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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1910.06659 | James McIntosh | J. R. McIntosh, J. Yao, Linbi Hong, J. Faller and P. Sajda | Ballistocardiogram artifact reduction in simultaneous EEG-fMRI using
deep learning | null | null | 10.1109/TBME.2020.3004548 | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: The concurrent recording of electroencephalography (EEG) and
functional magnetic resonance imaging (fMRI) is a technique that has received
much attention due to its potential for combined high temporal and spatial
resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact
caused by cardiac induced movement contaminates the EEG during EEG-fMRI
recordings. Removal of BCG in software has generally made use of linear
decompositions of the corrupted EEG. This is not ideal as the BCG signal is
non-stationary and propagates in a manner which is non-linearly dependent on
the electrocardiogram (ECG). In this paper, we present a novel method for BCG
artifact suppression using recurrent neural networks (RNNs). Methods: EEG
signals were recovered by training RNNs on the nonlinear mappings between ECG
and the BCG corrupted EEG. We evaluated our model's performance against the
commonly used Optimal Basis Set (OBS) method at the level of individual
subjects, and investigated generalization across subjects. Results: We show
that our algorithm can generate larger average power reduction of the BCG at
critical frequencies, while simultaneously improving task relevant EEG based
classification. Conclusion: The presented deep learning architecture can be
used to reduce BCG related artifacts in EEG-fMRI recordings. Significance: We
present a deep learning approach that can be used to suppress the BCG artifact
in EEG-fMRI without the use of additional hardware. This method may have scope
to be combined with current hardware methods, operate in real-time and be used
for direct modeling of the BCG.
| [
{
"created": "Tue, 15 Oct 2019 11:19:15 GMT",
"version": "v1"
}
] | 2020-06-29 | [
[
"McIntosh",
"J. R.",
""
],
[
"Yao",
"J.",
""
],
[
"Hong",
"Linbi",
""
],
[
"Faller",
"J.",
""
],
[
"Sajda",
"P.",
""
]
] | Objective: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal is non-stationary and propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs). Methods: EEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects. Results: We show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification. Conclusion: The presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings. Significance: We present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG. |
2402.07701 | Rupchand Sutradhar | Rupchand Sutradhar, D C Dalal | Combination Therapy for Chronic Hepatitis B Using Capsid Recycling
Inhibitor | null | null | null | null | q-bio.CB math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate the dynamics of hepatitis B virus infection
taking into account the implementation of combination therapy through
mathematical modeling. This model is established considering the interplay
between uninfected cells, infected cells, capsids, and viruses. Three drugs are
considered for specific roles (i) pegylated interferon (PEG IFN) for immune
modulation, (ii) lamivudine (LMV) as a reverse-transcriptase inhibitor, and
(iii) entecavir (ETV) to block capsid recycling. Using these drugs, three
combination therapies are introduced, specifically CT PEG IFN plus LMV, CT PEG
IFN plus ETV, and CT PEG IFN plus LMV plus ETV. As a result, when LMV is used
in combination therapy with PEG IFN and ETV, the impacts of ETV become
insignificant. In conclusion, if the appropriate drug effectively inhibits
reverse transcription, there is no need for an additional inhibitor to block
capsid recycling.
| [
{
"created": "Mon, 12 Feb 2024 15:08:49 GMT",
"version": "v1"
}
] | 2024-02-13 | [
[
"Sutradhar",
"Rupchand",
""
],
[
"Dalal",
"D C",
""
]
] | In this paper, we investigate the dynamics of hepatitis B virus infection taking into account the implementation of combination therapy through mathematical modeling. This model is established considering the interplay between uninfected cells, infected cells, capsids, and viruses. Three drugs are considered for specific roles (i) pegylated interferon (PEG IFN) for immune modulation, (ii) lamivudine (LMV) as a reverse-transcriptase inhibitor, and (iii) entecavir (ETV) to block capsid recycling. Using these drugs, three combination therapies are introduced, specifically CT PEG IFN plus LMV, CT PEG IFN plus ETV, and CT PEG IFN plus LMV plus ETV. As a result, when LMV is used in combination therapy with PEG IFN and ETV, the impacts of ETV become insignificant. In conclusion, if the appropriate drug effectively inhibits reverse transcription, there is no need for an additional inhibitor to block capsid recycling. |
1601.07447 | Alexei Drummond | Alexei J. Drummond and Tanja Stadler | Bayesian phylogenetic estimation of fossil ages | 28 pages, 8 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances have allowed for both morphological fossil evidence and
molecular sequences to be integrated into a single combined inference of
divergence dates under the rule of Bayesian probability. In particular the
fossilized birth-death tree prior and the Lewis-Mk model of discrete
morphological evolution allow for the estimation of both divergence times and
phylogenetic relationships between fossil and extant taxa. We exploit this
statistical framework to investigate the internal consistency of these models
by producing phylogenetic estimates of the age of each fossil in turn, within
two rich and well-characterized data sets of fossil and extant species
(penguins and canids). We find that the estimation accuracy of fossil ages is
generally high with credible intervals seldom excluding the true age and median
relative error in the two data sets of 5.7% and 13.2% respectively. The median
relative standard error (RSD) was 9.2% and 7.2% respectively, suggesting good
precision, although with some outliers. In fact in the two data sets we analyze
the phylogenetic estimates of fossil age is on average < 2 My from the midpoint
age of the geological strata from which it was excavated. The high level of
internal consistency found in our analyses suggests that the Bayesian
statistical model employed is an adequate fit for both the geological and
morphological data, and provides evidence from real data that the framework
used can accurately model the evolution of discrete morphological traits coded
from fossil and extant taxa. We anticipate that this approach will have diverse
applications beyond divergence time dating, including dating fossils that are
temporally unconstrained, testing of the "morphological clock", and for
uncovering potential model misspecification and/or data errors when
controversial phylogenetic hypotheses are obtained based on combined divergence
dating analyses.
| [
{
"created": "Wed, 27 Jan 2016 16:55:07 GMT",
"version": "v1"
},
{
"created": "Tue, 3 May 2016 05:28:00 GMT",
"version": "v2"
}
] | 2016-05-04 | [
[
"Drummond",
"Alexei J.",
""
],
[
"Stadler",
"Tanja",
""
]
] | Recent advances have allowed for both morphological fossil evidence and molecular sequences to be integrated into a single combined inference of divergence dates under the rule of Bayesian probability. In particular the fossilized birth-death tree prior and the Lewis-Mk model of discrete morphological evolution allow for the estimation of both divergence times and phylogenetic relationships between fossil and extant taxa. We exploit this statistical framework to investigate the internal consistency of these models by producing phylogenetic estimates of the age of each fossil in turn, within two rich and well-characterized data sets of fossil and extant species (penguins and canids). We find that the estimation accuracy of fossil ages is generally high with credible intervals seldom excluding the true age and median relative error in the two data sets of 5.7% and 13.2% respectively. The median relative standard error (RSD) was 9.2% and 7.2% respectively, suggesting good precision, although with some outliers. In fact in the two data sets we analyze the phylogenetic estimates of fossil age is on average < 2 My from the midpoint age of the geological strata from which it was excavated. The high level of internal consistency found in our analyses suggests that the Bayesian statistical model employed is an adequate fit for both the geological and morphological data, and provides evidence from real data that the framework used can accurately model the evolution of discrete morphological traits coded from fossil and extant taxa. We anticipate that this approach will have diverse applications beyond divergence time dating, including dating fossils that are temporally unconstrained, testing of the "morphological clock", and for uncovering potential model misspecification and/or data errors when controversial phylogenetic hypotheses are obtained based on combined divergence dating analyses. |
1112.2563 | Alessandra Andreoni | Luca Nardo, Giovanna Tosi, Maria Bondani, Roberto S. Accolla,
Alessandra Andreoni | Polymorphic gene conferring susceptibility to insulin-dependent diabetes
mellitus typed by ps-resolved FRET on nonamplified genomic DNA | 10 pp including 4 figs. and 1 table | null | null | null | q-bio.GN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work concerns the identification of the allelic sequences of the DQB1
gene of the human leukocyte antigen system conferring susceptibility to the
development of insulin-dependent diabetes mellitus (IDDM) in DNA samples with
no need of PCR amplification. Our method is based on the time-resolved analysis
of a F\"orster energy-transfer mechanism that occurs in a dual-labeled
fluorescent probe specific for the base sequence of the allelic variant of
interest. Such an oligonucleotide probe is labeled, at the two ends, by a pair
of chromophores that operate as donor and acceptor in a F\"orster resonant
energy-transfer. The donor fluorescence is quenched with an efficiency that is
strongly dependent on the donor-to-acceptor distance, hence on the
configuration of the probe after hybridization with the DNA containing or not
the selected allelic sequence. By time-correlated single-photon counting,
performed with an excitation/detection system endowed with 30-ps resolution, we
measure the time-resolved fluorescence decay of the donor and discriminate, by
means of the decay time value, the DNA bearing the allele conferring
susceptibility to IDDM from the DNAs bearing any other sequence in the same
region of the DQB1 gene.
| [
{
"created": "Mon, 12 Dec 2011 14:38:44 GMT",
"version": "v1"
}
] | 2011-12-13 | [
[
"Nardo",
"Luca",
""
],
[
"Tosi",
"Giovanna",
""
],
[
"Bondani",
"Maria",
""
],
[
"Accolla",
"Roberto S.",
""
],
[
"Andreoni",
"Alessandra",
""
]
] | This work concerns the identification of the allelic sequences of the DQB1 gene of the human leukocyte antigen system conferring susceptibility to the development of insulin-dependent diabetes mellitus (IDDM) in DNA samples with no need of PCR amplification. Our method is based on the time-resolved analysis of a F\"orster energy-transfer mechanism that occurs in a dual-labeled fluorescent probe specific for the base sequence of the allelic variant of interest. Such an oligonucleotide probe is labeled, at the two ends, by a pair of chromophores that operate as donor and acceptor in a F\"orster resonant energy-transfer. The donor fluorescence is quenched with an efficiency that is strongly dependent on the donor-to-acceptor distance, hence on the configuration of the probe after hybridization with the DNA containing or not the selected allelic sequence. By time-correlated single-photon counting, performed with an excitation/detection system endowed with 30-ps resolution, we measure the time-resolved fluorescence decay of the donor and discriminate, by means of the decay time value, the DNA bearing the allele conferring susceptibility to IDDM from the DNAs bearing any other sequence in the same region of the DQB1 gene. |
1712.09254 | Haiguang Liu | Yihua Wang, Daqi Yu, Qi Ouyang, Haiguang Liu | The determinant factors for model resolutions obtained using CryoEM
method | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The CryoEM single particle imaging method has recently received broad
attention in the field of structural biology for determining the structures of
biological molecules. The structures can be resolved to near-atomic resolutions
after rending a large number of CryoEM images measuring molecules in different
orientations. However, the factors for model resolution need to be further
explored. Here, we provide a theoretical framework in conjunction with
numerical simulations to gauge the influence of several key factors that are
determinant in model resolution. We found that the number of measured
projection images and the quality of each measurement (quantified using average
signal-noise-ratio) can be combined to a single factor, which is dominant to
the constructed model resolution. Furthermore, the intrinsic thermal motion of
the molecules and the defocus levels of the electron microscope both have
significant effects on the model resolution. These effects can be
quantitatively summarized using an analytical formula that provides a
theoretical guideline on structure resolutions for given experimental
measurements.
| [
{
"created": "Tue, 26 Dec 2017 12:39:22 GMT",
"version": "v1"
}
] | 2017-12-27 | [
[
"Wang",
"Yihua",
""
],
[
"Yu",
"Daqi",
""
],
[
"Ouyang",
"Qi",
""
],
[
"Liu",
"Haiguang",
""
]
] | The CryoEM single particle imaging method has recently received broad attention in the field of structural biology for determining the structures of biological molecules. The structures can be resolved to near-atomic resolutions after rending a large number of CryoEM images measuring molecules in different orientations. However, the factors for model resolution need to be further explored. Here, we provide a theoretical framework in conjunction with numerical simulations to gauge the influence of several key factors that are determinant in model resolution. We found that the number of measured projection images and the quality of each measurement (quantified using average signal-noise-ratio) can be combined to a single factor, which is dominant to the constructed model resolution. Furthermore, the intrinsic thermal motion of the molecules and the defocus levels of the electron microscope both have significant effects on the model resolution. These effects can be quantitatively summarized using an analytical formula that provides a theoretical guideline on structure resolutions for given experimental measurements. |
1907.12654 | Rudy Arthur | R. Arthur and A. Nicholson | Selection Principles for Gaia | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Gaia hypothesis considers the life-environment coupled system as a single
entity that acts to regulate and maintain habitable conditions on Earth. In
this paper we discuss three mechanisms which could potentially lead to Gaia:
Selection by Survival, Sequential Selection and Entropic Hierarchy. We use the
Tangled Nature Model of co-evolution as a common framework for investigating
all three, using an extended version of the standard model to elaborate on Gaia
as an example of an entropic hierarchy. This idea, which combines sequential
selection together with a reservoir of diversity that acts as a 'memory',
implies a tendency towards growth and increasing resilience of the Gaian system
over time. We then discuss how Gaian memory could be realised in practice via
the microbial seed bank, climate refugia and lateral gene transfer and conclude
by discussing testable implications of an entropic hierarchy for the study of
Earth history and the search for life in the universe. This paper adds to the
existing taxonomy of Gaia hypotheses to suggest an "Entropic Gaia" where we
argue that increasing biomass, complexity and enhanced habitability over time
is a statistically likely feature of a co-evolving system.
| [
{
"created": "Mon, 29 Jul 2019 21:07:38 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Mar 2021 15:58:56 GMT",
"version": "v2"
},
{
"created": "Tue, 23 Nov 2021 11:54:57 GMT",
"version": "v3"
}
] | 2021-11-24 | [
[
"Arthur",
"R.",
""
],
[
"Nicholson",
"A.",
""
]
] | The Gaia hypothesis considers the life-environment coupled system as a single entity that acts to regulate and maintain habitable conditions on Earth. In this paper we discuss three mechanisms which could potentially lead to Gaia: Selection by Survival, Sequential Selection and Entropic Hierarchy. We use the Tangled Nature Model of co-evolution as a common framework for investigating all three, using an extended version of the standard model to elaborate on Gaia as an example of an entropic hierarchy. This idea, which combines sequential selection together with a reservoir of diversity that acts as a 'memory', implies a tendency towards growth and increasing resilience of the Gaian system over time. We then discuss how Gaian memory could be realised in practice via the microbial seed bank, climate refugia and lateral gene transfer and conclude by discussing testable implications of an entropic hierarchy for the study of Earth history and the search for life in the universe. This paper adds to the existing taxonomy of Gaia hypotheses to suggest an "Entropic Gaia" where we argue that increasing biomass, complexity and enhanced habitability over time is a statistically likely feature of a co-evolving system. |
2010.06078 | Seung Suk Kang | Seung Suk Kang, Ph.D., Scott R. Sponheim, Ph.D., Kelvin O. Lim, M.D | Interoception Underlies The Therapeutic Effects of Mindfulness
Meditation for Post-Traumatic Stress Disorder: A Randomized Clinical Trial | 17 pages, 5 figures, 1 table | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mindfulness-based interventions have proven its efficacy in treating
post-traumatic stress disorder (PTSD), but the underlying neurobiological
mechanism is unknown. To determine the neurobiological mechanism of action of
mindfulness-based stress reduction (MBSR) treating PTSD, we conducted a
randomized clinical trial (RCT) in which 98 veterans with PTSD were randomly
assigned to receive MBSR therapy (n = 47) or present-centered group therapy
(PCGT; n = 51; an active-control condition). Pre- and post-intervention
measures of PTSD symptom severity (PTSD Checklist) and brain activity measures
of electroencephalography (EEG) were assessed, including spectral power of
spontaneous neural oscillatory activities during resting and meditation
periods, time-frequency (TF) power of cognitive task-related brain responses,
and TF power of heartbeat-evoked brain responses (HEBR) that reflect cardiac
interoceptive brain responses during resting and meditation. Compared to
controls, the MBSR group had greater improvements in PTSD symptoms, spontaneous
EEG alpha (8-13 Hz) power in posterior sites, task-related frontal theta power
(4-7 Hz in 140-220 ms post-stimulus), and frontal theta HEBR (3-5 Hz and
265-328 ms post-R-peak). Latent difference score modeling found that only the
changes in the frontal theta HEBR mediated the MBSR treatment effect. Brain
source-level analysis found that the theta HEBR changes in the anterior
cingulate cortex, anterior insular cortex, and the lateral prefrontal cortex
predicted PTSD symptom improvements. These results indicated that mindfulness
meditation improves spontaneous brain activities reflecting internally oriented
relaxation and brain functions of attentional control. However, interoceptive
brain capacity enhanced by MBSR appears to be the primary cerebral mechanism
that regulates emotional disturbances and improves anxiety symptoms of PTSD.
| [
{
"created": "Mon, 12 Oct 2020 23:41:40 GMT",
"version": "v1"
}
] | 2020-10-14 | [
[
"Kang",
"Seung Suk",
""
],
[
"D.",
"Ph.",
""
],
[
"Sponheim",
"Scott R.",
""
],
[
"D.",
"Ph.",
""
],
[
"Lim",
"Kelvin O.",
""
],
[
"D",
"M.",
""
]
] | Mindfulness-based interventions have proven its efficacy in treating post-traumatic stress disorder (PTSD), but the underlying neurobiological mechanism is unknown. To determine the neurobiological mechanism of action of mindfulness-based stress reduction (MBSR) treating PTSD, we conducted a randomized clinical trial (RCT) in which 98 veterans with PTSD were randomly assigned to receive MBSR therapy (n = 47) or present-centered group therapy (PCGT; n = 51; an active-control condition). Pre- and post-intervention measures of PTSD symptom severity (PTSD Checklist) and brain activity measures of electroencephalography (EEG) were assessed, including spectral power of spontaneous neural oscillatory activities during resting and meditation periods, time-frequency (TF) power of cognitive task-related brain responses, and TF power of heartbeat-evoked brain responses (HEBR) that reflect cardiac interoceptive brain responses during resting and meditation. Compared to controls, the MBSR group had greater improvements in PTSD symptoms, spontaneous EEG alpha (8-13 Hz) power in posterior sites, task-related frontal theta power (4-7 Hz in 140-220 ms post-stimulus), and frontal theta HEBR (3-5 Hz and 265-328 ms post-R-peak). Latent difference score modeling found that only the changes in the frontal theta HEBR mediated the MBSR treatment effect. Brain source-level analysis found that the theta HEBR changes in the anterior cingulate cortex, anterior insular cortex, and the lateral prefrontal cortex predicted PTSD symptom improvements. These results indicated that mindfulness meditation improves spontaneous brain activities reflecting internally oriented relaxation and brain functions of attentional control. However, interoceptive brain capacity enhanced by MBSR appears to be the primary cerebral mechanism that regulates emotional disturbances and improves anxiety symptoms of PTSD. |
q-bio/0511013 | M. D. Betterton | Joshua Downer, Joel R. Sevinsky, Natalie G. Ahn, Katheryn A. Resing,
M. D. Betterton | Incorporating expression data in metabolic modeling: a case study of
lactate dehydrogenase | In press, Journal of Theoretical Biology. 27 pages, 9 figures | J Theor Biol 240: 464-474 (2006) | 10.1016/j.jtbi.2005.10.007 | null | q-bio.SC q-bio.QM | null | Integrating biological information from different sources to understand
cellular processes is an important problem in systems biology. We use data from
mRNA expression arrays and chemical kinetics to formulate a metabolic model
relevant to K562 erythroleukemia cells. MAP kinase pathway activation alters
the expression of metabolic enzymes in K562 cells. Our array data show changes
in expression of lactate dehydrogenase (LDH) isoforms after treatment with
phorbol 12-myristate 13-acetate (PMA), which activates MAP kinase signaling. We
model the change in lactate production which occurs when the MAP kinase pathway
is activated, using a non-equilibrium, chemical-kinetic model of homolactic
fermentation. In particular, we examine the role of LDH isoforms, which
catalyze the conversion of pyruvate to lactate. Changes in the isoform ratio
are not the primary determinant of the production of lactate. Rather, the total
concentration of LDH controls the lactate concentration.
| [
{
"created": "Sat, 12 Nov 2005 20:30:13 GMT",
"version": "v1"
}
] | 2011-11-09 | [
[
"Downer",
"Joshua",
""
],
[
"Sevinsky",
"Joel R.",
""
],
[
"Ahn",
"Natalie G.",
""
],
[
"Resing",
"Katheryn A.",
""
],
[
"Betterton",
"M. D.",
""
]
] | Integrating biological information from different sources to understand cellular processes is an important problem in systems biology. We use data from mRNA expression arrays and chemical kinetics to formulate a metabolic model relevant to K562 erythroleukemia cells. MAP kinase pathway activation alters the expression of metabolic enzymes in K562 cells. Our array data show changes in expression of lactate dehydrogenase (LDH) isoforms after treatment with phorbol 12-myristate 13-acetate (PMA), which activates MAP kinase signaling. We model the change in lactate production which occurs when the MAP kinase pathway is activated, using a non-equilibrium, chemical-kinetic model of homolactic fermentation. In particular, we examine the role of LDH isoforms, which catalyze the conversion of pyruvate to lactate. Changes in the isoform ratio are not the primary determinant of the production of lactate. Rather, the total concentration of LDH controls the lactate concentration. |
2109.01130 | Jeremie Unterberger M | Jeremie Unterberger, Philippe Nghe | Stoechiometric and dynamical autocatalysis for diluted chemical reaction
networks | 43 pages | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Autocatalysis underlies the ability of chemical and biochemical systems to
replicate. Recently, Blokhuis et al. gave a stoechiometric definition of
autocatalysis for reaction networks, stating the existence of a combination of
reactions such that the balance for all autocatalytic species is strictly
positive, and investigated minimal autocatalytic networks, called {\em
autocatalytic cores}. By contrast, spontaneous autocatalysis -- namely,
exponential amplification of all species internal to a reaction network,
starting from a diluted regime, i.e. low concentrations -- is a dynamical
property.
We introduce here a topological condition (Top) for autocatalysis, namely:
restricting the reaction network description to highly diluted species, we
assume existence of a strongly connected component possessing at least one
reaction with multiple products (including multiple copies of a single
species). We find this condition to be necessary and sufficient for
stoechiometric autocatalysis. When degradation reactions have small enough
rates, the topological condition further ensures dynamical autocatalysis,
characterized by a strictly positive Lyapunov exponent giving the instantaneous
exponential growth rate of the system.
The proof is generally based on the study of auxiliary Markov chains. We
provide as examples general autocatalytic cores of Type I and Type III in the
typology of Blokhuis et al. In a companion article, Lyapunov exponents and the
behavior in the growth regime are studied quantitatively beyond the present
diluted regime .
| [
{
"created": "Thu, 2 Sep 2021 17:55:36 GMT",
"version": "v1"
},
{
"created": "Sun, 23 Jan 2022 20:44:43 GMT",
"version": "v2"
}
] | 2022-01-25 | [
[
"Unterberger",
"Jeremie",
""
],
[
"Nghe",
"Philippe",
""
]
] | Autocatalysis underlies the ability of chemical and biochemical systems to replicate. Recently, Blokhuis et al. gave a stoechiometric definition of autocatalysis for reaction networks, stating the existence of a combination of reactions such that the balance for all autocatalytic species is strictly positive, and investigated minimal autocatalytic networks, called {\em autocatalytic cores}. By contrast, spontaneous autocatalysis -- namely, exponential amplification of all species internal to a reaction network, starting from a diluted regime, i.e. low concentrations -- is a dynamical property. We introduce here a topological condition (Top) for autocatalysis, namely: restricting the reaction network description to highly diluted species, we assume existence of a strongly connected component possessing at least one reaction with multiple products (including multiple copies of a single species). We find this condition to be necessary and sufficient for stoechiometric autocatalysis. When degradation reactions have small enough rates, the topological condition further ensures dynamical autocatalysis, characterized by a strictly positive Lyapunov exponent giving the instantaneous exponential growth rate of the system. The proof is generally based on the study of auxiliary Markov chains. We provide as examples general autocatalytic cores of Type I and Type III in the typology of Blokhuis et al. In a companion article, Lyapunov exponents and the behavior in the growth regime are studied quantitatively beyond the present diluted regime . |
2407.00408 | Aram Mohammed | Kocher Omer Salih, Aram Akram Mohammed, Jamal Mahmood Faraj, Anwar
Mohammed Raouf, Nawroz Abdul-Razzak Tahir | Rooting behavior of pomegranate (Punica granatum L.) hardwood cuttings
in relation to genotype and irrigation frequency | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | The study was conducted to determine the best irrigation frequency for
rooting hardwood cuttings of some pomegranate genotypes that are cultivated in
Halabja province, Kurdistan Region, Iraq. The hardwood cuttings were collected
from 11 genotypes, which were 'Salakhani Trsh' (G1), 'Salakhani Mekhosh' (G2),
'Amriki' (G3), 'Twekl Sury Trsh' (G4), 'Twekl Astury Naw Spy' (G5), 'Hanara
Sherina' (G6), 'Kawa Hanary Sherin' (G7), 'Kawa Hanary Trsh' (G8), 'Malesay
Twekl Asture' (G9), 'Malesay Twekl Tank' (G10), and 'Sura Hanary Trsh' (G11).
The genotypes were subjected to irrigation applications by 1-day, 2-day, 7-day,
or 10-day frequencies. Among pomegranates, G11, G6, and G7 produced 95, 90, and
83% rooting percentages, which were significantly higher than the rest of other
genotypes. The lowest rooting percentages (28, 36, 38, and 40%) were found in
G1, G5, G3, and G10, respectively. The effect of irrigation frequencies on the
genotypes confirmed that a 7-day frequency was the best irrigation frequency to
achieve the maximum rooting percentages (93, 86, 80, 73, 53, and 40%) in G6,
G9, G2, G4, G3, and G1, respectively. In contrast, the minimum rooting
percentage (20%) was recorded in G3 with a 1-day frequency and in G1 with
10-day frequency. In this study, it was found that the cuttings of G11, G6, and
G7 had the best ability to form roots, and irrigation with a 7-day frequency
was the best for the cuttings of all the 11 pomegranate genotypes investigated.
| [
{
"created": "Sat, 29 Jun 2024 11:22:55 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Salih",
"Kocher Omer",
""
],
[
"Mohammed",
"Aram Akram",
""
],
[
"Faraj",
"Jamal Mahmood",
""
],
[
"Raouf",
"Anwar Mohammed",
""
],
[
"Tahir",
"Nawroz Abdul-Razzak",
""
]
] | The study was conducted to determine the best irrigation frequency for rooting hardwood cuttings of some pomegranate genotypes that are cultivated in Halabja province, Kurdistan Region, Iraq. The hardwood cuttings were collected from 11 genotypes, which were 'Salakhani Trsh' (G1), 'Salakhani Mekhosh' (G2), 'Amriki' (G3), 'Twekl Sury Trsh' (G4), 'Twekl Astury Naw Spy' (G5), 'Hanara Sherina' (G6), 'Kawa Hanary Sherin' (G7), 'Kawa Hanary Trsh' (G8), 'Malesay Twekl Asture' (G9), 'Malesay Twekl Tank' (G10), and 'Sura Hanary Trsh' (G11). The genotypes were subjected to irrigation applications by 1-day, 2-day, 7-day, or 10-day frequencies. Among pomegranates, G11, G6, and G7 produced 95, 90, and 83% rooting percentages, which were significantly higher than the rest of other genotypes. The lowest rooting percentages (28, 36, 38, and 40%) were found in G1, G5, G3, and G10, respectively. The effect of irrigation frequencies on the genotypes confirmed that a 7-day frequency was the best irrigation frequency to achieve the maximum rooting percentages (93, 86, 80, 73, 53, and 40%) in G6, G9, G2, G4, G3, and G1, respectively. In contrast, the minimum rooting percentage (20%) was recorded in G3 with a 1-day frequency and in G1 with 10-day frequency. In this study, it was found that the cuttings of G11, G6, and G7 had the best ability to form roots, and irrigation with a 7-day frequency was the best for the cuttings of all the 11 pomegranate genotypes investigated. |
2301.12696 | Niv DeMalach | Niv DeMalach, Jaime Kigel, Marcelo Sternberg | Contrasting dynamics of seed banks and standing vegetation of annuals
and perennials along a rainfall gradient | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The soil seed bank is a major component of plant communities. However,
long-term analyses of the dynamics of the seed bank and the ensuing vegetation
are rare. Here, we studied the dynamics in plant communities with high
dominance of annuals in Mediterranean, semiarid, and arid ecosystems for nine
consecutive years. For annuals, we hypothesized that the density of the seed
bank would be more stable than the density of the standing herbaceous
vegetation. Moreover, we predicted that differences in temporal variability
between the seed bank and the vegetation would increase with aridity, where
year-to-year rainfall variability is higher. We found that the temporal
variability at the population level (assessed as the standard deviation of the
loge-transformed density) of the nine dominant annuals in each site did not
differ between the seed bank and the ensuing vegetation in any of the sites.
For the total density of annuals, patterns depended on aridity. In the
Mediterranean site, the temporal variability was similar in the seed bank and
the vegetation (0.40 vs. 0.40). Still, in the semiarid and arid sites,
variability in the seed bank was lower than in the vegetation (0.49 vs. 1.01
and 0.63 vs. 1.38, respectively). This difference between the population-level
patterns and the total density of annuals can be related to the lower
population synchrony in their seed bank. In contrast, for the herbaceous
perennials (all species combined), the seed bank variability was higher than in
the vegetation. Overall, our results highlight the role of the seed bank in
buffering the annual vegetation density with increasing climatic uncertainty
typical in aridity gradients. This role is crucial under the increasing
uncertainty imposed by climatic change in the region.
| [
{
"created": "Mon, 30 Jan 2023 07:03:55 GMT",
"version": "v1"
}
] | 2023-01-31 | [
[
"DeMalach",
"Niv",
""
],
[
"Kigel",
"Jaime",
""
],
[
"Sternberg",
"Marcelo",
""
]
] | The soil seed bank is a major component of plant communities. However, long-term analyses of the dynamics of the seed bank and the ensuing vegetation are rare. Here, we studied the dynamics in plant communities with high dominance of annuals in Mediterranean, semiarid, and arid ecosystems for nine consecutive years. For annuals, we hypothesized that the density of the seed bank would be more stable than the density of the standing herbaceous vegetation. Moreover, we predicted that differences in temporal variability between the seed bank and the vegetation would increase with aridity, where year-to-year rainfall variability is higher. We found that the temporal variability at the population level (assessed as the standard deviation of the loge-transformed density) of the nine dominant annuals in each site did not differ between the seed bank and the ensuing vegetation in any of the sites. For the total density of annuals, patterns depended on aridity. In the Mediterranean site, the temporal variability was similar in the seed bank and the vegetation (0.40 vs. 0.40). Still, in the semiarid and arid sites, variability in the seed bank was lower than in the vegetation (0.49 vs. 1.01 and 0.63 vs. 1.38, respectively). This difference between the population-level patterns and the total density of annuals can be related to the lower population synchrony in their seed bank. In contrast, for the herbaceous perennials (all species combined), the seed bank variability was higher than in the vegetation. Overall, our results highlight the role of the seed bank in buffering the annual vegetation density with increasing climatic uncertainty typical in aridity gradients. This role is crucial under the increasing uncertainty imposed by climatic change in the region. |
1906.07289 | Nhat Tran | Nhat Tran and Jean Gao | Network Representation of Large-Scale Heterogeneous RNA Sequences with
Integration of Diverse Multi-omics, Interactions, and Annotations Data | null | Pacific Symposium on Biocomputing, Vol 25, 2020 | 10.1142/9789811215636_0044 | null | q-bio.MN cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Long non-coding RNA, microRNA, and messenger RNA enable key regulations of
various biological processes through a variety of diverse interaction
mechanisms. Identifying the interactions and cross-talk between these
heterogeneous RNA classes is essential in order to uncover the functional role
of individual RNA transcripts, especially for unannotated and newly-discovered
RNA sequences with no known interactions. Recently, sequence-based deep
learning and network embedding methods are becoming promising approaches that
can either predict RNA-RNA interactions from a sequence or infer missing
interactions from patterns that may exist in the network topology. However, the
majority of these methods have several limitations, eg, the inability to
perform inductive predictions, to distinguish the directionality of
interactions, or to integrate various sequence, interaction, and annotation
biological datasets. We proposed a novel deep learning-based framework,
rna2rna, which learns from RNA sequences to produce a low-dimensional embedding
that preserves the proximities in both the interactions topology and the
functional affinity topology. In this proposed embedding space, we have
designated a two-part" source and target contexts" to capture the targeting and
receptive fields of each RNA transcript, while encapsulating the heterogenous
cross-talk interactions between lncRNAs and miRNAs. From experimental results,
our method exhibits superior performance in AUPR rates compared to state-of-art
approaches at predicting missing interactions in different RNA-RNA interaction
databases and was shown to accurately perform link predictions to novel RNA
sequences not seen at training time, even without any prior information.
Additional results suggest that our proposed framework can capture a manifold
for heterogeneous RNA sequences to discover novel functional annotations.
| [
{
"created": "Mon, 17 Jun 2019 22:28:01 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Aug 2019 20:24:35 GMT",
"version": "v2"
},
{
"created": "Tue, 8 Dec 2020 20:04:52 GMT",
"version": "v3"
}
] | 2020-12-10 | [
[
"Tran",
"Nhat",
""
],
[
"Gao",
"Jean",
""
]
] | Long non-coding RNA, microRNA, and messenger RNA enable key regulations of various biological processes through a variety of diverse interaction mechanisms. Identifying the interactions and cross-talk between these heterogeneous RNA classes is essential in order to uncover the functional role of individual RNA transcripts, especially for unannotated and newly-discovered RNA sequences with no known interactions. Recently, sequence-based deep learning and network embedding methods are becoming promising approaches that can either predict RNA-RNA interactions from a sequence or infer missing interactions from patterns that may exist in the network topology. However, the majority of these methods have several limitations, eg, the inability to perform inductive predictions, to distinguish the directionality of interactions, or to integrate various sequence, interaction, and annotation biological datasets. We proposed a novel deep learning-based framework, rna2rna, which learns from RNA sequences to produce a low-dimensional embedding that preserves the proximities in both the interactions topology and the functional affinity topology. In this proposed embedding space, we have designated a two-part" source and target contexts" to capture the targeting and receptive fields of each RNA transcript, while encapsulating the heterogenous cross-talk interactions between lncRNAs and miRNAs. From experimental results, our method exhibits superior performance in AUPR rates compared to state-of-art approaches at predicting missing interactions in different RNA-RNA interaction databases and was shown to accurately perform link predictions to novel RNA sequences not seen at training time, even without any prior information. Additional results suggest that our proposed framework can capture a manifold for heterogeneous RNA sequences to discover novel functional annotations. |
2012.05912 | Jordi Mu\~noz-Mar\'i | Anna Mateo-Sanchis, Jordi Munoz-Mari, Manuel Campos-Taberner, Javier
Garcia-Haro, Gustau Camps-Valls | Gap Filling of Biophysical Parameter Time Series with Multi-Output
Gaussian Processes | 4 pages, 3 figures | 2018 IEEE International Geoscience and Remote Sensing Symposium,
Valencia, 2018, pp. 4039-4042 | 10.1109/IGARSS.2018.8519254 | null | q-bio.QM cs.LG physics.data-an stat.ML | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this work we evaluate multi-output (MO) Gaussian Process (GP) models based
on the linear model of coregionalization (LMC) for estimation of biophysical
parameter variables under a gap filling setup. In particular, we focus on LAI
and fAPAR over rice areas. We show how this problem cannot be solved with
standard single-output (SO) GP models, and how the proposed MO-GP models are
able to successfully predict these variables even in high missing data regimes,
by implicitly performing an across-domain information transfer.
| [
{
"created": "Fri, 11 Dec 2020 13:10:19 GMT",
"version": "v1"
}
] | 2020-12-14 | [
[
"Mateo-Sanchis",
"Anna",
""
],
[
"Munoz-Mari",
"Jordi",
""
],
[
"Campos-Taberner",
"Manuel",
""
],
[
"Garcia-Haro",
"Javier",
""
],
[
"Camps-Valls",
"Gustau",
""
]
] | In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer. |
2111.12825 | William Streilein | Gwendolyn Gettliffe, Adam Norige, Ted Londner, Jonathan Saunders,
Dieter Schuldt, William Streilein | Engineering and Implementation of SimAEN | arXiv admin note: substantial text overlap with arXiv:2012.04399 | null | null | null | q-bio.QM cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents SimAEN, an agent-based simulation whose purpose is to
assist public health in understanding and controlling AEN. SimAEN models a
population of interacting individuals, or 'agents', in which COVID-19 is
spreading. These individuals interact with a public health system that includes
Automated Exposure Notifiation (AEN) and Manual Contact Tracing (MCT). These
interactions influence when individuals enter and leave quarantine, affecting
the spread of the simulated disease. Over 70 user-configurable parameters
influence the outcome of SimAEN's simulations. These parameters allow the user
to tailor SimAEN to a specific public health jurisdiction and to test the
effects of various interventions, including different sensitivity settings of
AEN.
| [
{
"created": "Wed, 24 Nov 2021 22:38:20 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Dec 2021 20:51:38 GMT",
"version": "v2"
}
] | 2021-12-20 | [
[
"Gettliffe",
"Gwendolyn",
""
],
[
"Norige",
"Adam",
""
],
[
"Londner",
"Ted",
""
],
[
"Saunders",
"Jonathan",
""
],
[
"Schuldt",
"Dieter",
""
],
[
"Streilein",
"William",
""
]
] | This paper presents SimAEN, an agent-based simulation whose purpose is to assist public health in understanding and controlling AEN. SimAEN models a population of interacting individuals, or 'agents', in which COVID-19 is spreading. These individuals interact with a public health system that includes Automated Exposure Notifiation (AEN) and Manual Contact Tracing (MCT). These interactions influence when individuals enter and leave quarantine, affecting the spread of the simulated disease. Over 70 user-configurable parameters influence the outcome of SimAEN's simulations. These parameters allow the user to tailor SimAEN to a specific public health jurisdiction and to test the effects of various interventions, including different sensitivity settings of AEN. |
2202.08147 | Pietro Bongini | Pietro Bongini, Franco Scarselli, Monica Bianchini, Giovanna Maria
Dimitri, Niccol\`o Pancino, Pietro Li\`o | Modular multi-source prediction of drug side-effects with DruGNN | 19 pages, 3 figures | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Drug Side-Effects (DSEs) have a high impact on public health, care system
costs, and drug discovery processes. Predicting the probability of
side-effects, before their occurrence, is fundamental to reduce this impact, in
particular on drug discovery. Candidate molecules could be screened before
undergoing clinical trials, reducing the costs in time, money, and health of
the participants. Drug side-effects are triggered by complex biological
processes involving many different entities, from drug structures to
protein-protein interactions. To predict their occurrence, it is necessary to
integrate data from heterogeneous sources. In this work, such heterogeneous
data is integrated into a graph dataset, expressively representing the
relational information between different entities, such as drug molecules and
genes. The relational nature of the dataset represents an important novelty for
drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to
predict DSEs on our dataset with very promising results. GNNs are deep learning
models that can process graph-structured data, with minimal information loss,
and have been applied on a wide variety of biological tasks. Our experimental
results confirm the advantage of using relationships between data entities,
suggesting interesting future developments in this scope. The experimentation
also shows the importance of specific subsets of data in determining
associations between drugs and side-effects.
| [
{
"created": "Tue, 15 Feb 2022 09:41:05 GMT",
"version": "v1"
}
] | 2022-02-17 | [
[
"Bongini",
"Pietro",
""
],
[
"Scarselli",
"Franco",
""
],
[
"Bianchini",
"Monica",
""
],
[
"Dimitri",
"Giovanna Maria",
""
],
[
"Pancino",
"Niccolò",
""
],
[
"Liò",
"Pietro",
""
]
] | Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants. Drug side-effects are triggered by complex biological processes involving many different entities, from drug structures to protein-protein interactions. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities, such as drug molecules and genes. The relational nature of the dataset represents an important novelty for drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results. GNNs are deep learning models that can process graph-structured data, with minimal information loss, and have been applied on a wide variety of biological tasks. Our experimental results confirm the advantage of using relationships between data entities, suggesting interesting future developments in this scope. The experimentation also shows the importance of specific subsets of data in determining associations between drugs and side-effects. |
2209.15584 | Benjamin Berkels | Karina Ruzaeva, Kira K\"usters, Wolfgang Wiechert, Benjamin Berkels,
Marco Oldiges, Katharina N\"oh | Automated Characterization of Catalytically Active Inclusion Body
Production in Biotechnological Screening Systems | null | 2022 IEEE 44th International Engineering in Medicine and Biology
Conference (EMBC) | 10.1109/EMBC48229.2022.9871325 | null | q-bio.QM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We here propose an automated pipeline for the microscopy image-based
characterization of catalytically active inclusion bodies (CatIBs), which
includes a fully automatic experimental high-throughput workflow combined with
a hybrid approach for multi-object microbial cell segmentation. For automated
microscopy, a CatIB producer strain was cultivated in a microbioreactor from
which samples were injected into a flow chamber. The flow chamber was fixed
under a microscope and an integrated camera took a series of images per sample.
To explore heterogeneity of CatIB development during the cultivation and track
the size and quantity of CatIBs over time, a hybrid image processing pipeline
approach was developed, which combines an ML-based detection of in-focus cells
with model-based segmentation. The experimental setup in combination with an
automated image analysis unlocks high-throughput screening of CatIB production,
saving time and resources.
Biotechnological relevance - CatIBs have wide application in synthetic
chemistry and biocatalysis, but also could have future biomedical applications
such as therapeutics. The proposed hybrid automatic image processing pipeline
can be adjusted to treat comparable biological microorganisms, where fully
data-driven ML-based segmentation approaches are not feasible due to the lack
of training data. Our work is the first step towards image-based bioprocess
control.
| [
{
"created": "Fri, 30 Sep 2022 16:53:16 GMT",
"version": "v1"
}
] | 2022-10-03 | [
[
"Ruzaeva",
"Karina",
""
],
[
"Küsters",
"Kira",
""
],
[
"Wiechert",
"Wolfgang",
""
],
[
"Berkels",
"Benjamin",
""
],
[
"Oldiges",
"Marco",
""
],
[
"Nöh",
"Katharina",
""
]
] | We here propose an automated pipeline for the microscopy image-based characterization of catalytically active inclusion bodies (CatIBs), which includes a fully automatic experimental high-throughput workflow combined with a hybrid approach for multi-object microbial cell segmentation. For automated microscopy, a CatIB producer strain was cultivated in a microbioreactor from which samples were injected into a flow chamber. The flow chamber was fixed under a microscope and an integrated camera took a series of images per sample. To explore heterogeneity of CatIB development during the cultivation and track the size and quantity of CatIBs over time, a hybrid image processing pipeline approach was developed, which combines an ML-based detection of in-focus cells with model-based segmentation. The experimental setup in combination with an automated image analysis unlocks high-throughput screening of CatIB production, saving time and resources. Biotechnological relevance - CatIBs have wide application in synthetic chemistry and biocatalysis, but also could have future biomedical applications such as therapeutics. The proposed hybrid automatic image processing pipeline can be adjusted to treat comparable biological microorganisms, where fully data-driven ML-based segmentation approaches are not feasible due to the lack of training data. Our work is the first step towards image-based bioprocess control. |
0809.0332 | Sergey Gavrilets | Sergey Gavrilets, Edgar A. Duenez-Guzman and Michael D. Vose | Dynamics of alliance formation and the egalitarian revolution | 37 pages, 15 figures | null | 10.1371/journal.pone.0003293 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Arguably the most influential force in human history is the formation of
social coalitions and alliances (i.e., long-lasting coalitions) and their
impact on individual power. In most great ape species, coalitions occur at
individual and group levels and among both kin and non-kin. Nonetheless, ape
societies remain essentially hierarchical, and coalitions rarely weaken social
inequality. In contrast, human hunter-gatherers show a remarkable tendency to
egalitarianism, and human coalitions and alliances occur not only among
individuals and groups, but also among groups of groups. Here, we develop a
stochastic model describing the emergence of networks of allies resulting from
within-group competition for status or mates between individuals utilizing
dyadic information. The model shows that alliances often emerge in a phase
transition-like fashion if the group size, awareness, aggressiveness, and
persuasiveness of individuals are large and the decay rate of individual
affinities is small. With cultural inheritance of social networks, a single
leveling alliance including all group members can emerge in several
generations. Our results suggest that a rapid transition from a hierarchical
society of great apes to an egalitarian society of hunter-gatherers (often
referred to as "egalitarian revolution") could indeed follow an increase in
human cognitive abilities. The establishment of stable group-wide egalitarian
alliances creates conditions promoting the origin of cultural norms favoring
the group interests over those of individuals.
| [
{
"created": "Mon, 1 Sep 2008 23:55:53 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Gavrilets",
"Sergey",
""
],
[
"Duenez-Guzman",
"Edgar A.",
""
],
[
"Vose",
"Michael D.",
""
]
] | Arguably the most influential force in human history is the formation of social coalitions and alliances (i.e., long-lasting coalitions) and their impact on individual power. In most great ape species, coalitions occur at individual and group levels and among both kin and non-kin. Nonetheless, ape societies remain essentially hierarchical, and coalitions rarely weaken social inequality. In contrast, human hunter-gatherers show a remarkable tendency to egalitarianism, and human coalitions and alliances occur not only among individuals and groups, but also among groups of groups. Here, we develop a stochastic model describing the emergence of networks of allies resulting from within-group competition for status or mates between individuals utilizing dyadic information. The model shows that alliances often emerge in a phase transition-like fashion if the group size, awareness, aggressiveness, and persuasiveness of individuals are large and the decay rate of individual affinities is small. With cultural inheritance of social networks, a single leveling alliance including all group members can emerge in several generations. Our results suggest that a rapid transition from a hierarchical society of great apes to an egalitarian society of hunter-gatherers (often referred to as "egalitarian revolution") could indeed follow an increase in human cognitive abilities. The establishment of stable group-wide egalitarian alliances creates conditions promoting the origin of cultural norms favoring the group interests over those of individuals. |
q-bio/0608034 | Yonghong Chen Dr. | Yonghong Chen, Steven L. Bressler, Mingzhou Ding | Frequency decomposition of conditional Granger causality and application
to multivariate neural field potential data | 18 pages, 6 figures, Journal published | Journal of Neuroscience Methods, 150(2), 2006: 228-237 | null | null | q-bio.NC q-bio.QM | null | It is often useful in multivariate time series analysis to determine
statistical causal relations between different time series. Granger causality
is a fundamental measure for this purpose. Yet the traditional pairwise
approach to Granger causality analysis may not clearly distinguish between
direct causal influences from one time series to another and indirect ones
acting through a third time series. In order to differentiate direct from
indirect Granger causality, a conditional Granger causality measure in the
frequency domain is derived based on a partition matrix technique. Simulations
and an application to neural field potential time series are demonstrated to
validate the method.
| [
{
"created": "Wed, 23 Aug 2006 13:29:15 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Chen",
"Yonghong",
""
],
[
"Bressler",
"Steven L.",
""
],
[
"Ding",
"Mingzhou",
""
]
] | It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method. |
2212.14113 | Dhananjay Bhaskar | Dhananjay Bhaskar, William Y. Zhang, Alexandria Volkening, Bj\"orn
Sandstede, Ian Y. Wong | Topological Data Analysis of Spatial Patterning in Heterogeneous Cell
Populations: Clustering and Sorting with Varying Cell-Cell Adhesion | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Different cell types aggregate and sort into hierarchical architectures
during the formation of animal tissues. The resulting spatial organization
depends (in part) on the strength of adhesion of one cell type to itself
relative to other cell types. However, automated and unsupervised
classification of these multicellular spatial patterns remains challenging,
particularly given their structural diversity and biological variability.
Recent developments based on topological data analysis are intriguing to reveal
similarities in tissue architecture, but these methods remain computationally
expensive. In this article, we show that multicellular patterns organized from
two interacting cell types can be efficiently represented through persistence
images. Our optimized combination of dimensionality reduction via autoencoders,
combined with hierarchical clustering, achieved high classification accuracy
for simulations with constant cell numbers. We further demonstrate that
persistence images can be normalized to improve classification for simulations
with varying cell numbers due to proliferation. Finally, we systematically
consider the importance of incorporating different topological features as well
as information about each cell type to improve classification accuracy. We
envision that topological machine learning based on persistence images will
enable versatile and robust classification of complex tissue architectures that
occur in development and disease.
| [
{
"created": "Wed, 28 Dec 2022 22:30:53 GMT",
"version": "v1"
},
{
"created": "Mon, 31 Jul 2023 21:35:57 GMT",
"version": "v2"
}
] | 2023-08-02 | [
[
"Bhaskar",
"Dhananjay",
""
],
[
"Zhang",
"William Y.",
""
],
[
"Volkening",
"Alexandria",
""
],
[
"Sandstede",
"Björn",
""
],
[
"Wong",
"Ian Y.",
""
]
] | Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. However, automated and unsupervised classification of these multicellular spatial patterns remains challenging, particularly given their structural diversity and biological variability. Recent developments based on topological data analysis are intriguing to reveal similarities in tissue architecture, but these methods remain computationally expensive. In this article, we show that multicellular patterns organized from two interacting cell types can be efficiently represented through persistence images. Our optimized combination of dimensionality reduction via autoencoders, combined with hierarchical clustering, achieved high classification accuracy for simulations with constant cell numbers. We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation. Finally, we systematically consider the importance of incorporating different topological features as well as information about each cell type to improve classification accuracy. We envision that topological machine learning based on persistence images will enable versatile and robust classification of complex tissue architectures that occur in development and disease. |
1906.08631 | Inbar Seroussi | Inbar Seroussi, Nir Levy, Daniela Paolotti, Nir Sochen, and Elad
Yom-Tov | On the use of multiple compartment epidemiological models to describe
the dynamics of influenza in Europe | null | null | null | null | q-bio.PE physics.soc-ph stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a multiple compartment Susceptible-Infected-Recovered (SIR) model
to analyze the spread of several infectious diseases through different
geographic areas. Additionally, we propose a data-quality sensitive
optimization framework for fitting this model to observed data.
We fit the model to the temporal profile of the number of people infected by
one of six influenza strains in Europe over $7$ influenza seasons. In addition
to describing the temporal and spatial spread of influenza, the model provides
an estimate of the inter-country and intra-country infection and recovery rates
of each strain and in each season. We find that disease parameters remain
relatively stable, with a correlation greater than $0.5$ over seasons and
stains. Clustering of influenza strains by the inferred disease parameters is
consistent with genome sub-types. Surprisingly, our analysis suggests that
inter-country human mobility plays a negligible role in the spread of influenza
in Europe. Finally, we show that the model allows the estimation of disease
load in countries with poor or none existent data from the disease load in
adjacent countries.
Our findings reveal information on the spreading mechanism of influenza and
on disease parameters. These can be used to assist in disease surveillance and
in control of influenza as well as of other infectious pathogens in a
heterogenic environment.
| [
{
"created": "Tue, 18 Jun 2019 20:41:54 GMT",
"version": "v1"
}
] | 2019-06-21 | [
[
"Seroussi",
"Inbar",
""
],
[
"Levy",
"Nir",
""
],
[
"Paolotti",
"Daniela",
""
],
[
"Sochen",
"Nir",
""
],
[
"Yom-Tov",
"Elad",
""
]
] | We develop a multiple compartment Susceptible-Infected-Recovered (SIR) model to analyze the spread of several infectious diseases through different geographic areas. Additionally, we propose a data-quality sensitive optimization framework for fitting this model to observed data. We fit the model to the temporal profile of the number of people infected by one of six influenza strains in Europe over $7$ influenza seasons. In addition to describing the temporal and spatial spread of influenza, the model provides an estimate of the inter-country and intra-country infection and recovery rates of each strain and in each season. We find that disease parameters remain relatively stable, with a correlation greater than $0.5$ over seasons and stains. Clustering of influenza strains by the inferred disease parameters is consistent with genome sub-types. Surprisingly, our analysis suggests that inter-country human mobility plays a negligible role in the spread of influenza in Europe. Finally, we show that the model allows the estimation of disease load in countries with poor or none existent data from the disease load in adjacent countries. Our findings reveal information on the spreading mechanism of influenza and on disease parameters. These can be used to assist in disease surveillance and in control of influenza as well as of other infectious pathogens in a heterogenic environment. |
2001.00837 | Brigitte Sola | Guergana Tchakarska (GPMCND), Brigitte Sola (GPMCND) | The double dealing of cyclin D1 | null | Cell Cycle, Taylor & Francis, 2010, pp.1-16 | 10.1080/15384101.2019.1706903 | null | q-bio.SC q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The cell cycle is tightly regulated by cyclins and their catalytic moieties,
the cyclin-dependent kinases (CDKs). Cyclin D1, in association with CDK4/6,
acts as a mitogenic sensor and integrates extracellular mitogenic signals and
cell cycle progression. When deregulated (overexpressed, accumulated,
inappropriately located), cyclin D1 becomes an oncogene and is recognized as a
driver of solid tumors and hemopathies. Recent studies on the oncogenic roles
of cyclin D1 reported non-canonical functions dependent on the partners of
cyclin D1 and its location within tumor cells or tissues. Support for these new
functions was provided by various mouse models of oncogenesis. Finally,
proteomic and transcriptomic data identified complex cyclin D1 networks. This
review focuses on these aspects of cyclin D1 pathophysiology, which may be
crucial for targeted therapy.
| [
{
"created": "Fri, 3 Jan 2020 13:52:39 GMT",
"version": "v1"
}
] | 2020-01-06 | [
[
"Tchakarska",
"Guergana",
"",
"GPMCND"
],
[
"Sola",
"Brigitte",
"",
"GPMCND"
]
] | The cell cycle is tightly regulated by cyclins and their catalytic moieties, the cyclin-dependent kinases (CDKs). Cyclin D1, in association with CDK4/6, acts as a mitogenic sensor and integrates extracellular mitogenic signals and cell cycle progression. When deregulated (overexpressed, accumulated, inappropriately located), cyclin D1 becomes an oncogene and is recognized as a driver of solid tumors and hemopathies. Recent studies on the oncogenic roles of cyclin D1 reported non-canonical functions dependent on the partners of cyclin D1 and its location within tumor cells or tissues. Support for these new functions was provided by various mouse models of oncogenesis. Finally, proteomic and transcriptomic data identified complex cyclin D1 networks. This review focuses on these aspects of cyclin D1 pathophysiology, which may be crucial for targeted therapy. |
1404.5515 | Yi Ming Zou | Yi Ming Zou | An Algorithm for Detecting Fixed Points of Boolean Networks | A shorter version of this paper appeared in the conference proceeding
of ICME 2013 (Beijing), pp. 670 - 673 | null | null | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the applications of Boolean networks to modeling biological systems, an
important computational problem is the detection of the fixed points of these
networks. This is an NP-complete problem in general. There have been various
attempts to develop algorithms to address the computation need for large size
Boolean networks. The existing methods are usually based on known algorithms
and thus limited to the situations where these known algorithms can apply. In
this paper, we propose a novel approach to this problem. We show that any
system of Boolean equations is equivalent to one Boolean equation, and thus it
is possible to divide the polynomial equation system which defines the fixed
points of a Boolean network into subsystems that can be solved easily. After
solving these subsystems and thus reducing the number of states, we can combine
the solutions to obtain all fixed points of the given network. This approach
does not depend on other algorithms and it is straightforward and easy to
implement. We show that our method can handle large size Boolean networks, and
demonstrate its effectiveness by using MAPLE to compute the fixed points of
Boolean networks with hundreds of nodes and thousands of interactions.
| [
{
"created": "Tue, 22 Apr 2014 14:34:57 GMT",
"version": "v1"
}
] | 2014-04-23 | [
[
"Zou",
"Yi Ming",
""
]
] | In the applications of Boolean networks to modeling biological systems, an important computational problem is the detection of the fixed points of these networks. This is an NP-complete problem in general. There have been various attempts to develop algorithms to address the computation need for large size Boolean networks. The existing methods are usually based on known algorithms and thus limited to the situations where these known algorithms can apply. In this paper, we propose a novel approach to this problem. We show that any system of Boolean equations is equivalent to one Boolean equation, and thus it is possible to divide the polynomial equation system which defines the fixed points of a Boolean network into subsystems that can be solved easily. After solving these subsystems and thus reducing the number of states, we can combine the solutions to obtain all fixed points of the given network. This approach does not depend on other algorithms and it is straightforward and easy to implement. We show that our method can handle large size Boolean networks, and demonstrate its effectiveness by using MAPLE to compute the fixed points of Boolean networks with hundreds of nodes and thousands of interactions. |
1812.04525 | Alejandro F Villaverde | Alejandro F. Villaverde | Observability and Structural Identifiability of Nonlinear Biological
Systems | Accepted for publication in the special issue "Computational Methods
for Identification and Modelling of Complex Biological Systems" of Complexity | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Observability is a modelling property that describes the possibility of
inferring the internal state of a system from observations of its output. A
related property, structural identifiability, refers to the theoretical
possibility of determining the parameter values from the output. In fact,
structural identifiability becomes a particular case of observability if the
parameters are considered as constant state variables. It is possible to
simultaneously analyse the observability and structural identifiability of a
model using the conceptual tools of differential geometry. Many complex
biological processes can be described by systems of nonlinear ordinary
differential equations, and can therefore be analysed with this approach. The
purpose of this review article is threefold: (I) to serve as a tutorial on
observability and structural identifiability of nonlinear systems, using the
differential geometry approach for their analysis; (II) to review recent
advances in the field; and (III) to identify open problems and suggest new
avenues for research in this area.
| [
{
"created": "Tue, 11 Dec 2018 16:30:10 GMT",
"version": "v1"
}
] | 2018-12-12 | [
[
"Villaverde",
"Alejandro F.",
""
]
] | Observability is a modelling property that describes the possibility of inferring the internal state of a system from observations of its output. A related property, structural identifiability, refers to the theoretical possibility of determining the parameter values from the output. In fact, structural identifiability becomes a particular case of observability if the parameters are considered as constant state variables. It is possible to simultaneously analyse the observability and structural identifiability of a model using the conceptual tools of differential geometry. Many complex biological processes can be described by systems of nonlinear ordinary differential equations, and can therefore be analysed with this approach. The purpose of this review article is threefold: (I) to serve as a tutorial on observability and structural identifiability of nonlinear systems, using the differential geometry approach for their analysis; (II) to review recent advances in the field; and (III) to identify open problems and suggest new avenues for research in this area. |
2407.16249 | Zaineb Ajra | Zaineb Ajra and Binbin Xu and G\'erard Dray and Jacky Montmain and
St\'ephane Perrey | How Does a Single EEG Channel Tell Us About Brain States in
Brain-Computer Interfaces ? | Accepted in the 16th International Conference on Human System
Interaction 2024, Paris, France | null | 10.1109/HSI61632.2024.10613592 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Over recent decades, neuroimaging tools, particularly electroencephalography
(EEG), have revolutionized our understanding of the brain and its functions.
EEG is extensively used in traditional brain-computer interface (BCI) systems
due to its low cost, non-invasiveness, and high temporal resolution. This makes
it invaluable for identifying different brain states relevant to both medical
and non-medical applications. Although this practice is widely recognized,
current methods are mainly confined to lab or clinical environments because
they rely on data from multiple EEG electrodes covering the entire head.
Nonetheless, a significant advancement for these applications would be their
adaptation for "real-world" use, using portable devices with a single-channel.
In this study, we tackle this challenge through two distinct strategies: the
first approach involves training models with data from multiple channels and
then testing new trials on data from a single channel individually. The second
method focuses on training with data from a single channel and then testing the
performances of the models on data from all the other channels individually. To
efficiently classify cognitive tasks from EEG data, we propose Convolutional
Neural Networks (CNNs) with only a few parameters and fast learnable
spectral-temporal features. We demonstrated the feasibility of these approaches
on EEG data recorded during mental arithmetic and motor imagery tasks from
three datasets. We achieved the highest accuracies of 100%, 91.55% and 73.45%
in binary and 3-class classification on specific channels across three
datasets. This study can contribute to the development of single-channel BCI
and provides a robust EEG biomarker for brain states classification.
| [
{
"created": "Tue, 23 Jul 2024 07:38:06 GMT",
"version": "v1"
}
] | 2024-08-14 | [
[
"Ajra",
"Zaineb",
""
],
[
"Xu",
"Binbin",
""
],
[
"Dray",
"Gérard",
""
],
[
"Montmain",
"Jacky",
""
],
[
"Perrey",
"Stéphane",
""
]
] | Over recent decades, neuroimaging tools, particularly electroencephalography (EEG), have revolutionized our understanding of the brain and its functions. EEG is extensively used in traditional brain-computer interface (BCI) systems due to its low cost, non-invasiveness, and high temporal resolution. This makes it invaluable for identifying different brain states relevant to both medical and non-medical applications. Although this practice is widely recognized, current methods are mainly confined to lab or clinical environments because they rely on data from multiple EEG electrodes covering the entire head. Nonetheless, a significant advancement for these applications would be their adaptation for "real-world" use, using portable devices with a single-channel. In this study, we tackle this challenge through two distinct strategies: the first approach involves training models with data from multiple channels and then testing new trials on data from a single channel individually. The second method focuses on training with data from a single channel and then testing the performances of the models on data from all the other channels individually. To efficiently classify cognitive tasks from EEG data, we propose Convolutional Neural Networks (CNNs) with only a few parameters and fast learnable spectral-temporal features. We demonstrated the feasibility of these approaches on EEG data recorded during mental arithmetic and motor imagery tasks from three datasets. We achieved the highest accuracies of 100%, 91.55% and 73.45% in binary and 3-class classification on specific channels across three datasets. This study can contribute to the development of single-channel BCI and provides a robust EEG biomarker for brain states classification. |
2305.14366 | Yo Kobayashi Dr. | Yo Kobayashi | Information processing via human soft tissue | null | null | null | null | q-bio.NC cs.AI cs.HC cs.LG cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | This study demonstrates that the soft biological tissues of humans can be
used as a type of soft body in physical reservoir computing. Soft biological
tissues possess characteristics such as stress-strain nonlinearity and
viscoelasticity that satisfy the requirements for physical reservoir computing,
including nonlinearity and memory. The aim of this study was to utilize the
dynamics of human soft tissues as a physical reservoir for the emulation of
nonlinear dynamical systems. To demonstrate this concept, joint angle data
during motion in the flexion-extension direction of the wrist joint, and
ultrasound images of the muscles associated with that motion, were acquired
from human participants. The input to the system was the angle of the wrist
joint, while the deformation field within the muscle (obtained from ultrasound
images) represented the state of the reservoir. The results indicate that the
dynamics of soft tissue have a positive impact on the computational task of
emulating nonlinear dynamical systems. This research suggests that the soft
tissue of humans can be used as a potential computational resource.
| [
{
"created": "Wed, 17 May 2023 06:59:26 GMT",
"version": "v1"
}
] | 2023-05-25 | [
[
"Kobayashi",
"Yo",
""
]
] | This study demonstrates that the soft biological tissues of humans can be used as a type of soft body in physical reservoir computing. Soft biological tissues possess characteristics such as stress-strain nonlinearity and viscoelasticity that satisfy the requirements for physical reservoir computing, including nonlinearity and memory. The aim of this study was to utilize the dynamics of human soft tissues as a physical reservoir for the emulation of nonlinear dynamical systems. To demonstrate this concept, joint angle data during motion in the flexion-extension direction of the wrist joint, and ultrasound images of the muscles associated with that motion, were acquired from human participants. The input to the system was the angle of the wrist joint, while the deformation field within the muscle (obtained from ultrasound images) represented the state of the reservoir. The results indicate that the dynamics of soft tissue have a positive impact on the computational task of emulating nonlinear dynamical systems. This research suggests that the soft tissue of humans can be used as a potential computational resource. |
2202.00763 | Madeline Galbraith | Madeline Galbraith, Federico Bocci, Jos\'e N. Onuchic | Stochastic fluctuations promote ordered pattern formation of cells in
the Notch-Delta signaling pathway | null | null | 10.1371/journal.pcbi.1010306 | null | q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | The Notch-Delta signaling pathway mediates cell differentiation implicated in
many regulatory processes including spatiotemporal patterning in tissues by
promoting alternate cell fates between neighboring cells. At the multicellular
level, this "lateral inhibition" principle leads to checkerboard patterns of
Sender and Receiver cells. While it is well known that stochasticity modulates
cell fate specification, little is known about how stochastic fluctuations at
the cellular level propagate during multicell pattern formation. Here, we model
stochastic fluctuations in the Notch-Delta pathway in the presence of two
different noise types - shot and white - for a multicell system. The results
show that intermediate fluctuations reduce disorder and guide the multicell
lattice toward more checkerboard like patterns. By further analyzing cell fate
transition events, we demonstrate that intermediate noise amplitudes provide
enough perturbation to facilitate "proofreading" of disordered patterns and
cause cells to switch to their correct ordered state. Conversely, high noise
can override environmental signals coming from neighboring cells and lead to
switching between ordered and disordered patterns. Therefore, in analogy with
spin glass systems, intermediate noise levels allow the multicell Notch system
to escape frustrated patterns and relax towards the lower energy checkerboard
pattern while at large noise levels the system is unable to find this ordered
base of attraction.
| [
{
"created": "Tue, 1 Feb 2022 21:17:09 GMT",
"version": "v1"
}
] | 2022-10-12 | [
[
"Galbraith",
"Madeline",
""
],
[
"Bocci",
"Federico",
""
],
[
"Onuchic",
"José N.",
""
]
] | The Notch-Delta signaling pathway mediates cell differentiation implicated in many regulatory processes including spatiotemporal patterning in tissues by promoting alternate cell fates between neighboring cells. At the multicellular level, this "lateral inhibition" principle leads to checkerboard patterns of Sender and Receiver cells. While it is well known that stochasticity modulates cell fate specification, little is known about how stochastic fluctuations at the cellular level propagate during multicell pattern formation. Here, we model stochastic fluctuations in the Notch-Delta pathway in the presence of two different noise types - shot and white - for a multicell system. The results show that intermediate fluctuations reduce disorder and guide the multicell lattice toward more checkerboard like patterns. By further analyzing cell fate transition events, we demonstrate that intermediate noise amplitudes provide enough perturbation to facilitate "proofreading" of disordered patterns and cause cells to switch to their correct ordered state. Conversely, high noise can override environmental signals coming from neighboring cells and lead to switching between ordered and disordered patterns. Therefore, in analogy with spin glass systems, intermediate noise levels allow the multicell Notch system to escape frustrated patterns and relax towards the lower energy checkerboard pattern while at large noise levels the system is unable to find this ordered base of attraction. |
2312.05340 | Michael Plainer | Michael Plainer, Hannes St\"ark, Charlotte Bunne, Stephan G\"unnemann | Transition Path Sampling with Boltzmann Generator-based MCMC Moves | Spotlight at NeurIPS 2023 Generative AI and Biology Workshop | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sampling all possible transition paths between two 3D states of a molecular
system has various applications ranging from catalyst design to drug discovery.
Current approaches to sample transition paths use Markov chain Monte Carlo and
rely on time-intensive molecular dynamics simulations to find new paths. Our
approach operates in the latent space of a normalizing flow that maps from the
molecule's Boltzmann distribution to a Gaussian, where we propose new paths
without requiring molecular simulations. Using alanine dipeptide, we explore
Metropolis-Hastings acceptance criteria in the latent space for exact sampling
and investigate different latent proposal mechanisms.
| [
{
"created": "Fri, 8 Dec 2023 20:05:33 GMT",
"version": "v1"
},
{
"created": "Tue, 28 May 2024 14:50:41 GMT",
"version": "v2"
}
] | 2024-05-29 | [
[
"Plainer",
"Michael",
""
],
[
"Stärk",
"Hannes",
""
],
[
"Bunne",
"Charlotte",
""
],
[
"Günnemann",
"Stephan",
""
]
] | Sampling all possible transition paths between two 3D states of a molecular system has various applications ranging from catalyst design to drug discovery. Current approaches to sample transition paths use Markov chain Monte Carlo and rely on time-intensive molecular dynamics simulations to find new paths. Our approach operates in the latent space of a normalizing flow that maps from the molecule's Boltzmann distribution to a Gaussian, where we propose new paths without requiring molecular simulations. Using alanine dipeptide, we explore Metropolis-Hastings acceptance criteria in the latent space for exact sampling and investigate different latent proposal mechanisms. |
2405.17847 | Manuel Lladser | Sean S. Svihla, Manuel E. Lladser | Sparsification of Phylogenetic Covariance Matrices of $k$-Regular Trees | 17 pages, 5 figures, final version to appear in the Proceedings of
the 35th International Conference on Probabilistic, Combinatorial and
Asymptotic Methods for the Analysis of Algorithms (AofA2024) | null | null | null | q-bio.PE cs.DM math.CO math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Consider a tree $T=(V,E)$ with root $\circ$ and edge length function
$\ell:E\to\mathbb{R}_+$. The phylogenetic covariance matrix of $T$ is the
matrix $C$ with rows and columns indexed by $L$, the leaf set of $T$, with
entries $C(i,j):=\sum_{e\in[i\wedge j,o]}\ell(e)$, for each $i,j\in L$. Recent
work [15] has shown that the phylogenetic covariance matrix of a large, random
binary tree $T$ is significantly sparsified with overwhelmingly high
probability under a change-of-basis with respect to the so-called Haar-like
wavelets of $T$. This finding notably enables manipulating the spectrum of
covariance matrices of large binary trees without the necessity to store them
in computer memory but instead performing two post-order traversals of the
tree. Building on the methods of [15], this manuscript further advances their
sparsification result to encompass the broader class of $k$-regular trees, for
any given $k\ge2$. This extension is achieved by refining existing asymptotic
formulas for the mean and variance of the internal path length of random
$k$-regular trees, utilizing hypergeometric function properties and identities.
| [
{
"created": "Tue, 28 May 2024 05:52:19 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Svihla",
"Sean S.",
""
],
[
"Lladser",
"Manuel E.",
""
]
] | Consider a tree $T=(V,E)$ with root $\circ$ and edge length function $\ell:E\to\mathbb{R}_+$. The phylogenetic covariance matrix of $T$ is the matrix $C$ with rows and columns indexed by $L$, the leaf set of $T$, with entries $C(i,j):=\sum_{e\in[i\wedge j,o]}\ell(e)$, for each $i,j\in L$. Recent work [15] has shown that the phylogenetic covariance matrix of a large, random binary tree $T$ is significantly sparsified with overwhelmingly high probability under a change-of-basis with respect to the so-called Haar-like wavelets of $T$. This finding notably enables manipulating the spectrum of covariance matrices of large binary trees without the necessity to store them in computer memory but instead performing two post-order traversals of the tree. Building on the methods of [15], this manuscript further advances their sparsification result to encompass the broader class of $k$-regular trees, for any given $k\ge2$. This extension is achieved by refining existing asymptotic formulas for the mean and variance of the internal path length of random $k$-regular trees, utilizing hypergeometric function properties and identities. |
2004.01575 | Chong Qi | Chong Qi, Daniel Karlsson, Karl Sallmen, Ramon Wyss | Model studies on the COVID-19 pandemic in Sweden | null | null | null | null | q-bio.PE physics.bio-ph physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the increases of infections and deaths in Sweden caused by COVID-19
with several different models: Firstly an analytical susceptible-infected (SI)
model and the standard susceptible-infected-recovered (SIR) model. Then within
the SIR framework we study the susceptible-infected-deceased (SID)
correlations. All models reproduce well the number of infected cases and give
similar predictions. What causes us deep concern is the large number of deaths
projected by the SI and SID models. Our analysis shows that, irrespective of
the possible uncertainty of our model prediction, the next few days can be
critical for determining the future evolution of the death cases (Updated April
02).
| [
{
"created": "Fri, 3 Apr 2020 13:58:33 GMT",
"version": "v1"
}
] | 2020-04-06 | [
[
"Qi",
"Chong",
""
],
[
"Karlsson",
"Daniel",
""
],
[
"Sallmen",
"Karl",
""
],
[
"Wyss",
"Ramon",
""
]
] | We study the increases of infections and deaths in Sweden caused by COVID-19 with several different models: Firstly an analytical susceptible-infected (SI) model and the standard susceptible-infected-recovered (SIR) model. Then within the SIR framework we study the susceptible-infected-deceased (SID) correlations. All models reproduce well the number of infected cases and give similar predictions. What causes us deep concern is the large number of deaths projected by the SI and SID models. Our analysis shows that, irrespective of the possible uncertainty of our model prediction, the next few days can be critical for determining the future evolution of the death cases (Updated April 02). |
2404.08019 | Lena Podina | Lena Podina, Ali Ghodsi, Mohammad Kohandel | Learning Chemotherapy Drug Action via Universal Physics-Informed Neural
Networks | null | null | null | null | q-bio.QM cs.LG physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | Quantitative systems pharmacology (QSP) is widely used to assess drug effects
and toxicity before the drug goes to clinical trial. However, significant
manual distillation of the literature is needed in order to construct a QSP
model. Parameters may need to be fit, and simplifying assumptions of the model
need to be made. In this work, we apply Universal Physics-Informed Neural
Networks (UPINNs) to learn unknown components of various differential equations
that model chemotherapy pharmacodynamics. We learn three commonly employed
chemotherapeutic drug actions (log-kill, Norton-Simon, and E_max) from
synthetic data. Then, we use the UPINN method to fit the parameters for several
synthetic datasets simultaneously. Finally, we learn the net proliferation rate
in a model of doxorubicin (a chemotherapeutic) pharmacodynamics. As these are
only toy examples, we highlight the usefulness of UPINNs in learning unknown
terms in pharmacodynamic and pharmacokinetic models.
| [
{
"created": "Thu, 11 Apr 2024 01:30:05 GMT",
"version": "v1"
}
] | 2024-04-15 | [
[
"Podina",
"Lena",
""
],
[
"Ghodsi",
"Ali",
""
],
[
"Kohandel",
"Mohammad",
""
]
] | Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Physics-Informed Neural Networks (UPINNs) to learn unknown components of various differential equations that model chemotherapy pharmacodynamics. We learn three commonly employed chemotherapeutic drug actions (log-kill, Norton-Simon, and E_max) from synthetic data. Then, we use the UPINN method to fit the parameters for several synthetic datasets simultaneously. Finally, we learn the net proliferation rate in a model of doxorubicin (a chemotherapeutic) pharmacodynamics. As these are only toy examples, we highlight the usefulness of UPINNs in learning unknown terms in pharmacodynamic and pharmacokinetic models. |
2106.15928 | Edilson Arruda | Edilson F. Arruda and Dayse H. Pastore and Claudia M. Dias and
Fabricio O. Ourique | Reinfection and low cross-immunity as drivers of epidemic resurgence
under high seroprevalence: a model-based approach with application to
Amazonas, Brazil | null | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper introduces a new multi-strain epidemic model with reinfection and
cross-immunity to provide insights into the resurgence of the COVID-19 epidemic
in an area with reportedly high seroprevalence due to a largely unmitigated
outbreak: the state of Amazonas, Brazil. Although high seroprevalence could
have been expected to trigger herd immunity and prevent further waves in the
state, we have observed persistent levels of infection after the first wave and
eventually the emergence of a second viral strain just before an augmented
second wave. Our experiments suggest that the persistent levels of infection
after the first wave may be due to reinfection, whereas the higher peak at the
second wave can be explained by the emergence of the second variant and a low
level of cross-immunity between the original and the second variant. Finally,
the proposed model provides insights into the effect of reinfection and
cross-immunity on the long-term spread of an unmitigated epidemic.
| [
{
"created": "Wed, 30 Jun 2021 09:30:34 GMT",
"version": "v1"
}
] | 2021-07-01 | [
[
"Arruda",
"Edilson F.",
""
],
[
"Pastore",
"Dayse H.",
""
],
[
"Dias",
"Claudia M.",
""
],
[
"Ourique",
"Fabricio O.",
""
]
] | This paper introduces a new multi-strain epidemic model with reinfection and cross-immunity to provide insights into the resurgence of the COVID-19 epidemic in an area with reportedly high seroprevalence due to a largely unmitigated outbreak: the state of Amazonas, Brazil. Although high seroprevalence could have been expected to trigger herd immunity and prevent further waves in the state, we have observed persistent levels of infection after the first wave and eventually the emergence of a second viral strain just before an augmented second wave. Our experiments suggest that the persistent levels of infection after the first wave may be due to reinfection, whereas the higher peak at the second wave can be explained by the emergence of the second variant and a low level of cross-immunity between the original and the second variant. Finally, the proposed model provides insights into the effect of reinfection and cross-immunity on the long-term spread of an unmitigated epidemic. |
2208.14915 | Thomas Fink | Thomas Fink | Mortality equation characterizes the dynamics of an aging population | null | null | null | null | q-bio.PE cond-mat.stat-mech nlin.AO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Aging is thought to be a consequence of intrinsic breakdowns in how genetic
information is processed. But mounting experimental evidence suggests that
aging can be slowed. To help resolve this mystery, I derive a mortality
equation which characterizes the dynamics of an evolving population with a
given maximum age. Remarkably, while the spectrum of eigenvalues that govern
the evolution depends on the fitness, how they change with the maximum age is
independent of fitness. This makes it possible to establish the conditions
under which programmed aging can provide an evolutionary benefit.
| [
{
"created": "Wed, 31 Aug 2022 15:14:38 GMT",
"version": "v1"
}
] | 2022-09-01 | [
[
"Fink",
"Thomas",
""
]
] | Aging is thought to be a consequence of intrinsic breakdowns in how genetic information is processed. But mounting experimental evidence suggests that aging can be slowed. To help resolve this mystery, I derive a mortality equation which characterizes the dynamics of an evolving population with a given maximum age. Remarkably, while the spectrum of eigenvalues that govern the evolution depends on the fitness, how they change with the maximum age is independent of fitness. This makes it possible to establish the conditions under which programmed aging can provide an evolutionary benefit. |
1912.06386 | Nadav M. Shnerb | Immanuel Meyer and Nadav M. Shnerb | Evolutionary dynamics in populations with fluctuating size | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal environmental variations are ubiquitous in nature, yet most of the
theoretical works in population genetics and evolution assume fixed
environment. Here we analyze the effect of variations in carrying capacity on
the fate of a mutant type. We consider a two-state Moran model, where selection
intensity at equilibrium may differ (in amplitude and in sign) from selection
during periods of sharp growth and sharp decline. Using Kimura's diffusion
approximation we present simple formulae for effective population size and
effective selection, and use it to calculate the chance of ultimate fixation,
the time to fixation and the time to absorption (either fixation or loss). Our
analysis shows perfect agreement with numerical solutions for neutral,
beneficial and deleterious mutant. The contributions of different processes to
the mean and the variance of abundance variations are additive and commutative.
As a result, when selection intensity $s$ is weak such that ${\cal O}(s^2)$
terms are negligible, periodic or stochastic environmental variations yield
identical results.
| [
{
"created": "Fri, 13 Dec 2019 10:10:16 GMT",
"version": "v1"
}
] | 2019-12-16 | [
[
"Meyer",
"Immanuel",
""
],
[
"Shnerb",
"Nadav M.",
""
]
] | Temporal environmental variations are ubiquitous in nature, yet most of the theoretical works in population genetics and evolution assume fixed environment. Here we analyze the effect of variations in carrying capacity on the fate of a mutant type. We consider a two-state Moran model, where selection intensity at equilibrium may differ (in amplitude and in sign) from selection during periods of sharp growth and sharp decline. Using Kimura's diffusion approximation we present simple formulae for effective population size and effective selection, and use it to calculate the chance of ultimate fixation, the time to fixation and the time to absorption (either fixation or loss). Our analysis shows perfect agreement with numerical solutions for neutral, beneficial and deleterious mutant. The contributions of different processes to the mean and the variance of abundance variations are additive and commutative. As a result, when selection intensity $s$ is weak such that ${\cal O}(s^2)$ terms are negligible, periodic or stochastic environmental variations yield identical results. |
1804.04679 | Felipe Caycedo-Soler PhD | Felipe Caycedo-Soler, James Lim, Santiago Oviedo-Casado, Niek F. van
Hulst, Susana F. Huelga and Martin B. Plenio | On the theory of excitonic delocalization for robust vibronic dynamics
in LH2 | 5 pages main text with 3 figures, 7 pages supporting information with
4 figures | J. Phys. Chem. Lett. 9, 3446 (2018) | 10.1021/acs.jpclett.8b00933 | null | q-bio.BM physics.chem-ph quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nonlinear spectroscopy has revealed long-lasting oscillations in the optical
response of a variety of photosynthetic complexes. Different theoretical models
which involve the coherent coupling of electronic (excitonic) or
electronic-vibrational (vibronic) degrees of freedom have been put forward to
explain these observations. The ensuing debate concerning the relevance of
either one or the other mechanism may have obscured their potential synergy. To
illustrate this synergy, we quantify how the excitonic delocalization in the
LH2 unit of Rhodopseudomonas Acidophila purple bacterium, leads to correlations
of excitonic energy fluctuations, relevant coherent vibronic coupling and,
importantly, a decrease in the excitonic dephasing rates. Combining these
effects, we identify a feasible origin for the long-lasting oscillations
observed in fluorescent traces from time-delayed two-pulse single molecule
experiments performed on this photosynthetic complex.
| [
{
"created": "Thu, 15 Mar 2018 12:58:03 GMT",
"version": "v1"
}
] | 2018-07-02 | [
[
"Caycedo-Soler",
"Felipe",
""
],
[
"Lim",
"James",
""
],
[
"Oviedo-Casado",
"Santiago",
""
],
[
"van Hulst",
"Niek F.",
""
],
[
"Huelga",
"Susana F.",
""
],
[
"Plenio",
"Martin B.",
""
]
] | Nonlinear spectroscopy has revealed long-lasting oscillations in the optical response of a variety of photosynthetic complexes. Different theoretical models which involve the coherent coupling of electronic (excitonic) or electronic-vibrational (vibronic) degrees of freedom have been put forward to explain these observations. The ensuing debate concerning the relevance of either one or the other mechanism may have obscured their potential synergy. To illustrate this synergy, we quantify how the excitonic delocalization in the LH2 unit of Rhodopseudomonas Acidophila purple bacterium, leads to correlations of excitonic energy fluctuations, relevant coherent vibronic coupling and, importantly, a decrease in the excitonic dephasing rates. Combining these effects, we identify a feasible origin for the long-lasting oscillations observed in fluorescent traces from time-delayed two-pulse single molecule experiments performed on this photosynthetic complex. |
1110.5123 | Osamu Narikiyo | Tatsuro Yamashita and Osamu Narikiyo | Codon Capture and Ambiguous Intermediate Scenarios of Genetic Code
Evolution | arXiv admin note: substantial text overlap with arXiv:0901.0181 | null | null | null | q-bio.GN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using the shape space of codons and tRNAs we give a physical description of
the genetic code evolution on the basis of the codon capture and ambiguous
intermediate scenarios in a consistent manner. In the lowest dimensional
version of our description, a physical quantity, codon level is introduced. In
terms of the codon levels two scenarios are typically classified into two
different routes of the evolutional process. In the case of the ambiguous
intermediate scenario we perform an evolutional simulation implemented cost
selection of amino acids and confirm a rapid transition of the code change.
Such rapidness reduces uncomfortableness of the non-unique translation of the
code at intermediate state that is the weakness of the scenario. In the case of
the codon capture scenario the survival against mutations under the mutational
pressure minimizing GC content in genomes is simulated and it is demonstrated
that cells which experience only neutral mutations survive.
| [
{
"created": "Mon, 24 Oct 2011 03:04:12 GMT",
"version": "v1"
}
] | 2011-10-25 | [
[
"Yamashita",
"Tatsuro",
""
],
[
"Narikiyo",
"Osamu",
""
]
] | Using the shape space of codons and tRNAs we give a physical description of the genetic code evolution on the basis of the codon capture and ambiguous intermediate scenarios in a consistent manner. In the lowest dimensional version of our description, a physical quantity, codon level is introduced. In terms of the codon levels two scenarios are typically classified into two different routes of the evolutional process. In the case of the ambiguous intermediate scenario we perform an evolutional simulation implemented cost selection of amino acids and confirm a rapid transition of the code change. Such rapidness reduces uncomfortableness of the non-unique translation of the code at intermediate state that is the weakness of the scenario. In the case of the codon capture scenario the survival against mutations under the mutational pressure minimizing GC content in genomes is simulated and it is demonstrated that cells which experience only neutral mutations survive. |
2107.07047 | J. C. Phillips | Marcelo A. Moret, Gilney F. Zebende and J. C. Phillips | Why and How Coronavirus Has Evolved to Be Uniquely Contagious, with
Uniquely Successful Stable Vaccines | 8 pages | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Spike proteins, 1200 amino acids, are divided into two nearly equal parts, S1
and S2. We review here phase transition theory, implemented quantitatively by
thermodynamic scaling. The theory explains the evolution of Coronavirus
extremely high contagiousness caused by a few mutations from CoV2003 to CoV2019
identified among hundreds in S1. The theory previously predicted the
unprecedented success of spike-based vaccines. Here we analyze impressive
successes by McClellan et al., 2020, in stabilizing their original S2P vaccine
to Hexapro. Hexapro has expanded the two proline mutations of S2P, 2017, to six
combined proline mutations in S2. Their four new mutations are the result of
surveying 100 possibilities in their detailed structure-based context Our
analysis, based on only sparse publicly available data, suggests new proline
mutations could improve the Hexapro combination to Octapro or beyond.
| [
{
"created": "Thu, 1 Jul 2021 20:53:03 GMT",
"version": "v1"
}
] | 2021-07-16 | [
[
"Moret",
"Marcelo A.",
""
],
[
"Zebende",
"Gilney F.",
""
],
[
"Phillips",
"J. C.",
""
]
] | Spike proteins, 1200 amino acids, are divided into two nearly equal parts, S1 and S2. We review here phase transition theory, implemented quantitatively by thermodynamic scaling. The theory explains the evolution of Coronavirus extremely high contagiousness caused by a few mutations from CoV2003 to CoV2019 identified among hundreds in S1. The theory previously predicted the unprecedented success of spike-based vaccines. Here we analyze impressive successes by McClellan et al., 2020, in stabilizing their original S2P vaccine to Hexapro. Hexapro has expanded the two proline mutations of S2P, 2017, to six combined proline mutations in S2. Their four new mutations are the result of surveying 100 possibilities in their detailed structure-based context Our analysis, based on only sparse publicly available data, suggests new proline mutations could improve the Hexapro combination to Octapro or beyond. |
2108.12365 | Kapila Gunasekera PhD | Kapila Gunasekera, Daniel W\"uthrich, Sophie Braga-Lagache, Manfred
Heller, Torsten Ochsenreiter | Proteome remodelling during development from blood to insect-form
Trypanosoma brucei quantified by SILAC and mass spectrometry | 14 pages, 7 figures | BMC Genomics volume 13, 556 (2012) | 10.1186/1471-2164-13-556 | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Background: Trypanosoma brucei is the causative agent of human African
sleeping sickness and Nagana in cattle. In addition to being an important
pathogen T. brucei has developed into a model system in cell biology. Results:
Using Stable Isotope Labelling of Amino acids in Cell culture (SILAC) in
combination with mass spectrometry we determined the abundance of >1600
proteins in the long slender (LS), short stumpy (SS) mammalian bloodstream form
stages relative to the procyclic (PC) insect-form stage. In total we identified
2645 proteins, corresponding to ~30% of the total proteome and for the first
time present a comprehensive overview of relative protein levels in three life
stages of the parasite. Conclusions: We can show the extent of pre-adaptation
in the SS cells, especially at the level of the mitochondrial proteome. The
comparison to a previously published report on monomorphic in vitro grown
bloodstream and procyclic T. brucei indicates a loss of stringent regulation
particularly of mitochondrial proteins in these cells when compared to the
pleomorphic in vivo situation. In order to better understand the different
levels of gene expression regulation in this organism we compared mRNA steady
state abundance with the relative protein abundance-changes and detected
moderate but significant correlation indicating that trypanosomes possess a
significant repertoire of translational and posttranslational mechanisms to
regulate protein abundance.
| [
{
"created": "Fri, 27 Aug 2021 15:57:56 GMT",
"version": "v1"
}
] | 2021-08-30 | [
[
"Gunasekera",
"Kapila",
""
],
[
"Wüthrich",
"Daniel",
""
],
[
"Braga-Lagache",
"Sophie",
""
],
[
"Heller",
"Manfred",
""
],
[
"Ochsenreiter",
"Torsten",
""
]
] | Background: Trypanosoma brucei is the causative agent of human African sleeping sickness and Nagana in cattle. In addition to being an important pathogen T. brucei has developed into a model system in cell biology. Results: Using Stable Isotope Labelling of Amino acids in Cell culture (SILAC) in combination with mass spectrometry we determined the abundance of >1600 proteins in the long slender (LS), short stumpy (SS) mammalian bloodstream form stages relative to the procyclic (PC) insect-form stage. In total we identified 2645 proteins, corresponding to ~30% of the total proteome and for the first time present a comprehensive overview of relative protein levels in three life stages of the parasite. Conclusions: We can show the extent of pre-adaptation in the SS cells, especially at the level of the mitochondrial proteome. The comparison to a previously published report on monomorphic in vitro grown bloodstream and procyclic T. brucei indicates a loss of stringent regulation particularly of mitochondrial proteins in these cells when compared to the pleomorphic in vivo situation. In order to better understand the different levels of gene expression regulation in this organism we compared mRNA steady state abundance with the relative protein abundance-changes and detected moderate but significant correlation indicating that trypanosomes possess a significant repertoire of translational and posttranslational mechanisms to regulate protein abundance. |
1804.05266 | Daniel Larremore | Vidit Agrawal, Andrew B. Cowley, Qusay Alfaori, Juan G. Restrepo,
Daniel B. Larremore, Woodrow L. Shew | Robust entropy requires strong and balanced excitatory and inhibitory
synapses | null | null | 10.1063/1.5043429 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is widely appreciated that well-balanced excitation and inhibition are
necessary for proper function in neural networks. However, in principle, such
balance could be achieved by many possible configurations of excitatory and
inhibitory strengths, and relative numbers of excitatory and inhibitory
neurons. For instance, a given level of excitation could be balanced by either
numerous inhibitory neurons with weak synapses, or few inhibitory neurons with
strong synapses. Among the continuum of different but balanced configurations,
why should any particular configuration be favored? Here we address this
question in the context of the entropy of network dynamics by studying an
analytically tractable network of binary neurons. We find that entropy is
highest at the boundary between excitation-dominant and inhibition-dominant
regimes. Entropy also varies along this boundary with a trade-off between high
and robust entropy: weak synapse strengths yield high network entropy which is
fragile to parameter variations, while strong synapse strengths yield a lower,
but more robust, network entropy. In the case where inhibitory and excitatory
synapses are constrained to have similar strength, we find that a small, but
non-zero fraction of inhibitory neurons, like that seen in mammalian cortex,
results in robust and relatively high entropy.
| [
{
"created": "Sat, 14 Apr 2018 19:08:00 GMT",
"version": "v1"
}
] | 2018-11-14 | [
[
"Agrawal",
"Vidit",
""
],
[
"Cowley",
"Andrew B.",
""
],
[
"Alfaori",
"Qusay",
""
],
[
"Restrepo",
"Juan G.",
""
],
[
"Larremore",
"Daniel B.",
""
],
[
"Shew",
"Woodrow L.",
""
]
] | It is widely appreciated that well-balanced excitation and inhibition are necessary for proper function in neural networks. However, in principle, such balance could be achieved by many possible configurations of excitatory and inhibitory strengths, and relative numbers of excitatory and inhibitory neurons. For instance, a given level of excitation could be balanced by either numerous inhibitory neurons with weak synapses, or few inhibitory neurons with strong synapses. Among the continuum of different but balanced configurations, why should any particular configuration be favored? Here we address this question in the context of the entropy of network dynamics by studying an analytically tractable network of binary neurons. We find that entropy is highest at the boundary between excitation-dominant and inhibition-dominant regimes. Entropy also varies along this boundary with a trade-off between high and robust entropy: weak synapse strengths yield high network entropy which is fragile to parameter variations, while strong synapse strengths yield a lower, but more robust, network entropy. In the case where inhibitory and excitatory synapses are constrained to have similar strength, we find that a small, but non-zero fraction of inhibitory neurons, like that seen in mammalian cortex, results in robust and relatively high entropy. |
1407.6074 | Shi Chen | Shi Chen, Amiyaal Ilany, Brad J. White, Michael W. Sanderson, and
Cristina Lanzas | Spatial-Temporal Dynamics of High-Resolution Animal Social Networks:
What Can We Learn from Domestic Animals? | 4 figures | null | 10.1371/journal.pone.0129253 | null | q-bio.PE cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies of animal social networks have significantly increased our
understanding of animal behavior, social interactions, and many important
ecological and epidemiological processes. However, most of the studies are at
low temporal and spatial resolution due to the difficulty in recording accurate
contact information. Domestic animals such as cattle have social behavior and
serve as an excellent study system because their position can be explicitly and
continuously tracked, allowing their social networks to be accurately
constructed. We used radio-frequency tags to accurately track cattle position
and analyze high-resolution cattle social networks. We tested the hypothesis of
temporal stationarity and spatial homogeneity in these high-resolution networks
and demonstrated substantial spatial-temporal heterogeneity during different
daily time periods (feeding and non-feeding) and in different areas of the pen
(grain bunk, water trough, hay bunk, and other general pen area). The social
network structure is analyzed using global network characteristics (network
density, exponential random graph model structure), subgroup clustering
(modularity), triadic property (transitivity), and dyadic interactions
(correlation coefficient from a quadratic assignment procedure). Cattle tend to
have the strongest and most consistent contacts with others around the hay bunk
during the feeding time. These results cannot be determined from data at lower
spatial (aggregated at entire pen level) or temporal (aggregated at daily
level) resolution. These results reveal new insights for real-time animal
social network structure dynamics, providing more accurate descriptions that
allow more accurate modeling of multiple (both direct and indirect) disease
transmission pathways.
| [
{
"created": "Wed, 23 Jul 2014 00:41:09 GMT",
"version": "v1"
}
] | 2017-02-08 | [
[
"Chen",
"Shi",
""
],
[
"Ilany",
"Amiyaal",
""
],
[
"White",
"Brad J.",
""
],
[
"Sanderson",
"Michael W.",
""
],
[
"Lanzas",
"Cristina",
""
]
] | Recent studies of animal social networks have significantly increased our understanding of animal behavior, social interactions, and many important ecological and epidemiological processes. However, most of the studies are at low temporal and spatial resolution due to the difficulty in recording accurate contact information. Domestic animals such as cattle have social behavior and serve as an excellent study system because their position can be explicitly and continuously tracked, allowing their social networks to be accurately constructed. We used radio-frequency tags to accurately track cattle position and analyze high-resolution cattle social networks. We tested the hypothesis of temporal stationarity and spatial homogeneity in these high-resolution networks and demonstrated substantial spatial-temporal heterogeneity during different daily time periods (feeding and non-feeding) and in different areas of the pen (grain bunk, water trough, hay bunk, and other general pen area). The social network structure is analyzed using global network characteristics (network density, exponential random graph model structure), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure). Cattle tend to have the strongest and most consistent contacts with others around the hay bunk during the feeding time. These results cannot be determined from data at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. These results reveal new insights for real-time animal social network structure dynamics, providing more accurate descriptions that allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways. |
q-bio/0703002 | Claudius Gros | Claudius Gros | Autonomous Dynamics in Neural networks: The dHAN Concept and Associative
Thought Processes | null | Ninth Granada Lectures, AIP Conference Proceedings, Vol. 887, 129
(2007) | 10.1063/1.2709594 | null | q-bio.NC cond-mat.dis-nn | null | The neural activity of the human brain is dominated by self-sustained
activities. External sensory stimuli influence this autonomous activity but
they do not drive the brain directly. Most standard artificial neural network
models are however input driven and do not show spontaneous activities.
It constitutes a challenge to develop organizational principles for
controlled, self-sustained activity in artificial neural networks. Here we
propose and examine the dHAN concept for autonomous associative thought
processes in dense and homogeneous associative networks. An associative
thought-process is characterized, within this approach, by a time-series of
transient attractors. Each transient state corresponds to a stored information,
a memory. The subsequent transient states are characterized by large
associative overlaps, which are identical to acquired patterns. Memory states,
the acquired patterns, have such a dual functionality.
In this approach the self-sustained neural activity has a central functional
role. The network acquires a discrimination capability, as external stimuli
need to compete with the autonomous activity. Noise in the input is readily
filtered-out.
Hebbian learning of external patterns occurs coinstantaneous with the ongoing
associative thought process. The autonomous dynamics needs a long-term
working-point optimization which acquires within the dHAN concept a dual
functionality: It stabilizes the time development of the associative thought
process and limits runaway synaptic growth, which generically occurs otherwise
in neural networks with self-induced activities and Hebbian-type learning
rules.
| [
{
"created": "Thu, 1 Mar 2007 10:18:30 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Gros",
"Claudius",
""
]
] | The neural activity of the human brain is dominated by self-sustained activities. External sensory stimuli influence this autonomous activity but they do not drive the brain directly. Most standard artificial neural network models are however input driven and do not show spontaneous activities. It constitutes a challenge to develop organizational principles for controlled, self-sustained activity in artificial neural networks. Here we propose and examine the dHAN concept for autonomous associative thought processes in dense and homogeneous associative networks. An associative thought-process is characterized, within this approach, by a time-series of transient attractors. Each transient state corresponds to a stored information, a memory. The subsequent transient states are characterized by large associative overlaps, which are identical to acquired patterns. Memory states, the acquired patterns, have such a dual functionality. In this approach the self-sustained neural activity has a central functional role. The network acquires a discrimination capability, as external stimuli need to compete with the autonomous activity. Noise in the input is readily filtered-out. Hebbian learning of external patterns occurs coinstantaneous with the ongoing associative thought process. The autonomous dynamics needs a long-term working-point optimization which acquires within the dHAN concept a dual functionality: It stabilizes the time development of the associative thought process and limits runaway synaptic growth, which generically occurs otherwise in neural networks with self-induced activities and Hebbian-type learning rules. |
1609.06556 | Ognjen Perisic | Ognjen Peri\v{s}i\'c | Heterodimer binding scaffolds recognition via the analysis of
kinetically hot residues | arXiv admin note: text overlap with arXiv:1312.7376 | null | null | null | q-bio.BM cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Physical interactions between proteins are often difficult to decipher. The
aim of this paper is to present an algorithm designed to recognize binding
patches and supporting structural scaffolds of interacting heterodimer protein
chains using the Gaussian Network Model (GNM). The recognition is based on the
(self)adjustable identification of kinetically hot residues, i.e., residues
with highest contributions to the weighted sum of fastest modes per chain
extracted via GNM, and their connection to possible binding scaffolds. The
algorithm adjusts the number of modes used in the GNM's weighted sum
calculation using the ratio of predicted and expected numbers of target
residues (contact and first layer residues). This approach produces very good
results when applied to chains forming heterodimers, especially with dimers
with high chain length ratios. The protocol's ability to recognize near native
decoys was compared to the ability of the statistical potential of Lu and
Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical
potential produced better overall results, but in a number of cases its
predicting ability was comparable, or even worse than the ability of the
adjustable GNM approach. The results presented in this paper suggest that in
heterodimers, at least one partnering chain has interacting scaffold determined
by the immovable kinetically hot residues. In many cases interacting chains
(especially if being of noticeably different sizes), either behave as rigid
lock and key, or exhibit opposite dynamic behaviors. While the binding surface
of one of the chains is rigid and stable, its partner's interacting scaffold is
more flexible and adaptable.
Authors note: The approach described here was initially given as a rough
draft in 2013 [1]. The next manuscript will describe the behavior of protein
dimers incorrectly characterized with the present approach.
| [
{
"created": "Fri, 1 Jan 2016 11:48:32 GMT",
"version": "v1"
}
] | 2016-09-22 | [
[
"Perišić",
"Ognjen",
""
]
] | Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer protein chains using the Gaussian Network Model (GNM). The recognition is based on the (self)adjustable identification of kinetically hot residues, i.e., residues with highest contributions to the weighted sum of fastest modes per chain extracted via GNM, and their connection to possible binding scaffolds. The algorithm adjusts the number of modes used in the GNM's weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and first layer residues). This approach produces very good results when applied to chains forming heterodimers, especially with dimers with high chain length ratios. The protocol's ability to recognize near native decoys was compared to the ability of the statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even worse than the ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers, at least one partnering chain has interacting scaffold determined by the immovable kinetically hot residues. In many cases interacting chains (especially if being of noticeably different sizes), either behave as rigid lock and key, or exhibit opposite dynamic behaviors. While the binding surface of one of the chains is rigid and stable, its partner's interacting scaffold is more flexible and adaptable. Authors note: The approach described here was initially given as a rough draft in 2013 [1]. The next manuscript will describe the behavior of protein dimers incorrectly characterized with the present approach. |
1007.4521 | Tsvi Tlusty | Guy Shinar, Erez Dekel, Tsvi Tlusty, Uri Alon | Rules for biological regulation based on error minimization | biological physics, complex networks, systems biology,
transcriptional regulation
http://www.weizmann.ac.il/complex/tlusty/papers/PNAS2006.pdf
http://www.pnas.org/content/103/11/3999.full | PNAS March 14, 2006 vol. 103 no. 11 3999-4004 | 10.1073/pnas.0506610103 | null | q-bio.BM physics.bio-ph q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The control of gene expression involves complex mechanisms that show large
variation in design. For example, genes can be turned on either by the binding
of an activator (positive control) or the unbinding of a repressor (negative
control). What determines the choice of mode of control for each gene? This
study proposes rules for gene regulation based on the assumption that free
regulatory sites are exposed to nonspecific binding errors, whereas sites bound
to their cognate regulators are protected from errors. Hence, the selected
mechanisms keep the sites bound to their designated regulators for most of the
time, thus minimizing fitness-reducing errors. This offers an explanation of
the empirically demonstrated Savageau demand rule: Genes that are needed often
in the natural environment tend to be regulated by activators, and rarely
needed genes tend to be regulated by repressors; in both cases, sites are bound
for most of the time, and errors are minimized. The fitness advantage of error
minimization appears to be readily selectable. The present approach can also
generate rules for multi-regulator systems. The error-minimization framework
raises several experimentally testable hypotheses. It may also apply to other
biological regulation systems, such as those involving protein-protein
interactions.
| [
{
"created": "Mon, 26 Jul 2010 17:57:21 GMT",
"version": "v1"
}
] | 2010-07-27 | [
[
"Shinar",
"Guy",
""
],
[
"Dekel",
"Erez",
""
],
[
"Tlusty",
"Tsvi",
""
],
[
"Alon",
"Uri",
""
]
] | The control of gene expression involves complex mechanisms that show large variation in design. For example, genes can be turned on either by the binding of an activator (positive control) or the unbinding of a repressor (negative control). What determines the choice of mode of control for each gene? This study proposes rules for gene regulation based on the assumption that free regulatory sites are exposed to nonspecific binding errors, whereas sites bound to their cognate regulators are protected from errors. Hence, the selected mechanisms keep the sites bound to their designated regulators for most of the time, thus minimizing fitness-reducing errors. This offers an explanation of the empirically demonstrated Savageau demand rule: Genes that are needed often in the natural environment tend to be regulated by activators, and rarely needed genes tend to be regulated by repressors; in both cases, sites are bound for most of the time, and errors are minimized. The fitness advantage of error minimization appears to be readily selectable. The present approach can also generate rules for multi-regulator systems. The error-minimization framework raises several experimentally testable hypotheses. It may also apply to other biological regulation systems, such as those involving protein-protein interactions. |
1402.5447 | Nicol\'o Fusi | Nicolo Fusi, Christoph Lippert, Neil D. Lawrence and Oliver Stegle | Genetic Analysis of Transformed Phenotypes | null | null | null | null | q-bio.GN stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Linear mixed models (LMMs) are a powerful and established tool for studying
genotype-phenotype relationships. A limiting assumption of LMMs is that the
residuals are Gaussian distributed, a requirement that rarely holds in
practice. Violations of this assumption can lead to false conclusions and
losses in power, and hence it is common practice to pre-process the phenotypic
values to make them Gaussian, for instance by applying logarithmic or other
non-linear transformations. Unfortunately, different phenotypes require
different specific transformations, and choosing a "good" transformation is in
general challenging and subjective. Here, we present an extension of the LMM
that estimates an optimal transformation from the observed data. In extensive
simulations and applications to real data from human, mouse and yeast we show
that using such optimal transformations lead to increased power in genome-wide
association studies and higher accuracy in heritability estimates and phenotype
predictions.
| [
{
"created": "Fri, 21 Feb 2014 23:28:52 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Feb 2014 03:26:22 GMT",
"version": "v2"
}
] | 2014-08-10 | [
[
"Fusi",
"Nicolo",
""
],
[
"Lippert",
"Christoph",
""
],
[
"Lawrence",
"Neil D.",
""
],
[
"Stegle",
"Oliver",
""
]
] | Linear mixed models (LMMs) are a powerful and established tool for studying genotype-phenotype relationships. A limiting assumption of LMMs is that the residuals are Gaussian distributed, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and losses in power, and hence it is common practice to pre-process the phenotypic values to make them Gaussian, for instance by applying logarithmic or other non-linear transformations. Unfortunately, different phenotypes require different specific transformations, and choosing a "good" transformation is in general challenging and subjective. Here, we present an extension of the LMM that estimates an optimal transformation from the observed data. In extensive simulations and applications to real data from human, mouse and yeast we show that using such optimal transformations lead to increased power in genome-wide association studies and higher accuracy in heritability estimates and phenotype predictions. |
1604.04935 | Kasthuri Kannan | Kasthuri Kannan and Adriana Heguy | Why Mutant Allele Frequencies in Oncogenes Peak Around 0.40 and Rapidly
Decrease? | Main article 6 pages. Contains Abstract, Main Article, 1
Supplementary Figure, 1 Supplementary Method. Supplementary Table
(Supplementary Table 1) and MATLAB code available upon request | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The mutant allele frequencies in oncogenes peak around 0.40 and rapidly
decrease. In this article, we explain why this is the case. Invoking a key
result from mathematical analysis in our model, namely, the inverse function
theorem, we estimate the selection pressures of the mutations as a function of
germline allele frequencies. Under complete dominance of oncogenic mutations,
this selection function is expected to be linearly correlated with the
distribution of the mutant alleles. We demonstrate that this is the case by
investigating the allele frequencies of mutations in oncogenes across various
cancer types, validating our model for mean effective selection. Consistent
with the population genetics model of fitness, the selection function fits a
gamma distribution curve that accurately describes the trend of the mutant
allele frequencies. While existing equations for selection explain evolution at
low allele frequencies, our equations are general formulas for natural
selection under complete dominance operating at all frequencies. We show that
selection exhibits linear behavior at all times, favoring dominant alleles with
respect to the change in recessive allele frequency. Also, these equations
show, selection behaves like power-law against the recessive alleles at low
dominant allele frequency.
| [
{
"created": "Sun, 17 Apr 2016 22:29:12 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Apr 2016 15:14:45 GMT",
"version": "v2"
}
] | 2016-04-26 | [
[
"Kannan",
"Kasthuri",
""
],
[
"Heguy",
"Adriana",
""
]
] | The mutant allele frequencies in oncogenes peak around 0.40 and rapidly decrease. In this article, we explain why this is the case. Invoking a key result from mathematical analysis in our model, namely, the inverse function theorem, we estimate the selection pressures of the mutations as a function of germline allele frequencies. Under complete dominance of oncogenic mutations, this selection function is expected to be linearly correlated with the distribution of the mutant alleles. We demonstrate that this is the case by investigating the allele frequencies of mutations in oncogenes across various cancer types, validating our model for mean effective selection. Consistent with the population genetics model of fitness, the selection function fits a gamma distribution curve that accurately describes the trend of the mutant allele frequencies. While existing equations for selection explain evolution at low allele frequencies, our equations are general formulas for natural selection under complete dominance operating at all frequencies. We show that selection exhibits linear behavior at all times, favoring dominant alleles with respect to the change in recessive allele frequency. Also, these equations show, selection behaves like power-law against the recessive alleles at low dominant allele frequency. |
2311.17970 | Marko Petkovi\'c | Marko Petkovi\'c, Vlado Menkovski | Description Generation using Variational Auto-Encoders for precursor
microRNA | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Micro RNAs (miRNA) are a type of non-coding RNA, which are involved in gene
regulation and can be associated with diseases such as cancer, cardiovascular
and neurological diseases. As such, identifying the entire genome of miRNA can
be of great relevance. Since experimental methods for novel precursor miRNA
(pre-miRNA) detection are complex and expensive, computational detection using
ML could be useful. Existing ML methods are often complex black boxes, which do
not create an interpretable structural description of pre-miRNA. In this paper,
we propose a novel framework, which makes use of generative modeling through
Variational Auto-Encoders to uncover the generative factors of pre-miRNA. After
training the VAE, the pre-miRNA description is developed using a decision tree
on the lower dimensional latent space. Applying the framework to miRNA
classification, we obtain a high reconstruction and classification performance,
while also developing an accurate miRNA description.
| [
{
"created": "Wed, 29 Nov 2023 15:41:45 GMT",
"version": "v1"
}
] | 2023-12-01 | [
[
"Petković",
"Marko",
""
],
[
"Menkovski",
"Vlado",
""
]
] | Micro RNAs (miRNA) are a type of non-coding RNA, which are involved in gene regulation and can be associated with diseases such as cancer, cardiovascular and neurological diseases. As such, identifying the entire genome of miRNA can be of great relevance. Since experimental methods for novel precursor miRNA (pre-miRNA) detection are complex and expensive, computational detection using ML could be useful. Existing ML methods are often complex black boxes, which do not create an interpretable structural description of pre-miRNA. In this paper, we propose a novel framework, which makes use of generative modeling through Variational Auto-Encoders to uncover the generative factors of pre-miRNA. After training the VAE, the pre-miRNA description is developed using a decision tree on the lower dimensional latent space. Applying the framework to miRNA classification, we obtain a high reconstruction and classification performance, while also developing an accurate miRNA description. |
1812.03609 | Nikita Novikov | Nikita Novikov, Boris Gutkin | Role of NMDA conductance in average firing rate shifts caused by
external periodic forcing | null | Phys. Rev. E 101, 052408 (2020) | 10.1103/PhysRevE.101.052408 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A widely accepted view of computations in the brain relies on population
coding, where the neural ensemble firing rate is modulated in a stable manner
to transmit information and perform various cognitive tasks. At the same time,
oscillatory neural activity is specifically modulated in frequency, coherence
and power during cognitive performance. How the firing rate and oscillations
interact remains a salient question. In this paper, we develop a theory for the
interactions between oscillatory signals and the firing rate of neural
populations based on activity of non-linear voltage-dependent NMDA synapses.
Notably, we show under which conditions oscillatory inputs can control the mean
firing rate without loss of stability. Using mathematical analysis and
simulations of mean-field models, we demonstrate that presence of NMDA synapses
on both the excitatory and the inhibitory neurons is critical for sinusoidal
oscillations to significantly and stably increase the firing rate. We
characterize the oscillation-induced mean firing rate shift as a function of
the fast and slow synaptic weights and demonstrate the parameter region, in
which the effect under investigation is mostly pronounced. Results of our work
may help identify the properties of neural circuits that allow for constructive
control of the firing rate codes by large-scale neural oscillations.
| [
{
"created": "Mon, 10 Dec 2018 03:42:35 GMT",
"version": "v1"
}
] | 2020-05-27 | [
[
"Novikov",
"Nikita",
""
],
[
"Gutkin",
"Boris",
""
]
] | A widely accepted view of computations in the brain relies on population coding, where the neural ensemble firing rate is modulated in a stable manner to transmit information and perform various cognitive tasks. At the same time, oscillatory neural activity is specifically modulated in frequency, coherence and power during cognitive performance. How the firing rate and oscillations interact remains a salient question. In this paper, we develop a theory for the interactions between oscillatory signals and the firing rate of neural populations based on activity of non-linear voltage-dependent NMDA synapses. Notably, we show under which conditions oscillatory inputs can control the mean firing rate without loss of stability. Using mathematical analysis and simulations of mean-field models, we demonstrate that presence of NMDA synapses on both the excitatory and the inhibitory neurons is critical for sinusoidal oscillations to significantly and stably increase the firing rate. We characterize the oscillation-induced mean firing rate shift as a function of the fast and slow synaptic weights and demonstrate the parameter region, in which the effect under investigation is mostly pronounced. Results of our work may help identify the properties of neural circuits that allow for constructive control of the firing rate codes by large-scale neural oscillations. |
1401.5048 | Michael Deem | Man Chen and Michael W. Deem | Hierarchy of Gene Expression Data is Predictive of Future Breast Cancer
Outcome | 14 pages, 5 figures | Phys. Biol. 10 (2013) 056006 | 10.1088/1478-3975/10/5/056006 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We calculate measures of hierarchy in gene and tissue networks of breast
cancer patients. We find that the likelihood of metastasis in the future is
correlated with increased values of network hierarchy for expression networks
of cancer-associated genes, due to correlated expression of cancer-specific
pathways. Conversely, future metastasis and quick relapse times are negatively
correlated with values of network hierarchy in the expression network of all
genes, due to dedifferentiation of gene pathways and circuits. These results
suggest that hierarchy of gene expression may be useful as an additional
biomarker for breast cancer prognosis.
| [
{
"created": "Mon, 20 Jan 2014 20:36:41 GMT",
"version": "v1"
}
] | 2014-01-21 | [
[
"Chen",
"Man",
""
],
[
"Deem",
"Michael W.",
""
]
] | We calculate measures of hierarchy in gene and tissue networks of breast cancer patients. We find that the likelihood of metastasis in the future is correlated with increased values of network hierarchy for expression networks of cancer-associated genes, due to correlated expression of cancer-specific pathways. Conversely, future metastasis and quick relapse times are negatively correlated with values of network hierarchy in the expression network of all genes, due to dedifferentiation of gene pathways and circuits. These results suggest that hierarchy of gene expression may be useful as an additional biomarker for breast cancer prognosis. |
1908.01488 | Charlotte Recapet | Charlotte R\'ecapet (ECOBIOP), Mathilde Arriv\'e (IBMP), Blandine
Doligez, Pierre Bize | Antioxidant capacity is repeatable across years but does not
consistently correlate with a marker of peroxidation in a free-living
passerine bird | null | Journal of Comparative Physiology B, Springer Verlag, 2019, 189
(2), pp.283-298 | 10.1007/s00360-019-01211-1 | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Oxidative stress occurs when reactive oxygen species (ROS) exceed antioxidant
defences, which can have deleterious effects on cell function, health and
survival. Therefore, organisms are expected to finely regulate pro-oxidant and
antioxidant processes. ROS are mainly produced through aerobic metabolism and
vary in response to changes in energetic requirements, whereas antioxidants may
be enhanced, depleted or show no changes in response to changes in ROS levels.
We investigated the repeatability, within-individual variation and correlation
across different conditions of two plasmatic markers of the oxidative balance
in 1108 samples from 635 free-living adult collared flycatchers (Ficedula
albicollis). We sought to manipulate energy constraints by increasing wing load
in 2012 and 2013 and by providing additional food in 2014. We then tested the
relative importance of within- and between-individual variation on reactive
oxygen metabolites (ROMs), a marker of lipid and protein peroxidation, and on
non-enzymatic antioxidant defences (OXY test). We also investigated whether the
experimental treatments modified the correlation between markers. Antioxidant
defences were repeatable (range of repeatability estimates = 0.128--0.581),
whereas ROMs were not (0--0.061). Antioxidants varied neither between
incubation and nestling feeding nor between sexes. ROMs increased from
incubation to nestling feeding in females and were higher in females than
males. Antioxidant defences and ROM concentration were globally positively
correlated, but the correlation varied between experimental conditions and
between years. Hence, the management of oxidative balance in wild animals
appears flexible under variable environmental conditions, an observation which
should be confirmed over a wider range of markers.
| [
{
"created": "Mon, 5 Aug 2019 06:50:51 GMT",
"version": "v1"
}
] | 2019-08-06 | [
[
"Récapet",
"Charlotte",
"",
"ECOBIOP"
],
[
"Arrivé",
"Mathilde",
"",
"IBMP"
],
[
"Doligez",
"Blandine",
""
],
[
"Bize",
"Pierre",
""
]
] | Oxidative stress occurs when reactive oxygen species (ROS) exceed antioxidant defences, which can have deleterious effects on cell function, health and survival. Therefore, organisms are expected to finely regulate pro-oxidant and antioxidant processes. ROS are mainly produced through aerobic metabolism and vary in response to changes in energetic requirements, whereas antioxidants may be enhanced, depleted or show no changes in response to changes in ROS levels. We investigated the repeatability, within-individual variation and correlation across different conditions of two plasmatic markers of the oxidative balance in 1108 samples from 635 free-living adult collared flycatchers (Ficedula albicollis). We sought to manipulate energy constraints by increasing wing load in 2012 and 2013 and by providing additional food in 2014. We then tested the relative importance of within- and between-individual variation on reactive oxygen metabolites (ROMs), a marker of lipid and protein peroxidation, and on non-enzymatic antioxidant defences (OXY test). We also investigated whether the experimental treatments modified the correlation between markers. Antioxidant defences were repeatable (range of repeatability estimates = 0.128--0.581), whereas ROMs were not (0--0.061). Antioxidants varied neither between incubation and nestling feeding nor between sexes. ROMs increased from incubation to nestling feeding in females and were higher in females than males. Antioxidant defences and ROM concentration were globally positively correlated, but the correlation varied between experimental conditions and between years. Hence, the management of oxidative balance in wild animals appears flexible under variable environmental conditions, an observation which should be confirmed over a wider range of markers. |
1606.09277 | Dominika Lyzwa | Dominika Lyzwa and Florentin W\"org\"otter | Response and noise correlations to complex natural sounds in the
auditory midbrain | 19 pages, 10 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How natural communication sounds are spatially represented across the
inferior colliculus, the main center of convergence for auditory information in
the midbrain, is not known. The neural representation of the acoustic stimuli
results from the interplay of locally differing input and the organization of
spectral and temporal neural preferences that change gradually across the
nucleus. This raises the question how similar the neural representation of the
communication sounds is across these gradients of neural preferences, and
whether it also changes gradually. Multi-unit cluster spike trains were
recorded from guinea pigs presented with a spectrotemporally rich set of eleven
species-specific communication sounds. Using cross-correlation, we analyzed the
response similarity of spiking activity across a broad frequency range for
similarly and differently frequency-tuned neurons. Furthermore, we separated
the contribution of the stimulus to the correlations to investigate whether
similarity is only attributable to the stimulus, or, whether interactions exist
between the multi-unit clusters that lead to correlations and whether these
follow the same representation as the response similarity. We found that
similarity of responses is dependent on the neurons' spatial distance for
similarly and differently frequency-tuned neurons, and that similarity
decreases gradually with spatial distance. Significant neural correlations
exist, and contribute to the response similarity. Our findings suggest that for
multi-unit clusters in the mammalian inferior colliculus, the gradual response
similarity with spatial distance to natural complex sounds is shaped by neural
interactions and the gradual organization of neural preferences.
| [
{
"created": "Wed, 29 Jun 2016 20:42:29 GMT",
"version": "v1"
}
] | 2016-07-01 | [
[
"Lyzwa",
"Dominika",
""
],
[
"Wörgötter",
"Florentin",
""
]
] | How natural communication sounds are spatially represented across the inferior colliculus, the main center of convergence for auditory information in the midbrain, is not known. The neural representation of the acoustic stimuli results from the interplay of locally differing input and the organization of spectral and temporal neural preferences that change gradually across the nucleus. This raises the question how similar the neural representation of the communication sounds is across these gradients of neural preferences, and whether it also changes gradually. Multi-unit cluster spike trains were recorded from guinea pigs presented with a spectrotemporally rich set of eleven species-specific communication sounds. Using cross-correlation, we analyzed the response similarity of spiking activity across a broad frequency range for similarly and differently frequency-tuned neurons. Furthermore, we separated the contribution of the stimulus to the correlations to investigate whether similarity is only attributable to the stimulus, or, whether interactions exist between the multi-unit clusters that lead to correlations and whether these follow the same representation as the response similarity. We found that similarity of responses is dependent on the neurons' spatial distance for similarly and differently frequency-tuned neurons, and that similarity decreases gradually with spatial distance. Significant neural correlations exist, and contribute to the response similarity. Our findings suggest that for multi-unit clusters in the mammalian inferior colliculus, the gradual response similarity with spatial distance to natural complex sounds is shaped by neural interactions and the gradual organization of neural preferences. |
2205.15364 | Zheng Yao | Zhiqin Zhu, Zheng Yao, Guanqiu Qi, Neal Mazur, Baisen Cong | Associative Learning Mechanism for Drug-Target Interaction Prediction | The extended and final version of this paper has been published with
open access modality in the CAAI Transactions on Intelligence Technology and
can be found at link LINK HERE. Please refer to the TRIT published version in
your scientific papers | Zhiqin Zhu (2023) 1-20 | 10.1049/cit2.12194 | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As a necessary process in drug development, finding a drug compound that can
selectively bind to a specific protein is highly challenging and costly.
Drug-target affinity (DTA), which represents the strength of drug-target
interaction (DTI), has played an important role in the DTI prediction task over
the past decade. Although deep learning has been applied to DTA-related
research, existing solutions ignore fundamental correlations between molecular
substructures in molecular representation learning of drug compound
molecules/protein targets. Moreover, traditional methods lack the
interpretability of the DTA prediction process. This results in missing feature
information of intermolecular interactions, thereby affecting prediction
performance. Therefore, this paper proposes a DTA prediction method with
interactive learning and an autoencoder mechanism. The proposed model enhances
the corresponding ability to capture the feature information of a single
molecular sequence by the drug/protein molecular representation learning module
and supplements the information interaction between molecular sequence pairs by
the interactive information learning module. The DTA value prediction module
fuses the drug-target pair interaction information to output the predicted
value of DTA. Additionally, this paper theoretically proves that the proposed
method maximizes evidence lower bound (ELBO) for the joint distribution of the
DTA prediction model, which enhances the consistency of the probability
distribution between the actual value and the predicted value. The experimental
results confirm mutual transformer-drug target affinity (MT-DTA) achieves
better performance than other comparative methods.
| [
{
"created": "Tue, 24 May 2022 14:25:28 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Jun 2022 07:46:35 GMT",
"version": "v2"
},
{
"created": "Mon, 25 Jul 2022 07:03:42 GMT",
"version": "v3"
},
{
"created": "Thu, 28 Jul 2022 07:22:14 GMT",
"version": "v4"
},
{
"created": "Fri, 15 Dec 2023 15:02:34 GMT",
"version": "v5"
}
] | 2023-12-18 | [
[
"Zhu",
"Zhiqin",
""
],
[
"Yao",
"Zheng",
""
],
[
"Qi",
"Guanqiu",
""
],
[
"Mazur",
"Neal",
""
],
[
"Cong",
"Baisen",
""
]
] | As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. Although deep learning has been applied to DTA-related research, existing solutions ignore fundamental correlations between molecular substructures in molecular representation learning of drug compound molecules/protein targets. Moreover, traditional methods lack the interpretability of the DTA prediction process. This results in missing feature information of intermolecular interactions, thereby affecting prediction performance. Therefore, this paper proposes a DTA prediction method with interactive learning and an autoencoder mechanism. The proposed model enhances the corresponding ability to capture the feature information of a single molecular sequence by the drug/protein molecular representation learning module and supplements the information interaction between molecular sequence pairs by the interactive information learning module. The DTA value prediction module fuses the drug-target pair interaction information to output the predicted value of DTA. Additionally, this paper theoretically proves that the proposed method maximizes evidence lower bound (ELBO) for the joint distribution of the DTA prediction model, which enhances the consistency of the probability distribution between the actual value and the predicted value. The experimental results confirm mutual transformer-drug target affinity (MT-DTA) achieves better performance than other comparative methods. |
2304.13737 | Melanie F. Pradier | Melanie F. Pradier, Niranjani Prasad, Paidamoyo Chapfuwa, Sahra
Ghalebikesabi, Max Ilse, Steven Woodhouse, Rebecca Elyanow, Javier Zazo,
Javier Gonzalez, Julia Greissl, Edward Meeds | AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recent advances in immunomics have shown that T-cell receptor (TCR)
signatures can accurately predict active or recent infection by leveraging the
high specificity of TCR binding to disease antigens. However, the extreme
diversity of the adaptive immune repertoire presents challenges in reliably
identifying disease-specific TCRs. Population genetics and sequencing depth can
also have strong systematic effects on repertoires, which requires careful
consideration when developing diagnostic models. We present an Adaptive Immune
Repertoire-Invariant Variational Autoencoder (AIRIVA), a generative model that
learns a low-dimensional, interpretable, and compositional representation of
TCR repertoires to disentangle such systematic effects in repertoires. We apply
AIRIVA to two infectious disease case-studies: COVID-19 (natural infection and
vaccination) and the Herpes Simplex Virus (HSV-1 and HSV-2), and empirically
show that we can disentangle the individual disease signals. We further
demonstrate AIRIVA's capability to: learn from unlabelled samples; generate
in-silico TCR repertoires by intervening on the latent factors; and identify
disease-associated TCRs validated using TCR annotations from external assay
data.
| [
{
"created": "Wed, 26 Apr 2023 14:40:35 GMT",
"version": "v1"
}
] | 2023-04-28 | [
[
"Pradier",
"Melanie F.",
""
],
[
"Prasad",
"Niranjani",
""
],
[
"Chapfuwa",
"Paidamoyo",
""
],
[
"Ghalebikesabi",
"Sahra",
""
],
[
"Ilse",
"Max",
""
],
[
"Woodhouse",
"Steven",
""
],
[
"Elyanow",
"Rebecca",
""
],
[
"Zazo",
"Javier",
""
],
[
"Gonzalez",
"Javier",
""
],
[
"Greissl",
"Julia",
""
],
[
"Meeds",
"Edward",
""
]
] | Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens. However, the extreme diversity of the adaptive immune repertoire presents challenges in reliably identifying disease-specific TCRs. Population genetics and sequencing depth can also have strong systematic effects on repertoires, which requires careful consideration when developing diagnostic models. We present an Adaptive Immune Repertoire-Invariant Variational Autoencoder (AIRIVA), a generative model that learns a low-dimensional, interpretable, and compositional representation of TCR repertoires to disentangle such systematic effects in repertoires. We apply AIRIVA to two infectious disease case-studies: COVID-19 (natural infection and vaccination) and the Herpes Simplex Virus (HSV-1 and HSV-2), and empirically show that we can disentangle the individual disease signals. We further demonstrate AIRIVA's capability to: learn from unlabelled samples; generate in-silico TCR repertoires by intervening on the latent factors; and identify disease-associated TCRs validated using TCR annotations from external assay data. |
1605.04959 | Julio Augusto Freyre-Gonz\'alez | Miguel A. Ibarra-Arellano, Adri\'an I. Campos-Gonz\'alez, Luis G.
Trevi\~no-Quintanilla, Andreas Tauch and Julio A. Freyre-Gonz\'alez | Abasy Atlas: A comprehensive inventory of systems, global network
properties and systems-level elements across bacteria | 25 pages, 8 figures, 1 table | Database (2016) 2016 : baw089 | 10.1093/database/baw089 | null | q-bio.MN q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The availability of databases electronically encoding curated regulatory
networks and of high-throughput technologies and methods to discover regulatory
interactions provides an invaluable source of data to understand the principles
underpinning the organization and evolution of these networks responsible for
cellular regulation. Nevertheless, data on these sources never goes beyond the
regulon level despite the fact that regulatory networks are complex
hierarchical-modular structures still challenging our understanding. This
brings the necessity for an inventory of systems across a large range of
organisms, a key step to rendering feasible comparative systems biology
approaches. In this work, we take the first step towards a global understanding
of the regulatory networks organization by making a cartography of the
functional architectures of diverse bacteria. Abasy (Across-bacteria systems)
Atlas provides a comprehensive inventory of annotated functional systems,
global network properties, and systems-level elements (global regulators,
modular genes shaping functional systems, basal machinery genes, and
intermodular genes) predicted by the natural decomposition approach for
reconstructed and meta-curated regulatory networks across a large range of
bacteria, including pathogenically and biotechnologically relevant organisms.
The meta-curation of regulatory datasets provides the most complete and
reliable set of regulatory interactions currently available. Abasy Atlas
contains systems and system-level elements for 50 regulatory networks
comprising 78,649 regulatory interactions covering 42 bacteria in nine taxa,
containing 3,708 regulons and 1,776 systems. All this brings together a large
corpus of data that will surely inspire studies to generate hypothesis
regarding the principles governing the evolution and organization of systems
and the functional architectures controlling them.
| [
{
"created": "Mon, 16 May 2016 21:47:02 GMT",
"version": "v1"
}
] | 2016-10-31 | [
[
"Ibarra-Arellano",
"Miguel A.",
""
],
[
"Campos-González",
"Adrián I.",
""
],
[
"Treviño-Quintanilla",
"Luis G.",
""
],
[
"Tauch",
"Andreas",
""
],
[
"Freyre-González",
"Julio A.",
""
]
] | The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties, and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes, and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78,649 regulatory interactions covering 42 bacteria in nine taxa, containing 3,708 regulons and 1,776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them. |
1406.2195 | Nicolas Le Nov\`ere | Massimo Lai, Denis Brun, Stuart J Edelstein, Nicolas Le Nov\`ere | Modulation of calmodulin lobes by different targets: an allosteric model
with hemiconcerted conformational transitions | null | null | 10.1371/journal.pcbi.1004063 | null | q-bio.MN q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Calmodulin, the ubiquitous calcium-activated second messenger in eukaryotes,
is an extremely versatile molecule involved in many biological processes:
muscular contraction, synaptic plasticity, circadian rhythm, and cell cycle,
among others. The protein is structurally organised into two globular lobes,
joined by a flexible linker. Calcium modulates calmodulin activity by favoring
a conformational transition of each lobe from a closed conformation to an open
conformation. Most targets have a strong preference for one conformation over
the other, and depending on the free calcium concentration in a cell,
particular sets of targets will preferentially interact with calmodulin. In
turn, targets can increase or decrease the calcium affinity of the calmodulin
molecules to which they bind. Interestingly, experiments with the tryptic
fragments showed that most targets have a much lower affinity for the N-lobe
than for the C-lobe. Hence, the latter predominates in the formation of most
calmodulin-target complexes. We showed that a relatively simple allosteric
mechanism, based the classic MWC model, can capture the observed modulation of
both the isolated C-lobe, and intact calmodulin, by individual targets.
Moreover, our model can be naturally extended to study how the calcium affinity
of a single pool of calmodulin is modulated by a mixture of competing targets
in vivo.
| [
{
"created": "Mon, 9 Jun 2014 14:33:54 GMT",
"version": "v1"
}
] | 2015-06-19 | [
[
"Lai",
"Massimo",
""
],
[
"Brun",
"Denis",
""
],
[
"Edelstein",
"Stuart J",
""
],
[
"Novère",
"Nicolas Le",
""
]
] | Calmodulin, the ubiquitous calcium-activated second messenger in eukaryotes, is an extremely versatile molecule involved in many biological processes: muscular contraction, synaptic plasticity, circadian rhythm, and cell cycle, among others. The protein is structurally organised into two globular lobes, joined by a flexible linker. Calcium modulates calmodulin activity by favoring a conformational transition of each lobe from a closed conformation to an open conformation. Most targets have a strong preference for one conformation over the other, and depending on the free calcium concentration in a cell, particular sets of targets will preferentially interact with calmodulin. In turn, targets can increase or decrease the calcium affinity of the calmodulin molecules to which they bind. Interestingly, experiments with the tryptic fragments showed that most targets have a much lower affinity for the N-lobe than for the C-lobe. Hence, the latter predominates in the formation of most calmodulin-target complexes. We showed that a relatively simple allosteric mechanism, based the classic MWC model, can capture the observed modulation of both the isolated C-lobe, and intact calmodulin, by individual targets. Moreover, our model can be naturally extended to study how the calcium affinity of a single pool of calmodulin is modulated by a mixture of competing targets in vivo. |
1202.2670 | Noel Malod-Dognin | Inken Wohlers (MAC4), No\"el Malod-Dognin (INRIA Sophia Antipolis),
Rumen Andonov (INRIA - IRISA), Gunnar W. Klau (MAC4) | CSA: Comprehensive comparison of pairwise protein structure alignments | Preprint, submitted to Nucleic Acids Research | N° RR-7874 (2012) | null | RR-7874 | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | CSA is a web server for the comprehensive comparison of pairwise protein
structure alignments. Its exact alignment engine computes either optimal,
top-scoring alignments or heuristic alignments with quality guarantee for the
inter-residue distance based scorings of contact map overlap, PAUL, DALI and
MATRAS. These and additional, uploaded alignments are compared using a number
of quality measures and intuitive visualizations. CSA brings new insight into
the structural relationship of the protein pairs under investigation and is a
valuable tool for studying structural similarities. It is available at
http://csa.project.cwi.nl
| [
{
"created": "Mon, 13 Feb 2012 09:21:41 GMT",
"version": "v1"
}
] | 2012-02-14 | [
[
"Wohlers",
"Inken",
"",
"MAC4"
],
[
"Malod-Dognin",
"Noël",
"",
"INRIA Sophia Antipolis"
],
[
"Andonov",
"Rumen",
"",
"INRIA - IRISA"
],
[
"Klau",
"Gunnar W.",
"",
"MAC4"
]
] | CSA is a web server for the comprehensive comparison of pairwise protein structure alignments. Its exact alignment engine computes either optimal, top-scoring alignments or heuristic alignments with quality guarantee for the inter-residue distance based scorings of contact map overlap, PAUL, DALI and MATRAS. These and additional, uploaded alignments are compared using a number of quality measures and intuitive visualizations. CSA brings new insight into the structural relationship of the protein pairs under investigation and is a valuable tool for studying structural similarities. It is available at http://csa.project.cwi.nl |
1907.05596 | Ferruccio Pisanello | Filippo Pisano, Marco Pisanello, Massimo De Vittorio, Ferruccio
Pisanello | Single-cell micro- and nano-photonic technologies | null | null | null | null | q-bio.CB physics.app-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Since the advent of optogenetics, technology development has focused on new
methods to optically interact with single nervous cells. This gave rise to the
field of photonic neural interfaces, intended as the set of technologies that
can modify light radiation in either a linear or non-linear fashion to control
and/or monitor cellular functions. These include the use of plasmonic effects,
up-conversion, electron transfer and integrated light steering, with some of
them already implemented in vivo. This article will review available approaches
in this framework, with a particular emphasis on methods operating at the
single-unit level or having the potential to reach single-cell resolution.
| [
{
"created": "Fri, 12 Jul 2019 07:15:53 GMT",
"version": "v1"
}
] | 2019-07-15 | [
[
"Pisano",
"Filippo",
""
],
[
"Pisanello",
"Marco",
""
],
[
"De Vittorio",
"Massimo",
""
],
[
"Pisanello",
"Ferruccio",
""
]
] | Since the advent of optogenetics, technology development has focused on new methods to optically interact with single nervous cells. This gave rise to the field of photonic neural interfaces, intended as the set of technologies that can modify light radiation in either a linear or non-linear fashion to control and/or monitor cellular functions. These include the use of plasmonic effects, up-conversion, electron transfer and integrated light steering, with some of them already implemented in vivo. This article will review available approaches in this framework, with a particular emphasis on methods operating at the single-unit level or having the potential to reach single-cell resolution. |
1105.0880 | Antonio Scialdone | Antonio Scialdone and Mario Nicodemi | Diffusion-based DNA target colocalization by thermodynamic mechanisms | null | Development 137:3877 (2010) | 10.1242/dev.053322 | null | q-bio.GN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In eukaryotic cell nuclei, a variety of DNA interactions with nuclear
elements occur, which, in combination with intra- and inter- chromosomal
cross-talks, shape a functional 3D architecture. In some cases they are
organized by active, i.e. actin/myosin, motors. More often, however, they have
been related to passive diffusion mechanisms. Yet, the crucial questions on how
DNA loci recognize their target and are reliably shuttled to their destination
by Brownian diffusion are still open. Here, we complement the current
experimental scenario by considering a physics model, in which the interaction
between distant loci is mediated by diffusing bridging molecules. We show that,
in such a system, the mechanism underlying target recognition and
colocalization is a thermodynamic switch-like process (a phase transition) that
only occurs if the concentration and affinity of binding molecules is above a
threshold, or else stable contacts are not possible. We also briefly discuss
the kinetics of this "passive-shuttling" process, as produced by random
diffusion of DNA loci and their binders, and derive predictions based on the
effects of genomic modifications and deletions.
| [
{
"created": "Wed, 4 May 2011 17:45:28 GMT",
"version": "v1"
}
] | 2011-05-05 | [
[
"Scialdone",
"Antonio",
""
],
[
"Nicodemi",
"Mario",
""
]
] | In eukaryotic cell nuclei, a variety of DNA interactions with nuclear elements occur, which, in combination with intra- and inter- chromosomal cross-talks, shape a functional 3D architecture. In some cases they are organized by active, i.e. actin/myosin, motors. More often, however, they have been related to passive diffusion mechanisms. Yet, the crucial questions on how DNA loci recognize their target and are reliably shuttled to their destination by Brownian diffusion are still open. Here, we complement the current experimental scenario by considering a physics model, in which the interaction between distant loci is mediated by diffusing bridging molecules. We show that, in such a system, the mechanism underlying target recognition and colocalization is a thermodynamic switch-like process (a phase transition) that only occurs if the concentration and affinity of binding molecules is above a threshold, or else stable contacts are not possible. We also briefly discuss the kinetics of this "passive-shuttling" process, as produced by random diffusion of DNA loci and their binders, and derive predictions based on the effects of genomic modifications and deletions. |
1412.3064 | Peteris Daugulis | Peteris Daugulis | Homomorphisms of connectome graphs | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose to study homomorphisms of connectome graphs. Homomorphisms can be
studied as sequences of elementary homomorphisms - folds, which identify pairs
of vertices. Several fold types are defined. Initial computation results for
some connectome graphs are described.
| [
{
"created": "Mon, 8 Dec 2014 14:50:50 GMT",
"version": "v1"
}
] | 2014-12-10 | [
[
"Daugulis",
"Peteris",
""
]
] | We propose to study homomorphisms of connectome graphs. Homomorphisms can be studied as sequences of elementary homomorphisms - folds, which identify pairs of vertices. Several fold types are defined. Initial computation results for some connectome graphs are described. |
2001.08369 | Takahiro Ezaki | Takahiro Ezaki, Yu Himeno, Takamitsu Watanabe, Naoki Masuda | Modeling state-transition dynamics in resting-state brain signals by the
hidden Markov and Gaussian mixture models | Sample code is available at
https://github.com/tkEzaki/gmm_hmm_comparison | Eur. J. Neurosci. 54:5404-5416 (2021) | 10.1111/ejn.15386 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies have proposed that one can summarize brain activity into
dynamics among a relatively small number of hidden states and that such an
approach is a promising tool for revealing brain function. Hidden Markov models
(HMMs) are a prevalent approach to inferring such neural dynamics among
discrete brain states. However, the impact of assuming Markovian structure in
neural time series data has not been sufficiently examined. Here, to address
this situation and examine the performance of the HMM, we compare the model
with the Gaussian mixture model (GMM), which is with no temporal regularization
and thus a statistically simpler model than the HMM, by applying both models to
synthetic time series generated from empirical resting-state functional
magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various
sampling frequencies, lengths of recording per participant, numbers of
participants, and numbers of independent component signals. We find that the
HMM attains a better accuracy of estimating the hidden state than the GMM in a
majority of cases. However, we also find that the accuracy of the GMM is
comparable to that of the HMM under the condition that the sampling frequency
is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data is relatively short.
These results suggest that the GMM can be a viable alternative to the HMM for
investigating hidden-state dynamics under this condition.
| [
{
"created": "Thu, 23 Jan 2020 04:56:53 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Apr 2020 08:31:43 GMT",
"version": "v2"
},
{
"created": "Wed, 1 Sep 2021 03:01:45 GMT",
"version": "v3"
}
] | 2021-09-02 | [
[
"Ezaki",
"Takahiro",
""
],
[
"Himeno",
"Yu",
""
],
[
"Watanabe",
"Takamitsu",
""
],
[
"Masuda",
"Naoki",
""
]
] | Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants, and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data is relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition. |
0709.3602 | Mathieu Coppey | Mathieu Coppey, Alexander M. Berezhkovskii, Stuart C. Sealfon,
Stanislav Y. Shvartsman | Time and length scales of autocrine signals in three dimensions | 15 pages | Biophys J. 2007 Sep 15;93(6):1917-22 | 10.1529/biophysj.107.109736 | null | q-bio.QM q-bio.SC | null | A model of autocrine signaling in cultures of suspended cells is developed on
the basis of the effective medium approximation. The fraction of autocrine
ligands, the mean and distribution of distances traveled by paracrine ligands
before binding, as well as the mean and distribution of the ligand lifetime are
derived. Interferon signaling by dendritic immune cells is considered as an
illustration.
| [
{
"created": "Sat, 22 Sep 2007 20:27:22 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Coppey",
"Mathieu",
""
],
[
"Berezhkovskii",
"Alexander M.",
""
],
[
"Sealfon",
"Stuart C.",
""
],
[
"Shvartsman",
"Stanislav Y.",
""
]
] | A model of autocrine signaling in cultures of suspended cells is developed on the basis of the effective medium approximation. The fraction of autocrine ligands, the mean and distribution of distances traveled by paracrine ligands before binding, as well as the mean and distribution of the ligand lifetime are derived. Interferon signaling by dendritic immune cells is considered as an illustration. |
1707.02930 | Kun Zhang | Kun Zhang and Jin Wang | Exploring the underlying mechanisms of Xenopus laevis embryonic cell
cycle | 24 pages, 11 figures | null | null | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell cycle is an indispensable process in the proliferation and development.
Despite significant efforts, global quantification and physical understanding
are still challenging. In this study, we explored the mechanisms of Xenopus
laevis embryonic cell cycle by quantifying the underlying landscape and flux.
We uncovered the irregular Mexican hat landscape of the Xenopus laevis
embryonic cell cycle with several local basins and barriers on the oscillation
path. The local basins characterize the different phases of Xenopus laevis
embryonic cell cycle and the local barriers represent the checkpoints. The
checkpoint mechanism of cell cycle is revealed by the landscape basins and
barriers. While landscape shape determines the stabilities of the states on the
oscillation path, the curl flux force determines the stability of the cell
cycle flow. Replication is fundamental for biology of living. From our
quantitative study here, we see that replication can not proceed without energy
input. In fact, the curl flux originated from energy or nutrition supply
determines the speed of the cell cycle and guarantees the progression. Speed of
cell cycle is a hallmark of cancer. Through landscape and flux analysis, one
can identify the key elements for controlling the speed. This can help to
design effective strategy for drug discovery against cancer.
| [
{
"created": "Fri, 30 Jun 2017 09:42:26 GMT",
"version": "v1"
}
] | 2017-07-11 | [
[
"Zhang",
"Kun",
""
],
[
"Wang",
"Jin",
""
]
] | Cell cycle is an indispensable process in the proliferation and development. Despite significant efforts, global quantification and physical understanding are still challenging. In this study, we explored the mechanisms of Xenopus laevis embryonic cell cycle by quantifying the underlying landscape and flux. We uncovered the irregular Mexican hat landscape of the Xenopus laevis embryonic cell cycle with several local basins and barriers on the oscillation path. The local basins characterize the different phases of Xenopus laevis embryonic cell cycle and the local barriers represent the checkpoints. The checkpoint mechanism of cell cycle is revealed by the landscape basins and barriers. While landscape shape determines the stabilities of the states on the oscillation path, the curl flux force determines the stability of the cell cycle flow. Replication is fundamental for biology of living. From our quantitative study here, we see that replication can not proceed without energy input. In fact, the curl flux originated from energy or nutrition supply determines the speed of the cell cycle and guarantees the progression. Speed of cell cycle is a hallmark of cancer. Through landscape and flux analysis, one can identify the key elements for controlling the speed. This can help to design effective strategy for drug discovery against cancer. |
1001.0546 | Joshua Milstein | Yih-Fan Chen, J. N. Milstein and J. -C. Meiners | Femtonewton Forces Can Control Protein-Meditated DNA Looping | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that minuscule entropic forces, on the order of 100 fN, can prevent
the formation of DNA loops--a ubiquitous means of regulating the expression of
genes. We observe a tenfold decrease in the rate of LacI-mediated DNA loop
formation when a tension of 200 fN is applied to the substrate DNA, biasing the
thermal fluctuations that drive loop formation and breakdown events.
Conversely, once looped, the DNA-protein complex is insensitive to applied
force. Our measurements are in excellent agreement with a simple polymer model
of loop formation in DNA, and show that an anti-parallel topology is the
preferred LacI-DNA loop conformation for a generic loop-forming construct.
| [
{
"created": "Mon, 4 Jan 2010 17:51:16 GMT",
"version": "v1"
}
] | 2010-01-05 | [
[
"Chen",
"Yih-Fan",
""
],
[
"Milstein",
"J. N.",
""
],
[
"Meiners",
"J. -C.",
""
]
] | We show that minuscule entropic forces, on the order of 100 fN, can prevent the formation of DNA loops--a ubiquitous means of regulating the expression of genes. We observe a tenfold decrease in the rate of LacI-mediated DNA loop formation when a tension of 200 fN is applied to the substrate DNA, biasing the thermal fluctuations that drive loop formation and breakdown events. Conversely, once looped, the DNA-protein complex is insensitive to applied force. Our measurements are in excellent agreement with a simple polymer model of loop formation in DNA, and show that an anti-parallel topology is the preferred LacI-DNA loop conformation for a generic loop-forming construct. |
1702.07711 | Andrew Krause | Andrew L. Krause, Dmitry Beliaev, Robert A. Van Gorder, Sarah L.
Waters | Lattice and Continuum Modelling of a Bioactive Porous Tissue Scaffold | 38 pages, 16 figures. This version includes a much-expanded
introduction, and a new section on nonlinear diffusion in addition to polish
throughout | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A contemporary procedure to grow artificial tissue is to seed cells onto a
porous biomaterial scaffold and culture it within a perfusion bioreactor to
facilitate the transport of nutrients to growing cells. Typical models of cell
growth for tissue engineering applications make use of spatially homogeneous or
spatially continuous equations to model cell growth, flow of culture medium,
nutrient transport, and their interactions. The network structure of the
physical porous scaffold is often incorporated through parameters in these
models, either phenomenologically or through techniques like mathematical
homogenization. We derive a model on a square grid lattice to demonstrate the
importance of explicitly modelling the network structure of the porous
scaffold, and compare results from this model with those from a modified
continuum model from the literature. We capture two-way coupling between cell
growth and fluid flow by allowing cells to block pores, and by allowing the
shear stress of the fluid to affect cell growth and death. We explore a range
of parameters for both models, and demonstrate quantitative and qualitative
differences between predictions from each of these approaches, including
spatial pattern formation and local oscillations in cell density present only
in the lattice model. These differences suggest that for some parameter
regimes, corresponding to specific cell types and scaffold geometries, the
lattice model gives qualitatively different model predictions than typical
continuum models. Our results inform model selection for bioactive porous
tissue scaffolds, aiding in the development of successful tissue engineering
experiments and eventually clinically successful technologies.
| [
{
"created": "Fri, 24 Feb 2017 14:34:38 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Apr 2017 00:13:18 GMT",
"version": "v2"
},
{
"created": "Sun, 29 Apr 2018 14:05:35 GMT",
"version": "v3"
}
] | 2018-05-01 | [
[
"Krause",
"Andrew L.",
""
],
[
"Beliaev",
"Dmitry",
""
],
[
"Van Gorder",
"Robert A.",
""
],
[
"Waters",
"Sarah L.",
""
]
] | A contemporary procedure to grow artificial tissue is to seed cells onto a porous biomaterial scaffold and culture it within a perfusion bioreactor to facilitate the transport of nutrients to growing cells. Typical models of cell growth for tissue engineering applications make use of spatially homogeneous or spatially continuous equations to model cell growth, flow of culture medium, nutrient transport, and their interactions. The network structure of the physical porous scaffold is often incorporated through parameters in these models, either phenomenologically or through techniques like mathematical homogenization. We derive a model on a square grid lattice to demonstrate the importance of explicitly modelling the network structure of the porous scaffold, and compare results from this model with those from a modified continuum model from the literature. We capture two-way coupling between cell growth and fluid flow by allowing cells to block pores, and by allowing the shear stress of the fluid to affect cell growth and death. We explore a range of parameters for both models, and demonstrate quantitative and qualitative differences between predictions from each of these approaches, including spatial pattern formation and local oscillations in cell density present only in the lattice model. These differences suggest that for some parameter regimes, corresponding to specific cell types and scaffold geometries, the lattice model gives qualitatively different model predictions than typical continuum models. Our results inform model selection for bioactive porous tissue scaffolds, aiding in the development of successful tissue engineering experiments and eventually clinically successful technologies. |
2203.13198 | Johanne Hizanidis | Dimitrios Chalkiadakis and Johanne Hizanidis | Dynamical properties of neuromorphic Josephson junctions | null | null | null | null | q-bio.NC cond-mat.supr-con nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuromorphic computing exploits the dynamical analogy between many physical
systems and neuron biophysics. Superconductor systems, in particular, are
excellent candidates for neuromorphic devices due to their capacity to operate
in great speeds and with low energy dissipation compared to their silicon
counterparts. In this study we revisit a prior work on Josephson Junction-based
"neurons" in order to identify the exact dynamical mechanisms underlying the
system's neuron-like properties and reveal new complex behaviors which are
relevant for neurocomputation and the design of superconducting neuromorphic
devices. Our work lies at the intersection of superconducting physics and
theoretical neuroscience, both viewed under a common framework, that of
nonlinear dynamics theory.
| [
{
"created": "Fri, 28 Jan 2022 15:32:55 GMT",
"version": "v1"
},
{
"created": "Sat, 21 May 2022 17:57:27 GMT",
"version": "v2"
}
] | 2022-05-24 | [
[
"Chalkiadakis",
"Dimitrios",
""
],
[
"Hizanidis",
"Johanne",
""
]
] | Neuromorphic computing exploits the dynamical analogy between many physical systems and neuron biophysics. Superconductor systems, in particular, are excellent candidates for neuromorphic devices due to their capacity to operate in great speeds and with low energy dissipation compared to their silicon counterparts. In this study we revisit a prior work on Josephson Junction-based "neurons" in order to identify the exact dynamical mechanisms underlying the system's neuron-like properties and reveal new complex behaviors which are relevant for neurocomputation and the design of superconducting neuromorphic devices. Our work lies at the intersection of superconducting physics and theoretical neuroscience, both viewed under a common framework, that of nonlinear dynamics theory. |
2003.13754 | Connor Coley | Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen | Autonomous discovery in the chemical sciences part I: Progress | Revised version available at 10.1002/anie.201909987 | null | 10.1002/anie.201909987 | null | q-bio.QM cs.AI cs.RO stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This two-part review examines how automation has contributed to different
aspects of discovery in the chemical sciences. In this first part, we describe
a classification for discoveries of physical matter (molecules, materials,
devices), processes, and models and how they are unified as search problems. We
then introduce a set of questions and considerations relevant to assessing the
extent of autonomy. Finally, we describe many case studies of discoveries
accelerated by or resulting from computer assistance and automation from the
domains of synthetic chemistry, drug discovery, inorganic chemistry, and
materials science. These illustrate how rapid advancements in hardware
automation and machine learning continue to transform the nature of
experimentation and modelling.
Part two reflects on these case studies and identifies a set of open
challenges for the field.
| [
{
"created": "Mon, 30 Mar 2020 19:11:31 GMT",
"version": "v1"
}
] | 2020-04-01 | [
[
"Coley",
"Connor W.",
""
],
[
"Eyke",
"Natalie S.",
""
],
[
"Jensen",
"Klavs F.",
""
]
] | This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modelling. Part two reflects on these case studies and identifies a set of open challenges for the field. |
1909.04660 | Marzio Pennisi | Marco Viceconti, Miguel A. Ju\'arez, Cristina Curreli, Marzio Pennisi,
Giulia Russo, Francesco Pappalardo | POSITION PAPER: Credibility of In Silico Trial Technologies: A
Theoretical Framing | 11 pages | null | null | null | q-bio.QM cs.CE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Different research communities have developed various approaches to assess
the credibility of predictive models. Each approach usually works well for a
specific type of model, and under some epistemic conditions that are normally
satisfied within that specific research domain. Some regulatory agencies
recently started to consider evidences of safety and efficacy on new medical
products obtained using computer modelling and simulation (which is referred to
as In Silico Trials); this has raised the attention in the computational
medicine research community on the regulatory science aspects of this emerging
discipline. But this poses a foundational problem: in the domain of biomedical
research the use of computer modelling is relatively recent, without a widely
accepted epistemic framing for problem of model credibility. Also, because of
the inherent complexity of living organisms, biomedical modellers tend to use a
variety of modelling methods, sometimes mixing them in the solution of a single
problem. In such context merely adopting credibility approaches developed
within other research community might not be appropriate. In this position
paper we propose a theoretical framing for the problem of assessing the
credibility of a predictive models for In Silico Trials, which accounts for the
epistemic specificity of this research field and is general enough to be used
for different type of models.
| [
{
"created": "Tue, 10 Sep 2019 07:29:36 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Oct 2019 14:29:20 GMT",
"version": "v2"
}
] | 2019-10-25 | [
[
"Viceconti",
"Marco",
""
],
[
"Juárez",
"Miguel A.",
""
],
[
"Curreli",
"Cristina",
""
],
[
"Pennisi",
"Marzio",
""
],
[
"Russo",
"Giulia",
""
],
[
"Pappalardo",
"Francesco",
""
]
] | Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for problem of model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research community might not be appropriate. In this position paper we propose a theoretical framing for the problem of assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models. |
2301.01144 | Gerald Cooray PhD | Gerald K. Cooray, Richard E. Rosch, Karl J. Friston | Modelling cortical network dynamics | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We consider the theoretical constraints on interactions between coupled
cortical columns. Each column comprises a set of neural populations, where each
population is modelled as a neural mass. The existence of semi-stable states
within a cortical column has been shown to be dependent on the type of
interaction between the constituent neuronal subpopulations, i.e., the form of
the implicit synaptic convolution kernels. Current-to-current coupling has been
shown, in contrast to potential-to-current coupling, to create semi-stable
states within a cortical column. In this analytic and numerical study, the
interaction between semi-stable states is characterized by equations of motion
for ensemble activity. We show that for small excitations, the dynamics follow
the Kuramoto model. However, in contrast to previous work, we derive coupled
equations between phase and amplitude dynamics. This affords the possibility of
defining connectivity as a dynamic variable. The turbulent flow of phase
dynamics found in networks of Kuramoto oscillators indicate turbulent changes
in dynamic connectivity for coupled cortical columns. Crucially, this is
something that has been recorded in epileptic seizures. We used the results we
derived to estimate a seizure propagation model, which allowed for relatively
straightforward inversions using variational Laplace (a.k.a., Dynamic Causal
Modelling). The face validity of the ensuing seizure propagation model was
established using simulated data as a prelude to future work; which will
investigate dynamic connectivity from empirical data. This model also allows
predictions of seizure evolution, induced by virtual lesions to synaptic
connectivity: something that could be of clinical use, when applied to epilepsy
surgical cases.
| [
{
"created": "Tue, 3 Jan 2023 15:09:30 GMT",
"version": "v1"
}
] | 2023-01-04 | [
[
"Cooray",
"Gerald K.",
""
],
[
"Rosch",
"Richard E.",
""
],
[
"Friston",
"Karl J.",
""
]
] | We consider the theoretical constraints on interactions between coupled cortical columns. Each column comprises a set of neural populations, where each population is modelled as a neural mass. The existence of semi-stable states within a cortical column has been shown to be dependent on the type of interaction between the constituent neuronal subpopulations, i.e., the form of the implicit synaptic convolution kernels. Current-to-current coupling has been shown, in contrast to potential-to-current coupling, to create semi-stable states within a cortical column. In this analytic and numerical study, the interaction between semi-stable states is characterized by equations of motion for ensemble activity. We show that for small excitations, the dynamics follow the Kuramoto model. However, in contrast to previous work, we derive coupled equations between phase and amplitude dynamics. This affords the possibility of defining connectivity as a dynamic variable. The turbulent flow of phase dynamics found in networks of Kuramoto oscillators indicate turbulent changes in dynamic connectivity for coupled cortical columns. Crucially, this is something that has been recorded in epileptic seizures. We used the results we derived to estimate a seizure propagation model, which allowed for relatively straightforward inversions using variational Laplace (a.k.a., Dynamic Causal Modelling). The face validity of the ensuing seizure propagation model was established using simulated data as a prelude to future work; which will investigate dynamic connectivity from empirical data. This model also allows predictions of seizure evolution, induced by virtual lesions to synaptic connectivity: something that could be of clinical use, when applied to epilepsy surgical cases. |
1906.05136 | Mandev Gill | Guy Baele, Mandev S. Gill, Philippe Lemey, Marc A. Suchard | Markov-modulated continuous-time Markov chains to identify site- and
branch-specific evolutionary variation | 30 pages, 8 figures | null | null | null | q-bio.PE stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Markov models of character substitution on phylogenies form the foundation of
phylogenetic inference frameworks. Early models made the simplifying assumption
that the substitution process is homogeneous over time and across sites in the
molecular sequence alignment. While standard practice adopts extensions that
accommodate heterogeneity of substitution rates across sites, heterogeneity in
the process over time in a site-specific manner remains frequently overlooked.
This is problematic, as evolutionary processes that act at the molecular level
are highly variable, subjecting different sites to different selective
constraints over time, impacting their substitution behaviour. We propose
incorporating time variability through Markov-modulated models (MMMs) that
allow the substitution process (including relative character exchange rates as
well as the overall substitution rate) that models the evolution at an
individual site to vary across lineages. We implement a general MMM framework
in BEAST, a popular Bayesian phylogenetic inference software package, allowing
researchers to compose a wide range of MMMs through flexible XML specification.
Using examples from bacterial, viral and plastid genome evolution, we show that
MMMs impact phylogenetic tree estimation and can substantially improve model
fit compared to standard substitution models. Through simulations, we show that
marginal likelihood estimation accurately identifies the generative model and
does not systematically prefer the more parameter-rich MMMs. In order to
mitigate the increased computational demands associated with MMMs, our
implementation exploits recently developed updates to BEAGLE, a
high-performance computational library for phylogenetic inference.
| [
{
"created": "Wed, 12 Jun 2019 13:46:38 GMT",
"version": "v1"
}
] | 2019-06-13 | [
[
"Baele",
"Guy",
""
],
[
"Gill",
"Mandev S.",
""
],
[
"Lemey",
"Philippe",
""
],
[
"Suchard",
"Marc A.",
""
]
] | Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behaviour. We propose incorporating time variability through Markov-modulated models (MMMs) that allow the substitution process (including relative character exchange rates as well as the overall substitution rate) that models the evolution at an individual site to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. In order to mitigate the increased computational demands associated with MMMs, our implementation exploits recently developed updates to BEAGLE, a high-performance computational library for phylogenetic inference. |
1803.10597 | Ru Zhang | Ru Zhang and Yanjun Liu and James T Townsend | A Theoretical Study of Process Dependence for Critical Statistics in
Standard Serial Models and Standard Parallel Models | arXiv admin note: substantial text overlap with arXiv:1712.00528 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Critical parts of the definitions of standard serial and standard parallel
modes refer to stochastic independence. Standard serial models are defined by
stochastic independence and identical distributions of their processing times.
Processing times in the serial models are identical to the intercompletion time
statistics. Similarly, standard parallel models assume stochastically
independent and identical processing times. Their processing times are
equivalent to the statistic known as total completion times. Little is known
about what standard serial models can predict for the total completion time or
what standard parallel models can predict for the intercompletion times. Here
we demonstrate that standard serial models possess a tendency to predict a
positive dependence for the total completion times with that always being true
in the case of a single processing order. However, with mixtures of processing
orders, standard serial models may predict negative dependence of the total
completion times. Comparably, standard parallel models typically predict
neither independence of the intercompletion times nor identical distributions.
In fact, standard parallel models predict increasing intercompletion times as
the individual channels continue to finish. Nevertheless, dramatically
increasing hazard functions of the channels can defeat that tendency. And,
standard parallel models can predict intercompletion time independence but only
when individual channel distributions are exponential. Finally, we use these
and ancillary mathematical results to conclude that standard serial and
standard parallel models can never perfectly mimic one another. Therefore, our
findings set the stage for explicit model testing between these classes.
| [
{
"created": "Mon, 19 Mar 2018 07:02:09 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Feb 2019 20:22:00 GMT",
"version": "v2"
}
] | 2019-02-13 | [
[
"Zhang",
"Ru",
""
],
[
"Liu",
"Yanjun",
""
],
[
"Townsend",
"James T",
""
]
] | Critical parts of the definitions of standard serial and standard parallel modes refer to stochastic independence. Standard serial models are defined by stochastic independence and identical distributions of their processing times. Processing times in the serial models are identical to the intercompletion time statistics. Similarly, standard parallel models assume stochastically independent and identical processing times. Their processing times are equivalent to the statistic known as total completion times. Little is known about what standard serial models can predict for the total completion time or what standard parallel models can predict for the intercompletion times. Here we demonstrate that standard serial models possess a tendency to predict a positive dependence for the total completion times with that always being true in the case of a single processing order. However, with mixtures of processing orders, standard serial models may predict negative dependence of the total completion times. Comparably, standard parallel models typically predict neither independence of the intercompletion times nor identical distributions. In fact, standard parallel models predict increasing intercompletion times as the individual channels continue to finish. Nevertheless, dramatically increasing hazard functions of the channels can defeat that tendency. And, standard parallel models can predict intercompletion time independence but only when individual channel distributions are exponential. Finally, we use these and ancillary mathematical results to conclude that standard serial and standard parallel models can never perfectly mimic one another. Therefore, our findings set the stage for explicit model testing between these classes. |
2005.01955 | Mykhailo Potomkin | Shawn D. Ryan, Zachary McCarthy, Mykhailo Potomkin | Motor protein transport along inhomogeneous microtubules | null | null | null | null | q-bio.SC cond-mat.soft math.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many cellular processes rely on the cell's ability to transport material to
and from the nucleus. Networks consisting of many microtubules and actin
filaments are key to this transport. Recently, the inhibition of intracellular
transport has been implicated in neurodegenerative diseases such as Alzheimer's
disease and Amyotrophic Lateral Sclerosis (ALS). Furthermore, microtubules may
contain so-called defective regions where motor protein velocity is reduced due
to accumulation of other motors and microtubule associated proteins. In this
work, we propose a new mathematical model describing the motion of motor
proteins on microtubules which incorporate a defective region. We take a
mean-field approach derived from a first principle lattice model to study motor
protein dynamics and density profiles. In particular, given a set of model
parameters we obtain a closed-form expression for the equilibrium density
profile along a given microtubule. We then verify the analytic results using
mathematical analysis on the discrete model and Monte Carlo simulations. This
work will contribute to the fundamental understanding of inhomogeneous
microtubules providing insight into microscopic interactions that may result in
the onset of neurodegenerative diseases. Our results for inhomogeneous
microtubules are consistent with prior work studying the homogeneous case.
| [
{
"created": "Tue, 5 May 2020 05:59:06 GMT",
"version": "v1"
}
] | 2020-05-06 | [
[
"Ryan",
"Shawn D.",
""
],
[
"McCarthy",
"Zachary",
""
],
[
"Potomkin",
"Mykhailo",
""
]
] | Many cellular processes rely on the cell's ability to transport material to and from the nucleus. Networks consisting of many microtubules and actin filaments are key to this transport. Recently, the inhibition of intracellular transport has been implicated in neurodegenerative diseases such as Alzheimer's disease and Amyotrophic Lateral Sclerosis (ALS). Furthermore, microtubules may contain so-called defective regions where motor protein velocity is reduced due to accumulation of other motors and microtubule associated proteins. In this work, we propose a new mathematical model describing the motion of motor proteins on microtubules which incorporate a defective region. We take a mean-field approach derived from a first principle lattice model to study motor protein dynamics and density profiles. In particular, given a set of model parameters we obtain a closed-form expression for the equilibrium density profile along a given microtubule. We then verify the analytic results using mathematical analysis on the discrete model and Monte Carlo simulations. This work will contribute to the fundamental understanding of inhomogeneous microtubules providing insight into microscopic interactions that may result in the onset of neurodegenerative diseases. Our results for inhomogeneous microtubules are consistent with prior work studying the homogeneous case. |
2007.00268 | Angeline Pendergrass | Angeline G. Pendergrass, Kristie L. Ebi, and Micah B. Hahn | Reply to Zhang et al.: Linear regression does not encapsulate the effect
of non-pharmaceutical interventions on the number of COVID-19 cases | Submitted as a letter to Proceedings of the National Academy of
Sciences of the United States of America | null | null | null | q-bio.PE physics.ao-ph | http://creativecommons.org/licenses/by/4.0/ | Zhang et al. (2020) used linear regression to quantify the effect of
lockdowns on the number of cases of COVID-19. We show using differential
equations from the susceptible-exposed-infected-recovered (SEIR) model and with
an example from another location not previously considered that the Zhang et
al. analysis should not be considered sound evidence that mask mandates are
sufficient to control or the primary factor controlling the spread of COVID-19.
| [
{
"created": "Wed, 1 Jul 2020 06:32:36 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Jul 2020 06:56:43 GMT",
"version": "v2"
},
{
"created": "Fri, 7 Aug 2020 06:32:42 GMT",
"version": "v3"
},
{
"created": "Wed, 2 Sep 2020 22:27:13 GMT",
"version": "v4"
},
{
"created": "Mon, 28 Sep 2020 19:07:53 GMT",
"version": "v5"
},
{
"created": "Mon, 5 Oct 2020 02:50:05 GMT",
"version": "v6"
}
] | 2020-10-06 | [
[
"Pendergrass",
"Angeline G.",
""
],
[
"Ebi",
"Kristie L.",
""
],
[
"Hahn",
"Micah B.",
""
]
] | Zhang et al. (2020) used linear regression to quantify the effect of lockdowns on the number of cases of COVID-19. We show using differential equations from the susceptible-exposed-infected-recovered (SEIR) model and with an example from another location not previously considered that the Zhang et al. analysis should not be considered sound evidence that mask mandates are sufficient to control or the primary factor controlling the spread of COVID-19. |
1610.06859 | Alexandra-M. Reimers | Alexandra-M. Reimers, Henning Knoop, Alexander Bockmayr, Ralf Steuer | Evaluating the stoichiometric and energetic constraints of
cyanobacterial diurnal growth | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cyanobacteria are an integral part of the Earth's biogeochemical cycles and a
promising resource for the synthesis of renewable bioproducts from atmospheric
CO2 . Growth and metabolism of cyanobacteria are inherently tied to the diurnal
rhythm of light availability. As yet, however, insight into the stoichiometric
and energetic constraints of cyanobacterial diurnal growth is limited. Here, we
develop a computational platform to evaluate the optimality of diurnal
phototrophic growth using a high-quality genome-scale metabolic reconstruction
of the cyanobacterium Synechococcus elongatus PCC 7942. We formulate
phototrophic growth as a self-consistent autocatalytic process and evaluate the
resulting time-dependent resource allocation problem using constraint-based
analysis. Based on a narrow and well defined set of parameters, our approach
results in an ab initio prediction of growth properties over a full diurnal
cycle. In particular, our approach allows us to study the optimality of
metabolite partitioning during diurnal growth. The cyclic pattern of glycogen
accumulation, an emergent property of the model, has timing characteristics
that are shown to be a trade-off between conflicting cellular objectives. The
approach presented here provides insight into the time-dependent resource
allocation problem of phototrophic diurnal growth and may serve as a general
framework to evaluate the optimality of metabolic strategies that evolved in
photosynthetic organisms under diurnal conditions.
| [
{
"created": "Fri, 21 Oct 2016 17:06:44 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Oct 2016 11:02:28 GMT",
"version": "v2"
}
] | 2016-10-25 | [
[
"Reimers",
"Alexandra-M.",
""
],
[
"Knoop",
"Henning",
""
],
[
"Bockmayr",
"Alexander",
""
],
[
"Steuer",
"Ralf",
""
]
] | Cyanobacteria are an integral part of the Earth's biogeochemical cycles and a promising resource for the synthesis of renewable bioproducts from atmospheric CO2 . Growth and metabolism of cyanobacteria are inherently tied to the diurnal rhythm of light availability. As yet, however, insight into the stoichiometric and energetic constraints of cyanobacterial diurnal growth is limited. Here, we develop a computational platform to evaluate the optimality of diurnal phototrophic growth using a high-quality genome-scale metabolic reconstruction of the cyanobacterium Synechococcus elongatus PCC 7942. We formulate phototrophic growth as a self-consistent autocatalytic process and evaluate the resulting time-dependent resource allocation problem using constraint-based analysis. Based on a narrow and well defined set of parameters, our approach results in an ab initio prediction of growth properties over a full diurnal cycle. In particular, our approach allows us to study the optimality of metabolite partitioning during diurnal growth. The cyclic pattern of glycogen accumulation, an emergent property of the model, has timing characteristics that are shown to be a trade-off between conflicting cellular objectives. The approach presented here provides insight into the time-dependent resource allocation problem of phototrophic diurnal growth and may serve as a general framework to evaluate the optimality of metabolic strategies that evolved in photosynthetic organisms under diurnal conditions. |
1309.6066 | Nicolas Langlade | Gwena\"elle Marchand, V\^an Anh Huynh-Thu, Nolan Kane, Sandrine
Arribat, Didier Var\`es, David Rengel, Sandrine Balzergue, Loren Rieseberg,
Patrick Vincourt, Pierre Geurts, Matthieu Vignes, Nicolas B. Langlade | Bridging physiological and evolutionary time scales in a gene regulatory
network | New Phytologist, 2014, in press | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene regulatory networks (GRN) govern phenotypic adaptations and reflect the
trade-offs between physiological responses and evolutionary adaptation that act
at different time scales. To identify patterns of molecular function and
genetic diversity in GRNs, we studied the drought response of the common
sunflower, Helianthus annuus, and how the underlying GRN is related to its
evolution. We examined the responses of 32,423 expressed sequences to drought
and to abscisic acid and selected 145 co-expressed transcripts. We
characterized their regulatory relationships in nine kinetic studies based on
different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian
graphical model and a random forest algorithm and studied the genetic
differentiation among populations (FST) at nodes. We identified two main hubs
in the network that transport nitrate in guard cells. This suggests that
nitrate transport is a critical aspect of sunflower physiological response to
drought. We observed that differentiation of the network genes in elite
sunflower cultivars is correlated with their position and connectivity. This
systems biology approach combined molecular data at different time scales and
identified important physiological processes. At the evolutionary level, we
propose that network topology could influence responses to human selection and
possibly adaptation to dry environments.
| [
{
"created": "Tue, 24 Sep 2013 07:35:24 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Mar 2014 13:54:09 GMT",
"version": "v2"
}
] | 2014-03-20 | [
[
"Marchand",
"Gwenaëlle",
""
],
[
"Huynh-Thu",
"Vân Anh",
""
],
[
"Kane",
"Nolan",
""
],
[
"Arribat",
"Sandrine",
""
],
[
"Varès",
"Didier",
""
],
[
"Rengel",
"David",
""
],
[
"Balzergue",
"Sandrine",
""
],
[
"Rieseberg",
"Loren",
""
],
[
"Vincourt",
"Patrick",
""
],
[
"Geurts",
"Pierre",
""
],
[
"Vignes",
"Matthieu",
""
],
[
"Langlade",
"Nicolas B.",
""
]
] | Gene regulatory networks (GRN) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution. We examined the responses of 32,423 expressed sequences to drought and to abscisic acid and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian graphical model and a random forest algorithm and studied the genetic differentiation among populations (FST) at nodes. We identified two main hubs in the network that transport nitrate in guard cells. This suggests that nitrate transport is a critical aspect of sunflower physiological response to drought. We observed that differentiation of the network genes in elite sunflower cultivars is correlated with their position and connectivity. This systems biology approach combined molecular data at different time scales and identified important physiological processes. At the evolutionary level, we propose that network topology could influence responses to human selection and possibly adaptation to dry environments. |
1312.2950 | Marcos A. Trevisan | Mar\'ia Florencia Assaneo, Marcos A. Trevisan | Revisiting the two-mass model of the vocal folds | 7 pages, 5 figures | Papers in Physics 5, 050004 (2013) | 10.4279/PIP.050004 | null | q-bio.NC | http://creativecommons.org/licenses/by/3.0/ | Realistic mathematical modeling of voice production has been recently boosted
by applications to different fields like bioprosthetics, quality speech
synthesis and pathological diagnosis. In this work, we revisit a two-mass model
of the vocal folds that includes accurate fluid mechanics for the air passage
through the folds and nonlinear properties of the tissue. We present the
bifurcation diagram for such a system, focusing on the dynamical properties of
two regimes of interest: the onset of oscillations and the normal phonation
regime. We also show theoretical support to the nonlinear nature of the elastic
properties of the folds tissue by comparing theoretical isofrequency curves
with reported experimental data.
| [
{
"created": "Tue, 10 Dec 2013 13:33:18 GMT",
"version": "v1"
}
] | 2013-12-12 | [
[
"Assaneo",
"María Florencia",
""
],
[
"Trevisan",
"Marcos A.",
""
]
] | Realistic mathematical modeling of voice production has been recently boosted by applications to different fields like bioprosthetics, quality speech synthesis and pathological diagnosis. In this work, we revisit a two-mass model of the vocal folds that includes accurate fluid mechanics for the air passage through the folds and nonlinear properties of the tissue. We present the bifurcation diagram for such a system, focusing on the dynamical properties of two regimes of interest: the onset of oscillations and the normal phonation regime. We also show theoretical support to the nonlinear nature of the elastic properties of the folds tissue by comparing theoretical isofrequency curves with reported experimental data. |
2102.08213 | Edilson Arruda | Edilson F. Arruda, Rodrigo e Alvim Alexandre, Marcelo D. Fragoso,
Jo\~ao B. R. do val, Sinnu S. Thomas | A Novel Stochastic Epidemic Model with Application to COVID-19 | null | null | 10.1016/j.isatra.2023.06.018 | null | q-bio.QM cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper we propose a novel SEIR stochastic epidemic model. A
distinguishing feature of this new model is that it allows us to consider a set
up under general latency and infectious period distributions. To some extent,
queuing systems with infinitely many servers and a Markov chain with
time-varying transition rate are the very technical underpinning of the paper.
Although more general, the Markov chain is as tractable as previous models for
exponentially distributed latency and infection periods. It is also
significantly simpler and more tractable than semi-Markov models with a similar
level of generality. Based on the notion of stochastic stability, we derive a
sufficient condition for a shrinking epidemic in terms of the queuing system's
occupation rate that drives the dynamics. Relying on this condition, we propose
a class of ad-hoc stabilising mitigation strategies that seek to keep a
balanced occupation rate after a prescribed mitigation-free period. We validate
the approach in the light of recent data on the COVID-19 epidemic and assess
the effect of different stabilising strategies. The results suggest that it is
possible to curb the epidemic with various occupation rate levels, as long as
the mitigation is not excessively procrastinated.
| [
{
"created": "Tue, 16 Feb 2021 15:24:45 GMT",
"version": "v1"
}
] | 2023-06-29 | [
[
"Arruda",
"Edilson F.",
""
],
[
"Alexandre",
"Rodrigo e Alvim",
""
],
[
"Fragoso",
"Marcelo D.",
""
],
[
"val",
"João B. R. do",
""
],
[
"Thomas",
"Sinnu S.",
""
]
] | In this paper we propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a set up under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate are the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly simpler and more tractable than semi-Markov models with a similar level of generality. Based on the notion of stochastic stability, we derive a sufficient condition for a shrinking epidemic in terms of the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of recent data on the COVID-19 epidemic and assess the effect of different stabilising strategies. The results suggest that it is possible to curb the epidemic with various occupation rate levels, as long as the mitigation is not excessively procrastinated. |
1610.08552 | Julio Augusto Freyre-Gonz\'alez | Julio A. Freyre-Gonz\'alez and Andreas Tauch | Functional architecture and global properties of the Corynebacterium
glutamicum regulatory network: novel insights from a dataset with a high
genomic coverage | 24 pages, 8 figures, 2 tables | Journal of Biotechnology 257:199-210 (2017) | 10.1016/j.jbiotec.2016.10.025 | null | q-bio.MN q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil
bacterium able to grow on a diversity of carbon sources like sugars and organic
acids. It is a biotechnological relevant organism because of its highly
efficient ability to biosynthesize amino acids, such as L-glutamic acid and
L-lysine. Here, we reconstructed the most complete C. glutamicum regulatory
network to date and comprehensively analyzed its global organizational
properties, systems-level features and functional architecture. Our analyses
show the tremendous power of Abasy Atlas to study the functional organization
of regulatory networks. We created two models of the C. glutamicum regulatory
network: all-evidences (containing both weak and strong supported interactions,
genomic coverage = 73%) and strongly-supported (only accounting for strongly
supported evidences, genomic coverage = 71%). Using state-of-the-art
methodologies, we prove that power-law behaviors truly govern the connectivity
and clustering coefficient distributions. We found a non-previously reported
circuit motif that we named complex feed-forward motif. We highlighted the
importance of feedback loops for the functional architecture, beyond whether
they are statistically over-represented or not in the network. We show that the
previously reported top-down approach is inadequate to infer the hierarchy
governing a regulatory network because feedback bridges different hierarchical
layers, and the top-down approach disregards the presence of intermodular genes
shaping the integration layer. Our findings all together further support a
diamond-shaped, three-layered hierarchy exhibiting some feedback between
processing and coordination layers, which is shaped by four classes of
systems-level elements: global regulators, locally autonomous modules, basal
machinery and intermodular genes.
| [
{
"created": "Wed, 26 Oct 2016 21:19:57 GMT",
"version": "v1"
}
] | 2020-12-29 | [
[
"Freyre-González",
"Julio A.",
""
],
[
"Tauch",
"Andreas",
""
]
] | Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil bacterium able to grow on a diversity of carbon sources like sugars and organic acids. It is a biotechnological relevant organism because of its highly efficient ability to biosynthesize amino acids, such as L-glutamic acid and L-lysine. Here, we reconstructed the most complete C. glutamicum regulatory network to date and comprehensively analyzed its global organizational properties, systems-level features and functional architecture. Our analyses show the tremendous power of Abasy Atlas to study the functional organization of regulatory networks. We created two models of the C. glutamicum regulatory network: all-evidences (containing both weak and strong supported interactions, genomic coverage = 73%) and strongly-supported (only accounting for strongly supported evidences, genomic coverage = 71%). Using state-of-the-art methodologies, we prove that power-law behaviors truly govern the connectivity and clustering coefficient distributions. We found a non-previously reported circuit motif that we named complex feed-forward motif. We highlighted the importance of feedback loops for the functional architecture, beyond whether they are statistically over-represented or not in the network. We show that the previously reported top-down approach is inadequate to infer the hierarchy governing a regulatory network because feedback bridges different hierarchical layers, and the top-down approach disregards the presence of intermodular genes shaping the integration layer. Our findings all together further support a diamond-shaped, three-layered hierarchy exhibiting some feedback between processing and coordination layers, which is shaped by four classes of systems-level elements: global regulators, locally autonomous modules, basal machinery and intermodular genes. |
2101.07356 | Yekbun Adiguzel | Yekbun Adiguzel | Peptides of H. sapiens and P. falciparum that are predicted to bind
strongly to HLA-A*24:02 and homologous to a SARS-CoV-2 peptide | 37 pages, 4 figures | null | 10.1016/j.actatropica.2021.106013 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Aim: This study is looking for a common pathogenicity between SARS-CoV-2 and
plasmodium species, in individuals with certain HLA serotypes. Methods: 1-)
Tblastx searches of SARS-CoV-2 are performed by limiting searches to plasmodium
species that infect human. 2-) Aligned sequences in the respective organisms'
proteomes are searched with blastp. 3-) Binding predictions of the identified
SARS-CoV-2 peptide to MHC class I supertype representatives are performed. 4-)
Blastp searches of predicted-epitopes that bind strongly to the identified HLA
allele are performed by limiting searches to human and to the plasmodium
species. 5-) Peptides with minimum 60 % identity to the predicted-epitopes are
found in results. 6-) Peptides among those, which bind strongly to the same HLA
allele, are predicted. 7-) Step-4 is repeated by limiting searches to human,
for peptides sourced by limiting searches to plasmodium species at step-4. 8-)
Step-5 and 6 are performed with results of 7. Results: CFLGYFCTCYFGLFC peptide
of SARS-CoV-2 has the highest identity to P. vivax. Its GYFCTCYFGLF and
YFCTCYFGLF parts are predicted to bind strongly to HLA-A*24:02. Results
obtained only for peptides homologous to YFCTCYFGLF, as follows: YYCARRFGLF,
YYCHCPFGVF, and YYCQQYFFLF are potential HLA-A*24:02 epitopes in the human
proteome. Among FFYTFYFELF, YFVACLFILF, and YFPTITFHLF peptides in the
plasmodium species' proteomes with strong binding affinity to HLA-A*24:02, only
FFYTFYFELF of P. falciparum is homologous to the potential HLA-A*24:02 epitope
YFYLFSLELF in the human proteome. Conclusion: Immune responses to the
identified-peptides with similar sequences and strong binding affinities to
HLA-A*24:02 may lead to autoimmune response risk in individuals with
HLA-A*24:02 serotypes, upon getting infected with SARS-CoV-2 or P. falciparum.
| [
{
"created": "Mon, 18 Jan 2021 22:52:07 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Feb 2021 00:19:52 GMT",
"version": "v2"
},
{
"created": "Thu, 11 Feb 2021 00:11:28 GMT",
"version": "v3"
},
{
"created": "Tue, 23 Feb 2021 06:19:14 GMT",
"version": "v4"
},
{
"created": "Thu, 27 May 2021 04:21:09 GMT",
"version": "v5"
}
] | 2021-06-22 | [
[
"Adiguzel",
"Yekbun",
""
]
] | Aim: This study is looking for a common pathogenicity between SARS-CoV-2 and plasmodium species, in individuals with certain HLA serotypes. Methods: 1-) Tblastx searches of SARS-CoV-2 are performed by limiting searches to plasmodium species that infect human. 2-) Aligned sequences in the respective organisms' proteomes are searched with blastp. 3-) Binding predictions of the identified SARS-CoV-2 peptide to MHC class I supertype representatives are performed. 4-) Blastp searches of predicted-epitopes that bind strongly to the identified HLA allele are performed by limiting searches to human and to the plasmodium species. 5-) Peptides with minimum 60 % identity to the predicted-epitopes are found in results. 6-) Peptides among those, which bind strongly to the same HLA allele, are predicted. 7-) Step-4 is repeated by limiting searches to human, for peptides sourced by limiting searches to plasmodium species at step-4. 8-) Step-5 and 6 are performed with results of 7. Results: CFLGYFCTCYFGLFC peptide of SARS-CoV-2 has the highest identity to P. vivax. Its GYFCTCYFGLF and YFCTCYFGLF parts are predicted to bind strongly to HLA-A*24:02. Results obtained only for peptides homologous to YFCTCYFGLF, as follows: YYCARRFGLF, YYCHCPFGVF, and YYCQQYFFLF are potential HLA-A*24:02 epitopes in the human proteome. Among FFYTFYFELF, YFVACLFILF, and YFPTITFHLF peptides in the plasmodium species' proteomes with strong binding affinity to HLA-A*24:02, only FFYTFYFELF of P. falciparum is homologous to the potential HLA-A*24:02 epitope YFYLFSLELF in the human proteome. Conclusion: Immune responses to the identified-peptides with similar sequences and strong binding affinities to HLA-A*24:02 may lead to autoimmune response risk in individuals with HLA-A*24:02 serotypes, upon getting infected with SARS-CoV-2 or P. falciparum. |
2407.19112 | Alexandre Matov | Alexandre Matov | Mitosis, Cytoskeleton Regulation, and Drug Resistance in Receptor Triple
Negative Breast Cancer | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | During cell division, the receptor triple-negative MDA-MB-231 mitotic
spindles are the largest in comparison to other BC cell lines. Many of the
MDA-MB-231 spindles exhibit rapid lateral twisting during metaphase, which
remains unaffected by knockdown of the oncogene Myc and treatment with
inhibitors of the serine/threonine-protein kinase B-Raf and the epidermal
growth factor receptor (EGFR), alone or in any combination. The MDA-MB-231
cells are the most aggressive and rapidly form metastatic tumors in xenograft
transplant models, and exhibited very high proliferation rates when plated as
three-dimensional cultures in Matrigel. Quantitative image analysis of
microtubules (MTs) in six BC cell lines - MDA-MB-231 (receptor negative),
HCC-1143 (receptor negative), HCC-3153 (receptor negative), ZR75B (estrogen
receptor-positive), LY2 (progesterone receptor-positive), HCC-1428 (estrogen
receptor-positive, progesterone receptor-positive) - demonstrated that the
rotational spindle rocking of MDA-MB-231 cells during metaphase appears coupled
with a significant increase in MT polymerization rates during interphase, which
likely shortens interphase and accelerates cell cycle progression and mitotic
entry. Unlike the uniform treadmilling rates of about 21 um/min in kinetochore
MTs during metaphase we measured across cell lines, MDA-MB-231 cells in
interphase exhibit the fastest MT polymerization dynamics of about 19 um/min
and this is coupled with abnormal mitotic spindle oscillations of almost 30
um/min. This aberrant behavior in MDA-MB-231 spindles may represent a
therapeutically targetable disrupted mechanism of spindle positioning in
receptor triple-negative breast cancer (TNBC) cells leading to tumor
aggressiveness. In this manuscript, we outline a strategy for the selection of
the most optimal tubulin inhibitor based on the ability to affect MT dynamics.
| [
{
"created": "Fri, 26 Jul 2024 22:20:36 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Aug 2024 07:31:21 GMT",
"version": "v2"
},
{
"created": "Mon, 5 Aug 2024 01:58:14 GMT",
"version": "v3"
},
{
"created": "Wed, 7 Aug 2024 16:11:10 GMT",
"version": "v4"
}
] | 2024-08-08 | [
[
"Matov",
"Alexandre",
""
]
] | During cell division, the receptor triple-negative MDA-MB-231 mitotic spindles are the largest in comparison to other BC cell lines. Many of the MDA-MB-231 spindles exhibit rapid lateral twisting during metaphase, which remains unaffected by knockdown of the oncogene Myc and treatment with inhibitors of the serine/threonine-protein kinase B-Raf and the epidermal growth factor receptor (EGFR), alone or in any combination. The MDA-MB-231 cells are the most aggressive and rapidly form metastatic tumors in xenograft transplant models, and exhibited very high proliferation rates when plated as three-dimensional cultures in Matrigel. Quantitative image analysis of microtubules (MTs) in six BC cell lines - MDA-MB-231 (receptor negative), HCC-1143 (receptor negative), HCC-3153 (receptor negative), ZR75B (estrogen receptor-positive), LY2 (progesterone receptor-positive), HCC-1428 (estrogen receptor-positive, progesterone receptor-positive) - demonstrated that the rotational spindle rocking of MDA-MB-231 cells during metaphase appears coupled with a significant increase in MT polymerization rates during interphase, which likely shortens interphase and accelerates cell cycle progression and mitotic entry. Unlike the uniform treadmilling rates of about 21 um/min in kinetochore MTs during metaphase we measured across cell lines, MDA-MB-231 cells in interphase exhibit the fastest MT polymerization dynamics of about 19 um/min and this is coupled with abnormal mitotic spindle oscillations of almost 30 um/min. This aberrant behavior in MDA-MB-231 spindles may represent a therapeutically targetable disrupted mechanism of spindle positioning in receptor triple-negative breast cancer (TNBC) cells leading to tumor aggressiveness. In this manuscript, we outline a strategy for the selection of the most optimal tubulin inhibitor based on the ability to affect MT dynamics. |
1212.0795 | Srinandan Dasmahapatra | Srinandan Dasmahapatra | Model of haplotype and phenotype in the evolution of a duplicated
autoregulatory activator | 52 pages, including 11 figures | Journal of Theoretical Biology 325 (2013) | 10.1016/j.jtbi.2013.01.025 | null | q-bio.MN physics.bio-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene duplication is believed to play a major role in the evolution of genomic
complexity. The presence of a duplicate removes the constraint of natural
selection upon the gene, leading to its likely loss of function or,
occasionally, the gain of a novel one. Alternately, a pleiotropic gene might
partition its functions among its duplicates, thus preserving both copies.
Duplicate genes is not a novelty for diploid genotypes, but only for
haplotypes. In this paper, we study the consequences of regulatory interactions
in diploid genotypes and explore how the context of allelic interactions gives
rise to dynamical phenotypes that enable duplicate genes to spread in a
population. The regulatory network we study is that of a single autoregulatory
activator gene, and the two copies of the gene diverge either as alleles in a
diploid species or as duplicates in haploids. These differences are in their
transcriptional ability -- either via alterations to its activating domain, or
to its cis-regulatory binding repertoire. When cis-regulatory changes are
introduced that partition multiple regulatory triggers among the duplicates, it
is shown that mutually exclusive expression states of the duplicates that
emerge are accompanied by a back-up facility: when a highly expressed gene is
deleted, the previously unexpressed duplicate copy compensates for it. The
diploid version of the regulatory network model can account for allele-specific
expression variants, and a model of inheritance of the haplotype network
enables us to trace the evolutionary consequence of heterozygous phenotypes.
This is modelled for the variations in the activating domain of one copy,
whereby stable as well as transiently bursting oscillations ensue in single
cells. The evolutionary model shows that these phenotypic states accessible to
a diploid, heterozygous genotype enable the spread of a duplicated haplotype.
| [
{
"created": "Tue, 4 Dec 2012 17:23:11 GMT",
"version": "v1"
}
] | 2015-05-06 | [
[
"Dasmahapatra",
"Srinandan",
""
]
] | Gene duplication is believed to play a major role in the evolution of genomic complexity. The presence of a duplicate removes the constraint of natural selection upon the gene, leading to its likely loss of function or, occasionally, the gain of a novel one. Alternately, a pleiotropic gene might partition its functions among its duplicates, thus preserving both copies. Duplicate genes is not a novelty for diploid genotypes, but only for haplotypes. In this paper, we study the consequences of regulatory interactions in diploid genotypes and explore how the context of allelic interactions gives rise to dynamical phenotypes that enable duplicate genes to spread in a population. The regulatory network we study is that of a single autoregulatory activator gene, and the two copies of the gene diverge either as alleles in a diploid species or as duplicates in haploids. These differences are in their transcriptional ability -- either via alterations to its activating domain, or to its cis-regulatory binding repertoire. When cis-regulatory changes are introduced that partition multiple regulatory triggers among the duplicates, it is shown that mutually exclusive expression states of the duplicates that emerge are accompanied by a back-up facility: when a highly expressed gene is deleted, the previously unexpressed duplicate copy compensates for it. The diploid version of the regulatory network model can account for allele-specific expression variants, and a model of inheritance of the haplotype network enables us to trace the evolutionary consequence of heterozygous phenotypes. This is modelled for the variations in the activating domain of one copy, whereby stable as well as transiently bursting oscillations ensue in single cells. The evolutionary model shows that these phenotypic states accessible to a diploid, heterozygous genotype enable the spread of a duplicated haplotype. |
1807.04325 | Shatrunjai Singh | Shatrunjai P. Singh, Swagata Karkare, Sudhir M. Baswan and Vijendra P.
Singh | Agglomerative Hierarchical Clustering Analysis of co/multi-morbidities | 18 pages, 3 figures, 2 tables | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although co/multi-morbidities are associated with significant increase in
mortality, the lack of appropriate quantitative exploratory techniques often
impede their analysis. In the current study, we study the clustering of
multimorbid patients in the Texas patient population. To this end we employ
agglomerative hierarchical clustering to find clusters within the patient
population. The analysis revealed the presence of nine distinct, clinically
relevant clusters of co/multi-morbidities within the study population of
interest. This technique provides a quantitative exploratory analysis of the
co/multi-morbidities present in a specific population.
| [
{
"created": "Wed, 11 Jul 2018 19:47:36 GMT",
"version": "v1"
}
] | 2018-07-13 | [
[
"Singh",
"Shatrunjai P.",
""
],
[
"Karkare",
"Swagata",
""
],
[
"Baswan",
"Sudhir M.",
""
],
[
"Singh",
"Vijendra P.",
""
]
] | Although co/multi-morbidities are associated with significant increase in mortality, the lack of appropriate quantitative exploratory techniques often impede their analysis. In the current study, we study the clustering of multimorbid patients in the Texas patient population. To this end we employ agglomerative hierarchical clustering to find clusters within the patient population. The analysis revealed the presence of nine distinct, clinically relevant clusters of co/multi-morbidities within the study population of interest. This technique provides a quantitative exploratory analysis of the co/multi-morbidities present in a specific population. |
2403.05314 | Bozhen Hu | Bozhen Hu, Cheng Tan, Lirong Wu, Jiangbin Zheng, Jun Xia, Zhangyang
Gao, Zicheng Liu, Fandi Wu, Guijun Zhang, Stan Z. Li | Advances of Deep Learning in Protein Science: A Comprehensive Survey | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Protein representation learning plays a crucial role in understanding the
structure and function of proteins, which are essential biomolecules involved
in various biological processes. In recent years, deep learning has emerged as
a powerful tool for protein modeling due to its ability to learn complex
patterns and representations from large-scale protein data. This comprehensive
survey aims to provide an overview of the recent advances in deep learning
techniques applied to protein science. The survey begins by introducing the
developments of deep learning based protein models and emphasizes the
importance of protein representation learning in drug discovery, protein
engineering, and function annotation. It then delves into the fundamentals of
deep learning, including convolutional neural networks, recurrent neural
networks, attention models, and graph neural networks in modeling protein
sequences, structures, and functions, and explores how these techniques can be
used to extract meaningful features and capture intricate relationships within
protein data. Next, the survey presents various applications of deep learning
in the field of proteins, including protein structure prediction,
protein-protein interaction prediction, protein function prediction, etc.
Furthermore, it highlights the challenges and limitations of these deep
learning techniques and also discusses potential solutions and future
directions for overcoming these challenges. This comprehensive survey provides
a valuable resource for researchers and practitioners in the field of proteins
who are interested in harnessing the power of deep learning techniques. By
consolidating the latest advancements and discussing potential avenues for
improvement, this review contributes to the ongoing progress in protein
research and paves the way for future breakthroughs in the field.
| [
{
"created": "Fri, 8 Mar 2024 13:45:32 GMT",
"version": "v1"
}
] | 2024-03-11 | [
[
"Hu",
"Bozhen",
""
],
[
"Tan",
"Cheng",
""
],
[
"Wu",
"Lirong",
""
],
[
"Zheng",
"Jiangbin",
""
],
[
"Xia",
"Jun",
""
],
[
"Gao",
"Zhangyang",
""
],
[
"Liu",
"Zicheng",
""
],
[
"Wu",
"Fandi",
""
],
[
"Zhang",
"Guijun",
""
],
[
"Li",
"Stan Z.",
""
]
] | Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes. In recent years, deep learning has emerged as a powerful tool for protein modeling due to its ability to learn complex patterns and representations from large-scale protein data. This comprehensive survey aims to provide an overview of the recent advances in deep learning techniques applied to protein science. The survey begins by introducing the developments of deep learning based protein models and emphasizes the importance of protein representation learning in drug discovery, protein engineering, and function annotation. It then delves into the fundamentals of deep learning, including convolutional neural networks, recurrent neural networks, attention models, and graph neural networks in modeling protein sequences, structures, and functions, and explores how these techniques can be used to extract meaningful features and capture intricate relationships within protein data. Next, the survey presents various applications of deep learning in the field of proteins, including protein structure prediction, protein-protein interaction prediction, protein function prediction, etc. Furthermore, it highlights the challenges and limitations of these deep learning techniques and also discusses potential solutions and future directions for overcoming these challenges. This comprehensive survey provides a valuable resource for researchers and practitioners in the field of proteins who are interested in harnessing the power of deep learning techniques. By consolidating the latest advancements and discussing potential avenues for improvement, this review contributes to the ongoing progress in protein research and paves the way for future breakthroughs in the field. |
1610.05189 | Karolis Uziela | Karolis Uziela, David Men\'endez Hurtado, Bj\"orn Wallner and Arne
Elofsson | ProQ3D: Improved model quality assessments using Deep Learning | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Summary: Protein quality assessment is a long-standing problem in
bioinformatics. For more than a decade we have developed state-of-art
predictors by carefully selecting and optimising inputs to a machine learning
method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and
0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a
protein. Here, we show that a substantial improvement can be obtained using
exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector
machine by a deep neural network. This improves the Pearson correlation to 0.90
(0.85 using ProQ2 input features).
Availability: ProQ3D is freely available both as a webserver and a
stand-alone program at http://proq3.bioinfo.se/
| [
{
"created": "Mon, 17 Oct 2016 16:11:57 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Oct 2016 19:01:18 GMT",
"version": "v2"
}
] | 2016-10-19 | [
[
"Uziela",
"Karolis",
""
],
[
"Hurtado",
"David Menéndez",
""
],
[
"Wallner",
"Björn",
""
],
[
"Elofsson",
"Arne",
""
]
] | Summary: Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features). Availability: ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/ |
q-bio/0606021 | Sung Joon Moon | Sung Joon Moon, B. Nabet, Naomi E. Leonard, Simon A. Levin, I. G.
Kevrekidis | Heterogeneous animal group models and their group-level alignment
dynamics; an equation-free approach | final form; accepted for publication in Journal of Theoretical
Biology | null | null | null | q-bio.QM q-bio.PE | null | We study coarse-grained (group-level) alignment dynamics of individual-based
animal group models for {\it heterogeneous} populations consisting of informed
(on preferred directions) and uninformed individuals. The orientation of each
individual is characterized by an angle, whose dynamics are nonlinearly coupled
with those of all the other individuals, with an explicit dependence on the
difference between the individual's orientation and the instantaneous average
direction. Choosing convenient coarse-grained variables (suggested by
uncertainty quantification methods) that account for rapidly developing
correlations during initial transients, we perform efficient computations of
coarse-grained steady states and their bifurcation analysis. We circumvent the
derivation of coarse-grained governing equations, following an equation-free
computational approach.
| [
{
"created": "Fri, 16 Jun 2006 04:42:54 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Dec 2006 16:55:14 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Moon",
"Sung Joon",
""
],
[
"Nabet",
"B.",
""
],
[
"Leonard",
"Naomi E.",
""
],
[
"Levin",
"Simon A.",
""
],
[
"Kevrekidis",
"I. G.",
""
]
] | We study coarse-grained (group-level) alignment dynamics of individual-based animal group models for {\it heterogeneous} populations consisting of informed (on preferred directions) and uninformed individuals. The orientation of each individual is characterized by an angle, whose dynamics are nonlinearly coupled with those of all the other individuals, with an explicit dependence on the difference between the individual's orientation and the instantaneous average direction. Choosing convenient coarse-grained variables (suggested by uncertainty quantification methods) that account for rapidly developing correlations during initial transients, we perform efficient computations of coarse-grained steady states and their bifurcation analysis. We circumvent the derivation of coarse-grained governing equations, following an equation-free computational approach. |
1505.07012 | Jicun Wang-Michelitsch | Jicun Wang-Michelitsch, Thomas M Michelitsch | Development of age spots as a result of accumulation of aged cells in
aged skin | 9 pages | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Age spots are the brown spots that develop in the skin but change in color
and shape with time. To understand the mechanism of development of age spots,
characteristics of age spots are analyzed by Misrepair mechanism, a mechanism
introduced in Misrepair-accumulation aging theory. An age spot is
pathologically a group of aggregated basal cells, which contain lipofuscin
bodies. Accumulation of lipofuscin bodies is a sign of aging of a cell.
Characteristics of age spots include: inhomogeneity in distribution, growing
flatly before becoming protruding, irregularity on shape, inhomogeneity on the
color and on the protruding degree of a spot, and softness of a protruding
spot. After analyzing these characteristics, we make a hypothesis on the
process of development of an age spot. A. Aging of a tissue is the basis for
development of age spots. B. A flat spot results from accumulation of
lipofuscin containing cells. When an aged cell remains, this cell can
accelerate the aging of its neighbor cells by increasing damage sensitivity and
reducing repair efficiency of the local tissue. By a viscous circle, more and
more neighbor cells become aged and they form a flat spot, which has an
irregular shape. C. A protruding spot develops when some of the cells in a flat
spot die and release lipofuscin bodies. For the survival of an organism, the un
degradable lipofuscin bodies have to be isolated by a capsule made by fibrotic
membrane, for maintaining the structural integrity of local epidermis.
Successive deaths of lipofuscin containing cells make the capsule include more
and more dead substances by layers of fibrotic membrane. In this way, the spot
"grows" in three-dimension, resulting in protruding of the spot. In conclusion,
development of an age spot is a result of accumulation of aged cells in aged
skin.
| [
{
"created": "Tue, 26 May 2015 15:31:05 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Feb 2018 10:45:30 GMT",
"version": "v2"
}
] | 2018-02-06 | [
[
"Wang-Michelitsch",
"Jicun",
""
],
[
"Michelitsch",
"Thomas M",
""
]
] | Age spots are the brown spots that develop in the skin but change in color and shape with time. To understand the mechanism of development of age spots, characteristics of age spots are analyzed by Misrepair mechanism, a mechanism introduced in Misrepair-accumulation aging theory. An age spot is pathologically a group of aggregated basal cells, which contain lipofuscin bodies. Accumulation of lipofuscin bodies is a sign of aging of a cell. Characteristics of age spots include: inhomogeneity in distribution, growing flatly before becoming protruding, irregularity on shape, inhomogeneity on the color and on the protruding degree of a spot, and softness of a protruding spot. After analyzing these characteristics, we make a hypothesis on the process of development of an age spot. A. Aging of a tissue is the basis for development of age spots. B. A flat spot results from accumulation of lipofuscin containing cells. When an aged cell remains, this cell can accelerate the aging of its neighbor cells by increasing damage sensitivity and reducing repair efficiency of the local tissue. By a viscous circle, more and more neighbor cells become aged and they form a flat spot, which has an irregular shape. C. A protruding spot develops when some of the cells in a flat spot die and release lipofuscin bodies. For the survival of an organism, the un degradable lipofuscin bodies have to be isolated by a capsule made by fibrotic membrane, for maintaining the structural integrity of local epidermis. Successive deaths of lipofuscin containing cells make the capsule include more and more dead substances by layers of fibrotic membrane. In this way, the spot "grows" in three-dimension, resulting in protruding of the spot. In conclusion, development of an age spot is a result of accumulation of aged cells in aged skin. |
1512.03344 | Nihar Sheth | Vishal N. Koparde, Hardik I. Parikh, Steven P. Bradley and Nihar U.
Sheth | MEEPTOOLS: A maximum expected error based FASTQ read filtering and
trimming toolkit | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Next generation sequencing technology rapidly produces massive volume of data
and quality control of this sequencing data is essential to any genomic
analysis. Here we present MEEPTOOLS, which is a collection of open-source tools
based on maximum expected error as a percentage of read length (MEEP score) to
filter, trim, truncate and assess next generation DNA sequencing data in FASTQ
file format. MEEPTOOLS provides a non-traditional approach towards read
filtering/trimming based on maximum error probabilities of the bases in the
read on a non-logarithmic scale. This method simultaneously retains more
reliable bases and removes more unreliable bases than the traditional quality
filtering strategies.
| [
{
"created": "Thu, 10 Dec 2015 17:53:58 GMT",
"version": "v1"
}
] | 2015-12-11 | [
[
"Koparde",
"Vishal N.",
""
],
[
"Parikh",
"Hardik I.",
""
],
[
"Bradley",
"Steven P.",
""
],
[
"Sheth",
"Nihar U.",
""
]
] | Next generation sequencing technology rapidly produces massive volume of data and quality control of this sequencing data is essential to any genomic analysis. Here we present MEEPTOOLS, which is a collection of open-source tools based on maximum expected error as a percentage of read length (MEEP score) to filter, trim, truncate and assess next generation DNA sequencing data in FASTQ file format. MEEPTOOLS provides a non-traditional approach towards read filtering/trimming based on maximum error probabilities of the bases in the read on a non-logarithmic scale. This method simultaneously retains more reliable bases and removes more unreliable bases than the traditional quality filtering strategies. |
1710.05183 | Thomas Dean | Thomas Dean | Inferring Mesoscale Models of Neural Computation | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent years have seen dramatic progress in the development of techniques for
measuring the activity and connectivity of large populations of neurons in the
brain. However, as these techniques grow ever more powerful---allowing us to
even contemplate measuring every neuron in entire brain---a new problem arises:
how do we make sense of the mountains of data that these techniques produce?
Here, we argue that the time is ripe for building an intermediate or
"mesoscale" computational theory that can bridge between single-cell
(microscale) accounts of neural function and behavioral (macroscale) accounts
of animal cognition and environmental complexity. Just as digital accounts of
computation in conventional computers abstract away the non-essential dynamics
of the analog circuits that implementing gates and registers, so too a
computational account of animal cognition can afford to abstract from the
non-essential dynamics of neurons. We argue that the geometry of neural
circuits is essential in explaining the computational limitations and
technological innovations inherent in biological information processing. We
propose a blueprint for how to employ tools from modern machine learning to
automatically infer a satisfying mesoscale account of neural computation that
combines functional and structural data, with an emphasis on learning and
exploiting regularity and repeating motifs in neuronal circuits. Rather than
suggest a specific theory, we present a new class of scientific instruments
that can enable neuroscientists to design, propose, implement and test
mesoscale theories of neural computation.
| [
{
"created": "Sat, 14 Oct 2017 13:26:42 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Oct 2017 12:19:22 GMT",
"version": "v2"
}
] | 2017-10-20 | [
[
"Dean",
"Thomas",
""
]
] | Recent years have seen dramatic progress in the development of techniques for measuring the activity and connectivity of large populations of neurons in the brain. However, as these techniques grow ever more powerful---allowing us to even contemplate measuring every neuron in entire brain---a new problem arises: how do we make sense of the mountains of data that these techniques produce? Here, we argue that the time is ripe for building an intermediate or "mesoscale" computational theory that can bridge between single-cell (microscale) accounts of neural function and behavioral (macroscale) accounts of animal cognition and environmental complexity. Just as digital accounts of computation in conventional computers abstract away the non-essential dynamics of the analog circuits that implementing gates and registers, so too a computational account of animal cognition can afford to abstract from the non-essential dynamics of neurons. We argue that the geometry of neural circuits is essential in explaining the computational limitations and technological innovations inherent in biological information processing. We propose a blueprint for how to employ tools from modern machine learning to automatically infer a satisfying mesoscale account of neural computation that combines functional and structural data, with an emphasis on learning and exploiting regularity and repeating motifs in neuronal circuits. Rather than suggest a specific theory, we present a new class of scientific instruments that can enable neuroscientists to design, propose, implement and test mesoscale theories of neural computation. |
2005.06777 | Chika Koyama | Chika Koyama, Taichi Haruna, Satoshi Hagihira, Kazuto Yamashita | Power-law distribution in Burst-suppression on electroencephalogram of
dogs | 18 pages, 2 figures, 3 tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Burst-suppression (BS) is a reliable electroencephalogram (EEG) indicator of
excessive deep anesthesia common in mammals. Since some intermittent events are
known to follow a power-law, we investigated the power-law hypothesis in BS by
comparing it with alternative functions focusing on flattish periods and
developed a new method for detecting BS as an application of statistical model
in dogs. Young-adult 6 beagles and senior 6 beagles were anesthetized with
sevoflurane 2.0%-5.0% and three of 64 sec EEG (256 Hz) from Fpz-T4 via scalp
electrodes were recorded. Three thresholds for peak-to-peak voltage were set:
mean value of peak-to-peak voltage at sevoflurane 2.0% in each dog (AS2%),
3mcrV, and 5mcrV. The subthreshold periods were discriminated as $\tau$ events.
We fitted the empirical probability distribution of $\tau$ by a power-law
distribution and an exponential distribution. These two distributions were
compared by the normalized log-likelihood ratio test to see which distribution
was better fit. At sevoflurane 2.0%-3.0%, by any threshold, the exponential
distribution became better fit in all dogs. The power-law distribution became
better fit only when BS expressed on EEG. No strict threshold was required for
detection of onset of BS. We showed a transition from exponential behavior to
power-law behavior on the right tail of $\tau$ distributions in response to the
appearance of suppression waves with increasing anesthetic. This will be a
robust tool for BS detection.
| [
{
"created": "Thu, 14 May 2020 07:47:41 GMT",
"version": "v1"
}
] | 2020-05-15 | [
[
"Koyama",
"Chika",
""
],
[
"Haruna",
"Taichi",
""
],
[
"Hagihira",
"Satoshi",
""
],
[
"Yamashita",
"Kazuto",
""
]
] | Burst-suppression (BS) is a reliable electroencephalogram (EEG) indicator of excessive deep anesthesia common in mammals. Since some intermittent events are known to follow a power-law, we investigated the power-law hypothesis in BS by comparing it with alternative functions focusing on flattish periods and developed a new method for detecting BS as an application of statistical model in dogs. Young-adult 6 beagles and senior 6 beagles were anesthetized with sevoflurane 2.0%-5.0% and three of 64 sec EEG (256 Hz) from Fpz-T4 via scalp electrodes were recorded. Three thresholds for peak-to-peak voltage were set: mean value of peak-to-peak voltage at sevoflurane 2.0% in each dog (AS2%), 3mcrV, and 5mcrV. The subthreshold periods were discriminated as $\tau$ events. We fitted the empirical probability distribution of $\tau$ by a power-law distribution and an exponential distribution. These two distributions were compared by the normalized log-likelihood ratio test to see which distribution was better fit. At sevoflurane 2.0%-3.0%, by any threshold, the exponential distribution became better fit in all dogs. The power-law distribution became better fit only when BS expressed on EEG. No strict threshold was required for detection of onset of BS. We showed a transition from exponential behavior to power-law behavior on the right tail of $\tau$ distributions in response to the appearance of suppression waves with increasing anesthetic. This will be a robust tool for BS detection. |
2309.03910 | Mamata Das | Mamata Das, Selvakumar K. and P.J.A. Alphonse | Analyzing and Comparing Omicron Lineage Variants Protein-Protein
Interaction Network using Centrality Measure | 14 pages, 15 figures, SN Computer Science | SN Computer Science.4(2023)299 | 10.1007/s42979-023-01685-5 | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | The Worldwide spread of the Omicron lineage variants has now been confirmed.
It is crucial to understand the process of cellular life and to discover new
drugs need to identify the important proteins in a protein interaction network
(PPIN). PPINs are often represented by graphs in bioinformatics, which describe
cell processes. There are some proteins that have significant influences on
these tissues, and which play a crucial role in regulating them. The discovery
of new drugs is aided by the study of significant proteins. These significant
proteins can be found by reducing the graph and using graph analysis. Studies
examining protein interactions in the Omicron lineage (B.1.1.529) and its
variants (BA.5, BA.4, BA.3, BA.2, BA.1.1, BA.1) are not yet available. Studying
Omicron has been intended to find a significant protein. 68 nodes represent 68
proteins and 52 edges represent the relationship among the protein in the
network. A few entrality measures are computed namely page rank centrality
(PRC), degree centrality (DC), closeness centrality (CC), and betweenness
centrality (BC) together with node degree and Local Clustering Co-efficient
(LCC). We also discover 18 network clusters using Markov clustering. 8
significant proteins (candidate gene of Omicron lineage variants) were detected
among the 68 proteins, including AHSG, KCNK1, KCNQ1, MAPT, NR1H4, PSMC2, PTPN11
and, UBE21 which scored the highest among the Omicron proteins. It is found
that in the variant of Omicron protein-protein interaction networks, the MAPT
protein's impact is the most significant.
| [
{
"created": "Wed, 9 Aug 2023 04:56:02 GMT",
"version": "v1"
}
] | 2024-08-06 | [
[
"Das",
"Mamata",
""
],
[
"K.",
"Selvakumar",
""
],
[
"Alphonse",
"P. J. A.",
""
]
] | The Worldwide spread of the Omicron lineage variants has now been confirmed. It is crucial to understand the process of cellular life and to discover new drugs need to identify the important proteins in a protein interaction network (PPIN). PPINs are often represented by graphs in bioinformatics, which describe cell processes. There are some proteins that have significant influences on these tissues, and which play a crucial role in regulating them. The discovery of new drugs is aided by the study of significant proteins. These significant proteins can be found by reducing the graph and using graph analysis. Studies examining protein interactions in the Omicron lineage (B.1.1.529) and its variants (BA.5, BA.4, BA.3, BA.2, BA.1.1, BA.1) are not yet available. Studying Omicron has been intended to find a significant protein. 68 nodes represent 68 proteins and 52 edges represent the relationship among the protein in the network. A few entrality measures are computed namely page rank centrality (PRC), degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC) together with node degree and Local Clustering Co-efficient (LCC). We also discover 18 network clusters using Markov clustering. 8 significant proteins (candidate gene of Omicron lineage variants) were detected among the 68 proteins, including AHSG, KCNK1, KCNQ1, MAPT, NR1H4, PSMC2, PTPN11 and, UBE21 which scored the highest among the Omicron proteins. It is found that in the variant of Omicron protein-protein interaction networks, the MAPT protein's impact is the most significant. |
2004.04874 | Zhi Liu | Zhi Liu, Jianwei Wang, Yuyu Xu, Mengchen Guo, Kai Mi, Rui Xu, Yang
Pei, Qiangkun Zhang, Xiaoting Luan, Zhibin Hu, Xingyin Liu# | Implications of the virus-encoded miRNA and host miRNA in the
pathogenicity of SARS-CoV-2 | 24 pages,7 figures and 2 supplementary figures | null | null | null | q-bio.GN q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The outbreak of COVID-19 caused by SARS-CoV-2 has rapidly spread worldwide
and has caused over 1,400,000 infections and 80,000 deaths. There are currently
no drugs or vaccines with proven efficacy for its prevention and little
knowledge was known about the pathogenicity mechanism of SARS-CoV-2 infection.
Previous studies showed both virus and host-derived MicroRNAs (miRNAs) played
crucial roles in the pathology of virus infection. In this study, we use
computational approaches to scan the SARS-CoV-2 genome for putative miRNAs and
predict the virus miRNA targets on virus and human genome as well as the host
miRNAs targets on virus genome. Furthermore, we explore miRNAs involved
dysregulation caused by the virus infection. Our results implicated that the
immune response and cytoskeleton organization are two of the most notable
biological processes regulated by the infection-modulated miRNAs. Impressively,
we found hsa-miR-4661-3p was predicted to target the S gene of SARS-CoV-2, and
a virus-encoded miRNA MR147-3p could enhance the expression of TMPRSS2 with the
function of strengthening SARS-CoV-2 infection in the gut. The study may
provide important clues for the mechisms of pathogenesis of SARS-CoV-2.
| [
{
"created": "Fri, 10 Apr 2020 01:34:39 GMT",
"version": "v1"
}
] | 2020-04-13 | [
[
"Liu",
"Zhi",
""
],
[
"Wang",
"Jianwei",
""
],
[
"Xu",
"Yuyu",
""
],
[
"Guo",
"Mengchen",
""
],
[
"Mi",
"Kai",
""
],
[
"Xu",
"Rui",
""
],
[
"Pei",
"Yang",
""
],
[
"Zhang",
"Qiangkun",
""
],
[
"Luan",
"Xiaoting",
""
],
[
"Hu",
"Zhibin",
""
],
[
"Liu#",
"Xingyin",
""
]
] | The outbreak of COVID-19 caused by SARS-CoV-2 has rapidly spread worldwide and has caused over 1,400,000 infections and 80,000 deaths. There are currently no drugs or vaccines with proven efficacy for its prevention and little knowledge was known about the pathogenicity mechanism of SARS-CoV-2 infection. Previous studies showed both virus and host-derived MicroRNAs (miRNAs) played crucial roles in the pathology of virus infection. In this study, we use computational approaches to scan the SARS-CoV-2 genome for putative miRNAs and predict the virus miRNA targets on virus and human genome as well as the host miRNAs targets on virus genome. Furthermore, we explore miRNAs involved dysregulation caused by the virus infection. Our results implicated that the immune response and cytoskeleton organization are two of the most notable biological processes regulated by the infection-modulated miRNAs. Impressively, we found hsa-miR-4661-3p was predicted to target the S gene of SARS-CoV-2, and a virus-encoded miRNA MR147-3p could enhance the expression of TMPRSS2 with the function of strengthening SARS-CoV-2 infection in the gut. The study may provide important clues for the mechisms of pathogenesis of SARS-CoV-2. |
q-bio/0701010 | Emmanuel Tannenbaum | Nikhil Ghandi, Gonen Ashkenasy, and Emmanuel Tannenbaum | Associative learning in biochemical networks | 14 pages, 3 figures, submitted to J. Theor. Biol | null | null | null | q-bio.MN q-bio.SC | null | We develop a simple, chemostat-based model illustrating how a process
analogous to associative learning can occur in a biochemical network.
Associative learning is a form of learning whereby a system "learns" to
associate two stimuli with one another. In our model, two types of replicating
molecules, denoted A and B, are present in some initial concentration in the
chemostat. Molecules A and B are stimulated to replicate by some growth
factors, denoted GA and GB, respectively. It is also assumed that A and B can
covalently link, and that the conjugated molecule can be stimulated by either
the GA or GB growth factors (and can be degraded). We show that, if the
chemostat is stimulated by both growth factors for a certain time, followed by
a time gap during which the chemostat is not stimulated at all, and if the
chemostat is then stimulated again by only one of the growth factors, then
there will be a transient increase in the number of molecules activated by the
other growth factor. Therefore, the chemostat bears the imprint of earlier,
simultaneous stimulation with both growth factors, which is indicative of
associative learning. It is interesting to note that the dynamics of our model
is consistent with various aspects of Pavlov's original series of associative
learning experiments in dogs. We discuss how associative learning can
potentially be performed in vitro within RNA, DNA, or peptide networks. We also
highlight how such a mechanism could potentially be involved in genomic
evolution, and suggest bioinformatics studies that could be used to find
evidence for associative learning processes at work inside living cells.
| [
{
"created": "Thu, 4 Jan 2007 20:50:55 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Ghandi",
"Nikhil",
""
],
[
"Ashkenasy",
"Gonen",
""
],
[
"Tannenbaum",
"Emmanuel",
""
]
] | We develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. In our model, two types of replicating molecules, denoted A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted GA and GB, respectively. It is also assumed that A and B can covalently link, and that the conjugated molecule can be stimulated by either the GA or GB growth factors (and can be degraded). We show that, if the chemostat is stimulated by both growth factors for a certain time, followed by a time gap during which the chemostat is not stimulated at all, and if the chemostat is then stimulated again by only one of the growth factors, then there will be a transient increase in the number of molecules activated by the other growth factor. Therefore, the chemostat bears the imprint of earlier, simultaneous stimulation with both growth factors, which is indicative of associative learning. It is interesting to note that the dynamics of our model is consistent with various aspects of Pavlov's original series of associative learning experiments in dogs. We discuss how associative learning can potentially be performed in vitro within RNA, DNA, or peptide networks. We also highlight how such a mechanism could potentially be involved in genomic evolution, and suggest bioinformatics studies that could be used to find evidence for associative learning processes at work inside living cells. |
1910.06884 | Matthew Hall | Matthew S Hall, Joseph T Decker, Lonnie D Shea | Towards systems tissue engineering: elucidating the dynamics, spatial
coordination, and individual cells driving emergent behaviors | 17 pages, 2 figures | null | null | null | q-bio.TO q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biomaterial systems have allowed for the in vitro production of complex,
emergent tissue behaviors that were not possible with conventional 2D culture
systems allowing for analysis of the normal development as well as disease
processes. We propose that the path towards developing the design parameters
for biomaterial systems lies with identifying the molecular drivers of emergent
behavior through leveraging technological advances in systems biology,
including single cell omics, genetic engineering, and high content imaging.
This research focus, which we term systems tissue engineering, can uniquely
interrogate the mechanisms by which complex tissue behaviors emerge with the
potential to capture the contribution of i) dynamic regulation of tissue
development and dysregulation, ii) single cell heterogeneity and the function
of rare cell types, and iii) the spatial distribution and structure of
individual cells and cell types within a tissue. Collectively, systems tissue
engineering can facilitate the identification of biomaterial design parameters
that will accelerate basic science discovery and translation.
| [
{
"created": "Tue, 15 Oct 2019 15:56:50 GMT",
"version": "v1"
}
] | 2019-10-16 | [
[
"Hall",
"Matthew S",
""
],
[
"Decker",
"Joseph T",
""
],
[
"Shea",
"Lonnie D",
""
]
] | Biomaterial systems have allowed for the in vitro production of complex, emergent tissue behaviors that were not possible with conventional 2D culture systems allowing for analysis of the normal development as well as disease processes. We propose that the path towards developing the design parameters for biomaterial systems lies with identifying the molecular drivers of emergent behavior through leveraging technological advances in systems biology, including single cell omics, genetic engineering, and high content imaging. This research focus, which we term systems tissue engineering, can uniquely interrogate the mechanisms by which complex tissue behaviors emerge with the potential to capture the contribution of i) dynamic regulation of tissue development and dysregulation, ii) single cell heterogeneity and the function of rare cell types, and iii) the spatial distribution and structure of individual cells and cell types within a tissue. Collectively, systems tissue engineering can facilitate the identification of biomaterial design parameters that will accelerate basic science discovery and translation. |
1910.08453 | Ulrich S. Schwarz | Justin Grewe and Ulrich S. Schwarz (Heidelberg University) | Mechanosensitive Self-Assembly of Myosin II Minifilaments | 15 pages, 6 figures, 4 supplemental figures | Phys. Rev. E 101, 022402 (2020) | 10.1103/PhysRevE.101.022402 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-assembly and force generation are two central processes in biological
systems that usually are considered in separation. However, the signals that
activate non-muscle myosin II molecular motors simultaneously lead to
self-assembly into myosin II minifilaments as well as progression of the motor
heads through the crossbridge cycle. Here we investigate theoretically the
possible effects of coupling these two processes. Our assembly model, which
builds upon a consensus architecture of the minifilament, predicts a critical
aggregation concentration at which the assembly kinetics slows down
dramatically. The combined model predicts that increasing actin filament
concentration and force both lead to a decrease in the critical aggregation
concentration. We suggest that due to these effects, myosin II minifilaments in
a filamentous context might be in a critical state that reacts faster to
varying conditions than in solution. We finally compare our model to
experiments by simulating fluorescence recovery after photobleaching.
| [
{
"created": "Fri, 18 Oct 2019 14:58:17 GMT",
"version": "v1"
}
] | 2020-02-12 | [
[
"Grewe",
"Justin",
"",
"Heidelberg University"
],
[
"Schwarz",
"Ulrich S.",
"",
"Heidelberg University"
]
] | Self-assembly and force generation are two central processes in biological systems that usually are considered in separation. However, the signals that activate non-muscle myosin II molecular motors simultaneously lead to self-assembly into myosin II minifilaments as well as progression of the motor heads through the crossbridge cycle. Here we investigate theoretically the possible effects of coupling these two processes. Our assembly model, which builds upon a consensus architecture of the minifilament, predicts a critical aggregation concentration at which the assembly kinetics slows down dramatically. The combined model predicts that increasing actin filament concentration and force both lead to a decrease in the critical aggregation concentration. We suggest that due to these effects, myosin II minifilaments in a filamentous context might be in a critical state that reacts faster to varying conditions than in solution. We finally compare our model to experiments by simulating fluorescence recovery after photobleaching. |
2012.04217 | Yuzhen Qin | Yuzhen Qin, Tommaso Menara, Danielle S. Bassett, Fabio Pasqualetti | Phase-Amplitude Coupling in Neuronal Oscillator Networks | 6 pages, 5 figures | Phys. Rev. Research 3, 023218 (2021) | 10.1103/PhysRevResearch.3.023218 | null | q-bio.NC math.DS nlin.AO nlin.PS | http://creativecommons.org/licenses/by/4.0/ | Phase-amplitude coupling (PAC) describes the phenomenon where the power of a
high-frequency oscillation evolves with the phase of a low-frequency one. We
propose a model that explains the emergence of PAC in two commonly-accepted
architectures in the brain, namely, a high-frequency neural oscillation driven
by an external low-frequency input and two interacting local oscillations with
distinct, locally-generated frequencies. We further propose an interconnection
structure for brain regions and demonstrate that low-frequency phase synchrony
can integrate high-frequency activities regulated by local PAC and control the
direction of information flow across distant regions.
| [
{
"created": "Tue, 8 Dec 2020 05:02:08 GMT",
"version": "v1"
}
] | 2021-06-30 | [
[
"Qin",
"Yuzhen",
""
],
[
"Menara",
"Tommaso",
""
],
[
"Bassett",
"Danielle S.",
""
],
[
"Pasqualetti",
"Fabio",
""
]
] | Phase-amplitude coupling (PAC) describes the phenomenon where the power of a high-frequency oscillation evolves with the phase of a low-frequency one. We propose a model that explains the emergence of PAC in two commonly-accepted architectures in the brain, namely, a high-frequency neural oscillation driven by an external low-frequency input and two interacting local oscillations with distinct, locally-generated frequencies. We further propose an interconnection structure for brain regions and demonstrate that low-frequency phase synchrony can integrate high-frequency activities regulated by local PAC and control the direction of information flow across distant regions. |
2311.08076 | Raffaello Potestio | Raffaele Fiorentini, Thomas Tarenzi, Giovanni Mattiotti, Raffaello
Potestio | The optimal resolution level of a protein is an emergent property of its
structure and dynamics | null | null | null | null | q-bio.BM cond-mat.soft cond-mat.stat-mech | http://creativecommons.org/licenses/by/4.0/ | Molecular dynamics simulations provide a wealth of data whose in-depth
analysis can be computationally demanding and, sometimes, even unnecessary.
Dimensionality reduction techniques are thus routinely employed to simplify and
improve the interpretation of trajectories focusing on specific subsets of the
system's atoms; a still open problem, in this context, is to determine the
optimal resolution level of the molecule, i.e. the smallest number of atoms
needed to preserve the largest information content from the full atomistic
trajectory. Here, we introduce the protein optimal resolution identification
method (PROPRE), an unsupervised approach built on information theory
principles that determines the smallest number of atoms that need to be
retained in order to attain a synthetic yet informative description of a
protein. By applying the method to a protein dataset and two particular case
studies, we show that this number is typically between 1.5 and 2 times the
amount of residues in a protein; furthermore, the degree of structural
variability of the system influences the optimal resolution level importantly,
in that a broader range of large-scale conformations correlates with fewer
retained sites. The PROPRE method is implemented in efficient and user-friendly
python scripts, which are made available for download on a github repository.
| [
{
"created": "Tue, 14 Nov 2023 11:01:36 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Nov 2023 06:39:28 GMT",
"version": "v2"
},
{
"created": "Wed, 6 Dec 2023 16:17:54 GMT",
"version": "v3"
},
{
"created": "Sun, 4 Feb 2024 11:42:52 GMT",
"version": "v4"
}
] | 2024-02-06 | [
[
"Fiorentini",
"Raffaele",
""
],
[
"Tarenzi",
"Thomas",
""
],
[
"Mattiotti",
"Giovanni",
""
],
[
"Potestio",
"Raffaello",
""
]
] | Molecular dynamics simulations provide a wealth of data whose in-depth analysis can be computationally demanding and, sometimes, even unnecessary. Dimensionality reduction techniques are thus routinely employed to simplify and improve the interpretation of trajectories focusing on specific subsets of the system's atoms; a still open problem, in this context, is to determine the optimal resolution level of the molecule, i.e. the smallest number of atoms needed to preserve the largest information content from the full atomistic trajectory. Here, we introduce the protein optimal resolution identification method (PROPRE), an unsupervised approach built on information theory principles that determines the smallest number of atoms that need to be retained in order to attain a synthetic yet informative description of a protein. By applying the method to a protein dataset and two particular case studies, we show that this number is typically between 1.5 and 2 times the amount of residues in a protein; furthermore, the degree of structural variability of the system influences the optimal resolution level importantly, in that a broader range of large-scale conformations correlates with fewer retained sites. The PROPRE method is implemented in efficient and user-friendly python scripts, which are made available for download on a github repository. |
2303.03128 | Henrique Lima | Dimitri Marques Abramov, Constantino Tsallis, and Henrique Santos Lima | Neural complexity -- Statistical-mechanical approach of human
electroencephalograms | null | null | 10.1038/s41598-023-37219-5 | null | q-bio.NC cond-mat.stat-mech physics.med-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The brain is a complex system whose understanding enables potentially deeper
approaches to mental phenomena. Dynamics of wide classes of complex systems
have been satisfactorily described within $q$-statistics, a current
generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human
electroencephalograms of typical human adults (EEG), very specifically their
inter-occurrence times across an arbitrarily chosen threshold of the signal
(observed, for instance, at the midparietal location in scalp). The
distributions of these inter-occurrence times differ from those usually
emerging within BG statistical mechanics. They are instead well approached
within the $q$-statistical theory, based on non-additive entropies
characterized by the index $q$. The present method points towards a suitable
tool for quantitatively accessing brain complexity, thus potentially opening
useful studies of the properties of both typical and altered brain physiology.
| [
{
"created": "Mon, 6 Mar 2023 13:41:08 GMT",
"version": "v1"
}
] | 2023-08-15 | [
[
"Abramov",
"Dimitri Marques",
""
],
[
"Tsallis",
"Constantino",
""
],
[
"Lima",
"Henrique Santos",
""
]
] | The brain is a complex system whose understanding enables potentially deeper approaches to mental phenomena. Dynamics of wide classes of complex systems have been satisfactorily described within $q$-statistics, a current generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human electroencephalograms of typical human adults (EEG), very specifically their inter-occurrence times across an arbitrarily chosen threshold of the signal (observed, for instance, at the midparietal location in scalp). The distributions of these inter-occurrence times differ from those usually emerging within BG statistical mechanics. They are instead well approached within the $q$-statistical theory, based on non-additive entropies characterized by the index $q$. The present method points towards a suitable tool for quantitatively accessing brain complexity, thus potentially opening useful studies of the properties of both typical and altered brain physiology. |
2103.06114 | Timothy Verstynen | Timothy Verstynen, Konrad Kording | A critical reappraisal of predicting suicidal ideation using fMRI | 6 pages, 1 table | null | null | null | q-bio.NC stat.ML | http://creativecommons.org/licenses/by/4.0/ | For many psychiatric disorders, neuroimaging offers a potential for
revolutionizing diagnosis, and potentially treatment, by providing access to
preverbal mental processes. In their study "Machine learning of neural
representations of suicide and emotion concepts identifies suicidal youth."1,
Just and colleagues report that a Naive Bayes classifier, trained on voxelwise
fMRI responses in human participants during the presentation of words and
concepts related to mortality, can predict whether an individual had reported
having suicidal ideations with a classification accuracy of 91%. Here we report
a reappraisal of the methods employed by the authors, including re-analysis of
the same data set, that calls into question the accuracy of the authors
findings. The analysis is a case study in the dangers of overfitting in machine
learning.
| [
{
"created": "Wed, 10 Mar 2021 15:08:57 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Oct 2021 19:58:35 GMT",
"version": "v2"
}
] | 2021-11-02 | [
[
"Verstynen",
"Timothy",
""
],
[
"Kording",
"Konrad",
""
]
] | For many psychiatric disorders, neuroimaging offers a potential for revolutionizing diagnosis, and potentially treatment, by providing access to preverbal mental processes. In their study "Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth."1, Just and colleagues report that a Naive Bayes classifier, trained on voxelwise fMRI responses in human participants during the presentation of words and concepts related to mortality, can predict whether an individual had reported having suicidal ideations with a classification accuracy of 91%. Here we report a reappraisal of the methods employed by the authors, including re-analysis of the same data set, that calls into question the accuracy of the authors findings. The analysis is a case study in the dangers of overfitting in machine learning. |
1404.0936 | Stefan Z Stefanov | I. Trifonova, G. Kurteva, S. Z. Stefanov | Success of Chemotherapy in Soft Matter | 5 pages | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The success of chemotharapy in soft matter as a survival is found in the
paper. Therefore, it is found the analogous tumor stretching force in soft
matter; ultrasonography is performed for this tumor; restoration in soft matter
with such a tumor is found; Bayes estimate of the probability of chemotherapy
success is derived from the transferred chemical energy and from soft matter
entropy; survival probability is juxtaposed to this probability of success.
| [
{
"created": "Thu, 20 Mar 2014 11:56:35 GMT",
"version": "v1"
}
] | 2014-05-19 | [
[
"Trifonova",
"I.",
""
],
[
"Kurteva",
"G.",
""
],
[
"Stefanov",
"S. Z.",
""
]
] | The success of chemotharapy in soft matter as a survival is found in the paper. Therefore, it is found the analogous tumor stretching force in soft matter; ultrasonography is performed for this tumor; restoration in soft matter with such a tumor is found; Bayes estimate of the probability of chemotherapy success is derived from the transferred chemical energy and from soft matter entropy; survival probability is juxtaposed to this probability of success. |
1806.02435 | Caroline Holmes | Caroline M. Holmes, Ilya Nemenman, Daniel B. Weissman | Increased adaptability to rapid environmental change can more than make
up for the two-fold cost of males | 15 pages, 3 figures | null | 10.1209/0295-5075/123/58001 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The famous "two-fold cost of sex" is really the cost of anisogamy -- why
should females mate with males who do not contribute resources to offspring,
rather than isogamous partners who contribute equally? In typical anisogamous
populations, a single very fit male can have an enormous number of offspring,
far larger than is possible for any female or isogamous individual. If the
sexual selection on males aligns with the natural selection on females,
anisogamy thus allows much more rapid adaptation via super-successful males. We
show via simulations that this effect can be sufficient to overcome the
two-fold cost and maintain anisogamy against isogamy in populations adapting to
environmental change. The key quantity is the variance in male fitness -- if
this exceeds what is possible in an isogamous population, anisogamous
populations can win out in direct competition by adapting faster.
| [
{
"created": "Wed, 6 Jun 2018 21:37:55 GMT",
"version": "v1"
}
] | 2018-09-26 | [
[
"Holmes",
"Caroline M.",
""
],
[
"Nemenman",
"Ilya",
""
],
[
"Weissman",
"Daniel B.",
""
]
] | The famous "two-fold cost of sex" is really the cost of anisogamy -- why should females mate with males who do not contribute resources to offspring, rather than isogamous partners who contribute equally? In typical anisogamous populations, a single very fit male can have an enormous number of offspring, far larger than is possible for any female or isogamous individual. If the sexual selection on males aligns with the natural selection on females, anisogamy thus allows much more rapid adaptation via super-successful males. We show via simulations that this effect can be sufficient to overcome the two-fold cost and maintain anisogamy against isogamy in populations adapting to environmental change. The key quantity is the variance in male fitness -- if this exceeds what is possible in an isogamous population, anisogamous populations can win out in direct competition by adapting faster. |
2310.20575 | Jacopo Grilli | Solmaz Golmohammadi, Mina Zarei, Jacopo Grilli | The effect of demographic stochasticity on predatory-prey oscillations | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-sa/4.0/ | The ecological dynamics of interacting predator and prey populations can
display sustained oscillations, as for instance predicted by the
Rosenzweig-MacArthur predator-prey model. The presence of demographic
stochasticity, due to the finiteness of population sizes, alters the amplitude
and frequency of these oscillations. Here we present a method for
characterizing the effects of demographic stochasticity on the limit cycle
attractor of the Rosenzweig-MacArthur. We show that an angular Brownian motion
well describes the frequency oscillations. In the vicinity of the bifurcation
point, we obtain an analytical approximation for the angular diffusion
constant. This approximation accurately captures the effect of demographic
stochasticity across parameter values.
| [
{
"created": "Tue, 31 Oct 2023 16:09:50 GMT",
"version": "v1"
}
] | 2023-11-01 | [
[
"Golmohammadi",
"Solmaz",
""
],
[
"Zarei",
"Mina",
""
],
[
"Grilli",
"Jacopo",
""
]
] | The ecological dynamics of interacting predator and prey populations can display sustained oscillations, as for instance predicted by the Rosenzweig-MacArthur predator-prey model. The presence of demographic stochasticity, due to the finiteness of population sizes, alters the amplitude and frequency of these oscillations. Here we present a method for characterizing the effects of demographic stochasticity on the limit cycle attractor of the Rosenzweig-MacArthur. We show that an angular Brownian motion well describes the frequency oscillations. In the vicinity of the bifurcation point, we obtain an analytical approximation for the angular diffusion constant. This approximation accurately captures the effect of demographic stochasticity across parameter values. |
1302.0029 | Rhiju Das | Sergey Lyskov, Fang-Chieh Chou, Shane \'O Conch\'uir, Bryan S. Der,
Kevin Drew, Daisuke Kuroda, Jianqing Xu, Brian D. Weitzner, P. Douglas
Renfrew, Parin Sripakdeevong, Benjamin Borgo, James J. Havranek, Brian
Kuhlman, Tanja Kortemme, Richard Bonneau, Jeffrey J. Gray, Rhiju Das | Serverification of Molecular Modeling Applications: the Rosetta Online
Server that Includes Everyone (ROSIE) | null | null | 10.1371/journal.pone.0063906 | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Rosetta molecular modeling software package provides experimentally
tested and rapidly evolving tools for the 3D structure prediction and
high-resolution design of proteins, nucleic acids, and a growing number of
non-natural polymers. Despite its free availability to academic users and
improving documentation, use of Rosetta has largely remained confined to
developers and their immediate collaborators due to the code's difficulty of
use, the requirement for large computational resources, and the unavailability
of servers for most of the Rosetta applications. Here, we present a unified web
framework for Rosetta applications called ROSIE (Rosetta Online Server that
Includes Everyone). ROSIE provides (a) a common user interface for Rosetta
protocols, (b) a stable application programming interface for developers to add
additional protocols, (c) a flexible back-end to allow leveraging of computer
cluster resources shared by RosettaCommons member institutions, and (d)
centralized administration by the RosettaCommons to ensure continuous
maintenance. This paper describes the ROSIE server infrastructure, a
step-by-step 'serverification' protocol for use by Rosetta developers, and the
deployment of the first nine ROSIE applications by six separate developer
teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance,
Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated
by the number and diversity of these applications, ROSIE offers a general and
speedy paradigm for serverification of Rosetta applications that incurs
negligible cost to developers and lowers barriers to Rosetta use for the
broader biological community. ROSIE is available at
http://rosie.rosettacommons.org.
| [
{
"created": "Thu, 31 Jan 2013 22:39:53 GMT",
"version": "v1"
}
] | 2015-06-12 | [
[
"Lyskov",
"Sergey",
""
],
[
"Chou",
"Fang-Chieh",
""
],
[
"Conchúir",
"Shane Ó",
""
],
[
"Der",
"Bryan S.",
""
],
[
"Drew",
"Kevin",
""
],
[
"Kuroda",
"Daisuke",
""
],
[
"Xu",
"Jianqing",
""
],
[
"Weitzner",
"Brian D.",
""
],
[
"Renfrew",
"P. Douglas",
""
],
[
"Sripakdeevong",
"Parin",
""
],
[
"Borgo",
"Benjamin",
""
],
[
"Havranek",
"James J.",
""
],
[
"Kuhlman",
"Brian",
""
],
[
"Kortemme",
"Tanja",
""
],
[
"Bonneau",
"Richard",
""
],
[
"Gray",
"Jeffrey J.",
""
],
[
"Das",
"Rhiju",
""
]
] | The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code's difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step 'serverification' protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org. |
1206.1864 | Tobias Reichenbach | Tobias Reichenbach and A. J. Hudspeth | Discrimination of low-frequency tones employs temporal fine structure | 12 pages, 3 figures | PLoS ONE 7, e45579 (2012) | 10.1371/journal.pone.0045579 | null | q-bio.NC physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An auditory neuron can preserve the temporal fine structure of a
low-frequency tone by phase-locking its response to the stimulus. Apart from
sound localization, however, little is known about the role of this temporal
information for signal processing in the brain. Through psychoacoustic studies
we provide direct evidence that humans employ temporal fine structure to
discriminate between frequencies. To this end we construct tones that are based
on a single frequency but in which, through the concatenation of wavelets, the
phase changes randomly every few cycles. We then test the frequency
discrimination of these phase-changing tones, of control tones without phase
changes, and of short tones that consist of a single wavelets. For carrier
frequencies below a few kilohertz we find that phase changes systematically
worsen frequency discrimination. No such effect appears for higher carrier
frequencies at which temporal information is not available in the central
auditory system.
| [
{
"created": "Fri, 8 Jun 2012 20:07:16 GMT",
"version": "v1"
}
] | 2012-09-21 | [
[
"Reichenbach",
"Tobias",
""
],
[
"Hudspeth",
"A. J.",
""
]
] | An auditory neuron can preserve the temporal fine structure of a low-frequency tone by phase-locking its response to the stimulus. Apart from sound localization, however, little is known about the role of this temporal information for signal processing in the brain. Through psychoacoustic studies we provide direct evidence that humans employ temporal fine structure to discriminate between frequencies. To this end we construct tones that are based on a single frequency but in which, through the concatenation of wavelets, the phase changes randomly every few cycles. We then test the frequency discrimination of these phase-changing tones, of control tones without phase changes, and of short tones that consist of a single wavelets. For carrier frequencies below a few kilohertz we find that phase changes systematically worsen frequency discrimination. No such effect appears for higher carrier frequencies at which temporal information is not available in the central auditory system. |
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