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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2106.07865 | Brandon Munn | Brandon R. Munn, Eli J. M\"uller, and James M. Shine | Noradrenergic neuromodulation of nonlinear bursting neurons controls
critical dynamics | 17 pages, 5 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In order to remain adaptable to a dynamic environment, neural activity must
be simultaneously both sensitive and stable. To solve this problem, the brain
has been hypothesised to sit near a critical boundary. Yet, precisely how
criticality and these opposing information processing modes are implemented in
the brain remains elusive. A potential solution to this problem involves
modulating intrinsically nonlinear neurons within the cerebral cortex with
neuromodulatory neurotransmitters such as noradrenaline, a highly-conserved
chemical released from the pontine locus coeruleus. Here we confirm that
neuronal spiking in mice is poised close to the critical point of a branching
process and that time-varying signatures of criticality fluctuate with
neuromodulatory tone, as assessed by dynamic alterations in pupil diameter. We
explore these results theoretically by creating a dual-compartment model of
non-linear pyramidal neurons - capable of both regular spike and bursting modes
- that replicates our main empirical findings of slightly subcritical dynamics.
We then probe our model at a resolution impossible in vivo to demonstrate that
noradrenaline differentially alters spiking- and bursting-criticality to
facilitate sensitive and stable dynamics following an inverted-U profile that
peaks at intermediate noradrenergic tone. Finally, we demonstrate that this
intermediate noradrenergic regime displays burst avalanches with power-law size
and duration distributions and scaling relationship belonging to the
universality class of self-organized criticality. Our results confirm that the
noradrenergic ascending arousal system acts as a control parameter for emergent
critical dynamics in the brain. This methodology could be extended to explore
other neuromodulators as control parameters of the brain.
| [
{
"created": "Tue, 15 Jun 2021 03:59:16 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Feb 2022 22:32:10 GMT",
"version": "v2"
},
{
"created": "Thu, 6 Apr 2023 10:35:42 GMT",
"version": "v3"
}
] | 2023-04-07 | [
[
"Munn",
"Brandon R.",
""
],
[
"Müller",
"Eli J.",
""
],
[
"Shine",
"James M.",
""
]
] | In order to remain adaptable to a dynamic environment, neural activity must be simultaneously both sensitive and stable. To solve this problem, the brain has been hypothesised to sit near a critical boundary. Yet, precisely how criticality and these opposing information processing modes are implemented in the brain remains elusive. A potential solution to this problem involves modulating intrinsically nonlinear neurons within the cerebral cortex with neuromodulatory neurotransmitters such as noradrenaline, a highly-conserved chemical released from the pontine locus coeruleus. Here we confirm that neuronal spiking in mice is poised close to the critical point of a branching process and that time-varying signatures of criticality fluctuate with neuromodulatory tone, as assessed by dynamic alterations in pupil diameter. We explore these results theoretically by creating a dual-compartment model of non-linear pyramidal neurons - capable of both regular spike and bursting modes - that replicates our main empirical findings of slightly subcritical dynamics. We then probe our model at a resolution impossible in vivo to demonstrate that noradrenaline differentially alters spiking- and bursting-criticality to facilitate sensitive and stable dynamics following an inverted-U profile that peaks at intermediate noradrenergic tone. Finally, we demonstrate that this intermediate noradrenergic regime displays burst avalanches with power-law size and duration distributions and scaling relationship belonging to the universality class of self-organized criticality. Our results confirm that the noradrenergic ascending arousal system acts as a control parameter for emergent critical dynamics in the brain. This methodology could be extended to explore other neuromodulators as control parameters of the brain. |
2104.12571 | Sebastian Lotter | Sebastian Lotter and Lukas Brand and Maximilian Sch\"afer and Robert
Schober | Statistical Modeling of Airborne Virus Transmission Through Imperfectly
Fitted Face Masks | 7 pages, 5 figures, 1 table. Presented at the 8th ACM International
Conference on Nanoscale Computing and Communication (NANOCOM) | null | 10.1145/3477206.3477478 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rapid emergence and the disastrous impact of the severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) pandemic on public health, societies, and
economies around the world has created an urgent need for understanding the
pathways critical for virus transmission. Airborne virus transmission by
asymptomatic SARS-CoV-2-infected individuals is considered to be a major
contributor to the spread of SARS-CoV-2 and social distancing and wearing of
face masks in public have been implemented as countermeasures in many
countries. However, a comprehensive risk assessment framework for the airborne
transmission of SARS-CoV-2 incorporating realistic assumptions on the
filtration of infectious aerosols (IAs) by face masks is not available yet. In
particular, in most end-to-end models for airborne virus transmission, it is
neglected that the stochastic spread of IAs through imperfectly fitted face
masks depends on the dynamics of the breathing of the wearer. In this paper, we
consider airborne virus transmission from an infected but asymptomatic person
to a healthy person, both wearing imperfectly fitted face masks, in an indoor
environment. By framing the end-to-end virus transmission as a Molecular
Communications (MC) system, we obtain a statistical description of the number
of IAs inhaled by the healthy person subject to the respective configurations
of the face masks of both persons. We demonstrate that the exhalation and
inhalation air flow dynamics have a significant impact on the stochastic
filtering of IAs by the face masks. Furthermore, we show that the fit of the
face mask of the infected person can highly impact the infection probability.
We conclude that the proposed MC model may contribute a valuable assessment
tool to fight the spread of SARS-CoV-2 as it encompasses the randomness of the
transmission process and enables comprehensive risk analysis beyond statistical
averages.
| [
{
"created": "Mon, 26 Apr 2021 13:40:42 GMT",
"version": "v1"
},
{
"created": "Sat, 1 May 2021 14:03:11 GMT",
"version": "v2"
},
{
"created": "Mon, 16 Jan 2023 13:50:00 GMT",
"version": "v3"
}
] | 2023-01-18 | [
[
"Lotter",
"Sebastian",
""
],
[
"Brand",
"Lukas",
""
],
[
"Schäfer",
"Maximilian",
""
],
[
"Schober",
"Robert",
""
]
] | The rapid emergence and the disastrous impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic on public health, societies, and economies around the world has created an urgent need for understanding the pathways critical for virus transmission. Airborne virus transmission by asymptomatic SARS-CoV-2-infected individuals is considered to be a major contributor to the spread of SARS-CoV-2 and social distancing and wearing of face masks in public have been implemented as countermeasures in many countries. However, a comprehensive risk assessment framework for the airborne transmission of SARS-CoV-2 incorporating realistic assumptions on the filtration of infectious aerosols (IAs) by face masks is not available yet. In particular, in most end-to-end models for airborne virus transmission, it is neglected that the stochastic spread of IAs through imperfectly fitted face masks depends on the dynamics of the breathing of the wearer. In this paper, we consider airborne virus transmission from an infected but asymptomatic person to a healthy person, both wearing imperfectly fitted face masks, in an indoor environment. By framing the end-to-end virus transmission as a Molecular Communications (MC) system, we obtain a statistical description of the number of IAs inhaled by the healthy person subject to the respective configurations of the face masks of both persons. We demonstrate that the exhalation and inhalation air flow dynamics have a significant impact on the stochastic filtering of IAs by the face masks. Furthermore, we show that the fit of the face mask of the infected person can highly impact the infection probability. We conclude that the proposed MC model may contribute a valuable assessment tool to fight the spread of SARS-CoV-2 as it encompasses the randomness of the transmission process and enables comprehensive risk analysis beyond statistical averages. |
2401.06959 | Shirui Bian | Shirui Bian, Ruisong Zhou, Wei Lin, Chunhe Li | Quantifying energy landscape of oscillatory systems: Explosion,
pre-solution, and diffusion decomposition | 13 pages, 4 figures | null | null | null | q-bio.QM q-bio.MN | http://creativecommons.org/publicdomain/zero/1.0/ | The energy landscape theory finds its both extensive and intensive
application in studying stochastic dynamics of physical and biological systems.
Although the weighted summation of the Gaussian approximation (WSGA) approach
has been proposed for quantifying the energy landscape in multistable systems
by solving the diffusion equation approximately from moment equations, we are
still lacking an accurate approach for quantifying the energy landscape of the
periodic oscillatory systems. To address this challenge, we propose an
approach, called the diffusion decomposition of the Gaussian approximation
(DDGA). Using typical oscillatory systems as examples, we demonstrate the
efficacy of the proposed DDGA in quantifying the energy landscape of
oscillatory systems and corresponding stochastic dynamics, in comparison with
existing approaches. By further applying the DDGA to a high-dimensional cell
cycle network, we are able to uncover more intricate biological mechanisms in
cell cycle, which cannot be discerned using previously developed approaches.
| [
{
"created": "Sat, 13 Jan 2024 03:15:13 GMT",
"version": "v1"
}
] | 2024-01-17 | [
[
"Bian",
"Shirui",
""
],
[
"Zhou",
"Ruisong",
""
],
[
"Lin",
"Wei",
""
],
[
"Li",
"Chunhe",
""
]
] | The energy landscape theory finds its both extensive and intensive application in studying stochastic dynamics of physical and biological systems. Although the weighted summation of the Gaussian approximation (WSGA) approach has been proposed for quantifying the energy landscape in multistable systems by solving the diffusion equation approximately from moment equations, we are still lacking an accurate approach for quantifying the energy landscape of the periodic oscillatory systems. To address this challenge, we propose an approach, called the diffusion decomposition of the Gaussian approximation (DDGA). Using typical oscillatory systems as examples, we demonstrate the efficacy of the proposed DDGA in quantifying the energy landscape of oscillatory systems and corresponding stochastic dynamics, in comparison with existing approaches. By further applying the DDGA to a high-dimensional cell cycle network, we are able to uncover more intricate biological mechanisms in cell cycle, which cannot be discerned using previously developed approaches. |
1809.01699 | Robert Grossman | Robert L. Grossman | Data Lakes, Clouds and Commons: A Review of Platforms for Analyzing and
Sharing Genomic Data | 28 pages, 4 figures | null | null | null | q-bio.GN cs.CY | http://creativecommons.org/licenses/by/4.0/ | Data commons collate data with cloud computing infrastructure and commonly
used software services, tools and applications to create biomedical resources
for the large-scale management, analysis, harmonization, and sharing of
biomedical data. Over the past few years, data commons have been used to
analyze, harmonize and share large scale genomics datasets. Data ecosystems can
be built by interoperating multiple data commons. It can be quite labor
intensive to curate, import and analyze the data in a data commons. Data lakes
provide an alternative to data commons and simply provide access to data, with
the data curation and analysis deferred until later and delegated to those that
access the data. We review software platforms for managing, analyzing and
sharing genomic data, with an emphasis on data commons, but also covering data
ecosystems and data lakes.
| [
{
"created": "Wed, 5 Sep 2018 19:24:13 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Dec 2018 19:57:33 GMT",
"version": "v2"
}
] | 2018-12-27 | [
[
"Grossman",
"Robert L.",
""
]
] | Data commons collate data with cloud computing infrastructure and commonly used software services, tools and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical data. Over the past few years, data commons have been used to analyze, harmonize and share large scale genomics datasets. Data ecosystems can be built by interoperating multiple data commons. It can be quite labor intensive to curate, import and analyze the data in a data commons. Data lakes provide an alternative to data commons and simply provide access to data, with the data curation and analysis deferred until later and delegated to those that access the data. We review software platforms for managing, analyzing and sharing genomic data, with an emphasis on data commons, but also covering data ecosystems and data lakes. |
1402.0385 | Guido Tiana | R. Capelli, C. Paissoni, P. Sormanni and G. Tiana | Iterative derivation of effective potentials to sample the
conformational space of proteins at atomistic scale | null | null | 10.1063/1.4876219 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The current capacity of computers makes it possible to perform simulations of
small systems with portable, explicit-solvent potentials achieving high degree
of accuracy. However, simplified models must be employed to exploit the
behaviour of large systems or to perform systematic scans of smaller systems.
While powerful algorithms are available to facilitate the sampling of the
conformational space, successful applications of such models are hindered by
the availability of simple enough potentials able to satisfactorily reproduce
known properties of the system. We develop an interatomic potential to account
for a number of properties of proteins in a computationally economic way. The
potential is defined within an all-atom, implicit solvent model by contact
functions between the different atom types. The associated numerical values can
be optimised by an iterative Monte Carlo scheme on any available experimental
data, provided that they are expressible as thermal averages of some
conformational properties. We test this model on three different proteins, for
which we also perform a scan of all possible point mutations with explicit
conformational sampling. The resulting models, optimised solely on a subset of
native distances, not only reproduce the native conformations within a few
Angstroms from the experimental ones, but show the cooperative transition
between native and denatured state and correctly predict the measured
free--energy changes associated with point mutations. Moreover, differently
from other structure-based models, our method leaves a residual degree of
frustration, which is known to be present in protein molecules.
| [
{
"created": "Mon, 3 Feb 2014 14:11:01 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Capelli",
"R.",
""
],
[
"Paissoni",
"C.",
""
],
[
"Sormanni",
"P.",
""
],
[
"Tiana",
"G.",
""
]
] | The current capacity of computers makes it possible to perform simulations of small systems with portable, explicit-solvent potentials achieving high degree of accuracy. However, simplified models must be employed to exploit the behaviour of large systems or to perform systematic scans of smaller systems. While powerful algorithms are available to facilitate the sampling of the conformational space, successful applications of such models are hindered by the availability of simple enough potentials able to satisfactorily reproduce known properties of the system. We develop an interatomic potential to account for a number of properties of proteins in a computationally economic way. The potential is defined within an all-atom, implicit solvent model by contact functions between the different atom types. The associated numerical values can be optimised by an iterative Monte Carlo scheme on any available experimental data, provided that they are expressible as thermal averages of some conformational properties. We test this model on three different proteins, for which we also perform a scan of all possible point mutations with explicit conformational sampling. The resulting models, optimised solely on a subset of native distances, not only reproduce the native conformations within a few Angstroms from the experimental ones, but show the cooperative transition between native and denatured state and correctly predict the measured free--energy changes associated with point mutations. Moreover, differently from other structure-based models, our method leaves a residual degree of frustration, which is known to be present in protein molecules. |
1004.2072 | Vadas Gintautas | Michael I. Ham, Vadas Gintautas, Guenter W. Gross | Spontaneous coordinated activity in cultured networks: Analysis of
multiple ignition sites, primary circuits, burst phase delay distributions
and functional structures | 4 pages, 3 figures. Presented at 6th International Meeting on
Substrate-Integrated Micro Electrode Arrays in Reutlingen in July 2008. | Published in 6th International Meeting on Substrate-Integrated
Micro Electrode Arrays, 2008. | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | All higher order central nervous systems exhibit spontaneous neural activity,
though the purpose and mechanistic origin of such activity remains poorly
understood. We explore the ignition and spread of collective spontaneous
electrophysiological burst activity in networks of cultured cortical neurons
growing on microelectrode arrays using information theory and
first-spike-in-burst analysis methods. We show the presence of burst leader
neurons, which form a mono-synaptically connected primary circuit, and initiate
a majority of network bursts. Leader/follower firing delay times form
temporally stable positively skewed distributions. Blocking inhibitory synapses
usually results in shorter delay times with reduced variance. These
distributions are generalized characterizations of internal network dynamics
and provide estimates of pair-wise synaptic distances. We show that mutual
information between neural nodes is a function of distance, which is maintained
under disinhibition. The resulting analysis produces specific quantitative
constraints and insights into the activation patterns of collective neuronal
activity in self-organized cortical networks, which may prove useful for models
emulating spontaneously active systems.
| [
{
"created": "Mon, 12 Apr 2010 22:23:15 GMT",
"version": "v1"
}
] | 2010-04-14 | [
[
"Ham",
"Michael I.",
""
],
[
"Gintautas",
"Vadas",
""
],
[
"Gross",
"Guenter W.",
""
]
] | All higher order central nervous systems exhibit spontaneous neural activity, though the purpose and mechanistic origin of such activity remains poorly understood. We explore the ignition and spread of collective spontaneous electrophysiological burst activity in networks of cultured cortical neurons growing on microelectrode arrays using information theory and first-spike-in-burst analysis methods. We show the presence of burst leader neurons, which form a mono-synaptically connected primary circuit, and initiate a majority of network bursts. Leader/follower firing delay times form temporally stable positively skewed distributions. Blocking inhibitory synapses usually results in shorter delay times with reduced variance. These distributions are generalized characterizations of internal network dynamics and provide estimates of pair-wise synaptic distances. We show that mutual information between neural nodes is a function of distance, which is maintained under disinhibition. The resulting analysis produces specific quantitative constraints and insights into the activation patterns of collective neuronal activity in self-organized cortical networks, which may prove useful for models emulating spontaneously active systems. |
1606.08233 | Raul Isea | Raul Isea and Karl E Lonngren | A Preliminary Mathematical Model for the Dynamic Transmission of Dengue,
Chikungunya and Zika | 5 pages, 2 figures, open access | American Journal of Modern Physics and Application. 2016. Vol 3(2)
11-15 | null | null | q-bio.PE | http://creativecommons.org/publicdomain/zero/1.0/ | Aedes aegypti is a known vector of Dengue, Chikungunya and Zika and the goal
of this study is to propose the first mathematical model to describe the
dynamic transmission of these three diseases. We present two preliminary models
that consist of the SEIR model for the human populations and an SEI model for
the vector to describe (a) the single transmission dynamics of dengue,
Chikungunya or Zika, and (b) any possible coinfection between two diseases in
the same population. In order to do that, we obtain an analytical solution of
the system of 17 and 30 coupled differential equations for each model
respectively, and later obtain the eigenvalues by analyzing the Jacobian matrix
in order to begin the development of a surveillance system to prevent the
spread of these three diseases.
| [
{
"created": "Mon, 27 Jun 2016 12:17:24 GMT",
"version": "v1"
}
] | 2016-06-28 | [
[
"Isea",
"Raul",
""
],
[
"Lonngren",
"Karl E",
""
]
] | Aedes aegypti is a known vector of Dengue, Chikungunya and Zika and the goal of this study is to propose the first mathematical model to describe the dynamic transmission of these three diseases. We present two preliminary models that consist of the SEIR model for the human populations and an SEI model for the vector to describe (a) the single transmission dynamics of dengue, Chikungunya or Zika, and (b) any possible coinfection between two diseases in the same population. In order to do that, we obtain an analytical solution of the system of 17 and 30 coupled differential equations for each model respectively, and later obtain the eigenvalues by analyzing the Jacobian matrix in order to begin the development of a surveillance system to prevent the spread of these three diseases. |
1710.03563 | Laszlo Pecze | Michael Dougoud, Christian Mazza, Beat Schwaller, Laszlo Pecze | Extending the mathematical palette for developmental pattern formation:
Piebaldism | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Piebaldism usually manifests as white areas of fur, hair or skin due to the
absence of pigment-producing cells in those regions. The distribution of the
white and colored zones does not follow the classical Turing patterns. Here we
present a modeling framework for pattern formation that enables to easily
modify the relationship between three factors with different feedback
mechanisms. These factors consist of two diffusing factors and a
cell-autonomous immobile transcription factor. Globally the model allowed to
distinguishing four different situations. Two situations result in the
production of classical Turing patterns; regularly spaced spots and labyrinth
patterns. Moreover, an initial slope in the activation of the transcription
factor produces straight lines. The third situation does not lead to patterns,
but results in different homogeneous color tones. Finally, the fourth one sheds
new light on the possible mechanisms leading to the formation of piebald
patterns exemplified by the random patterns on the fur of some cow strains and
Dalmatian dogs. We demonstrate that these piebald patterns are of transient
nature, develop from random initial conditions and rely on a system's
bi-stability. The main novelty lies in our finding that the presence of a
cell-autonomous factor not only expands the range of reaction diffusion
parameters in which a pattern may arise, but also extends the pattern-forming
abilities of the reaction-diffusion equations.
| [
{
"created": "Tue, 10 Oct 2017 13:24:12 GMT",
"version": "v1"
}
] | 2017-10-11 | [
[
"Dougoud",
"Michael",
""
],
[
"Mazza",
"Christian",
""
],
[
"Schwaller",
"Beat",
""
],
[
"Pecze",
"Laszlo",
""
]
] | Piebaldism usually manifests as white areas of fur, hair or skin due to the absence of pigment-producing cells in those regions. The distribution of the white and colored zones does not follow the classical Turing patterns. Here we present a modeling framework for pattern formation that enables to easily modify the relationship between three factors with different feedback mechanisms. These factors consist of two diffusing factors and a cell-autonomous immobile transcription factor. Globally the model allowed to distinguishing four different situations. Two situations result in the production of classical Turing patterns; regularly spaced spots and labyrinth patterns. Moreover, an initial slope in the activation of the transcription factor produces straight lines. The third situation does not lead to patterns, but results in different homogeneous color tones. Finally, the fourth one sheds new light on the possible mechanisms leading to the formation of piebald patterns exemplified by the random patterns on the fur of some cow strains and Dalmatian dogs. We demonstrate that these piebald patterns are of transient nature, develop from random initial conditions and rely on a system's bi-stability. The main novelty lies in our finding that the presence of a cell-autonomous factor not only expands the range of reaction diffusion parameters in which a pattern may arise, but also extends the pattern-forming abilities of the reaction-diffusion equations. |
2106.01602 | Ignazio Lazzizzera | Ignazio Lazzizzera | The SIR model towards he data. One year of Covid-19 pandemic in Italy
case study and plausible "real" numbers | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | In this work, the SIR epidemiological model is reformulated so to highlight
the important {\em effective reproduction number}, as well as to account for
the {\em generation time}, inverse of the {\em incidence rate}, and the {\em
infectious period} (or {\em removal period}), inverse of the {\em removal
rate}. The aim is to check whether the relationships the model poses among the
various observables are actually found in the data. The study case of the
second through the third wave of the Covid-19 pandemic in Italy is taken. Given
its scale invariance, initially the model is tested with reference to the curve
of swab-confirmed infectious individuals only. It is found to match the data if
the given curve of the {\em removed} (that is healed or deceased) individuals
is assumed underestimated by a factor of about 3 together with other related
curves. Contextually, the {\em generation time} and the {\em removal period},
as well as the {\em effective reproduction number}, are obtained fitting the
SIR equations to the data; the outcomes prove to be in good agreement with
those of other works. Then, using knowledge of the proportion of Covid-19
transmissions likely occurring from individuals who didn't develop symptoms,
thus mainly undetected, an estimate of the {\em "true numbers"} of the epidemic
is obtained, looking also in good agreement with results from other, completely
different works. The line of this work is new and the procedures are
computationally really inexpensive.
| [
{
"created": "Thu, 3 Jun 2021 05:32:14 GMT",
"version": "v1"
},
{
"created": "Sun, 25 Jul 2021 09:45:35 GMT",
"version": "v2"
}
] | 2021-07-27 | [
[
"Lazzizzera",
"Ignazio",
""
]
] | In this work, the SIR epidemiological model is reformulated so to highlight the important {\em effective reproduction number}, as well as to account for the {\em generation time}, inverse of the {\em incidence rate}, and the {\em infectious period} (or {\em removal period}), inverse of the {\em removal rate}. The aim is to check whether the relationships the model poses among the various observables are actually found in the data. The study case of the second through the third wave of the Covid-19 pandemic in Italy is taken. Given its scale invariance, initially the model is tested with reference to the curve of swab-confirmed infectious individuals only. It is found to match the data if the given curve of the {\em removed} (that is healed or deceased) individuals is assumed underestimated by a factor of about 3 together with other related curves. Contextually, the {\em generation time} and the {\em removal period}, as well as the {\em effective reproduction number}, are obtained fitting the SIR equations to the data; the outcomes prove to be in good agreement with those of other works. Then, using knowledge of the proportion of Covid-19 transmissions likely occurring from individuals who didn't develop symptoms, thus mainly undetected, an estimate of the {\em "true numbers"} of the epidemic is obtained, looking also in good agreement with results from other, completely different works. The line of this work is new and the procedures are computationally really inexpensive. |
2208.05228 | Ijaz Gul | Ijaz Gul, Changyue Liu, Yuan Xi, Zhicheng Du, Shiyao Zhai, Zhengyang
Lei, Chen Qun, Muhammad Akmal Raheem, Qian He, Zhang Haihui, Canyang Zhang,
Runming Wang, Sanyang Han, Du Ke, Peiwu Qin | Current and perspective sensing methods for monkeypox virus: a
reemerging zoonosis in its infancy | 36 pages, 5 figures, 1 table | null | null | null | q-bio.QM q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Objectives The review is dedicated to evaluate the current monkeypox virus
(MPXV) detection methods, discuss their pros and cons, and provide recommended
solutions to the problems.
Methods The literature for this review is identified through searches in
PubMed, Web of Science, Google Scholar, ResearchGate, and Science Direct
advanced search for articles published in English without any start date until
June, 2022, by use of the terms "monkeypox virus" or "poxvirus" along with
"diagnosis"; "PCR"; "real-time PCR"; "LAMP"; "RPA"; "immunoassay";
"reemergence"; "biothreat"; "endemic", and "multi-country outbreak" and also,
by tracking citations of the relevant papers. The most relevant articles are
included in the review.
Results Our literature review shows that PCR is the gold standard method for
MPXV detection. In addition, loop-mediated isothermal amplification (LAMP) and
recombinase polymerase amplification (RPA) have been reported as alternatives
to PCR. Immunodiagnostics, whole particle detection, and image-based detection
are the non-nucleic acid-based MPXV detection modalities.
Conclusions PCR is easy to leverage and adapt for a quick response to an
outbreak, but the PCR-based MPXV detection approaches may not be suitable for
marginalized settings. Limited progress has been made towards innovations in
MPXV diagnostics, providing room for the development of novel detection
techniques for this virus.
| [
{
"created": "Wed, 10 Aug 2022 09:03:02 GMT",
"version": "v1"
}
] | 2022-08-11 | [
[
"Gul",
"Ijaz",
""
],
[
"Liu",
"Changyue",
""
],
[
"Xi",
"Yuan",
""
],
[
"Du",
"Zhicheng",
""
],
[
"Zhai",
"Shiyao",
""
],
[
"Lei",
"Zhengyang",
""
],
[
"Qun",
"Chen",
""
],
[
"Raheem",
"Muhammad Akmal",
""
],
[
"He",
"Qian",
""
],
[
"Haihui",
"Zhang",
""
],
[
"Zhang",
"Canyang",
""
],
[
"Wang",
"Runming",
""
],
[
"Han",
"Sanyang",
""
],
[
"Ke",
"Du",
""
],
[
"Qin",
"Peiwu",
""
]
] | Objectives The review is dedicated to evaluate the current monkeypox virus (MPXV) detection methods, discuss their pros and cons, and provide recommended solutions to the problems. Methods The literature for this review is identified through searches in PubMed, Web of Science, Google Scholar, ResearchGate, and Science Direct advanced search for articles published in English without any start date until June, 2022, by use of the terms "monkeypox virus" or "poxvirus" along with "diagnosis"; "PCR"; "real-time PCR"; "LAMP"; "RPA"; "immunoassay"; "reemergence"; "biothreat"; "endemic", and "multi-country outbreak" and also, by tracking citations of the relevant papers. The most relevant articles are included in the review. Results Our literature review shows that PCR is the gold standard method for MPXV detection. In addition, loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA) have been reported as alternatives to PCR. Immunodiagnostics, whole particle detection, and image-based detection are the non-nucleic acid-based MPXV detection modalities. Conclusions PCR is easy to leverage and adapt for a quick response to an outbreak, but the PCR-based MPXV detection approaches may not be suitable for marginalized settings. Limited progress has been made towards innovations in MPXV diagnostics, providing room for the development of novel detection techniques for this virus. |
1807.09400 | Nicholas Chia | Stephanie Danni Song, Patricio Jeraldo, Jun Chen, Nicholas Chia | Extreme value analysis of gut microbial alterations in colorectal cancer | null | Phys. Rev. E 99, 032413 (2019) | 10.1103/PhysRevE.99.032413 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gut microbes play a key role in colorectal carcinogenesis, yet reaching a
consensus on microbial signatures remains a challenge. This is in part due to a
reliance on mean value estimates. We present an extreme value analysis for
overcoming these limitations. By characterizing a power law fit to the relative
abundances of microbes, we capture the same microbial signatures as more
complex meta-analyses. Importantly, we show that our method is robust to the
variations inherent in microbial community profiling and point to future
directions for developing sensitive, reliable analytical methods.
| [
{
"created": "Wed, 25 Jul 2018 00:15:21 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Jan 2019 21:03:48 GMT",
"version": "v2"
},
{
"created": "Wed, 13 Feb 2019 14:05:54 GMT",
"version": "v3"
}
] | 2019-03-27 | [
[
"Song",
"Stephanie Danni",
""
],
[
"Jeraldo",
"Patricio",
""
],
[
"Chen",
"Jun",
""
],
[
"Chia",
"Nicholas",
""
]
] | Gut microbes play a key role in colorectal carcinogenesis, yet reaching a consensus on microbial signatures remains a challenge. This is in part due to a reliance on mean value estimates. We present an extreme value analysis for overcoming these limitations. By characterizing a power law fit to the relative abundances of microbes, we capture the same microbial signatures as more complex meta-analyses. Importantly, we show that our method is robust to the variations inherent in microbial community profiling and point to future directions for developing sensitive, reliable analytical methods. |
1411.6843 | Jose Fontanari | Mauro Santos, E\"ors Szathm\'ary and Jos\'e F. Fontanari | Phenotypic Plasticity, the Baldwin Effect, and the Speeding up of
Evolution: the Computational Roots of an Illusion | null | Journal of Theoretical Biology 371, 127-136 (2015) | 10.1016/j.jtbi.2015.02.012 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An increasing number of dissident voices claim that the standard
neo-Darwinian view of genes as 'leaders' and phenotypes as 'followers' during
the process of adaptive evolution should be turned on its head. This idea is
older than the rediscovery of Mendel's laws of inheritance and has been given
several names before its final 'Baldwin effect' label. A condition for this
effect is that environmentally induced variation such as phenotypic plasticity
or learning is crucial for the initial establishment of a population. This
gives the necessary time for natural selection to act on genetic variation and
the adaptive trait can be eventually encoded in the genotype. An influential
paper published in the late 1980s showed the Baldwin effect to happen in
computer simulations, and claimed that it was crucial to solve a difficult
adaptive task. This generated much excitement among scholars in various
disciplines that regard neo-Darwinian accounts to explain the evolutionary
emergence of high-order phenotypic traits such as consciousness or language
almost hopeless. Here, we use analytical and computational approaches to show
that a standard population genetics treatment can easily crack what the
scientific community has granted as an unsolvable adaptive problem without
learning. The Baldwin effect is once again in need of convincing theoretical
foundations.
| [
{
"created": "Tue, 25 Nov 2014 12:55:08 GMT",
"version": "v1"
}
] | 2015-03-10 | [
[
"Santos",
"Mauro",
""
],
[
"Szathmáry",
"Eörs",
""
],
[
"Fontanari",
"José F.",
""
]
] | An increasing number of dissident voices claim that the standard neo-Darwinian view of genes as 'leaders' and phenotypes as 'followers' during the process of adaptive evolution should be turned on its head. This idea is older than the rediscovery of Mendel's laws of inheritance and has been given several names before its final 'Baldwin effect' label. A condition for this effect is that environmentally induced variation such as phenotypic plasticity or learning is crucial for the initial establishment of a population. This gives the necessary time for natural selection to act on genetic variation and the adaptive trait can be eventually encoded in the genotype. An influential paper published in the late 1980s showed the Baldwin effect to happen in computer simulations, and claimed that it was crucial to solve a difficult adaptive task. This generated much excitement among scholars in various disciplines that regard neo-Darwinian accounts to explain the evolutionary emergence of high-order phenotypic traits such as consciousness or language almost hopeless. Here, we use analytical and computational approaches to show that a standard population genetics treatment can easily crack what the scientific community has granted as an unsolvable adaptive problem without learning. The Baldwin effect is once again in need of convincing theoretical foundations. |
1805.01425 | Delfim F. M. Torres | Jasmina Djordjevic, Cristiana J. Silva, Delfim F. M. Torres | A stochastic SICA epidemic model for HIV transmission | This is a preprint of a paper whose final and definite form is with
'Applied Mathematics Letters', ISSN 0893-9659. Submitted 22/Jan/2018; Revised
03/May/2018; Accepted for publication 03/May/2018 | Appl. Math. Lett. 84 (2018), 168--175 | 10.1016/j.aml.2018.05.005 | null | q-bio.PE math.CA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a stochastic SICA epidemic model for HIV transmission, described
by stochastic ordinary differential equations, and discuss its perturbation by
environmental white noise. Existence and uniqueness of the global positive
solution to the stochastic HIV system is proven, and conditions under which
extinction and persistence in mean hold, are given. The theoretical results are
illustrated via numerical simulations.
| [
{
"created": "Thu, 3 May 2018 16:58:07 GMT",
"version": "v1"
}
] | 2018-05-30 | [
[
"Djordjevic",
"Jasmina",
""
],
[
"Silva",
"Cristiana J.",
""
],
[
"Torres",
"Delfim F. M.",
""
]
] | We propose a stochastic SICA epidemic model for HIV transmission, described by stochastic ordinary differential equations, and discuss its perturbation by environmental white noise. Existence and uniqueness of the global positive solution to the stochastic HIV system is proven, and conditions under which extinction and persistence in mean hold, are given. The theoretical results are illustrated via numerical simulations. |
2106.09979 | Taisuke Kobayashi | Taisuke Kobayashi, Eiji Watanabe | Artificial Perception Meets Psychophysics, Revealing a Fundamental Law
of Illusory Motion | 15 pages, 11 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rotating Snakes is a visual illusion in which a stationary design is
perceived to move dramatically. In the current study, the mechanism that
generates perception of motion was analyzed using a combination of
psychophysics experiments and deep neural network models that mimic human
vision. We prepared three- and four-color illusion-like designs with a wide
range of luminance and measured their strength of induced rotational motion. As
a result, we discovered the fundamental law that the effect of the four-color
snake rotation illusion was successfully enhanced by the combination of two
perceptual motion vectors produced by the two three-color designs. In years to
come, deep neural network technology will be one of the most effective tools
not only for engineering applications but also for human perception research.
| [
{
"created": "Fri, 18 Jun 2021 07:59:18 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Jun 2021 06:06:02 GMT",
"version": "v2"
},
{
"created": "Thu, 24 Jun 2021 05:26:51 GMT",
"version": "v3"
},
{
"created": "Fri, 11 Feb 2022 06:17:19 GMT",
"version": "v4"
},
{
"created": "Wed, 30 Mar 2022 08:03:29 GMT",
"version": "v5"
}
] | 2022-03-31 | [
[
"Kobayashi",
"Taisuke",
""
],
[
"Watanabe",
"Eiji",
""
]
] | Rotating Snakes is a visual illusion in which a stationary design is perceived to move dramatically. In the current study, the mechanism that generates perception of motion was analyzed using a combination of psychophysics experiments and deep neural network models that mimic human vision. We prepared three- and four-color illusion-like designs with a wide range of luminance and measured their strength of induced rotational motion. As a result, we discovered the fundamental law that the effect of the four-color snake rotation illusion was successfully enhanced by the combination of two perceptual motion vectors produced by the two three-color designs. In years to come, deep neural network technology will be one of the most effective tools not only for engineering applications but also for human perception research. |
2006.12479 | Nana Geraldine Cabo Bizet Dr | Nana Geraldine Cabo Bizet, Dami\'an Kaloni Mayorga Pe\~na | Time-dependent and time-independent SIR models applied to the COVID-19
outbreak in Argentina, Brazil, Colombia, Mexico and South Africa | 26 pages, 5 figures, 9 tables | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the SIR epidemiological model applied to the evolution of
COVID-19 with two approaches. In the first place we fit a global SIR model,
with time delay, and constant parameters throughout the outbreak, including the
contagion rate. The contention measures are reflected on an effective reduced
susceptible population $N_{eff}$. In the second approach we consider a
time-dependent contagion rate that reflects the contention measures either
through a step by step fitting process or by following an exponential decay. In
this last model the population is considered the one of the country $N$. In the
linear region of the differential equations, when the total population $N$ is
large the predictions are independent of $N$. We apply these methodologies to
study the spread of the pandemic in Argentina, Brazil, Colombia, Mexico, and
South Africa for which the infection peaks are yet to be reached. In all of
these cases we provide estimates for the reproduction and recovery rates. The
scenario for a time varying contagion rate is optimistic, considering that
reasonable measures are taken such that the reproduction factor $R_0$ decreases
exponentially. The measured values for the recovery rate $\gamma$ are reported
finding a universality of this parameter over various countries. We discuss the
correspondence between the global SIR with effective population $N_{eff}$ and
the evolution of the time local SIR.
| [
{
"created": "Mon, 22 Jun 2020 17:55:52 GMT",
"version": "v1"
}
] | 2020-06-23 | [
[
"Bizet",
"Nana Geraldine Cabo",
""
],
[
"Peña",
"Damián Kaloni Mayorga",
""
]
] | We consider the SIR epidemiological model applied to the evolution of COVID-19 with two approaches. In the first place we fit a global SIR model, with time delay, and constant parameters throughout the outbreak, including the contagion rate. The contention measures are reflected on an effective reduced susceptible population $N_{eff}$. In the second approach we consider a time-dependent contagion rate that reflects the contention measures either through a step by step fitting process or by following an exponential decay. In this last model the population is considered the one of the country $N$. In the linear region of the differential equations, when the total population $N$ is large the predictions are independent of $N$. We apply these methodologies to study the spread of the pandemic in Argentina, Brazil, Colombia, Mexico, and South Africa for which the infection peaks are yet to be reached. In all of these cases we provide estimates for the reproduction and recovery rates. The scenario for a time varying contagion rate is optimistic, considering that reasonable measures are taken such that the reproduction factor $R_0$ decreases exponentially. The measured values for the recovery rate $\gamma$ are reported finding a universality of this parameter over various countries. We discuss the correspondence between the global SIR with effective population $N_{eff}$ and the evolution of the time local SIR. |
2403.01332 | Tyler Ross | Tyler D. Ross, Ashwin Gopinath | Chaining thoughts and LLMs to learn DNA structural biophysics | null | null | null | null | q-bio.QM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | The future development of an AI scientist, a tool that is capable of
integrating a variety of experimental data and generating testable hypotheses,
holds immense potential. So far, bespoke machine learning models have been
created to specialize in singular scientific tasks, but otherwise lack the
flexibility of a general purpose model. Here, we show that a general purpose
large language model, chatGPT 3.5-turbo, can be fine-tuned to learn the
structural biophysics of DNA. We find that both fine-tuning models to return
chain-of-thought responses and chaining together models fine-tuned for subtasks
have an enhanced ability to analyze and design DNA sequences and their
structures.
| [
{
"created": "Sat, 2 Mar 2024 22:38:01 GMT",
"version": "v1"
}
] | 2024-03-05 | [
[
"Ross",
"Tyler D.",
""
],
[
"Gopinath",
"Ashwin",
""
]
] | The future development of an AI scientist, a tool that is capable of integrating a variety of experimental data and generating testable hypotheses, holds immense potential. So far, bespoke machine learning models have been created to specialize in singular scientific tasks, but otherwise lack the flexibility of a general purpose model. Here, we show that a general purpose large language model, chatGPT 3.5-turbo, can be fine-tuned to learn the structural biophysics of DNA. We find that both fine-tuning models to return chain-of-thought responses and chaining together models fine-tuned for subtasks have an enhanced ability to analyze and design DNA sequences and their structures. |
0801.3940 | Eleonora Alfinito Dr. | Eleonora Alfinito, Cecilia Pennetta, Lino Reggiani | Topological change and impedance spectrum of rat olfactory receptor I7:
A comparative analysis with bovine rhodopsin and bacterior | 6 pages, 8 figures | J. Appl. Phys. 105 084703 (2009) | 10.1063/1.3100210 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a theoretical investigation on possible selection of olfactory
receptors (ORs) as sensing components of nanobiosensors. Accordingly, we
generate the impedance spectra of the rat OR I7 in the native and activated
state and analyze their differences. In this way, we connect the protein
morphological transformation, caused by the sensing action, with its change of
electrical impedance. The results are compared with those obtained by studying
the best known protein of the GPCR family: bovine rhodopsin. Our investigations
indicate that a change in morphology goes with a change in impedance spectrum
mostly associated with a decrease of the static impedance up to about 60 % of
the initial value, in qualitative agreement with existing experiments on rat OR
I7. The predictiveness of the model is tested successfully for the case of
recent experiments on bacteriorhodopsin. The present results point to a
promising development of a new class of nanobiosensors based on the electrical
properties of GPCR and other sensing proteins.
| [
{
"created": "Fri, 25 Jan 2008 13:27:51 GMT",
"version": "v1"
},
{
"created": "Tue, 19 May 2009 08:20:58 GMT",
"version": "v2"
}
] | 2011-02-17 | [
[
"Alfinito",
"Eleonora",
""
],
[
"Pennetta",
"Cecilia",
""
],
[
"Reggiani",
"Lino",
""
]
] | We present a theoretical investigation on possible selection of olfactory receptors (ORs) as sensing components of nanobiosensors. Accordingly, we generate the impedance spectra of the rat OR I7 in the native and activated state and analyze their differences. In this way, we connect the protein morphological transformation, caused by the sensing action, with its change of electrical impedance. The results are compared with those obtained by studying the best known protein of the GPCR family: bovine rhodopsin. Our investigations indicate that a change in morphology goes with a change in impedance spectrum mostly associated with a decrease of the static impedance up to about 60 % of the initial value, in qualitative agreement with existing experiments on rat OR I7. The predictiveness of the model is tested successfully for the case of recent experiments on bacteriorhodopsin. The present results point to a promising development of a new class of nanobiosensors based on the electrical properties of GPCR and other sensing proteins. |
1908.06671 | Francesco Cagnetta | F. Cagnetta, D. Michieletto and D. Marenduzzo | A nonequilibrium strategy for fast target search on the genome | 5 pages, 5 figures | Phys. Rev. Lett. 124, 198101 (2020) | 10.1103/PhysRevLett.124.198101 | null | q-bio.SC cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vital biological processes such as genome repair require fast and efficient
binding of selected proteins to specific target sites on DNA. Here we propose
an active target search mechanism based on "chromophoresis", the dynamics of
DNA-binding proteins up or down gradients in the density of epigenetic marks,
or colours (biochemical tags on the genome). We focus on a set of proteins that
deposit marks from which they are repelled---a case which is only encountered
away from thermodynamic equilibrium. For suitable ranges of kinetic parameter
values, chromophoretic proteins can perform unidirectional motion and are
optimally redistributed along the genome. Importantly, they can also locally
unravel a region of the genome which is collapsed due to self-interactions and
"dive" deep into its core, for a striking enhancement of the efficiency of
target search on such an inaccessible substrate. We discuss the potential
relevance of chromophoresis for the location of DNA lesions.
| [
{
"created": "Mon, 19 Aug 2019 09:57:07 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Apr 2020 06:37:00 GMT",
"version": "v2"
},
{
"created": "Thu, 23 Apr 2020 10:04:33 GMT",
"version": "v3"
}
] | 2020-05-20 | [
[
"Cagnetta",
"F.",
""
],
[
"Michieletto",
"D.",
""
],
[
"Marenduzzo",
"D.",
""
]
] | Vital biological processes such as genome repair require fast and efficient binding of selected proteins to specific target sites on DNA. Here we propose an active target search mechanism based on "chromophoresis", the dynamics of DNA-binding proteins up or down gradients in the density of epigenetic marks, or colours (biochemical tags on the genome). We focus on a set of proteins that deposit marks from which they are repelled---a case which is only encountered away from thermodynamic equilibrium. For suitable ranges of kinetic parameter values, chromophoretic proteins can perform unidirectional motion and are optimally redistributed along the genome. Importantly, they can also locally unravel a region of the genome which is collapsed due to self-interactions and "dive" deep into its core, for a striking enhancement of the efficiency of target search on such an inaccessible substrate. We discuss the potential relevance of chromophoresis for the location of DNA lesions. |
1106.3771 | Yann Ponty | Yann Ponty (INRIA Saclay - Ile de France, LIX), C\'edric Saule (INRIA
Saclay - Ile de France, LRI, IRIC) | A Combinatorial Framework for Designing (Pseudoknotted) RNA Algorithms | null | 11th Workshop on Algorithms in Bioinformatics (WABI'11) (2011) | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We extend an hypergraph representation, introduced by Finkelstein and
Roytberg, to unify dynamic programming algorithms in the context of RNA folding
with pseudoknots. Classic applications of RNA dynamic programming energy
minimization, partition function, base-pair probabilities...) are reformulated
within this framework, giving rise to very simple algorithms. This
reformulation allows one to conceptually detach the conformation space/energy
model -- captured by the hypergraph model -- from the specific application,
assuming unambiguity of the decomposition. To ensure the latter property, we
propose a new combinatorial methodology based on generating functions. We
extend the set of generic applications by proposing an exact algorithm for
extracting generalized moments in weighted distribution, generalizing a prior
contribution by Miklos and al. Finally, we illustrate our full-fledged
programme on three exemplary conformation spaces (secondary structures,
Akutsu's simple type pseudoknots and kissing hairpins). This readily gives sets
of algorithms that are either novel or have complexity comparable to classic
implementations for minimization and Boltzmann ensemble applications of dynamic
programming.
| [
{
"created": "Sun, 19 Jun 2011 18:50:54 GMT",
"version": "v1"
}
] | 2011-06-21 | [
[
"Ponty",
"Yann",
"",
"INRIA Saclay - Ile de France, LIX"
],
[
"Saule",
"Cédric",
"",
"INRIA\n Saclay - Ile de France, LRI, IRIC"
]
] | We extend an hypergraph representation, introduced by Finkelstein and Roytberg, to unify dynamic programming algorithms in the context of RNA folding with pseudoknots. Classic applications of RNA dynamic programming energy minimization, partition function, base-pair probabilities...) are reformulated within this framework, giving rise to very simple algorithms. This reformulation allows one to conceptually detach the conformation space/energy model -- captured by the hypergraph model -- from the specific application, assuming unambiguity of the decomposition. To ensure the latter property, we propose a new combinatorial methodology based on generating functions. We extend the set of generic applications by proposing an exact algorithm for extracting generalized moments in weighted distribution, generalizing a prior contribution by Miklos and al. Finally, we illustrate our full-fledged programme on three exemplary conformation spaces (secondary structures, Akutsu's simple type pseudoknots and kissing hairpins). This readily gives sets of algorithms that are either novel or have complexity comparable to classic implementations for minimization and Boltzmann ensemble applications of dynamic programming. |
1903.10120 | Andrey Kuznetsov | Ivan A. Kuznetsov and Andrey V. Kuznetsov | Modeling transport and mean age of dense core vesicles in large axonal
arbors | Added an acknowledgement to Holger Metzler | Proceedings of the Royal Society A, vol. 475, 20190284, 2019 | 10.1098/rspa.2019.0284 | null | q-bio.NC q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A model simulating transport of dense core vesicles (DCVs) in type II axonal
terminals of Drosophila motoneurons has been developed. The morphology of type
II terminals is characterized by the large number of en passant boutons. The
lack of both scaled up DCV transport and scaled down DCV capture in boutons
results in a less efficient supply of DCVs to distal boutons. Furthermore, the
large number of boutons that DCVs pass as they move anterogradely, until they
reach the most distal bouton, may lead to the capture of a majority of DCVs
before they turn around in the most distal bouton to move in the retrograde
direction. This may lead to a reduced retrograde flux of DCVs and a lack of DCV
circulation in type II terminals. The developed model simulates DCV
concentrations in boutons, DCV fluxes between the boutons, age density
distributions of DCVs, and the mean age of DCVs in various boutons. Unlike
published experimental observations, our model predicts DCV circulation in type
II terminals after these terminals are filled to saturation. This disagreement
is likely because experimentally observed terminals were not at steady-state,
but rather were accumulating DCVs for later release. Our estimates show that
the number of DCVs in the transiting state is much smaller than that in the
resident state. DCVs traveling in the axon, rather than DCVs transiting in the
terminal, may provide a reserve of DCVs for replenishing boutons after a
release. The techniques for modeling transport of DCVs developed in our paper
can be used to model the transport of other organelles in axons.
| [
{
"created": "Mon, 25 Mar 2019 03:44:22 GMT",
"version": "v1"
},
{
"created": "Thu, 2 May 2019 17:57:58 GMT",
"version": "v2"
},
{
"created": "Sat, 7 Sep 2019 14:33:09 GMT",
"version": "v3"
},
{
"created": "Sat, 1 Feb 2020 18:36:09 GMT",
"version": "v4"
}
] | 2020-02-04 | [
[
"Kuznetsov",
"Ivan A.",
""
],
[
"Kuznetsov",
"Andrey V.",
""
]
] | A model simulating transport of dense core vesicles (DCVs) in type II axonal terminals of Drosophila motoneurons has been developed. The morphology of type II terminals is characterized by the large number of en passant boutons. The lack of both scaled up DCV transport and scaled down DCV capture in boutons results in a less efficient supply of DCVs to distal boutons. Furthermore, the large number of boutons that DCVs pass as they move anterogradely, until they reach the most distal bouton, may lead to the capture of a majority of DCVs before they turn around in the most distal bouton to move in the retrograde direction. This may lead to a reduced retrograde flux of DCVs and a lack of DCV circulation in type II terminals. The developed model simulates DCV concentrations in boutons, DCV fluxes between the boutons, age density distributions of DCVs, and the mean age of DCVs in various boutons. Unlike published experimental observations, our model predicts DCV circulation in type II terminals after these terminals are filled to saturation. This disagreement is likely because experimentally observed terminals were not at steady-state, but rather were accumulating DCVs for later release. Our estimates show that the number of DCVs in the transiting state is much smaller than that in the resident state. DCVs traveling in the axon, rather than DCVs transiting in the terminal, may provide a reserve of DCVs for replenishing boutons after a release. The techniques for modeling transport of DCVs developed in our paper can be used to model the transport of other organelles in axons. |
1612.05882 | Giovanni Bussi | Ji\v{r}\'i \v{S}poner, Giovanni Bussi, Petr Stadlbauer, Petra
K\"uhrov\'a, Pavel Ban\'a\v{s}, Barira Islam, Shozeb Haider, Stephen Neidle,
Michal Otyepka | Folding of guanine quadruplex molecules -- funnel-like mechanism or
kinetic partitioning? An overview from MD simulation studies | \c{opyright} 2016. This manuscript version is made available under
the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | BBA-Gen. Subjects 1861, 1246 (2017) | 10.1016/j.bbagen.2016.12.008 | null | q-bio.BM physics.bio-ph physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Guanine quadruplexes (GQs) play vital roles in many cellular
processes and are of much interest as drug targets. In contrast to the
availability of many structural studies, there is still limited knowledge on GQ
folding.
Scope of review: We review recent molecular dynamics (MD) simulation studies
of the folding of GQs, with an emphasis paid to the human telomeric DNA GQ. We
explain the basic principles and limitations of all types of MD methods used to
study unfolding and folding in a way accessible to non-specialists. We discuss
the potential role of G-hairpin, G-triplex and alternative GQ intermediates in
the folding process. We argue that, in general, folding of GQs is fundamentally
different from funneled folding of small fast-folding proteins, and can be best
described by a kinetic partitioning (KP) mechanism. KP is a competition between
at least two (but often many) well-separated and structurally different
conformational ensembles.
Major conclusions: The KP mechanism is the only plausible way to explain
experiments reporting long time-scales of GQ folding and the existence of
long-lived sub-states. A significant part of the natural partitioning of the
free energy landscape of GQs comes from the ability of the GQ-forming sequences
to populate a large number of syn-anti patterns in their G-tracts. The extreme
complexity of the KP of GQs typically prevents an appropriate description of
the folding landscape using just a few order parameters or collective
variables.
General significance: We reconcile available computational and experimental
studies of GQ folding and formulate basic principles characterizing GQ folding
landscapes
| [
{
"created": "Sun, 18 Dec 2016 09:58:16 GMT",
"version": "v1"
}
] | 2020-05-05 | [
[
"Šponer",
"Jiří",
""
],
[
"Bussi",
"Giovanni",
""
],
[
"Stadlbauer",
"Petr",
""
],
[
"Kührová",
"Petra",
""
],
[
"Banáš",
"Pavel",
""
],
[
"Islam",
"Barira",
""
],
[
"Haider",
"Shozeb",
""
],
[
"Neidle",
"Stephen",
""
],
[
"Otyepka",
"Michal",
""
]
] | Background: Guanine quadruplexes (GQs) play vital roles in many cellular processes and are of much interest as drug targets. In contrast to the availability of many structural studies, there is still limited knowledge on GQ folding. Scope of review: We review recent molecular dynamics (MD) simulation studies of the folding of GQs, with an emphasis paid to the human telomeric DNA GQ. We explain the basic principles and limitations of all types of MD methods used to study unfolding and folding in a way accessible to non-specialists. We discuss the potential role of G-hairpin, G-triplex and alternative GQ intermediates in the folding process. We argue that, in general, folding of GQs is fundamentally different from funneled folding of small fast-folding proteins, and can be best described by a kinetic partitioning (KP) mechanism. KP is a competition between at least two (but often many) well-separated and structurally different conformational ensembles. Major conclusions: The KP mechanism is the only plausible way to explain experiments reporting long time-scales of GQ folding and the existence of long-lived sub-states. A significant part of the natural partitioning of the free energy landscape of GQs comes from the ability of the GQ-forming sequences to populate a large number of syn-anti patterns in their G-tracts. The extreme complexity of the KP of GQs typically prevents an appropriate description of the folding landscape using just a few order parameters or collective variables. General significance: We reconcile available computational and experimental studies of GQ folding and formulate basic principles characterizing GQ folding landscapes |
2112.06256 | Pierre Haas | Pierre A. Haas and Maria A. Gutierrez and Nuno M. Oliveira and Raymond
E. Goldstein | Stabilization of Microbial Communities by Responsive Phenotypic
Switching | 27 pages, 13 figures, (structure of paper reorganized, typos
corrected) | null | null | null | q-bio.PE cond-mat.soft | http://creativecommons.org/licenses/by/4.0/ | Clonal microbes can switch between different phenotypes and recent
theoretical work has shown that stochastic switching between these
subpopulations can stabilize microbial communities. This phenotypic switching
need not be stochastic, however, but could also be in response to environmental
factors, both biotic and abiotic. Here, motivated by the bacterial persistence
phenotype, we explore the ecological effects of such responsive switching by
analyzing phenotypic switching in response to competing species. We show that
the stability of microbial communities with responsive switching differs
generically from that of communities with stochastic switching only. To
understand the mechanisms by which responsive switching stabilizes coexistence,
we go on to analyze simple two-species models. Combining exact results and
numerical simulations, we extend the classical stability results for the
competition of two species without phenotypic variation to the case in which
one species switches, stochastically and responsively, between two phenotypes.
In particular, we show that responsive switching can stabilize coexistence even
when stochastic switching on its own does not affect the stability of the
community.
| [
{
"created": "Sun, 12 Dec 2021 15:08:52 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Jun 2022 07:52:22 GMT",
"version": "v2"
}
] | 2022-06-15 | [
[
"Haas",
"Pierre A.",
""
],
[
"Gutierrez",
"Maria A.",
""
],
[
"Oliveira",
"Nuno M.",
""
],
[
"Goldstein",
"Raymond E.",
""
]
] | Clonal microbes can switch between different phenotypes and recent theoretical work has shown that stochastic switching between these subpopulations can stabilize microbial communities. This phenotypic switching need not be stochastic, however, but could also be in response to environmental factors, both biotic and abiotic. Here, motivated by the bacterial persistence phenotype, we explore the ecological effects of such responsive switching by analyzing phenotypic switching in response to competing species. We show that the stability of microbial communities with responsive switching differs generically from that of communities with stochastic switching only. To understand the mechanisms by which responsive switching stabilizes coexistence, we go on to analyze simple two-species models. Combining exact results and numerical simulations, we extend the classical stability results for the competition of two species without phenotypic variation to the case in which one species switches, stochastically and responsively, between two phenotypes. In particular, we show that responsive switching can stabilize coexistence even when stochastic switching on its own does not affect the stability of the community. |
1812.10227 | Yangsong Zhang | Yangsong Zhang, Erwei Yin, Fali Li, Yu Zhang, Daqing Guo, Dezhong Yao,
Peng Xu | Hierarchical feature fusion framework for frequency recognition in
SSVEP-based BCIs | 25 pages, 9 figures | null | null | null | q-bio.NC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Effective frequency recognition algorithms are critical in steady-state
visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this
study, we present a hierarchical feature fusion framework which can be used to
design high-performance frequency recognition methods. The proposed framework
includes two primary technique for fusing features: spatial dimension fusion
(SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained
using a weighted strategy with a nonlinear function. To assess our novel
methods, we used the correlated component analysis (CORRCA) method to
investigate the efficiency and effectiveness of the proposed framework.
Experimental results were obtained from a benchmark dataset of thirty-five
subjects and indicate that the extended CORRCA method used within the framework
significantly outperforms the original CORCCA method. Accordingly, the proposed
framework holds promise to enhance the performance of frequency recognition
methods in SSVEP-based BCIs.
| [
{
"created": "Wed, 26 Dec 2018 05:06:16 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Mar 2019 14:41:35 GMT",
"version": "v2"
}
] | 2019-03-22 | [
[
"Zhang",
"Yangsong",
""
],
[
"Yin",
"Erwei",
""
],
[
"Li",
"Fali",
""
],
[
"Zhang",
"Yu",
""
],
[
"Guo",
"Daqing",
""
],
[
"Yao",
"Dezhong",
""
],
[
"Xu",
"Peng",
""
]
] | Effective frequency recognition algorithms are critical in steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this study, we present a hierarchical feature fusion framework which can be used to design high-performance frequency recognition methods. The proposed framework includes two primary technique for fusing features: spatial dimension fusion (SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained using a weighted strategy with a nonlinear function. To assess our novel methods, we used the correlated component analysis (CORRCA) method to investigate the efficiency and effectiveness of the proposed framework. Experimental results were obtained from a benchmark dataset of thirty-five subjects and indicate that the extended CORRCA method used within the framework significantly outperforms the original CORCCA method. Accordingly, the proposed framework holds promise to enhance the performance of frequency recognition methods in SSVEP-based BCIs. |
2402.10251 | Zhizhang Yuan | Zhizhang Yuan, Daoze Zhang, Junru Chen, Gefei Gu, Yang Yang | Brant-2: Foundation Model for Brain Signals | 14 pages, 7 figures | null | null | null | q-bio.NC cs.AI cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Foundational models benefit from pre-training on large amounts of unlabeled
data and enable strong performance in a wide variety of applications with a
small amount of labeled data. Such models can be particularly effective in
analyzing brain signals, as this field encompasses numerous application
scenarios, and it is costly to perform large-scale annotation. In this work, we
present the largest foundation model in brain signals, Brant-2. Compared to
Brant, a foundation model designed for intracranial neural signals, Brant-2 not
only exhibits robustness towards data variations and modeling scales but also
can be applied to a broader range of brain neural data. By experimenting on an
extensive range of tasks, we demonstrate that Brant-2 is adaptive to various
application scenarios in brain signals. Further analyses reveal the scalability
of the Brant-2, validate each component's effectiveness, and showcase our
model's ability to maintain performance in scenarios with scarce labels.
| [
{
"created": "Thu, 15 Feb 2024 16:04:11 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Feb 2024 12:32:53 GMT",
"version": "v2"
},
{
"created": "Wed, 6 Mar 2024 09:04:32 GMT",
"version": "v3"
},
{
"created": "Thu, 28 Mar 2024 13:55:31 GMT",
"version": "v4"
}
] | 2024-03-29 | [
[
"Yuan",
"Zhizhang",
""
],
[
"Zhang",
"Daoze",
""
],
[
"Chen",
"Junru",
""
],
[
"Gu",
"Gefei",
""
],
[
"Yang",
"Yang",
""
]
] | Foundational models benefit from pre-training on large amounts of unlabeled data and enable strong performance in a wide variety of applications with a small amount of labeled data. Such models can be particularly effective in analyzing brain signals, as this field encompasses numerous application scenarios, and it is costly to perform large-scale annotation. In this work, we present the largest foundation model in brain signals, Brant-2. Compared to Brant, a foundation model designed for intracranial neural signals, Brant-2 not only exhibits robustness towards data variations and modeling scales but also can be applied to a broader range of brain neural data. By experimenting on an extensive range of tasks, we demonstrate that Brant-2 is adaptive to various application scenarios in brain signals. Further analyses reveal the scalability of the Brant-2, validate each component's effectiveness, and showcase our model's ability to maintain performance in scenarios with scarce labels. |
1912.07925 | Joachim Krug | Benjamin Schmiegelt and Joachim Krug | Accessibility Percolation on Cartesian Power Graphs | 43 pages, 9 figures, 2 tables | Journal of Mathematical Biology 86, article number 46 (2023) | 10.1007/s00285-023-01882-z | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A fitness landscape is a mapping from a space of discrete genotypes to the
real numbers. A path in a fitness landscape is a sequence of genotypes
connected by single mutational steps. Such a path is said to be accessible if
the fitness values of the genotypes encountered along the path increase
monotonically. We study accessible paths on random fitness landscapes of the
House-of-Cards type, on which fitness values are independent, identically and
continuously distributed random variables. The genotype space is taken to be a
Cartesian power graph $\mathcal{A}^L$, where $L$ is the number of genetic loci
and the allele graph $\mathcal{A}$ encodes the possible allelic states and
mutational transitions on one locus. The probability of existence of accessible
paths between two genotypes at a distance linear in $L$ displays a transition
from 0 to a positive value at a threshold $\beta_c$ for the fitness difference
between the initial and final genotype. We derive a lower bound on $\beta_c$
for general $\mathcal{A}$ and show that this bound is tight for a large class
of allele graphs. Our results generalize previous results for accessibility
percolation on the biallelic hypercube, and compare favorably to published
numerical results for multiallelic Hamming graphs.
| [
{
"created": "Tue, 17 Dec 2019 11:11:36 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Apr 2021 13:58:49 GMT",
"version": "v2"
},
{
"created": "Thu, 12 Jan 2023 13:28:46 GMT",
"version": "v3"
},
{
"created": "Mon, 20 Feb 2023 17:46:49 GMT",
"version": "v4"
}
] | 2023-02-21 | [
[
"Schmiegelt",
"Benjamin",
""
],
[
"Krug",
"Joachim",
""
]
] | A fitness landscape is a mapping from a space of discrete genotypes to the real numbers. A path in a fitness landscape is a sequence of genotypes connected by single mutational steps. Such a path is said to be accessible if the fitness values of the genotypes encountered along the path increase monotonically. We study accessible paths on random fitness landscapes of the House-of-Cards type, on which fitness values are independent, identically and continuously distributed random variables. The genotype space is taken to be a Cartesian power graph $\mathcal{A}^L$, where $L$ is the number of genetic loci and the allele graph $\mathcal{A}$ encodes the possible allelic states and mutational transitions on one locus. The probability of existence of accessible paths between two genotypes at a distance linear in $L$ displays a transition from 0 to a positive value at a threshold $\beta_c$ for the fitness difference between the initial and final genotype. We derive a lower bound on $\beta_c$ for general $\mathcal{A}$ and show that this bound is tight for a large class of allele graphs. Our results generalize previous results for accessibility percolation on the biallelic hypercube, and compare favorably to published numerical results for multiallelic Hamming graphs. |
1005.0103 | Giuseppe Jurman | Giuseppe Jurman, Roberto Visintainer, Cesare Furlanello | An introduction to spectral distances in networks (extended version) | null | G. Jurman, R. Visintainer, C. Furlanello. An introduction to
spectral distances in networks. Frontiers in Artificial Intelligence and
Applications, 226:227-234, 2011 | null | null | q-bio.MN math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many functions have been recently defined to assess the similarity among
networks as tools for quantitative comparison. They stem from very different
frameworks - and they are tuned for dealing with different situations. Here we
show an overview of the spectral distances, highlighting their behavior in some
basic cases of static and dynamic synthetic and real networks.
| [
{
"created": "Sat, 1 May 2010 20:44:04 GMT",
"version": "v1"
},
{
"created": "Tue, 25 May 2010 16:06:54 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Oct 2010 16:21:14 GMT",
"version": "v3"
}
] | 2012-08-21 | [
[
"Jurman",
"Giuseppe",
""
],
[
"Visintainer",
"Roberto",
""
],
[
"Furlanello",
"Cesare",
""
]
] | Many functions have been recently defined to assess the similarity among networks as tools for quantitative comparison. They stem from very different frameworks - and they are tuned for dealing with different situations. Here we show an overview of the spectral distances, highlighting their behavior in some basic cases of static and dynamic synthetic and real networks. |
1808.07259 | Yu Sun | Yu Sun and Siv G.E. Andersson | SSCU: an R/Bioconductor package for analyzing selective profile in
synonymous codon usage | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background Synonymous codon choice is mainly affected by mutation and
selection. For the majority of genes within a genome, mutational pressure is
the major driving force, but selective strength can be strong and dominant for
specific set of genes or codons. More specifically, the selective strength on
translational efficiency and accuracy increases with the gene's expression
level. Many statistical approaches have been developed to evaluate and quantify
the selective profile in codon usage, including S index and Akashi's test, but
no program or pipeline has been developed that includes these tests and
automates the calculation.
Results In this study, we release an R package SSCU (selective strength for
codon usage, v2.4.0), which includes tools for codon usage analyses. The
package identifies optimal codons using two approaches (comparative and
correlative methods), implements well-established statistics for detecting
codon selection, such as S index, Akashi's test, and estimates standard genomic
statistics, such as genomic GC3, RSCU and Nc.
Conclusions The package is useful for researchers working on the codon usage
analysis, and thus has general interest to the biological research community.
The package is deposited and curated at the Bioconductor site, and has
currently been downloaded for more than 2000 times and ranked as top 50%
packages.
| [
{
"created": "Wed, 22 Aug 2018 07:52:25 GMT",
"version": "v1"
}
] | 2018-08-23 | [
[
"Sun",
"Yu",
""
],
[
"Andersson",
"Siv G. E.",
""
]
] | Background Synonymous codon choice is mainly affected by mutation and selection. For the majority of genes within a genome, mutational pressure is the major driving force, but selective strength can be strong and dominant for specific set of genes or codons. More specifically, the selective strength on translational efficiency and accuracy increases with the gene's expression level. Many statistical approaches have been developed to evaluate and quantify the selective profile in codon usage, including S index and Akashi's test, but no program or pipeline has been developed that includes these tests and automates the calculation. Results In this study, we release an R package SSCU (selective strength for codon usage, v2.4.0), which includes tools for codon usage analyses. The package identifies optimal codons using two approaches (comparative and correlative methods), implements well-established statistics for detecting codon selection, such as S index, Akashi's test, and estimates standard genomic statistics, such as genomic GC3, RSCU and Nc. Conclusions The package is useful for researchers working on the codon usage analysis, and thus has general interest to the biological research community. The package is deposited and curated at the Bioconductor site, and has currently been downloaded for more than 2000 times and ranked as top 50% packages. |
1903.01233 | Simon Girel | Simon Girel (ICJ, DRACULA), Christophe Arpin (CIRI), Jacqueline Marvel
(CIRI), Olivier Gandrillon (DRACULA, LBMC UMR 5239), Fabien Crauste (DRACULA,
ICJ) | Model-based assessment of the Role of Uneven Partitioning of Molecular
Content on Heterogeneity and Regulation of Differentiation in CD8 T-cell
Immune Responses | Frontiers in Immunology, Frontiers, In press | null | 10.3389/fimmu.2019.00230 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Activation of naive CD8 T-cells can lead to the generation of multiple
effector and memory subsets. Multiple parameters associated with activation
conditions are involved in generating this diversity that is associated with
heterogeneous molecular contents of activated cells. Although naive cell
polarisation upon antigenic stimulation and the resulting asymmetric division
are known to be a major source of heterogeneity and cell fate regulation, the
consequences of stochastic uneven partitioning of molecular content upon
subsequent divisions remain unclear yet. Here we aim at studying the impact of
uneven partitioning on molecular-content heterogeneity and then on the immune
response dynamics at the cellular level. To do so, we introduce a multiscale
mathematical model of the CD8 T-cell immune response in the lymph node. In the
model, cells are described as agents evolving and interacting in a 2D
environment while a set of differential equations, embedded in each cell,
models the regulation of intra and extracellular proteins involved in cell
differentiation. Based on the analysis of in silico data at the single cell
level, we 1 show that immune response dynamics can be explained by the
molecular-content heterogeneity generated by uneven partitioning at cell
division. In particular, uneven partitioning acts as a regulator of cell
differentiation and induces the emergence of two coexisting sub-populations of
cells exhibiting antagonistic fates. We show that the degree of unevenness of
molecular partitioning, along all cell divisions, affects the outcome of the
immune response and can promote the generation of memory cells.
| [
{
"created": "Thu, 7 Feb 2019 15:01:31 GMT",
"version": "v1"
}
] | 2019-03-05 | [
[
"Girel",
"Simon",
"",
"ICJ, DRACULA"
],
[
"Arpin",
"Christophe",
"",
"CIRI"
],
[
"Marvel",
"Jacqueline",
"",
"CIRI"
],
[
"Gandrillon",
"Olivier",
"",
"DRACULA, LBMC UMR 5239"
],
[
"Crauste",
"Fabien",
"",
"DRACULA,\n ICJ"
]
] | Activation of naive CD8 T-cells can lead to the generation of multiple effector and memory subsets. Multiple parameters associated with activation conditions are involved in generating this diversity that is associated with heterogeneous molecular contents of activated cells. Although naive cell polarisation upon antigenic stimulation and the resulting asymmetric division are known to be a major source of heterogeneity and cell fate regulation, the consequences of stochastic uneven partitioning of molecular content upon subsequent divisions remain unclear yet. Here we aim at studying the impact of uneven partitioning on molecular-content heterogeneity and then on the immune response dynamics at the cellular level. To do so, we introduce a multiscale mathematical model of the CD8 T-cell immune response in the lymph node. In the model, cells are described as agents evolving and interacting in a 2D environment while a set of differential equations, embedded in each cell, models the regulation of intra and extracellular proteins involved in cell differentiation. Based on the analysis of in silico data at the single cell level, we 1 show that immune response dynamics can be explained by the molecular-content heterogeneity generated by uneven partitioning at cell division. In particular, uneven partitioning acts as a regulator of cell differentiation and induces the emergence of two coexisting sub-populations of cells exhibiting antagonistic fates. We show that the degree of unevenness of molecular partitioning, along all cell divisions, affects the outcome of the immune response and can promote the generation of memory cells. |
0909.1935 | Natalja Strelkowa | Natalja Strelkowa, Mauricio Barahona | Switchable Genetic Oscillator Operating in Quasi-Stable Mode | 24 pages, 5 main figures | null | 10.1098/rsif.2009.0487 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ring topologies of repressing genes have qualitatively different long-term
dynamics if the number of genes is odd (they oscillate) or even (they exhibit
bistability). However, these attractors may not fully explain the observed
behavior in transient and stochastic environments such as the cell. We show
here that even repressilators possess quasi-stable, travelling-wave periodic
solutions that are reachable, long-lived and robust to parameter changes. These
solutions underlie the sustained oscillations observed in even rings in the
stochastic regime, even if these circuits are expected to behave as switches.
The existence of such solutions can also be exploited for control purposes:
operation of the system around the quasi-stable orbit allows us to turn on and
off the oscillations reliably and on demand. We illustrate these ideas with a
simple protocol based on optical interference that can induce oscillations
robustly both in the stochastic and deterministic regimes.
| [
{
"created": "Thu, 10 Sep 2009 12:31:34 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Nov 2009 21:06:04 GMT",
"version": "v2"
}
] | 2010-01-22 | [
[
"Strelkowa",
"Natalja",
""
],
[
"Barahona",
"Mauricio",
""
]
] | Ring topologies of repressing genes have qualitatively different long-term dynamics if the number of genes is odd (they oscillate) or even (they exhibit bistability). However, these attractors may not fully explain the observed behavior in transient and stochastic environments such as the cell. We show here that even repressilators possess quasi-stable, travelling-wave periodic solutions that are reachable, long-lived and robust to parameter changes. These solutions underlie the sustained oscillations observed in even rings in the stochastic regime, even if these circuits are expected to behave as switches. The existence of such solutions can also be exploited for control purposes: operation of the system around the quasi-stable orbit allows us to turn on and off the oscillations reliably and on demand. We illustrate these ideas with a simple protocol based on optical interference that can induce oscillations robustly both in the stochastic and deterministic regimes. |
1909.07540 | Yuri A. Dabaghian | Yuri Dabaghian | Topological stability of the hippocampal spatial map and synaptic
transience | 14 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatial awareness in mammals is based on internalized representations of the
environment---cognitive maps---encoded by networks of spiking neurons. Although
behavioral studies suggest that these maps can remain stable for long periods,
it is also well-known that the underlying networks of synaptic connections
constantly change their architecture due to various forms of neuronal
plasticity. This raises a principal question: how can a dynamic network encode
a stable map of space? In the following, we discuss some recent results
obtained in this direction using an algebro-topological modeling approach,
which demonstrate that emergence of stable cognitive maps produced by networks
with transient architectures is not only possible, but may be a generic
phenomenon.
| [
{
"created": "Tue, 17 Sep 2019 01:17:22 GMT",
"version": "v1"
}
] | 2019-09-18 | [
[
"Dabaghian",
"Yuri",
""
]
] | Spatial awareness in mammals is based on internalized representations of the environment---cognitive maps---encoded by networks of spiking neurons. Although behavioral studies suggest that these maps can remain stable for long periods, it is also well-known that the underlying networks of synaptic connections constantly change their architecture due to various forms of neuronal plasticity. This raises a principal question: how can a dynamic network encode a stable map of space? In the following, we discuss some recent results obtained in this direction using an algebro-topological modeling approach, which demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is not only possible, but may be a generic phenomenon. |
1812.08328 | Duc Nguyen | Duc Duy Nguyen, Guo-Wei Wei | Algebraic graph learning of protein-ligand binding affinity | 13 pages, 6 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although algebraic graph theory based models have been widely applied in
physical modeling and molecular studies, they are typically incompetent in the
analysis and prediction of biomolecular properties when compared with other
quantitative approaches. There is a need to explore the capability and
limitation of algebraic graph theory for molecular and biomolecular modeling,
analysis, and prediction. In this work, we propose novel algebraic graph
learning (AGL) models that encode high-dimensional physical and biological
information into intrinsically low-dimensional representations. The proposed
AGL model introduces multiscale weighted colored subgraphs to describe crucial
molecular and biomolecular interactions via graph invariants associated with
the graph Laplacian, its pseudo-inverse, and adjacent matrix. Additionally, the
AGL models are incorporated with an advanced machine learning algorithm to
connect the low-dimensional graph representation of biomolecular structures
with their macroscopic properties. Three popular protein-ligand binding
affinity benchmarks, namely CASF-2007, CASF-2013, and CASF-2016, are employed
to validate the accuracy, robustness, and reliability of the present AGL model.
Numerical results indicate that the proposed AGL method outperforms the other
state-of-the-art methods in the binding affinity predictions of the
protein-ligand complexes.
| [
{
"created": "Thu, 20 Dec 2018 02:33:47 GMT",
"version": "v1"
}
] | 2018-12-21 | [
[
"Nguyen",
"Duc Duy",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Although algebraic graph theory based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties when compared with other quantitative approaches. There is a need to explore the capability and limitation of algebraic graph theory for molecular and biomolecular modeling, analysis, and prediction. In this work, we propose novel algebraic graph learning (AGL) models that encode high-dimensional physical and biological information into intrinsically low-dimensional representations. The proposed AGL model introduces multiscale weighted colored subgraphs to describe crucial molecular and biomolecular interactions via graph invariants associated with the graph Laplacian, its pseudo-inverse, and adjacent matrix. Additionally, the AGL models are incorporated with an advanced machine learning algorithm to connect the low-dimensional graph representation of biomolecular structures with their macroscopic properties. Three popular protein-ligand binding affinity benchmarks, namely CASF-2007, CASF-2013, and CASF-2016, are employed to validate the accuracy, robustness, and reliability of the present AGL model. Numerical results indicate that the proposed AGL method outperforms the other state-of-the-art methods in the binding affinity predictions of the protein-ligand complexes. |
2104.01279 | John Zobolas | John Zobolas, Pedro T. Monteiro, Martin Kuiper and {\AA}smund Flobak | Boolean function metrics can assist modelers to check and choose logical
rules | 30 pages, 4 Figures, 3 Tables | Journal of Theoretical Biology 109 (2022) | 10.1016/j.jtbi.2022.111025 | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Computational models of biological processes provide one of the most powerful
methods for a detailed analysis of the mechanisms that drive the behavior of
complex systems. Logic-based modeling has enhanced our understanding and
interpretation of those systems. Defining rules that determine how the output
activity of biological entities is regulated by their respective inputs has
proven to be challenging, due to increasingly larger models and the presence of
noise in data, allowing multiple model parameterizations to fit the
experimental observations.
We present several Boolean function metrics that provide modelers with the
appropriate framework to analyze the impact of a particular model
parameterization. We demonstrate the link between a semantic characterization
of a Boolean function and its consistency with the model's underlying
regulatory structure. We further define the properties that outline such
consistency and show that several of the Boolean functions under study violate
them, questioning their biological plausibility and subsequent use. We also
illustrate that regulatory functions can have major differences with regard to
their asymptotic output behavior, with some of them being biased towards
specific Boolean outcomes when others are dependent on the ratio between
activating and inhibitory regulators.
Application results show that in a specific signaling cancer network, the
function bias can be used to guide the choice of logical operators for a model
that matches data observations. Moreover, graph analysis indicates that the
standardized Boolean function bias becomes more prominent with increasing
numbers of regulators, confirming the fact that rule specification can
effectively determine regulatory outcome despite the complex dynamics of
biological networks.
| [
{
"created": "Fri, 2 Apr 2021 23:58:42 GMT",
"version": "v1"
}
] | 2022-02-08 | [
[
"Zobolas",
"John",
""
],
[
"Monteiro",
"Pedro T.",
""
],
[
"Kuiper",
"Martin",
""
],
[
"Flobak",
"Åsmund",
""
]
] | Computational models of biological processes provide one of the most powerful methods for a detailed analysis of the mechanisms that drive the behavior of complex systems. Logic-based modeling has enhanced our understanding and interpretation of those systems. Defining rules that determine how the output activity of biological entities is regulated by their respective inputs has proven to be challenging, due to increasingly larger models and the presence of noise in data, allowing multiple model parameterizations to fit the experimental observations. We present several Boolean function metrics that provide modelers with the appropriate framework to analyze the impact of a particular model parameterization. We demonstrate the link between a semantic characterization of a Boolean function and its consistency with the model's underlying regulatory structure. We further define the properties that outline such consistency and show that several of the Boolean functions under study violate them, questioning their biological plausibility and subsequent use. We also illustrate that regulatory functions can have major differences with regard to their asymptotic output behavior, with some of them being biased towards specific Boolean outcomes when others are dependent on the ratio between activating and inhibitory regulators. Application results show that in a specific signaling cancer network, the function bias can be used to guide the choice of logical operators for a model that matches data observations. Moreover, graph analysis indicates that the standardized Boolean function bias becomes more prominent with increasing numbers of regulators, confirming the fact that rule specification can effectively determine regulatory outcome despite the complex dynamics of biological networks. |
0906.2232 | Thomas Gurry | Thomas Gurry, Ozan Kahramanogullari, Robert G. Endres | Biophysical mechanism for Ras-nanocluster formation and signaling in
plasma membrane | 8 figures. PLoS ONE, in press | null | 10.1371/journal.pone.0006148 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ras GTPases are lipid-anchored G proteins which play a fundamental role in
cell signaling processes. Electron micrographs of immunogold-labeled Ras have
shown that membrane-bound Ras molecules segregate into nanocluster domains.
Several models have been developed in attempts to obtain quantitative
descriptions of nanocluster formation, but all have relied on assumptions such
as a constant, expression-level independent ratio of Ras in clusters to Ras
monomers (cluster/monomer ratio). However, this assumption is inconsistent with
the law of mass action. Here, we present a biophysical model of Ras clustering
based on short-range attraction and long-range repulsion between Ras molecules
in the membrane. To test this model, we performed Monte Carlo simulations and
compared statistical clustering properties with experimental data. We find that
we can recover the experimentally-observed clustering across a range of Ras
expression levels, without assuming a constant cluster/monomer ratio or the
existence of lipid rafts. In addition, our model makes predictions about the
signaling properties of Ras nanoclusters in support of the idea that Ras
nanoclusters act as an analog-digital-analog converter for high fidelity
signaling.
| [
{
"created": "Fri, 12 Jun 2009 00:45:03 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Gurry",
"Thomas",
""
],
[
"Kahramanogullari",
"Ozan",
""
],
[
"Endres",
"Robert G.",
""
]
] | Ras GTPases are lipid-anchored G proteins which play a fundamental role in cell signaling processes. Electron micrographs of immunogold-labeled Ras have shown that membrane-bound Ras molecules segregate into nanocluster domains. Several models have been developed in attempts to obtain quantitative descriptions of nanocluster formation, but all have relied on assumptions such as a constant, expression-level independent ratio of Ras in clusters to Ras monomers (cluster/monomer ratio). However, this assumption is inconsistent with the law of mass action. Here, we present a biophysical model of Ras clustering based on short-range attraction and long-range repulsion between Ras molecules in the membrane. To test this model, we performed Monte Carlo simulations and compared statistical clustering properties with experimental data. We find that we can recover the experimentally-observed clustering across a range of Ras expression levels, without assuming a constant cluster/monomer ratio or the existence of lipid rafts. In addition, our model makes predictions about the signaling properties of Ras nanoclusters in support of the idea that Ras nanoclusters act as an analog-digital-analog converter for high fidelity signaling. |
q-bio/0406009 | Nicholas Chia | Ralf Bundschuh and Nicholas Chia | Finite Width Model Sequence Comparison | null | null | null | null | q-bio.QM | null | Sequence comparison is a widely used computational technique in modern
molecular biology. In spite of the frequent use of sequence comparisons the
important problem of assigning statistical significance to a given degree of
similarity is still outstanding. Analytical approaches to filling this gap
usually make use of an approximation that neglects certain correlations in the
disorder underlying the sequence comparison algorithm. Here, we use the longest
common subsequence problem, a prototype sequence comparison problem, to
analytically establish that this approximation does make a difference to
certain sequence comparison statistics. In the course of establishing this
difference we develop a method that can systematically deal with these disorder
correlations.
| [
{
"created": "Thu, 3 Jun 2004 17:09:35 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bundschuh",
"Ralf",
""
],
[
"Chia",
"Nicholas",
""
]
] | Sequence comparison is a widely used computational technique in modern molecular biology. In spite of the frequent use of sequence comparisons the important problem of assigning statistical significance to a given degree of similarity is still outstanding. Analytical approaches to filling this gap usually make use of an approximation that neglects certain correlations in the disorder underlying the sequence comparison algorithm. Here, we use the longest common subsequence problem, a prototype sequence comparison problem, to analytically establish that this approximation does make a difference to certain sequence comparison statistics. In the course of establishing this difference we develop a method that can systematically deal with these disorder correlations. |
2211.13673 | Celine Teplitsky | David L\'opez-Idi\'aquez (CEFE), C\'eline Teplitsky (CEFE), Arnaud
Gr\'egoire (CEFE), Am\'elie Fargevieille (CEFE), Mar\'ia del Rey (CEFE),
Christophe de Franceschi (CEFE), Anne Charmantier (CEFE), Claire Doutrelant
(CEFE) | Long-Term Decrease in Coloration: A Consequence of Climate Change? | null | The American Naturalist, 2022, 200 (1), pp.32-47 | 10.1086/719655 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Climate change has been shown to affect fitness-related traits in a wide
range of taxa; for instance, warming leads to phenological advancements in many
plant and animal species. The influence of climate change on social and
secondary sexual traits, that are associated with fitness due to their role as
quality signals, is however unknown. Here, we use more than 5800 observations
collected on two Mediterranean blue tit subspecies (Cyanistes caeruleus
caeruleus and C.c. ogliastrae) to explore whether blue crown and yellow breast
patch colourations have changed over the past 15 years. Our data suggests that
colouration has become duller and less chromatic in both sexes. In addition, in
the Corsican C.c. ogliastrae, but not in the mainland C.c. caeruleus, the
decrease is associated with an increase in temperature at moult. Quantitative
genetic analyses do not reveal any microevolutionary change in the colour
traits along the study period, strongly suggesting that the observed change
over time was caused by a plastic response to the environmental conditions.
Overall, this study suggests that ornamental colourations could become less
conspicuous due to warming, revealing climate change effects on sexual and
social ornaments and calling for further research on the proximate mechanisms
behind these effects.
| [
{
"created": "Thu, 24 Nov 2022 15:39:46 GMT",
"version": "v1"
}
] | 2022-11-28 | [
[
"López-Idiáquez",
"David",
"",
"CEFE"
],
[
"Teplitsky",
"Céline",
"",
"CEFE"
],
[
"Grégoire",
"Arnaud",
"",
"CEFE"
],
[
"Fargevieille",
"Amélie",
"",
"CEFE"
],
[
"del Rey",
"María",
"",
"CEFE"
],
[
"de Franceschi",
"Christophe",
"",
"CEFE"
],
[
"Charmantier",
"Anne",
"",
"CEFE"
],
[
"Doutrelant",
"Claire",
"",
"CEFE"
]
] | Climate change has been shown to affect fitness-related traits in a wide range of taxa; for instance, warming leads to phenological advancements in many plant and animal species. The influence of climate change on social and secondary sexual traits, that are associated with fitness due to their role as quality signals, is however unknown. Here, we use more than 5800 observations collected on two Mediterranean blue tit subspecies (Cyanistes caeruleus caeruleus and C.c. ogliastrae) to explore whether blue crown and yellow breast patch colourations have changed over the past 15 years. Our data suggests that colouration has become duller and less chromatic in both sexes. In addition, in the Corsican C.c. ogliastrae, but not in the mainland C.c. caeruleus, the decrease is associated with an increase in temperature at moult. Quantitative genetic analyses do not reveal any microevolutionary change in the colour traits along the study period, strongly suggesting that the observed change over time was caused by a plastic response to the environmental conditions. Overall, this study suggests that ornamental colourations could become less conspicuous due to warming, revealing climate change effects on sexual and social ornaments and calling for further research on the proximate mechanisms behind these effects. |
1910.01726 | Jianhong Chen | Jianhong Chen, Huang Huang, Wenrui Hao, Jinchao Xu | A machine learning method correlating pulse pressure wave data with
pregnancy | null | null | null | null | q-bio.QM cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pulse feeling, representing the tactile arterial palpation of the heartbeat,
has been widely used in traditional Chinese medicine (TCM) to diagnose various
diseases. The quantitative relationship between the pulse wave and health
conditions however has not been investigated in modern medicine. In this paper,
we explored the correlation between pulse pressure wave (PPW), rather than the
pulse key features in TCM, and pregnancy by using deep learning technology.
This computational approach shows that the accuracy of pregnancy detection by
the PPW is 84% with an AUC of 91%. Our study is a proof of concept of pulse
diagnosis and will also motivate further sophisticated investigations on pulse
waves.
| [
{
"created": "Thu, 3 Oct 2019 21:35:05 GMT",
"version": "v1"
}
] | 2019-10-07 | [
[
"Chen",
"Jianhong",
""
],
[
"Huang",
"Huang",
""
],
[
"Hao",
"Wenrui",
""
],
[
"Xu",
"Jinchao",
""
]
] | Pulse feeling, representing the tactile arterial palpation of the heartbeat, has been widely used in traditional Chinese medicine (TCM) to diagnose various diseases. The quantitative relationship between the pulse wave and health conditions however has not been investigated in modern medicine. In this paper, we explored the correlation between pulse pressure wave (PPW), rather than the pulse key features in TCM, and pregnancy by using deep learning technology. This computational approach shows that the accuracy of pregnancy detection by the PPW is 84% with an AUC of 91%. Our study is a proof of concept of pulse diagnosis and will also motivate further sophisticated investigations on pulse waves. |
0802.1902 | Nicolas Vuillerme | Nicolas Vuillerme (TIMC), Nicolas Pinsault (TIMC), Jacques Vaillant
(TIMC) | Postural control during quiet standing following cervical muscular
fatigue: effects of changes in sensory inputs | null | Neuroscience Letters 378, 3 (2005) 135-9 | 10.1016/j.neulet.2004.12.024 | null | q-bio.NC | null | The purpose of the present experiment was to investigate the effects of
cervical muscular fatigue on postural control during quiet standing under
different conditions of reliability and/or availability of somatosensory inputs
from the plantar soles and the ankles and visual information. To this aim, 14
young healthy adults were asked to sway as little as possible in three sensory
conditions (No vision, No vision-Foam support and Vision) executed in two
conditions of No fatigue and Fatigue of the scapula elevator muscles. Centre of
foot pressure (CoP) displacements were recorded using a force platform. Results
showed that (1) the cervical muscular fatigue yielded increased CoP
displacements in the absence of vision, (2) this effect was more accentuated
when somatosensation was degraded by standing on a foam surface and (3) the
availability of vision allowed the individuals to suppress this destabilising
effect. On the whole, these findings not only stress the importance of intact
cervical neuromuscular function on postural control during quiet standing, but
also suggest a reweigthing of sensory cues in balance control following
cervical muscular fatigue by increasing the reliance on the somatosensory
inputs from the plantar soles and the ankles and visual information.
| [
{
"created": "Wed, 13 Feb 2008 20:08:49 GMT",
"version": "v1"
}
] | 2008-02-14 | [
[
"Vuillerme",
"Nicolas",
"",
"TIMC"
],
[
"Pinsault",
"Nicolas",
"",
"TIMC"
],
[
"Vaillant",
"Jacques",
"",
"TIMC"
]
] | The purpose of the present experiment was to investigate the effects of cervical muscular fatigue on postural control during quiet standing under different conditions of reliability and/or availability of somatosensory inputs from the plantar soles and the ankles and visual information. To this aim, 14 young healthy adults were asked to sway as little as possible in three sensory conditions (No vision, No vision-Foam support and Vision) executed in two conditions of No fatigue and Fatigue of the scapula elevator muscles. Centre of foot pressure (CoP) displacements were recorded using a force platform. Results showed that (1) the cervical muscular fatigue yielded increased CoP displacements in the absence of vision, (2) this effect was more accentuated when somatosensation was degraded by standing on a foam surface and (3) the availability of vision allowed the individuals to suppress this destabilising effect. On the whole, these findings not only stress the importance of intact cervical neuromuscular function on postural control during quiet standing, but also suggest a reweigthing of sensory cues in balance control following cervical muscular fatigue by increasing the reliance on the somatosensory inputs from the plantar soles and the ankles and visual information. |
1704.03150 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Stochastic Spike Synchronization in A Small-World Neural Network with
Spike-Timing-Dependent Plasticity | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the Watts-Strogatz small-world network consisting of subthreshold
neurons which exhibit noise-induced spikings. This neuronal network has
adaptive dynamic synaptic strengths governed by the spike-timing-dependent
plasticity (STDP). In previous works without STDP, stochastic spike
synchronization (SSS) between noise-induced spikings of subthreshold neurons
was found to occur in a range of intermediate noise intensities through
competition between the constructive and the destructive roles of noise. Here,
we investigate the effect of additive STDP on the SSS by varying the noise
intensity. Occurrence of a "Matthew" effect in synaptic plasticity is found due
to a positive feedback process. As a result, good synchronization gets better
via long-term potentiation (LTP) of synaptic strengths, while bad
synchronization gets worse via long-term depression (LTD). Emergence of LTP and
LTD of synaptic strengths are intensively investigated via microscopic studies
based on the pair-correlations between the pre- and the post-synaptic IISRs
(instantaneous individual spike rates) as well as the distributions of time
delays between the pre- and the post-synaptic spike times. Furthermore, the
effects of multiplicative STDP (which depends on the states) on the SSS are
also studied and discussed in comparison with the case of additive STDP.
| [
{
"created": "Tue, 11 Apr 2017 05:42:39 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Apr 2017 01:56:32 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Apr 2017 02:27:33 GMT",
"version": "v3"
},
{
"created": "Mon, 14 Aug 2017 19:16:44 GMT",
"version": "v4"
}
] | 2017-08-16 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | We consider the Watts-Strogatz small-world network consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). In previous works without STDP, stochastic spike synchronization (SSS) between noise-induced spikings of subthreshold neurons was found to occur in a range of intermediate noise intensities through competition between the constructive and the destructive roles of noise. Here, we investigate the effect of additive STDP on the SSS by varying the noise intensity. Occurrence of a "Matthew" effect in synaptic plasticity is found due to a positive feedback process. As a result, good synchronization gets better via long-term potentiation (LTP) of synaptic strengths, while bad synchronization gets worse via long-term depression (LTD). Emergence of LTP and LTD of synaptic strengths are intensively investigated via microscopic studies based on the pair-correlations between the pre- and the post-synaptic IISRs (instantaneous individual spike rates) as well as the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, the effects of multiplicative STDP (which depends on the states) on the SSS are also studied and discussed in comparison with the case of additive STDP. |
2405.03601 | Tatiana Levanova | Sergey V. Stasenko, Sergey M. Olenin, Eugene A. Grines, Tatiana A.
Levanova | Firing rate model for brain rhythms controlled by astrocytes | 12 pages, 4 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | We propose a new mean-field model of brain rhythms governed by astrocytes.
This theoretical framework describes how astrocytes can regulate neuronal
activity and contribute to the generation of brain rhythms. The model describes
at the population level the interactions between two large groups of excitatory
and inhibitory neurons. The excitatory population is governed by astrocytes via
a so-called tripartite synapse. This approach allows us to describe how the
interactions between different groups of neurons and astrocytes can give rise
to various patterns of synchronized activity and transitions between them.
Using methods of nonlinear analysis we show that astrocytic modulation can lead
to a change in the period and amplitude of oscillations in the populations of
neurons.
| [
{
"created": "Mon, 6 May 2024 16:14:19 GMT",
"version": "v1"
}
] | 2024-05-07 | [
[
"Stasenko",
"Sergey V.",
""
],
[
"Olenin",
"Sergey M.",
""
],
[
"Grines",
"Eugene A.",
""
],
[
"Levanova",
"Tatiana A.",
""
]
] | We propose a new mean-field model of brain rhythms governed by astrocytes. This theoretical framework describes how astrocytes can regulate neuronal activity and contribute to the generation of brain rhythms. The model describes at the population level the interactions between two large groups of excitatory and inhibitory neurons. The excitatory population is governed by astrocytes via a so-called tripartite synapse. This approach allows us to describe how the interactions between different groups of neurons and astrocytes can give rise to various patterns of synchronized activity and transitions between them. Using methods of nonlinear analysis we show that astrocytic modulation can lead to a change in the period and amplitude of oscillations in the populations of neurons. |
1401.6004 | Clinton Goss Ph.D. | Eric B. Miller and Clinton F. Goss | An Exploration of Physiological Responses to the Native American Flute | 17 pages, 7 figures, 4 tables, presented at ISQRMM 2013, Athens, GA,
July 26, 2013 | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/3.0/ | This pilot study explored physiological responses to playing and listening to
the Native American flute. Autonomic, electroencephalographic (EEG), and heart
rate variability (HRV) metrics were recorded while participants (N = 15) played
flutes and listened to several styles of music. Flute playing was accompanied
by an 84% increase in HRV (p < .001). EEG theta (4-8 Hz) activity increased
while playing flutes (p = .007) and alpha (8-12 Hz) increased while playing
lower-pitched flutes (p = .009). Increase in alpha from baseline to the flute
playing conditions strongly correlated with experience playing Native American
flutes (r = +.700). Wide-band beta (12-25 Hz) decreased from the silence
conditions when listening to solo Native American flute music (p = .013). The
findings of increased HRV, increasing slow-wave rhythms, and decreased beta
support the hypothesis that Native American flutes, particularly those with
lower pitches, may have a role in music therapy contexts. We conclude that the
Native American flute may merit a more prominent role in music therapy and that
a study of the effects of flute playing on clinical conditions, such as
post-traumatic stress disorder (PTSD), asthma, chronic obstructive pulmonary
disease (COPD), hypertension, anxiety, and major depressive disorder, is
warranted.
| [
{
"created": "Wed, 22 Jan 2014 02:44:01 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Jan 2014 03:57:28 GMT",
"version": "v2"
}
] | 2014-01-27 | [
[
"Miller",
"Eric B.",
""
],
[
"Goss",
"Clinton F.",
""
]
] | This pilot study explored physiological responses to playing and listening to the Native American flute. Autonomic, electroencephalographic (EEG), and heart rate variability (HRV) metrics were recorded while participants (N = 15) played flutes and listened to several styles of music. Flute playing was accompanied by an 84% increase in HRV (p < .001). EEG theta (4-8 Hz) activity increased while playing flutes (p = .007) and alpha (8-12 Hz) increased while playing lower-pitched flutes (p = .009). Increase in alpha from baseline to the flute playing conditions strongly correlated with experience playing Native American flutes (r = +.700). Wide-band beta (12-25 Hz) decreased from the silence conditions when listening to solo Native American flute music (p = .013). The findings of increased HRV, increasing slow-wave rhythms, and decreased beta support the hypothesis that Native American flutes, particularly those with lower pitches, may have a role in music therapy contexts. We conclude that the Native American flute may merit a more prominent role in music therapy and that a study of the effects of flute playing on clinical conditions, such as post-traumatic stress disorder (PTSD), asthma, chronic obstructive pulmonary disease (COPD), hypertension, anxiety, and major depressive disorder, is warranted. |
1511.06546 | Antti Honkela | Aravind Sankar, Brandon Malone, Sion Bayliss, Ben Pascoe, Guillaume
M\'eric, Matthew D. Hitchings, Samuel K. Sheppard, Edward J. Feil, Jukka
Corander and Antti Honkela | Bayesian identification of bacterial strains from sequencing data | 16 pages, 7 figures | null | null | null | q-bio.GN q-bio.QM stat.AP | http://creativecommons.org/licenses/by/4.0/ | Rapidly assaying the diversity of a bacterial species present in a sample
obtained from a hospital patient or an evironmental source has become possible
after recent technological advances in DNA sequencing. For several applications
it is important to accurately identify the presence and estimate relative
abundances of the target organisms from short sequence reads obtained from a
sample. This task is particularly challenging when the set of interest includes
very closely related organisms, such as different strains of pathogenic
bacteria, which can vary considerably in terms of virulence, resistance and
spread. Using advanced Bayesian statistical modelling and computation
techniques we introduce a novel pipeline for bacterial identification that is
shown to outperform the currently leading pipeline for this purpose. Our
approach enables fast and accurate sequence-based identification of bacterial
strains while using only modest computational resources. Hence it provides a
useful tool for a wide spectrum of applications, including rapid clinical
diagnostics to distinguish among closely related strains causing nosocomial
infections. The software implementation is available at
https://github.com/PROBIC/BIB
| [
{
"created": "Fri, 20 Nov 2015 10:12:06 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Feb 2016 17:16:45 GMT",
"version": "v2"
}
] | 2016-02-18 | [
[
"Sankar",
"Aravind",
""
],
[
"Malone",
"Brandon",
""
],
[
"Bayliss",
"Sion",
""
],
[
"Pascoe",
"Ben",
""
],
[
"Méric",
"Guillaume",
""
],
[
"Hitchings",
"Matthew D.",
""
],
[
"Sheppard",
"Samuel K.",
""
],
[
"Feil",
"Edward J.",
""
],
[
"Corander",
"Jukka",
""
],
[
"Honkela",
"Antti",
""
]
] | Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at https://github.com/PROBIC/BIB |
1301.0960 | Amy Maxmen | Amy Maxmen | Sea Spider development: How the encysting Anoplodactylus eroticus
matures from a buoyant nymph to a grounded adult | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to understand how animals evolved over time, biologists must learn
how their body parts form during their development. The following is a detailed
description of how one species of sea spider transforms from a hatchling to an
8-legged adult. Pycnogonids, or sea spiders, comprise a primitive lineage of
arthropods. As such, they hold potential to reveal insights into arthropod
evolution. Recent phylogenetic analyses have supported their position as either
basal chelicerates or as a separate, fifth major lineage of extant arthropods.
Disagreements concerning pycnogonid relations to other arthropods is partly a
product of too few primary observations of pycnogonid anatomy and development.
This investigation of post-embryonic development of the pycnogonid
Anoplodactylus eroticus employs multiple techniques of anatomical observation,
including Nomarski optics, scanning electron microscopy and fluorescence
microscopy, in order to thoroughly document the life cycle.
After the second post-embryonic stage, larvae of A. eroticus burrow within a
hydroid and undergo morphogenesis. Larvae emerge from the hydroid and
simultaneously molt into the juvenile stage. Over the course of post-embryonic
development there are eight stages preceding the mature adult. All structures,
except for the anteriormost appendages, the chelifores, undergo some degree of
transformation. Chelifores are present prior to hatching and remain mobile over
the course of development. Some larger issues important to arthropod evolution
are addressed, such as the equivalent of a germband and labrum in pycnogonids.
Post-embryonic development of A. eroticus provides an example counteracting
previous reports of anamorphic development and a four-segmented head in the
pycnogonid ground pattern, findings that were extrapolated to fit the ground
pattern of Arthropoda.
| [
{
"created": "Sun, 6 Jan 2013 02:29:53 GMT",
"version": "v1"
}
] | 2013-01-08 | [
[
"Maxmen",
"Amy",
""
]
] | In order to understand how animals evolved over time, biologists must learn how their body parts form during their development. The following is a detailed description of how one species of sea spider transforms from a hatchling to an 8-legged adult. Pycnogonids, or sea spiders, comprise a primitive lineage of arthropods. As such, they hold potential to reveal insights into arthropod evolution. Recent phylogenetic analyses have supported their position as either basal chelicerates or as a separate, fifth major lineage of extant arthropods. Disagreements concerning pycnogonid relations to other arthropods is partly a product of too few primary observations of pycnogonid anatomy and development. This investigation of post-embryonic development of the pycnogonid Anoplodactylus eroticus employs multiple techniques of anatomical observation, including Nomarski optics, scanning electron microscopy and fluorescence microscopy, in order to thoroughly document the life cycle. After the second post-embryonic stage, larvae of A. eroticus burrow within a hydroid and undergo morphogenesis. Larvae emerge from the hydroid and simultaneously molt into the juvenile stage. Over the course of post-embryonic development there are eight stages preceding the mature adult. All structures, except for the anteriormost appendages, the chelifores, undergo some degree of transformation. Chelifores are present prior to hatching and remain mobile over the course of development. Some larger issues important to arthropod evolution are addressed, such as the equivalent of a germband and labrum in pycnogonids. Post-embryonic development of A. eroticus provides an example counteracting previous reports of anamorphic development and a four-segmented head in the pycnogonid ground pattern, findings that were extrapolated to fit the ground pattern of Arthropoda. |
2006.16379 | Ian Craig | Laurentz E. Olivier, Stefan Botha and Ian K. Craig | Optimized lockdown strategies for curbing the spread of COVID-19: A
South African case study | 11 pages, 7 figures, 4 tables | null | 10.1109/ACCESS.2020.3037415 | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | To curb the spread of COVID-19, many governments around the world have
implemented tiered lockdowns with varying degrees of stringency. Lockdown
levels are typically increased when the disease spreads and reduced when the
disease abates. A predictive control approach is used to develop optimized
lockdown strategies for curbing the spread of COVID-19. The strategies are then
applied to South African data. The South African case is of interest as the
South African government has defined five distinct levels of lockdown, which
serves as a discrete control input. An epidemiological model for the spread of
COVID-19 in South Africa was previously developed, and is used in conjunction
with a hybrid model predictive controller to optimize lockdown management under
different policy scenarios. Scenarios considered include how to flatten the
curve to a level that the healthcare system can cope with, how to balance lives
and livelihoods, and what impact the compliance of the population to the
lockdown measures has on the spread of COVID-19. The main purpose of this paper
is to show what the optimal lockdown level should be given the policy that is
in place, as determined by the closed-loop feedback controller.
| [
{
"created": "Mon, 29 Jun 2020 20:59:08 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Nov 2020 06:58:52 GMT",
"version": "v2"
}
] | 2020-11-16 | [
[
"Olivier",
"Laurentz E.",
""
],
[
"Botha",
"Stefan",
""
],
[
"Craig",
"Ian K.",
""
]
] | To curb the spread of COVID-19, many governments around the world have implemented tiered lockdowns with varying degrees of stringency. Lockdown levels are typically increased when the disease spreads and reduced when the disease abates. A predictive control approach is used to develop optimized lockdown strategies for curbing the spread of COVID-19. The strategies are then applied to South African data. The South African case is of interest as the South African government has defined five distinct levels of lockdown, which serves as a discrete control input. An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios. Scenarios considered include how to flatten the curve to a level that the healthcare system can cope with, how to balance lives and livelihoods, and what impact the compliance of the population to the lockdown measures has on the spread of COVID-19. The main purpose of this paper is to show what the optimal lockdown level should be given the policy that is in place, as determined by the closed-loop feedback controller. |
1304.1865 | Alessandro Torcini Dr | Kaare Mikkelsen, Alberto Imparato, Alessandro Torcini | Emergence of slow collective oscillations in neural networks with spike
timing dependent plasticity | 8 pages, 6 figures to appear in Physical Review Letters plus
additional material | Phys. Rev. Lett. 110, 208101 (2013) | 10.1103/PhysRevLett.110.208101 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The collective dynamics of excitatory pulse coupled neurons with spike timing
dependent plasticity (STDP) is studied. The introduction of STDP induces
persistent irregular oscillations between strongly and weakly synchronized
states, reminiscent of brain activity during slow-wave sleep. We explain the
oscillations by a mechanism, the Sisyphus Effect, caused by a continuous
feedback between the synaptic adjustments and the coherence in the neural
firing. Due to this effect, the synaptic weights have oscillating equilibrium
values, and this prevents the system from relaxing into a stationary
macroscopic state.
| [
{
"created": "Sat, 6 Apr 2013 08:18:24 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Apr 2013 17:32:48 GMT",
"version": "v2"
}
] | 2013-05-21 | [
[
"Mikkelsen",
"Kaare",
""
],
[
"Imparato",
"Alberto",
""
],
[
"Torcini",
"Alessandro",
""
]
] | The collective dynamics of excitatory pulse coupled neurons with spike timing dependent plasticity (STDP) is studied. The introduction of STDP induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state. |
1310.7501 | Daniel Beard | Jason N. Bazil, Karl D. Stamm, Xing Li, Timothy J. Nelson, Aoy
Tomita-Mitchell, and Daniel A. Beard | The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart | 18 pages; 4 figures | null | 10.1371/journal.pone.0100842 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cardiac development is complex, multiscale process encompassing cell fate
adoption, differentiation and morphogenesis. To elucidate pathways underlying
this process, a recently developed algorithm to reverse engineer gene
regulatory networks was applied to time-course microarray data obtained from
the developing mouse heart. The algorithm generates many different putative
network topologies that are capable of explaining the experimental data via
model simulation. To cull specious network interactions, thousands of
topologies are merged and filtered to generate a scale-free, hierarchical
network. The network is validated with known gene interactions and used to
identify regulatory pathways critical to the developing mammalian heart. The
predicted gene interactions are prioritized using semantic similarity and gene
profile uniqueness metrics. Using these metrics, the network is expanded to
include all known mouse genes to form the most likely cardiogenic gene
regulatory network. The method outlined herein provides an informative approach
to network inference and leads to clear testable hypotheses related to gene
regulation.
| [
{
"created": "Mon, 28 Oct 2013 17:21:53 GMT",
"version": "v1"
}
] | 2017-02-08 | [
[
"Bazil",
"Jason N.",
""
],
[
"Stamm",
"Karl D.",
""
],
[
"Li",
"Xing",
""
],
[
"Nelson",
"Timothy J.",
""
],
[
"Tomita-Mitchell",
"Aoy",
""
],
[
"Beard",
"Daniel A.",
""
]
] | Cardiac development is complex, multiscale process encompassing cell fate adoption, differentiation and morphogenesis. To elucidate pathways underlying this process, a recently developed algorithm to reverse engineer gene regulatory networks was applied to time-course microarray data obtained from the developing mouse heart. The algorithm generates many different putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of topologies are merged and filtered to generate a scale-free, hierarchical network. The network is validated with known gene interactions and used to identify regulatory pathways critical to the developing mammalian heart. The predicted gene interactions are prioritized using semantic similarity and gene profile uniqueness metrics. Using these metrics, the network is expanded to include all known mouse genes to form the most likely cardiogenic gene regulatory network. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation. |
2306.10905 | Mattia Miotto | Mattia Miotto, Simone Scalise, Marco Leonetti, Giancarlo Ruocco,
Giovanna Peruzzi, Giorgio Gosti | Determining cancer cells division strategy | 16 pages, 5 figures | null | null | null | q-bio.CB physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Heterogeneity in the size distribution of cancer cell populations has been
recently linked to drug resistance and invasiveness. However, despite many
progresses have been made in understanding how such heterogeneous size
distributions arise in fast-proliferating cell types -like bacteria and yeast-,
comprehensive investigations on cancer cell populations are still lacking
mainly due to the difficulties of monitoring the proliferation of the time
scales typical of mammalian cells. From a reductionist cell dynamics point of
view, the strategies allowing size homeostasis are roughly grouped into three
classes, \emph{i.e.} timer, sizer, or adder. These strategies are empirically
distinguishable given the phenomenological measurable relationship between the
cell size at birth and at division, which requires following the proliferation
at the single-cell level.
Here, we show how it is possible to infer the growth regime and division
strategy of leukemia cell populations using live cell fluorescence labeling and
flow cytometry in combination with a quantitative analytical model where both
cell growth and division rates depend on powers of the cell size.
Using our novel approach, we found that the dynamics of the size distribution
of leukemia Jurkat T-cells is quantitatively reproduced by (i) a sizer-like
division strategy, with (ii) division times following an Erlang distribution
given by the sum of at least three independent exponentially-distributed times
and (iii) fluctuations up to 15\% of the inherited fraction of size at division
with respect to the mother cell size. Finally, we note that our experimental
and theoretical apparatus can be easily extended to other cell types and
environmental conditions, allowing for a comprehensive characterization of the
growth and division model different cells can adopt.
| [
{
"created": "Mon, 19 Jun 2023 13:05:30 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Oct 2023 10:29:25 GMT",
"version": "v2"
}
] | 2023-10-23 | [
[
"Miotto",
"Mattia",
""
],
[
"Scalise",
"Simone",
""
],
[
"Leonetti",
"Marco",
""
],
[
"Ruocco",
"Giancarlo",
""
],
[
"Peruzzi",
"Giovanna",
""
],
[
"Gosti",
"Giorgio",
""
]
] | Heterogeneity in the size distribution of cancer cell populations has been recently linked to drug resistance and invasiveness. However, despite many progresses have been made in understanding how such heterogeneous size distributions arise in fast-proliferating cell types -like bacteria and yeast-, comprehensive investigations on cancer cell populations are still lacking mainly due to the difficulties of monitoring the proliferation of the time scales typical of mammalian cells. From a reductionist cell dynamics point of view, the strategies allowing size homeostasis are roughly grouped into three classes, \emph{i.e.} timer, sizer, or adder. These strategies are empirically distinguishable given the phenomenological measurable relationship between the cell size at birth and at division, which requires following the proliferation at the single-cell level. Here, we show how it is possible to infer the growth regime and division strategy of leukemia cell populations using live cell fluorescence labeling and flow cytometry in combination with a quantitative analytical model where both cell growth and division rates depend on powers of the cell size. Using our novel approach, we found that the dynamics of the size distribution of leukemia Jurkat T-cells is quantitatively reproduced by (i) a sizer-like division strategy, with (ii) division times following an Erlang distribution given by the sum of at least three independent exponentially-distributed times and (iii) fluctuations up to 15\% of the inherited fraction of size at division with respect to the mother cell size. Finally, we note that our experimental and theoretical apparatus can be easily extended to other cell types and environmental conditions, allowing for a comprehensive characterization of the growth and division model different cells can adopt. |
2006.14304 | Irina Higgins | Irina Higgins, Le Chang, Victoria Langston, Demis Hassabis,
Christopher Summerfield, Doris Tsao, Matthew Botvinick | Unsupervised deep learning identifies semantic disentanglement in single
inferotemporal neurons | null | null | 10.1038/s41467-021-26751-5 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep supervised neural networks trained to classify objects have emerged as
popular models of computation in the primate ventral stream. These models
represent information with a high-dimensional distributed population code,
implying that inferotemporal (IT) responses are also too complex to interpret
at the single-neuron level. We challenge this view by modelling neural
responses to faces in the macaque IT with a deep unsupervised generative model,
beta-VAE. Unlike deep classifiers, beta-VAE "disentangles" sensory data into
interpretable latent factors, such as gender or hair length. We found a
remarkable correspondence between the generative factors discovered by the
model and those coded by single IT neurons. Moreover, we were able to
reconstruct face images using the signals from just a handful of cells. This
suggests that the ventral visual stream may be optimising the disentangling
objective, producing a neural code that is low-dimensional and semantically
interpretable at the single-unit level.
| [
{
"created": "Thu, 25 Jun 2020 10:50:51 GMT",
"version": "v1"
}
] | 2022-01-19 | [
[
"Higgins",
"Irina",
""
],
[
"Chang",
"Le",
""
],
[
"Langston",
"Victoria",
""
],
[
"Hassabis",
"Demis",
""
],
[
"Summerfield",
"Christopher",
""
],
[
"Tsao",
"Doris",
""
],
[
"Botvinick",
"Matthew",
""
]
] | Deep supervised neural networks trained to classify objects have emerged as popular models of computation in the primate ventral stream. These models represent information with a high-dimensional distributed population code, implying that inferotemporal (IT) responses are also too complex to interpret at the single-neuron level. We challenge this view by modelling neural responses to faces in the macaque IT with a deep unsupervised generative model, beta-VAE. Unlike deep classifiers, beta-VAE "disentangles" sensory data into interpretable latent factors, such as gender or hair length. We found a remarkable correspondence between the generative factors discovered by the model and those coded by single IT neurons. Moreover, we were able to reconstruct face images using the signals from just a handful of cells. This suggests that the ventral visual stream may be optimising the disentangling objective, producing a neural code that is low-dimensional and semantically interpretable at the single-unit level. |
1309.5076 | Wilten Nicola | Wilten Nicola and Sue Ann Campbell | Mean-Field Models for Heterogeneous Networks of Two-Dimensional
Integrate and Fire Models | 39 pages, 12 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analytically derive mean-field models for all-to-all coupled networks of
heterogeneous, adapting, two-dimensional integrate and fire neurons. The class
of models we consider includes the Izhikevich, adaptive exponential, and
quartic integrate and fire models. The heterogeneity in the parameters leads to
different moment closure assumptions that can be made in the derivation of the
mean-field model from the population density equation for the large network.
Three different moment closure assumptions lead to three different mean-field
systems. These systems can be used for distinct purposes such as bifurcation
analysis of the large networks, prediction of steady state firing rate
distributions, parameter estimation for actual neurons, and faster exploration
of the parameter space. We use the mean-field systems to analyze adaptation
induced bursting under realistic sources of heterogeneity in multiple
parameters. Our analysis demonstrates that the presence of heterogeneity causes
the Hopf bifurcation associated with the emergence of bursting to change from
sub-critical to super-critical. This is confirmed with numerical simulations of
the full network for biologically reasonable parameter values. This change
decreases the plausibility of adaptation being the cause of bursting in
hippocampal area CA3, an area with a sizable population of heavily coupled,
strongly adapting neurons.
| [
{
"created": "Thu, 19 Sep 2013 19:23:37 GMT",
"version": "v1"
}
] | 2013-09-20 | [
[
"Nicola",
"Wilten",
""
],
[
"Campbell",
"Sue Ann",
""
]
] | We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential, and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons, and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons. |
1503.07998 | Benjamin Schmid | Benjamin Schmid and Jan Huisken | Real-time multi-view deconvolution | 8 pages, 5 figures, submitted to Bioinformatics | null | null | null | q-bio.QM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In light-sheet microscopy, overall image content and resolution are improved
by acquiring and fusing multiple views of the sample from different directions.
State-of-the-art multi-view (MV) deconvolution employs the point spread
functions (PSF) of the different views to simultaneously fuse and deconvolve
the images in 3D, but processing takes a multiple of the acquisition time and
constitutes the bottleneck in the imaging pipeline. Here we show that MV
deconvolution in 3D can finally be achieved in real-time by reslicing the
acquired data and processing cross-sectional planes individually on the
massively parallel architecture of a graphics processing unit (GPU).
| [
{
"created": "Fri, 27 Mar 2015 09:17:27 GMT",
"version": "v1"
}
] | 2015-03-30 | [
[
"Schmid",
"Benjamin",
""
],
[
"Huisken",
"Jan",
""
]
] | In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions. State-of-the-art multi-view (MV) deconvolution employs the point spread functions (PSF) of the different views to simultaneously fuse and deconvolve the images in 3D, but processing takes a multiple of the acquisition time and constitutes the bottleneck in the imaging pipeline. Here we show that MV deconvolution in 3D can finally be achieved in real-time by reslicing the acquired data and processing cross-sectional planes individually on the massively parallel architecture of a graphics processing unit (GPU). |
2306.14080 | Qianqian Wang | Qianqian Wang, Wei Wang, Yuqi Fang, P.-T. Yap, Hongtu Zhu, Hong-Jun
Li, Lishan Qiao and Mingxia Liu | Leveraging Brain Modularity Prior for Interpretable Representation
Learning of fMRI | null | null | null | null | q-bio.QM cs.LG q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect
spontaneous neural activities in brain and is widely used for brain disorder
analysis.Previous studies propose to extract fMRI representations through
diverse machine/deep learning methods for subsequent analysis. But the learned
features typically lack biological interpretability, which limits their
clinical utility. From the view of graph theory, the brain exhibits a
remarkable modular structure in spontaneous brain functional networks, with
each module comprised of functionally interconnected brain regions-of-interest
(ROIs). However, most existing learning-based methods for fMRI analysis fail to
adequately utilize such brain modularity prior. In this paper, we propose a
Brain Modularity-constrained dynamic Representation learning (BMR) framework
for interpretable fMRI analysis, consisting of three major components: (1)
dynamic graph construction, (2) dynamic graph learning via a novel
modularity-constrained graph neural network(MGNN), (3) prediction and biomarker
detection for interpretable fMRI analysis. Especially, three core
neurocognitive modules (i.e., salience network, central executive network, and
default mode network) are explicitly incorporated into the MGNN, encouraging
the nodes/ROIs within the same module to share similar representations. To
further enhance discriminative ability of learned features, we also encourage
the MGNN to preserve the network topology of input graphs via a graph topology
reconstruction constraint. Experimental results on 534 subjects with rs-fMRI
scans from two datasets validate the effectiveness of the proposed method. The
identified discriminative brain ROIs and functional connectivities can be
regarded as potential fMRI biomarkers to aid in clinical diagnosis.
| [
{
"created": "Sat, 24 Jun 2023 23:45:47 GMT",
"version": "v1"
}
] | 2023-06-27 | [
[
"Wang",
"Qianqian",
""
],
[
"Wang",
"Wei",
""
],
[
"Fang",
"Yuqi",
""
],
[
"Yap",
"P. -T.",
""
],
[
"Zhu",
"Hongtu",
""
],
[
"Li",
"Hong-Jun",
""
],
[
"Qiao",
"Lishan",
""
],
[
"Liu",
"Mingxia",
""
]
] | Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in brain and is widely used for brain disorder analysis.Previous studies propose to extract fMRI representations through diverse machine/deep learning methods for subsequent analysis. But the learned features typically lack biological interpretability, which limits their clinical utility. From the view of graph theory, the brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, most existing learning-based methods for fMRI analysis fail to adequately utilize such brain modularity prior. In this paper, we propose a Brain Modularity-constrained dynamic Representation learning (BMR) framework for interpretable fMRI analysis, consisting of three major components: (1) dynamic graph construction, (2) dynamic graph learning via a novel modularity-constrained graph neural network(MGNN), (3) prediction and biomarker detection for interpretable fMRI analysis. Especially, three core neurocognitive modules (i.e., salience network, central executive network, and default mode network) are explicitly incorporated into the MGNN, encouraging the nodes/ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we also encourage the MGNN to preserve the network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis. |
2008.00131 | Rebekah Rogers | Rebekah L. Rogers, Stephanie L. Grizzard, James E. Titus-McQuillan,
Katherine Bockrath, Sagar Patel, John P. Wares, Jeffrey T. Garner, Cathy C.
Moore | Gene family amplification facilitates adaptation in freshwater Unionid
bivalve Megalonaias nervosa | Main text 42 pages, 1 table 8 figures; SI 12 pages, 8 tables, 2
figures; Gene tree phylogenies added to directly address incomplete lineage
sorting | null | 10.1111/mec.15786 | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As organisms are faced with intense rapidly changing selective pressures, new
genetic material is required to facilitate adaptation. Among sources of genetic
novelty, gene duplications and transposable elements (TEs) offer new genes or
new regulatory patterns that can facilitate evolutionary change. With advances
in genome sequencing it is possible to gain a broader view of how gene family
proliferation and TE content evolve in non-model species when populations
become threatened. Freshwater bivalves (Unionidae) currently face severe
anthropogenic challenges. Over 70% of species in the United States are
threatened, endangered or extinct due to pollution, damming of waterways, and
overfishing. We have created a reference genome for M. nervosa to determine how
genome content has evolved in the face of these widespread environmental
challenges. We observe a burst of recent transposable element proliferation
causing a 382 Mb expansion in genome content. Gene family expansion is common,
with a duplication rate of 1.16 x 10^-8 per gene per generation. Cytochrome
P450, ABC transporters, Hsp70 genes, von Willebrand proteins, chitin metabolism
genes, mitochondria eating proteins, and opsin gene families have experienced
significantly greater amplification and show signatures of selection. We use
evolutionary theory to assess the relative contribution of SNPs and
duplications in evolutionary change. Estimates suggest that gene family
evolution may offer an exceptional substrate of genetic variation in M.
nervosa, with Psgv=0.185 compared with Psgv=0.067 for single nucleotide
changes. Hence, we suggest that gene family evolution is a source of "hopeful
monsters" within the genome that facilitate adaptation.
| [
{
"created": "Sat, 1 Aug 2020 00:22:50 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Nov 2020 15:33:19 GMT",
"version": "v2"
}
] | 2021-01-06 | [
[
"Rogers",
"Rebekah L.",
""
],
[
"Grizzard",
"Stephanie L.",
""
],
[
"Titus-McQuillan",
"James E.",
""
],
[
"Bockrath",
"Katherine",
""
],
[
"Patel",
"Sagar",
""
],
[
"Wares",
"John P.",
""
],
[
"Garner",
"Jeffrey T.",
""
],
[
"Moore",
"Cathy C.",
""
]
] | As organisms are faced with intense rapidly changing selective pressures, new genetic material is required to facilitate adaptation. Among sources of genetic novelty, gene duplications and transposable elements (TEs) offer new genes or new regulatory patterns that can facilitate evolutionary change. With advances in genome sequencing it is possible to gain a broader view of how gene family proliferation and TE content evolve in non-model species when populations become threatened. Freshwater bivalves (Unionidae) currently face severe anthropogenic challenges. Over 70% of species in the United States are threatened, endangered or extinct due to pollution, damming of waterways, and overfishing. We have created a reference genome for M. nervosa to determine how genome content has evolved in the face of these widespread environmental challenges. We observe a burst of recent transposable element proliferation causing a 382 Mb expansion in genome content. Gene family expansion is common, with a duplication rate of 1.16 x 10^-8 per gene per generation. Cytochrome P450, ABC transporters, Hsp70 genes, von Willebrand proteins, chitin metabolism genes, mitochondria eating proteins, and opsin gene families have experienced significantly greater amplification and show signatures of selection. We use evolutionary theory to assess the relative contribution of SNPs and duplications in evolutionary change. Estimates suggest that gene family evolution may offer an exceptional substrate of genetic variation in M. nervosa, with Psgv=0.185 compared with Psgv=0.067 for single nucleotide changes. Hence, we suggest that gene family evolution is a source of "hopeful monsters" within the genome that facilitate adaptation. |
2406.00382 | Michele Piana | Davide Parodi, Edoardo Dighero, Giorgia Biddau, Francesca D'Amico,
Matteo Bauckneht, Cecilia Marini, Sara Garbarino, Cristina Campi, Michele
Piana, Gianmario Sambuceti | Localized FDG loss in lung cancer lesions | null | null | null | null | q-bio.TO stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: Analysis of [18F]-Fluorodeoxyglucose (FDG) kinetics in cancer has
been most often limited to the evaluation of the average uptake over relatively
large volumes. Nevertheless, tumor lesion almost contains inflammatory
infiltrates whose cells are characterized by a significant radioactivity
washout due to the hydrolysis of FDG-6P catalyzed by glucose-6P phosphatase.
The present study aimed to verify whether voxel-wise compartmental analysis of
dynamic imaging can identify tumor regions characterized by tracer washout.
Materials & Methods: The study included 11 patients with lung cancer submitted
to PET/CT imaging for staging purposes. Tumor was defined by drawing a volume
of interest loosely surrounding the lesion and considering all inside voxels
with standardized uptake value (SUV) >40% of the maximum. After 20 minutes
dynamic imaging centered on the heart, eight whole body scans were repeated.
Six parametric maps were progressively generated by computing six regression
lines that considered all eight frames, the last seven ones, and so on, up to
the last three. Results: Progressively delaying the starting point of
regression line computation identified a progressive increase in the prevalence
of voxels with a negative slope. Conclusions: The voxel-wise parametric maps
provided by compartmental analysis permits to identify a measurable volume
characterized by radioactivity washout. The spatial localization of this
pattern is compatible with the recognized preferential site of inflammatory
infiltrates populating the tumor stroma and might improve the power of FDG
imaging in monitoring the effectiveness of treatments aimed to empower the host
immune response against the cancer.
| [
{
"created": "Sat, 1 Jun 2024 09:41:48 GMT",
"version": "v1"
}
] | 2024-06-04 | [
[
"Parodi",
"Davide",
""
],
[
"Dighero",
"Edoardo",
""
],
[
"Biddau",
"Giorgia",
""
],
[
"D'Amico",
"Francesca",
""
],
[
"Bauckneht",
"Matteo",
""
],
[
"Marini",
"Cecilia",
""
],
[
"Garbarino",
"Sara",
""
],
[
"Campi",
"Cristina",
""
],
[
"Piana",
"Michele",
""
],
[
"Sambuceti",
"Gianmario",
""
]
] | Purpose: Analysis of [18F]-Fluorodeoxyglucose (FDG) kinetics in cancer has been most often limited to the evaluation of the average uptake over relatively large volumes. Nevertheless, tumor lesion almost contains inflammatory infiltrates whose cells are characterized by a significant radioactivity washout due to the hydrolysis of FDG-6P catalyzed by glucose-6P phosphatase. The present study aimed to verify whether voxel-wise compartmental analysis of dynamic imaging can identify tumor regions characterized by tracer washout. Materials & Methods: The study included 11 patients with lung cancer submitted to PET/CT imaging for staging purposes. Tumor was defined by drawing a volume of interest loosely surrounding the lesion and considering all inside voxels with standardized uptake value (SUV) >40% of the maximum. After 20 minutes dynamic imaging centered on the heart, eight whole body scans were repeated. Six parametric maps were progressively generated by computing six regression lines that considered all eight frames, the last seven ones, and so on, up to the last three. Results: Progressively delaying the starting point of regression line computation identified a progressive increase in the prevalence of voxels with a negative slope. Conclusions: The voxel-wise parametric maps provided by compartmental analysis permits to identify a measurable volume characterized by radioactivity washout. The spatial localization of this pattern is compatible with the recognized preferential site of inflammatory infiltrates populating the tumor stroma and might improve the power of FDG imaging in monitoring the effectiveness of treatments aimed to empower the host immune response against the cancer. |
2005.09108 | Sebastian Lotter | Sebastian Lotter, Arman Ahmadzadeh, Robert Schober | Synaptic Channel Modeling for DMC: Neurotransmitter Uptake and Spillover
in the Tripartite Synapse | 42 pages, 8 figures, 1 table. Accepted for publication in IEEE
Transactions on Communications. This article is the extended version of the
conference paper arXiv:1912.04025 | null | 10.1109/TCOMM.2020.3040318 | null | q-bio.SC cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Diffusive Molecular Communication (DMC), information is transmitted by
diffusing molecules. Synaptic signaling, as a natural implementation of this
paradigm, encompasses functional components that, once understood, can
facilitate the development of synthetic DMC systems. To unleash this potential,
however, a thorough understanding of the synaptic communication channel based
on biophysical principles is needed. Since synaptic transmission critically
depends also on non-neural cells, such understanding requires the consideration
of the so-called tripartite synapse. In this paper, we develop a comprehensive
channel model of the tripartite synapse encompassing a three-dimensional,
finite-size spatial model of the synaptic cleft, molecule uptake at the
presynaptic neuron and at glial cells, reversible binding to individual
receptors at the postsynaptic neuron, and spillover to the extrasynaptic space.
Based on this model, we derive analytical time domain expressions for the
channel impulse response (CIR) of the synaptic DMC system and for the number of
molecules taken up at the presynaptic neuron and at glial cells, respectively.
These expressions provide insight into the impact of macroscopic physical
channel parameters on the decay rate of the CIR and the reuptake rate, and
reveal fundamental limits for synaptic signal transmission induced by chemical
reaction kinetics and the channel geometry. Adapted to realistic parameters,
our model produces plausible results when compared to experimental and
simulation studies and we provide results from particle-based computer
simulations to further validate the analytical model. The proposed
comprehensive channel model admits a wide range of synaptic configurations
making it suitable for the investigation of many practically relevant
questions, such as the impact of glial cell uptake and spillover on signal
transmission in the tripartite synapse.
| [
{
"created": "Mon, 18 May 2020 21:52:56 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Nov 2020 12:26:08 GMT",
"version": "v2"
}
] | 2020-11-26 | [
[
"Lotter",
"Sebastian",
""
],
[
"Ahmadzadeh",
"Arman",
""
],
[
"Schober",
"Robert",
""
]
] | In Diffusive Molecular Communication (DMC), information is transmitted by diffusing molecules. Synaptic signaling, as a natural implementation of this paradigm, encompasses functional components that, once understood, can facilitate the development of synthetic DMC systems. To unleash this potential, however, a thorough understanding of the synaptic communication channel based on biophysical principles is needed. Since synaptic transmission critically depends also on non-neural cells, such understanding requires the consideration of the so-called tripartite synapse. In this paper, we develop a comprehensive channel model of the tripartite synapse encompassing a three-dimensional, finite-size spatial model of the synaptic cleft, molecule uptake at the presynaptic neuron and at glial cells, reversible binding to individual receptors at the postsynaptic neuron, and spillover to the extrasynaptic space. Based on this model, we derive analytical time domain expressions for the channel impulse response (CIR) of the synaptic DMC system and for the number of molecules taken up at the presynaptic neuron and at glial cells, respectively. These expressions provide insight into the impact of macroscopic physical channel parameters on the decay rate of the CIR and the reuptake rate, and reveal fundamental limits for synaptic signal transmission induced by chemical reaction kinetics and the channel geometry. Adapted to realistic parameters, our model produces plausible results when compared to experimental and simulation studies and we provide results from particle-based computer simulations to further validate the analytical model. The proposed comprehensive channel model admits a wide range of synaptic configurations making it suitable for the investigation of many practically relevant questions, such as the impact of glial cell uptake and spillover on signal transmission in the tripartite synapse. |
1212.4996 | Alberto d'Onofrio | Sebastiano de Franciscis and Alberto d'Onofrio | Cellular polarization: interaction between extrinsic bounded noises and
wave-pinning mechanism | 21 pages, 9 figures | null | 10.1103/PhysRevE.88.032709 | null | q-bio.MN cond-mat.stat-mech nlin.PS q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cued and un-cued cell polarization is a fundamental mechanism in cell
biology. As an alternative to the classical Turing bifurcation, it has been
proposed that the cell polarity might onset by means of the well-known
phenomenon of wave-pinning (Gamba et al, PNAS, 2005). A particularly simple and
elegant model of wave-pinning has been proposed by Edelstein-Keshet and
coworkers (Biop. J., 2008). However, biomolecular networks do communicate with
other networks as well as with the external world. As such, their dynamics has
to be considered as perturbed by extrinsic noises. These noises may have both a
spatial and a temporal correlation, but any case they must be bounded to
preserve the biological meaningfulness of the perturbed parameters. Here we
numerically show that the inclusion of external spatio-temporal bounded
perturbations may sometime destroy the polarized state. The polarization loss
depends on both the extent of temporal and spatial correlations, and on the
kind of adopted noise. Namely, independently of the specific model of noise, an
increase of the spatial correlation induces an increase of the probability of
polarization. However, if the noise is spatially homogeneous then the
polarization is lost in the majority of cases. On the contrary, an increase of
the temporal autocorrelation of the noise induces an effect that depends on the
noise model.
| [
{
"created": "Thu, 20 Dec 2012 11:57:57 GMT",
"version": "v1"
}
] | 2015-06-12 | [
[
"de Franciscis",
"Sebastiano",
""
],
[
"d'Onofrio",
"Alberto",
""
]
] | Cued and un-cued cell polarization is a fundamental mechanism in cell biology. As an alternative to the classical Turing bifurcation, it has been proposed that the cell polarity might onset by means of the well-known phenomenon of wave-pinning (Gamba et al, PNAS, 2005). A particularly simple and elegant model of wave-pinning has been proposed by Edelstein-Keshet and coworkers (Biop. J., 2008). However, biomolecular networks do communicate with other networks as well as with the external world. As such, their dynamics has to be considered as perturbed by extrinsic noises. These noises may have both a spatial and a temporal correlation, but any case they must be bounded to preserve the biological meaningfulness of the perturbed parameters. Here we numerically show that the inclusion of external spatio-temporal bounded perturbations may sometime destroy the polarized state. The polarization loss depends on both the extent of temporal and spatial correlations, and on the kind of adopted noise. Namely, independently of the specific model of noise, an increase of the spatial correlation induces an increase of the probability of polarization. However, if the noise is spatially homogeneous then the polarization is lost in the majority of cases. On the contrary, an increase of the temporal autocorrelation of the noise induces an effect that depends on the noise model. |
2209.01792 | Tobias Paul | Jochen Blath, Tobias Paul, Andr\'as Tobi\'as, Maite Wilke Berenguer | The impact of dormancy on evolutionary branching | 17 pages, 6 figures | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate the consequences of dormancy in the `rare
mutation' and `large population' regime of stochastic adaptive dynamics.
Starting from an individual-based micro-model, we first derive the polymorphic
evolution sequence of the population, based on previous work by Baar and Bovier
(2018). After passing to a second `small mutations' limit, we arrive at the
canonical equation of adaptive dynamics, and state a corresponding criterion
for evolutionary branching, extending a previous result of Champagnat and
M\'el\'eard (2011).
The criterion allows a quantitative and qualitative analysis of the effects
of dormancy in the well-known model of Dieckmann and Doebeli (1999) for
sympatric speciation. In fact, a quite intuitive picture merges: Dormancy
enlarges the parameter range for evolutionary branching, increases the carrying
capacity and niche width of the post-branching sub-populations, and, depending
on the model parameters, can either increase or decrease the `speed of
adaptation' of populations. Finally, dormancy increases diversity by increasing
the genetic distance between subpopulations.
| [
{
"created": "Mon, 5 Sep 2022 07:03:02 GMT",
"version": "v1"
}
] | 2022-09-07 | [
[
"Blath",
"Jochen",
""
],
[
"Paul",
"Tobias",
""
],
[
"Tobiás",
"András",
""
],
[
"Berenguer",
"Maite Wilke",
""
]
] | In this paper, we investigate the consequences of dormancy in the `rare mutation' and `large population' regime of stochastic adaptive dynamics. Starting from an individual-based micro-model, we first derive the polymorphic evolution sequence of the population, based on previous work by Baar and Bovier (2018). After passing to a second `small mutations' limit, we arrive at the canonical equation of adaptive dynamics, and state a corresponding criterion for evolutionary branching, extending a previous result of Champagnat and M\'el\'eard (2011). The criterion allows a quantitative and qualitative analysis of the effects of dormancy in the well-known model of Dieckmann and Doebeli (1999) for sympatric speciation. In fact, a quite intuitive picture merges: Dormancy enlarges the parameter range for evolutionary branching, increases the carrying capacity and niche width of the post-branching sub-populations, and, depending on the model parameters, can either increase or decrease the `speed of adaptation' of populations. Finally, dormancy increases diversity by increasing the genetic distance between subpopulations. |
1605.07792 | Mark Leake | Zhaokun Zhou, Mark C. Leake | Force spectroscopy in studying infection | null | null | null | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biophysical force spectroscopy tools - for example optical tweezers, magnetic
tweezers, atomic force microscopy, - have been used to study elastic,
mechanical, conformational and dynamic properties of single biological
specimens from single proteins to whole cells to reveal information not
accessible by ensemble average methods such as X-ray crystallography, mass
spectroscopy, gel electrophoresis and so on. Here we review the application of
these tools on a range of infection-related questions from antibody-inhibited
protein processivity to virus-cell adhesion. In each case we focus on how the
instrumental design tailored to the biological system in question translates
into the functionality suitable for that particular study. The unique insights
that force spectroscopy has gained to complement knowledge learned through
population averaging techniques in interrogating biomolecular details prove to
be instrumental in therapeutic innovations such as those in structure-based
drug design.
| [
{
"created": "Wed, 25 May 2016 09:19:50 GMT",
"version": "v1"
}
] | 2016-06-09 | [
[
"Zhou",
"Zhaokun",
""
],
[
"Leake",
"Mark C.",
""
]
] | Biophysical force spectroscopy tools - for example optical tweezers, magnetic tweezers, atomic force microscopy, - have been used to study elastic, mechanical, conformational and dynamic properties of single biological specimens from single proteins to whole cells to reveal information not accessible by ensemble average methods such as X-ray crystallography, mass spectroscopy, gel electrophoresis and so on. Here we review the application of these tools on a range of infection-related questions from antibody-inhibited protein processivity to virus-cell adhesion. In each case we focus on how the instrumental design tailored to the biological system in question translates into the functionality suitable for that particular study. The unique insights that force spectroscopy has gained to complement knowledge learned through population averaging techniques in interrogating biomolecular details prove to be instrumental in therapeutic innovations such as those in structure-based drug design. |
1308.2547 | Bal\'azs Madas | Bal\'azs G. Madas, Katalin Varga | Biophysical modelling of the effects of inhaled radon progeny on the
bronchial epithelium for the estimation of the relationships applied in the
two stage clonal expansion model of carcinogenesis | paper presented in the conference EPRBioDose2013 (Leiden, the
Netherlands, March 24-28, 2013) and submitted to Radiation Protection
Dosimetry published by Oxford University Press, 9 pages, 4 figures | null | 10.1093/rpd/ncu125 | null | q-bio.TO physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is a considerable debate between research groups applying the two stage
clonal expansion model for lung cancer risk estimation, whether radon exposure
affects initiation and transformation or promotion. The objective of the
present study is to quantify the effects of radon progeny on these stages with
biophysical models. For this purpose, numerical models of mutation induction
and clonal growth were applied in order to estimate how initiation,
transformation and promotion rates depend on tissue dose rate. It was found
that rates of initiation and transformation increase monotonically with dose
rate, while effective promotion rate decreases with time, but increases in a
supralinear fashion with dose rate. Despite the uncertainty of the results due
to the lack of experimental data, present study suggests that effects of radon
exposure on both mutational events and clonal growth are significant, and
should be considered in epidemiological analyses applying mathematical models
of carcinogenesis.
| [
{
"created": "Mon, 12 Aug 2013 13:03:27 GMT",
"version": "v1"
}
] | 2014-04-23 | [
[
"Madas",
"Balázs G.",
""
],
[
"Varga",
"Katalin",
""
]
] | There is a considerable debate between research groups applying the two stage clonal expansion model for lung cancer risk estimation, whether radon exposure affects initiation and transformation or promotion. The objective of the present study is to quantify the effects of radon progeny on these stages with biophysical models. For this purpose, numerical models of mutation induction and clonal growth were applied in order to estimate how initiation, transformation and promotion rates depend on tissue dose rate. It was found that rates of initiation and transformation increase monotonically with dose rate, while effective promotion rate decreases with time, but increases in a supralinear fashion with dose rate. Despite the uncertainty of the results due to the lack of experimental data, present study suggests that effects of radon exposure on both mutational events and clonal growth are significant, and should be considered in epidemiological analyses applying mathematical models of carcinogenesis. |
1907.03223 | Johannes Kleiner | Johannes Kleiner | Mathematical Models of Consciousness | null | null | 10.3390/e22060609 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, promising mathematical models have been suggested which aim
to describe conscious experience and its relation to the physical domain.
Whereas the axioms and metaphysical ideas of these theories have been carefully
motivated, their mathematical formalism has not. In this article we aim to
remedy this situation. We give an account of what warrants mathematical
representation of phenomenal experience, derive a general mathematical
framework which takes into account consciousness' epistemic context and study
which mathematical structures some of the key characteristics of conscious
experience imply, showing precisely where mathematical approaches allow to go
beyond what the standard methodology can do. The result is a general
mathematical framework for models of consciousness that can be employed in the
theory-building process.
| [
{
"created": "Sun, 7 Jul 2019 05:35:24 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Apr 2020 10:44:33 GMT",
"version": "v2"
}
] | 2020-07-15 | [
[
"Kleiner",
"Johannes",
""
]
] | In recent years, promising mathematical models have been suggested which aim to describe conscious experience and its relation to the physical domain. Whereas the axioms and metaphysical ideas of these theories have been carefully motivated, their mathematical formalism has not. In this article we aim to remedy this situation. We give an account of what warrants mathematical representation of phenomenal experience, derive a general mathematical framework which takes into account consciousness' epistemic context and study which mathematical structures some of the key characteristics of conscious experience imply, showing precisely where mathematical approaches allow to go beyond what the standard methodology can do. The result is a general mathematical framework for models of consciousness that can be employed in the theory-building process. |
0906.0756 | Jorge F. Mejias | Jorge F. Mejias, Joaquin J. Torres | Emergence of resonances in neural systems: the interplay between
threshold adaptation and short-term synaptic plasticity | 24 pages, 8 figures, submitted to Neural Computation | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we study the detection of weak stimuli by spiking neurons in the
presence of certain level of noisy background neural activity. Our study has
focused in the realistic assumption that the synapses in the network present
activity-dependent processes, such as short-term synaptic depression and
facilitation. Employing mean-field techniques as well as numerical simulations,
we found that there are two possible noise levels which optimize signal
transmission. This new finding is in contrast with the classical theory of
stochastic resonance which is able to predict only one optimal level of noise.
We found that the complex interplay between the nonlinear dynamics of the
neuron threshold and the activity-dependent synaptic mechanisms is responsible
for this new phenomenology. Our results are confirmed by employing a more
realistic FitzHugh-Nagumo neuron model, which displays threshold variability,
as well as by considering more realistic synaptic models. We support our
findings with recent experimental data of stochastic resonance in the human
tactile blink reflex.
| [
{
"created": "Wed, 3 Jun 2009 17:58:27 GMT",
"version": "v1"
}
] | 2009-06-04 | [
[
"Mejias",
"Jorge F.",
""
],
[
"Torres",
"Joaquin J.",
""
]
] | In this work we study the detection of weak stimuli by spiking neurons in the presence of certain level of noisy background neural activity. Our study has focused in the realistic assumption that the synapses in the network present activity-dependent processes, such as short-term synaptic depression and facilitation. Employing mean-field techniques as well as numerical simulations, we found that there are two possible noise levels which optimize signal transmission. This new finding is in contrast with the classical theory of stochastic resonance which is able to predict only one optimal level of noise. We found that the complex interplay between the nonlinear dynamics of the neuron threshold and the activity-dependent synaptic mechanisms is responsible for this new phenomenology. Our results are confirmed by employing a more realistic FitzHugh-Nagumo neuron model, which displays threshold variability, as well as by considering more realistic synaptic models. We support our findings with recent experimental data of stochastic resonance in the human tactile blink reflex. |
2008.08667 | Bernadette Stolz | Bernadette J. Stolz, Jakob Kaeppler, Bostjan Markelc, Franziska Mech,
Florian Lipsmeier, Ruth J. Muschel, Helen M. Byrne, Heather A. Harrington | Multiscale Topology Characterises Dynamic Tumour Vascular Networks | null | null | null | null | q-bio.QM math.AT q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Advances in imaging techniques enable high resolution 3D visualisation of
vascular networks over time and reveal abnormal structural features such as
twists and loops, and their quantification is an active area of research. Here
we showcase how topological data analysis (TDA), the mathematical field that
studies `shape' of data, can characterise the geometric, spatial and temporal
organisation of vascular networks. We propose two topological lenses to study
vasculature, which capture inherent multi-scale features and vessel
connectivity, and surpass the single scale analysis of existing methods. We
analyse images collected using intravital and ultramicroscopy modalities and
quantify spatio-temporal variation of twists, loops, and avascular regions
(voids) in 3D vascular networks. This topological approach validates and
quantifies known qualitative trends such as dynamic changes in tortuosity and
loops in response to antibodies that modulate vessel sprouting; furthermore, it
quantifies the effect of radiotherapy on vessel architecture.
| [
{
"created": "Wed, 19 Aug 2020 21:06:27 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Mar 2022 16:23:33 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Apr 2022 20:06:24 GMT",
"version": "v3"
}
] | 2022-04-28 | [
[
"Stolz",
"Bernadette J.",
""
],
[
"Kaeppler",
"Jakob",
""
],
[
"Markelc",
"Bostjan",
""
],
[
"Mech",
"Franziska",
""
],
[
"Lipsmeier",
"Florian",
""
],
[
"Muschel",
"Ruth J.",
""
],
[
"Byrne",
"Helen M.",
""
],
[
"Harrington",
"Heather A.",
""
]
] | Advances in imaging techniques enable high resolution 3D visualisation of vascular networks over time and reveal abnormal structural features such as twists and loops, and their quantification is an active area of research. Here we showcase how topological data analysis (TDA), the mathematical field that studies `shape' of data, can characterise the geometric, spatial and temporal organisation of vascular networks. We propose two topological lenses to study vasculature, which capture inherent multi-scale features and vessel connectivity, and surpass the single scale analysis of existing methods. We analyse images collected using intravital and ultramicroscopy modalities and quantify spatio-temporal variation of twists, loops, and avascular regions (voids) in 3D vascular networks. This topological approach validates and quantifies known qualitative trends such as dynamic changes in tortuosity and loops in response to antibodies that modulate vessel sprouting; furthermore, it quantifies the effect of radiotherapy on vessel architecture. |
1301.2694 | Ian Dworkin | Chris H. Chandler, Sudarshan Chari and Ian Dworkin | Does your gene need a background check? How genetic background impacts
the analysis of mutations, genes, and evolution | In review at Trends in Genetics | Trends in Genetics 2013 29(6):358-366 | 10.1016/j.tig.2013.01.009 | null | q-bio.GN q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The premise of genetic analysis is that a causal link exists between
phenotypic and allelic variation. Yet it has long been documented that mutant
phenotypes are not a simple result of a single DNA lesion, but rather are due
to interactions of the focal allele with other genes and the environment.
Although an experimentally rigorous approach, focusing on individual mutations
and isogenic control strains, has facilitated amazing progress within genetics
and related fields, a glimpse back suggests that a vast complexity has been
omitted from our current understanding of allelic effects. Armed with
traditional genetic analyses and the foundational knowledge they have provided,
we argue that the time and tools are ripe to return to the under-explored
aspects of gene function and embrace the context-dependent nature of genetic
effects. We assert that a broad understanding of genetic effects and the
evolutionary dynamics of alleles requires identifying how mutational outcomes
depend upon the wild-type genetic background. Furthermore, we discuss how best
to exploit genetic background effects to broaden genetic research programs.
| [
{
"created": "Sat, 12 Jan 2013 15:59:35 GMT",
"version": "v1"
}
] | 2014-06-04 | [
[
"Chandler",
"Chris H.",
""
],
[
"Chari",
"Sudarshan",
""
],
[
"Dworkin",
"Ian",
""
]
] | The premise of genetic analysis is that a causal link exists between phenotypic and allelic variation. Yet it has long been documented that mutant phenotypes are not a simple result of a single DNA lesion, but rather are due to interactions of the focal allele with other genes and the environment. Although an experimentally rigorous approach, focusing on individual mutations and isogenic control strains, has facilitated amazing progress within genetics and related fields, a glimpse back suggests that a vast complexity has been omitted from our current understanding of allelic effects. Armed with traditional genetic analyses and the foundational knowledge they have provided, we argue that the time and tools are ripe to return to the under-explored aspects of gene function and embrace the context-dependent nature of genetic effects. We assert that a broad understanding of genetic effects and the evolutionary dynamics of alleles requires identifying how mutational outcomes depend upon the wild-type genetic background. Furthermore, we discuss how best to exploit genetic background effects to broaden genetic research programs. |
2301.08698 | Laurent Evain | Laurent Evain, Jean-Jacques Loeb | Geometric approach for non pharmaceutical interventions in epidemiology | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Various non pharmaceutical interventions have been settled to minimise the
burden of the COVID-19 outbreak. We build a framework to analyse the dynamics
of non pharmaceutical interventions, to distinguish between mitigations
measures leading to objective scientific improvements and mitigations based on
both political and scientific considerations. We analyse two possible
strategies within this framework. Namely, we consider mitigations driven by the
limited resources of the health system and mitigations where a constant set of
measures is applied at different moments. We describe the optimal interventions
for these scenarios. Our approach is mathematical and involves sir differential
systems, it is qualitative and geometrical rather than computational. Along
with the analysis of these scenarios, we collect several results that may be
useful on their own, in particular on the ground when the variables are not
known in real time.
| [
{
"created": "Fri, 20 Jan 2023 17:35:47 GMT",
"version": "v1"
}
] | 2023-01-23 | [
[
"Evain",
"Laurent",
""
],
[
"Loeb",
"Jean-Jacques",
""
]
] | Various non pharmaceutical interventions have been settled to minimise the burden of the COVID-19 outbreak. We build a framework to analyse the dynamics of non pharmaceutical interventions, to distinguish between mitigations measures leading to objective scientific improvements and mitigations based on both political and scientific considerations. We analyse two possible strategies within this framework. Namely, we consider mitigations driven by the limited resources of the health system and mitigations where a constant set of measures is applied at different moments. We describe the optimal interventions for these scenarios. Our approach is mathematical and involves sir differential systems, it is qualitative and geometrical rather than computational. Along with the analysis of these scenarios, we collect several results that may be useful on their own, in particular on the ground when the variables are not known in real time. |
2106.11799 | Mos\`e Manni | Mos\`e Manni, Matthew R Berkeley, Mathieu Seppey, Felipe A Simao,
Evgeny M Zdobnov | BUSCO update: novel and streamlined workflows along with broader and
deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and
viral genomes | for supplementary files, see
https://gitlab.com/ezlab/busco_preprint_2021 | null | null | null | q-bio.GN q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Methods for evaluating the quality of genomic and metagenomic data are
essential to aid genome assembly and to correctly interpret the results of
subsequent analyses. BUSCO estimates the completeness and redundancy of
processed genomic data based on universal single-copy orthologs. Here we
present new functionalities and major improvements of the BUSCO software, as
well as the renewal and expansion of the underlying datasets in sync with the
OrthoDB v10 release. Among the major novelties, BUSCO now enables phylogenetic
placement of the input sequence to automatically select the most appropriate
dataset for the assessment, allowing the analysis of metagenome-assembled
genomes of unknown origin. A newly-introduced genome workflow increases the
efficiency and runtimes especially on large eukaryotic genomes. BUSCO is the
only tool capable of assessing both eukaryotic and prokaryotic species, and can
be applied to various data types, from genome assemblies and metagenomic bins,
to transcriptomes and gene sets.
| [
{
"created": "Tue, 22 Jun 2021 14:12:18 GMT",
"version": "v1"
}
] | 2021-06-23 | [
[
"Manni",
"Mosè",
""
],
[
"Berkeley",
"Matthew R",
""
],
[
"Seppey",
"Mathieu",
""
],
[
"Simao",
"Felipe A",
""
],
[
"Zdobnov",
"Evgeny M",
""
]
] | Methods for evaluating the quality of genomic and metagenomic data are essential to aid genome assembly and to correctly interpret the results of subsequent analyses. BUSCO estimates the completeness and redundancy of processed genomic data based on universal single-copy orthologs. Here we present new functionalities and major improvements of the BUSCO software, as well as the renewal and expansion of the underlying datasets in sync with the OrthoDB v10 release. Among the major novelties, BUSCO now enables phylogenetic placement of the input sequence to automatically select the most appropriate dataset for the assessment, allowing the analysis of metagenome-assembled genomes of unknown origin. A newly-introduced genome workflow increases the efficiency and runtimes especially on large eukaryotic genomes. BUSCO is the only tool capable of assessing both eukaryotic and prokaryotic species, and can be applied to various data types, from genome assemblies and metagenomic bins, to transcriptomes and gene sets. |
1608.02621 | Bohdan Khomtchouk | Bohdan B. Khomtchouk, Edmund Weitz, Claes Wahlestedt | The Machine that Builds Itself: How the Strengths of Lisp Family
Languages Facilitate Building Complex and Flexible Bioinformatic Models | 9 pages | Briefings in Bioinformatics, Volume 19, Issue 3, 2018, pp. 537-543 | 10.1093/bib/bbw130 | null | q-bio.OT cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the need for expanding the presence of the Lisp family of
programming languages in bioinformatics and computational biology research.
Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the
creation of powerful and flexible software models that are required for complex
and rapidly evolving domains like biology. We will point out several important
key features that distinguish languages of the Lisp family from other
programming languages and we will explain how these features can aid
researchers in becoming more productive and creating better code. We will also
show how these features make these languages ideal tools for artificial
intelligence and machine learning applications. We will specifically stress the
advantages of domain-specific languages (DSL): languages which are specialized
to a particular area and thus not only facilitate easier research problem
formulation, but also aid in the establishment of standards and best
programming practices as applied to the specific research field at hand. DSLs
are particularly easy to build in Common Lisp, the most comprehensive Lisp
dialect, which is commonly referred to as the "programmable programming
language." We are convinced that Lisp grants programmers unprecedented power to
build increasingly sophisticated artificial intelligence systems that may
ultimately transform machine learning and AI research in bioinformatics and
computational biology.
| [
{
"created": "Mon, 8 Aug 2016 20:58:32 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Sep 2016 03:29:49 GMT",
"version": "v2"
}
] | 2021-02-03 | [
[
"Khomtchouk",
"Bohdan B.",
""
],
[
"Weitz",
"Edmund",
""
],
[
"Wahlestedt",
"Claes",
""
]
] | We address the need for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the creation of powerful and flexible software models that are required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the "programmable programming language." We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. |
1206.3340 | Sree Vamsee Chintapalli dr | Sree V. Chintapalli, Gaurav Bhardwaj, Jagadish Babu, Loukia
Hadjiyianni, Yoojin Hong, Zhenhai Zhang, Xiaofan Zhou, Hong Ma, Andriy
Anishkin, Damian B. van Rossum, Randen L. Patterson | Extraction of Deep Phylogenetic Signal and Improved Resolution of
Evolutionary Events within the recA/RAD51 Phylogeny | 21 pages, 11 figures, 1 table | null | null | null | q-bio.GN q-bio.MN q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recA/RAD51 gene family encodes a diverse set of recombinase proteins that
effect homologous recombination, DNA-repair, and genome stability. The recA
gene family is expressed in almost all species of Eubacteria, Archaea, and
Eukaryotes, and even in some viruses. To date, efforts to resolve the deep
evolutionary origins of this ancient protein family have been hindered, in
part, by the high sequence divergence between families (i.e. ~30% identity
between paralogous groups). Through (i) large taxon sampling, (ii) the use of a
phylogenetic algorithm designed for measuring highly divergent paralogs, and
(iii) novel Evolutionary Spatial Dynamics simulation and analytical tools, we
obtained a robust, parsimonious and more refined phylogenetic history of the
recA/RAD51 superfamily. Taken together, our model for the evolution of
recA/RAD51 family provides a better understanding of ancient origin of recA
proteins and multiple events leading to the diversification of recA homologs in
eukaryotes, including the discovery of additional RAD51 sub-families.
| [
{
"created": "Thu, 14 Jun 2012 22:02:09 GMT",
"version": "v1"
}
] | 2012-06-18 | [
[
"Chintapalli",
"Sree V.",
""
],
[
"Bhardwaj",
"Gaurav",
""
],
[
"Babu",
"Jagadish",
""
],
[
"Hadjiyianni",
"Loukia",
""
],
[
"Hong",
"Yoojin",
""
],
[
"Zhang",
"Zhenhai",
""
],
[
"Zhou",
"Xiaofan",
""
],
[
"Ma",
"Hong",
""
],
[
"Anishkin",
"Andriy",
""
],
[
"van Rossum",
"Damian B.",
""
],
[
"Patterson",
"Randen L.",
""
]
] | The recA/RAD51 gene family encodes a diverse set of recombinase proteins that effect homologous recombination, DNA-repair, and genome stability. The recA gene family is expressed in almost all species of Eubacteria, Archaea, and Eukaryotes, and even in some viruses. To date, efforts to resolve the deep evolutionary origins of this ancient protein family have been hindered, in part, by the high sequence divergence between families (i.e. ~30% identity between paralogous groups). Through (i) large taxon sampling, (ii) the use of a phylogenetic algorithm designed for measuring highly divergent paralogs, and (iii) novel Evolutionary Spatial Dynamics simulation and analytical tools, we obtained a robust, parsimonious and more refined phylogenetic history of the recA/RAD51 superfamily. Taken together, our model for the evolution of recA/RAD51 family provides a better understanding of ancient origin of recA proteins and multiple events leading to the diversification of recA homologs in eukaryotes, including the discovery of additional RAD51 sub-families. |
0709.0025 | Ping Ao | P Ao | Borges Dilemma, Fundamental Laws, and Systems Biology | 4 pages | null | null | null | q-bio.QM q-bio.OT | null | I reason here that the known folk law in biology that there is no general law
in biology because of exceptions is false. The (quantitative) systems biology
offers the potential to solve the Borges Dilemma, by transcending it. There
have already a plenty of indications on this trend.
| [
{
"created": "Sat, 1 Sep 2007 00:16:45 GMT",
"version": "v1"
}
] | 2007-09-04 | [
[
"Ao",
"P",
""
]
] | I reason here that the known folk law in biology that there is no general law in biology because of exceptions is false. The (quantitative) systems biology offers the potential to solve the Borges Dilemma, by transcending it. There have already a plenty of indications on this trend. |
2307.13608 | Marco Pegoraro | Marco Pegoraro, Cl\'ementine Domin\'e, Emanuele Rodol\`a, Petar
Veli\v{c}kovi\'c, Andreea Deac | Geometric Epitope and Paratope Prediction | null | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Antibody-antigen interactions play a crucial role in identifying and
neutralizing harmful foreign molecules. In this paper, we investigate the
optimal representation for predicting the binding sites in the two molecules
and emphasize the importance of geometric information. Specifically, we compare
different geometric deep learning methods applied to proteins' inner (I-GEP)
and outer (O-GEP) structures. We incorporate 3D coordinates and spectral
geometric descriptors as input features to fully leverage the geometric
information. Our research suggests that surface-based models are more efficient
than other methods, and our O-GEP experiments have achieved state-of-the-art
results with significant performance improvements.
| [
{
"created": "Sun, 28 May 2023 12:51:42 GMT",
"version": "v1"
}
] | 2023-07-26 | [
[
"Pegoraro",
"Marco",
""
],
[
"Dominé",
"Clémentine",
""
],
[
"Rodolà",
"Emanuele",
""
],
[
"Veličković",
"Petar",
""
],
[
"Deac",
"Andreea",
""
]
] | Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules. In this paper, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the importance of geometric information. Specifically, we compare different geometric deep learning methods applied to proteins' inner (I-GEP) and outer (O-GEP) structures. We incorporate 3D coordinates and spectral geometric descriptors as input features to fully leverage the geometric information. Our research suggests that surface-based models are more efficient than other methods, and our O-GEP experiments have achieved state-of-the-art results with significant performance improvements. |
2210.11478 | Matthew Bryan | Matthew J. Bryan (1), Linxing Preston Jiang (1), Rajesh P N Rao (1)
((1) Neural Systems Laboratory, Paul G. Allen School of Computer Science &
Engineering, University of Washington) | Neural Co-Processors for Restoring Brain Function: Results from a
Cortical Model of Grasping | 45 pages, 19 figures. Submitted the IOP Journal of Neural Engineering | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | Objective: A major challenge in designing closed-loop brain-computer
interfaces is finding optimal stimulation patterns as a function of ongoing
neural activity for different subjects and objectives. Approach: To achieve
goal-directed closed-loop neurostimulation, we propose "neural co-processors"
which use artificial neural networks and deep learning to learn optimal
closed-loop stimulation policies, shaping neural activity and bridging injured
neural circuits for targeted repair and rehabilitation. The co-processor adapts
the stimulation policy as the biological circuit itself adapts to the
stimulation, achieving a form of brain-device co-adaptation. Here we use
simulations to lay the groundwork for future in vivo tests of neural
co-processors. We leverage a cortical model of grasping, to which we applied
various forms of simulated lesions, allowing us to develop the critical
learning algorithms and study adaptations to non-stationarity. Main results:
Our simulations show the ability of a neural co-processor to learn a
stimulation policy using a supervised learning approach, and to adapt that
policy as the underlying brain and sensors change. Our co-processor
successfully co-adapted with the simulated brain to accomplish the
reach-and-grasp task after a variety of lesions were applied, achieving
recovery towards healthy function. Significance: Our results provide the first
proof-of-concept demonstration of a co-processor for adaptive
activity-dependent closed-loop neurostimulation, optimizing for a
rehabilitation goal. While a gap remains between simulations and applications,
our results provide insights on how co-processors may be developed for learning
complex adaptive stimulation policies for a variety of neural rehabilitation
and neuroprosthetic applications.
| [
{
"created": "Wed, 19 Oct 2022 04:13:33 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Mar 2023 22:25:33 GMT",
"version": "v2"
}
] | 2023-03-22 | [
[
"Bryan",
"Matthew J.",
""
],
[
"Jiang",
"Linxing Preston",
""
],
[
"Rao",
"Rajesh P N",
""
]
] | Objective: A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and objectives. Approach: To achieve goal-directed closed-loop neurostimulation, we propose "neural co-processors" which use artificial neural networks and deep learning to learn optimal closed-loop stimulation policies, shaping neural activity and bridging injured neural circuits for targeted repair and rehabilitation. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for future in vivo tests of neural co-processors. We leverage a cortical model of grasping, to which we applied various forms of simulated lesions, allowing us to develop the critical learning algorithms and study adaptations to non-stationarity. Main results: Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied, achieving recovery towards healthy function. Significance: Our results provide the first proof-of-concept demonstration of a co-processor for adaptive activity-dependent closed-loop neurostimulation, optimizing for a rehabilitation goal. While a gap remains between simulations and applications, our results provide insights on how co-processors may be developed for learning complex adaptive stimulation policies for a variety of neural rehabilitation and neuroprosthetic applications. |
2207.02014 | Alexander Stewart | Saul Acevedo and Alexander J. Stewart | Eco-evolutionary tradeoffs in the dynamics of prion strain competition | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Prion and prion-like molecules are a type of self replicating aggregate
protein that have been implicated in a variety of neurodegenerative diseases.
Over recent decades the molecular dynamics of prions have been characterized
both empirically and through mathematical models, providing insights into the
epidemiology of prion diseases, and the impact of prions on the evolution of
cellular processes. At the same time, a variety of evidence indicates that
prions are themselves capable of a form of evolution, in which changes to their
structure that impact their rate of growth or fragmentation are replicated,
making such changes subject to natural selection. Here we study the role of
such selection in shaping the characteristics of prions under the nucleated
polymerization model (NPM). We show that fragmentation rates evolve to an
evolutionary stable value which balances rapid reproduction of $PrP^{Sc}$
aggregates with the need to produce stable polymers. We further show that this
evolved fragmentation rate differs in general from the rate that optimizes
transmission between cells. We find that under the NPM, prions that are both
evolutionary stable and optimized for transmission have a characteristic length
of $3n$, i.e three times the critical length below which they become unstable.
Finally we study the dynamics of inter-cellular competition between strains,
and show that the eco-evolutionary tradeoff between intra- and inter-cellular
competition favors coexistence.
| [
{
"created": "Tue, 5 Jul 2022 12:51:26 GMT",
"version": "v1"
}
] | 2022-07-06 | [
[
"Acevedo",
"Saul",
""
],
[
"Stewart",
"Alexander J.",
""
]
] | Prion and prion-like molecules are a type of self replicating aggregate protein that have been implicated in a variety of neurodegenerative diseases. Over recent decades the molecular dynamics of prions have been characterized both empirically and through mathematical models, providing insights into the epidemiology of prion diseases, and the impact of prions on the evolution of cellular processes. At the same time, a variety of evidence indicates that prions are themselves capable of a form of evolution, in which changes to their structure that impact their rate of growth or fragmentation are replicated, making such changes subject to natural selection. Here we study the role of such selection in shaping the characteristics of prions under the nucleated polymerization model (NPM). We show that fragmentation rates evolve to an evolutionary stable value which balances rapid reproduction of $PrP^{Sc}$ aggregates with the need to produce stable polymers. We further show that this evolved fragmentation rate differs in general from the rate that optimizes transmission between cells. We find that under the NPM, prions that are both evolutionary stable and optimized for transmission have a characteristic length of $3n$, i.e three times the critical length below which they become unstable. Finally we study the dynamics of inter-cellular competition between strains, and show that the eco-evolutionary tradeoff between intra- and inter-cellular competition favors coexistence. |
1611.00313 | Joaquin Rapela | Joaquin Rapela and Marissa Westerfield and Jeanne Townsend and Scott
Makeig | A new foreperiod effect on single-trial phase coherence. Part I:
existence and relevance | null | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Expecting events in time leads to more efficient behavior. A remarkable early
finding in the study of temporal expectancy is the foreperiod effect on
reaction times; i.e., the influence or reaction time of the time period between
a warning signal and an imperative stimulus to which subjects are instructed to
respond as quickly as possible. Recently it has been shown that the phase of
oscillatory activity preceding stimulus presentation is related to behavior.
Here we connect both of these findings by reporting a novel foreperiod effect
on the inter-trial phase coherence of the electroencephalogram (EEG) triggered
by stimuli to which subjects are instructed not to respond. Inter-trial phase
coherence has been used to describe regularities in phases of groups of trials
time locked to an event of interest. We propose a single-trial measure of
inter-trial phase coherence and prove its soundness. Equipped with this
measure, and using a multivariate decoding method, we demonstrate that the
foreperiod duration in and audiovisual attention-shifting task modulates
single-trial phase coherence. In principle, this modulation could be an
artifact of the decoding method used to detect it. We show that this is not the
case, since the modulation can also be observed using a simple averaging
method. We show that the strength of this modulation correlates with subject
behavior (both error rates and mean-reaction times). We anticipate that the new
foreperiod effect on inter-trial phase coherence, and the decoding method used
here to detect it, will be important tools to understand cognition at the
single-trial level. In Part II of this manuscript, we support this claim, by
showing that changes in attention modulate the strength of the new foreperiod
effect on a trial-by-trial basis.
| [
{
"created": "Fri, 26 Aug 2016 18:50:12 GMT",
"version": "v1"
}
] | 2016-11-02 | [
[
"Rapela",
"Joaquin",
""
],
[
"Westerfield",
"Marissa",
""
],
[
"Townsend",
"Jeanne",
""
],
[
"Makeig",
"Scott",
""
]
] | Expecting events in time leads to more efficient behavior. A remarkable early finding in the study of temporal expectancy is the foreperiod effect on reaction times; i.e., the influence or reaction time of the time period between a warning signal and an imperative stimulus to which subjects are instructed to respond as quickly as possible. Recently it has been shown that the phase of oscillatory activity preceding stimulus presentation is related to behavior. Here we connect both of these findings by reporting a novel foreperiod effect on the inter-trial phase coherence of the electroencephalogram (EEG) triggered by stimuli to which subjects are instructed not to respond. Inter-trial phase coherence has been used to describe regularities in phases of groups of trials time locked to an event of interest. We propose a single-trial measure of inter-trial phase coherence and prove its soundness. Equipped with this measure, and using a multivariate decoding method, we demonstrate that the foreperiod duration in and audiovisual attention-shifting task modulates single-trial phase coherence. In principle, this modulation could be an artifact of the decoding method used to detect it. We show that this is not the case, since the modulation can also be observed using a simple averaging method. We show that the strength of this modulation correlates with subject behavior (both error rates and mean-reaction times). We anticipate that the new foreperiod effect on inter-trial phase coherence, and the decoding method used here to detect it, will be important tools to understand cognition at the single-trial level. In Part II of this manuscript, we support this claim, by showing that changes in attention modulate the strength of the new foreperiod effect on a trial-by-trial basis. |
2003.00327 | Gerhard Mayer | Gerhard Mayer | Modelling techniques for biomolecular networks | 44 pages | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | First we shortly review the different kinds of network modelling methods for
systems biology with an emphasis on the different subtypes of logical models,
which we review in more detail. Then we show the advantages of Boolean networks
models over more mechanistic modelling types like differential equation
techniques. Then follows an overlook about connections between different kinds
of models and how they can be converted to each other. We also give a short
overview about the mathematical frameworks for modelling of logical networks
and list available software packages for logical modelling. Then we give an
overview about the available standards and ontologies for storing such logical
systems biology models and their results. In the end we give a short review
about the difference between quantitative and qualitative models and describe
the mathematics that specifically deals with qualitative modelling.
| [
{
"created": "Sat, 29 Feb 2020 18:42:56 GMT",
"version": "v1"
}
] | 2020-03-03 | [
[
"Mayer",
"Gerhard",
""
]
] | First we shortly review the different kinds of network modelling methods for systems biology with an emphasis on the different subtypes of logical models, which we review in more detail. Then we show the advantages of Boolean networks models over more mechanistic modelling types like differential equation techniques. Then follows an overlook about connections between different kinds of models and how they can be converted to each other. We also give a short overview about the mathematical frameworks for modelling of logical networks and list available software packages for logical modelling. Then we give an overview about the available standards and ontologies for storing such logical systems biology models and their results. In the end we give a short review about the difference between quantitative and qualitative models and describe the mathematics that specifically deals with qualitative modelling. |
1402.4063 | Michael Goldberg | Byron C. Williams, Joshua J. Filter, Kristina A. Blake-Hodek, Brian E.
Wadzinski, Nicholas J. Fuda, David Shalloway, and Michael L. Goldberg | Greatwall-phosphorylated Endosulfine is Both an Inhibitor and a
Substrate of PP2A-B55 Heterotrimers | 65 pages of text, 11 figures, 10 supplementary figures. This paper
will be published in the journal eLife | null | null | null | q-bio.SC q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During M phase, Endosulfine (Endos) family proteins are phosphorylated by
Greatwall kinase (Gwl), and the resultant pEndos inhibits the phosphatase
PP2A-B55, which would otherwise prematurely reverse many CDK-driven
phosphorylations. We show here that PP2A-B55 is the enzyme responsible for
dephosphorylating pEndos during M phase exit. The kinetic parameters for
PP2A-B55's action on pEndos are orders of magnitude lower than those for
CDK-phosphorylated substrates, suggesting a simple model for PP2A-B55
regulation that we call inhibition by unfair competition. As the name suggests,
during M phase PP2A-B55's attention is diverted to pEndos, which binds much
more avidly and is dephosphorylated more slowly than other substrates. When Gwl
is inactivated during the M phase-to-interphase transition, the dynamic balance
changes: pEndos dephosphorylated by PP2A-B55 cannot be replaced, so the
phosphatase can refocus its attention on CDK-phosphorylated substrates. This
mechanism explains simultaneously how PP2A-B55 and Gwl together regulate
pEndos, and how pEndos controls PP2A-B55.
| [
{
"created": "Mon, 17 Feb 2014 17:10:42 GMT",
"version": "v1"
}
] | 2014-02-18 | [
[
"Williams",
"Byron C.",
""
],
[
"Filter",
"Joshua J.",
""
],
[
"Blake-Hodek",
"Kristina A.",
""
],
[
"Wadzinski",
"Brian E.",
""
],
[
"Fuda",
"Nicholas J.",
""
],
[
"Shalloway",
"David",
""
],
[
"Goldberg",
"Michael L.",
""
]
] | During M phase, Endosulfine (Endos) family proteins are phosphorylated by Greatwall kinase (Gwl), and the resultant pEndos inhibits the phosphatase PP2A-B55, which would otherwise prematurely reverse many CDK-driven phosphorylations. We show here that PP2A-B55 is the enzyme responsible for dephosphorylating pEndos during M phase exit. The kinetic parameters for PP2A-B55's action on pEndos are orders of magnitude lower than those for CDK-phosphorylated substrates, suggesting a simple model for PP2A-B55 regulation that we call inhibition by unfair competition. As the name suggests, during M phase PP2A-B55's attention is diverted to pEndos, which binds much more avidly and is dephosphorylated more slowly than other substrates. When Gwl is inactivated during the M phase-to-interphase transition, the dynamic balance changes: pEndos dephosphorylated by PP2A-B55 cannot be replaced, so the phosphatase can refocus its attention on CDK-phosphorylated substrates. This mechanism explains simultaneously how PP2A-B55 and Gwl together regulate pEndos, and how pEndos controls PP2A-B55. |
1709.04090 | Yanjun Qi Dr. | Chandan Singh, Beilun Wang, Yanjun Qi | A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of
Heterogeneous Neural Connectivity Graphs | 8 pages | null | null | null | q-bio.NC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Determining functional brain connectivity is crucial to understanding the
brain and neural differences underlying disorders such as autism. Recent
studies have used Gaussian graphical models to learn brain connectivity via
statistical dependencies across brain regions from neuroimaging. However,
previous studies often fail to properly incorporate priors tailored to
neuroscience, such as preferring shorter connections. To remedy this problem,
the paper here introduces a novel, weighted-$\ell_1$, multi-task graphical
model (W-SIMULE). This model elegantly incorporates a flexible prior, along
with a parallelizable formulation. Additionally, W-SIMULE extends the
often-used Gaussian assumption, leading to considerable performance increases.
Here, applications to fMRI data show that W-SIMULE succeeds in determining
functional connectivity in terms of (1) log-likelihood, (2) finding edges that
differentiate groups, and (3) classifying different groups based on their
connectivity, achieving 58.6\% accuracy on the ABIDE dataset. Having
established W-SIMULE's effectiveness, it links four key areas to autism, all of
which are consistent with the literature. Due to its elegant domain adaptivity,
W-SIMULE can be readily applied to various data types to effectively estimate
connectivity.
| [
{
"created": "Wed, 13 Sep 2017 00:05:20 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Sep 2017 15:18:23 GMT",
"version": "v2"
}
] | 2017-09-22 | [
[
"Singh",
"Chandan",
""
],
[
"Wang",
"Beilun",
""
],
[
"Qi",
"Yanjun",
""
]
] | Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism. Recent studies have used Gaussian graphical models to learn brain connectivity via statistical dependencies across brain regions from neuroimaging. However, previous studies often fail to properly incorporate priors tailored to neuroscience, such as preferring shorter connections. To remedy this problem, the paper here introduces a novel, weighted-$\ell_1$, multi-task graphical model (W-SIMULE). This model elegantly incorporates a flexible prior, along with a parallelizable formulation. Additionally, W-SIMULE extends the often-used Gaussian assumption, leading to considerable performance increases. Here, applications to fMRI data show that W-SIMULE succeeds in determining functional connectivity in terms of (1) log-likelihood, (2) finding edges that differentiate groups, and (3) classifying different groups based on their connectivity, achieving 58.6\% accuracy on the ABIDE dataset. Having established W-SIMULE's effectiveness, it links four key areas to autism, all of which are consistent with the literature. Due to its elegant domain adaptivity, W-SIMULE can be readily applied to various data types to effectively estimate connectivity. |
1407.2269 | Krystyna Lukierska-Walasek | Krystyna Lukierska-Walasek, Krzysztof Topolski, Krzysztof Trojanowski | Statistical distributions and entropy considerations in gene codes | 11 pages, 18 figures. arXiv admin note: text overlap with
arXiv:1401.4561 | null | null | null | q-bio.GN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In our paper selected linguistic features of genomes to study the statistics
of the gene codes are considered. We present the information theory from which
it follows that if the system is described by distributions of hyperbolic type
it leads to the possibility of entropy loss and stability. We show that the
histograms of gene lengths are similar to that of language words. We show the
correspondence between presented theory and results for the number of
replicated genes and replicated fragments of genes in genomes for Borelia
burgdorferi, Escherichia coli and Saccharomyces cerevisiae S288c.
| [
{
"created": "Fri, 4 Jul 2014 19:19:18 GMT",
"version": "v1"
}
] | 2014-07-10 | [
[
"Lukierska-Walasek",
"Krystyna",
""
],
[
"Topolski",
"Krzysztof",
""
],
[
"Trojanowski",
"Krzysztof",
""
]
] | In our paper selected linguistic features of genomes to study the statistics of the gene codes are considered. We present the information theory from which it follows that if the system is described by distributions of hyperbolic type it leads to the possibility of entropy loss and stability. We show that the histograms of gene lengths are similar to that of language words. We show the correspondence between presented theory and results for the number of replicated genes and replicated fragments of genes in genomes for Borelia burgdorferi, Escherichia coli and Saccharomyces cerevisiae S288c. |
q-bio/0309028 | Bijan Pesaran | P. P. Mitra and B. Pesaran | Analysis of Dynamic Brain Imaging Data | 40 pages; 26 figures with subparts including 3 figures as .gif files.
Originally submitted to the neuro-sys archive which was never publicly
announced (was 9804003) | null | 10.1016/S0006-3495(99)77236-X | CMP-002 | q-bio.NC q-bio.QM | null | Modern imaging techniques for probing brain function, including functional
Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging,
and magnetoencephalography, generate large data sets with complex content. In
this paper we develop appropriate techniques of analysis and visualization of
such imaging data, in order to separate the signal from the noise, as well as
to characterize the signal. The techniques developed fall into the general
category of multivariate time series analysis, and in particular we extensively
use the multitaper framework of spectral analysis. We develop specific
protocols for the analysis of fMRI, optical imaging and MEG data, and
illustrate the techniques by applications to real data sets generated by these
imaging modalities. In general, the analysis protocols involve two distinct
stages: `noise' characterization and suppression, and `signal' characterization
and visualization. An important general conclusion of our study is the utility
of a frequency-based representation, with short, moving analysis windows to
account for non-stationarity in the data. Of particular note are (a) the
development of a decomposition technique (`space-frequency singular value
decomposition') that is shown to be a useful means of characterizing the image
data, and (b) the development of an algorithm, based on multitaper methods, for
the removal of approximately periodic physiological artifacts arising from
cardiac and respiratory sources.
| [
{
"created": "Mon, 20 Apr 1998 21:02:27 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Mitra",
"P. P.",
""
],
[
"Pesaran",
"B.",
""
]
] | Modern imaging techniques for probing brain function, including functional Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques of analysis and visualization of such imaging data, in order to separate the signal from the noise, as well as to characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: `noise' characterization and suppression, and `signal' characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for non-stationarity in the data. Of particular note are (a) the development of a decomposition technique (`space-frequency singular value decomposition') that is shown to be a useful means of characterizing the image data, and (b) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources. |
1904.00099 | Dean Koro\v{s}ak | Dean Koro\v{s}ak, Marjan Slak Rupnik | Random matrix analysis of Ca$^{2+}$ signals in $\beta$-cell collectives | null | null | null | null | q-bio.CB cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Even within small organs like pancreatic islets, different endocrine cell
types and subtypes form a heterogeneous collective to sense the chemical
composition of the extracellular solution and compute an adequate hormonal
output. Erroneous cellular processing and hormonal output due to challenged
heterogeneity result in various disorders with diabetes mellitus as a flagship
metabolic disease. Here we attempt to address the aforementioned functional
heterogeneity with comparing pairwise cell-cell cross-correlations obtained
from simultaneous measurements of cytosolic calcium responses in hundreds of
islet cells in an optical plane to statistical properties of correlations
predicted by the random matrix theory (RMT). We find that the bulk of the
empirical eigenvalue spectrum is almost completely described by RMT prediction,
however, the deviating eigenvalues that exist below and above RMT spectral
edges suggest that there are local and extended modes driving the correlations.
We show that empirical nearest neighbor spacing of eigenvalues follows
universal RMT properties regardless of glucose stimulation, but that number
variance displays clear separation from RMT prediction and can differentiate
between empirical spectra obtained under non-stimulated and stimulated
conditions. We suggest that RMT approach provides a sensitive tool to assess
the functional cell heterogeneity and its effects on the spatio-temporal
dynamics a collective of beta cells in pancreatic islets in physiological
resting and stimulatory conditions.
| [
{
"created": "Fri, 29 Mar 2019 21:53:14 GMT",
"version": "v1"
}
] | 2019-04-02 | [
[
"Korošak",
"Dean",
""
],
[
"Rupnik",
"Marjan Slak",
""
]
] | Even within small organs like pancreatic islets, different endocrine cell types and subtypes form a heterogeneous collective to sense the chemical composition of the extracellular solution and compute an adequate hormonal output. Erroneous cellular processing and hormonal output due to challenged heterogeneity result in various disorders with diabetes mellitus as a flagship metabolic disease. Here we attempt to address the aforementioned functional heterogeneity with comparing pairwise cell-cell cross-correlations obtained from simultaneous measurements of cytosolic calcium responses in hundreds of islet cells in an optical plane to statistical properties of correlations predicted by the random matrix theory (RMT). We find that the bulk of the empirical eigenvalue spectrum is almost completely described by RMT prediction, however, the deviating eigenvalues that exist below and above RMT spectral edges suggest that there are local and extended modes driving the correlations. We show that empirical nearest neighbor spacing of eigenvalues follows universal RMT properties regardless of glucose stimulation, but that number variance displays clear separation from RMT prediction and can differentiate between empirical spectra obtained under non-stimulated and stimulated conditions. We suggest that RMT approach provides a sensitive tool to assess the functional cell heterogeneity and its effects on the spatio-temporal dynamics a collective of beta cells in pancreatic islets in physiological resting and stimulatory conditions. |
2201.09683 | Javier Jarillo | Javier Jarillo, Francisco J. Cao-Garc\'ia, and Frederik De Laender | Spatial and Ecological Scaling of Stability in Spatial Community
Networks | null | Front. Ecol. Evol. 10 (2022) 861537 | 10.3389/fevo.2022.861537 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are many scales at which to quantify stability in spatial and
ecological networks. Local-scale analyses focus on specific nodes of the
spatial network, while regional-scale analyses consider the whole network.
Similarly, species- and community-level analyses either account for single
species or for the whole community. Furthermore, stability itself can be
defined in multiple ways, including resistance (the inverse of the relative
displacement caused by a perturbation), initial resilience (the rate of return
after a perturbation), and invariability (the inverse of the relative amplitude
of the population fluctuations). Here, we analyze the scale-dependence of these
stability properties. More specifically, we ask how spatial scale (local vs
regional) and ecological scale (species vs community) influence these stability
properties. We find that regional initial resilience is the weighted arithmetic
mean of the local initial resiliences. The regional resistance is the harmonic
mean of local resistances, which makes regional resistance particularly
vulnerable to nodes with low stability, unlike regional initial resilience.
Analogous results hold for the relationship between community- and
species-level initial resilience and resistance. Both resistance and initial
resilience are ``scale-free'' properties: regional and community values are
simply the biomass-weighted means of the local and species values,
respectively. Thus, one can easily estimate both stability metrics of whole
networks from partial sampling. In contrast, invariability generally is greater
at the regional and community-level than at the local and species-level,
respectively. Hence, estimating the invariability of spatial or ecological
networks from measurements at the local or species level is more complicated,
requiring an unbiased estimate of the network (i.e. region or community) size.
| [
{
"created": "Mon, 24 Jan 2022 13:28:47 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Oct 2022 08:30:39 GMT",
"version": "v2"
}
] | 2022-10-19 | [
[
"Jarillo",
"Javier",
""
],
[
"Cao-García",
"Francisco J.",
""
],
[
"De Laender",
"Frederik",
""
]
] | There are many scales at which to quantify stability in spatial and ecological networks. Local-scale analyses focus on specific nodes of the spatial network, while regional-scale analyses consider the whole network. Similarly, species- and community-level analyses either account for single species or for the whole community. Furthermore, stability itself can be defined in multiple ways, including resistance (the inverse of the relative displacement caused by a perturbation), initial resilience (the rate of return after a perturbation), and invariability (the inverse of the relative amplitude of the population fluctuations). Here, we analyze the scale-dependence of these stability properties. More specifically, we ask how spatial scale (local vs regional) and ecological scale (species vs community) influence these stability properties. We find that regional initial resilience is the weighted arithmetic mean of the local initial resiliences. The regional resistance is the harmonic mean of local resistances, which makes regional resistance particularly vulnerable to nodes with low stability, unlike regional initial resilience. Analogous results hold for the relationship between community- and species-level initial resilience and resistance. Both resistance and initial resilience are ``scale-free'' properties: regional and community values are simply the biomass-weighted means of the local and species values, respectively. Thus, one can easily estimate both stability metrics of whole networks from partial sampling. In contrast, invariability generally is greater at the regional and community-level than at the local and species-level, respectively. Hence, estimating the invariability of spatial or ecological networks from measurements at the local or species level is more complicated, requiring an unbiased estimate of the network (i.e. region or community) size. |
1407.0867 | Gibin Powathil | Gibin G Powathil, Alastair J Munro, Mark AJ Chaplain and Maciej Swat | Bystander effects and their implications for clinical radiation therapy:
Insights from multiscale in silico experiments | null | null | 10.1016/j.jtbi.2016.04.010 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Radiotherapy is a commonly used treatment for cancer and is usually given in
varying doses. At low radiation doses relatively few cells die as a direct
response to radiation but secondary radiation effects such as DNA mutation or
bystander effects affect many cells. Consequently it is at low radiation levels
where an understanding of bystander effects is essential in designing novel
therapies with superior clinical outcomes. In this article, we use a hybrid
multiscale mathematical model to study the direct effects of radiation as well
as radiation-induced bystander effects on both tumour cells and normal cells.
We show that bystander responses may play a major role in mediating radiation
damage to cells at low-doses of radiotherapy, doing more damage than that due
to direct radiation. The survival curves derived from our computational
simulations showed an area of hyper-radiosensitivity at low-doses that are not
obtained using a traditional radiobiological model.
| [
{
"created": "Thu, 3 Jul 2014 11:22:29 GMT",
"version": "v1"
}
] | 2016-06-16 | [
[
"Powathil",
"Gibin G",
""
],
[
"Munro",
"Alastair J",
""
],
[
"Chaplain",
"Mark AJ",
""
],
[
"Swat",
"Maciej",
""
]
] | Radiotherapy is a commonly used treatment for cancer and is usually given in varying doses. At low radiation doses relatively few cells die as a direct response to radiation but secondary radiation effects such as DNA mutation or bystander effects affect many cells. Consequently it is at low radiation levels where an understanding of bystander effects is essential in designing novel therapies with superior clinical outcomes. In this article, we use a hybrid multiscale mathematical model to study the direct effects of radiation as well as radiation-induced bystander effects on both tumour cells and normal cells. We show that bystander responses may play a major role in mediating radiation damage to cells at low-doses of radiotherapy, doing more damage than that due to direct radiation. The survival curves derived from our computational simulations showed an area of hyper-radiosensitivity at low-doses that are not obtained using a traditional radiobiological model. |
2004.08491 | Saroj Jayasinghe | Anuruddha Abeygunasekera and Saroj Jayasinghe | Is the anti-filarial drug diethylcarbamazine useful to treat COVID-19? | 9 pages | null | null | null | q-bio.SC q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The SARS-CoV-2 virus has resulted in a devastating pandemic of COVID-19.
Exploring compounds that could offer a breakthrough in treatment is the need of
the hour. Re-positioning cheap, freely available and safe drugs is a priority.
The paper proposes evidence for the potential use of diethylcarbamazine (DEC)
in the treatment of COVID-19. DEC has inhibitory effects on arachidonic acid
metabolism to prostaglandins, little known anti-viral effects on animal
retroviruses and demonstrated anti-inflammatory actions in animal models of
lung inflammation indicating the need to explore this hypothesis further. We
believe this is the first time DEC is being proposed to treat COVID-19.
| [
{
"created": "Sat, 18 Apr 2020 00:18:08 GMT",
"version": "v1"
}
] | 2020-04-21 | [
[
"Abeygunasekera",
"Anuruddha",
""
],
[
"Jayasinghe",
"Saroj",
""
]
] | The SARS-CoV-2 virus has resulted in a devastating pandemic of COVID-19. Exploring compounds that could offer a breakthrough in treatment is the need of the hour. Re-positioning cheap, freely available and safe drugs is a priority. The paper proposes evidence for the potential use of diethylcarbamazine (DEC) in the treatment of COVID-19. DEC has inhibitory effects on arachidonic acid metabolism to prostaglandins, little known anti-viral effects on animal retroviruses and demonstrated anti-inflammatory actions in animal models of lung inflammation indicating the need to explore this hypothesis further. We believe this is the first time DEC is being proposed to treat COVID-19. |
2006.01550 | Paul Reiser | Paul A. Reiser | Modified SIR Model Yielding a Logistic Solution | null | null | null | null | q-bio.PE | http://creativecommons.org/publicdomain/zero/1.0/ | The SIR pandemic model suffers from an unrealistic assumption: The rate of
removal from the infectious class of individuals is assumed to be proportional
to the number of infectious individuals. This means that a change in the rate
of infection is simultaneous with an equal change in the rate of removal. A
more realistic assumption is that an individual is removed at a certain time
interval after having been infected. A simple modified SIR model is proposed
which implements this delay, resulting in a single delay differential equation
which comprises the model. A solution to this equation which is applicable to a
pandemic is of the form A+B L(t) where L(t) is a logistic function, and A and B
are constants. While the classical SIR model is often an oversimplification of
pandemic behavior, it is instructive in that many of the fundamental dynamics
and descriptors of pandemics are clearly and simply defined. The logistic model
is generally used descriptively, dealing as it does with only the susceptible
and infected classes and the rate of transfer between them. The present model
presents a full but modified SIR model with a simpler logistic solution which
is more realistic and equally instructive.
| [
{
"created": "Tue, 2 Jun 2020 12:16:19 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Feb 2021 02:15:47 GMT",
"version": "v2"
},
{
"created": "Mon, 22 Feb 2021 00:26:24 GMT",
"version": "v3"
}
] | 2021-02-23 | [
[
"Reiser",
"Paul A.",
""
]
] | The SIR pandemic model suffers from an unrealistic assumption: The rate of removal from the infectious class of individuals is assumed to be proportional to the number of infectious individuals. This means that a change in the rate of infection is simultaneous with an equal change in the rate of removal. A more realistic assumption is that an individual is removed at a certain time interval after having been infected. A simple modified SIR model is proposed which implements this delay, resulting in a single delay differential equation which comprises the model. A solution to this equation which is applicable to a pandemic is of the form A+B L(t) where L(t) is a logistic function, and A and B are constants. While the classical SIR model is often an oversimplification of pandemic behavior, it is instructive in that many of the fundamental dynamics and descriptors of pandemics are clearly and simply defined. The logistic model is generally used descriptively, dealing as it does with only the susceptible and infected classes and the rate of transfer between them. The present model presents a full but modified SIR model with a simpler logistic solution which is more realistic and equally instructive. |
2004.01207 | Mario Villalobos-Arias Dr. | Mario Villalobos-Arias | Estimation of population infected by Covid-19 using regression
Generalized logistics and optimization heuristics | 16 pages, 12 figures, prediction for covid-19, the document is in
Spanish | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this work, a proposal for the estimation of the populations using logistic
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 in Spanish: En este trabajos se presenta una propuesta para la
estimaci\'on de la poblaciones usando ajuste de curvas del tipo log\'istica.
Este tipo de curvas se utilizan para el estudio de crecimiento de poblaciones,
en este casos poblaci\'on de personas infectadas por el virus Covid-19; y
tambi\'en se puede utilizar para aproximar la curva de supervivencia que se
utiliza en estudios actuariales y otras similares
| [
{
"created": "Thu, 2 Apr 2020 18:06:17 GMT",
"version": "v1"
}
] | 2020-04-06 | [
[
"Villalobos-Arias",
"Mario",
""
]
] | In this work, a proposal for the estimation of the populations using logistic 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 in Spanish: En este trabajos se presenta una propuesta para la estimaci\'on de la poblaciones usando ajuste de curvas del tipo log\'istica. Este tipo de curvas se utilizan para el estudio de crecimiento de poblaciones, en este casos poblaci\'on de personas infectadas por el virus Covid-19; y tambi\'en se puede utilizar para aproximar la curva de supervivencia que se utiliza en estudios actuariales y otras similares |
1209.5549 | David Balduzzi | David Balduzzi and Michel Besserve | Towards a learning-theoretic analysis of spike-timing dependent
plasticity | To appear in Adv. Neural Inf. Proc. Systems | null | null | null | q-bio.NC cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper suggests a learning-theoretic perspective on how synaptic
plasticity benefits global brain functioning. We introduce a model, the
selectron, that (i) arises as the fast time constant limit of leaky
integrate-and-fire neurons equipped with spiking timing dependent plasticity
(STDP) and (ii) is amenable to theoretical analysis. We show that the selectron
encodes reward estimates into spikes and that an error bound on spikes is
controlled by a spiking margin and the sum of synaptic weights. Moreover, the
efficacy of spikes (their usefulness to other reward maximizing selectrons)
also depends on total synaptic strength. Finally, based on our analysis, we
propose a regularized version of STDP, and show the regularization improves the
robustness of neuronal learning when faced with multiple stimuli.
| [
{
"created": "Tue, 25 Sep 2012 09:23:41 GMT",
"version": "v1"
}
] | 2012-09-26 | [
[
"Balduzzi",
"David",
""
],
[
"Besserve",
"Michel",
""
]
] | This paper suggests a learning-theoretic perspective on how synaptic plasticity benefits global brain functioning. We introduce a model, the selectron, that (i) arises as the fast time constant limit of leaky integrate-and-fire neurons equipped with spiking timing dependent plasticity (STDP) and (ii) is amenable to theoretical analysis. We show that the selectron encodes reward estimates into spikes and that an error bound on spikes is controlled by a spiking margin and the sum of synaptic weights. Moreover, the efficacy of spikes (their usefulness to other reward maximizing selectrons) also depends on total synaptic strength. Finally, based on our analysis, we propose a regularized version of STDP, and show the regularization improves the robustness of neuronal learning when faced with multiple stimuli. |
1809.05970 | Brian Cohn | Brian A. Cohn, Dilan D. Shah, Ali Marjaninejad, Martin Shapiro, Serhan
Ulkumen, Christopher M. Laine, Francisco J. Valero-Cuevas, Kenneth H.
Hayashida, Sarah Ingersoll | Quantifying and attenuating pathologic tremor in virtual reality | 3 pages; 3 figures | null | null | null | q-bio.QM cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a virtual reality (VR) experience that creates a research-grade
benchmark in assessing patients with active upper-limb tremor, while
simultaneously offering the opportunity for patients to engage with VR
experiences without their pathologic tremor. Accurate and precise use of
handheld motion controllers in VR gaming applications may be limited for
patients with upper limb tremor. In parallel, objective tools measuring tremor
are not in widespread, routine clinical use. We used a commercially available
VR system and designed a challenging virtual-balloon-popping test mimicking a
common nose-to-target pointing task used by medical practitioners to
subjectively evaluate tremor in the exam room. Within our VR experience, we
offer a software mode which uses a low-pass filter to adjust hand position and
pointing orientation over a series of past data points. This digital filter
creates a smoothing function for hand movement which effectively removes the
patient's tremor in the VR representation. While the patient completes trials
of the reaching task, quantitative data on the pathologic tremor is digitally
recorded. With speed, accuracy, and the tremor components computed across three
axes of movement, patients can be evaluated for their tremor amplitudes in a
quantitative, replicable, and enjoyable manner. Removal of tremor in digital
space may allow patients having significant upper limb tremor to have both an
objective clinical measurement of symptoms while providing patients positive
feedback and interaction.
| [
{
"created": "Sun, 16 Sep 2018 22:23:04 GMT",
"version": "v1"
}
] | 2018-09-18 | [
[
"Cohn",
"Brian A.",
""
],
[
"Shah",
"Dilan D.",
""
],
[
"Marjaninejad",
"Ali",
""
],
[
"Shapiro",
"Martin",
""
],
[
"Ulkumen",
"Serhan",
""
],
[
"Laine",
"Christopher M.",
""
],
[
"Valero-Cuevas",
"Francisco J.",
""
],
[
"Hayashida",
"Kenneth H.",
""
],
[
"Ingersoll",
"Sarah",
""
]
] | We present a virtual reality (VR) experience that creates a research-grade benchmark in assessing patients with active upper-limb tremor, while simultaneously offering the opportunity for patients to engage with VR experiences without their pathologic tremor. Accurate and precise use of handheld motion controllers in VR gaming applications may be limited for patients with upper limb tremor. In parallel, objective tools measuring tremor are not in widespread, routine clinical use. We used a commercially available VR system and designed a challenging virtual-balloon-popping test mimicking a common nose-to-target pointing task used by medical practitioners to subjectively evaluate tremor in the exam room. Within our VR experience, we offer a software mode which uses a low-pass filter to adjust hand position and pointing orientation over a series of past data points. This digital filter creates a smoothing function for hand movement which effectively removes the patient's tremor in the VR representation. While the patient completes trials of the reaching task, quantitative data on the pathologic tremor is digitally recorded. With speed, accuracy, and the tremor components computed across three axes of movement, patients can be evaluated for their tremor amplitudes in a quantitative, replicable, and enjoyable manner. Removal of tremor in digital space may allow patients having significant upper limb tremor to have both an objective clinical measurement of symptoms while providing patients positive feedback and interaction. |
1711.05574 | Laszlo Pecze | Mich\"ael Dougoud, Christian Mazza, Beat Schwaller, Laszlo Pecze | The phenomenon of growing surface interference explains the rosette
pattern of jaguar | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One possible mechanism to explain how animals got their coat patterns was
proposed by Alan Turing. He assumed that two kinds of morphogens diffuse on a
surface and interact with each other, generating a reaction-diffusion
mechanism. We developed a new framework for pattern generation incorporating a
non-diffusing transcription factor in the system. The diffusion factors (one
inhibitor and one activator) acting on cell surface receptors modulate the
activity of a transcription factor. The difference in the local concentration
of diffusion factors is translated into the degree of activation of
transcription factors. The speed of this process determines then pattern
formation velocity, i.e. the elapsed time from an initial noisy situation to a
final developed pattern. If the pattern formation velocity slows down compared
to the growth of the surface, the phenomenon of "growing surface interference"
occurs. We find that this phenomenon might explain the rosette pattern observed
on different types of felids and the pale stripes found between the regular
black stripes of zebras. We also investigate the dynamics between pattern
formation velocity and growth and to what extent a pattern may freeze on
growing domains.
| [
{
"created": "Wed, 15 Nov 2017 14:03:56 GMT",
"version": "v1"
}
] | 2017-11-16 | [
[
"Dougoud",
"Michäel",
""
],
[
"Mazza",
"Christian",
""
],
[
"Schwaller",
"Beat",
""
],
[
"Pecze",
"Laszlo",
""
]
] | One possible mechanism to explain how animals got their coat patterns was proposed by Alan Turing. He assumed that two kinds of morphogens diffuse on a surface and interact with each other, generating a reaction-diffusion mechanism. We developed a new framework for pattern generation incorporating a non-diffusing transcription factor in the system. The diffusion factors (one inhibitor and one activator) acting on cell surface receptors modulate the activity of a transcription factor. The difference in the local concentration of diffusion factors is translated into the degree of activation of transcription factors. The speed of this process determines then pattern formation velocity, i.e. the elapsed time from an initial noisy situation to a final developed pattern. If the pattern formation velocity slows down compared to the growth of the surface, the phenomenon of "growing surface interference" occurs. We find that this phenomenon might explain the rosette pattern observed on different types of felids and the pale stripes found between the regular black stripes of zebras. We also investigate the dynamics between pattern formation velocity and growth and to what extent a pattern may freeze on growing domains. |
2108.09926 | Akash Gupta | Rishal Aggarwal, Akash Gupta, U Deva Priyakumar | APObind: A Dataset of Ligand Unbound Protein Conformations for Machine
Learning Applications in De Novo Drug Design | Accepted in The 2021 ICML Workshop on Computational Biology | null | null | null | q-bio.BM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein-ligand complex structures have been utilised to design benchmark
machine learning methods that perform important tasks related to drug design
such as receptor binding site detection, small molecule docking and binding
affinity prediction. However, these methods are usually trained on only ligand
bound (or holo) conformations of the protein and therefore are not guaranteed
to perform well when the protein structure is in its native unbound
conformation (or apo), which is usually the conformation available for a newly
identified receptor. A primary reason for this is that the local structure of
the binding site usually changes upon ligand binding. To facilitate solutions
for this problem, we propose a dataset called APObind that aims to provide apo
conformations of proteins present in the PDBbind dataset, a popular dataset
used in drug design. Furthermore, we explore the performance of methods
specific to three use cases on this dataset, through which, the importance of
validating them on the APObind dataset is demonstrated.
| [
{
"created": "Mon, 23 Aug 2021 04:29:38 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Aug 2021 04:37:33 GMT",
"version": "v2"
}
] | 2021-08-26 | [
[
"Aggarwal",
"Rishal",
""
],
[
"Gupta",
"Akash",
""
],
[
"Priyakumar",
"U Deva",
""
]
] | Protein-ligand complex structures have been utilised to design benchmark machine learning methods that perform important tasks related to drug design such as receptor binding site detection, small molecule docking and binding affinity prediction. However, these methods are usually trained on only ligand bound (or holo) conformations of the protein and therefore are not guaranteed to perform well when the protein structure is in its native unbound conformation (or apo), which is usually the conformation available for a newly identified receptor. A primary reason for this is that the local structure of the binding site usually changes upon ligand binding. To facilitate solutions for this problem, we propose a dataset called APObind that aims to provide apo conformations of proteins present in the PDBbind dataset, a popular dataset used in drug design. Furthermore, we explore the performance of methods specific to three use cases on this dataset, through which, the importance of validating them on the APObind dataset is demonstrated. |
2102.07036 | Joaquin Torres | Jorge Pretel and Joaquin J. Torres and J. Marro | EEGs disclose significant brain activity correlated with synaptic
fickleness | 6 pages, 6 figures | null | null | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We here study a network of synaptic relations mingling excitatory and
inhibitory neuron nodes that displays oscillations quite similar to
electroencephalogram (EEG) brain waves, and identify abrupt variations brought
about by swift synaptic mediations. We thus conclude that corresponding changes
in EEG series surely come from the slowdown of the activity in neuron
populations due to synaptic restrictions. The latter happens to generate an
imbalance between excitation and inhibition causing a quick explosive increase
of excitatory activity, which turns out to be a (first-order) transition among
dynamic mental phases. Besides, near this phase transition, our model system
exhibits waves with a strong component in the so-called \textit{delta-theta
domain} that coexist with fast oscillations. These findings provide a simple
explanation for the observed \textit{delta-gamma} and \textit{theta-gamma
modulation} in actual brains, and open a serious and versatile path to
understand deeply large amounts of apparently erratic, easily accessible brain
data.
| [
{
"created": "Sun, 14 Feb 2021 00:10:06 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Feb 2021 10:22:54 GMT",
"version": "v2"
}
] | 2021-02-17 | [
[
"Pretel",
"Jorge",
""
],
[
"Torres",
"Joaquin J.",
""
],
[
"Marro",
"J.",
""
]
] | We here study a network of synaptic relations mingling excitatory and inhibitory neuron nodes that displays oscillations quite similar to electroencephalogram (EEG) brain waves, and identify abrupt variations brought about by swift synaptic mediations. We thus conclude that corresponding changes in EEG series surely come from the slowdown of the activity in neuron populations due to synaptic restrictions. The latter happens to generate an imbalance between excitation and inhibition causing a quick explosive increase of excitatory activity, which turns out to be a (first-order) transition among dynamic mental phases. Besides, near this phase transition, our model system exhibits waves with a strong component in the so-called \textit{delta-theta domain} that coexist with fast oscillations. These findings provide a simple explanation for the observed \textit{delta-gamma} and \textit{theta-gamma modulation} in actual brains, and open a serious and versatile path to understand deeply large amounts of apparently erratic, easily accessible brain data. |
2006.13693 | Richard Q Bao | Richard Bao, August Chen, Jethin Gowda, Shiva Mudide | PECAIQR: A Model for Infectious Disease Applied to the Covid-19 Epidemic | null | null | null | null | q-bio.PE cs.LG physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Covid-19 pandemic has made clear the need to improve modern multivariate
time-series forecasting models. Current state of the art predictions of future
daily deaths and, especially, hospital resource usage have confidence intervals
that are unacceptably wide. Policy makers and hospitals require accurate
forecasts to make informed decisions on passing legislation and allocating
resources. We used US county-level data on daily deaths and population
statistics to forecast future deaths. We extended the SIR epidemiological model
to a novel model we call the PECAIQR model. It adds several new variables and
parameters to the naive SIR model by taking into account the ramifications of
the partial quarantining implemented in the US. We fitted data to the model
parameters with numerical integration. Because of the fit degeneracy in
parameter space and non-constant nature of the parameters, we developed several
methods to optimize our fit, such as training on the data tail and training on
specific policy regimes. We use cross-validation to tune our hyper parameters
at the county level and generate a CDF for future daily deaths. For predictions
made from training data up to May 25th, we consistently obtained an averaged
pinball loss score of 0.096 on a 14 day forecast. We finally present examples
of possible avenues for utility from our model. We generate longer-time horizon
predictions over various 1-month windows in the past, forecast how many medical
resources such as ventilators and ICU beds will be needed in counties, and
evaluate the efficacy of our model in other countries.
| [
{
"created": "Wed, 17 Jun 2020 17:59:55 GMT",
"version": "v1"
}
] | 2020-06-25 | [
[
"Bao",
"Richard",
""
],
[
"Chen",
"August",
""
],
[
"Gowda",
"Jethin",
""
],
[
"Mudide",
"Shiva",
""
]
] | The Covid-19 pandemic has made clear the need to improve modern multivariate time-series forecasting models. Current state of the art predictions of future daily deaths and, especially, hospital resource usage have confidence intervals that are unacceptably wide. Policy makers and hospitals require accurate forecasts to make informed decisions on passing legislation and allocating resources. We used US county-level data on daily deaths and population statistics to forecast future deaths. We extended the SIR epidemiological model to a novel model we call the PECAIQR model. It adds several new variables and parameters to the naive SIR model by taking into account the ramifications of the partial quarantining implemented in the US. We fitted data to the model parameters with numerical integration. Because of the fit degeneracy in parameter space and non-constant nature of the parameters, we developed several methods to optimize our fit, such as training on the data tail and training on specific policy regimes. We use cross-validation to tune our hyper parameters at the county level and generate a CDF for future daily deaths. For predictions made from training data up to May 25th, we consistently obtained an averaged pinball loss score of 0.096 on a 14 day forecast. We finally present examples of possible avenues for utility from our model. We generate longer-time horizon predictions over various 1-month windows in the past, forecast how many medical resources such as ventilators and ICU beds will be needed in counties, and evaluate the efficacy of our model in other countries. |
1707.03302 | Matthew Spencer | Matthew Spencer | Equivalence relations on ecosystems | 17 pages, 3 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In abstract terms, ecosystem ecology is about determining when two
ecosystems, superficially different, are alike in some deeper way. An external
observer can choose any ecosystem property as being important. In contrast, two
ecosystems are equivalent from the point of view of the organisms they contain
if and only if for each species, the proportional population growth rate does
not differ between the ecosystems. Comparative studies of ecosystems should
therefore focus on patterns in proportional population growth rates, rather
than patterns in other properties such as relative abundances. Popular
activities such as measuring dissimilarity, and representing dissimilarity via
ordination, can then be done from the point of view of the organisms in
ecosystems. Summarizing the state of an ecosystem under this approach remains
challenging. In general, the dynamics on equivalence classes of ecosystems
defined in this way are structurally different from the dynamics of ecosystems
as seen by an external observer. This may limit the extent to which natural
selection can act on ecosystem structure.
| [
{
"created": "Tue, 11 Jul 2017 14:30:54 GMT",
"version": "v1"
}
] | 2017-07-12 | [
[
"Spencer",
"Matthew",
""
]
] | In abstract terms, ecosystem ecology is about determining when two ecosystems, superficially different, are alike in some deeper way. An external observer can choose any ecosystem property as being important. In contrast, two ecosystems are equivalent from the point of view of the organisms they contain if and only if for each species, the proportional population growth rate does not differ between the ecosystems. Comparative studies of ecosystems should therefore focus on patterns in proportional population growth rates, rather than patterns in other properties such as relative abundances. Popular activities such as measuring dissimilarity, and representing dissimilarity via ordination, can then be done from the point of view of the organisms in ecosystems. Summarizing the state of an ecosystem under this approach remains challenging. In general, the dynamics on equivalence classes of ecosystems defined in this way are structurally different from the dynamics of ecosystems as seen by an external observer. This may limit the extent to which natural selection can act on ecosystem structure. |
q-bio/0402030 | Ajit Kumar Mohanty | Ajit Kumar Mohanty (NPD, BARC, India) | A Barrier Penetration Model for DNA Double Strand Separation | 32 pages, 8 figures | null | null | null | q-bio.QM | null | A barrier penetration model has been proposed to explain the spontaneous
melting of the DNA oligomers into two separate single strands whereas the
partially melted intermediate states are shown to be the bound state solution
of the same effective potential that generates the barrier.
| [
{
"created": "Fri, 13 Feb 2004 05:45:38 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Mohanty",
"Ajit Kumar",
"",
"NPD, BARC, India"
]
] | A barrier penetration model has been proposed to explain the spontaneous melting of the DNA oligomers into two separate single strands whereas the partially melted intermediate states are shown to be the bound state solution of the same effective potential that generates the barrier. |
1911.12909 | Xiao-Jun Cai | Xiaojun Cai (1), Ruidong Li (2), Changcheng Sheng (1,3), Yifeng Tao
(2), Quanbao Zhang (2), Xiaofei Zhang (2), Juan Li (2), Conghuan Shen (2),
Xiaoyan Qiu (1), Zhengxin Wang (2), Zheng Jiao (1) | Systematic external evaluation of published population pharmacokinetic
models for tacrolimus in adult liver transplant recipients | null | Eur.J.Pharm.Sci.145(2020)105237 | 10.1016/j.ejps.2020.105237 | EJPS-D-19-01454 | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background:Diverse tacrolimus population pharmacokinetic models in adult
liver transplant recipients have been established to describe the PK
characteristics of tacrolimus in the last two decades. However, their
extrapolated predictive performance remains unclear.Therefore,in this study,we
aimed to evaluate their external predictability and identify their potential
influencing factors. Methods:The external predictability of each selected popPK
model was evaluated using an independent dataset of 84 patients with 572 trough
concentrations prospectively collected from Huashan Hospital. Prediction and
simulation based diagnostics and Bayesian forecasting were conducted to
evaluate model predictability. Furthermore, the effect of model structure on
the predictive performance was investigated.Results:Sixteen published popPK
models were assessed. In prediction-based diagnostics,the prediction error
within 30% was below 50% in all the published models. The simulation based
normalised prediction distribution error test and visual predictive check
indicated large discrepancies between the observations and simulations in most
of the models. Bayesian forecasting showed improvement in model predictability
with two to three prior observations. Additionally, the predictive performance
of the nonlinear Michaelis Menten model was superior to that of linear
compartment models,indicating the underlying nonlinear kinetics of tacrolimus
in liver transplant recipients.Conclusions:The published models performed
inadequately in prediction and simulation based diagnostics. Bayesian
forecasting may improve the predictive performance of the models. Furthermore,
nonlinear kinetics of tacrolimus may be mainly caused by the properties of the
drug itself, and incorporating nonlinear kinetics may be considered to improve
model predictability.
| [
{
"created": "Fri, 29 Nov 2019 00:43:13 GMT",
"version": "v1"
}
] | 2020-02-20 | [
[
"Cai",
"Xiaojun",
""
],
[
"Li",
"Ruidong",
""
],
[
"Sheng",
"Changcheng",
""
],
[
"Tao",
"Yifeng",
""
],
[
"Zhang",
"Quanbao",
""
],
[
"Zhang",
"Xiaofei",
""
],
[
"Li",
"Juan",
""
],
[
"Shen",
"Conghuan",
""
],
[
"Qiu",
"Xiaoyan",
""
],
[
"Wang",
"Zhengxin",
""
],
[
"Jiao",
"Zheng",
""
]
] | Background:Diverse tacrolimus population pharmacokinetic models in adult liver transplant recipients have been established to describe the PK characteristics of tacrolimus in the last two decades. However, their extrapolated predictive performance remains unclear.Therefore,in this study,we aimed to evaluate their external predictability and identify their potential influencing factors. Methods:The external predictability of each selected popPK model was evaluated using an independent dataset of 84 patients with 572 trough concentrations prospectively collected from Huashan Hospital. Prediction and simulation based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. Furthermore, the effect of model structure on the predictive performance was investigated.Results:Sixteen published popPK models were assessed. In prediction-based diagnostics,the prediction error within 30% was below 50% in all the published models. The simulation based normalised prediction distribution error test and visual predictive check indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting showed improvement in model predictability with two to three prior observations. Additionally, the predictive performance of the nonlinear Michaelis Menten model was superior to that of linear compartment models,indicating the underlying nonlinear kinetics of tacrolimus in liver transplant recipients.Conclusions:The published models performed inadequately in prediction and simulation based diagnostics. Bayesian forecasting may improve the predictive performance of the models. Furthermore, nonlinear kinetics of tacrolimus may be mainly caused by the properties of the drug itself, and incorporating nonlinear kinetics may be considered to improve model predictability. |
1605.09073 | Yu Hu | Yu Hu, Steven L. Brunton, Nicholas Cain, Stefan Mihalas, J. Nathan
Kutz, Eric Shea-Brown | Feedback through graph motifs relates structure and function in complex
networks | 31 pages, 20 figures | Phys. Rev. E 98, 062312 (2018) | 10.1103/PhysRevE.98.062312 | null | q-bio.NC cond-mat.dis-nn physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In physics, biology and engineering, network systems abound. How does the
connectivity of a network system combine with the behavior of its individual
components to determine its collective function? We approach this question for
networks with linear time-invariant dynamics by relating internal network
feedbacks to the statistical prevalence of connectivity motifs, a set of
surprisingly simple and local statistics of connectivity. This results in a
reduced order model of the network input-output dynamics in terms of motifs
structures. As an example, the new formulation dramatically simplifies the
classic Erdos-Renyi graph, reducing the overall network behavior to one
proportional feedback wrapped around the dynamics of a single node. For general
networks, higher-order motifs systematically provide further layers and types
of feedback to regulate the network response. Thus, the local connectivity
shapes temporal and spectral processing by the network as a whole, and we show
how this enables robust, yet tunable, functionality such as extending the time
constant with which networks remember past signals. The theory also extends to
networks composed from heterogeneous nodes with distinct dynamics and
connectivity, and patterned input to (and readout from) subsets of nodes. These
statistical descriptions provide a powerful theoretical framework to understand
the functionality of real-world network systems, as we illustrate with examples
including the mouse brain connectome.
| [
{
"created": "Sun, 29 May 2016 22:58:04 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Dec 2018 16:33:43 GMT",
"version": "v2"
}
] | 2018-12-19 | [
[
"Hu",
"Yu",
""
],
[
"Brunton",
"Steven L.",
""
],
[
"Cain",
"Nicholas",
""
],
[
"Mihalas",
"Stefan",
""
],
[
"Kutz",
"J. Nathan",
""
],
[
"Shea-Brown",
"Eric",
""
]
] | In physics, biology and engineering, network systems abound. How does the connectivity of a network system combine with the behavior of its individual components to determine its collective function? We approach this question for networks with linear time-invariant dynamics by relating internal network feedbacks to the statistical prevalence of connectivity motifs, a set of surprisingly simple and local statistics of connectivity. This results in a reduced order model of the network input-output dynamics in terms of motifs structures. As an example, the new formulation dramatically simplifies the classic Erdos-Renyi graph, reducing the overall network behavior to one proportional feedback wrapped around the dynamics of a single node. For general networks, higher-order motifs systematically provide further layers and types of feedback to regulate the network response. Thus, the local connectivity shapes temporal and spectral processing by the network as a whole, and we show how this enables robust, yet tunable, functionality such as extending the time constant with which networks remember past signals. The theory also extends to networks composed from heterogeneous nodes with distinct dynamics and connectivity, and patterned input to (and readout from) subsets of nodes. These statistical descriptions provide a powerful theoretical framework to understand the functionality of real-world network systems, as we illustrate with examples including the mouse brain connectome. |
2405.05293 | Sergei Voloboev | Sergei Voloboev | A Review on Fragment-based De Novo 2D Molecule Generation | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | In the field of computational molecule generation, an essential task in the
discovery of new chemical compounds, fragment-based deep generative models are
a leading approach, consistently achieving state-of-the-art results in
molecular design benchmarks as of 2023. We present a detailed comparative
assessment of their architectures, highlighting their unique approaches to
molecular fragmentation and generative modeling. This review also includes
comparisons of output quality, generation speed, and the current limitations of
specific models. We also highlight promising avenues for future research that
could bridge fragment-based models to real-world applications.
| [
{
"created": "Wed, 8 May 2024 09:38:38 GMT",
"version": "v1"
}
] | 2024-05-10 | [
[
"Voloboev",
"Sergei",
""
]
] | In the field of computational molecule generation, an essential task in the discovery of new chemical compounds, fragment-based deep generative models are a leading approach, consistently achieving state-of-the-art results in molecular design benchmarks as of 2023. We present a detailed comparative assessment of their architectures, highlighting their unique approaches to molecular fragmentation and generative modeling. This review also includes comparisons of output quality, generation speed, and the current limitations of specific models. We also highlight promising avenues for future research that could bridge fragment-based models to real-world applications. |
1603.01297 | Bal\'azs Madas | Bal\'azs G. Madas | Radon induced hyperplasia: effective adaptation reducing the local doses
in the bronchial epithelium | 13 pages, 5 figures, 1 table | null | null | null | q-bio.TO physics.bio-ph physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is experimental and histological evidence that chronic irritation and
cell death may cause hyperplasia in the exposed tissue. As the heterogeneous
deposition of inhaled radon progeny results in high local doses at the peak of
the bronchial bifurcations, it was proposed earlier that hyperplasia occurs in
these deposition hot spots upon chronic radon exposure. The objective of the
present study is to quantify how the induction of basal cell hyperplasia
modulates the microdosimetric consequences of a given radon exposure. For this
purpose, numerical epithelium models were generated with spherical cell nuclei
of six different cell types based on histological data. Basal cell hyperplasia
was modelled by epithelium models with additional basal cells and increased
epithelium thickness. Microdosimetry for alpha-particles was performed by an
own-developed Monte-Carlo code. Results show that the average tissue dose, and
the average hit number and dose of basal cells decrease by the increase of the
measure of hyperplasia. Hit and dose distribution reveal that the induction of
hyperplasia may result in a basal cell pool which is shielded from alpha
radiation. It highlights that the exposure history affects the microdosimetric
consequences of a present exposure, while the biological and health effects may
also depend on previous exposures. The induction of hyperplasia can be
considered as a radioadaptive response at the tissue level. Such an adaptation
of the tissue challenges the validity of the application of the dose dose rate
effectiveness factor from a mechanistic point of view. As the location of
radiosensitive target cells may change due to previous exposures, dosimetry
models considering the tissue geometry characteristic of normal conditions may
be inappropriate for dose estimation in case of protracted exposures. As
internal exposures are frequently chronic, such changes in tissue...
| [
{
"created": "Thu, 3 Mar 2016 22:06:42 GMT",
"version": "v1"
}
] | 2016-03-07 | [
[
"Madas",
"Balázs G.",
""
]
] | There is experimental and histological evidence that chronic irritation and cell death may cause hyperplasia in the exposed tissue. As the heterogeneous deposition of inhaled radon progeny results in high local doses at the peak of the bronchial bifurcations, it was proposed earlier that hyperplasia occurs in these deposition hot spots upon chronic radon exposure. The objective of the present study is to quantify how the induction of basal cell hyperplasia modulates the microdosimetric consequences of a given radon exposure. For this purpose, numerical epithelium models were generated with spherical cell nuclei of six different cell types based on histological data. Basal cell hyperplasia was modelled by epithelium models with additional basal cells and increased epithelium thickness. Microdosimetry for alpha-particles was performed by an own-developed Monte-Carlo code. Results show that the average tissue dose, and the average hit number and dose of basal cells decrease by the increase of the measure of hyperplasia. Hit and dose distribution reveal that the induction of hyperplasia may result in a basal cell pool which is shielded from alpha radiation. It highlights that the exposure history affects the microdosimetric consequences of a present exposure, while the biological and health effects may also depend on previous exposures. The induction of hyperplasia can be considered as a radioadaptive response at the tissue level. Such an adaptation of the tissue challenges the validity of the application of the dose dose rate effectiveness factor from a mechanistic point of view. As the location of radiosensitive target cells may change due to previous exposures, dosimetry models considering the tissue geometry characteristic of normal conditions may be inappropriate for dose estimation in case of protracted exposures. As internal exposures are frequently chronic, such changes in tissue... |
0705.2491 | Kazuya Ishibashi | Kazuya Ishibashi, Kosuke Hamaguchi, and Masato Okada | Sparse and Dense Encoding in Layered Associative Network of Spiking
Neurons | null | null | 10.1143/JPSJ.76.124801 | null | q-bio.NC | null | A synfire chain is a simple neural network model which can propagate stable
synchronous spikes called a pulse packet and widely researched. However how
synfire chains coexist in one network remains to be elucidated. We have studied
the activity of a layered associative network of Leaky Integrate-and-Fire
neurons in which connection we embed memory patterns by the Hebbian Learning.
We analyzed their activity by the Fokker-Planck method. In our previous report,
when a half of neurons belongs to each memory pattern (memory pattern rate
$F=0.5$), the temporal profiles of the network activity is split into
temporally clustered groups called sublattices under certain input conditions.
In this study, we show that when the network is sparsely connected ($F<0.5$),
synchronous firings of the memory pattern are promoted. On the contrary, the
densely connected network ($F>0.5$) inhibit synchronous firings. The sparseness
and denseness also effect the basin of attraction and the storage capacity of
the embedded memory patterns. We show that the sparsely(densely) connected
networks enlarge(shrink) the basion of attraction and increase(decrease) the
storage capacity.
| [
{
"created": "Thu, 17 May 2007 07:18:34 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Ishibashi",
"Kazuya",
""
],
[
"Hamaguchi",
"Kosuke",
""
],
[
"Okada",
"Masato",
""
]
] | A synfire chain is a simple neural network model which can propagate stable synchronous spikes called a pulse packet and widely researched. However how synfire chains coexist in one network remains to be elucidated. We have studied the activity of a layered associative network of Leaky Integrate-and-Fire neurons in which connection we embed memory patterns by the Hebbian Learning. We analyzed their activity by the Fokker-Planck method. In our previous report, when a half of neurons belongs to each memory pattern (memory pattern rate $F=0.5$), the temporal profiles of the network activity is split into temporally clustered groups called sublattices under certain input conditions. In this study, we show that when the network is sparsely connected ($F<0.5$), synchronous firings of the memory pattern are promoted. On the contrary, the densely connected network ($F>0.5$) inhibit synchronous firings. The sparseness and denseness also effect the basin of attraction and the storage capacity of the embedded memory patterns. We show that the sparsely(densely) connected networks enlarge(shrink) the basion of attraction and increase(decrease) the storage capacity. |
1505.05644 | Aristides Moustakas | Marianna Louca, Ioannis N. Vogiatzakis, and Aristides Moustakas | Modelling the combined effects of land use and climatic changes:
coupling bioclimatic modelling with markov-chain cellular automata in a case
study in Cyprus | to appear (in press in Ecological Informatics (2015)) | null | null | null | q-bio.PE cs.CY cs.DM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two endemic plant species in the Mediterranean island of Cyprus, Crocus
cyprius and Ophrys kotschyi, were used as a case study. We have coupled climate
change scenarios, and land use change models with species distribution models.
Future land use scenarios were modelled by initially calculating the rate of
current land use changes between two time snapshots (2000 and 2006) on the
island, and based on these transition probabilities markov-chain cellular
automata were used to generate future land use changes for 2050. Climate change
scenarios A1B, A2, B1 and B2A were derived from the IPCC reports. Species
climatic preferences were derived from their current distributions using
classification trees while habitats preferences were derived from the Red Data
Book of the Flora of Cyprus. A bioclimatic model for Crocus cyprius was built
using mean temperature of wettest quarter, max temperature of warmest month and
precipitation seasonality, while for Ophrys kotchyi the bioclimatic model was
built using precipitation of wettest month, mean temperature of warmest
quarter, isothermality, precipitation of coldest quarter, and annual
precipitation. Sequentially, simulation scenarios were performed regarding
future species distributions by accounting climate alone and both climate and
land use changes. The distribution of the two species resulting from the
bioclimatic models was then filtered by future land use changes, providing the
species projected potential distribution. The species projected potential
distribution varies depending on the type and scenario used, but many of both
species current sites/locations are projected to be outside their future
potential distribution. Our results demonstrate the importance of including
both land use and climatic changes in predictive species modeling.
| [
{
"created": "Thu, 21 May 2015 08:25:16 GMT",
"version": "v1"
}
] | 2015-05-22 | [
[
"Louca",
"Marianna",
""
],
[
"Vogiatzakis",
"Ioannis N.",
""
],
[
"Moustakas",
"Aristides",
""
]
] | Two endemic plant species in the Mediterranean island of Cyprus, Crocus cyprius and Ophrys kotschyi, were used as a case study. We have coupled climate change scenarios, and land use change models with species distribution models. Future land use scenarios were modelled by initially calculating the rate of current land use changes between two time snapshots (2000 and 2006) on the island, and based on these transition probabilities markov-chain cellular automata were used to generate future land use changes for 2050. Climate change scenarios A1B, A2, B1 and B2A were derived from the IPCC reports. Species climatic preferences were derived from their current distributions using classification trees while habitats preferences were derived from the Red Data Book of the Flora of Cyprus. A bioclimatic model for Crocus cyprius was built using mean temperature of wettest quarter, max temperature of warmest month and precipitation seasonality, while for Ophrys kotchyi the bioclimatic model was built using precipitation of wettest month, mean temperature of warmest quarter, isothermality, precipitation of coldest quarter, and annual precipitation. Sequentially, simulation scenarios were performed regarding future species distributions by accounting climate alone and both climate and land use changes. The distribution of the two species resulting from the bioclimatic models was then filtered by future land use changes, providing the species projected potential distribution. The species projected potential distribution varies depending on the type and scenario used, but many of both species current sites/locations are projected to be outside their future potential distribution. Our results demonstrate the importance of including both land use and climatic changes in predictive species modeling. |
1104.3457 | Sergei Rybalko | Sergei Rybalko and Sergei Larionov and Maria Poptsova and Alexander
Loskutov | Intermittency as a universal characteristic of the complete chromosome
DNA sequences of eukaryotes: From protozoa to human genomes | 4 pages, 5 figures | null | 10.1103/PhysRevE.84.042902 | null | q-bio.GN cond-mat.soft nlin.CD q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large-scale dynamical properties of complete chromosome DNA sequences of
eukaryotes are considered. By the proposed deterministic models with
intermittency and symbolic dynamics we describe a wide spectrum of large-scale
patterns inherent in these sequences, such as segmental duplications, tandem
repeats, and other complex sequence structures. It is shown that the recently
discovered gene number balance on the strands is not of random nature, and a
complete chromosome DNA sequence exhibits the properties of deterministic
chaos.
| [
{
"created": "Mon, 18 Apr 2011 12:35:47 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Apr 2011 12:49:31 GMT",
"version": "v2"
}
] | 2015-05-27 | [
[
"Rybalko",
"Sergei",
""
],
[
"Larionov",
"Sergei",
""
],
[
"Poptsova",
"Maria",
""
],
[
"Loskutov",
"Alexander",
""
]
] | Large-scale dynamical properties of complete chromosome DNA sequences of eukaryotes are considered. By the proposed deterministic models with intermittency and symbolic dynamics we describe a wide spectrum of large-scale patterns inherent in these sequences, such as segmental duplications, tandem repeats, and other complex sequence structures. It is shown that the recently discovered gene number balance on the strands is not of random nature, and a complete chromosome DNA sequence exhibits the properties of deterministic chaos. |
2101.05746 | Rastine Merat | Aurore Bugi-Marteyn, Fanny Noulet, Nicolas Liaudet and Rastine Merat | A mutual information-based in vivo monitoring of adaptive response to
targeted therapies in melanoma | 9 pages, 5 figures Corrected typos | Neoplasia 23 (2021) pp. 775-782 | 10.1016/j.neo.2021.06.009 | null | q-bio.TO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The mechanisms of adaptive resistance to genetic-based targeted therapies of
solid malignancies have been the subject of intense research. These studies
hold great promise for finding co-targetable hub/pathways which in turn would
control the downstream non-genetic mechanisms of adaptive resistance. Many such
mechanisms have been described in the paradigmatic BRAF-mutated melanoma model
of adaptive response to BRAF inhibition. Currently, a major challenge for these
mechanistic studies is to confirm in vivo, at the single-cell proteomic level,
the existence of dependencies between the co-targeted hub/pathways and their
downstream effectors. Moreover, the drug-induced in vivo modulation of these
dependencies needs to be demonstrated. Here, we implement such
single-cell-based in vivo expression dependency quantification using
immunohistochemistry (IHC)-based analyses of sequential biopsies in two
xenograft models. These mimic phase 2 and 3 trials in our own therapeutic
strategy to prevent the adaptive response to BRAF inhibition. In this
mechanistic model, the dependencies between the targeted Li2CO3-inducible hub
HuR and the resistance effectors are more likely time-shifted and transient
since the minority of HuRLow cells, which act as a reservoir of adaptive
plasticity, switch to a HuRHigh state as they paradoxically proliferate under
BRAF inhibition. Nevertheless, we show that a copula/kernel density estimator
(KDE)-based quantification of mutual information (MI) efficiently captures, at
the individual level, the dependencies between HuR and two relevant resistance
markers pERK and EGFR, and outperforms classic expression correlation
coefficients. Ultimately, the validation of MI as a predictive IHC-based metric
of response to our therapeutic strategy will be carried in clinical trials.
| [
{
"created": "Sun, 10 Jan 2021 18:35:51 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Jan 2021 07:56:48 GMT",
"version": "v2"
}
] | 2021-07-09 | [
[
"Bugi-Marteyn",
"Aurore",
""
],
[
"Noulet",
"Fanny",
""
],
[
"Liaudet",
"Nicolas",
""
],
[
"Merat",
"Rastine",
""
]
] | The mechanisms of adaptive resistance to genetic-based targeted therapies of solid malignancies have been the subject of intense research. These studies hold great promise for finding co-targetable hub/pathways which in turn would control the downstream non-genetic mechanisms of adaptive resistance. Many such mechanisms have been described in the paradigmatic BRAF-mutated melanoma model of adaptive response to BRAF inhibition. Currently, a major challenge for these mechanistic studies is to confirm in vivo, at the single-cell proteomic level, the existence of dependencies between the co-targeted hub/pathways and their downstream effectors. Moreover, the drug-induced in vivo modulation of these dependencies needs to be demonstrated. Here, we implement such single-cell-based in vivo expression dependency quantification using immunohistochemistry (IHC)-based analyses of sequential biopsies in two xenograft models. These mimic phase 2 and 3 trials in our own therapeutic strategy to prevent the adaptive response to BRAF inhibition. In this mechanistic model, the dependencies between the targeted Li2CO3-inducible hub HuR and the resistance effectors are more likely time-shifted and transient since the minority of HuRLow cells, which act as a reservoir of adaptive plasticity, switch to a HuRHigh state as they paradoxically proliferate under BRAF inhibition. Nevertheless, we show that a copula/kernel density estimator (KDE)-based quantification of mutual information (MI) efficiently captures, at the individual level, the dependencies between HuR and two relevant resistance markers pERK and EGFR, and outperforms classic expression correlation coefficients. Ultimately, the validation of MI as a predictive IHC-based metric of response to our therapeutic strategy will be carried in clinical trials. |
2204.07362 | Keon-Woo Lee | Keon-Woo Lee and Dae-Hyeok Lee and Sung-Jin Kim and Seong-Whan Lee | Decoding Neural Correlation of Language-Specific Imagined Speech using
EEG Signals | Accepted in EMBC 2022 | null | null | null | q-bio.NC cs.HC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Speech impairments due to cerebral lesions and degenerative disorders can be
devastating. For humans with severe speech deficits, imagined speech in the
brain-computer interface has been a promising hope for reconstructing the
neural signals of speech production. However, studies in the EEG-based imagined
speech domain still have some limitations due to high variability in spatial
and temporal information and low signal-to-noise ratio. In this paper, we
investigated the neural signals for two groups of native speakers with two
tasks with different languages, English and Chinese. Our assumption was that
English, a non-tonal and phonogram-based language, would have spectral
differences in neural computation compared to Chinese, a tonal and
ideogram-based language. The results showed the significant difference in the
relative power spectral density between English and Chinese in specific
frequency band groups. Also, the spatial evaluation of Chinese native speakers
in the theta band was distinctive during the imagination task. Hence, this
paper would suggest the key spectral and spatial information of word
imagination with specialized language while decoding the neural signals of
speech.
| [
{
"created": "Fri, 15 Apr 2022 07:41:04 GMT",
"version": "v1"
}
] | 2022-04-18 | [
[
"Lee",
"Keon-Woo",
""
],
[
"Lee",
"Dae-Hyeok",
""
],
[
"Kim",
"Sung-Jin",
""
],
[
"Lee",
"Seong-Whan",
""
]
] | Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface has been a promising hope for reconstructing the neural signals of speech production. However, studies in the EEG-based imagined speech domain still have some limitations due to high variability in spatial and temporal information and low signal-to-noise ratio. In this paper, we investigated the neural signals for two groups of native speakers with two tasks with different languages, English and Chinese. Our assumption was that English, a non-tonal and phonogram-based language, would have spectral differences in neural computation compared to Chinese, a tonal and ideogram-based language. The results showed the significant difference in the relative power spectral density between English and Chinese in specific frequency band groups. Also, the spatial evaluation of Chinese native speakers in the theta band was distinctive during the imagination task. Hence, this paper would suggest the key spectral and spatial information of word imagination with specialized language while decoding the neural signals of speech. |
2306.13832 | David Benrimoh | David Benrimoh, Victoria L. Fisher, Rashina Seabury, Ely Sibarium,
Catalina Mourgues, Doris Chen, Albert Powers | Evidence for Reduced Sensory Precision and Increased Reliance on Priors
in Hallucination-Prone Individuals in a General Population Sample | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | There is increasing evidence that people with hallucinations overweight
perceptual beliefs relative to incoming sensory evidence. Much past work
demonstrating prior overweighting has used simple, non-linguistic stimuli.
However, auditory hallucinations in psychosis are often complex and linguistic.
There may be an interaction between the type of auditory information being
processed and its perceived quality in engendering hallucinations. We
administered a linguistic version of the Conditioned Hallucinations (CH) task
to an online sample of 88 general population participants. Metrics related to
hallucination-proneness, recent auditory hallucinations, stimulus thresholds,
and stimulus detection were collected; data was used to fit parameters of a
Hierarchical Gaussian Filter model of perceptual inference to determine how
latent perceptual states influenced task behavior. Replicating past results,
higher CH rates were associated with measures of higher hallucination-proneness
and recent hallucinatory experiences; CH rates were positively correlated with
increased prior weighting; and increased prior weighting was related to recent
hallucinatory experiences. Unlike past results, participants with recent
hallucinatory experiences as well as those with higher hallucination-proneness
had higher stimulus thresholds, lower sensitivity to stimuli presented at the
highest threshold, and tended to have lower response confidence, consistent
with lower precision of sensory evidence. We show that hallucination-prone
individuals in the general population have increased conditioned hallucination
rates using a linguistic version of the CH task, and replicated the finding
that increased CH rates and recent hallucinations correlate with increased
prior weighting. Results support a role for reduced sensory precision in the
interplay between prior weighting and hallucination-proneness. *contributed
equally
| [
{
"created": "Sat, 24 Jun 2023 01:18:03 GMT",
"version": "v1"
}
] | 2023-06-27 | [
[
"Benrimoh",
"David",
""
],
[
"Fisher",
"Victoria L.",
""
],
[
"Seabury",
"Rashina",
""
],
[
"Sibarium",
"Ely",
""
],
[
"Mourgues",
"Catalina",
""
],
[
"Chen",
"Doris",
""
],
[
"Powers",
"Albert",
""
]
] | There is increasing evidence that people with hallucinations overweight perceptual beliefs relative to incoming sensory evidence. Much past work demonstrating prior overweighting has used simple, non-linguistic stimuli. However, auditory hallucinations in psychosis are often complex and linguistic. There may be an interaction between the type of auditory information being processed and its perceived quality in engendering hallucinations. We administered a linguistic version of the Conditioned Hallucinations (CH) task to an online sample of 88 general population participants. Metrics related to hallucination-proneness, recent auditory hallucinations, stimulus thresholds, and stimulus detection were collected; data was used to fit parameters of a Hierarchical Gaussian Filter model of perceptual inference to determine how latent perceptual states influenced task behavior. Replicating past results, higher CH rates were associated with measures of higher hallucination-proneness and recent hallucinatory experiences; CH rates were positively correlated with increased prior weighting; and increased prior weighting was related to recent hallucinatory experiences. Unlike past results, participants with recent hallucinatory experiences as well as those with higher hallucination-proneness had higher stimulus thresholds, lower sensitivity to stimuli presented at the highest threshold, and tended to have lower response confidence, consistent with lower precision of sensory evidence. We show that hallucination-prone individuals in the general population have increased conditioned hallucination rates using a linguistic version of the CH task, and replicated the finding that increased CH rates and recent hallucinations correlate with increased prior weighting. Results support a role for reduced sensory precision in the interplay between prior weighting and hallucination-proneness. *contributed equally |
2104.01256 | Deepak Gupta | Deepak Gupta, Stefano Garlaschi, Samir Suweis, Sandro Azaele, and Amos
Maritan | Effective Resource-Competition Model for Species Coexistence | 5 pages + 4 figures, Major revision | null | 10.1103/PhysRevLett.127.208101 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Local coexistence of species in large ecosystems is traditionally explained
within the broad framework of niche theory. However, its rationale hardly
justifies rich biodiversity observed in nearly homogeneous environments. Here
we consider a consumer-resource model in which a coarse-graining procedure
accounts for a variety of ecological mechanisms and leads to effective spatial
effects which favour species coexistence. Herein, we provide conditions for
several species to live in an environment with very few resources. In fact, the
model displays two different phases depending on whether the number of
surviving species is larger or smaller than the number of resources. We obtain
conditions whereby a species can successfully colonize a pool of coexisting
species. Finally, we analytically compute the distribution of the population
sizes of coexisting species. Numerical simulations as well as empirical
distributions of population sizes support our analytical findings.
| [
{
"created": "Fri, 2 Apr 2021 22:02:50 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Oct 2021 00:06:38 GMT",
"version": "v2"
}
] | 2021-11-17 | [
[
"Gupta",
"Deepak",
""
],
[
"Garlaschi",
"Stefano",
""
],
[
"Suweis",
"Samir",
""
],
[
"Azaele",
"Sandro",
""
],
[
"Maritan",
"Amos",
""
]
] | Local coexistence of species in large ecosystems is traditionally explained within the broad framework of niche theory. However, its rationale hardly justifies rich biodiversity observed in nearly homogeneous environments. Here we consider a consumer-resource model in which a coarse-graining procedure accounts for a variety of ecological mechanisms and leads to effective spatial effects which favour species coexistence. Herein, we provide conditions for several species to live in an environment with very few resources. In fact, the model displays two different phases depending on whether the number of surviving species is larger or smaller than the number of resources. We obtain conditions whereby a species can successfully colonize a pool of coexisting species. Finally, we analytically compute the distribution of the population sizes of coexisting species. Numerical simulations as well as empirical distributions of population sizes support our analytical findings. |
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