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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1806.02824 | Samuel Chiquita | Samuel Chiquita | A theoretical framework for retinal computations: insights from textbook
knowledge | null | null | null | ICBASUP preprint 18-06 | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Neural circuits in the retina divide the incoming visual scene into more than
a dozen distinct representations that are sent on to central brain areas, such
as the lateral geniculate nucleus and the superior colliculus. The retina can
be viewed as a parallel image processor made of a multitude of small
computational devices. Neural circuits of the retina are constituted by various
cell types that separate the incoming visual information in different channels.
Visual information is processed by retinal neural circuits and several
computations are performed extracting distinct features from the visual scene.
The aim of this article is to understand the computational basis involved in
processing visual information which finally leads to several feature detectors.
Therefore, the elements that form the basis of retinal computations will be
explored by explaining how oscillators can lead to a final output with
computational meaning. Linear versus nonlinear systems will be presented and
the retina will be placed in the context of a nonlinear system. Finally,
simulations will be presented exploring the concept of the retina as a
nonlinear system which can perform understandable computations converting a
known input into a predictable output.
| [
{
"created": "Thu, 7 Jun 2018 23:21:21 GMT",
"version": "v1"
}
] | 2018-06-11 | [
[
"Chiquita",
"Samuel",
""
]
] | Neural circuits in the retina divide the incoming visual scene into more than a dozen distinct representations that are sent on to central brain areas, such as the lateral geniculate nucleus and the superior colliculus. The retina can be viewed as a parallel image processor made of a multitude of small computational devices. Neural circuits of the retina are constituted by various cell types that separate the incoming visual information in different channels. Visual information is processed by retinal neural circuits and several computations are performed extracting distinct features from the visual scene. The aim of this article is to understand the computational basis involved in processing visual information which finally leads to several feature detectors. Therefore, the elements that form the basis of retinal computations will be explored by explaining how oscillators can lead to a final output with computational meaning. Linear versus nonlinear systems will be presented and the retina will be placed in the context of a nonlinear system. Finally, simulations will be presented exploring the concept of the retina as a nonlinear system which can perform understandable computations converting a known input into a predictable output. |
1612.01233 | Fangting Li | Teng Wang (1,3), Chenzi Jin (2,3), Fangting Li (2,3) ((1) School of
Life Sciences, Peking University, Beijing, China. (2) School of Physics,
Peking University, Beijing, China. (3) Center for Quantitative Biology,
Peking University, Beijing, China.) | Phosphorylation potential and chemical fluxes govern the biological
performance of multiple PdP cycles | 15 pages and 4 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fission yeast G2/M transition is regulated by a biochemical reaction networks
which contains four components: Cdc13, Cdc2, Wee1, and Cdc25. This circuit is
characterized by the ultrasensitive responses of Wee1 or Cdc25 to Cdc13/Cdc2
activity, and the bistability of Cdc2 activation. Previous work has shown that
this bistability is governed by phosphorylation energy. In this article, we
developed the kinetic model of this circuit and conducted further thermodynamic
analysis on the role of phosphorylation energy (&[Delta]G). We showed that
level &[Delta]G shapes the response curves of Wee1 or Cdc25 to Cdc2 and governs
the intrinsic noise level of Cdc2 activity. More importantly, the mutually
antagonistic chemical fluxes around the PdP cycles in G2/M circuit were shown
to act as a stabilizer of Cdc2 activity against &[Delta]G fluctuations. These
results suggests the fundamental role of free energy and chemical fluxes on the
sensitivity, bistability and robustness of G2/M transition.
| [
{
"created": "Mon, 5 Dec 2016 03:03:37 GMT",
"version": "v1"
}
] | 2016-12-06 | [
[
"Wang",
"Teng",
""
],
[
"Jin",
"Chenzi",
""
],
[
"Li",
"Fangting",
""
]
] | Fission yeast G2/M transition is regulated by a biochemical reaction networks which contains four components: Cdc13, Cdc2, Wee1, and Cdc25. This circuit is characterized by the ultrasensitive responses of Wee1 or Cdc25 to Cdc13/Cdc2 activity, and the bistability of Cdc2 activation. Previous work has shown that this bistability is governed by phosphorylation energy. In this article, we developed the kinetic model of this circuit and conducted further thermodynamic analysis on the role of phosphorylation energy (&[Delta]G). We showed that level &[Delta]G shapes the response curves of Wee1 or Cdc25 to Cdc2 and governs the intrinsic noise level of Cdc2 activity. More importantly, the mutually antagonistic chemical fluxes around the PdP cycles in G2/M circuit were shown to act as a stabilizer of Cdc2 activity against &[Delta]G fluctuations. These results suggests the fundamental role of free energy and chemical fluxes on the sensitivity, bistability and robustness of G2/M transition. |
q-bio/0405028 | Luciano da Fontoura Costa | Luciano da Fontoura Costa | The Heart of Protein-Protein Interaction Networks | 4 pages, 1 figure | null | null | null | q-bio.MN cond-mat.dis-nn | null | Recent developments in complex networks have paved the way to a series of
important biological insights, such as the fact that many of the essential
proteins of S. cerevisae corresponds to the so-called hubs of the respective
protein-protein interaction networks. Despite the special importance of hubs,
other types of nodes such as those corresponding to the network border, as well
as the innermost nodes, also deserve special attention. This work reports on
how the application of the concept of distance transform to networks showed
that a great deal of the innermost nodes correspond to essential proteins, with
interesting biological implications.
| [
{
"created": "Mon, 31 May 2004 23:45:47 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Costa",
"Luciano da Fontoura",
""
]
] | Recent developments in complex networks have paved the way to a series of important biological insights, such as the fact that many of the essential proteins of S. cerevisae corresponds to the so-called hubs of the respective protein-protein interaction networks. Despite the special importance of hubs, other types of nodes such as those corresponding to the network border, as well as the innermost nodes, also deserve special attention. This work reports on how the application of the concept of distance transform to networks showed that a great deal of the innermost nodes correspond to essential proteins, with interesting biological implications. |
1806.03881 | Michael Schmuker | Michael Schmuker, R\"udiger Kupper, Ad Aertsen, Thomas Wachtler,
Marc-Oliver Gewaltig | Feed-forward and noise-tolerant detection of feature homogeneity in
spiking networks with a latency code | Accepted for publication in Biological Cybernetics | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In studies of the visual system as well as in computer vision, the focus is
often on contrast edges. However, the primate visual system contains a large
number of cells that are insensitive to spatial contrast and, instead, respond
to uniform homogeneous illumination of their visual field. The purpose of this
information remains unclear. Here, we propose a mechanism that detects feature
homogeneity in visual areas, based on latency coding and spike time
coincidence, in a purely feed-forward and therefore rapid manner. We
demonstrate how homogeneity information can interact with information on
contrast edges to potentially support rapid image segmentation. Furthermore, we
analyze how neuronal crosstalk (noise) affects the mechanism's performance. We
show that the detrimental effects of crosstalk can be partly mitigated through
delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The
delay of the feed-forward inhibition allows effectively controlling the size of
the temporal integration window and, thereby, the coincidence threshold. The
proposed model is based on single-spike latency codes in a purely feed-forward
architecture that supports low-latency processing, making it an attractive
scheme of computation in spiking neuronal networks where rapid responses and
low spike counts are desired.
| [
{
"created": "Mon, 11 Jun 2018 09:40:46 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Aug 2020 19:14:17 GMT",
"version": "v2"
},
{
"created": "Tue, 15 Dec 2020 10:38:17 GMT",
"version": "v3"
},
{
"created": "Mon, 29 Mar 2021 12:41:38 GMT",
"version": "v4"
}
] | 2021-03-30 | [
[
"Schmuker",
"Michael",
""
],
[
"Kupper",
"Rüdiger",
""
],
[
"Aertsen",
"Ad",
""
],
[
"Wachtler",
"Thomas",
""
],
[
"Gewaltig",
"Marc-Oliver",
""
]
] | In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired. |
1506.01731 | Duncan Palmer | Duncan S. Palmer, Emily Adland, John A. Frater, Philip J.R. Goulder,
Thumbi Ndung'u, Philippa C. Matthews, Rodney E. Phillips, Roger Shapiro, Gil
McVean and Angela R. McLean | Predictable patterns of CTL escape and reversion across host populations
and viral subtypes in HIV-1 evolution | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The twin processes of viral evolutionary escape and reversion in response to
host immune pressure, in particular the cytotoxic T-lymphocyte (CTL) response,
shape Human Immunodeficiency Virus-1 sequence evolution in infected host
populations. The tempo of CTL escape and reversion is known to differ between
CTL escape variants in a given host population. Here, we ask: are rates of
escape and reversion comparable across infected host populations? For three
cohorts taken from three continents, we estimate escape and reversion rates at
23 escape sites in optimally defined Gag epitopes. We find consistent escape
rate estimates across the examined cohorts. Reversion rates are also consistent
between a Canadian and South African infected host population. Certain Gag
escape variants that incur a large replicative fitness cost are known to revert
rapidly upon transmission. However, the relationship between escape/reversion
rates and viral replicative capacity across a large number of epitopes has not
been interrogated. We investigate this relationship by examining $in$ $vitro$
replicative capacities of viral sequences with minimal variation: point escape
mutants induced in a lab strain. Remarkably, despite the complexities of
epistatic effects exemplified by pathways to escape in famous epitopes, and the
diversity of both hosts and viruses, CTL escape mutants which escape rapidly
tend to be those with the highest replicative capacity when applied as a single
point mutation. Similarly, mutants inducing the greatest costs to viral
replicative capacity tend to revert more quickly. These data suggest that
escape rates in Gag are consistent across host populations, and that in general
these rates are dominated by site specific effects upon viral replicative
capacity.
| [
{
"created": "Thu, 4 Jun 2015 21:07:50 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Nov 2015 19:45:07 GMT",
"version": "v2"
}
] | 2015-11-05 | [
[
"Palmer",
"Duncan S.",
""
],
[
"Adland",
"Emily",
""
],
[
"Frater",
"John A.",
""
],
[
"Goulder",
"Philip J. R.",
""
],
[
"Ndung'u",
"Thumbi",
""
],
[
"Matthews",
"Philippa C.",
""
],
[
"Phillips",
"Rodney E.",
""
],
[
"Shapiro",
"Roger",
""
],
[
"McVean",
"Gil",
""
],
[
"McLean",
"Angela R.",
""
]
] | The twin processes of viral evolutionary escape and reversion in response to host immune pressure, in particular the cytotoxic T-lymphocyte (CTL) response, shape Human Immunodeficiency Virus-1 sequence evolution in infected host populations. The tempo of CTL escape and reversion is known to differ between CTL escape variants in a given host population. Here, we ask: are rates of escape and reversion comparable across infected host populations? For three cohorts taken from three continents, we estimate escape and reversion rates at 23 escape sites in optimally defined Gag epitopes. We find consistent escape rate estimates across the examined cohorts. Reversion rates are also consistent between a Canadian and South African infected host population. Certain Gag escape variants that incur a large replicative fitness cost are known to revert rapidly upon transmission. However, the relationship between escape/reversion rates and viral replicative capacity across a large number of epitopes has not been interrogated. We investigate this relationship by examining $in$ $vitro$ replicative capacities of viral sequences with minimal variation: point escape mutants induced in a lab strain. Remarkably, despite the complexities of epistatic effects exemplified by pathways to escape in famous epitopes, and the diversity of both hosts and viruses, CTL escape mutants which escape rapidly tend to be those with the highest replicative capacity when applied as a single point mutation. Similarly, mutants inducing the greatest costs to viral replicative capacity tend to revert more quickly. These data suggest that escape rates in Gag are consistent across host populations, and that in general these rates are dominated by site specific effects upon viral replicative capacity. |
1103.3883 | Carlos Afonso | S\'everine Zirah, Carlos Afonso (IPCM), Uwe Linne, Thomas A Knappe,
Mohamed A Marahiel, Sylvie Rebuffat, Jean-Claude Tabet (IPCM) | Topoisomer Differentiation of Molecular Knots by FTICR MS: Lessons from
Class II Lasso Peptides | null | Journal of the American Society for Mass Spectrometry 22 (2011)
467-479 | 10.1007/s13361-010-0028-1 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lasso peptides constitute a class of bioactive peptides sharing a knotted
structure where the C-terminal tail of the peptide is threaded through and
trapped within an N-terminalmacrolactamring. The structural characterization of
lasso structures and differentiation from their unthreaded topoisomers is not
trivial and generally requires the use of complementary biochemical and
spectroscopic methods. Here we investigated two antimicrobial peptides
belonging to the class II lasso peptide family and their corresponding
unthreaded topoisomers: microcin J25 (MccJ25), which is known to yield
two-peptide product ions specific of the lasso structure under collisioninduced
dissociation (CID), and capistruin, for which CID does not permit to
unambiguously assign the lasso structure. The two pairs of topoisomers were
analyzed by electrospray ionization Fourier transform ion cyclotron resonance
mass spectrometry (ESI-FTICR MS) upon CID, infrared multiple photon
dissociation (IRMPD), and electron capture dissociation (ECD). CID and
ECDspectra clearly permitted to differentiate MccJ25 from its non-lasso
topoisomer MccJ25-Icm, while for capistruin, only ECD was informative and
showed different extent of hydrogen migration (formation of c\bullet/z from
c/z\bullet) for the threaded and unthreaded topoisomers. The ECD spectra of the
triply-charged MccJ25 and MccJ25-lcm showed a series of radical b-type product
ions {\eth}b0In{\TH}. We proposed that these ions are specific of
cyclic-branched peptides and result from a dual c/z\bullet and y/b
dissociation, in the ring and in the tail, respectively. This work shows the
potentiality of ECD for structural characterization of peptide topoisomers, as
well as the effect of conformation on hydrogen migration subsequent to electron
capture.
| [
{
"created": "Sun, 20 Mar 2011 20:21:23 GMT",
"version": "v1"
}
] | 2011-03-22 | [
[
"Zirah",
"Séverine",
"",
"IPCM"
],
[
"Afonso",
"Carlos",
"",
"IPCM"
],
[
"Linne",
"Uwe",
"",
"IPCM"
],
[
"Knappe",
"Thomas A",
"",
"IPCM"
],
[
"Marahiel",
"Mohamed A",
"",
"IPCM"
],
[
"Rebuffat",
"Sylvie",
"",
"IPCM"
],
[
"Tabet",
"Jean-Claude",
"",
"IPCM"
]
] | Lasso peptides constitute a class of bioactive peptides sharing a knotted structure where the C-terminal tail of the peptide is threaded through and trapped within an N-terminalmacrolactamring. The structural characterization of lasso structures and differentiation from their unthreaded topoisomers is not trivial and generally requires the use of complementary biochemical and spectroscopic methods. Here we investigated two antimicrobial peptides belonging to the class II lasso peptide family and their corresponding unthreaded topoisomers: microcin J25 (MccJ25), which is known to yield two-peptide product ions specific of the lasso structure under collisioninduced dissociation (CID), and capistruin, for which CID does not permit to unambiguously assign the lasso structure. The two pairs of topoisomers were analyzed by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS) upon CID, infrared multiple photon dissociation (IRMPD), and electron capture dissociation (ECD). CID and ECDspectra clearly permitted to differentiate MccJ25 from its non-lasso topoisomer MccJ25-Icm, while for capistruin, only ECD was informative and showed different extent of hydrogen migration (formation of c\bullet/z from c/z\bullet) for the threaded and unthreaded topoisomers. The ECD spectra of the triply-charged MccJ25 and MccJ25-lcm showed a series of radical b-type product ions {\eth}b0In{\TH}. We proposed that these ions are specific of cyclic-branched peptides and result from a dual c/z\bullet and y/b dissociation, in the ring and in the tail, respectively. This work shows the potentiality of ECD for structural characterization of peptide topoisomers, as well as the effect of conformation on hydrogen migration subsequent to electron capture. |
2202.11544 | Alec Sargood Mr | Alec Sargood | Gene Expression Time Delays in Reaction-Diffusion Systems | 81 pages, 64 figures, MSc Thesis | null | null | null | q-bio.CB nlin.PS | http://creativecommons.org/licenses/by/4.0/ | Gene expression time delays, modelling the complex biological processes of
gene transcription and translation, have been shown to play an important role
in cellular dynamics. Time delays, motivated by the gene expression process,
can also greatly affect the behaviour of reaction-diffusion systems. In this
dissertation, we explore their effects on Turing pattern mechanisms. By
incorporating time delays, modelled as both a fixed parameter and as a
continuous distribution, into classical reaction-diffusion systems that exhibit
Turing instabilities, we investigate the changing behaviour of these systems.
We find that an introduction of increasing time delay increases the time taken
for spatially inhomogeneous patterns to stabilise, and the two are related
linearly. We also present results to show, through a linear stability analysis,
that an increasing time delay can act both to expand or shrink the Turing space
of a certain reaction-diffusion mechanism, depending on the placement of
time-delayed terms. Significantly, we find that modelling time delays as a
continuous distribution has a negligible impact on qualitative or quantitative
aspects of the results seen compared with a fixed time delay of the mean of the
distribution. These findings serve to highlight the importance of considering
gene expression time delays when modelling biological patterning events, as
well as requiring a complete understanding of the cellular dynamics before
attempting to apply Turing mechanisms to explain biological phenomena. The
results also suggest, at least for the distributions considered in this
dissertation, that fixed delay and distributed delay models have almost
identical dynamics. This allows one to use simpler fixed delay models rather
than the more complicated distributed delay variants.
| [
{
"created": "Tue, 22 Feb 2022 08:47:49 GMT",
"version": "v1"
}
] | 2022-02-24 | [
[
"Sargood",
"Alec",
""
]
] | Gene expression time delays, modelling the complex biological processes of gene transcription and translation, have been shown to play an important role in cellular dynamics. Time delays, motivated by the gene expression process, can also greatly affect the behaviour of reaction-diffusion systems. In this dissertation, we explore their effects on Turing pattern mechanisms. By incorporating time delays, modelled as both a fixed parameter and as a continuous distribution, into classical reaction-diffusion systems that exhibit Turing instabilities, we investigate the changing behaviour of these systems. We find that an introduction of increasing time delay increases the time taken for spatially inhomogeneous patterns to stabilise, and the two are related linearly. We also present results to show, through a linear stability analysis, that an increasing time delay can act both to expand or shrink the Turing space of a certain reaction-diffusion mechanism, depending on the placement of time-delayed terms. Significantly, we find that modelling time delays as a continuous distribution has a negligible impact on qualitative or quantitative aspects of the results seen compared with a fixed time delay of the mean of the distribution. These findings serve to highlight the importance of considering gene expression time delays when modelling biological patterning events, as well as requiring a complete understanding of the cellular dynamics before attempting to apply Turing mechanisms to explain biological phenomena. The results also suggest, at least for the distributions considered in this dissertation, that fixed delay and distributed delay models have almost identical dynamics. This allows one to use simpler fixed delay models rather than the more complicated distributed delay variants. |
2008.00471 | Frank Julicher | Jonas Neipel, Jonathan Bauermann, Stefano Bo, Tyler Harmon and Frank
J\"ulicher | Power-Law Population Heterogeneity Governs Epidemic Waves | 34 pages, 8 figures | null | 10.1371/journal.pone.0239678 | null | q-bio.PE nlin.PS physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We generalize the Susceptible-Infected-Removed model for epidemics to take
into account generic effects of heterogeneity in the degree of susceptibility
to infection in the population. We introduce a single new parameter
corresponding to a power-law exponent of the susceptibility distribution that
characterizes the population heterogeneity. We show that our generalized model
is as simple as the original model which is contained as a limiting case.
Because of this simplicity, numerical solutions can be generated easily and key
properties of the epidemic wave can still be obtained exactly. In particular,
we present exact expressions for the herd immunity level, the final size of the
epidemic, as well as for the shape of the wave and for observables that can be
quantified during an epidemic. We find that in strongly heterogeneous
populations the epidemic reaches only a small fraction of the population. This
implies that the herd immunity level can be much lower than in commonly used
models with homogeneous populations. Using our model to analyze data for the
SARS-CoV-2 epidemic in Germany shows that the reported time course is
consistent with several scenarios characterized by different levels of
immunity. These scenarios differ in population heterogeneity and in the time
course of the infection rate, for example due to mitigation efforts or
seasonality. Our analysis reveals that quantifying the effects of mitigation
requires knowledge on the degree of heterogeneity in the population. Our work
shows that key effects of population heterogeneity can be captured without
increasing the complexity of the model. We show that information about
population heterogeneity will be key to understand how far an epidemic has
progressed and what can be expected for its future course.
| [
{
"created": "Sun, 2 Aug 2020 12:41:16 GMT",
"version": "v1"
}
] | 2021-01-27 | [
[
"Neipel",
"Jonas",
""
],
[
"Bauermann",
"Jonathan",
""
],
[
"Bo",
"Stefano",
""
],
[
"Harmon",
"Tyler",
""
],
[
"Jülicher",
"Frank",
""
]
] | We generalize the Susceptible-Infected-Removed model for epidemics to take into account generic effects of heterogeneity in the degree of susceptibility to infection in the population. We introduce a single new parameter corresponding to a power-law exponent of the susceptibility distribution that characterizes the population heterogeneity. We show that our generalized model is as simple as the original model which is contained as a limiting case. Because of this simplicity, numerical solutions can be generated easily and key properties of the epidemic wave can still be obtained exactly. In particular, we present exact expressions for the herd immunity level, the final size of the epidemic, as well as for the shape of the wave and for observables that can be quantified during an epidemic. We find that in strongly heterogeneous populations the epidemic reaches only a small fraction of the population. This implies that the herd immunity level can be much lower than in commonly used models with homogeneous populations. Using our model to analyze data for the SARS-CoV-2 epidemic in Germany shows that the reported time course is consistent with several scenarios characterized by different levels of immunity. These scenarios differ in population heterogeneity and in the time course of the infection rate, for example due to mitigation efforts or seasonality. Our analysis reveals that quantifying the effects of mitigation requires knowledge on the degree of heterogeneity in the population. Our work shows that key effects of population heterogeneity can be captured without increasing the complexity of the model. We show that information about population heterogeneity will be key to understand how far an epidemic has progressed and what can be expected for its future course. |
2101.07000 | David Schaller | David Schaller, Manuela Gei{\ss}, Marc Hellmuth, Peter F. Stadler | Least resolved trees for two-colored best match graphs | null | null | null | null | q-bio.PE cs.CC cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 2-colored best match graphs (2-BMGs) form a subclass of sink-free
bi-transitive graphs that appears in phylogenetic combinatorics. There, 2-BMGs
describe evolutionarily most closely related genes between a pair of species.
They are explained by a unique least resolved tree (LRT). Introducing the
concept of support vertices we derive an $O(|V|+|E|\log^2|V|)$-time algorithm
to recognize 2-BMGs and to construct its LRT. The approach can be extended to
also recognize binary-explainable 2-BMGs with the same complexity. An empirical
comparison emphasizes the efficiency of the new algorithm.
| [
{
"created": "Mon, 18 Jan 2021 11:03:54 GMT",
"version": "v1"
}
] | 2021-01-19 | [
[
"Schaller",
"David",
""
],
[
"Geiß",
"Manuela",
""
],
[
"Hellmuth",
"Marc",
""
],
[
"Stadler",
"Peter F.",
""
]
] | 2-colored best match graphs (2-BMGs) form a subclass of sink-free bi-transitive graphs that appears in phylogenetic combinatorics. There, 2-BMGs describe evolutionarily most closely related genes between a pair of species. They are explained by a unique least resolved tree (LRT). Introducing the concept of support vertices we derive an $O(|V|+|E|\log^2|V|)$-time algorithm to recognize 2-BMGs and to construct its LRT. The approach can be extended to also recognize binary-explainable 2-BMGs with the same complexity. An empirical comparison emphasizes the efficiency of the new algorithm. |
1908.00785 | Francois Coste | Fran\c{c}ois Coste (Dyliss) | Deep learning languages: a key fundamental shift from probabilities to
weights? | null | null | null | null | q-bio.OT cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent successes in language modeling, notably with deep learning methods,
coincide with a shift from probabilistic to weighted representations. We raise
here the question of the importance of this evolution, in the light of the
practical limitations of a classical and simple probabilistic modeling approach
for the classification of protein sequences and in relation to the need for
principled methods to learn non-probabilistic models.
| [
{
"created": "Fri, 2 Aug 2019 10:09:51 GMT",
"version": "v1"
}
] | 2019-08-05 | [
[
"Coste",
"François",
"",
"Dyliss"
]
] | Recent successes in language modeling, notably with deep learning methods, coincide with a shift from probabilistic to weighted representations. We raise here the question of the importance of this evolution, in the light of the practical limitations of a classical and simple probabilistic modeling approach for the classification of protein sequences and in relation to the need for principled methods to learn non-probabilistic models. |
0801.3764 | Indranil Mitra Mr | Sisir Roy, Indranil Mitra, Rodolfo Llinas | Non Markovian Noise mediated through Anamolous Diffusion within Ion
Channels | 10 pages, 6 figures | null | null | null | q-bio.NC q-bio.QM | null | It is quite clear from a wide range of experiments that gating phenomena of
ion channels is inherently stochastic. It has been discussed using BD
simulations in a recent paper that memory effects in ion transport is
negligible, unless the barrier height is high. In this brief report we like to
state using Differential Stochastic Methods (DSM's) that the Markovian property
of exponential dwell times do indeed give rise to a high barrier, which in turn
indicates that memory effects need not be ignored. We have thus constructed a
Generalized Langevin Equation which contains a combination of Non Markovian at
different time scales & Markovian processes and develop an algorithm to
describe the scheme of events. We see that the oscillatory function behaviour
with exponential decay is obtained in the Markovian limit and two distinct time
scales corresponding to the processes of diffusion & drift may be obtained from
preliminary simulation results. We propose that the results need much more
inspection and it will be worthwhile to reproduce using MD simulations. The
most important idea which we like to propose in this paper is that the rise of
time scales and memory effects may be inherently related to the differential
behaviour of shear viscosity in the cytoplasm & extracellular matrix.
| [
{
"created": "Thu, 24 Jan 2008 01:52:08 GMT",
"version": "v1"
}
] | 2008-01-25 | [
[
"Roy",
"Sisir",
""
],
[
"Mitra",
"Indranil",
""
],
[
"Llinas",
"Rodolfo",
""
]
] | It is quite clear from a wide range of experiments that gating phenomena of ion channels is inherently stochastic. It has been discussed using BD simulations in a recent paper that memory effects in ion transport is negligible, unless the barrier height is high. In this brief report we like to state using Differential Stochastic Methods (DSM's) that the Markovian property of exponential dwell times do indeed give rise to a high barrier, which in turn indicates that memory effects need not be ignored. We have thus constructed a Generalized Langevin Equation which contains a combination of Non Markovian at different time scales & Markovian processes and develop an algorithm to describe the scheme of events. We see that the oscillatory function behaviour with exponential decay is obtained in the Markovian limit and two distinct time scales corresponding to the processes of diffusion & drift may be obtained from preliminary simulation results. We propose that the results need much more inspection and it will be worthwhile to reproduce using MD simulations. The most important idea which we like to propose in this paper is that the rise of time scales and memory effects may be inherently related to the differential behaviour of shear viscosity in the cytoplasm & extracellular matrix. |
1611.03698 | Michael G. M\"uller | Michael G. M\"uller, Christos H. Papadimitriou, Wolfgang Maass, Robert
Legenstein | A model for structured information representation in neural networks | 23 pages, 5 figures | eNeuro 7 May 2020, 7 (3) ENEURO.0533-19.2020 | 10.1523/ENEURO.0533-19.2020 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans possess the capability to reason at an abstract level and to structure
information into abstract categories, but the underlying neural processes have
remained unknown. Experimental evidence has recently emerged for the
organization of an important aspect of abstract reasoning: for assigning words
to semantic roles in a sentence, such as agent (or subject) and patient (or
object). Using minimal assumptions, we show how such a binding of words to
semantic roles emerges in a generic spiking neural network through Hebbian
plasticity. The resulting model is consistent with the experimental data and
enables new computational functionalities such as structured information
retrieval, copying data, and comparisons. It thus provides a basis for the
implementation of more demanding cognitive computations by networks of spiking
neurons.
| [
{
"created": "Fri, 11 Nov 2016 13:33:35 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Apr 2018 09:43:21 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Mar 2019 16:41:57 GMT",
"version": "v3"
}
] | 2022-04-05 | [
[
"Müller",
"Michael G.",
""
],
[
"Papadimitriou",
"Christos H.",
""
],
[
"Maass",
"Wolfgang",
""
],
[
"Legenstein",
"Robert",
""
]
] | Humans possess the capability to reason at an abstract level and to structure information into abstract categories, but the underlying neural processes have remained unknown. Experimental evidence has recently emerged for the organization of an important aspect of abstract reasoning: for assigning words to semantic roles in a sentence, such as agent (or subject) and patient (or object). Using minimal assumptions, we show how such a binding of words to semantic roles emerges in a generic spiking neural network through Hebbian plasticity. The resulting model is consistent with the experimental data and enables new computational functionalities such as structured information retrieval, copying data, and comparisons. It thus provides a basis for the implementation of more demanding cognitive computations by networks of spiking neurons. |
2401.06938 | Javier L\'opez-De-La-Cruz | Javier L\'opez-de-la-Cruz, Mar\'ia P\'erez-Aranda, Ana Alcudia,
Bel\'en Begines, Tom\'as Caraballo, Elo\'isa Pajuelo, Pedro J. Ginel | Dynamics and numerical simulations to predict empirical antibiotic
treatment of multi-resistant Pseudomonas aeruginosa infection | null | null | 10.1016/j.cnsns.2020.105418 | null | q-bio.PE math.DS | http://creativecommons.org/licenses/by/4.0/ | This work discloses an epidemiological mathematical model to predict an
empirical treatment for dogs infected by Pseudomonas aeruginosa. This dangerous
pathogen is one of the leading causes of multi-resistant infections and can be
transmitted from dogs to humans. Numerical simulations and appropriated codes
were developed using Matlab software to gather information concerning long-time
dynamics of the susceptible, infected and recovered individuals. All data
compiled from the mathematical model was used to provide an appropriated
antibiotic sensitivity panel for this specific infection. In this study,
several variables have been included in this model to predict which treatment
should be prescribed in emergency cases, when there is no time to perform an
antibiogram or the cost of it could not be assumed. In particular, we highlight
the use of this model aiming to become part of the convenient toolbox of Public
Health research and decision-making in the design of the mitigation strategy of
bacterial pathogens.
| [
{
"created": "Sat, 13 Jan 2024 00:34:10 GMT",
"version": "v1"
}
] | 2024-01-17 | [
[
"López-de-la-Cruz",
"Javier",
""
],
[
"Pérez-Aranda",
"María",
""
],
[
"Alcudia",
"Ana",
""
],
[
"Begines",
"Belén",
""
],
[
"Caraballo",
"Tomás",
""
],
[
"Pajuelo",
"Eloísa",
""
],
[
"Ginel",
"Pedro J.",
""
]
] | This work discloses an epidemiological mathematical model to predict an empirical treatment for dogs infected by Pseudomonas aeruginosa. This dangerous pathogen is one of the leading causes of multi-resistant infections and can be transmitted from dogs to humans. Numerical simulations and appropriated codes were developed using Matlab software to gather information concerning long-time dynamics of the susceptible, infected and recovered individuals. All data compiled from the mathematical model was used to provide an appropriated antibiotic sensitivity panel for this specific infection. In this study, several variables have been included in this model to predict which treatment should be prescribed in emergency cases, when there is no time to perform an antibiogram or the cost of it could not be assumed. In particular, we highlight the use of this model aiming to become part of the convenient toolbox of Public Health research and decision-making in the design of the mitigation strategy of bacterial pathogens. |
1610.03217 | Yue Zhang | Yue Zhang | A Corrected Parsimony Criterion for Reconstructing Phylogenies | null | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In phylogenetic analysis, for non-molecular data, particularly morphology,
parsimony optimization is the most commonly employed approach. In the past and
present application of the parsimony principle, extra step numbers have been
added across different characters without proper justification. This practice,
however, has caused the impacts of characters to be inflated or deflated
without a valid reason. To resolve this methodological deficiency, here I
present a corrected parsimony criterion for reconstructing phylogenies. In
essence, character rather than step is the most fundamental unit. Accordingly,
the most parsimonious tree should maximize the sum or average of the
phylogenetic signals, quantified by retention index, contributed by each
character. Assigning proper weights to characters is a separate task that
requires information other than the intra-character step number changing range.
| [
{
"created": "Tue, 11 Oct 2016 07:16:37 GMT",
"version": "v1"
}
] | 2016-10-12 | [
[
"Zhang",
"Yue",
""
]
] | In phylogenetic analysis, for non-molecular data, particularly morphology, parsimony optimization is the most commonly employed approach. In the past and present application of the parsimony principle, extra step numbers have been added across different characters without proper justification. This practice, however, has caused the impacts of characters to be inflated or deflated without a valid reason. To resolve this methodological deficiency, here I present a corrected parsimony criterion for reconstructing phylogenies. In essence, character rather than step is the most fundamental unit. Accordingly, the most parsimonious tree should maximize the sum or average of the phylogenetic signals, quantified by retention index, contributed by each character. Assigning proper weights to characters is a separate task that requires information other than the intra-character step number changing range. |
0709.2824 | Simon Rosenfeld | Simon Rosenfeld | Pseudo-Random Fluctuations, Stochastic Cooperativity and Burstiness in
Dynamically Unstable High-Dimensional Biochemical Networks | 34 pages, 8 figures | null | null | null | q-bio.MN q-bio.GN | null | The goal of this paper is to outline a scenario of emerging stochasticity in
high-dimensional highly nonlinear systems, such as genetic regulatory networks
(GRN). We focus attention on the fact that in such systems confluence of all
the factors necessary for gene expression is a comparatively rare event, and
only massive redundancy makes such events sufficiently frequent. An immediate
consequence of this rareness is burstiness in mRNA and protein copy numbers, a
well known experimentally observed effect. We introduce the concept of
stochastic cooperativity and show that this phenomenon is a natural consequence
of high dimensionality coupled with highly nonlinearity of a dynamical system.
In mathematical terms, burstiness is associated with heavy-tailed probability
distributions of stochastic processes describing the dynamics of the system.
The sequence of stochastic cooperativity events allows for transition from
continuous deterministic dynamics expressed in terms of ordinary differential
equations (ODE) to discrete stochastic dynamics expressed in terms of Langevin
and Fokker-Plank equations. We demonstrate also that high-dimensional nonlinear
systems, even in the absence of explicit mechanisms for suppressing inherent
instability, may nevertheless reside in a state of stationary pseudo-random
fluctuations which for all practical purposes may be regarded as stochastic
process. This type of stochastic behavior is an inherent property of such
systems and requires neither an external random force, nor highly specialized
conditions of bistability.
| [
{
"created": "Tue, 18 Sep 2007 13:32:07 GMT",
"version": "v1"
}
] | 2007-09-19 | [
[
"Rosenfeld",
"Simon",
""
]
] | The goal of this paper is to outline a scenario of emerging stochasticity in high-dimensional highly nonlinear systems, such as genetic regulatory networks (GRN). We focus attention on the fact that in such systems confluence of all the factors necessary for gene expression is a comparatively rare event, and only massive redundancy makes such events sufficiently frequent. An immediate consequence of this rareness is burstiness in mRNA and protein copy numbers, a well known experimentally observed effect. We introduce the concept of stochastic cooperativity and show that this phenomenon is a natural consequence of high dimensionality coupled with highly nonlinearity of a dynamical system. In mathematical terms, burstiness is associated with heavy-tailed probability distributions of stochastic processes describing the dynamics of the system. The sequence of stochastic cooperativity events allows for transition from continuous deterministic dynamics expressed in terms of ordinary differential equations (ODE) to discrete stochastic dynamics expressed in terms of Langevin and Fokker-Plank equations. We demonstrate also that high-dimensional nonlinear systems, even in the absence of explicit mechanisms for suppressing inherent instability, may nevertheless reside in a state of stationary pseudo-random fluctuations which for all practical purposes may be regarded as stochastic process. This type of stochastic behavior is an inherent property of such systems and requires neither an external random force, nor highly specialized conditions of bistability. |
1307.0305 | Denis Menshykau | Srivathsan Adivarahan, Denis Menshykau, Odysse Michos and Dagmar Iber | Dynamic Image-Based Modelling of Kidney Branching Morphogenesis | null | null | null | null | q-bio.TO q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Kidney branching morphogenesis has been studied extensively, but the
mechanism that defines the branch points is still elusive. Here we obtained a
2D movie of kidney branching morphogenesis in culture to test different models
of branching morphogenesis with physiological growth dynamics. We carried out
image segmentation and calculated the displacement fields between the frames.
The models were subsequently solved on the 2D domain, that was extracted from
the movie. We find that Turing patterns are sensitive to the initial conditions
when solved on the epithelial shapes. A previously proposed diffusion-dependent
geometry effect allowed us to reproduce the growth fields reasonably well, both
for an inhibitor of branching that was produced in the epithelium, and for an
inducer of branching that was produced in the mesenchyme. The latter could be
represented by Glial-derived neurotrophic factor (GDNF), which is expressed in
the mesenchyme and induces outgrowth of ureteric branches. Considering that the
Turing model represents the interaction between the GDNF and its receptor RET
very well and that the model reproduces the relevant expression patterns in
developing wildtype and mutant kidneys, it is well possible that a combination
of the Turing mechanism and the geometry effect control branching
morphogenesis.
| [
{
"created": "Mon, 1 Jul 2013 09:00:43 GMT",
"version": "v1"
}
] | 2013-07-02 | [
[
"Adivarahan",
"Srivathsan",
""
],
[
"Menshykau",
"Denis",
""
],
[
"Michos",
"Odysse",
""
],
[
"Iber",
"Dagmar",
""
]
] | Kidney branching morphogenesis has been studied extensively, but the mechanism that defines the branch points is still elusive. Here we obtained a 2D movie of kidney branching morphogenesis in culture to test different models of branching morphogenesis with physiological growth dynamics. We carried out image segmentation and calculated the displacement fields between the frames. The models were subsequently solved on the 2D domain, that was extracted from the movie. We find that Turing patterns are sensitive to the initial conditions when solved on the epithelial shapes. A previously proposed diffusion-dependent geometry effect allowed us to reproduce the growth fields reasonably well, both for an inhibitor of branching that was produced in the epithelium, and for an inducer of branching that was produced in the mesenchyme. The latter could be represented by Glial-derived neurotrophic factor (GDNF), which is expressed in the mesenchyme and induces outgrowth of ureteric branches. Considering that the Turing model represents the interaction between the GDNF and its receptor RET very well and that the model reproduces the relevant expression patterns in developing wildtype and mutant kidneys, it is well possible that a combination of the Turing mechanism and the geometry effect control branching morphogenesis. |
1511.00631 | Andrew Mugler | Edward Roob III, Nicola Trendel, Pieter Rein ten Wolde, Andrew Mugler | Molecular clustering digitizes signaling and increases fidelity | 12 pages, 4 figures | Biophysical Journal 110(7):1661-1669, 2016 | 10.1016/j.bpj.2016.02.031 | null | q-bio.MN physics.bio-ph q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many membrane-bound molecules in cells form small clusters. It has been
hypothesized that these clusters convert an analog extracellular signal into a
digital intracellular signal and that this conversion increases signaling
fidelity. However, the mechanism by which clusters digitize a signal and the
subsequent effects on fidelity remain poorly understood. Here we demonstrate
using a stochastic model of cooperative cluster formation that sufficient
cooperation leads to digital signaling. We show that despite reducing the
number of output states, which decreases fidelity, digitization also reduces
noise in the system, which increases fidelity. The tradeoff between these
effects leads to an optimal cluster size that agrees with experimental
measurements.
| [
{
"created": "Mon, 2 Nov 2015 18:58:24 GMT",
"version": "v1"
}
] | 2019-08-28 | [
[
"Roob",
"Edward",
"III"
],
[
"Trendel",
"Nicola",
""
],
[
"Wolde",
"Pieter Rein ten",
""
],
[
"Mugler",
"Andrew",
""
]
] | Many membrane-bound molecules in cells form small clusters. It has been hypothesized that these clusters convert an analog extracellular signal into a digital intracellular signal and that this conversion increases signaling fidelity. However, the mechanism by which clusters digitize a signal and the subsequent effects on fidelity remain poorly understood. Here we demonstrate using a stochastic model of cooperative cluster formation that sufficient cooperation leads to digital signaling. We show that despite reducing the number of output states, which decreases fidelity, digitization also reduces noise in the system, which increases fidelity. The tradeoff between these effects leads to an optimal cluster size that agrees with experimental measurements. |
2302.02386 | JunJie Wee | JunJie Wee, Ginestra Bianconi, Kelin Xia | Persistent Dirac for molecular representation | 22 pages, 7 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Molecular representations are of fundamental importance for the modeling and
analysis of molecular systems. Representation models and in general approaches
based on topological data analysis (TDA) have demonstrated great success in
various steps of drug design and materials discovery. Here we develop a
mathematically rigorous computational framework for molecular representation
based on the persistent Dirac operator. The properties of the spectrum of the
discrete weighted and unweighted Dirac matrices are systemically discussed and
used to demonstrate the geometric and topological properties of both
non-homology and homology eigenvectors of real molecular structures. This
allows us to asses the influence of weighting schemes on the information
encoded in the Dirac eigenspectrum. A series of physical persistent attributes,
which characterize the spectrum of the Dirac matrices across a filtration, are
proposed and used as efficient molecular fingerprints. Finally, our persistent
Dirac-based model is used for clustering molecular configurations from nine
types of organic-inorganic halide perovskites. We found that our model can
cluster the structures very well, demonstrating the representation and
featurization power of the current approach.
| [
{
"created": "Sun, 5 Feb 2023 14:07:03 GMT",
"version": "v1"
}
] | 2023-02-07 | [
[
"Wee",
"JunJie",
""
],
[
"Bianconi",
"Ginestra",
""
],
[
"Xia",
"Kelin",
""
]
] | Molecular representations are of fundamental importance for the modeling and analysis of molecular systems. Representation models and in general approaches based on topological data analysis (TDA) have demonstrated great success in various steps of drug design and materials discovery. Here we develop a mathematically rigorous computational framework for molecular representation based on the persistent Dirac operator. The properties of the spectrum of the discrete weighted and unweighted Dirac matrices are systemically discussed and used to demonstrate the geometric and topological properties of both non-homology and homology eigenvectors of real molecular structures. This allows us to asses the influence of weighting schemes on the information encoded in the Dirac eigenspectrum. A series of physical persistent attributes, which characterize the spectrum of the Dirac matrices across a filtration, are proposed and used as efficient molecular fingerprints. Finally, our persistent Dirac-based model is used for clustering molecular configurations from nine types of organic-inorganic halide perovskites. We found that our model can cluster the structures very well, demonstrating the representation and featurization power of the current approach. |
2002.06053 | Hakime \"Ozt\"urk | Hakime \"Ozt\"urk, Arzucan \"Ozg\"ur, Philippe Schwaller, Teodoro
Laino, Elif Ozkirimli | Exploring Chemical Space using Natural Language Processing Methodologies
for Drug Discovery | null | null | 10.1016/j.drudis.2020.01.020 | null | q-bio.BM cs.CL cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Text-based representations of chemicals and proteins can be thought of as
unstructured languages codified by humans to describe domain-specific
knowledge. Advances in natural language processing (NLP) methodologies in the
processing of spoken languages accelerated the application of NLP to elucidate
hidden knowledge in textual representations of these biochemical entities and
then use it to construct models to predict molecular properties or to design
novel molecules. This review outlines the impact made by these advances on drug
discovery and aims to further the dialogue between medicinal chemists and
computer scientists.
| [
{
"created": "Mon, 10 Feb 2020 21:02:05 GMT",
"version": "v1"
}
] | 2020-02-17 | [
[
"Öztürk",
"Hakime",
""
],
[
"Özgür",
"Arzucan",
""
],
[
"Schwaller",
"Philippe",
""
],
[
"Laino",
"Teodoro",
""
],
[
"Ozkirimli",
"Elif",
""
]
] | Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural language processing (NLP) methodologies in the processing of spoken languages accelerated the application of NLP to elucidate hidden knowledge in textual representations of these biochemical entities and then use it to construct models to predict molecular properties or to design novel molecules. This review outlines the impact made by these advances on drug discovery and aims to further the dialogue between medicinal chemists and computer scientists. |
1808.09828 | Arni S.R. Srinivasa Rao | Steven G. Krantz, Peter Polyakov, Arni S.R. Srinivasa Rao | True Epidemic Growth Construction Through Harmonic Analysis | 16 pages, new Figure on Meyer wavelets added | Journal of Theoretical Biology Volume 494, 7 June 2020, 110243 | 10.1016/j.jtbi.2020.110243 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we have proposed a two phase procedure (combining discrete
graphs and wavelets) for constructing a true epidemic growth. In the first
phase graph theory based approach was developed to update partial data
available and in the second phase we used this partial data to generate a
plausible complete data through wavelets. This procedure although novel and
implementable, still leave some questions unanswered.
| [
{
"created": "Wed, 29 Aug 2018 13:50:42 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Sep 2018 18:21:45 GMT",
"version": "v2"
}
] | 2021-06-15 | [
[
"Krantz",
"Steven G.",
""
],
[
"Polyakov",
"Peter",
""
],
[
"Rao",
"Arni S. R. Srinivasa",
""
]
] | In this paper, we have proposed a two phase procedure (combining discrete graphs and wavelets) for constructing a true epidemic growth. In the first phase graph theory based approach was developed to update partial data available and in the second phase we used this partial data to generate a plausible complete data through wavelets. This procedure although novel and implementable, still leave some questions unanswered. |
1402.1410 | Adriaan (Ard) A. Louis | Ard A Louis and Steffen Schaper | The arrival of the frequent: how bias in genotype-phenotype maps can
steer populations to local optima | full paper plus supplementary materials | PLoS ONE 9(2): e86635 (2014) | 10.1371/journal.pone.0086635 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genotype-phenotype (GP) maps specify how the random mutations that change
genotypes generate variation by altering phenotypes, which, in turn, can
trigger selection. Many GP maps share the following general properties: 1) The
number of genotypes $N_G$ is much larger than the number of selectable
phenotypes; 2) Neutral exploration changes the variation that is accessible to
the population; 3) The distribution of phenotype frequencies $F_p=N_p/N_G$,
with $N_p$ the number of genotypes mapping onto phenotype $p$, is highly
biased: the majority of genotypes map to only a small minority of the
phenotypes. Here we explore how these properties affect the evolutionary
dynamics of haploid Wright-Fisher models that are coupled to a simplified and
general random GP map or to a more complex RNA sequence to secondary structure
map. For both maps the probability of a mutation leading to a phenotype $p$
scales to first order as $F_p$, although for the RNA map there are further
correlations as well. By using mean-field theory, supported by computer
simulations, we show that the discovery time $T_p$ of a phenotype $p$ similarly
scales to first order as $1/F_p$ for a wide range of population sizes and
mutation rates in both the monomorphic and polymorphic regimes. These
differences in the rate at which variation arises can vary over many orders of
magnitude. Phenotypic variation with a larger $F_p$ is therefore be much more
likely to arise than variation with a small $F_p$. We show, using the RNA
model, that frequent phenotypes (with larger $F_p$) can fix in a population
even when alternative, but less frequent, phenotypes with much higher fitness
are potentially accessible. In other words, if the fittest never `arrive' on
the timescales of evolutionary change, then they can't fix. We call this highly
non-ergodic effect the `arrival of the frequent'.
| [
{
"created": "Thu, 6 Feb 2014 17:08:57 GMT",
"version": "v1"
}
] | 2014-02-07 | [
[
"Louis",
"Ard A",
""
],
[
"Schaper",
"Steffen",
""
]
] | Genotype-phenotype (GP) maps specify how the random mutations that change genotypes generate variation by altering phenotypes, which, in turn, can trigger selection. Many GP maps share the following general properties: 1) The number of genotypes $N_G$ is much larger than the number of selectable phenotypes; 2) Neutral exploration changes the variation that is accessible to the population; 3) The distribution of phenotype frequencies $F_p=N_p/N_G$, with $N_p$ the number of genotypes mapping onto phenotype $p$, is highly biased: the majority of genotypes map to only a small minority of the phenotypes. Here we explore how these properties affect the evolutionary dynamics of haploid Wright-Fisher models that are coupled to a simplified and general random GP map or to a more complex RNA sequence to secondary structure map. For both maps the probability of a mutation leading to a phenotype $p$ scales to first order as $F_p$, although for the RNA map there are further correlations as well. By using mean-field theory, supported by computer simulations, we show that the discovery time $T_p$ of a phenotype $p$ similarly scales to first order as $1/F_p$ for a wide range of population sizes and mutation rates in both the monomorphic and polymorphic regimes. These differences in the rate at which variation arises can vary over many orders of magnitude. Phenotypic variation with a larger $F_p$ is therefore be much more likely to arise than variation with a small $F_p$. We show, using the RNA model, that frequent phenotypes (with larger $F_p$) can fix in a population even when alternative, but less frequent, phenotypes with much higher fitness are potentially accessible. In other words, if the fittest never `arrive' on the timescales of evolutionary change, then they can't fix. We call this highly non-ergodic effect the `arrival of the frequent'. |
2305.13914 | Maik Sch\"unemann | Maik Sch\"unemann and Udo Ernst | Routing by spontaneous synchronization | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Selective attention allows to process stimuli which are behaviorally
relevant, while attenuating distracting information. However, it is an open
question what mechanisms implement selective routing, and how they are engaged
in dependence on behavioral need. Here we introduce a novel framework for
selective processing by spontaneous synchronization. Input signals become
organized into 'avalanches' of synchronized spikes which propagate to target
populations. Selective attention enhances spontaneous synchronization and
boosts signal transfer by a simple disinhibition of a control population,
without requiring changes in synaptic weights. Our framework is fully
analytically tractable and provides a complete understanding of all stages of
the routing mechanism, yielding closed-form expressions for input-output
correlations. Interestingly, although gamma oscillations can naturally occur
through a recurrent dynamics, we can formally show that the routing mechanism
itself does not require such oscillatory activity and works equally well if
synchronous events would be randomly shuffled over time. Our framework explains
a large range of physiological findings in a unified framework and makes
specific predictions about putative control mechanisms and their effects on
neural dynamics.
| [
{
"created": "Tue, 23 May 2023 10:39:00 GMT",
"version": "v1"
}
] | 2023-05-24 | [
[
"Schünemann",
"Maik",
""
],
[
"Ernst",
"Udo",
""
]
] | Selective attention allows to process stimuli which are behaviorally relevant, while attenuating distracting information. However, it is an open question what mechanisms implement selective routing, and how they are engaged in dependence on behavioral need. Here we introduce a novel framework for selective processing by spontaneous synchronization. Input signals become organized into 'avalanches' of synchronized spikes which propagate to target populations. Selective attention enhances spontaneous synchronization and boosts signal transfer by a simple disinhibition of a control population, without requiring changes in synaptic weights. Our framework is fully analytically tractable and provides a complete understanding of all stages of the routing mechanism, yielding closed-form expressions for input-output correlations. Interestingly, although gamma oscillations can naturally occur through a recurrent dynamics, we can formally show that the routing mechanism itself does not require such oscillatory activity and works equally well if synchronous events would be randomly shuffled over time. Our framework explains a large range of physiological findings in a unified framework and makes specific predictions about putative control mechanisms and their effects on neural dynamics. |
1612.08058 | Danielle Bassett | Lia Papadopoulos, Pablo Blinder, Henrik Ronellenfitsch, Florian Klimm,
Eleni Katifori, David Kleinfeld, Danielle S. Bassett | Embedding of biological distribution networks with differing
environmental constraints | 12 pages, 7 figures, plus supplement | null | null | null | q-bio.QM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distribution networks -- from vasculature to urban transportation systems --
are prevalent in both the natural and consumer worlds. These systems are
intrinsically physical in composition and are embedded into real space,
properties that lead to constraints on their topological organization. In this
study, we compare and contrast two types of biological distribution networks:
mycelial fungi and the vasculature system on the surface of rodent brains. Both
systems are alike in that they must route resources efficiently, but they are
also inherently distinct in terms of their growth mechanisms, and in that fungi
are not attached to a larger organism and must often function in unregulated
and varied environments. We begin by uncovering a common organizational
principle -- Rentian scaling -- that manifests as hierarchical network layout
in both physical and topological space. Simulated models of distribution
networks optimized for transport in the presence of fluctuations are also shown
to exhibit this feature in their embedding, with similar scaling exponents.
However, we also find clear differences in how the fungi and vasculature
balance tradeoffs in material cost, efficiency, and robustness. While the
vasculature appear well optimized for low cost, but relatively high efficiency,
the fungi tend to form more expensive but in turn more robust networks. These
differences may be driven by the distinct functions that each system must
perform, and the different habitats in which they reside. As a whole, this work
demonstrates that distribution networks contain a set of common, emergent
design features, as well as tailored optimizations.
| [
{
"created": "Fri, 23 Dec 2016 18:09:01 GMT",
"version": "v1"
}
] | 2016-12-26 | [
[
"Papadopoulos",
"Lia",
""
],
[
"Blinder",
"Pablo",
""
],
[
"Ronellenfitsch",
"Henrik",
""
],
[
"Klimm",
"Florian",
""
],
[
"Katifori",
"Eleni",
""
],
[
"Kleinfeld",
"David",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Distribution networks -- from vasculature to urban transportation systems -- are prevalent in both the natural and consumer worlds. These systems are intrinsically physical in composition and are embedded into real space, properties that lead to constraints on their topological organization. In this study, we compare and contrast two types of biological distribution networks: mycelial fungi and the vasculature system on the surface of rodent brains. Both systems are alike in that they must route resources efficiently, but they are also inherently distinct in terms of their growth mechanisms, and in that fungi are not attached to a larger organism and must often function in unregulated and varied environments. We begin by uncovering a common organizational principle -- Rentian scaling -- that manifests as hierarchical network layout in both physical and topological space. Simulated models of distribution networks optimized for transport in the presence of fluctuations are also shown to exhibit this feature in their embedding, with similar scaling exponents. However, we also find clear differences in how the fungi and vasculature balance tradeoffs in material cost, efficiency, and robustness. While the vasculature appear well optimized for low cost, but relatively high efficiency, the fungi tend to form more expensive but in turn more robust networks. These differences may be driven by the distinct functions that each system must perform, and the different habitats in which they reside. As a whole, this work demonstrates that distribution networks contain a set of common, emergent design features, as well as tailored optimizations. |
1602.01778 | Julia Pulwicki | Julia Pulwicki | Dynamics of Plant Growth; A Theory Based on Riemannian Geometry | PhD thesis, 188 pages. Accepted December 2015 at University of
Calgary, Canada | null | null | null | q-bio.TO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, a new model for macroscopic plant tissue growth based on
dynamical Riemannian geometry is presented. We treat 1D and 2D tissues as
continuous, deformable, growing geometries for sizes larger than 1mm. The
dynamics of the growing tissue are described by a set of coupled tensor
equations in non-Euclidean (curved) space. These coupled equations represent a
novel feedback mechanism between growth and curvature dynamics.
For 1D growth, numerical simulations are compared to two measures of root
growth. First, modular growth along the simulated root shows an elongation zone
common to many species of plant roots. Second, the relative elemental growth
rate (REGR) calculated in silico exhibits temporal dynamics recently
characterized in high-resolution root growth studies but which thus far lack a
biological hypothesis to explain them. Namely, the REGR can evolve from a
single peak localized near the root tip to a double-peak structure. In our
model, this is a direct consequence of considering growth as both a geometric
reaction-diffusion process and expansion due to a distributed source of new
materials.
In 2D, we study a circularly symmetric growing disk with emergent negative
curvatures. These results are compared against thin disk experiments, which are
a proxy model for plant leaves. These results also apply to the curvature
evolution and the inhomogeneous growth pattern of the Acetabularia cap. Lastly,
we extend the model to anisotropic disks and predict the growth dynamics for a
2D curved surface which develops an elongated shape with localized ruffling.
Our model also provides several measures of the dynamics of tissue growth.
These include the time evolution of the metric and velocity field, which are
dynamical variables in the model, as well as expansion, shear and rotation
which are deformation tensors characterizing the growth of the tissue.
| [
{
"created": "Thu, 4 Feb 2016 18:25:38 GMT",
"version": "v1"
}
] | 2016-02-05 | [
[
"Pulwicki",
"Julia",
""
]
] | In this work, a new model for macroscopic plant tissue growth based on dynamical Riemannian geometry is presented. We treat 1D and 2D tissues as continuous, deformable, growing geometries for sizes larger than 1mm. The dynamics of the growing tissue are described by a set of coupled tensor equations in non-Euclidean (curved) space. These coupled equations represent a novel feedback mechanism between growth and curvature dynamics. For 1D growth, numerical simulations are compared to two measures of root growth. First, modular growth along the simulated root shows an elongation zone common to many species of plant roots. Second, the relative elemental growth rate (REGR) calculated in silico exhibits temporal dynamics recently characterized in high-resolution root growth studies but which thus far lack a biological hypothesis to explain them. Namely, the REGR can evolve from a single peak localized near the root tip to a double-peak structure. In our model, this is a direct consequence of considering growth as both a geometric reaction-diffusion process and expansion due to a distributed source of new materials. In 2D, we study a circularly symmetric growing disk with emergent negative curvatures. These results are compared against thin disk experiments, which are a proxy model for plant leaves. These results also apply to the curvature evolution and the inhomogeneous growth pattern of the Acetabularia cap. Lastly, we extend the model to anisotropic disks and predict the growth dynamics for a 2D curved surface which develops an elongated shape with localized ruffling. Our model also provides several measures of the dynamics of tissue growth. These include the time evolution of the metric and velocity field, which are dynamical variables in the model, as well as expansion, shear and rotation which are deformation tensors characterizing the growth of the tissue. |
2001.03848 | Toshinori Namba | Toshinori Namba (1) and Shuji Ishihara (1,2) ((1) Graduate School of
Arts and Sciences, The University of Tokyo, (2) Universal Biology Institute,
The University of Tokyo) | Cytoskeleton polarity is essential in determining orientational order in
basal bodies of multi-ciliated cells | 30 pages, 8 figures (including Supporting Information), 2 tables | PLoS Comput Biol 16(2): e1007649 (2020) | 10.1371/journal.pcbi.1007649 | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synchronous and directed ciliary beating in trachea allows transport and
ejection of virus and dust from the body. This ciliary function depends on the
coordinated configuration of basal bodies (root of cilia) in apical cell
membrane. However, the mechanism for their formation remains unknown. In this
study, we show that the polarity in apical microtubule bundles plays a
significant role in the organization of basal bodies. A mathematical model
incorporating polarity has been formulated which provides a coherent
explanation and is able to reproduce experimental observations. We have
clarified both necessity ('why polarity is required for pattern formation') and
sufficiency ('how polarity works for pattern formation') of cytoskeleton
polarity for correct pattering of basal bodies with verification by
experimental data. This model further leads us to a possible mechanism for
cellular chirality.
| [
{
"created": "Sun, 12 Jan 2020 04:51:16 GMT",
"version": "v1"
}
] | 2020-03-19 | [
[
"Namba",
"Toshinori",
""
],
[
"Ishihara",
"Shuji",
""
]
] | Synchronous and directed ciliary beating in trachea allows transport and ejection of virus and dust from the body. This ciliary function depends on the coordinated configuration of basal bodies (root of cilia) in apical cell membrane. However, the mechanism for their formation remains unknown. In this study, we show that the polarity in apical microtubule bundles plays a significant role in the organization of basal bodies. A mathematical model incorporating polarity has been formulated which provides a coherent explanation and is able to reproduce experimental observations. We have clarified both necessity ('why polarity is required for pattern formation') and sufficiency ('how polarity works for pattern formation') of cytoskeleton polarity for correct pattering of basal bodies with verification by experimental data. This model further leads us to a possible mechanism for cellular chirality. |
2011.02893 | Chaochao Yan | Chaochao Yan and Qianggang Ding and Peilin Zhao and Shuangjia Zheng
and Jinyu Yang and Yang Yu and Junzhou Huang | RetroXpert: Decompose Retrosynthesis Prediction like a Chemist | 17 pages, to appear in NeurIPS 2020 | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Retrosynthesis is the process of recursively decomposing target molecules
into available building blocks. It plays an important role in solving problems
in organic synthesis planning. To automate or assist in the retrosynthesis
analysis, various retrosynthesis prediction algorithms have been proposed.
However, most of them are cumbersome and lack interpretability about their
predictions. In this paper, we devise a novel template-free algorithm for
automatic retrosynthetic expansion inspired by how chemists approach
retrosynthesis prediction. Our method disassembles retrosynthesis into two
steps: i) identify the potential reaction center of the target molecule through
a novel graph neural network and generate intermediate synthons, and ii)
generate the reactants associated with synthons via a robust reactant
generation model. While outperforming the state-of-the-art baselines by a
significant margin, our model also provides chemically reasonable
interpretation.
| [
{
"created": "Wed, 4 Nov 2020 04:35:34 GMT",
"version": "v1"
}
] | 2020-11-06 | [
[
"Yan",
"Chaochao",
""
],
[
"Ding",
"Qianggang",
""
],
[
"Zhao",
"Peilin",
""
],
[
"Zheng",
"Shuangjia",
""
],
[
"Yang",
"Jinyu",
""
],
[
"Yu",
"Yang",
""
],
[
"Huang",
"Junzhou",
""
]
] | Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been proposed. However, most of them are cumbersome and lack interpretability about their predictions. In this paper, we devise a novel template-free algorithm for automatic retrosynthetic expansion inspired by how chemists approach retrosynthesis prediction. Our method disassembles retrosynthesis into two steps: i) identify the potential reaction center of the target molecule through a novel graph neural network and generate intermediate synthons, and ii) generate the reactants associated with synthons via a robust reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation. |
2404.18785 | Paolo Rissone | Paolo Rissone, Marc Rico-Pasto, Steve Smith, and Felix Ritort | DNA Calorimetric Force Spectroscopy at Single Base Pair Resolution | Main: 23 pages, 4 figures, 1 table SI: 13 pages, 7 figures, 5 tables | null | null | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | DNA hybridization is a fundamental reaction with wide-ranging applications in
biotechnology. The nearest-neighbor (NN) model provides the most reliable
description of the energetics of duplex formation. Most DNA thermodynamics
studies have been done in melting experiments in bulk, of limited resolution
due to ensemble averaging. In contrast, single-molecule methods have reached
the maturity to derive DNA thermodynamics with unprecedented accuracy. We
combine single-DNA mechanical unzipping experiments using a temperature jump
optical trap with machine learning methods and derive the temperature-dependent
DNA energy parameters of the NN model. In particular, we measure the previously
unknown ten heat-capacity change parameters $\Delta C_p$, relevant for
thermodynamical predictions throughout the DNA stability range. Calorimetric
force spectroscopy establishes a groundbreaking methodology to accurately study
nucleic acids, from chemically modified DNA to RNA and DNA/RNA hybrid
structures.
| [
{
"created": "Mon, 29 Apr 2024 15:19:25 GMT",
"version": "v1"
}
] | 2024-04-30 | [
[
"Rissone",
"Paolo",
""
],
[
"Rico-Pasto",
"Marc",
""
],
[
"Smith",
"Steve",
""
],
[
"Ritort",
"Felix",
""
]
] | DNA hybridization is a fundamental reaction with wide-ranging applications in biotechnology. The nearest-neighbor (NN) model provides the most reliable description of the energetics of duplex formation. Most DNA thermodynamics studies have been done in melting experiments in bulk, of limited resolution due to ensemble averaging. In contrast, single-molecule methods have reached the maturity to derive DNA thermodynamics with unprecedented accuracy. We combine single-DNA mechanical unzipping experiments using a temperature jump optical trap with machine learning methods and derive the temperature-dependent DNA energy parameters of the NN model. In particular, we measure the previously unknown ten heat-capacity change parameters $\Delta C_p$, relevant for thermodynamical predictions throughout the DNA stability range. Calorimetric force spectroscopy establishes a groundbreaking methodology to accurately study nucleic acids, from chemically modified DNA to RNA and DNA/RNA hybrid structures. |
1411.0165 | Markus Meister | Markus Meister | Can Humans Really Discriminate 1 Trillion Odors? | 14 pages, 4 figures. Revised version has same technical content, more
introduction for non-experts, more thoughts in the discussion | null | 10.7554/eLife.07865 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A recent paper in a prominent science magazine claims to show that humans can
discriminate at least 1 trillion odors. The authors reached that conclusion
after performing just 260 comparisons of two smells, of which about half could
be discriminated. Furthermore the paper claims that the human ability to
discriminate smells vastly exceeds our abilities to discriminate colors or
musical tones. Here I show that all these statements are wrong by astronomical
factors. A reanalysis of the authors' experiments shows they are also
consistent with humans discriminating just 10 odors. The paper's extravagant
claims are based on errors of mathematical logic. Further analysis highlights
the importance of establishing how many dimensions the perceptual odor space
has. I review some arguments on the topic and propose experimental avenues
towards an answer.
| [
{
"created": "Sat, 1 Nov 2014 20:32:04 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Nov 2014 04:42:26 GMT",
"version": "v2"
}
] | 2016-09-09 | [
[
"Meister",
"Markus",
""
]
] | A recent paper in a prominent science magazine claims to show that humans can discriminate at least 1 trillion odors. The authors reached that conclusion after performing just 260 comparisons of two smells, of which about half could be discriminated. Furthermore the paper claims that the human ability to discriminate smells vastly exceeds our abilities to discriminate colors or musical tones. Here I show that all these statements are wrong by astronomical factors. A reanalysis of the authors' experiments shows they are also consistent with humans discriminating just 10 odors. The paper's extravagant claims are based on errors of mathematical logic. Further analysis highlights the importance of establishing how many dimensions the perceptual odor space has. I review some arguments on the topic and propose experimental avenues towards an answer. |
1502.04262 | Eduardo Izquierdo | Eduardo J. Izquierdo, Paul L. Williams, Randall D. Beer | Information flow through a model of the C. elegans klinotaxis circuit | null | PLoS ONE 10(10): e0140397. (2015) | 10.1371/journal.pone.0140397 | null | q-bio.NC cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding how information about external stimuli is transformed into
behavior is one of the central goals of neuroscience. Here we characterize the
information flow through a complete sensorimotor circuit: from stimulus, to
sensory neurons, to interneurons, to motor neurons, to muscles, to motion.
Specifically, we apply a recently developed framework for quantifying
information flow to a previously published ensemble of models of salt
klinotaxis in the nematode worm C. elegans. The models are grounded in the
neuroanatomy and currently known neurophysiology of the worm. The unknown model
parameters were optimized to reproduce the worm's behavior. Information flow
analysis reveals several key principles underlying how the models operate: (1)
Interneuron class AIY is responsible for integrating information about positive
and negative changes in concentration, and exhibits a strong left/right
information asymmetry. (2) Gap junctions play a crucial role in the transfer of
information responsible for the information symmetry observed in interneuron
class AIZ. (3) Neck motor neuron class SMB implements an information gating
mechanism that underlies the circuit's state-dependent response. (4) The neck
carries non-uniform distribution about changes in concentration. Thus, not all
directions of movement are equally informative. Each of these findings
corresponds to an experimental prediction that could be tested in the worm to
greatly refine our understanding of the neural circuit underlying klinotaxis.
Information flow analysis also allows us to explore how information flow
relates to underlying electrophysiology. Despite large variations in the neural
parameters of individual circuits, the overall information flow architecture
circuit is remarkably consistent across the ensemble, suggesting that
information flow analysis captures general principles of operation for the
klinotaxis circuit.
| [
{
"created": "Sun, 15 Feb 2015 00:19:58 GMT",
"version": "v1"
}
] | 2015-10-15 | [
[
"Izquierdo",
"Eduardo J.",
""
],
[
"Williams",
"Paul L.",
""
],
[
"Beer",
"Randall D.",
""
]
] | Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm C. elegans. The models are grounded in the neuroanatomy and currently known neurophysiology of the worm. The unknown model parameters were optimized to reproduce the worm's behavior. Information flow analysis reveals several key principles underlying how the models operate: (1) Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2) Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3) Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit's state-dependent response. (4) The neck carries non-uniform distribution about changes in concentration. Thus, not all directions of movement are equally informative. Each of these findings corresponds to an experimental prediction that could be tested in the worm to greatly refine our understanding of the neural circuit underlying klinotaxis. Information flow analysis also allows us to explore how information flow relates to underlying electrophysiology. Despite large variations in the neural parameters of individual circuits, the overall information flow architecture circuit is remarkably consistent across the ensemble, suggesting that information flow analysis captures general principles of operation for the klinotaxis circuit. |
q-bio/0406008 | David Marin | David Marin Roma, Ruadhan A. O'Flanagan, Andrei E. Ruckenstein,
Anirvan M. Sengupta, Ranjan Mukhopadhyay | Optimal Path to Epigenetic Switching | 5 pages. 2 figures, uses revtex 4. PR-E reviewed for publication | null | 10.1103/PhysRevE.71.011902 | null | q-bio.MN q-bio.CB | null | We use large deviation methods to calculate rates of noise-induced
transitions between states in multistable genetic networks. We analyze a
synthetic biochemical circuit, the toggle switch, and compare the results to
those obtained from a numerical solution of the master equation.
| [
{
"created": "Thu, 3 Jun 2004 01:41:23 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Jun 2004 20:50:30 GMT",
"version": "v2"
},
{
"created": "Mon, 22 Nov 2004 18:39:24 GMT",
"version": "v3"
}
] | 2013-05-29 | [
[
"Roma",
"David Marin",
""
],
[
"O'Flanagan",
"Ruadhan A.",
""
],
[
"Ruckenstein",
"Andrei E.",
""
],
[
"Sengupta",
"Anirvan M.",
""
],
[
"Mukhopadhyay",
"Ranjan",
""
]
] | We use large deviation methods to calculate rates of noise-induced transitions between states in multistable genetic networks. We analyze a synthetic biochemical circuit, the toggle switch, and compare the results to those obtained from a numerical solution of the master equation. |
1502.05176 | Guido Gigante | Guido Gigante, Gustavo Deco, Shimon Marom, and Paolo Del Giudice | Network events on multiple space and time scales in cultured neural
networks and in a stochastic rate model | 35 pages, 9 figures | PLoS Comput Biol 11(11): e1004547 (2015) | 10.1371/journal.pcbi.1004547 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a
variety of collective dynamical events such as network spikes, UP and DOWN
states, global oscillations, and avalanches. Though each of them have been
variously recognized in previous works as expressions of the excitability of
the cortical tissue and the associated nonlinear dynamics, a unified picture of
their determinant factors (dynamical and architectural) is desirable and not
yet available. Progress has also been partially hindered by the use of a
variety of statistical measures to define the network events of interest. We
propose here a common probabilistic definition of network events that, applied
to the firing activity of cultured neural networks, highlights the
co-occurrence of network spikes, power-law distributed avalanches, and
exponentially distributed `quasi-orbits', which offer a third type of
collective behavior. A rate model, including synaptic excitation and inhibition
with no imposed topology, synaptic short-term depression, and finite-size
noise, accounts for all these different, coexisting phenomena. We find that
their emergence is largely regulated by the proximity to an oscillatory
instability of the dynamics, where the non-linear excitable behavior leads to a
self-amplification of activity fluctuations over a wide range of scales in
space and time. In this sense, the cultured network dynamics is compatible with
an excitation-inhibition balance corresponding to a slightly sub-critical
regime. Finally, we propose and test a method to infer the characteristic time
of the fatigue process, from the observed time course of the network's firing
rate. Unlike the model, possessing a single fatigue mechanism, the cultured
network appears to show multiple time scales, signalling the possible
coexistence of different fatigue mechanisms.
| [
{
"created": "Wed, 18 Feb 2015 10:47:48 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Nov 2015 10:10:07 GMT",
"version": "v2"
}
] | 2015-11-30 | [
[
"Gigante",
"Guido",
""
],
[
"Deco",
"Gustavo",
""
],
[
"Marom",
"Shimon",
""
],
[
"Del Giudice",
"Paolo",
""
]
] | Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them have been variously recognized in previous works as expressions of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of their determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed `quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms. |
1306.3427 | Amaury Lambert | Amaury Lambert and Helen K. Alexander and Tanja Stadler | Phylogenetic analysis accounting for age-dependent death and sampling
with applications to epidemics | 30 pages, 2 figures | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The reconstruction of phylogenetic trees based on viral genetic sequence data
sequentially sampled from an epidemic provides estimates of the past
transmission dynamics, by fitting epidemiological models to these trees. To our
knowledge, none of the epidemiological models currently used in phylogenetics
can account for recovery rates and sampling rates dependent on the time elapsed
since transmission.
Here we introduce an epidemiological model where infectives leave the
epidemic, either by recovery or sampling, after some random time which may
follow an arbitrary distribution.
We derive an expression for the likelihood of the phylogenetic tree of
sampled infectives under our general epidemiological model. The analytic
concept developed in this paper will facilitate inference of past
epidemiological dynamics and provide an analytical framework for performing
very efficient simulations of phylogenetic trees under our model. The main idea
of our analytic study is that the non-Markovian epidemiological model giving
rise to phylogenetic trees growing vertically as time goes by, can be
represented by a Markovian "coalescent point process" growing horizontally by
the sequential addition of pairs of coalescence and sampling times.
As examples, we discuss two special cases of our general model, namely an
application to influenza and an application to HIV. Though phrased in
epidemiological terms, our framework can also be used for instance to fit
macroevolutionary models to phylogenies of extant and extinct species,
accounting for general species lifetime distributions.
| [
{
"created": "Fri, 14 Jun 2013 15:27:09 GMT",
"version": "v1"
}
] | 2013-06-17 | [
[
"Lambert",
"Amaury",
""
],
[
"Alexander",
"Helen K.",
""
],
[
"Stadler",
"Tanja",
""
]
] | The reconstruction of phylogenetic trees based on viral genetic sequence data sequentially sampled from an epidemic provides estimates of the past transmission dynamics, by fitting epidemiological models to these trees. To our knowledge, none of the epidemiological models currently used in phylogenetics can account for recovery rates and sampling rates dependent on the time elapsed since transmission. Here we introduce an epidemiological model where infectives leave the epidemic, either by recovery or sampling, after some random time which may follow an arbitrary distribution. We derive an expression for the likelihood of the phylogenetic tree of sampled infectives under our general epidemiological model. The analytic concept developed in this paper will facilitate inference of past epidemiological dynamics and provide an analytical framework for performing very efficient simulations of phylogenetic trees under our model. The main idea of our analytic study is that the non-Markovian epidemiological model giving rise to phylogenetic trees growing vertically as time goes by, can be represented by a Markovian "coalescent point process" growing horizontally by the sequential addition of pairs of coalescence and sampling times. As examples, we discuss two special cases of our general model, namely an application to influenza and an application to HIV. Though phrased in epidemiological terms, our framework can also be used for instance to fit macroevolutionary models to phylogenies of extant and extinct species, accounting for general species lifetime distributions. |
2407.09587 | Guillermo Abramson | Tom\'as Ignacio Gonz\'alez, Mar\'ia Fabiana Laguna and Guillermo
Abramson | A mean field analysis of the role of indirect transmission in emergent
infection events | Accepted in Physica A | Physica A 648:129933 (2024) | 10.1016/j.physa.2024.129933 | null | q-bio.PE nlin.AO physics.bio-ph | http://creativecommons.org/licenses/by-sa/4.0/ | We developed a mathematical model to investigate the role of indirect
transmission in the spread of infectious diseases, using the illustrative
example of sarcoptic mange as a case study. This disease can be transmitted
through direct contact between an infected host and a susceptible one, or
indirectly when potential hosts encounter infectious mites and larvae deposited
in the environment, commonly referred to as fomites. Our focus is on exploring
the potential of these infectious reservoirs as triggers for emerging infection
events and as stable reservoirs of the disease. To achieve this, our mean field
compartmental model incorporates the epidemiological dynamics driven by
indirect transmission via fomites. We identify different types of dynamics that
the system can go into, controlled by different levels of direct and indirect
transmission. Among these, we find a new regime where the disease can emerge
and persist over time solely through fomites, without the necessity for direct
transmission. This possibility of the system reveals an evolutionary pathway
that could enable the parasite to enhance its fitness beyond host co-evolution.
We also define a new threshold based on an effective reproductive number, that
enables us to predict the conditions for disease persistence. Our model allows
us to assess the potential effectiveness of various disease intervention
measures by incorporating a feature observed in real systems. We hope this
contributes to a better understanding of infectious disease outbreaks.
| [
{
"created": "Fri, 12 Jul 2024 16:51:45 GMT",
"version": "v1"
}
] | 2024-07-19 | [
[
"González",
"Tomás Ignacio",
""
],
[
"Laguna",
"María Fabiana",
""
],
[
"Abramson",
"Guillermo",
""
]
] | We developed a mathematical model to investigate the role of indirect transmission in the spread of infectious diseases, using the illustrative example of sarcoptic mange as a case study. This disease can be transmitted through direct contact between an infected host and a susceptible one, or indirectly when potential hosts encounter infectious mites and larvae deposited in the environment, commonly referred to as fomites. Our focus is on exploring the potential of these infectious reservoirs as triggers for emerging infection events and as stable reservoirs of the disease. To achieve this, our mean field compartmental model incorporates the epidemiological dynamics driven by indirect transmission via fomites. We identify different types of dynamics that the system can go into, controlled by different levels of direct and indirect transmission. Among these, we find a new regime where the disease can emerge and persist over time solely through fomites, without the necessity for direct transmission. This possibility of the system reveals an evolutionary pathway that could enable the parasite to enhance its fitness beyond host co-evolution. We also define a new threshold based on an effective reproductive number, that enables us to predict the conditions for disease persistence. Our model allows us to assess the potential effectiveness of various disease intervention measures by incorporating a feature observed in real systems. We hope this contributes to a better understanding of infectious disease outbreaks. |
2108.07534 | Anurag Sau | Anurag Sau, Sabyasachi Bhattacharya and Bapi Saha | Recognizing and prevention of probable regime shift in density regulated
and Allee type stochastic harvesting model with application to herring
conservation | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | An ecological system with multiple stable equilibria is prone to undergo
catastrophic change or regime shift from one steady-state to another. It should
be noted that, if one of the steady states is an extinction state, the
catastrophic change may lead to extinction. A suitable manual measure may
control the prevention of catastrophic changes of different species from one
equilibrium to another. We consider two stochastic models with linear and
nonlinear harvesting terms. We inspect either density regulation or Allee type
density regulated models [Saha et al., Ecological Modelling, 2013], which have
substantial applications in the herring fish population's viability study. Both
the deterministic models we consider here contain bi-stability under certain
restrictions, and in that case, one of the stable states is the extinction
state. We assume that the dynamical system under consideration is closed, i.e.,
immigration and emigration are absent. The demographic noise is introduced in
the system by substituting an ordinary differential equation with a stochastic
differential equation model, where the birth and death rates of the
deterministic process are used to obtain the instantaneous mean and variance in
the stochastic differential equation. Our study reveals that, the catastrophic
changes can be avoided manually by a suitable choice of handling time that will
eventually help to prevent the sudden extinction of the harvested population.
The entire study is illustrated through the herring population size data
obtained from the Global Population Dynamics Database (GPDD) and simulation
experiment.
| [
{
"created": "Tue, 17 Aug 2021 09:37:31 GMT",
"version": "v1"
}
] | 2021-08-18 | [
[
"Sau",
"Anurag",
""
],
[
"Bhattacharya",
"Sabyasachi",
""
],
[
"Saha",
"Bapi",
""
]
] | An ecological system with multiple stable equilibria is prone to undergo catastrophic change or regime shift from one steady-state to another. It should be noted that, if one of the steady states is an extinction state, the catastrophic change may lead to extinction. A suitable manual measure may control the prevention of catastrophic changes of different species from one equilibrium to another. We consider two stochastic models with linear and nonlinear harvesting terms. We inspect either density regulation or Allee type density regulated models [Saha et al., Ecological Modelling, 2013], which have substantial applications in the herring fish population's viability study. Both the deterministic models we consider here contain bi-stability under certain restrictions, and in that case, one of the stable states is the extinction state. We assume that the dynamical system under consideration is closed, i.e., immigration and emigration are absent. The demographic noise is introduced in the system by substituting an ordinary differential equation with a stochastic differential equation model, where the birth and death rates of the deterministic process are used to obtain the instantaneous mean and variance in the stochastic differential equation. Our study reveals that, the catastrophic changes can be avoided manually by a suitable choice of handling time that will eventually help to prevent the sudden extinction of the harvested population. The entire study is illustrated through the herring population size data obtained from the Global Population Dynamics Database (GPDD) and simulation experiment. |
2003.08824 | Alex De Visscher | Alex De Visscher | A COVID-19 Epidemiological Model for Community and Policy Maker Use | 21 pages, includes source code; revision: errors corrected in eqs.
(15)-(17) | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | An epidemiological model for COVID-19 was developed and implemented in
MATLAB/GNU Octave for use by public health practitioners, policy makers and the
general public. The model distinguishes four stages in the disease: infected,
sick, seriously sick, and better. The model was preliminarily parameterized
based on observations of the spread of the disease. The model is consistent
with a mortality rate of 1.5 %. Preliminary simulations with the model indicate
that concepts such as "herd immunity" and "flattening the curve" are highly
misleading in the context of this virus. Public policies based on these
concepts are inadequate to protect the population. Only reducing the R0 of the
virus below 1 is an effective strategy for maintaining the death burden of
COVID-19 within the normal range of seasonal flu. As R0 values estimated with
the model range from 2.82 worldwide outside of China and 3.83 in the Western
world in late February - early March 2020, this means social distancing with
effectiveness greater than 65 % (worldwide) or 75 % (Western world) are needed
to combat the virus successfully.
| [
{
"created": "Thu, 19 Mar 2020 14:34:30 GMT",
"version": "v1"
},
{
"created": "Sun, 22 Mar 2020 06:52:59 GMT",
"version": "v2"
}
] | 2020-03-24 | [
[
"De Visscher",
"Alex",
""
]
] | An epidemiological model for COVID-19 was developed and implemented in MATLAB/GNU Octave for use by public health practitioners, policy makers and the general public. The model distinguishes four stages in the disease: infected, sick, seriously sick, and better. The model was preliminarily parameterized based on observations of the spread of the disease. The model is consistent with a mortality rate of 1.5 %. Preliminary simulations with the model indicate that concepts such as "herd immunity" and "flattening the curve" are highly misleading in the context of this virus. Public policies based on these concepts are inadequate to protect the population. Only reducing the R0 of the virus below 1 is an effective strategy for maintaining the death burden of COVID-19 within the normal range of seasonal flu. As R0 values estimated with the model range from 2.82 worldwide outside of China and 3.83 in the Western world in late February - early March 2020, this means social distancing with effectiveness greater than 65 % (worldwide) or 75 % (Western world) are needed to combat the virus successfully. |
1303.3109 | Michal Komorowski | Micha{\l} W{\l}odarczyk, Tomasz Lipniacki and Micha{\l} Komorowski | Functional redundancy in the NF-\kappa B signalling pathway | null | null | null | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to represent intracellular biochemical dynamics via deterministic
and stochastic modelling is one of the crucial components to move biological
sciences in the observe-predict-control-design knowledge ladder. Compared to
the engineering or physics problems, dynamical models in quantitative biology
typically dependent on a relatively large number of parameters. Therefore, the
relationship between model parameters and dynamics is often prohibitively
difficult to determine. We developed a method to depict the input-output
relationship for multi-parametric stochastic and deterministic models via
information-theoretic quantification of similarity between model parameters and
modules. Identification of most information-theoretically orthogonal biological
components, provided mathematical language to precisely communicate and
visualise compensation like phenomena such as biological robustness, sloppiness
and statistical non-identifiability. A comprehensive analysis of the
multi-parameter NF-$\kappa$B signalling pathway demonstrates that the
information-theoretic similarity reflects a topological structure of the
network. Examination of the currently available experimental data on this
system reveals the number of identifiable parameters and suggests informative
experimental protocols.
| [
{
"created": "Wed, 13 Mar 2013 09:29:24 GMT",
"version": "v1"
}
] | 2013-03-14 | [
[
"Włodarczyk",
"Michał",
""
],
[
"Lipniacki",
"Tomasz",
""
],
[
"Komorowski",
"Michał",
""
]
] | The ability to represent intracellular biochemical dynamics via deterministic and stochastic modelling is one of the crucial components to move biological sciences in the observe-predict-control-design knowledge ladder. Compared to the engineering or physics problems, dynamical models in quantitative biology typically dependent on a relatively large number of parameters. Therefore, the relationship between model parameters and dynamics is often prohibitively difficult to determine. We developed a method to depict the input-output relationship for multi-parametric stochastic and deterministic models via information-theoretic quantification of similarity between model parameters and modules. Identification of most information-theoretically orthogonal biological components, provided mathematical language to precisely communicate and visualise compensation like phenomena such as biological robustness, sloppiness and statistical non-identifiability. A comprehensive analysis of the multi-parameter NF-$\kappa$B signalling pathway demonstrates that the information-theoretic similarity reflects a topological structure of the network. Examination of the currently available experimental data on this system reveals the number of identifiable parameters and suggests informative experimental protocols. |
2003.06038 | Tilo Schwalger | Bastian Pietras, No\'e Gallice, Tilo Schwalger | Low-dimensional firing-rate dynamics for populations of renewal-type
spiking neurons | 24 pages, 7 figures | Phys. Rev. E 102, 022407 (2020) | 10.1103/PhysRevE.102.022407 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The macroscopic dynamics of large populations of neurons can be
mathematically analyzed using low-dimensional firing-rate or neural-mass
models. However, these models fail to capture spike synchronization effects of
stochastic spiking neurons such as the non-stationary population response to
rapidly changing stimuli. Here, we derive low-dimensional firing-rate models
for homogeneous populations of general renewal-type neurons, including
integrate-and-fire models driven by white noise. Renewal models account for
neuronal refractoriness and spike synchronization dynamics. The derivation is
based on an eigenmode expansion of the associated refractory density equation,
which generalizes previous spectral methods for Fokker-Planck equations to
arbitrary renewal models. We find a simple relation between the eigenvalues,
which determine the characteristic time scales of the firing rate dynamics, and
the Laplace transform of the interspike interval density or the survival
function of the renewal process. Analytical expressions for the Laplace
transforms are readily available for many renewal models including the leaky
integrate-and-fire model. Retaining only the first eigenmode yields already an
adequate low-dimensional approximation of the firing-rate dynamics that
captures spike synchronization effects and fast transient dynamics at stimulus
onset. We explicitly demonstrate the validity of our model for a large
homogeneous population of Poisson neurons with absolute refractoriness, and
other renewal models that admit an explicit analytical calculation of the
eigenvalues. The here presented eigenmode expansion provides a systematic
framework for novel firing-rate models in computational neuroscience based on
spiking neuron dynamics with refractoriness.
| [
{
"created": "Thu, 12 Mar 2020 22:10:20 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Mar 2020 17:34:50 GMT",
"version": "v2"
}
] | 2023-04-20 | [
[
"Pietras",
"Bastian",
""
],
[
"Gallice",
"Noé",
""
],
[
"Schwalger",
"Tilo",
""
]
] | The macroscopic dynamics of large populations of neurons can be mathematically analyzed using low-dimensional firing-rate or neural-mass models. However, these models fail to capture spike synchronization effects of stochastic spiking neurons such as the non-stationary population response to rapidly changing stimuli. Here, we derive low-dimensional firing-rate models for homogeneous populations of general renewal-type neurons, including integrate-and-fire models driven by white noise. Renewal models account for neuronal refractoriness and spike synchronization dynamics. The derivation is based on an eigenmode expansion of the associated refractory density equation, which generalizes previous spectral methods for Fokker-Planck equations to arbitrary renewal models. We find a simple relation between the eigenvalues, which determine the characteristic time scales of the firing rate dynamics, and the Laplace transform of the interspike interval density or the survival function of the renewal process. Analytical expressions for the Laplace transforms are readily available for many renewal models including the leaky integrate-and-fire model. Retaining only the first eigenmode yields already an adequate low-dimensional approximation of the firing-rate dynamics that captures spike synchronization effects and fast transient dynamics at stimulus onset. We explicitly demonstrate the validity of our model for a large homogeneous population of Poisson neurons with absolute refractoriness, and other renewal models that admit an explicit analytical calculation of the eigenvalues. The here presented eigenmode expansion provides a systematic framework for novel firing-rate models in computational neuroscience based on spiking neuron dynamics with refractoriness. |
1803.01070 | Hue Sun Chan | Suman Das, Adam Eisen, Yi-Hsuan Lin and Hue Sun Chan | A Lattice Model of Charge-Pattern-Dependent Polyampholyte Phase
Separation | 33 pages, 12 figures, 1 table. Accepted for publication in the
Journal of Physical Chemistry B | Journal of Physical Chemistry B Vol. 122, pp. 5418-5431 (2018);
Correction: Vol. 122, p. 8111 (2018) | 10.1021/acs.jpcb.7b11723 | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In view of recent intense experimental and theoretical interests in the
biophysics of liquid-liquid phase separation (LLPS) of intrinsically disordered
proteins (IDPs), heteropolymer models with chain molecules configured as
self-avoiding walks on the simple cubic lattice are constructed to study how
phase behaviors depend on the sequence of monomers along the chains. To address
pertinent general principles, we focus primarily on two fully charged
50-monomer sequences with significantly different charge patterns. Each monomer
in our models occupies a single lattice site and all monomers interact via a
screened pairwise Coulomb potential. Phase diagrams are obtained by extensive
Monte Carlo sampling performed at multiple temperatures on ensembles of 300
chains in boxes of sizes ranging from $52\times 52\times 52$ to $246\times
246\times 246$ to simulate a large number of different systems with the overall
polymer volume fraction $\phi$ in each system varying from $0.001$ to $0.1$.
Phase separation in the model systems is characterized by the emergence of a
large cluster connected by inter-monomer nearest-neighbor lattice contacts and
by large fluctuations in local polymer density. The simulated critical
temperatures, $T_{\rm cr}$, of phase separation for the two sequences differ
significantly, whereby the sequence with a more "blocky" charge pattern
exhibits a substantially higher propensity to phase separate. The trend is
consistent with our sequence-specific random-phase-approximation (RPA) polymer
theory, but the variation of the simulated $T_{\rm cr}$ with a previously
proposed "sequence charge decoration" pattern parameter is milder than that
predicted by RPA. Ramifications of our findings for the development of
analytical theory and simulation protocols of IDP LLPS are discussed.
| [
{
"created": "Fri, 2 Mar 2018 23:23:15 GMT",
"version": "v1"
}
] | 2018-08-30 | [
[
"Das",
"Suman",
""
],
[
"Eisen",
"Adam",
""
],
[
"Lin",
"Yi-Hsuan",
""
],
[
"Chan",
"Hue Sun",
""
]
] | In view of recent intense experimental and theoretical interests in the biophysics of liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs), heteropolymer models with chain molecules configured as self-avoiding walks on the simple cubic lattice are constructed to study how phase behaviors depend on the sequence of monomers along the chains. To address pertinent general principles, we focus primarily on two fully charged 50-monomer sequences with significantly different charge patterns. Each monomer in our models occupies a single lattice site and all monomers interact via a screened pairwise Coulomb potential. Phase diagrams are obtained by extensive Monte Carlo sampling performed at multiple temperatures on ensembles of 300 chains in boxes of sizes ranging from $52\times 52\times 52$ to $246\times 246\times 246$ to simulate a large number of different systems with the overall polymer volume fraction $\phi$ in each system varying from $0.001$ to $0.1$. Phase separation in the model systems is characterized by the emergence of a large cluster connected by inter-monomer nearest-neighbor lattice contacts and by large fluctuations in local polymer density. The simulated critical temperatures, $T_{\rm cr}$, of phase separation for the two sequences differ significantly, whereby the sequence with a more "blocky" charge pattern exhibits a substantially higher propensity to phase separate. The trend is consistent with our sequence-specific random-phase-approximation (RPA) polymer theory, but the variation of the simulated $T_{\rm cr}$ with a previously proposed "sequence charge decoration" pattern parameter is milder than that predicted by RPA. Ramifications of our findings for the development of analytical theory and simulation protocols of IDP LLPS are discussed. |
1912.13017 | William Bialek | Rebecca J. Rousseau and William Bialek | Information costs in the control of protein synthesis | null | null | null | null | q-bio.SC cond-mat.stat-mech q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Efficient protein synthesis depends on the availability of charged tRNA
molecules. With 61 different codons, shifting the balance among the tRNA
abundances can lead to large changes in the protein synthesis rate. Previous
theoretical work has asked about the optimization of these abundances, and
there is some evidence that regulatory mechanisms bring cells close to this
optimum, on average. We formulate the tradeoff between the precision of control
and the efficiency of synthesis, asking for the maximum entropy distribution of
tRNA abundances consistent with a desired mean rate of protein synthesis. Our
analysis, using data from E. coli, indicates that reasonable synthesis rates
are consistent only with rather low entropies, so that the cell's regulatory
mechanisms must encode a large amount of information about the "correct" tRNA
abundances.
| [
{
"created": "Mon, 30 Dec 2019 17:24:52 GMT",
"version": "v1"
}
] | 2020-01-01 | [
[
"Rousseau",
"Rebecca J.",
""
],
[
"Bialek",
"William",
""
]
] | Efficient protein synthesis depends on the availability of charged tRNA molecules. With 61 different codons, shifting the balance among the tRNA abundances can lead to large changes in the protein synthesis rate. Previous theoretical work has asked about the optimization of these abundances, and there is some evidence that regulatory mechanisms bring cells close to this optimum, on average. We formulate the tradeoff between the precision of control and the efficiency of synthesis, asking for the maximum entropy distribution of tRNA abundances consistent with a desired mean rate of protein synthesis. Our analysis, using data from E. coli, indicates that reasonable synthesis rates are consistent only with rather low entropies, so that the cell's regulatory mechanisms must encode a large amount of information about the "correct" tRNA abundances. |
1506.04965 | Young-Ho Eom | Young-Ho Eom, Andrea Perna, Santo Fortunato, Eric Darrouzet, Guy
Theraulaz, Christian Jost | Network-based model of the growth of termite nests | 11 pages, 5 figures. Published in Phy. Rev. E. Supplement materials
are available at
http://journals.aps.org/pre/supplemental/10.1103/PhysRevE.92.062810/YHE-TermiteNestModel-SM-V1.2.pdf | Phys. Rev. E 92, 062810 (2015) | 10.1103/PhysRevE.92.062810 | null | q-bio.PE nlin.AO physics.bio-ph physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a model for the growth of the transportation network inside nests
of the social insect subfamily Termitinae (Isoptera, termitidae). These nests
consist of large chambers (nodes) connected by tunnels (edges). The model based
on the empirical analysis of the real nest networks combined with pruning (edge
removal, either random or weighted by betweenness centrality) and a memory
effect (preferential growth from the latest added chambers) successfully
predicts emergent nest properties (degree distribution, size of the largest
connected component, average path lengths, backbone link ratios, and local
graph redundancy). The two pruning alternatives can be associated with
different genuses in the subfamily. A sensitivity analysis on the pruning and
memory parameters indicates that Termitinae networks favor fast internal
transportation over efficient defense strategies against ant predators. Our
results provide an example of how complex network organization and efficient
network properties can be generated from simple building rules based on local
interactions and contribute to our understanding of the mechanisms that come
into play for the formation of termite networks and of biological
transportation networks in general.
| [
{
"created": "Tue, 16 Jun 2015 13:33:54 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Dec 2015 10:26:30 GMT",
"version": "v2"
}
] | 2015-12-11 | [
[
"Eom",
"Young-Ho",
""
],
[
"Perna",
"Andrea",
""
],
[
"Fortunato",
"Santo",
""
],
[
"Darrouzet",
"Eric",
""
],
[
"Theraulaz",
"Guy",
""
],
[
"Jost",
"Christian",
""
]
] | We present a model for the growth of the transportation network inside nests of the social insect subfamily Termitinae (Isoptera, termitidae). These nests consist of large chambers (nodes) connected by tunnels (edges). The model based on the empirical analysis of the real nest networks combined with pruning (edge removal, either random or weighted by betweenness centrality) and a memory effect (preferential growth from the latest added chambers) successfully predicts emergent nest properties (degree distribution, size of the largest connected component, average path lengths, backbone link ratios, and local graph redundancy). The two pruning alternatives can be associated with different genuses in the subfamily. A sensitivity analysis on the pruning and memory parameters indicates that Termitinae networks favor fast internal transportation over efficient defense strategies against ant predators. Our results provide an example of how complex network organization and efficient network properties can be generated from simple building rules based on local interactions and contribute to our understanding of the mechanisms that come into play for the formation of termite networks and of biological transportation networks in general. |
1311.3261 | Vu Dinh | Vu Dinh, Ann E. Rundell and Gregery T. Buzzard | Experimental Design for Dynamics Identification of Cellular Processes | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the problem of using nonlinear models to design experiments to
characterize the dynamics of cellular processes by using the approach of the
Maximally Informative Next Experiment (MINE), which was introduced in [W. Dong,
et al. Systems biology of the clock in neurospora crassa. {\em PLoS ONE}, page
e3105, 2008] and independently in [M. M. Donahue, et al. Experiment design
through dynamical characterization of non-linear systems biology models
utilising sparse grids. {\em IET System Biology}, 4:249--262, 2010]. In this
approach, existing data is used to define a probability distribution on the
parameters; the next measurement point is the one that yields the largest model
output variance with this distribution. Building upon this approach, we
introduce the Expected Dynamics Estimator (EDE), which is the expected value
using this distribution of the output as a function of time. We prove the
consistency of this estimator (uniform convergence to true dynamics) even when
the chosen experiments cluster in a finite set of points. We extend this proof
of consistency to various practical assumptions on noisy data and moderate
levels of model mismatch. Through the derivation and proof, we develop a
relaxed version of MINE that is more computationally tractable and robust than
the original formulation. The results are illustrated with numerical examples
on two nonlinear ordinary differential equation models of biomolecular and
cellular processes.
| [
{
"created": "Wed, 13 Nov 2013 19:27:02 GMT",
"version": "v1"
}
] | 2013-11-14 | [
[
"Dinh",
"Vu",
""
],
[
"Rundell",
"Ann E.",
""
],
[
"Buzzard",
"Gregery T.",
""
]
] | We address the problem of using nonlinear models to design experiments to characterize the dynamics of cellular processes by using the approach of the Maximally Informative Next Experiment (MINE), which was introduced in [W. Dong, et al. Systems biology of the clock in neurospora crassa. {\em PLoS ONE}, page e3105, 2008] and independently in [M. M. Donahue, et al. Experiment design through dynamical characterization of non-linear systems biology models utilising sparse grids. {\em IET System Biology}, 4:249--262, 2010]. In this approach, existing data is used to define a probability distribution on the parameters; the next measurement point is the one that yields the largest model output variance with this distribution. Building upon this approach, we introduce the Expected Dynamics Estimator (EDE), which is the expected value using this distribution of the output as a function of time. We prove the consistency of this estimator (uniform convergence to true dynamics) even when the chosen experiments cluster in a finite set of points. We extend this proof of consistency to various practical assumptions on noisy data and moderate levels of model mismatch. Through the derivation and proof, we develop a relaxed version of MINE that is more computationally tractable and robust than the original formulation. The results are illustrated with numerical examples on two nonlinear ordinary differential equation models of biomolecular and cellular processes. |
1610.09985 | Keisuke Ishihara | Keisuke Ishihara, Kirill S. Korolev, Timothy J. Mitchison | Physical Basis of Large Microtubule Aster Growth | 5 figures, supplementary information with 5 additional figures | eLife 2016;5:e19145 | 10.7554/eLife.19145 | null | q-bio.SC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Microtubule asters - radial arrays of microtubules organized by centrosomes -
play a fundamental role in the spatial coordination of animal cells. The
standard model of aster growth assumes a fixed number of microtubules
originating from the centrosomes. However, aster morphology in this model does
not scale with cell size, and we recently found evidence for non-centrosomal
microtubule nucleation. Here, we combine autocatalytic nucleation and
polymerization dynamics to develop a biophysical model of aster growth. Our
model predicts that asters expand as traveling waves and recapitulates all
major aspects of aster growth. As the nucleation rate increases, the model
predicts an explosive transition from stationary to growing asters with a
discontinuous jump of the growth velocity to a nonzero value. Experiments in
frog egg extract confirm the main theoretical predictions. Our results suggest
that asters observed in large frog and amphibian eggs are a meshwork of short,
unstable microtubules maintained by autocatalytic nucleation and provide a
paradigm for the assembly of robust and evolvable polymer networks.
| [
{
"created": "Mon, 31 Oct 2016 15:49:51 GMT",
"version": "v1"
}
] | 2018-07-02 | [
[
"Ishihara",
"Keisuke",
""
],
[
"Korolev",
"Kirill S.",
""
],
[
"Mitchison",
"Timothy J.",
""
]
] | Microtubule asters - radial arrays of microtubules organized by centrosomes - play a fundamental role in the spatial coordination of animal cells. The standard model of aster growth assumes a fixed number of microtubules originating from the centrosomes. However, aster morphology in this model does not scale with cell size, and we recently found evidence for non-centrosomal microtubule nucleation. Here, we combine autocatalytic nucleation and polymerization dynamics to develop a biophysical model of aster growth. Our model predicts that asters expand as traveling waves and recapitulates all major aspects of aster growth. As the nucleation rate increases, the model predicts an explosive transition from stationary to growing asters with a discontinuous jump of the growth velocity to a nonzero value. Experiments in frog egg extract confirm the main theoretical predictions. Our results suggest that asters observed in large frog and amphibian eggs are a meshwork of short, unstable microtubules maintained by autocatalytic nucleation and provide a paradigm for the assembly of robust and evolvable polymer networks. |
1310.1462 | Arun Konagurthu | Arun S. Konagurthu, Arthur M. Lesk, David Abramson, Peter J. Stuckey,
Lloyd Allison | Statistical Inference of a canonical dictionary of protein substructural
fragments | 17 pages, 3 Figures (Accepted for publication as a short paper in the
'The thirteenth International Conference on Data Mining (ICDM '13; Dallas,
Texas Dec 7-10 2013) | null | null | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proteins are biomolecules of life. They fold into a great variety of
three-dimensional (3D) shapes. Underlying these folding patterns are many
recurrent structural fragments or building blocks (analogous to `LEGO bricks').
This paper reports an innovative statistical inference approach to discover a
comprehensive dictionary of protein structural building blocks from a large
corpus of experimentally determined protein structures. Our approach is built
on the Bayesian and information-theoretic criterion of minimum message length.
To the best of our knowledge, this work is the first systematic and rigorous
treatment of a very important data mining problem that arises in the
cross-disciplinary area of structural bioinformatics. The quality of the
dictionary we find is demonstrated by its explanatory power -- any protein
within the corpus of known 3D structures can be dissected into successive
regions assigned to fragments from this dictionary. This induces a novel
one-dimensional representation of three-dimensional protein folding patterns,
suitable for application of the rich repertoire of character-string processing
algorithms, for rapid identification of folding patterns of newly-determined
structures. This paper presents the details of the methodology used to infer
the dictionary of building blocks, and is supported by illustrative examples to
demonstrate its effectiveness and utility.
| [
{
"created": "Sat, 5 Oct 2013 10:14:21 GMT",
"version": "v1"
}
] | 2013-10-08 | [
[
"Konagurthu",
"Arun S.",
""
],
[
"Lesk",
"Arthur M.",
""
],
[
"Abramson",
"David",
""
],
[
"Stuckey",
"Peter J.",
""
],
[
"Allison",
"Lloyd",
""
]
] | Proteins are biomolecules of life. They fold into a great variety of three-dimensional (3D) shapes. Underlying these folding patterns are many recurrent structural fragments or building blocks (analogous to `LEGO bricks'). This paper reports an innovative statistical inference approach to discover a comprehensive dictionary of protein structural building blocks from a large corpus of experimentally determined protein structures. Our approach is built on the Bayesian and information-theoretic criterion of minimum message length. To the best of our knowledge, this work is the first systematic and rigorous treatment of a very important data mining problem that arises in the cross-disciplinary area of structural bioinformatics. The quality of the dictionary we find is demonstrated by its explanatory power -- any protein within the corpus of known 3D structures can be dissected into successive regions assigned to fragments from this dictionary. This induces a novel one-dimensional representation of three-dimensional protein folding patterns, suitable for application of the rich repertoire of character-string processing algorithms, for rapid identification of folding patterns of newly-determined structures. This paper presents the details of the methodology used to infer the dictionary of building blocks, and is supported by illustrative examples to demonstrate its effectiveness and utility. |
1409.4976 | Rachel Bearon | Joseph Leedale and Anne Herrmann and James Bagnall and Andreas Fercher
and Dmitri Papkovsky and Violaine S\'ee and Rachel N. Bearon | Modeling the dynamics of hypoxia inducible factor-1{\alpha}
(HIF-1{\alpha}) within single cells and 3D cell culture systems | null | null | null | null | q-bio.CB q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | HIF (Hypoxia Inducible Factor) is an oxygen-regulated transcription factor
that mediates the intracellular response to hypoxia in human cells. There is
increasing evidence that cell signaling pathways encode temporal information,
and thus cell fate may be determined by the dynamics of protein levels. We have
developed a mathematical model to describe the transient dynamics of the
HIF-1{\alpha} protein measured in single cells subjected to hypoxic shock. The
essential characteristics of these data are modeled with a system of
differential equations describing the feedback inhibition between HIF-1{\alpha}
and Prolyl Hydroxylases (PHD) oxygen sensors. Heterogeneity in the single-cell
data is accounted for through parameter variation in the model. We previously
identified the PHD2 isoform as the main PHD responsible for controlling the
HIF-1{\alpha} transient response, and make here testable predictions regarding
HIF-1{\alpha} dynamics subject to repetitive hypoxic pulses. The model is
further developed to describe the dynamics of HIF-1{\alpha} in cells cultured
as 3D spheroids, with oxygen dynamics parameterized using experimental
measurements of oxygen within spheroids. We show that the dynamics of
HIF-1{\alpha} and transcriptional targets of HIF-1{\alpha} display a
non-monotone response to the oxygen dynamics. Specifically we demonstrate that
the dynamic transient behavior of HIF-1{\alpha} results in differential
dynamics in transcriptional targets.
| [
{
"created": "Wed, 17 Sep 2014 12:56:31 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Sep 2014 15:47:28 GMT",
"version": "v2"
}
] | 2014-09-23 | [
[
"Leedale",
"Joseph",
""
],
[
"Herrmann",
"Anne",
""
],
[
"Bagnall",
"James",
""
],
[
"Fercher",
"Andreas",
""
],
[
"Papkovsky",
"Dmitri",
""
],
[
"Sée",
"Violaine",
""
],
[
"Bearon",
"Rachel N.",
""
]
] | HIF (Hypoxia Inducible Factor) is an oxygen-regulated transcription factor that mediates the intracellular response to hypoxia in human cells. There is increasing evidence that cell signaling pathways encode temporal information, and thus cell fate may be determined by the dynamics of protein levels. We have developed a mathematical model to describe the transient dynamics of the HIF-1{\alpha} protein measured in single cells subjected to hypoxic shock. The essential characteristics of these data are modeled with a system of differential equations describing the feedback inhibition between HIF-1{\alpha} and Prolyl Hydroxylases (PHD) oxygen sensors. Heterogeneity in the single-cell data is accounted for through parameter variation in the model. We previously identified the PHD2 isoform as the main PHD responsible for controlling the HIF-1{\alpha} transient response, and make here testable predictions regarding HIF-1{\alpha} dynamics subject to repetitive hypoxic pulses. The model is further developed to describe the dynamics of HIF-1{\alpha} in cells cultured as 3D spheroids, with oxygen dynamics parameterized using experimental measurements of oxygen within spheroids. We show that the dynamics of HIF-1{\alpha} and transcriptional targets of HIF-1{\alpha} display a non-monotone response to the oxygen dynamics. Specifically we demonstrate that the dynamic transient behavior of HIF-1{\alpha} results in differential dynamics in transcriptional targets. |
2009.04006 | Kai Ueltzh\"offer | Kai Ueltzh\"offer | On the thermodynamics of prediction under dissipative adaptation | null | null | null | null | q-bio.NC cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | On the one hand, the dissipated heat of a thermodynamic work extraction
process upper bounds the non-predictive information, which the associated
system encodes about its environment. Thus, emergent information processing
capabilities can be understood from the perspective of a pressure towards high
thermodynamic efficiency. On the other hand, the second law of thermodynamics
plays a crucial role in the emergence of complex, self-organising dissipative
structures. Such structures are thermodynamically favoured, because they can
dissipate free energy reservoirs, which would not be accessible otherwise.
Thereby, they allow a closed system to move from one meta-stable state to
another meta-stable state of higher entropy. This paper will argue, that these
two views are not contradictory, but that their combination allows to
understand the transition from simple self-organising dissipative structures to
complex information processing systems. If the efficiency required by a
dissipative structure to harvest enough work from the channeled flow of free
energy to maintain its own structure is high, there is a drive for this system
to be predictive. Still, the existence of this dissipative system is
thermodynamically favoured, compared to a situation without any dissipative
structure. Due to the emergence of a hierarchy of dissipative systems, which by
themselves are non-equilibrium structures that can be dissipated, such a drive
develops naturally, as one ascends in this hierarchy further and further away
from the initial driving disequilibrium.
| [
{
"created": "Tue, 8 Sep 2020 21:55:16 GMT",
"version": "v1"
}
] | 2020-09-10 | [
[
"Ueltzhöffer",
"Kai",
""
]
] | On the one hand, the dissipated heat of a thermodynamic work extraction process upper bounds the non-predictive information, which the associated system encodes about its environment. Thus, emergent information processing capabilities can be understood from the perspective of a pressure towards high thermodynamic efficiency. On the other hand, the second law of thermodynamics plays a crucial role in the emergence of complex, self-organising dissipative structures. Such structures are thermodynamically favoured, because they can dissipate free energy reservoirs, which would not be accessible otherwise. Thereby, they allow a closed system to move from one meta-stable state to another meta-stable state of higher entropy. This paper will argue, that these two views are not contradictory, but that their combination allows to understand the transition from simple self-organising dissipative structures to complex information processing systems. If the efficiency required by a dissipative structure to harvest enough work from the channeled flow of free energy to maintain its own structure is high, there is a drive for this system to be predictive. Still, the existence of this dissipative system is thermodynamically favoured, compared to a situation without any dissipative structure. Due to the emergence of a hierarchy of dissipative systems, which by themselves are non-equilibrium structures that can be dissipated, such a drive develops naturally, as one ascends in this hierarchy further and further away from the initial driving disequilibrium. |
2301.08803 | Julio Guerrero | Julio Guerrero, Maria del Carmen Galiano, Giuseppe Orlando | Modeling COVID-19 pandemic with financial markets models: The case of
Ja\'en (Spain) | 15 pages, 12 figures, Latex document | null | null | null | q-bio.PE q-fin.MF | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The main objective of this work is to test whether some stochastic models
typically used in financial markets could be applied to the COVID-19 pandemic.
To this end we have implemented the ARIMAX and Cox-Ingersoll-Ross (CIR) models
originally designed for interest rate pricing but transformed by us into a
forecasting tool. For the latter, which we denoted CIR*, both the
Euler-Maruyama method and the Milstein method were used. Forecasts obtained
with the maximum likelihood method have been validated with 95\% confidence
intervals and with statistical measures of goodness of fit, such as the root
mean square error (RMSE). We demonstrate that the accuracy of the obtained
results is consistent with the observations and sufficiently accurate to the
point that the proposed CIR* framework could be considered a valid alternative
to the classical ARIMAX for modelling pandemics.
| [
{
"created": "Fri, 20 Jan 2023 21:01:46 GMT",
"version": "v1"
}
] | 2023-01-24 | [
[
"Guerrero",
"Julio",
""
],
[
"Galiano",
"Maria del Carmen",
""
],
[
"Orlando",
"Giuseppe",
""
]
] | The main objective of this work is to test whether some stochastic models typically used in financial markets could be applied to the COVID-19 pandemic. To this end we have implemented the ARIMAX and Cox-Ingersoll-Ross (CIR) models originally designed for interest rate pricing but transformed by us into a forecasting tool. For the latter, which we denoted CIR*, both the Euler-Maruyama method and the Milstein method were used. Forecasts obtained with the maximum likelihood method have been validated with 95\% confidence intervals and with statistical measures of goodness of fit, such as the root mean square error (RMSE). We demonstrate that the accuracy of the obtained results is consistent with the observations and sufficiently accurate to the point that the proposed CIR* framework could be considered a valid alternative to the classical ARIMAX for modelling pandemics. |
0908.1351 | Paolo Visco | Paolo Visco, Rosalind J. Allen, Satya N. Majumdar, Martin R. Evans | Switching and growth for microbial populations in catastrophic
responsive environments | 9 pages, 10 figures; replaced with revised version | Biophys. J. 98(7) 1099 (2010) | 10.1016/j.bpj.2009.11.049 | null | q-bio.PE cond-mat.soft cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phase variation, or stochastic switching between alternative states of gene
expression, is common among microbes, and may be important in coping with
changing environments. We use a theoretical model to assess whether such
switching is a good strategy for growth in environments with occasional
catastrophic events. We find that switching can be advantageous, but only when
the environment is responsive to the microbial population. In our model,
microbes switch randomly between two phenotypic states, with different growth
rates. The environment undergoes sudden "catastrophes", the probability of
which depends on the composition of the population. We derive a simple
analytical result for the population growth rate. For a responsive environment,
two alternative strategies emerge. In the "no switching" strategy, the
population maximises its instantaneous growth rate, regardless of catastrophes.
In the "switching" strategy, the microbial switching rate is tuned to minimise
the environmental response. Which of these strategies is most favourable
depends on the parameters of the model. Previous studies have shown that
microbial switching can be favourable when the environment changes in an
unresponsive fashion between several states. Here, we demonstrate an
alternative role for phase variation in allowing microbes to maximise their
growth in catastrophic responsive environments.
| [
{
"created": "Mon, 10 Aug 2009 16:24:59 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Apr 2010 15:03:52 GMT",
"version": "v2"
}
] | 2010-04-13 | [
[
"Visco",
"Paolo",
""
],
[
"Allen",
"Rosalind J.",
""
],
[
"Majumdar",
"Satya N.",
""
],
[
"Evans",
"Martin R.",
""
]
] | Phase variation, or stochastic switching between alternative states of gene expression, is common among microbes, and may be important in coping with changing environments. We use a theoretical model to assess whether such switching is a good strategy for growth in environments with occasional catastrophic events. We find that switching can be advantageous, but only when the environment is responsive to the microbial population. In our model, microbes switch randomly between two phenotypic states, with different growth rates. The environment undergoes sudden "catastrophes", the probability of which depends on the composition of the population. We derive a simple analytical result for the population growth rate. For a responsive environment, two alternative strategies emerge. In the "no switching" strategy, the population maximises its instantaneous growth rate, regardless of catastrophes. In the "switching" strategy, the microbial switching rate is tuned to minimise the environmental response. Which of these strategies is most favourable depends on the parameters of the model. Previous studies have shown that microbial switching can be favourable when the environment changes in an unresponsive fashion between several states. Here, we demonstrate an alternative role for phase variation in allowing microbes to maximise their growth in catastrophic responsive environments. |
2312.04605 | Axel Andersson | Axel Andersson, Gabriele Partel, Leslie Solorzano, Carolina W\"ahlby | Transcriptome-supervised classification of tissue morphology using deep
learning | Accepted for publication at IEEE International Symposium on
Biomedical Imaging (ISBI) 2020 | 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI
2020) | 10.1109/ISBI45749.2020.9098361 | null | q-bio.QM eess.IV | http://creativecommons.org/licenses/by/4.0/ | Deep learning has proven to successfully learn variations in tissue and cell
morphology. Training of such models typically relies on expensive manual
annotations. Here we conjecture that spatially resolved gene expression, e.i.,
the transcriptome, can be used as an alternative to manual annotations. In
particular, we trained five convolutional neural networks with patches of
different size extracted from locations defined by spatially resolved gene
expression. The network is trained to classify tissue morphology related to two
different genes, general tissue, as well as background, on an image of
fluorescence stained nuclei in a mouse brain coronal section. Performance is
evaluated on an independent tissue section from a different mouse brain,
reaching an average Dice score of 0.51. Results may indicate that novel
techniques for spatially resolved transcriptomics together with deep learning
may provide a unique and unbiased way to find genotype-phenotype relationships.
| [
{
"created": "Thu, 7 Dec 2023 09:26:39 GMT",
"version": "v1"
}
] | 2023-12-11 | [
[
"Andersson",
"Axel",
""
],
[
"Partel",
"Gabriele",
""
],
[
"Solorzano",
"Leslie",
""
],
[
"Wählby",
"Carolina",
""
]
] | Deep learning has proven to successfully learn variations in tissue and cell morphology. Training of such models typically relies on expensive manual annotations. Here we conjecture that spatially resolved gene expression, e.i., the transcriptome, can be used as an alternative to manual annotations. In particular, we trained five convolutional neural networks with patches of different size extracted from locations defined by spatially resolved gene expression. The network is trained to classify tissue morphology related to two different genes, general tissue, as well as background, on an image of fluorescence stained nuclei in a mouse brain coronal section. Performance is evaluated on an independent tissue section from a different mouse brain, reaching an average Dice score of 0.51. Results may indicate that novel techniques for spatially resolved transcriptomics together with deep learning may provide a unique and unbiased way to find genotype-phenotype relationships. |
2201.05593 | Sarah Mubeen | Sarah Mubeen, Alpha Tom Kodamullil, Martin Hofmann-Apitius, and Daniel
Domingo-Fern\'andez | On the influence of several factors on pathway enrichment analysis | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Pathway enrichment analysis has become a widely used knowledge-based approach
for the interpretation of biomedical data. Its popularity has led to an
explosion of both enrichment methods and pathway databases. While the elegance
of pathway enrichment lies in its simplicity, multiple factors can impact the
results of such an analysis which may not be accounted for. Researchers may
fail to give influential aspects their due, resorting instead to popular
methods and gene set collections, or default settings. Despite ongoing efforts
to establish set guidelines, meaningful results are still hampered by a lack of
consensus or gold standards around how enrichment analysis should be conducted.
Nonetheless, such concerns have prompted a series of benchmark studies
specifically focused on evaluating the influence of various factors on pathway
enrichment results. In this review, we organize and summarize the findings of
these benchmarks to provide a comprehensive overview on the influence of these
factors. Our work covers a broad spectrum of factors, spanning from
methodological assumptions to those related to prior biological knowledge, such
as pathway definitions and database choice. In doing so, we aim to shed light
on how these aspects can lead to insignificant, uninteresting, or even
contradictory results. Finally, we conclude the review by proposing future
benchmarks as well as solutions to overcome some of the challenges which
originate from the outlined factors.
| [
{
"created": "Fri, 14 Jan 2022 18:29:32 GMT",
"version": "v1"
}
] | 2022-01-17 | [
[
"Mubeen",
"Sarah",
""
],
[
"Kodamullil",
"Alpha Tom",
""
],
[
"Hofmann-Apitius",
"Martin",
""
],
[
"Domingo-Fernández",
"Daniel",
""
]
] | Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting, or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges which originate from the outlined factors. |
2401.06967 | Alexander Titus | B. Ross Katz, Abdul Khan, James York-Winegar, and Alexander J. Titus | NHANES-GCP: Leveraging the Google Cloud Platform and BigQuery ML for
reproducible machine learning with data from the National Health and
Nutrition Examination Survey | 7 pages, 1 figure | null | null | null | q-bio.QM cs.LG stat.AP | http://creativecommons.org/licenses/by/4.0/ | Summary: NHANES, the National Health and Nutrition Examination Survey, is a
program of studies led by the Centers for Disease Control and Prevention (CDC)
designed to assess the health and nutritional status of adults and children in
the United States (U.S.). NHANES data is frequently used by biostatisticians
and clinical scientists to study health trends across the U.S., but every
analysis requires extensive data management and cleaning before use and this
repetitive data engineering collectively costs valuable research time and
decreases the reproducibility of analyses. Here, we introduce NHANES-GCP, a
Cloud Development Kit for Terraform (CDKTF) Infrastructure-as-Code (IaC) and
Data Build Tool (dbt) resources built on the Google Cloud Platform (GCP) that
automates the data engineering and management aspects of working with NHANES
data. With current GCP pricing, NHANES-GCP costs less than $2 to run and less
than $15/yr of ongoing costs for hosting the NHANES data, all while providing
researchers with clean data tables that can readily be integrated for
large-scale analyses. We provide examples of leveraging BigQuery ML to carry
out the process of selecting data, integrating data, training machine learning
and statistical models, and generating results all from a single SQL-like
query. NHANES-GCP is designed to enhance the reproducibility of analyses and
create a well-engineered NHANES data resource for statistics, machine learning,
and fine-tuning Large Language Models (LLMs).
Availability and implementation" NHANES-GCP is available at
https://github.com/In-Vivo-Group/NHANES-GCP
| [
{
"created": "Sat, 13 Jan 2024 03:41:54 GMT",
"version": "v1"
}
] | 2024-01-17 | [
[
"Katz",
"B. Ross",
""
],
[
"Khan",
"Abdul",
""
],
[
"York-Winegar",
"James",
""
],
[
"Titus",
"Alexander J.",
""
]
] | Summary: NHANES, the National Health and Nutrition Examination Survey, is a program of studies led by the Centers for Disease Control and Prevention (CDC) designed to assess the health and nutritional status of adults and children in the United States (U.S.). NHANES data is frequently used by biostatisticians and clinical scientists to study health trends across the U.S., but every analysis requires extensive data management and cleaning before use and this repetitive data engineering collectively costs valuable research time and decreases the reproducibility of analyses. Here, we introduce NHANES-GCP, a Cloud Development Kit for Terraform (CDKTF) Infrastructure-as-Code (IaC) and Data Build Tool (dbt) resources built on the Google Cloud Platform (GCP) that automates the data engineering and management aspects of working with NHANES data. With current GCP pricing, NHANES-GCP costs less than $2 to run and less than $15/yr of ongoing costs for hosting the NHANES data, all while providing researchers with clean data tables that can readily be integrated for large-scale analyses. We provide examples of leveraging BigQuery ML to carry out the process of selecting data, integrating data, training machine learning and statistical models, and generating results all from a single SQL-like query. NHANES-GCP is designed to enhance the reproducibility of analyses and create a well-engineered NHANES data resource for statistics, machine learning, and fine-tuning Large Language Models (LLMs). Availability and implementation" NHANES-GCP is available at https://github.com/In-Vivo-Group/NHANES-GCP |
1201.2779 | Ulrich S. Schwarz | Carina M. Edwards and Ulrich S. Schwarz (U Heidelberg) | Force localization in contracting cell layers | 4 pages, 4 postscript figures | Phys. Rev. Lett., 107:128101, 2011 | 10.1103/PhysRevLett.107.128101 | null | q-bio.TO cond-mat.soft physics.bio-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epithelial cell layers on soft elastic substrates or pillar arrays are
commonly used as model systems for investigating the role of force in tissue
growth, maintenance and repair. Here we show analytically that the
experimentally observed localization of traction forces to the periphery of the
cell layers does not necessarily imply increased local cell activity, but
follows naturally from the elastic problem of a finite-sized contractile layer
coupled to an elastic foundation. For homogeneous contractility, the force
localization is determined by one dimensionless parameter interpolating between
linear and exponential force profiles for the extreme cases of very soft and
very stiff substrates, respectively. If contractility is sufficiently increased
at the periphery, outward directed displacements can occur at intermediate
positions, although the edge itself still retracts. We also show that
anisotropic extracellular stiffness leads to force localization in the stiffer
direction, as observed experimentally.
| [
{
"created": "Fri, 13 Jan 2012 09:34:26 GMT",
"version": "v1"
}
] | 2012-01-16 | [
[
"Edwards",
"Carina M.",
"",
"U Heidelberg"
],
[
"Schwarz",
"Ulrich S.",
"",
"U Heidelberg"
]
] | Epithelial cell layers on soft elastic substrates or pillar arrays are commonly used as model systems for investigating the role of force in tissue growth, maintenance and repair. Here we show analytically that the experimentally observed localization of traction forces to the periphery of the cell layers does not necessarily imply increased local cell activity, but follows naturally from the elastic problem of a finite-sized contractile layer coupled to an elastic foundation. For homogeneous contractility, the force localization is determined by one dimensionless parameter interpolating between linear and exponential force profiles for the extreme cases of very soft and very stiff substrates, respectively. If contractility is sufficiently increased at the periphery, outward directed displacements can occur at intermediate positions, although the edge itself still retracts. We also show that anisotropic extracellular stiffness leads to force localization in the stiffer direction, as observed experimentally. |
1805.00497 | Ahmad Maqboul | Ahmad Maqboul and Bakheet Elsadek | Expression profiles of TRPV1, TRPV4, TLR4 and ERK1/2 in the dorsal root
ganglionic neurons of a cancer-induced neuropathy rat model | PMID: 29637027, PMCID: PMC5889703 | Maqboul A, Elsadek B. (2018) Expression profiles of TRPV1, TRPV4,
TLR4 and ERK1/2 in the dorsal root ganglionic neurons of a cancer-induced
neuropathy rat model. PeerJ 6:e4622 https://doi.org/10.7717/peerj.4622 | 10.7717/peerj.4622 | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Background: The spread of tumors through neural routes is common in several
types of cancer in which patients suffer from a moderate-to-severe neuropathy,
neural damage and a distorted quality of life. Here we aim to examine the
expression profiles of transient receptor potential vanilloid 1 (TRPV1) and of
transient receptor potential vanilloid 4 (TRPV4), toll-like receptor 4 (TLR4)
and extracellular signal-regulated kinase (ERK1/2), and to assess the possible
therapeutic strategies through blockade of transient receptor potential (TRP)
channels. Methods: Cancer was induced within the sciatic nerves of male
Copenhagen rats, and tissues from dorsal root ganglia (DRG) were collected and
used for measurements of immunofluorescence and Western blotting. The TRPV1
antagonist capsazepine, the selective TRPV4 antagonist HC-067047 and the
calcium ions inhibitor ruthenium red were used to treat thermal and/or
mechanical hyperalgesia. Results: Transient receptor potential vanilloid 1
showed a lower expression in DRGs on days 7 and 14. The expression of TRPV4,
TLR4 and ERK1/2 showed an increase on day 3 then a decrease on days 7 and 14.
TRPV1 and TLR4 as well as TRPV4 and ERK1/2 co-existed on the same neuronal
cells. The neuropathic pain was reversed in dose-dependent manners by using the
TRP antagonists and the calcium ions inhibitor. Conclusion: The decreased
expression of TRPV1 and TRPV4 is associated with high activation. The increased
expression of TLR4 and ERK1/2 reveals earlier immune response and tumor
progression, respectively, and their ultimate decrease is an indicator of nerve
damage. We studied the possible role of TRPV1 and TRPV4 in transducing
cancer-induced hyperalgesia. The possible treatment strategies of
cancer-induced thermal and/or mechanical hyperalgesia using capsazepine,
HC-067047 and ruthenium red are examined.
| [
{
"created": "Tue, 1 May 2018 18:03:58 GMT",
"version": "v1"
}
] | 2018-05-03 | [
[
"Maqboul",
"Ahmad",
""
],
[
"Elsadek",
"Bakheet",
""
]
] | Background: The spread of tumors through neural routes is common in several types of cancer in which patients suffer from a moderate-to-severe neuropathy, neural damage and a distorted quality of life. Here we aim to examine the expression profiles of transient receptor potential vanilloid 1 (TRPV1) and of transient receptor potential vanilloid 4 (TRPV4), toll-like receptor 4 (TLR4) and extracellular signal-regulated kinase (ERK1/2), and to assess the possible therapeutic strategies through blockade of transient receptor potential (TRP) channels. Methods: Cancer was induced within the sciatic nerves of male Copenhagen rats, and tissues from dorsal root ganglia (DRG) were collected and used for measurements of immunofluorescence and Western blotting. The TRPV1 antagonist capsazepine, the selective TRPV4 antagonist HC-067047 and the calcium ions inhibitor ruthenium red were used to treat thermal and/or mechanical hyperalgesia. Results: Transient receptor potential vanilloid 1 showed a lower expression in DRGs on days 7 and 14. The expression of TRPV4, TLR4 and ERK1/2 showed an increase on day 3 then a decrease on days 7 and 14. TRPV1 and TLR4 as well as TRPV4 and ERK1/2 co-existed on the same neuronal cells. The neuropathic pain was reversed in dose-dependent manners by using the TRP antagonists and the calcium ions inhibitor. Conclusion: The decreased expression of TRPV1 and TRPV4 is associated with high activation. The increased expression of TLR4 and ERK1/2 reveals earlier immune response and tumor progression, respectively, and their ultimate decrease is an indicator of nerve damage. We studied the possible role of TRPV1 and TRPV4 in transducing cancer-induced hyperalgesia. The possible treatment strategies of cancer-induced thermal and/or mechanical hyperalgesia using capsazepine, HC-067047 and ruthenium red are examined. |
0904.0575 | Thomas Michelitsch | Jicun Wang (CUH), Thomas Michelitsch (IJLRA), Arne Wunderlin, Ravi
Mahadeva (CUH) | Aging as a consequence of misrepair -- a novel theory of aging | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is now increasingly realized that the underlying mechanism which governs
aging (ageing) is a complex interplay of genetic regulation and
damage-accumulation. "Aging as a result of accumulation of 'faults' on cellular
and molecular levels", has been proposed in the damage (fault)-accumulation
theory. However, this theory fails to explain some aging phenotypes such as
fibrosis and premature aging, since terms such as 'damage' and 'fault' are not
specified. Therefore we introduce some crucial modifications of this theory and
arrive at a novel theory: aging of the body is the result of accumulation of
Misrepair of tissue. It emphasizes: a) it is Misrepair, not the original
damage, that accumulates and leads to aging; and b) aging can occur at
different levels, however aging of the body takes place necessarily on the
tissue level, but not requiring the aging of cells/molecules. The
Misrepair-accumulation theory introduced in the present paper unifies the
understanding of the roles of environmental damage, repair, gene regulation,
and multicellular structure in the aging process. This theory gives
explanations for the aging phenotypes, premature aging, the difference of
longevity in different species, and it is consistent with the physical view on
complex systems.
| [
{
"created": "Fri, 3 Apr 2009 13:49:01 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jun 2009 12:45:17 GMT",
"version": "v2"
},
{
"created": "Wed, 30 Mar 2011 08:00:08 GMT",
"version": "v3"
}
] | 2011-03-31 | [
[
"Wang",
"Jicun",
"",
"CUH"
],
[
"Michelitsch",
"Thomas",
"",
"IJLRA"
],
[
"Wunderlin",
"Arne",
"",
"CUH"
],
[
"Mahadeva",
"Ravi",
"",
"CUH"
]
] | It is now increasingly realized that the underlying mechanism which governs aging (ageing) is a complex interplay of genetic regulation and damage-accumulation. "Aging as a result of accumulation of 'faults' on cellular and molecular levels", has been proposed in the damage (fault)-accumulation theory. However, this theory fails to explain some aging phenotypes such as fibrosis and premature aging, since terms such as 'damage' and 'fault' are not specified. Therefore we introduce some crucial modifications of this theory and arrive at a novel theory: aging of the body is the result of accumulation of Misrepair of tissue. It emphasizes: a) it is Misrepair, not the original damage, that accumulates and leads to aging; and b) aging can occur at different levels, however aging of the body takes place necessarily on the tissue level, but not requiring the aging of cells/molecules. The Misrepair-accumulation theory introduced in the present paper unifies the understanding of the roles of environmental damage, repair, gene regulation, and multicellular structure in the aging process. This theory gives explanations for the aging phenotypes, premature aging, the difference of longevity in different species, and it is consistent with the physical view on complex systems. |
2103.10164 | Mark Leake | Jack W Shepherd, Ed J Higgins, Adam J M Wollman, Mark C Leake | PySTACHIO: Python Single-molecule TrAcking stoiCHiometry Intensity and
simulatiOn, a flexible, extensible, beginner-friendly and optimized program
for analysis of single-molecule microscopy | null | null | null | null | q-bio.BM cs.PL physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As camera pixel arrays have grown larger and faster, and optical microscopy
techniques ever more refined, there has been an explosion in the quantity of
data acquired during routine light microcopy. At the single-molecule level,
analysis involves multiple steps and can rapidly become computationally
expensive, in some cases intractable on office workstations. Complex bespoke
software can present high activation barriers to entry for new users. Here, we
redevelop our quantitative single-molecule analysis routines into an optimized
and extensible Python program, with GUI and command-line implementations to
facilitate use on local machines and remote clusters, by beginners and advanced
users alike. We demonstrate that its performance is on par with previous MATLAB
implementations but runs an order of magnitude faster. We tested it against
challenge data and demonstrate its performance is comparable to
state-of-the-art analysis platforms. We show the code can extract fluorescence
intensity values for single reporter dye molecules and, using these, estimate
molecular stoichiometries and cellular copy numbers of fluorescently-labeled
biomolecules. It can evaluate 2D diffusion coefficients for the
characteristically short single-particle tracking data. To facilitate
benchmarking we include data simulation routines to compare different analysis
programs. Finally, we show that it works with 2-color data and enables
colocalization analysis based on overlap integration, to infer interactions
between differently labelled biomolecules. By making this freely available we
aim to make complex light microscopy single-molecule analysis more
democratized.
| [
{
"created": "Thu, 18 Mar 2021 10:59:55 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Mar 2021 09:35:27 GMT",
"version": "v2"
},
{
"created": "Mon, 5 Jul 2021 20:52:02 GMT",
"version": "v3"
}
] | 2021-07-07 | [
[
"Shepherd",
"Jack W",
""
],
[
"Higgins",
"Ed J",
""
],
[
"Wollman",
"Adam J M",
""
],
[
"Leake",
"Mark C",
""
]
] | As camera pixel arrays have grown larger and faster, and optical microscopy techniques ever more refined, there has been an explosion in the quantity of data acquired during routine light microcopy. At the single-molecule level, analysis involves multiple steps and can rapidly become computationally expensive, in some cases intractable on office workstations. Complex bespoke software can present high activation barriers to entry for new users. Here, we redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with GUI and command-line implementations to facilitate use on local machines and remote clusters, by beginners and advanced users alike. We demonstrate that its performance is on par with previous MATLAB implementations but runs an order of magnitude faster. We tested it against challenge data and demonstrate its performance is comparable to state-of-the-art analysis platforms. We show the code can extract fluorescence intensity values for single reporter dye molecules and, using these, estimate molecular stoichiometries and cellular copy numbers of fluorescently-labeled biomolecules. It can evaluate 2D diffusion coefficients for the characteristically short single-particle tracking data. To facilitate benchmarking we include data simulation routines to compare different analysis programs. Finally, we show that it works with 2-color data and enables colocalization analysis based on overlap integration, to infer interactions between differently labelled biomolecules. By making this freely available we aim to make complex light microscopy single-molecule analysis more democratized. |
1702.06513 | Andrew Sornborger | Zhuocheng Xiao, Jiwei Zhang, Andrew T. Sornborger, Louis Tao | Cusps Enable Line Attractors for Neural Computation | 7 pages, 5 figures | Phys. Rev. E 96, 052308 (2017) | 10.1103/PhysRevE.96.052308 | LA-UR-17-28766 | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Line attractors in neuronal networks have been suggested to be the basis of
many brain functions, such as working memory, oculomotor control, head
movement, locomotion, and sensory processing. In this paper, we make the
connection between line attractors and pulse-gating in feedforward neuronal
networks. In this context, because of their neutral stability along a
one-dimensional manifold, line attractors are associated with a
time-translational invariance that allows graded information to be propagated
from one neuronal population to the next. To understand how pulse-gating
manifests itself in a high-dimensional, non-linear, feedforward
integrate-and-fire network, we use a Fokker-Planck approach to analyze system
dynamics. We make a connection between pulse-gated propagation in the
Fokker-Planck and population-averaged mean-field (firing rate) models, then
identify an approximate line attractor in state space as the essential
structure underlying graded information propagation. An analysis of the line
attractor shows that it consists of three fixed points: a central saddle with
an unstable manifold along the line and stable manifolds orthogonal to the
line, which is surrounded on either side by stable fixed points. Along the
manifold defined by the fixed points, slow dynamics give rise to a ghost. We
show that this line attractor arises at a cusp catastrophe, where a fold
bifurcation develops as a function of synaptic noise; and that the ghost
dynamics near the fold of the cusp underly the robustness of the line
attractor. Understanding the dynamical aspects of this cusp catastrophe allows
us to show how line attractors can persist in biologically realistic neuronal
networks and how the interplay of pulse gating, synaptic coupling and neuronal
stochasticity can be used to enable attracting one-dimensional manifolds and
thus, dynamically control the processing of graded information.
| [
{
"created": "Tue, 21 Feb 2017 18:32:52 GMT",
"version": "v1"
},
{
"created": "Mon, 2 Oct 2017 15:35:52 GMT",
"version": "v2"
},
{
"created": "Tue, 3 Oct 2017 20:51:29 GMT",
"version": "v3"
},
{
"created": "Wed, 29 Nov 2017 16:39:04 GMT",
"version": "v4"
}
] | 2017-11-30 | [
[
"Xiao",
"Zhuocheng",
""
],
[
"Zhang",
"Jiwei",
""
],
[
"Sornborger",
"Andrew T.",
""
],
[
"Tao",
"Louis",
""
]
] | Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse-gating in feedforward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, non-linear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and thus, dynamically control the processing of graded information. |
1309.6589 | Liane Gabora | Liane Gabora | Mind: An Archaeological Perspective | 25 pages | In R. A. Bentley, H. D. G. Maschner, & C. Chippendale (Eds.),
Handbook of theories and methods in archaeology (pp. 283-296). Walnut Creek,
CA: Altamira Press.(2008) | null | null | q-bio.PE q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | What can relics of the past tell us about the thoughts and beliefs of the
people who invented and used them? Recent collaborations at the frontier of
archaeology, anthropology, and cognitive science are culminating in speculative
but nevertheless increasingly sophisticated efforts to unravel how modern human
cognition came about. By considering objects within their archaeological
context, we have begun to piece together something of the way of life of people
who inhabited particular locales, which in turn reflects their underlying
thought processes.
| [
{
"created": "Wed, 25 Sep 2013 17:43:37 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Jul 2019 19:38:24 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Jul 2019 19:47:00 GMT",
"version": "v3"
}
] | 2019-07-11 | [
[
"Gabora",
"Liane",
""
]
] | What can relics of the past tell us about the thoughts and beliefs of the people who invented and used them? Recent collaborations at the frontier of archaeology, anthropology, and cognitive science are culminating in speculative but nevertheless increasingly sophisticated efforts to unravel how modern human cognition came about. By considering objects within their archaeological context, we have begun to piece together something of the way of life of people who inhabited particular locales, which in turn reflects their underlying thought processes. |
2111.12806 | Benjamin F. Maier | Benjamin F. Maier, Marc Wiedermann, Angelique Burdinski, Pascal
Klamser, Mirjam A. Jenny, Cornelia Betsch, Dirk Brockmann | Germany's current COVID-19 crisis is mainly driven by the unvaccinated | 21 pages, 3 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vaccines are the most powerful pharmaceutical tool to combat the COVID-19
pandemic. While the majority (about 65%) of the German population were fully
vaccinated, incidence started growing exponentially in October 2021 with about
41% of recorded new cases aged twelve or above being symptomatic breakthrough
infections, presumably also contributing to the dynamics. At the time, it (i)
remains elusive how significant this contribution is and (ii) whether targeted
non-pharmaceutical interventions (NPIs) may stop the amplification of the
ongoing crisis. Here, we estimate that about 67%-76% of all new infections are
caused by unvaccinated individuals, implying that only 24%-33% are caused by
the vaccinated. Furthermore, we estimate 38%-51% of new infections to be caused
by unvaccinated individuals infecting other unvaccinated individuals. In total,
unvaccinated individuals are expected to be involved in 8-9 of 10 new
infections. We further show that decreasing the transmissibility of the
unvaccinated by, e.g. targeted NPIs, causes a steeper decrease in the effective
reproduction number $\mathcal{R}$ than decreasing the transmissibility of
vaccinated individuals, potentially leading to temporary epidemic control.
Furthermore, reducing contacts between vaccinated and unvaccinated individuals
serves to decrease $\mathcal R$ in a similar manner as increasing vaccine
uptake. Taken together, our results contribute to the public discourse
regarding policy changes in pandemic response and highlight the importance of
combined measures, such as vaccination campaigns and contact reduction, to
achieve epidemic control and preventing an overload of public health systems.
| [
{
"created": "Wed, 24 Nov 2021 21:30:32 GMT",
"version": "v1"
}
] | 2021-11-29 | [
[
"Maier",
"Benjamin F.",
""
],
[
"Wiedermann",
"Marc",
""
],
[
"Burdinski",
"Angelique",
""
],
[
"Klamser",
"Pascal",
""
],
[
"Jenny",
"Mirjam A.",
""
],
[
"Betsch",
"Cornelia",
""
],
[
"Brockmann",
"Dirk",
""
]
] | Vaccines are the most powerful pharmaceutical tool to combat the COVID-19 pandemic. While the majority (about 65%) of the German population were fully vaccinated, incidence started growing exponentially in October 2021 with about 41% of recorded new cases aged twelve or above being symptomatic breakthrough infections, presumably also contributing to the dynamics. At the time, it (i) remains elusive how significant this contribution is and (ii) whether targeted non-pharmaceutical interventions (NPIs) may stop the amplification of the ongoing crisis. Here, we estimate that about 67%-76% of all new infections are caused by unvaccinated individuals, implying that only 24%-33% are caused by the vaccinated. Furthermore, we estimate 38%-51% of new infections to be caused by unvaccinated individuals infecting other unvaccinated individuals. In total, unvaccinated individuals are expected to be involved in 8-9 of 10 new infections. We further show that decreasing the transmissibility of the unvaccinated by, e.g. targeted NPIs, causes a steeper decrease in the effective reproduction number $\mathcal{R}$ than decreasing the transmissibility of vaccinated individuals, potentially leading to temporary epidemic control. Furthermore, reducing contacts between vaccinated and unvaccinated individuals serves to decrease $\mathcal R$ in a similar manner as increasing vaccine uptake. Taken together, our results contribute to the public discourse regarding policy changes in pandemic response and highlight the importance of combined measures, such as vaccination campaigns and contact reduction, to achieve epidemic control and preventing an overload of public health systems. |
2008.05902 | Jacques Daniel | Jacques H. Daniel | On some predictions of the poly-tRNA model for the origin and evolution
of genetic coding | 22 pages, 8 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The poly-tRNA model was recently presented for the origin and evolution of
genetic coding. This model has led to a rather precise description of what
might have occurred at the beginning of protein synthesis in the first life
form. Here, we further discuss some interesting implications of this model.
First, the system of encoded peptide/protein synthesis appears to have started
and developed on the breeding ground of a rich RNA world, responsible for the
infancy of life existence and complexity. Furthermore, once protein synthesis
was fully established and apparently superseding the RNA world, and at a very
early stage of life beginnings, we already see what has been a recurrent theme
in the likely interpretation of modern comparative molecular studies on
species: the full ability of this nascent life entity to develop itself by
tinkering, using all kinds of available pieces to improve itself. Lastly, and
very instructively, it is deduced from this model that the first peptides to be
produced probably had unique properties, not shared by the arsenal of molecules
present hitherto, which allowed a functional connection between the then
omnipotent RNA world and the lipid membrane vesicles containing it. These
specific functions might have initially been at the origin of the Darwinian
selection of the full-blown protein-synthesis machinery.
| [
{
"created": "Wed, 12 Aug 2020 13:21:03 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Aug 2020 11:25:16 GMT",
"version": "v2"
}
] | 2020-08-17 | [
[
"Daniel",
"Jacques H.",
""
]
] | The poly-tRNA model was recently presented for the origin and evolution of genetic coding. This model has led to a rather precise description of what might have occurred at the beginning of protein synthesis in the first life form. Here, we further discuss some interesting implications of this model. First, the system of encoded peptide/protein synthesis appears to have started and developed on the breeding ground of a rich RNA world, responsible for the infancy of life existence and complexity. Furthermore, once protein synthesis was fully established and apparently superseding the RNA world, and at a very early stage of life beginnings, we already see what has been a recurrent theme in the likely interpretation of modern comparative molecular studies on species: the full ability of this nascent life entity to develop itself by tinkering, using all kinds of available pieces to improve itself. Lastly, and very instructively, it is deduced from this model that the first peptides to be produced probably had unique properties, not shared by the arsenal of molecules present hitherto, which allowed a functional connection between the then omnipotent RNA world and the lipid membrane vesicles containing it. These specific functions might have initially been at the origin of the Darwinian selection of the full-blown protein-synthesis machinery. |
2003.10236 | Victor M. Perez-Garcia | Odelaisy Leon-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan
Belmonte-Beitia, Salvador Chulian, Alvaro Martinez-Rubio, Maria Rosa, Antonio
Perez-Martinez, Manuel Ramirez Orellana, Victor M. Perez-Garcia | CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights
from mathematical models | null | null | 10.1016/j.cnsns.2020.105570 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Immunotherapies use components of the patient immune system to selectively
target cancer cells. The use of CAR T cells to treat B-cell malignancies
--leukaemias and lymphomas-- is one of the most successful examples, with many
patients experiencing long-lasting complete responses to this therapy. This
treatment works by extracting the patient's T cells and adding them the CAR
group, which enables them to recognize and target cells carrying the antigen
CD19+, that is expressed in these haematological tumors.
Here we put forward a mathematical model describing the time response of
leukaemias to the injection of CAR T-cells. The model accounts for mature and
progenitor B-cells, tumor cells, CAR T cells and side effects by incorporating
the main biological processes involved. The model explains the early
post-injection dynamics of the different compartments and the fact that the
number of CAR T cells injected does not critically affect the treatment
outcome. An explicit formula is found that provides the maximum CAR T cell
expansion in-vivo and the severity of side effects. Our mathematical model
captures other known features of the response to this immunotherapy. It also
predicts that CD19+ tumor relapses could be the result of the competition
between tumor and CAR T cells analogous to predator-prey dynamics. We discuss
this fact on the light of available evidences and the possibility of
controlling relapses by early re-challenging of the tumor with stored CAR T
cells.
| [
{
"created": "Wed, 18 Mar 2020 21:18:39 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Aug 2020 17:01:20 GMT",
"version": "v2"
}
] | 2020-12-02 | [
[
"Leon-Triana",
"Odelaisy",
""
],
[
"Sabir",
"Soukaina",
""
],
[
"Calvo",
"Gabriel F.",
""
],
[
"Belmonte-Beitia",
"Juan",
""
],
[
"Chulian",
"Salvador",
""
],
[
"Martinez-Rubio",
"Alvaro",
""
],
[
"Rosa",
"Maria",
""
],
[
"Perez-Martinez",
"Antonio",
""
],
[
"Orellana",
"Manuel Ramirez",
""
],
[
"Perez-Garcia",
"Victor M.",
""
]
] | Immunotherapies use components of the patient immune system to selectively target cancer cells. The use of CAR T cells to treat B-cell malignancies --leukaemias and lymphomas-- is one of the most successful examples, with many patients experiencing long-lasting complete responses to this therapy. This treatment works by extracting the patient's T cells and adding them the CAR group, which enables them to recognize and target cells carrying the antigen CD19+, that is expressed in these haematological tumors. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T-cells. The model accounts for mature and progenitor B-cells, tumor cells, CAR T cells and side effects by incorporating the main biological processes involved. The model explains the early post-injection dynamics of the different compartments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that provides the maximum CAR T cell expansion in-vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19+ tumor relapses could be the result of the competition between tumor and CAR T cells analogous to predator-prey dynamics. We discuss this fact on the light of available evidences and the possibility of controlling relapses by early re-challenging of the tumor with stored CAR T cells. |
1512.02182 | Riccardo Sacco Ph.D. | Chiara Lelli and Riccardo Sacco and Paola Causin and Manuela T.
Raimondi | A Poroelastic Mixture Model of Mechanobiological Processes in Tissue
Engineering. Part I: Mathematical Formulation | null | null | null | null | q-bio.TO math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An adequate control of cell response in tissue engineering applications is of
utmost importance to obtain products suitable to clinical practice. This paper
is the first part of a series of two connected publications in which we study
via mathematical tools the cultivation in bioreactors of articular
chondrocytes. The proposed model combines poroelastic theory of mixtures and
cellular population models into a framework including stress state and oxygen
tension as main determinants of engineered culture evolution. The special
mechanosensitivity of articular chondrocytes to the surrounding environment is
accounted for in the model through the novel concept of "force isotropy" acting
on the cell which is assumed as the promoting factor of the production of new
cells or extracellular matrix.
| [
{
"created": "Wed, 25 Nov 2015 12:24:34 GMT",
"version": "v1"
}
] | 2015-12-08 | [
[
"Lelli",
"Chiara",
""
],
[
"Sacco",
"Riccardo",
""
],
[
"Causin",
"Paola",
""
],
[
"Raimondi",
"Manuela T.",
""
]
] | An adequate control of cell response in tissue engineering applications is of utmost importance to obtain products suitable to clinical practice. This paper is the first part of a series of two connected publications in which we study via mathematical tools the cultivation in bioreactors of articular chondrocytes. The proposed model combines poroelastic theory of mixtures and cellular population models into a framework including stress state and oxygen tension as main determinants of engineered culture evolution. The special mechanosensitivity of articular chondrocytes to the surrounding environment is accounted for in the model through the novel concept of "force isotropy" acting on the cell which is assumed as the promoting factor of the production of new cells or extracellular matrix. |
1106.4192 | W B Langdon | W. B. Langdon, M. J. Arno | More Mouldy Data: Another mycoplasma gene jumps the silicon barrier into
the human genome | data directory contains results of AF241217 and DA466599 blast runs
by EBI in Cambridge | Evolutionary Computation, Machine Learning and Data Mining in
Bioinformatics Lecture Notes in Computer Science Volume 7246, 2012, pp
245-249 | 10.1007/978-3-642-29066-4_22 | RN/11/14 | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human genome sequence database contains DNA sequences very like those of
mycoplasma molds. It appears such moulds infect not only molecular Biology
laboratories but were picked up by experimenters from contaminated samples and
inserted into GenBank as if they were human. At least one mouldy EST (Expressed
Sequence Tag) has transferred from public databases to commercial tools
(Affymetrix HG-U133 plus 2.0 microarrays). We report a second example
(DA466599) and suggest there is a need to clean up genomic databases but fear
current tools will be inadequate to catch genes which have jumped the silicon
barrier.
| [
{
"created": "Tue, 21 Jun 2011 13:16:42 GMT",
"version": "v1"
}
] | 2014-05-02 | [
[
"Langdon",
"W. B.",
""
],
[
"Arno",
"M. J.",
""
]
] | The human genome sequence database contains DNA sequences very like those of mycoplasma molds. It appears such moulds infect not only molecular Biology laboratories but were picked up by experimenters from contaminated samples and inserted into GenBank as if they were human. At least one mouldy EST (Expressed Sequence Tag) has transferred from public databases to commercial tools (Affymetrix HG-U133 plus 2.0 microarrays). We report a second example (DA466599) and suggest there is a need to clean up genomic databases but fear current tools will be inadequate to catch genes which have jumped the silicon barrier. |
1502.00483 | Subhash Lele | Subhash R. Lele | Is non-informative Bayesian analysis appropriate for wildlife
management: survival of San Joaquin Kit Fox and declines in amphibian
populations | null | null | null | null | q-bio.QM stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational convenience has led to widespread use of Bayesian inference
with vague or flat priors to analyze state-space models in ecology. Vague
priors are claimed to be objective and to let the data speak. Neither of these
claims is valid. Statisticians have criticized the use of vague priors from
philosophical to computational to pragmatic reasons. Ecologists, however,
dismiss such criticisms as empty philosophical wonderings with no practical
implications. We illustrate that use of vague priors in population viability
analysis and occupancy models can have significant impact on the analysis and
can lead to strikingly different managerial decisions. Given the wide spread
applicability of the hierarchical models and uncritical use of non-informative
Bayesian analysis in ecology, researchers should be cautious about using the
vague priors as a default choice in practical situations.
| [
{
"created": "Mon, 2 Feb 2015 14:07:50 GMT",
"version": "v1"
}
] | 2015-02-03 | [
[
"Lele",
"Subhash R.",
""
]
] | Computational convenience has led to widespread use of Bayesian inference with vague or flat priors to analyze state-space models in ecology. Vague priors are claimed to be objective and to let the data speak. Neither of these claims is valid. Statisticians have criticized the use of vague priors from philosophical to computational to pragmatic reasons. Ecologists, however, dismiss such criticisms as empty philosophical wonderings with no practical implications. We illustrate that use of vague priors in population viability analysis and occupancy models can have significant impact on the analysis and can lead to strikingly different managerial decisions. Given the wide spread applicability of the hierarchical models and uncritical use of non-informative Bayesian analysis in ecology, researchers should be cautious about using the vague priors as a default choice in practical situations. |
1504.06273 | Andrei Zinovyev Dr. | Laurence Calzone, Emmanuel Barillot, Andrei Zinovyev | Predicting genetic interactions from Boolean models of biological
networks | Will appear in Integrative Biology | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genetic interaction can be defined as a deviation of the phenotypic
quantitative effect of a double gene mutation from the effect predicted from
single mutations using a simple (e.g., multiplicative or linear additive)
statistical model. Experimentally characterized genetic interaction networks in
model organisms provide important insights into relationships between different
biological functions. We describe a computational methodology allowing to
systematically and quantitatively characterize a Boolean mathematical model of
a biological network in terms of genetic interactions between all loss of
function and gain of function mutations with respect to all model phenotypes or
outputs. We use the probabilistic framework defined in MaBoSS software, based
on continuous time Markov chains and stochastic simulations. In addition, we
suggest several computational tools for studying the distribution of double
mutants in the space of model phenotype probabilities. We demonstrate this
methodology on three published models for each of which we derive the genetic
interaction networks and analyze their properties. We classify the obtained
interactions according to their class of epistasis, dependence on the chosen
initial conditions and phenotype. The use of this methodology for validating
mathematical models from experimental data and designing new experiments is
discussed.
| [
{
"created": "Thu, 23 Apr 2015 17:49:52 GMT",
"version": "v1"
}
] | 2015-04-24 | [
[
"Calzone",
"Laurence",
""
],
[
"Barillot",
"Emmanuel",
""
],
[
"Zinovyev",
"Andrei",
""
]
] | Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed. |
1705.10478 | Christian Schmidt | Christian Schmidt and Ursula van Rienen | Adaptive Estimation of the Neural Activation Extent in Computational
Volume Conductor Models of Deep Brain Stimulation | 12 pages | null | null | null | q-bio.NC cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: The aim of this study is to propose an adaptive scheme embedded
into an open-source environment for the estimation of the neural activation
extent during deep brain stimulation and to investigate the feasibility of
approximating the neural activation extent by thresholds of the field solution.
Methods: Open-source solutions for solving the field equation in volume
conductor models of deep brain stimulation and computing the neural activation
are embedded into a Python package to estimate the neural activation dependent
on the dielectric tissue properties and axon parameters by employing a
spatially adaptive scheme. Feasibility of the approximation of the neural
activation extent by field thresholds is investigated to further reduce the
computational expense. Results: The varying extents of neural activation for
different patient-specific dielectric properties were estimated with the
adaptive scheme. The results revealed the strong influence of the dielectric
properties of the encapsulation layer in the acute and chronic phase after
surgery. The computational time required to determine the neural activation
extent in each studied model case was substantially reduced. Conclusion: The
neural activation extent is altered by patient-specific parameters. Threshold
values of the electric potential and electric field norm facilitate a
computationally efficient method to estimate the neural activation extent.
Significance: The presented adaptive scheme is able to robustly determine
neural activation extents and field threshold estimates for varying dielectric
tissue properties and axon diameters while reducing substantially the
computational expense.
| [
{
"created": "Tue, 30 May 2017 07:12:21 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Jun 2017 14:50:12 GMT",
"version": "v2"
},
{
"created": "Mon, 7 Aug 2017 18:24:45 GMT",
"version": "v3"
}
] | 2017-08-09 | [
[
"Schmidt",
"Christian",
""
],
[
"van Rienen",
"Ursula",
""
]
] | Objective: The aim of this study is to propose an adaptive scheme embedded into an open-source environment for the estimation of the neural activation extent during deep brain stimulation and to investigate the feasibility of approximating the neural activation extent by thresholds of the field solution. Methods: Open-source solutions for solving the field equation in volume conductor models of deep brain stimulation and computing the neural activation are embedded into a Python package to estimate the neural activation dependent on the dielectric tissue properties and axon parameters by employing a spatially adaptive scheme. Feasibility of the approximation of the neural activation extent by field thresholds is investigated to further reduce the computational expense. Results: The varying extents of neural activation for different patient-specific dielectric properties were estimated with the adaptive scheme. The results revealed the strong influence of the dielectric properties of the encapsulation layer in the acute and chronic phase after surgery. The computational time required to determine the neural activation extent in each studied model case was substantially reduced. Conclusion: The neural activation extent is altered by patient-specific parameters. Threshold values of the electric potential and electric field norm facilitate a computationally efficient method to estimate the neural activation extent. Significance: The presented adaptive scheme is able to robustly determine neural activation extents and field threshold estimates for varying dielectric tissue properties and axon diameters while reducing substantially the computational expense. |
2208.13812 | Erin Kim | Erin Kim | Analysis of Cell Packing Behavior to Enhance Wound Assessment | 19 pages, 7 figures | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Wound assessment is a critical aspect of wound treatment, as the healing
progress of a wound determines the optimal approach to care. However, the
heterogeneity of burn wounds often complicates wound assessment, causing
inaccurate wound evaluation and ineffective treatment. Traditional wound
assessment methods such as Gross Area Reduction (GAR) and Percentage Area
Reduction (PAR) are prone to misinterpretation, due to irregular results.
Inaccurate wound assessment leads to higher rates of death and life-long
physical and psychological morbidities in burn patients, especially in
low-income communities that lack specialty care and medical resources.
Therefore, I propose a novel approach to wound assessment: wound healing from
the biophysical perspective of collective cell migration by analyzing cell
packing behavior. This approach was modeled through Voronoi Tessellation
simulations and applied to a wound healing system, where changes in the cell
morphology parameters of aspect ratio and shape index were plotted over time to
numerically evaluate the geometry of different cell migration packing patterns.
Experimental results demonstrate the effectiveness of measuring aspect ratio,
as a reduction in aspect ratio indicates that cell shapes become increasingly
rounded throughout wound closure. This is further proven when considering
physical principles in wound healing and changes in cell elongation. By placing
a microscope objective on a phone camera, it is possible to directly examine
any wound, with the calculations done on the phone as well. This efficient and
accurate mechanism can be especially useful in low-resource communities, as it
is accessible regardless of technical or medical background.
| [
{
"created": "Mon, 29 Aug 2022 18:03:26 GMT",
"version": "v1"
}
] | 2022-08-31 | [
[
"Kim",
"Erin",
""
]
] | Wound assessment is a critical aspect of wound treatment, as the healing progress of a wound determines the optimal approach to care. However, the heterogeneity of burn wounds often complicates wound assessment, causing inaccurate wound evaluation and ineffective treatment. Traditional wound assessment methods such as Gross Area Reduction (GAR) and Percentage Area Reduction (PAR) are prone to misinterpretation, due to irregular results. Inaccurate wound assessment leads to higher rates of death and life-long physical and psychological morbidities in burn patients, especially in low-income communities that lack specialty care and medical resources. Therefore, I propose a novel approach to wound assessment: wound healing from the biophysical perspective of collective cell migration by analyzing cell packing behavior. This approach was modeled through Voronoi Tessellation simulations and applied to a wound healing system, where changes in the cell morphology parameters of aspect ratio and shape index were plotted over time to numerically evaluate the geometry of different cell migration packing patterns. Experimental results demonstrate the effectiveness of measuring aspect ratio, as a reduction in aspect ratio indicates that cell shapes become increasingly rounded throughout wound closure. This is further proven when considering physical principles in wound healing and changes in cell elongation. By placing a microscope objective on a phone camera, it is possible to directly examine any wound, with the calculations done on the phone as well. This efficient and accurate mechanism can be especially useful in low-resource communities, as it is accessible regardless of technical or medical background. |
1311.1726 | Shuji Kaieda | Shuji Kaieda and Bertil Halle | Internal water and microsecond dynamics in myoglobin | 25 pages, 12 figures | J. Phys. Chem. B 117, 14676-14687 (2013) | 10.1021/jp409234g | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Myoglobin (Mb) binds diatomic ligands, like O$_2$, CO, and NO, in a cavity
that is only transiently accessible. Crystallography and molecular simulations
show that the ligands can migrate through an extensive network of transiently
connected cavities, but disagree on the locations and occupancy of internal
hydration sites. Here, we use water $^2$H and $^{17}$O magnetic relaxation
dispersion (MRD) to characterize the internal water molecules in Mb under
physiological conditions. We find that equine carbonmonoxy Mb contains 4.5
$\pm$ 1.0 ordered internal water molecules with a mean survival time of 5.6
$\pm$ 0.5 $\mu$s at 25 $^\circ$C. The likely location of these water molecules
are the four polar hydration sites, including one of the xenon-binding
cavities, that are fully occupied in all high-resolution crystal structures of
equine Mb. The finding that water escapes from these sites, located 17 -- 31
{\AA} apart in the protein, on the same $\mu$s time scale suggests a global
exchange mechanism. We propose that this mechanism involves transient
penetration of the protein by H-bonded water chains. Such a mechanism could
play a functional role by eliminating trapped ligands. In addition, the MRD
results indicate that two or three of the 11 histidine residues of equine Mb
undergo intramolecular hydrogen exchange on a $\mu$s time scale.
| [
{
"created": "Thu, 7 Nov 2013 16:09:58 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Nov 2013 13:43:50 GMT",
"version": "v2"
}
] | 2013-11-28 | [
[
"Kaieda",
"Shuji",
""
],
[
"Halle",
"Bertil",
""
]
] | Myoglobin (Mb) binds diatomic ligands, like O$_2$, CO, and NO, in a cavity that is only transiently accessible. Crystallography and molecular simulations show that the ligands can migrate through an extensive network of transiently connected cavities, but disagree on the locations and occupancy of internal hydration sites. Here, we use water $^2$H and $^{17}$O magnetic relaxation dispersion (MRD) to characterize the internal water molecules in Mb under physiological conditions. We find that equine carbonmonoxy Mb contains 4.5 $\pm$ 1.0 ordered internal water molecules with a mean survival time of 5.6 $\pm$ 0.5 $\mu$s at 25 $^\circ$C. The likely location of these water molecules are the four polar hydration sites, including one of the xenon-binding cavities, that are fully occupied in all high-resolution crystal structures of equine Mb. The finding that water escapes from these sites, located 17 -- 31 {\AA} apart in the protein, on the same $\mu$s time scale suggests a global exchange mechanism. We propose that this mechanism involves transient penetration of the protein by H-bonded water chains. Such a mechanism could play a functional role by eliminating trapped ligands. In addition, the MRD results indicate that two or three of the 11 histidine residues of equine Mb undergo intramolecular hydrogen exchange on a $\mu$s time scale. |
2408.07280 | Alphin J Thottupattu | Alphin J Thottupattu, Jayanthi Sivaswamy, Bharath Holla, Jithender
Saini | Understanding Brain Aging Across Populations: A Comprehensive Framework
for Structural Analysis | null | null | null | null | q-bio.NC stat.AP | http://creativecommons.org/licenses/by/4.0/ | Understanding distinct neurological aging patterns across various populations
is vital in the context of a globally aging populace. This study seeks to
unravel the structural variations in the aging brain, taking into consideration
different ethnic backgrounds. MRI data from Indian, Chinese, Japanese, and
Caucasian populations were analyzed using a two-pronged approach. Initially, a
group analysis was performed involving tissue segmentation through FSL-FAST,
examining gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF).
Subsequently, a continuous model-based analysis was employed, defining aging as
a diffeomorphic transformation, which facilitated a detailed intra- and
inter-population analysis, and examined both global anatomy and age-dependent
distances of each population in comparison to the Indian population. Detailed
insights into the spatial distribution of brain deformations over time were
obtained, with particular focus on anatomical changes relative to a reference
time point. The proposed comprehensive structural comparison framework, applied
to a sample dataset encompassing four distinct populations, represents a
pioneering effort to compare structural differences related to brain aging on a
global scale. Subsequent studies utilizing larger datasets and applying the
proposed analysis across groups based on various criteria will further advance
our understanding of aging-related changes.
| [
{
"created": "Wed, 31 Jul 2024 06:37:00 GMT",
"version": "v1"
}
] | 2024-08-15 | [
[
"Thottupattu",
"Alphin J",
""
],
[
"Sivaswamy",
"Jayanthi",
""
],
[
"Holla",
"Bharath",
""
],
[
"Saini",
"Jithender",
""
]
] | Understanding distinct neurological aging patterns across various populations is vital in the context of a globally aging populace. This study seeks to unravel the structural variations in the aging brain, taking into consideration different ethnic backgrounds. MRI data from Indian, Chinese, Japanese, and Caucasian populations were analyzed using a two-pronged approach. Initially, a group analysis was performed involving tissue segmentation through FSL-FAST, examining gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Subsequently, a continuous model-based analysis was employed, defining aging as a diffeomorphic transformation, which facilitated a detailed intra- and inter-population analysis, and examined both global anatomy and age-dependent distances of each population in comparison to the Indian population. Detailed insights into the spatial distribution of brain deformations over time were obtained, with particular focus on anatomical changes relative to a reference time point. The proposed comprehensive structural comparison framework, applied to a sample dataset encompassing four distinct populations, represents a pioneering effort to compare structural differences related to brain aging on a global scale. Subsequent studies utilizing larger datasets and applying the proposed analysis across groups based on various criteria will further advance our understanding of aging-related changes. |
1405.0518 | Rebekah Rogers | Rebekah L Rogers, Julie M Cridland, Ling Shao, Tina T Hu, Peter
Andolfatto, and Kevin R Thornton | Tandem duplications and the limits of natural selection in Drosophila
yakuba and Drosophila simulans | Updated draft with improved estimates of mutation rates, time to
establishment of sweeps, and probability of adaptation from standing
variation | null | 10.1371/journal.pone.0132184 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tandem duplications are an essential source of genetic novelty, and their
variation in natural populations is expected to influence adaptive walks. Here,
we describe evolutionary impacts of recently-derived, segregating tandem
duplications in Drosophila yakuba and Drosophila simulans. We observe an excess
of duplicated genes involved in defense against pathogens, insecticide
resistance, chorion development, cuticular peptides, and lipases or
endopeptidases associated with the accessory glands, suggesting that
duplications function in Red Queen dynamics and rapid evolution. We document
evidence of widespread selection on the D. simulans X, suggesting adaptation
through duplication is common on the X. Despite the evidence for positive
selection, duplicates display an excess of low frequency variants consistent
with largely detrimental impacts, limiting the variation that can effectively
facilitate adaptation. Although we observe hundreds of gene duplications, we
show that segregating variation is insufficient to provide duplicate copies of
the entire genome, and the number of duplications in the population spans
13.4\% of major chromosome arms in D. yakuba and 9.7\% in D. simulans. Whole
gene duplication rates are low at $1.17\times10^{-9}$ per gene per generation
in D. yakuba and $6.03\times10^{-10}$ per gene per generation in D. simulans,
suggesting long wait times for new mutations on the order of thousands of years
for the establishment of sweeps. Hence, in cases where adaption depends on
individual tandem duplications, evolution will be severely limited by mutation.
We observe low levels of parallel recruitment of the same duplicated gene in
different species, suggesting that the span of standing variation will define
evolutionary outcomes in spite of convergence across gene ontologies consistent
with rapidly evolving phenotypes.} }
| [
{
"created": "Fri, 2 May 2014 21:18:16 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Aug 2014 19:45:07 GMT",
"version": "v2"
}
] | 2015-08-13 | [
[
"Rogers",
"Rebekah L",
""
],
[
"Cridland",
"Julie M",
""
],
[
"Shao",
"Ling",
""
],
[
"Hu",
"Tina T",
""
],
[
"Andolfatto",
"Peter",
""
],
[
"Thornton",
"Kevin R",
""
]
] | Tandem duplications are an essential source of genetic novelty, and their variation in natural populations is expected to influence adaptive walks. Here, we describe evolutionary impacts of recently-derived, segregating tandem duplications in Drosophila yakuba and Drosophila simulans. We observe an excess of duplicated genes involved in defense against pathogens, insecticide resistance, chorion development, cuticular peptides, and lipases or endopeptidases associated with the accessory glands, suggesting that duplications function in Red Queen dynamics and rapid evolution. We document evidence of widespread selection on the D. simulans X, suggesting adaptation through duplication is common on the X. Despite the evidence for positive selection, duplicates display an excess of low frequency variants consistent with largely detrimental impacts, limiting the variation that can effectively facilitate adaptation. Although we observe hundreds of gene duplications, we show that segregating variation is insufficient to provide duplicate copies of the entire genome, and the number of duplications in the population spans 13.4\% of major chromosome arms in D. yakuba and 9.7\% in D. simulans. Whole gene duplication rates are low at $1.17\times10^{-9}$ per gene per generation in D. yakuba and $6.03\times10^{-10}$ per gene per generation in D. simulans, suggesting long wait times for new mutations on the order of thousands of years for the establishment of sweeps. Hence, in cases where adaption depends on individual tandem duplications, evolution will be severely limited by mutation. We observe low levels of parallel recruitment of the same duplicated gene in different species, suggesting that the span of standing variation will define evolutionary outcomes in spite of convergence across gene ontologies consistent with rapidly evolving phenotypes.} } |
2001.11080 | Pedro Carelli | Nastaran Lotfi, Antonio J. Fontenele, Tha\'is Feliciano, Leandro A. A.
Aguiar, Nivaldo A. P. de Vasconcelos, Carina Soares-Cunha, B\'arbara Coimbra,
Ana Jo\~ao Rodrigues, Nuno Sousa, Mauro Copelli, Pedro V. Carelli | Signatures of brain criticality unveiled by maximum entropy analysis
across cortical states | null | Phys. Rev. E 102, 012408 (2020) | 10.1103/PhysRevE.102.012408 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has recently been reported that statistical signatures of brain
criticality, obtained from distributions of neuronal avalanches, can depend on
the cortical state. We revisit these claims with a completely different and
independent approach, employing a maximum entropy model to test whether
signatures of criticality appear in urethane-anesthetized rats. To account for
the spontaneous variation of cortical state, we parse the time series and
perform the maximum entropy analysis as a function of the variability of the
population spiking activity. To compare data sets with different number of
neurons, we define a normalized distance to criticality that takes into account
the peak and width of the specific heat curve. We found an universal collapse
of the normalized distance to criticality dependence on the cortical state on
an animal by animal basis. This indicates a universal dynamics and a critical
point at an intermediate value of spiking variability.
| [
{
"created": "Wed, 29 Jan 2020 20:24:12 GMT",
"version": "v1"
}
] | 2020-08-05 | [
[
"Lotfi",
"Nastaran",
""
],
[
"Fontenele",
"Antonio J.",
""
],
[
"Feliciano",
"Thaís",
""
],
[
"Aguiar",
"Leandro A. A.",
""
],
[
"de Vasconcelos",
"Nivaldo A. P.",
""
],
[
"Soares-Cunha",
"Carina",
""
],
[
"Coimbra",
"Bárbara",
""
],
[
"Rodrigues",
"Ana João",
""
],
[
"Sousa",
"Nuno",
""
],
[
"Copelli",
"Mauro",
""
],
[
"Carelli",
"Pedro V.",
""
]
] | It has recently been reported that statistical signatures of brain criticality, obtained from distributions of neuronal avalanches, can depend on the cortical state. We revisit these claims with a completely different and independent approach, employing a maximum entropy model to test whether signatures of criticality appear in urethane-anesthetized rats. To account for the spontaneous variation of cortical state, we parse the time series and perform the maximum entropy analysis as a function of the variability of the population spiking activity. To compare data sets with different number of neurons, we define a normalized distance to criticality that takes into account the peak and width of the specific heat curve. We found an universal collapse of the normalized distance to criticality dependence on the cortical state on an animal by animal basis. This indicates a universal dynamics and a critical point at an intermediate value of spiking variability. |
1112.2771 | Zuo-Bing Wu | Zuo-Bing Wu | Global transposable characteristics in the yeast complete DNA sequence | 19 pages, 5 figures, 5 tables | Physica A 389 (2010) 5698-5705 | 10.1016/j.physa.2010.08.026 | null | q-bio.GN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Global transposable characteristics in the complete DNA sequence of the
Saccharomyces cevevisiae yeast is determined by using the metric representation
and recurrence plot methods. In the form of the correlation distance of
nucleotide strings, 16 chromosome sequences of the yeast, which are divided
into 5 groups, display 4 kinds of the fundamental transposable characteristics:
a short period increasing, a long quasi-period increasing, a long major value
and hardly relevant.
| [
{
"created": "Tue, 13 Dec 2011 02:01:11 GMT",
"version": "v1"
}
] | 2015-06-03 | [
[
"Wu",
"Zuo-Bing",
""
]
] | Global transposable characteristics in the complete DNA sequence of the Saccharomyces cevevisiae yeast is determined by using the metric representation and recurrence plot methods. In the form of the correlation distance of nucleotide strings, 16 chromosome sequences of the yeast, which are divided into 5 groups, display 4 kinds of the fundamental transposable characteristics: a short period increasing, a long quasi-period increasing, a long major value and hardly relevant. |
2207.14729 | Michael Levin | Lakshwin Shreesha and Michael Levin | Competency of the Developmental Layer Alters Evolutionary Dynamics in an
Artificial Embryogeny Model of Morphogenesis | 30 pages, 7 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Biological genotypes do not code directly for phenotypes; developmental
physiology is the control layer that separates genomes from capacities
ascertained by selection. A key aspect is competency, as cells are not a
passive material but descendants of unicellular organisms with complex
context-sensitive capabilities. We used an evolutionary simulation in the
context of minimal artificial embryogeny to probe the effects of different
degrees of cellular competency on evolutionary dynamics. Virtual embryos
consisted of a single axis of positional information values provided by cells'
genomes, operated upon by an evolutionary cycle in which embryos' fitness was
proportional to monotonicity of the axial gradient. Evolutionary dynamics were
evaluated in two modes: hardwired "mosaic" development (genotype directly
encodes phenotype), and a more realistic mode in which cells interact prior to
evaluation by the fitness function ("regulative" development). Even minimal
competency with respect to improving their position in the embryo results in
better performance of the evolutionary search. Crucially, we observed that as
competency of cells masks the raw fitness of the genomes, the phenotypic
fitness gains are then mostly due to improvements of cells' developmental
problem-solving capacities, not the structural genome. This suggests the
existence of a powerful ratchet mechanism: evolution progressively becomes
locked in to improvements in the intelligence of its agential substrate, with
reduced pressure on the structural genome. A feedback loop in which evolution
increasingly puts more effort into the developmental software than perfecting
the hardware explains the very puzzling divergence of genome from anatomy in
species like planaria, identifies a possible drive for scaling intelligence
over time, and suggests strategies for engineering novel systems in silico and
in bioengineering.
| [
{
"created": "Fri, 29 Jul 2022 15:01:49 GMT",
"version": "v1"
}
] | 2022-08-01 | [
[
"Shreesha",
"Lakshwin",
""
],
[
"Levin",
"Michael",
""
]
] | Biological genotypes do not code directly for phenotypes; developmental physiology is the control layer that separates genomes from capacities ascertained by selection. A key aspect is competency, as cells are not a passive material but descendants of unicellular organisms with complex context-sensitive capabilities. We used an evolutionary simulation in the context of minimal artificial embryogeny to probe the effects of different degrees of cellular competency on evolutionary dynamics. Virtual embryos consisted of a single axis of positional information values provided by cells' genomes, operated upon by an evolutionary cycle in which embryos' fitness was proportional to monotonicity of the axial gradient. Evolutionary dynamics were evaluated in two modes: hardwired "mosaic" development (genotype directly encodes phenotype), and a more realistic mode in which cells interact prior to evaluation by the fitness function ("regulative" development). Even minimal competency with respect to improving their position in the embryo results in better performance of the evolutionary search. Crucially, we observed that as competency of cells masks the raw fitness of the genomes, the phenotypic fitness gains are then mostly due to improvements of cells' developmental problem-solving capacities, not the structural genome. This suggests the existence of a powerful ratchet mechanism: evolution progressively becomes locked in to improvements in the intelligence of its agential substrate, with reduced pressure on the structural genome. A feedback loop in which evolution increasingly puts more effort into the developmental software than perfecting the hardware explains the very puzzling divergence of genome from anatomy in species like planaria, identifies a possible drive for scaling intelligence over time, and suggests strategies for engineering novel systems in silico and in bioengineering. |
1901.08114 | Yanlu Xie | Yanlu Xie, Yue Chen, Man Li | Convolution Forgetting Curve Model for Repeated Learning | 12 pages, 9 figures | null | null | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most of mathematic forgetting curve models fit well with the forgetting data
under the learning condition of one time rather than repeated. In the paper, a
convolution model of forgetting curve is proposed to simulate the memory
process during learning. In this model, the memory ability (i.e. the central
procedure in the working memory model) and learning material (i.e. the input in
the working memory model) is regarded as the system function and the input
function, respectively. The status of forgetting (i.e. the output in the
working memory model) is regarded as output function or the convolution result
of the memory ability and learning material. The model is applied to simulate
the forgetting curves in different situations. The results show that the model
is able to simulate the forgetting curves not only in one time learning
condition but also in multi-times condition. The model is further verified in
the experiments of Mandarin tone learning for Japanese learners. And the
predicted curve fits well on the test points.
| [
{
"created": "Sat, 19 Jan 2019 08:09:58 GMT",
"version": "v1"
}
] | 2019-01-25 | [
[
"Xie",
"Yanlu",
""
],
[
"Chen",
"Yue",
""
],
[
"Li",
"Man",
""
]
] | Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated. In the paper, a convolution model of forgetting curve is proposed to simulate the memory process during learning. In this model, the memory ability (i.e. the central procedure in the working memory model) and learning material (i.e. the input in the working memory model) is regarded as the system function and the input function, respectively. The status of forgetting (i.e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material. The model is applied to simulate the forgetting curves in different situations. The results show that the model is able to simulate the forgetting curves not only in one time learning condition but also in multi-times condition. The model is further verified in the experiments of Mandarin tone learning for Japanese learners. And the predicted curve fits well on the test points. |
2004.02406 | Mario Villalobos-Arias Dr. | Mario Villalobos-Arias | Using generalized logistics regression to forecast population infected
by Covid-19 | forecast to covid-19 using generalized logistics regression 14
figures, 18 pages | null | null | null | q-bio.PE math.OC | http://creativecommons.org/publicdomain/zero/1.0/ | In this work, a proposal to forecast the populations using generalized
logistics regression curve fitting is presented. This type of curve is used to
study population growth, in this case population of people infected with the
Covid-19 virus; and it can also be used to approximate the survival curve used
in actuarial and similar studies.
| [
{
"created": "Mon, 6 Apr 2020 05:27:40 GMT",
"version": "v1"
}
] | 2020-04-07 | [
[
"Villalobos-Arias",
"Mario",
""
]
] | In this work, a proposal to forecast the populations using generalized logistics regression curve fitting is presented. This type of curve is used to study population growth, in this case population of people infected with the Covid-19 virus; and it can also be used to approximate the survival curve used in actuarial and similar studies. |
2007.07736 | Vangelis Daskalakis | Athanasios A. Panagiotopoulos, Danai-Maria Kotzampasi, George
Sourvinos, Marilena Kampa, Stergios Pirintsos, Elias Castanas, Vangelis
Daskalakis | The natural polyphenol fortunellin and its structural analogs are
inhibitors of the SARS-CoV-2 main proteinase dimerization, as revealed by
molecular simulation studies | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 3CL-Pro (or M-Pro) is the SARS-CoV-2 main protease, acting as a homodimer, is
responsible for the cleavage of the large polyprotein 1ab transcript in
proteins acting on viral growth and replication. 3CL-Pro has been one of the
most studied SARS-CoV-2 proteins and the subject of therapeutic interventions,
targeting its catalytic domain. A number of drug candidates have been reported,
including some natural products. Here, we investigated in silico, through
binding and molecular dynamics simulations, the natural product space for the
identification of candidates of 3CL-Pro dimerization inhibitors. We report that
fortunellin (acacetin 7-O-neohesperidoside), a natural flavonoid O-glycoside,
is a potent inhibitor of 3CL-Pro dimerization. A search of the ZINC natural
products database identified another 16 related molecules, including apilin and
rhoifolin, with interesting pharmacological properties. We propose that
fortunellin and its structural analogs might be the basis of novel
pharmaceuticals and dietary supplements against SARS-CoV-2 induced COVID-19
disease.
| [
{
"created": "Wed, 15 Jul 2020 15:08:00 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Jul 2020 07:40:52 GMT",
"version": "v2"
},
{
"created": "Fri, 24 Jul 2020 11:06:18 GMT",
"version": "v3"
},
{
"created": "Tue, 15 Sep 2020 19:10:50 GMT",
"version": "v4"
}
] | 2020-09-17 | [
[
"Panagiotopoulos",
"Athanasios A.",
""
],
[
"Kotzampasi",
"Danai-Maria",
""
],
[
"Sourvinos",
"George",
""
],
[
"Kampa",
"Marilena",
""
],
[
"Pirintsos",
"Stergios",
""
],
[
"Castanas",
"Elias",
""
],
[
"Daskalakis",
"Vangelis",
""
]
] | 3CL-Pro (or M-Pro) is the SARS-CoV-2 main protease, acting as a homodimer, is responsible for the cleavage of the large polyprotein 1ab transcript in proteins acting on viral growth and replication. 3CL-Pro has been one of the most studied SARS-CoV-2 proteins and the subject of therapeutic interventions, targeting its catalytic domain. A number of drug candidates have been reported, including some natural products. Here, we investigated in silico, through binding and molecular dynamics simulations, the natural product space for the identification of candidates of 3CL-Pro dimerization inhibitors. We report that fortunellin (acacetin 7-O-neohesperidoside), a natural flavonoid O-glycoside, is a potent inhibitor of 3CL-Pro dimerization. A search of the ZINC natural products database identified another 16 related molecules, including apilin and rhoifolin, with interesting pharmacological properties. We propose that fortunellin and its structural analogs might be the basis of novel pharmaceuticals and dietary supplements against SARS-CoV-2 induced COVID-19 disease. |
1703.04184 | Theodore Hill | Theodore P. Hill | An Evolutionary Theory for the Variability Hypothesis | 34 pages; restructured Appendix; added 17 references; changed
citation/bibliography style | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An elementary biostatistical theory based on a selectivity-variability
principle is proposed to address a question raised by Charles Darwin, namely,
how one sex of a sexually dimorphic species might tend to evolve with greater
variability than the other sex. Briefly, the theory says that if one sex is
relatively selective then from one generation to the next, more variable
subpopulations of the opposite sex will generally tend to prevail over those
with lesser variability. Moreover, the perhaps less intuitive converse also
holds: if a sex is relatively non-selective, then less variable subpopulations
of the opposite sex will prevail over those with greater variability. This
theory requires certain regularity conditions on the distributions, but makes
no assumptions about differences in means between the sexes, nor does it
presume that one sex is selective and the other non-selective. Two mathematical
models of the selectivity-variability principle are presented: a discrete-time
one-step probabilistic model of short-term behavior with an example using
normally distributed perceived fitness values; and a continuous-time
deterministic model for the long-term asymptotic behavior of the expected sizes
of the subpopulations with an example using exponentially distributed fitness
levels.
| [
{
"created": "Sun, 12 Mar 2017 22:08:19 GMT",
"version": "v1"
},
{
"created": "Wed, 31 Oct 2018 23:58:59 GMT",
"version": "v10"
},
{
"created": "Thu, 28 Mar 2019 23:19:19 GMT",
"version": "v11"
},
{
"created": "Tue, 24 Sep 2019 21:54:13 GMT",
"version": "v12"
},
{
"created": "Fri, 31 Jan 2020 21:18:09 GMT",
"version": "v13"
},
{
"created": "Sat, 16 Jan 2021 22:41:38 GMT",
"version": "v14"
},
{
"created": "Sun, 19 Mar 2017 01:08:13 GMT",
"version": "v2"
},
{
"created": "Sat, 9 Sep 2017 22:33:08 GMT",
"version": "v3"
},
{
"created": "Mon, 18 Sep 2017 16:21:49 GMT",
"version": "v4"
},
{
"created": "Thu, 1 Feb 2018 01:01:30 GMT",
"version": "v5"
},
{
"created": "Tue, 6 Feb 2018 21:18:49 GMT",
"version": "v6"
},
{
"created": "Tue, 17 Apr 2018 22:00:54 GMT",
"version": "v7"
},
{
"created": "Mon, 21 May 2018 23:10:19 GMT",
"version": "v8"
},
{
"created": "Fri, 24 Aug 2018 21:07:51 GMT",
"version": "v9"
}
] | 2021-01-19 | [
[
"Hill",
"Theodore P.",
""
]
] | An elementary biostatistical theory based on a selectivity-variability principle is proposed to address a question raised by Charles Darwin, namely, how one sex of a sexually dimorphic species might tend to evolve with greater variability than the other sex. Briefly, the theory says that if one sex is relatively selective then from one generation to the next, more variable subpopulations of the opposite sex will generally tend to prevail over those with lesser variability. Moreover, the perhaps less intuitive converse also holds: if a sex is relatively non-selective, then less variable subpopulations of the opposite sex will prevail over those with greater variability. This theory requires certain regularity conditions on the distributions, but makes no assumptions about differences in means between the sexes, nor does it presume that one sex is selective and the other non-selective. Two mathematical models of the selectivity-variability principle are presented: a discrete-time one-step probabilistic model of short-term behavior with an example using normally distributed perceived fitness values; and a continuous-time deterministic model for the long-term asymptotic behavior of the expected sizes of the subpopulations with an example using exponentially distributed fitness levels. |
2112.09200 | Heiko Sch\"utt | Heiko H. Sch\"utt, Alexander D. Kipnis, J\"orn Diedrichsen, Nikolaus
Kriegeskorte | Statistical inference on representational geometries | revision submitted to Elife, 40 pages 9 figures | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuroscience has recently made much progress, expanding the complexity of
both neural-activity measurements and brain-computational models. However, we
lack robust methods for connecting theory and experiment by evaluating our new
big models with our new big data. Here, we introduce new inference methods
enabling researchers to evaluate and compare models based on the accuracy of
their predictions of representational geometries: A good model should
accurately predict the distances among the neural population representations
(e.g. of a set of stimuli). Our inference methods combine novel 2-factor
extensions of crossvalidation (to prevent overfitting to either subjects or
conditions from inflating our estimates of model accuracy) and bootstrapping
(to enable inferential model comparison with simultaneous generalization to
both new subjects and new conditions). We validate the inference methods on
data where the ground-truth model is known, by simulating data with deep neural
networks and by resampling of calcium imaging and functional MRI data. Results
demonstrate that the methods are valid and conclusions generalize correctly.
These data analysis methods are available in an open-source Python toolbox
(rsatoolbox.readthedocs.io).
| [
{
"created": "Thu, 16 Dec 2021 20:58:43 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Jul 2023 23:18:53 GMT",
"version": "v2"
}
] | 2023-07-06 | [
[
"Schütt",
"Heiko H.",
""
],
[
"Kipnis",
"Alexander D.",
""
],
[
"Diedrichsen",
"Jörn",
""
],
[
"Kriegeskorte",
"Nikolaus",
""
]
] | Neuroscience has recently made much progress, expanding the complexity of both neural-activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by resampling of calcium imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox (rsatoolbox.readthedocs.io). |
2208.10985 | Natasha Savage Dr | K. S. Bagdassarian, J. P. Etchells, N. S. Savage | A mathematical model integrates diverging PXY and MP interactions in
cambium development | 12 pages with embedded figures & tables in main manuscript, 6 pages
with embedded figure in supplement, 4 pages of references | null | null | null | q-bio.CB q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | The cambium is a meristematic tissue in plant stems. Here, cell divisions
occur that are required for radial growth of plant stems. Daughters of cell
divisions within the cambium differentiate into woody xylem cells towards the
inside of the stem, or phloem towards the outside. As such, a pattern of
xylem-cambium-phloem is present along the radial axis of the stem. A
ligand-receptor pair, TDIF-PXY promotes cell division in the cambium, as do the
phytohormones, cytokinin and auxin. An auxin response factor, MP, has been
proposed to initiate cambial cell divisions by promoting PXY expression,
however, MP has also been reported to repress cambial cell divisions later in
development where TDIF-PXY complexes are also reported to suppress MP activity.
Here, we used a mathematical modelling approach to investigate how MP cell
division-promoting activity and cell division-repressing activity might be
integrated into the same network as a negative feedback loop. In our model,
this feedback loop improved the ability of the cambium to pattern correctly and
was found to be required for normal patterning when MP was stable. The
implications of this model in early and late cambium development are discussed.
| [
{
"created": "Tue, 23 Aug 2022 14:07:21 GMT",
"version": "v1"
}
] | 2022-08-24 | [
[
"Bagdassarian",
"K. S.",
""
],
[
"Etchells",
"J. P.",
""
],
[
"Savage",
"N. S.",
""
]
] | The cambium is a meristematic tissue in plant stems. Here, cell divisions occur that are required for radial growth of plant stems. Daughters of cell divisions within the cambium differentiate into woody xylem cells towards the inside of the stem, or phloem towards the outside. As such, a pattern of xylem-cambium-phloem is present along the radial axis of the stem. A ligand-receptor pair, TDIF-PXY promotes cell division in the cambium, as do the phytohormones, cytokinin and auxin. An auxin response factor, MP, has been proposed to initiate cambial cell divisions by promoting PXY expression, however, MP has also been reported to repress cambial cell divisions later in development where TDIF-PXY complexes are also reported to suppress MP activity. Here, we used a mathematical modelling approach to investigate how MP cell division-promoting activity and cell division-repressing activity might be integrated into the same network as a negative feedback loop. In our model, this feedback loop improved the ability of the cambium to pattern correctly and was found to be required for normal patterning when MP was stable. The implications of this model in early and late cambium development are discussed. |
2205.05876 | Abdullah Alqarni | Abdullah Alqarni, Wei Wen, Ben C.P. Lam, John D. Crawford, Perminder
S. Sachdev, Jiyang Jiang | Hormonal Factors Moderate the Associations Between Vascular Risk Factors
and White Matter Hyperintensities | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Objective: To examine the moderation effects of hormonal factors on the
associations between vascular risk factors and white matter hyperintensities
(WMH) in men and women, separately. Methods: WMH were automatically segmented
and quantified in the UK Biobank dataset (N = 18,294). Generalised linear
models were applied to examine 1) the main effects of vascular (body mass
index, hip to waist ratio, pulse wave velocity, hypercholesterolemia, diabetes,
hypertension, smoking status) and hormonal (testosterone levels, contraceptive
pill, hormone replacement therapy, menopause) factors on WMH, and 2) the
moderation effects of hormonal factors on the relationship between vascular
risk factors and WMH volumes. Results: In men with testosterone levels one
standard deviation (SD) higher than the mean value, increased body mass index
and pulse wave velocity, and smoking were associated with higher WMH volumes.
The association between body mass index and WMH was more significant in the
periventricular white matter regions, whilst the relationship between pulse
wave velocity and WMH was restricted to deep white matter regions. Men with low
testosterone levels (one SD below the mean level) showed a significant
association between hypercholesterolemia and higher deep WMH volumes.
Hypertensive women showed higher WMH volumes than women without hypertension
regardless of whether hormone replacement therapy was used. However, higher WMH
volumes, especially in the deep white matter regions, were found in women who
did not use hormone replacement therapy or use it for a shorter duration.
Conclusion: These findings highlighted the importance of considering hormonal
risk factors in the prevention and management of WMH.
| [
{
"created": "Thu, 12 May 2022 04:50:54 GMT",
"version": "v1"
}
] | 2022-05-13 | [
[
"Alqarni",
"Abdullah",
""
],
[
"Wen",
"Wei",
""
],
[
"Lam",
"Ben C. P.",
""
],
[
"Crawford",
"John D.",
""
],
[
"Sachdev",
"Perminder S.",
""
],
[
"Jiang",
"Jiyang",
""
]
] | Objective: To examine the moderation effects of hormonal factors on the associations between vascular risk factors and white matter hyperintensities (WMH) in men and women, separately. Methods: WMH were automatically segmented and quantified in the UK Biobank dataset (N = 18,294). Generalised linear models were applied to examine 1) the main effects of vascular (body mass index, hip to waist ratio, pulse wave velocity, hypercholesterolemia, diabetes, hypertension, smoking status) and hormonal (testosterone levels, contraceptive pill, hormone replacement therapy, menopause) factors on WMH, and 2) the moderation effects of hormonal factors on the relationship between vascular risk factors and WMH volumes. Results: In men with testosterone levels one standard deviation (SD) higher than the mean value, increased body mass index and pulse wave velocity, and smoking were associated with higher WMH volumes. The association between body mass index and WMH was more significant in the periventricular white matter regions, whilst the relationship between pulse wave velocity and WMH was restricted to deep white matter regions. Men with low testosterone levels (one SD below the mean level) showed a significant association between hypercholesterolemia and higher deep WMH volumes. Hypertensive women showed higher WMH volumes than women without hypertension regardless of whether hormone replacement therapy was used. However, higher WMH volumes, especially in the deep white matter regions, were found in women who did not use hormone replacement therapy or use it for a shorter duration. Conclusion: These findings highlighted the importance of considering hormonal risk factors in the prevention and management of WMH. |
0806.1267 | Francois Bonneton | Fran\c{c}ois Bonneton (CGMC), Dominique Zelus (LBMC), Thomas Iwema
(CGMC), Marc Robinson-Rechavi (LBMC), Vincent Laudet (LBMC) | Rapid divergence of the ecdysone receptor in Diptera and Lepidoptera
suggests coevolution between ECR and USP-RXR | null | Molecular Biology and Evolution 4, 20 (2003) 541-553 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ecdysteroid hormones are major regulators in reproduction and development of
insects, including larval molts and metamorphosis. The functional ecdysone
receptor is a heterodimer of ECR (NR1H1) and USP-RXR (NR2B4), which is the
orthologue of vertebrate retinoid X receptors (RXR alpha, beta, gamma). Both
proteins belong to the superfamily of nuclear hormone receptors,
ligand-dependent transcription factors that share two conserved domains: the
DNA-binding domain (DBD) and the ligand-binding domain (LBD). In order to gain
further insight into the evolution of metamorphosis and gene regulation by
ecdysone in arthropods, we performed a phylogenetic analysis of both partners
of the heterodimer ECR/USP-RXR. Overall, 38 USP-RXR and 19 ECR protein
sequences, from 33 species, have been used for this analysis. Interestingly,
sequence alignments and structural comparisons reveal high divergence rates,
for both ECR and USP-RXR, specifically among Diptera and Lepidoptera. The most
impressive differences affect the ligand-binding domain of USP-RXR. In
addition, ECR sequences show variability in other domains, namely the
DNA-binding and the carboxy-terminal F domains. Our data provide the first
evidence that ECR and USP-RXR may have coevolved during holometabolous insect
diversification, leading to a functional divergence of the ecdysone receptor.
These results have general implications on fundamental aspects of insect
development, evolution of nuclear receptors, and the design of specific
insecticides.
| [
{
"created": "Sat, 7 Jun 2008 06:24:12 GMT",
"version": "v1"
}
] | 2008-12-18 | [
[
"Bonneton",
"François",
"",
"CGMC"
],
[
"Zelus",
"Dominique",
"",
"LBMC"
],
[
"Iwema",
"Thomas",
"",
"CGMC"
],
[
"Robinson-Rechavi",
"Marc",
"",
"LBMC"
],
[
"Laudet",
"Vincent",
"",
"LBMC"
]
] | Ecdysteroid hormones are major regulators in reproduction and development of insects, including larval molts and metamorphosis. The functional ecdysone receptor is a heterodimer of ECR (NR1H1) and USP-RXR (NR2B4), which is the orthologue of vertebrate retinoid X receptors (RXR alpha, beta, gamma). Both proteins belong to the superfamily of nuclear hormone receptors, ligand-dependent transcription factors that share two conserved domains: the DNA-binding domain (DBD) and the ligand-binding domain (LBD). In order to gain further insight into the evolution of metamorphosis and gene regulation by ecdysone in arthropods, we performed a phylogenetic analysis of both partners of the heterodimer ECR/USP-RXR. Overall, 38 USP-RXR and 19 ECR protein sequences, from 33 species, have been used for this analysis. Interestingly, sequence alignments and structural comparisons reveal high divergence rates, for both ECR and USP-RXR, specifically among Diptera and Lepidoptera. The most impressive differences affect the ligand-binding domain of USP-RXR. In addition, ECR sequences show variability in other domains, namely the DNA-binding and the carboxy-terminal F domains. Our data provide the first evidence that ECR and USP-RXR may have coevolved during holometabolous insect diversification, leading to a functional divergence of the ecdysone receptor. These results have general implications on fundamental aspects of insect development, evolution of nuclear receptors, and the design of specific insecticides. |
1211.3690 | Juliana Capitanio | Juliana Silva Capitanio and Richard W. Wozniak | Host Cell Factors Necessary for Influenza A Infection: Meta-Analysis of
Genome Wide Studies | 14 pages, 6 figure, 1 table, 1 supplementary table | null | 10.6084/m9.figshare.1248958 | null | q-bio.CB q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Influenza A virus belongs to the Orthomyxoviridae family. Influenza virus
infection occurs yearly in all countries of the world. It usually kills between
250,000 and 500,000 people and causes severe illness in millions more. Over the
last century alone we have seen 3 global influenza pandemics. The great human
and financial cost of this disease has made it the second most studied virus
today, behind HIV. Recently, several genome-wide RNA interference studies have
focused on identifying host molecules that participate in Influenza infection.
We used nine of these studies for this meta-analysis. Even though the overlap
among genes identified in multiple screens was small, network analysis
indicates that similar protein complexes and biological functions of the host
were present. As a result, several host gene complexes important for the
Influenza virus life cycle were identified. The biological function and the
relevance of each identified protein complex in the Influenza virus life cycle
is further detailed in this paper.
| [
{
"created": "Thu, 15 Nov 2012 18:29:38 GMT",
"version": "v1"
},
{
"created": "Fri, 30 Nov 2012 23:08:04 GMT",
"version": "v2"
}
] | 2014-11-25 | [
[
"Capitanio",
"Juliana Silva",
""
],
[
"Wozniak",
"Richard W.",
""
]
] | The Influenza A virus belongs to the Orthomyxoviridae family. Influenza virus infection occurs yearly in all countries of the world. It usually kills between 250,000 and 500,000 people and causes severe illness in millions more. Over the last century alone we have seen 3 global influenza pandemics. The great human and financial cost of this disease has made it the second most studied virus today, behind HIV. Recently, several genome-wide RNA interference studies have focused on identifying host molecules that participate in Influenza infection. We used nine of these studies for this meta-analysis. Even though the overlap among genes identified in multiple screens was small, network analysis indicates that similar protein complexes and biological functions of the host were present. As a result, several host gene complexes important for the Influenza virus life cycle were identified. The biological function and the relevance of each identified protein complex in the Influenza virus life cycle is further detailed in this paper. |
0811.2437 | Sorin Tanase-Nicola | Siebe B. van Albada, Sorin Tanase-Nicola and Pieter Rein ten Wolde | The switching dynamics of the bacterial flagellar motor | 7 pages, 6 figures, RevTeX4 | null | null | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many swimming bacteria are propelled by flagellar motors that stochastically
switch between the clockwise and counterclockwise rotation direction. While the
switching dynamics are one of the most important characteristics of flagellar
motors, the mechanisms that control switching are poorly understood. We present
a statistical-mechanical model of the flagellar rotary motor, which consists of
a number of stator proteins that drive the rotation of a ring of rotor
proteins, which in turn drives the rotation of a flagellar filament. At the
heart of our model is the assumption that the rotor protein complex can exist
in two conformational states corresponding to the two respective rotation
directions, and that switching between these states depends on interactions
with the stator proteins. This naturally couples the switching dynamics to the
rotation dynamics, making the switch sensitive to torque and speed. Another key
element of our model is that after a switching event, it takes time for the
load to build up, due to polymorphic transitions of the filament. Our model
predicts that this slow relaxation dynamics of the filament, in combination
with the load dependence of the switching frequency, leads to a characteristic
switching time, in agreement with recent observations.
| [
{
"created": "Fri, 14 Nov 2008 22:53:39 GMT",
"version": "v1"
}
] | 2008-11-18 | [
[
"van Albada",
"Siebe B.",
""
],
[
"Tanase-Nicola",
"Sorin",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | Many swimming bacteria are propelled by flagellar motors that stochastically switch between the clockwise and counterclockwise rotation direction. While the switching dynamics are one of the most important characteristics of flagellar motors, the mechanisms that control switching are poorly understood. We present a statistical-mechanical model of the flagellar rotary motor, which consists of a number of stator proteins that drive the rotation of a ring of rotor proteins, which in turn drives the rotation of a flagellar filament. At the heart of our model is the assumption that the rotor protein complex can exist in two conformational states corresponding to the two respective rotation directions, and that switching between these states depends on interactions with the stator proteins. This naturally couples the switching dynamics to the rotation dynamics, making the switch sensitive to torque and speed. Another key element of our model is that after a switching event, it takes time for the load to build up, due to polymorphic transitions of the filament. Our model predicts that this slow relaxation dynamics of the filament, in combination with the load dependence of the switching frequency, leads to a characteristic switching time, in agreement with recent observations. |
1307.5335 | Blake Stacey | M.A.M. de Aguiar, E. Rauch, B.C. Stacey, Y. Bar-Yam | Mean Field Approximation to a Spatial Host-Pathogen Model | 7 pages (1 of errata), 1 figure; original paper by MdA, ER and YB;
errata by MdA, BCS and YB. v2: additional misprints corrected and docketed | Physical Review E 67, 047102 (2003) | 10.1103/PhysRevE.67.047102 | null | q-bio.PE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the mean field approximation to a simple spatial host-pathogen model
that has been shown to display interesting evolutionary properties. We show
that previous derivations of the mean field equations for this model are
actually only low-density approximations to the true mean field limit. We
derive the correct equations and the corresponding equations including
pair-correlations. The process of invasion by a mutant type of pathogen is also
discussed. [This article was published as Physical Review E 67, 047102 (2003).
Errata for the published version are corrected here and explicitly listed at
the end of this document.]
| [
{
"created": "Fri, 19 Jul 2013 20:47:19 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Aug 2013 05:25:19 GMT",
"version": "v2"
}
] | 2013-08-28 | [
[
"de Aguiar",
"M. A. M.",
""
],
[
"Rauch",
"E.",
""
],
[
"Stacey",
"B. C.",
""
],
[
"Bar-Yam",
"Y.",
""
]
] | We study the mean field approximation to a simple spatial host-pathogen model that has been shown to display interesting evolutionary properties. We show that previous derivations of the mean field equations for this model are actually only low-density approximations to the true mean field limit. We derive the correct equations and the corresponding equations including pair-correlations. The process of invasion by a mutant type of pathogen is also discussed. [This article was published as Physical Review E 67, 047102 (2003). Errata for the published version are corrected here and explicitly listed at the end of this document.] |
2301.10709 | Kishore Vasan | Kishore Vasan and Deisy Gysi and Albert-Laszlo Barabasi | The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery | manuscript + SI | null | null | null | q-bio.QM cs.SI | http://creativecommons.org/licenses/by/4.0/ | The depth of knowledge offered by post-genomic medicine has carried the
promise of new drugs, and cures for multiple diseases. To explore the degree to
which this capability has materialized, we extract meta-data from 356,403
clinical trials spanning four decades, aiming to offer mechanistic insights
into the innovation practices in drug discovery. We find that convention
dominates over innovation, as over 96% of the recorded trials focus on
previously tested drug targets, and the tested drugs target only 12% of the
human interactome. If current patterns persist, it would take 170 years to
target all druggable proteins. We uncover two network-based fundamental
mechanisms that currently limit target discovery: preferential attachment,
leading to the repeated exploration of previously targeted proteins; and local
network effects, limiting exploration to proteins interacting with highly
explored proteins. We build on these insights to develop a quantitative
network-based model of drug discovery. We demonstrate that the model is able to
accurately recreate the exploration patterns observed in clinical trials. Most
importantly, we show that a network-based search strategy can widen the scope
of drug discovery by guiding exploration to novel proteins that are part of
under explored regions in the human interactome.
| [
{
"created": "Wed, 25 Jan 2023 17:21:35 GMT",
"version": "v1"
}
] | 2023-01-26 | [
[
"Vasan",
"Kishore",
""
],
[
"Gysi",
"Deisy",
""
],
[
"Barabasi",
"Albert-Laszlo",
""
]
] | The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model of drug discovery. We demonstrate that the model is able to accurately recreate the exploration patterns observed in clinical trials. Most importantly, we show that a network-based search strategy can widen the scope of drug discovery by guiding exploration to novel proteins that are part of under explored regions in the human interactome. |
1512.05703 | Benjamin Albrecht | Benjamin Albrecht | Computing a Relevant Set of Nonbinary Maximum Acyclic Agreement Forests | 28 pages | null | null | null | q-bio.PE cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There exist several methods dealing with the reconstruction of rooted
phylogenetic networks explaining different evolutionary histories given by
rooted binary phylogenetic trees. In practice, however, due to insufficient
information of the underlying data, phylogenetic trees are in general not
completely resolved and, thus, those methods can often not be applied to
biological data. In this work, we make a first important step to approach this
goal by presenting the first algorithm --- called allMulMAAFs --- that enables
the computation of all relevant nonbinary maximum acyclic agreement forests for
two rooted (nonbinary) phylogenetic trees on the same set of taxa. Notice that
our algorithm is part of the freely available software Hybroscale computing
minimum hybridization networks for a set of rooted (nonbinary) phylogenetic
trees on an overlapping set of taxa.
| [
{
"created": "Thu, 17 Dec 2015 18:10:20 GMT",
"version": "v1"
}
] | 2015-12-18 | [
[
"Albrecht",
"Benjamin",
""
]
] | There exist several methods dealing with the reconstruction of rooted phylogenetic networks explaining different evolutionary histories given by rooted binary phylogenetic trees. In practice, however, due to insufficient information of the underlying data, phylogenetic trees are in general not completely resolved and, thus, those methods can often not be applied to biological data. In this work, we make a first important step to approach this goal by presenting the first algorithm --- called allMulMAAFs --- that enables the computation of all relevant nonbinary maximum acyclic agreement forests for two rooted (nonbinary) phylogenetic trees on the same set of taxa. Notice that our algorithm is part of the freely available software Hybroscale computing minimum hybridization networks for a set of rooted (nonbinary) phylogenetic trees on an overlapping set of taxa. |
2211.01978 | Yimeng Chen | Yuancheng Sun, Yimeng Chen, Weizhi Ma, Wenhao Huang, Kang Liu, Zhiming
Ma, Wei-Ying Ma, Yanyan Lan | PEMP: Leveraging Physics Properties to Enhance Molecular Property
Prediction | 9 pages. Published in CIKM 2022 | null | 10.1145/3511808.3557142 | null | q-bio.BM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Molecular property prediction is essential for drug discovery. In recent
years, deep learning methods have been introduced to this area and achieved
state-of-the-art performances. However, most of existing methods ignore the
intrinsic relations between molecular properties which can be utilized to
improve the performances of corresponding prediction tasks. In this paper, we
propose a new approach, namely Physics properties Enhanced Molecular Property
prediction (PEMP), to utilize relations between molecular properties revealed
by previous physics theory and physical chemistry studies. Specifically, we
enhance the training of the chemical and physiological property predictors with
related physics property prediction tasks. We design two different methods for
PEMP, respectively based on multi-task learning and transfer learning. Both
methods include a model-agnostic molecule representation module and a property
prediction module. In our implementation, we adopt both the state-of-the-art
molecule embedding models under the supervised learning paradigm and the
pretraining paradigm as the molecule representation module of PEMP,
respectively. Experimental results on public benchmark MoleculeNet show that
the proposed methods have the ability to outperform corresponding
state-of-the-art models.
| [
{
"created": "Tue, 18 Oct 2022 07:40:58 GMT",
"version": "v1"
}
] | 2022-11-04 | [
[
"Sun",
"Yuancheng",
""
],
[
"Chen",
"Yimeng",
""
],
[
"Ma",
"Weizhi",
""
],
[
"Huang",
"Wenhao",
""
],
[
"Liu",
"Kang",
""
],
[
"Ma",
"Zhiming",
""
],
[
"Ma",
"Wei-Ying",
""
],
[
"Lan",
"Yanyan",
""
]
] | Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. In this paper, we propose a new approach, namely Physics properties Enhanced Molecular Property prediction (PEMP), to utilize relations between molecular properties revealed by previous physics theory and physical chemistry studies. Specifically, we enhance the training of the chemical and physiological property predictors with related physics property prediction tasks. We design two different methods for PEMP, respectively based on multi-task learning and transfer learning. Both methods include a model-agnostic molecule representation module and a property prediction module. In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively. Experimental results on public benchmark MoleculeNet show that the proposed methods have the ability to outperform corresponding state-of-the-art models. |
1307.5300 | Yoshiharu Maeno | Yoshiharu Maeno | Detecting a trend change in cross-border epidemic transmission | null | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A method for detecting a trend change in cross-border epidemic transmission
is developed for a standard epidemiological SIR compartment model and a
meta-population network model. The method is applicable to investigating the
efficacy of the implemented public health intervention in managing infectious
travelers across borders from a time series of the number of new cases reported
in multiple geographical regions. It is found that the change point of the
probability of travel movements was one week after the WHO worldwide alert on
the SARS outbreak in 2003. The alert was effective in managing infectious
travelers. On the other hand, it is found that the probability of travel
movements did not change at all for the flu pandemic in 2009. The pandemic did
not affect potential travelers despite the WHO alert.
| [
{
"created": "Thu, 18 Jul 2013 08:38:27 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Nov 2013 14:59:41 GMT",
"version": "v2"
},
{
"created": "Mon, 29 Jun 2015 01:55:13 GMT",
"version": "v3"
},
{
"created": "Tue, 30 Jun 2015 02:28:22 GMT",
"version": "v4"
},
{
"created": "Wed, 14 Oct 2015 07:05:50 GMT",
"version": "v5"
}
] | 2015-10-15 | [
[
"Maeno",
"Yoshiharu",
""
]
] | A method for detecting a trend change in cross-border epidemic transmission is developed for a standard epidemiological SIR compartment model and a meta-population network model. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders from a time series of the number of new cases reported in multiple geographical regions. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert. |
2101.07117 | Diego Santoro | Diego Santoro, Leonardo Pellegrina, Fabio Vandin | SPRISS: Approximating Frequent $k$-mers by Sampling Reads, and
Applications | Accepted to RECOMB 2021 | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The extraction of $k$-mers is a fundamental component in many complex
analyses of large next-generation sequencing datasets, including reads
classification in genomics and the characterization of RNA-seq datasets. The
extraction of all $k$-mers and their frequencies is extremely demanding in
terms of running time and memory, owing to the size of the data and to the
exponential number of $k$-mers to be considered. However, in several
applications, only frequent $k$-mers, which are $k$-mers appearing in a
relatively high proportion of the data, are required by the analysis. In this
work we present SPRISS, a new efficient algorithm to approximate frequent
$k$-mers and their frequencies in next-generation sequencing data. SPRISS
employs a simple yet powerful reads sampling scheme, which allows to extract a
representative subset of the dataset that can be used, in combination with any
$k$-mer counting algorithm, to perform downstream analyses in a fraction of the
time required by the analysis of the whole data, while obtaining comparable
answers. Our extensive experimental evaluation demonstrates the efficiency and
accuracy of SPRISS in approximating frequent $k$-mers, and shows that it can be
used in various scenarios, such as the comparison of metagenomic datasets and
the identification of discriminative $k$-mers, to extract insights in a
fraction of the time required by the analysis of the whole dataset.
| [
{
"created": "Mon, 18 Jan 2021 15:16:26 GMT",
"version": "v1"
}
] | 2021-01-19 | [
[
"Santoro",
"Diego",
""
],
[
"Pellegrina",
"Leonardo",
""
],
[
"Vandin",
"Fabio",
""
]
] | The extraction of $k$-mers is a fundamental component in many complex analyses of large next-generation sequencing datasets, including reads classification in genomics and the characterization of RNA-seq datasets. The extraction of all $k$-mers and their frequencies is extremely demanding in terms of running time and memory, owing to the size of the data and to the exponential number of $k$-mers to be considered. However, in several applications, only frequent $k$-mers, which are $k$-mers appearing in a relatively high proportion of the data, are required by the analysis. In this work we present SPRISS, a new efficient algorithm to approximate frequent $k$-mers and their frequencies in next-generation sequencing data. SPRISS employs a simple yet powerful reads sampling scheme, which allows to extract a representative subset of the dataset that can be used, in combination with any $k$-mer counting algorithm, to perform downstream analyses in a fraction of the time required by the analysis of the whole data, while obtaining comparable answers. Our extensive experimental evaluation demonstrates the efficiency and accuracy of SPRISS in approximating frequent $k$-mers, and shows that it can be used in various scenarios, such as the comparison of metagenomic datasets and the identification of discriminative $k$-mers, to extract insights in a fraction of the time required by the analysis of the whole dataset. |
1103.0216 | Alessandro Pelizzola | Mauro Faccin, Pierpaolo Bruscolini and Alessandro Pelizzola | Analysis of the Equilibrium and Kinetics of the Ankyrin Repeat Protein
Myotrophin | 27 pages, 7 figures | J. Chem. Phys. 134, 075102 (2011) | 10.1063/1.3535562 | null | q-bio.BM cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We apply the Wako-Saito-Munoz-Eaton model to the study of Myotrophin, a small
ankyrin repeat protein, whose folding equilibrium and kinetics have been
recently characterized experimentally. The model, which is a native-centric
with binary variables, provides a finer microscopic detail than the Ising
model, that has been recently applied to some different repeat proteins, while
being still amenable for an exact solution. In partial agreement with the
experiments, our results reveal a weakly three-state equilibrium and a
two-state-like kinetics of the wild type protein despite the presence of a
non-trivial free-energy profile. These features appear to be related to a
careful "design" of the free-energy landscape, so that mutations can alter this
picture, stabilizing some intermediates and changing the position of the
rate-limiting step. Also the experimental findings of two alternative pathways,
an N-terminal and a C-terminal one, are qualitatively confirmed, even if the
variations in the rates upon the experimental mutations cannot be
quantitatively reproduced. Interestingly, folding and unfolding pathway appear
to be different, even if closely related: a property that is not generally
considered in the phenomenological interpretation of the experimental data.
| [
{
"created": "Tue, 1 Mar 2011 16:55:14 GMT",
"version": "v1"
}
] | 2011-03-02 | [
[
"Faccin",
"Mauro",
""
],
[
"Bruscolini",
"Pierpaolo",
""
],
[
"Pelizzola",
"Alessandro",
""
]
] | We apply the Wako-Saito-Munoz-Eaton model to the study of Myotrophin, a small ankyrin repeat protein, whose folding equilibrium and kinetics have been recently characterized experimentally. The model, which is a native-centric with binary variables, provides a finer microscopic detail than the Ising model, that has been recently applied to some different repeat proteins, while being still amenable for an exact solution. In partial agreement with the experiments, our results reveal a weakly three-state equilibrium and a two-state-like kinetics of the wild type protein despite the presence of a non-trivial free-energy profile. These features appear to be related to a careful "design" of the free-energy landscape, so that mutations can alter this picture, stabilizing some intermediates and changing the position of the rate-limiting step. Also the experimental findings of two alternative pathways, an N-terminal and a C-terminal one, are qualitatively confirmed, even if the variations in the rates upon the experimental mutations cannot be quantitatively reproduced. Interestingly, folding and unfolding pathway appear to be different, even if closely related: a property that is not generally considered in the phenomenological interpretation of the experimental data. |
2303.05742 | Matthias Schott | Kerem Akdogan, Lucas Heger, Andrew Iskauskas, Friedemann Neuhaus,
Matthias Schott | JUNE-Germany: An Agent-Based Epidemiology Simulation including Multiple
Virus Strains, Vaccinations and Testing Campaigns | 10 pages, 11 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The June software package is an open-source framework for the detailed
simulation of epidemics based on social interactions in a virtual population
reflecting age, gender, ethnicity, and socio-economic indicators in England. In
this paper, we present a new version of the framework specifically adapted for
Germany, which allows the simulation of the entire German population using
publicly available information on households, schools, universities,
workplaces, and mobility data for Germany. Moreover, JuneGermany incorporates
testing and vaccination strategies within the population as well as the
simultaneous handling of several different virus strains. First validation
tests of the framework have been performed for the state of Rhineland
Palatinate based on data collected between October 2020 and December 2020 and
then extrapolated to March 2021, i.e. the end of the second wave.
| [
{
"created": "Fri, 10 Mar 2023 06:59:49 GMT",
"version": "v1"
}
] | 2023-03-13 | [
[
"Akdogan",
"Kerem",
""
],
[
"Heger",
"Lucas",
""
],
[
"Iskauskas",
"Andrew",
""
],
[
"Neuhaus",
"Friedemann",
""
],
[
"Schott",
"Matthias",
""
]
] | The June software package is an open-source framework for the detailed simulation of epidemics based on social interactions in a virtual population reflecting age, gender, ethnicity, and socio-economic indicators in England. In this paper, we present a new version of the framework specifically adapted for Germany, which allows the simulation of the entire German population using publicly available information on households, schools, universities, workplaces, and mobility data for Germany. Moreover, JuneGermany incorporates testing and vaccination strategies within the population as well as the simultaneous handling of several different virus strains. First validation tests of the framework have been performed for the state of Rhineland Palatinate based on data collected between October 2020 and December 2020 and then extrapolated to March 2021, i.e. the end of the second wave. |
0812.4583 | Manoj Gopalakrishnan | Melissa Reneaux (St.Stephens, Delhi) and Manoj Gopalakrishnan (HRI,
Allahabad and IIT Madras) | From random to directed motion: Understanding chemotaxis in E. Coli
within a simplified model | 12 pages, article submitted for proceedings of IPCMB2008, Bose
Institute, Kolkata (Dec 4-6,2008) | null | null | null | q-bio.CB cond-mat.stat-mech q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The bacterium E.Coli swims in a zig-zag manner, in a series of straight runs
and tumbles occurring alternately, with the run-durations dependent on the
local spatial gradient of chemo-attractants/repellants. This enables the
organism to move towards nutrient sources and move away from toxins. The signal
transduction network of E.Coli has been well-characterized, and theoretical
modeling has been used, with some success, in understanding its many remarkable
features, including the near-perfect adaptation to spatially uniform stimulus.
We study a reduced form of this network, with 3 methylation states for the
receptor instead of 5. We derive an analytical form of the response function of
the tumbling rate and use it to compute the drift velocity of the bacterium in
the presence of a weak spatial attractant gradient.
| [
{
"created": "Thu, 25 Dec 2008 00:54:17 GMT",
"version": "v1"
}
] | 2008-12-31 | [
[
"Reneaux",
"Melissa",
"",
"St.Stephens, Delhi"
],
[
"Gopalakrishnan",
"Manoj",
"",
"HRI,\n Allahabad and IIT Madras"
]
] | The bacterium E.Coli swims in a zig-zag manner, in a series of straight runs and tumbles occurring alternately, with the run-durations dependent on the local spatial gradient of chemo-attractants/repellants. This enables the organism to move towards nutrient sources and move away from toxins. The signal transduction network of E.Coli has been well-characterized, and theoretical modeling has been used, with some success, in understanding its many remarkable features, including the near-perfect adaptation to spatially uniform stimulus. We study a reduced form of this network, with 3 methylation states for the receptor instead of 5. We derive an analytical form of the response function of the tumbling rate and use it to compute the drift velocity of the bacterium in the presence of a weak spatial attractant gradient. |
1301.0068 | Guy Bresler | Guy Bresler, Ma'ayan Bresler, David Tse | Optimal Assembly for High Throughput Shotgun Sequencing | 26 pages, 18 figures | null | null | null | q-bio.GN cs.DS cs.IT math.IT q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a framework for the design of optimal assembly algorithms for
shotgun sequencing under the criterion of complete reconstruction. We derive a
lower bound on the read length and the coverage depth required for
reconstruction in terms of the repeat statistics of the genome. Building on
earlier works, we design a de Brujin graph based assembly algorithm which can
achieve very close to the lower bound for repeat statistics of a wide range of
sequenced genomes, including the GAGE datasets. The results are based on a set
of necessary and sufficient conditions on the DNA sequence and the reads for
reconstruction. The conditions can be viewed as the shotgun sequencing analogue
of Ukkonen-Pevzner's necessary and sufficient conditions for Sequencing by
Hybridization.
| [
{
"created": "Tue, 1 Jan 2013 08:52:44 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Jan 2013 03:51:20 GMT",
"version": "v2"
},
{
"created": "Mon, 18 Feb 2013 17:41:09 GMT",
"version": "v3"
}
] | 2013-02-20 | [
[
"Bresler",
"Guy",
""
],
[
"Bresler",
"Ma'ayan",
""
],
[
"Tse",
"David",
""
]
] | We present a framework for the design of optimal assembly algorithms for shotgun sequencing under the criterion of complete reconstruction. We derive a lower bound on the read length and the coverage depth required for reconstruction in terms of the repeat statistics of the genome. Building on earlier works, we design a de Brujin graph based assembly algorithm which can achieve very close to the lower bound for repeat statistics of a wide range of sequenced genomes, including the GAGE datasets. The results are based on a set of necessary and sufficient conditions on the DNA sequence and the reads for reconstruction. The conditions can be viewed as the shotgun sequencing analogue of Ukkonen-Pevzner's necessary and sufficient conditions for Sequencing by Hybridization. |
1105.5093 | Pascal Buenzli | P. R. Buenzli, J. Jeon, P. Pivonka, D. W. Smith and P. T. Cummings | Investigation of bone resorption within a cortical basic multicellular
unit using a lattice-based computational model | 17 pages, 11 figures, 1 table. Revised version: paper entirely
rewritten for a more biology-oriented readership. Technical points of model
description now in Appendix. Addition of two new figures (Fig. 5 and Fig. 9)
and removal of former Fig. 4 | Bone 50, 378-389 (2012) | 10.1016/j.bone.2011.10.021 | null | q-bio.TO physics.bio-ph physics.med-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we develop a lattice-based computational model focused on bone
resorption by osteoclasts in a single cortical basic multicellular unit (BMU).
Our model takes into account the interaction of osteoclasts with the bone
matrix, the interaction of osteoclasts with each other, the generation of
osteoclasts from a growing blood vessel, and the renewal of osteoclast nuclei
by cell fusion. All these features are shown to strongly influence the
geometrical properties of the developing resorption cavity including its size,
shape and progression rate, and are also shown to influence the distribution,
resorption pattern and trajectories of individual osteoclasts within the BMU.
We demonstrate that for certain parameter combinations, resorption cavity
shapes can be recovered from the computational model that closely resemble
resorption cavity shapes observed from microCT imaging of human cortical bone.
| [
{
"created": "Wed, 25 May 2011 17:39:56 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Aug 2011 04:02:14 GMT",
"version": "v2"
},
{
"created": "Tue, 22 Nov 2011 03:15:50 GMT",
"version": "v3"
}
] | 2015-03-19 | [
[
"Buenzli",
"P. R.",
""
],
[
"Jeon",
"J.",
""
],
[
"Pivonka",
"P.",
""
],
[
"Smith",
"D. W.",
""
],
[
"Cummings",
"P. T.",
""
]
] | In this paper we develop a lattice-based computational model focused on bone resorption by osteoclasts in a single cortical basic multicellular unit (BMU). Our model takes into account the interaction of osteoclasts with the bone matrix, the interaction of osteoclasts with each other, the generation of osteoclasts from a growing blood vessel, and the renewal of osteoclast nuclei by cell fusion. All these features are shown to strongly influence the geometrical properties of the developing resorption cavity including its size, shape and progression rate, and are also shown to influence the distribution, resorption pattern and trajectories of individual osteoclasts within the BMU. We demonstrate that for certain parameter combinations, resorption cavity shapes can be recovered from the computational model that closely resemble resorption cavity shapes observed from microCT imaging of human cortical bone. |
0811.3508 | Noa Sela | Noa Sela, Adi Stern, Wojciech Makalowski, Tal Pupko, Gil Ast | Transduplication resulted in the incorporation of two protein-coding
sequences into the Turmoil-1 transposable element of C. elegans | null | Biology Direct 2008, 3:41 | 10.1186/1745-6150-3-41 | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transposable elements may acquire unrelated gene fragments into their
sequences in a process called transduplication. Transduplication of
protein-coding genes is common in plants, but is unknown of in animals. Here,
we report that the Turmoil-1 transposable element in C. elegans has
incorporated two protein-coding sequences into its inverted terminal repeat
(ITR) sequences. The ITRs of Turmoil-1 contain a conserved RNA recognition
motif (RRM) that originated from the rsp- 2 gene and a fragment from the
protein-coding region of the cpg-3 gene. We further report that an open reading
frame specific to C. elegans may have been created as a result of a Turmoil-1
insertion. Mutations at the 5' splice site of this open reading frame may have
reactivated the transduplicated RRM motif
| [
{
"created": "Fri, 21 Nov 2008 10:48:20 GMT",
"version": "v1"
}
] | 2008-11-24 | [
[
"Sela",
"Noa",
""
],
[
"Stern",
"Adi",
""
],
[
"Makalowski",
"Wojciech",
""
],
[
"Pupko",
"Tal",
""
],
[
"Ast",
"Gil",
""
]
] | Transposable elements may acquire unrelated gene fragments into their sequences in a process called transduplication. Transduplication of protein-coding genes is common in plants, but is unknown of in animals. Here, we report that the Turmoil-1 transposable element in C. elegans has incorporated two protein-coding sequences into its inverted terminal repeat (ITR) sequences. The ITRs of Turmoil-1 contain a conserved RNA recognition motif (RRM) that originated from the rsp- 2 gene and a fragment from the protein-coding region of the cpg-3 gene. We further report that an open reading frame specific to C. elegans may have been created as a result of a Turmoil-1 insertion. Mutations at the 5' splice site of this open reading frame may have reactivated the transduplicated RRM motif |
2207.09715 | Giuseppe Petrillo | Eugenio Lippiello, Giuseppe Petrillo and Lucilla de Arcangelis | Estimating generation time of SARS-CoV-2 variants from the daily
incidence rate | 11 pages, 5 figures, Submission to SciPost | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The identification of the transmission parameters of a virus is fundamental
to identify the optimal public health strategy. These parameters can present
significant changes over time caused by genetic mutations or viral
recombination, making their continuous monitoring fundamental. Here we present
a method, suitable for this task, which uses as unique information the daily
number of reported cases. The method is based on a time since infection model
where transmission parameters are obtained by means of an efficient
maximization procedure of the likelihood. Applying the method to SARS-CoV-2
data in Italy we find an average generation time $z = 3.2 \pm 0.8$ days, during
the temporal window when the majority of infections can be attributed to the
Omicron variants. At the same time we find a significantly larger value $z =
6.2 \pm 1.1$ days, in the temporal window when spreading was dominated by the
Delta variant. We are also able to show that the presence of the Omicron
variant, characterized by a shorter $z$, was already detectable in the first
weeks of December 2021, in full agreement with results provided by sequences of
SARS-CoV-2 genomes reported in national databases. Our results therefore
indicate that the novel approach can indicate the existence of virus variants
resulting particularly useful in situations when information about genomic
sequencing is not yet available.
| [
{
"created": "Wed, 20 Jul 2022 07:32:36 GMT",
"version": "v1"
}
] | 2022-07-21 | [
[
"Lippiello",
"Eugenio",
""
],
[
"Petrillo",
"Giuseppe",
""
],
[
"de Arcangelis",
"Lucilla",
""
]
] | The identification of the transmission parameters of a virus is fundamental to identify the optimal public health strategy. These parameters can present significant changes over time caused by genetic mutations or viral recombination, making their continuous monitoring fundamental. Here we present a method, suitable for this task, which uses as unique information the daily number of reported cases. The method is based on a time since infection model where transmission parameters are obtained by means of an efficient maximization procedure of the likelihood. Applying the method to SARS-CoV-2 data in Italy we find an average generation time $z = 3.2 \pm 0.8$ days, during the temporal window when the majority of infections can be attributed to the Omicron variants. At the same time we find a significantly larger value $z = 6.2 \pm 1.1$ days, in the temporal window when spreading was dominated by the Delta variant. We are also able to show that the presence of the Omicron variant, characterized by a shorter $z$, was already detectable in the first weeks of December 2021, in full agreement with results provided by sequences of SARS-CoV-2 genomes reported in national databases. Our results therefore indicate that the novel approach can indicate the existence of virus variants resulting particularly useful in situations when information about genomic sequencing is not yet available. |
1212.4786 | Timoth\'ee Flutre | Timoth\'ee Flutre, Xiaoquan Wen, Jonathan Pritchard, Matthew Stephens | A statistical framework for joint eQTL analysis in multiple tissues | Summitted to PLoS Genetics | PLoS Genetics 2013, Vol. 9, No. 5 | 10.1371/journal.pgen.1003486 | null | q-bio.QM q-bio.GN stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and
widely-adopted approach to identifying putative regulatory variants and linking
them to specific genes. Up to now eQTL studies have been conducted in a
relatively narrow range of tissues or cell types. However, understanding the
biology of organismal phenotypes will involve understanding regulation in
multiple tissues, and ongoing studies are collecting eQTL data in dozens of
cell types. Here we present a statistical framework for powerfully detecting
eQTLs in multiple tissues or cell types (or, more generally, multiple
subgroups). The framework explicitly models the potential for each eQTL to be
active in some tissues and inactive in others. By modeling the sharing of
active eQTLs among tissues this framework increases power to detect eQTLs that
are present in more than one tissue compared with "tissue-by-tissue" analyses
that examine each tissue separately. Conversely, by modeling the inactivity of
eQTLs in some tissues, the framework allows the proportion of eQTLs shared
across different tissues to be formally estimated as parameters of a model,
addressing the difficulties of accounting for incomplete power when comparing
overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our
framework to re-analyze data from transformed B cells, T cells and fibroblasts
we find that it substantially increases power compared with tissue-by-tissue
analysis, identifying 63% more genes with eQTLs (at FDR=0.05). Further the
results suggest that, in contrast to previous analyses of the same data, the
majority of eQTLs detectable in these data are shared among all three tissues.
| [
{
"created": "Wed, 19 Dec 2012 18:32:23 GMT",
"version": "v1"
}
] | 2013-08-12 | [
[
"Flutre",
"Timothée",
""
],
[
"Wen",
"Xiaoquan",
""
],
[
"Pritchard",
"Jonathan",
""
],
[
"Stephens",
"Matthew",
""
]
] | Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely-adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells and fibroblasts we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR=0.05). Further the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues. |
2406.01505 | Tyler Cassidy | Chiara Villa, Philip K Maini, Alexander P Browning, Adrianne L Jenner,
Sara Hamis and Tyler Cassidy | Reducing phenotype-structured PDE models of cancer evolution to systems
of ODEs: a generalised moment dynamics approach | 23 pages, 1 figure | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Intratumour phenotypic heterogeneity is nowadays understood to play a
critical role in disease progression and treatment failure. Accordingly, there
has been increasing interest in the development of mathematical models capable
of capturing its role in cancer cell adaptation. This can be systematically
achieved by means of models comprising phenotype-structured nonlocal partial
differential equations, tracking the evolution of the phenotypic density
distribution of the cell population, which may be compared to gene and protein
expression distributions obtained experimentally. Nevertheless, given the high
analytical and computational cost of solving these models, much is to be gained
from reducing them to systems of ordinary differential equations for the
moments of the distribution. We propose a generalised method of
model-reduction, relying on the use of a moment generating function, Taylor
series expansion and truncation closure, to reduce a nonlocal
reaction-advection-diffusion equation, with general phenotypic drift and
proliferation rate functions, to a system of moment equations up to arbitrary
order. Our method extends previous results in the literature, which we address
via two examples, by removing any \textit{a priori} assumption on the shape of
the distribution, and provides a flexible framework for mathematical modellers
to account for the role of phenotypic heterogeneity in cancer adaptive
dynamics, in a simpler mathematical framework.
| [
{
"created": "Mon, 3 Jun 2024 16:33:54 GMT",
"version": "v1"
}
] | 2024-06-04 | [
[
"Villa",
"Chiara",
""
],
[
"Maini",
"Philip K",
""
],
[
"Browning",
"Alexander P",
""
],
[
"Jenner",
"Adrianne L",
""
],
[
"Hamis",
"Sara",
""
],
[
"Cassidy",
"Tyler",
""
]
] | Intratumour phenotypic heterogeneity is nowadays understood to play a critical role in disease progression and treatment failure. Accordingly, there has been increasing interest in the development of mathematical models capable of capturing its role in cancer cell adaptation. This can be systematically achieved by means of models comprising phenotype-structured nonlocal partial differential equations, tracking the evolution of the phenotypic density distribution of the cell population, which may be compared to gene and protein expression distributions obtained experimentally. Nevertheless, given the high analytical and computational cost of solving these models, much is to be gained from reducing them to systems of ordinary differential equations for the moments of the distribution. We propose a generalised method of model-reduction, relying on the use of a moment generating function, Taylor series expansion and truncation closure, to reduce a nonlocal reaction-advection-diffusion equation, with general phenotypic drift and proliferation rate functions, to a system of moment equations up to arbitrary order. Our method extends previous results in the literature, which we address via two examples, by removing any \textit{a priori} assumption on the shape of the distribution, and provides a flexible framework for mathematical modellers to account for the role of phenotypic heterogeneity in cancer adaptive dynamics, in a simpler mathematical framework. |
2210.12960 | ZangHee Cho | Zang-Hee Cho, Sun-Ha Paek, Young-Bo Kim, Taigyoun Cho, Hyejin Jeong,
Haigun Lee | Human Cognition and Language Processing with Neural-Lexicon Hypothesis | 11 pages, 6 figures, 14 supplementary figures, 1 supplementary table | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cognition and language seem closely related to the human cognitive process,
although they have not been studied and investigated in detail. Our brain is
too complex to fully comprehend the structures and connectivity, as well as its
functions, with the currently available technology such as
electro-encephalography, positron emission tomography, or functional magnetic
resonance imaging, and neurobiological data. Therefore, the exploration of
neurobiological processes, such as cognition, requires substantially more
related evidences, especially from in-vivo human experiments. Cognition and
language are of inter-disciplinary nature and additional methodological support
is needed from other disciplines, such as deep learning in the field of
artificial intelligence, for example. In this paper, we have attempted to
explain the neural mechanisms underlying "cognition and language processing" or
"cognition or thinking" using a novel neural network model with several newly
emerging developments such as neuronal resonance, in-vivo human fiber
tractography or connectivity data, Engram and Hebbian hypothesis, human memory
formation in the high brain areas, deep learning, and more recently developed
neural memory concepts, the neural lexicon. The neural lexicon is developed via
language by repeated exposure to the neural system, similar to multilayer
signal processing in deep learning. We have derived a neural model to explain
how human "cognition and language processing" or "cognition and thinking"
works, with a focus on language, a universal medium of the human society.
Although the proposed hypothesis is not fully based on experimental evidences,
a substantial portion of the observations in this study is directly and
indirectly supported by recent experimental findings and the theoretical bases
of deep learning research.
| [
{
"created": "Mon, 24 Oct 2022 05:31:09 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Oct 2022 13:36:00 GMT",
"version": "v2"
}
] | 2022-10-26 | [
[
"Cho",
"Zang-Hee",
""
],
[
"Paek",
"Sun-Ha",
""
],
[
"Kim",
"Young-Bo",
""
],
[
"Cho",
"Taigyoun",
""
],
[
"Jeong",
"Hyejin",
""
],
[
"Lee",
"Haigun",
""
]
] | Cognition and language seem closely related to the human cognitive process, although they have not been studied and investigated in detail. Our brain is too complex to fully comprehend the structures and connectivity, as well as its functions, with the currently available technology such as electro-encephalography, positron emission tomography, or functional magnetic resonance imaging, and neurobiological data. Therefore, the exploration of neurobiological processes, such as cognition, requires substantially more related evidences, especially from in-vivo human experiments. Cognition and language are of inter-disciplinary nature and additional methodological support is needed from other disciplines, such as deep learning in the field of artificial intelligence, for example. In this paper, we have attempted to explain the neural mechanisms underlying "cognition and language processing" or "cognition or thinking" using a novel neural network model with several newly emerging developments such as neuronal resonance, in-vivo human fiber tractography or connectivity data, Engram and Hebbian hypothesis, human memory formation in the high brain areas, deep learning, and more recently developed neural memory concepts, the neural lexicon. The neural lexicon is developed via language by repeated exposure to the neural system, similar to multilayer signal processing in deep learning. We have derived a neural model to explain how human "cognition and language processing" or "cognition and thinking" works, with a focus on language, a universal medium of the human society. Although the proposed hypothesis is not fully based on experimental evidences, a substantial portion of the observations in this study is directly and indirectly supported by recent experimental findings and the theoretical bases of deep learning research. |
1805.07058 | Michael Watson | Michael G. Watson, Helen M. Byrne, Charlie Macaskill, Mary R.
Myerscough | A Two-Phase Model of Early Fibrous Cap Formation in Atherosclerosis | null | null | null | null | q-bio.CB q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Atherosclerotic plaque growth is characterised by chronic inflammation that
promotes accumulation of cellular debris and extracellular fat in the inner
artery wall. This material is highly thrombogenic, and plaque rupture can lead
to the formation of blood clots that occlude major arteries and cause
myocardial infarction or stroke. In advanced plaques, vascular smooth muscle
cells (SMCs) migrate from deeper in the artery wall to synthesise a cap of
fibrous tissue that stabilises the plaque and sequesters the thrombogenic
plaque content from the bloodstream. The fibrous cap provides crucial
protection against the clinical consequences of atherosclerosis, but the
mechanisms of cap formation are poorly understood. In particular, it is unclear
why certain plaques become stable and robust while others become fragile and
vulnerable to rupture.
We develop a multiphase model with non-standard boundary conditions to
investigate early fibrous cap formation in the atherosclerotic plaque. The
model is parameterised using a range of in vitro and in vivo data, and includes
highly nonlinear mechanisms of SMC proliferation and migration in response to
an endothelium-derived chemical signal. We demonstrate that the model SMC
population naturally evolves towards a steady-state, and predict a rate of cap
formation and a final plaque SMC content consistent with experimental
observations in mice. Parameter sensitivity simulations show that SMC
proliferation makes a limited contribution to cap formation, and highlight that
stable cap formation relies on a critical balance between SMC recruitment to
the plaque, SMC migration within the plaque and SMC loss by apoptosis. The
model represents the first detailed in silico study of fibrous cap formation in
atherosclerosis, and establishes a multiphase modelling framework that can be
readily extended to investigate many other aspects of plaque development.
| [
{
"created": "Fri, 18 May 2018 05:53:11 GMT",
"version": "v1"
}
] | 2018-05-21 | [
[
"Watson",
"Michael G.",
""
],
[
"Byrne",
"Helen M.",
""
],
[
"Macaskill",
"Charlie",
""
],
[
"Myerscough",
"Mary R.",
""
]
] | Atherosclerotic plaque growth is characterised by chronic inflammation that promotes accumulation of cellular debris and extracellular fat in the inner artery wall. This material is highly thrombogenic, and plaque rupture can lead to the formation of blood clots that occlude major arteries and cause myocardial infarction or stroke. In advanced plaques, vascular smooth muscle cells (SMCs) migrate from deeper in the artery wall to synthesise a cap of fibrous tissue that stabilises the plaque and sequesters the thrombogenic plaque content from the bloodstream. The fibrous cap provides crucial protection against the clinical consequences of atherosclerosis, but the mechanisms of cap formation are poorly understood. In particular, it is unclear why certain plaques become stable and robust while others become fragile and vulnerable to rupture. We develop a multiphase model with non-standard boundary conditions to investigate early fibrous cap formation in the atherosclerotic plaque. The model is parameterised using a range of in vitro and in vivo data, and includes highly nonlinear mechanisms of SMC proliferation and migration in response to an endothelium-derived chemical signal. We demonstrate that the model SMC population naturally evolves towards a steady-state, and predict a rate of cap formation and a final plaque SMC content consistent with experimental observations in mice. Parameter sensitivity simulations show that SMC proliferation makes a limited contribution to cap formation, and highlight that stable cap formation relies on a critical balance between SMC recruitment to the plaque, SMC migration within the plaque and SMC loss by apoptosis. The model represents the first detailed in silico study of fibrous cap formation in atherosclerosis, and establishes a multiphase modelling framework that can be readily extended to investigate many other aspects of plaque development. |
0906.2145 | Robert Endres | Robert G. Endres | Polar Chemoreceptor Clustering by Coupled Trimers of Dimers | 11 pages, 6 figures, and 1 table | Biophys J 96(2): 453-463 (2009) | 10.1016/j.bpj.2008.10.021 | null | q-bio.SC q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Receptors of bacterial chemotaxis form clusters at the cell poles, where
clusters act as "antennas" to amplify small changes in ligand concentration.
Interestingly, chemoreceptors cluster at multiple length scales. At the
smallest scale, receptors form dimers, which assemble into stable timers of
dimers. At a large scale, trimers form large polar clusters composed of
thousands of receptors. Although much is known about the signaling properties
emerging from receptor clusters, it is unknown how receptors localize at the
cell poles and what the cluster-size determining factors are. Here, we present
a model of polar receptor clustering based on coupled trimers of dimers, where
cluster size is determined as a minimum of the cluster-membrane free energy.
This energy has contributions from the cluster-membrane elastic energy,
penalizing large clusters due to their high intrinsic curvature, and
receptor-receptor coupling favoring large clusters. We find that the reduced
cluster-membrane curvature mismatch at the curved cell poles leads to large and
robust polar clusters in line with experimental observation, while lateral
clusters are efficiently suppressed.
| [
{
"created": "Thu, 11 Jun 2009 16:01:51 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Endres",
"Robert G.",
""
]
] | Receptors of bacterial chemotaxis form clusters at the cell poles, where clusters act as "antennas" to amplify small changes in ligand concentration. Interestingly, chemoreceptors cluster at multiple length scales. At the smallest scale, receptors form dimers, which assemble into stable timers of dimers. At a large scale, trimers form large polar clusters composed of thousands of receptors. Although much is known about the signaling properties emerging from receptor clusters, it is unknown how receptors localize at the cell poles and what the cluster-size determining factors are. Here, we present a model of polar receptor clustering based on coupled trimers of dimers, where cluster size is determined as a minimum of the cluster-membrane free energy. This energy has contributions from the cluster-membrane elastic energy, penalizing large clusters due to their high intrinsic curvature, and receptor-receptor coupling favoring large clusters. We find that the reduced cluster-membrane curvature mismatch at the curved cell poles leads to large and robust polar clusters in line with experimental observation, while lateral clusters are efficiently suppressed. |
q-bio/0510037 | Emilio Salinas | Emilio Salinas | Context-dependent selection of visuomotor maps | 22 pages, 10 figures. Article available from:
http://www.biomedcentral.com/1471-2202/5/47 | BMC Neuroscience 5:47, 2005 | 10.1186/1471-2202-5-47 | null | q-bio.NC | null | Behavior results from the integration of ongoing sensory signals and
contextual information in various forms, such as past experience, expectations,
current goals, etc. Thus, the response to a specific stimulus, say the ringing
of a doorbell, varies depending on whether you are at home or in someone else's
house. What is the neural basis of this flexibility? What mechanism is capable
of selecting, in a context-dependent way, an adequate response to a given
stimulus? One possibility is based on a nonlinear neural representation in
which context information regulates the gain of stimulus-evoked responses. Here
I explore the properties of this mechanism. By means of three hypothetical
visuomotor tasks, I study a class of neural network models in which any one of
several possible stimulus-response maps or rules can be selected according to
context. The underlying mechanism based on gain modulation has three key
features: (1) modulating the sensory responses is equivalent to switching on or
off different subpopulations of neurons, (2) context does not need to be
represented continuously, although this is advantageous for generalization, and
(3) context-dependent selection is independent of the discriminability of the
stimuli. In all cases, the contextual cues can quickly turn on or off a
sensory-motor map, effectively changing the functional connectivity between
inputs and outputs in the networks. The model predicts that sensory responses
that are nonlinearly modulated by arbitrary context signals should be found in
behavioral situations that involve choosing or switching between multiple
sensory-motor maps.
| [
{
"created": "Tue, 18 Oct 2005 20:42:58 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Salinas",
"Emilio",
""
]
] | Behavior results from the integration of ongoing sensory signals and contextual information in various forms, such as past experience, expectations, current goals, etc. Thus, the response to a specific stimulus, say the ringing of a doorbell, varies depending on whether you are at home or in someone else's house. What is the neural basis of this flexibility? What mechanism is capable of selecting, in a context-dependent way, an adequate response to a given stimulus? One possibility is based on a nonlinear neural representation in which context information regulates the gain of stimulus-evoked responses. Here I explore the properties of this mechanism. By means of three hypothetical visuomotor tasks, I study a class of neural network models in which any one of several possible stimulus-response maps or rules can be selected according to context. The underlying mechanism based on gain modulation has three key features: (1) modulating the sensory responses is equivalent to switching on or off different subpopulations of neurons, (2) context does not need to be represented continuously, although this is advantageous for generalization, and (3) context-dependent selection is independent of the discriminability of the stimuli. In all cases, the contextual cues can quickly turn on or off a sensory-motor map, effectively changing the functional connectivity between inputs and outputs in the networks. The model predicts that sensory responses that are nonlinearly modulated by arbitrary context signals should be found in behavioral situations that involve choosing or switching between multiple sensory-motor maps. |
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