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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1802.04488 | Guillaume Le Treut | Guillaume Le Treut, Fran\c{c}ois K\'ep\`es, Henri Orland | A polymer model for the quantitative reconstruction of 3d chromosome
architecture from Hi-C and GAM data | 76 pages, 43 figures | Biophysical Journal 115, 2286-2294, December 18, 2018 | 10.1016/j.bpj.2018.10.032 | null | q-bio.QM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is widely believed that the folding of the chromosome in the nucleus has a
major effect on genetic expression. For example co-regulated genes in several
species have been shown to colocalize in space despite being far away on the
DNA sequence. In this manuscript, we present a new method to model the
three-dimensional structure of the chromosome in live cells, based on DNA-DNA
interactions measured in high-throughput chromosome conformation capture
experiments (Hi-C) and genome architecture mapping experiments (GAM). Our
approach incorporates a polymer model, and directly uses the contact
probabilities measured in Hi-C and GAM experiments rather than estimates of
average distances between genomic loci. Specifically, we model the chromosome
as a Gaussian polymer with harmonic interactions and extract the coupling
coefficients best reproducing the experimental contact probabilities. In
contrast to existing methods, we give an exact expression of the contact
probabilities at thermodynamic equilibrium. The Gaussian effective model (GEM)
reconstructed with our method reproduces experimental contacts with high
accuracy. We also show how Brownian Dynamics simulations of our reconstructed
GEM can be used to study chromatin organization, and possibly give some clue
about its dynamics.
| [
{
"created": "Tue, 13 Feb 2018 07:23:17 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Apr 2020 06:55:21 GMT",
"version": "v2"
}
] | 2020-04-29 | [
[
"Treut",
"Guillaume Le",
""
],
[
"Képès",
"François",
""
],
[
"Orland",
"Henri",
""
]
] | It is widely believed that the folding of the chromosome in the nucleus has a major effect on genetic expression. For example co-regulated genes in several species have been shown to colocalize in space despite being far away on the DNA sequence. In this manuscript, we present a new method to model the three-dimensional structure of the chromosome in live cells, based on DNA-DNA interactions measured in high-throughput chromosome conformation capture experiments (Hi-C) and genome architecture mapping experiments (GAM). Our approach incorporates a polymer model, and directly uses the contact probabilities measured in Hi-C and GAM experiments rather than estimates of average distances between genomic loci. Specifically, we model the chromosome as a Gaussian polymer with harmonic interactions and extract the coupling coefficients best reproducing the experimental contact probabilities. In contrast to existing methods, we give an exact expression of the contact probabilities at thermodynamic equilibrium. The Gaussian effective model (GEM) reconstructed with our method reproduces experimental contacts with high accuracy. We also show how Brownian Dynamics simulations of our reconstructed GEM can be used to study chromatin organization, and possibly give some clue about its dynamics. |
0705.2704 | Danielle Rojas-Rousse | Auguste Ndoutoume, Danielle Rousse (IRBII), Roland Allemand | Rythmes d'activit\'e locomotrice chez deux insectes parasito\"ides
sympatriques : Eupelmus orientalis et Eupelmus vuilleti (Hym\'enopt\`ere,
Eupelmidae) | null | Comptes Rendus Biologies 329 (2006) 476-482 | null | null | q-bio.PE | null | With an automatic image analysis device, we studied the temporal distribution
of the locomotor activity of E. orientalis and E. vuilleti during 24 h, and
over several days to know whether the activity rhythms of these two Eupelmidae
play a role in their competitive interactions. The analysis of locomotor
activity rhythms of E. orientalis and E. vuilleti shows that the locomotor
activity of both species presents daily cyclic variations. These two Eupelmidae
have similar activity rhythms. Displacements of these parasitoids essentially
take place during the photophase. But the activity of E. vuilleti is earlier,
because the individuals of this species start their activity on average 4 to 5
h earlier than those of E. orientalis. E. vuilleti begins its displacements
several hours before the onset of lighting, whereas E. orientalis is active
only in the presence of the light. This shift of starting activity is thus a
factor allowing these concurrent species to minimize their interactions during
the cohabitation period in traditional granaries after the harvests of cowpea.
| [
{
"created": "Fri, 18 May 2007 14:36:08 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Ndoutoume",
"Auguste",
"",
"IRBII"
],
[
"Rousse",
"Danielle",
"",
"IRBII"
],
[
"Allemand",
"Roland",
""
]
] | With an automatic image analysis device, we studied the temporal distribution of the locomotor activity of E. orientalis and E. vuilleti during 24 h, and over several days to know whether the activity rhythms of these two Eupelmidae play a role in their competitive interactions. The analysis of locomotor activity rhythms of E. orientalis and E. vuilleti shows that the locomotor activity of both species presents daily cyclic variations. These two Eupelmidae have similar activity rhythms. Displacements of these parasitoids essentially take place during the photophase. But the activity of E. vuilleti is earlier, because the individuals of this species start their activity on average 4 to 5 h earlier than those of E. orientalis. E. vuilleti begins its displacements several hours before the onset of lighting, whereas E. orientalis is active only in the presence of the light. This shift of starting activity is thus a factor allowing these concurrent species to minimize their interactions during the cohabitation period in traditional granaries after the harvests of cowpea. |
1701.02599 | Vaibhav Wasnik | Vaibhav Wasnik | Issues in data expansion in understanding criticality in biological
systems | 6 pages | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | At the point of a second order phase transition also termed as a critical
point, systems display long range order and their macroscopic behaviors are
independent of the microscopic details making up the system. Due to these
properties, it has long been speculated that biological systems that show
similar behavior despite having very different microscopics, may be operating
near a critical point. Recent methods in neuroscience are making it possible to
explore whether criticality exists in neural networks. Despite being large in
size, many data sets are still only a minute sample of the neural system and
methods towards expanding these data sets have to be considered in order to
study the existence of criticality. In this work we develop an analytical
method of expanding a dataset to the large N limit so that statements about the
critical nature of the data set could be made. We also show using a particular
dataset analyzed computationally in literature that expanding data sets keeping
the moments of the original data set need not lead to unique values of the
critical temperature when the large N limit is considered analytically, despite
the mirage of them appearing to do so when analyzed computationally. This
suggests that not all available data sets from experiments are amenable for
understanding the critically of the underlying system.
| [
{
"created": "Tue, 10 Jan 2017 14:06:21 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Jul 2017 08:55:17 GMT",
"version": "v2"
}
] | 2017-07-18 | [
[
"Wasnik",
"Vaibhav",
""
]
] | At the point of a second order phase transition also termed as a critical point, systems display long range order and their macroscopic behaviors are independent of the microscopic details making up the system. Due to these properties, it has long been speculated that biological systems that show similar behavior despite having very different microscopics, may be operating near a critical point. Recent methods in neuroscience are making it possible to explore whether criticality exists in neural networks. Despite being large in size, many data sets are still only a minute sample of the neural system and methods towards expanding these data sets have to be considered in order to study the existence of criticality. In this work we develop an analytical method of expanding a dataset to the large N limit so that statements about the critical nature of the data set could be made. We also show using a particular dataset analyzed computationally in literature that expanding data sets keeping the moments of the original data set need not lead to unique values of the critical temperature when the large N limit is considered analytically, despite the mirage of them appearing to do so when analyzed computationally. This suggests that not all available data sets from experiments are amenable for understanding the critically of the underlying system. |
0708.2061 | Eduardo Candelario-Jalil | E. Candelario-Jalil, S. M. Al-Dalain, R. Castillo, G. Martinez, O. S.
Fernandez | Selective vulnerability to kainate-induced oxidative damage in different
rat brain regions | null | Journal of Applied Toxicology 21(5): 403-407 (2001) | null | null | q-bio.TO | null | Some markers of oxidative injury were measured in different rat brain areas
(hippocampus, cerebral cortex, striatum, hypothalamus, amygdala/piriform cortex
and cerebellum) after the systemic administration of an excitotoxic dose of
kainic acid (KA, 9 mg kg(-1) i.p.) at two different sampling times (24 and 48
h). Kainic acid was able to lower markedly (P < 0.05) the glutathione (GSH)
levels in hippocampus, cerebellum and amygdala/piriform cortex (maximal
reduction at 24 h). In a similar way, lipid peroxidation, as assessed by
malonaldehyde and 4-hydroxyalkenal levels, significantly increased (P < 0.05)
in hippocampus, cerebellum and amygdala/piriform cortex mainly at 24 h after
KA. In addition, hippocampal superoxide dismutase (SOD) activity decreased
significantly (P < 0.05) with respect to basal levels by 24 h after KA
application. On the other hand, brain areas such as hypothalamus, striatum and
cerebral cortex seem to be less susceptible to KA excitotoxicity. According to
these findings, the pattern of oxidative injury induced by systemically
administered KA seems to be highly region-specific. Further, our results have
shown that a lower antioxidant status (GSH and SOD) seems not to play an
important role in the selective vulnerability of certain brain regions because
it correlates poorly with increases in markers of oxidative damage.
| [
{
"created": "Wed, 15 Aug 2007 15:31:13 GMT",
"version": "v1"
}
] | 2007-08-16 | [
[
"Candelario-Jalil",
"E.",
""
],
[
"Al-Dalain",
"S. M.",
""
],
[
"Castillo",
"R.",
""
],
[
"Martinez",
"G.",
""
],
[
"Fernandez",
"O. S.",
""
]
] | Some markers of oxidative injury were measured in different rat brain areas (hippocampus, cerebral cortex, striatum, hypothalamus, amygdala/piriform cortex and cerebellum) after the systemic administration of an excitotoxic dose of kainic acid (KA, 9 mg kg(-1) i.p.) at two different sampling times (24 and 48 h). Kainic acid was able to lower markedly (P < 0.05) the glutathione (GSH) levels in hippocampus, cerebellum and amygdala/piriform cortex (maximal reduction at 24 h). In a similar way, lipid peroxidation, as assessed by malonaldehyde and 4-hydroxyalkenal levels, significantly increased (P < 0.05) in hippocampus, cerebellum and amygdala/piriform cortex mainly at 24 h after KA. In addition, hippocampal superoxide dismutase (SOD) activity decreased significantly (P < 0.05) with respect to basal levels by 24 h after KA application. On the other hand, brain areas such as hypothalamus, striatum and cerebral cortex seem to be less susceptible to KA excitotoxicity. According to these findings, the pattern of oxidative injury induced by systemically administered KA seems to be highly region-specific. Further, our results have shown that a lower antioxidant status (GSH and SOD) seems not to play an important role in the selective vulnerability of certain brain regions because it correlates poorly with increases in markers of oxidative damage. |
q-bio/0601026 | Jie Liang | Xiang Li and Jie Liang | Knowledge-based energy functions for computational studies of proteins | 57 pages, 6 figures. To be published in a book by Springer | null | 10.1007/978-0-387-68372-0_3 | null | q-bio.BM | null | This chapter discusses theoretical framework and methods for developing
knowledge-based potential functions essential for protein structure prediction,
protein-protein interaction, and protein sequence design. We discuss in some
details about the Miyazawa-Jernigan contact statistical potential,
distance-dependent statistical potentials, as well as geometric statistical
potentials. We also describe a geometric model for developing both linear and
non-linear potential functions by optimization. Applications of knowledge-based
potential functions in protein-decoy discrimination, in protein-protein
interactions, and in protein design are then described. Several issues of
knowledge-based potential functions are finally discussed.
| [
{
"created": "Thu, 19 Jan 2006 05:40:23 GMT",
"version": "v1"
}
] | 2015-06-26 | [
[
"Li",
"Xiang",
""
],
[
"Liang",
"Jie",
""
]
] | This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed. |
1607.03957 | Aleksandra Walczak | Jonathan Desponds, Huy Tran, Teresa Ferraro, Tanguy Lucas, Carmina
Perez Romero, Aurelien Guillou, Cecile Fradin, Mathieu Coppey, Nathalie
Dostatni, and Aleksandra M. Walczak | Precision of readout at the hunchback gene: analyzing short
transcription time traces in living fly embryos | null | null | 10.1371/journal.pcbi.1005256 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The simultaneous expression of the hunchback gene in the numerous nuclei of
the developing fly embryo gives us a unique opportunity to study how
transcription is regulated in living organisms. A recently developed MS2-MCP
technique for imaging nascent messenger RNA in living Drosophila embryos allows
us to quantify the dynamics of the developmental transcription process. The
initial measurement of the morphogens by the hunchback promoter takes place
during very short cell cycles, not only giving each nucleus little time for a
precise readout, but also resulting in short time traces of transcription.
Additionally, the relationship between the measured signal and the promoter
state depends on the molecular design of the reporting probe. We develop an
analysis approach based on tailor made autocorrelation functions that overcomes
the short trace problems and quantifies the dynamics of transcription
initiation. Based on live imaging data, we identify signatures of bursty
transcription initiation from the hunchback promoter. We show that the
precision of the expression of the hunchback gene to measure its position along
the anterior-posterior axis is low both at the boundary and in the anterior
even at cycle 13, suggesting additional post-transcriptional averaging
mechanisms to provide the precision observed in fixed embryos.
| [
{
"created": "Wed, 13 Jul 2016 23:23:49 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Nov 2016 22:12:42 GMT",
"version": "v2"
}
] | 2017-02-08 | [
[
"Desponds",
"Jonathan",
""
],
[
"Tran",
"Huy",
""
],
[
"Ferraro",
"Teresa",
""
],
[
"Lucas",
"Tanguy",
""
],
[
"Romero",
"Carmina Perez",
""
],
[
"Guillou",
"Aurelien",
""
],
[
"Fradin",
"Cecile",
""
],
[
"Coppey",
"Mathieu",
""
],
[
"Dostatni",
"Nathalie",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] | The simultaneous expression of the hunchback gene in the numerous nuclei of the developing fly embryo gives us a unique opportunity to study how transcription is regulated in living organisms. A recently developed MS2-MCP technique for imaging nascent messenger RNA in living Drosophila embryos allows us to quantify the dynamics of the developmental transcription process. The initial measurement of the morphogens by the hunchback promoter takes place during very short cell cycles, not only giving each nucleus little time for a precise readout, but also resulting in short time traces of transcription. Additionally, the relationship between the measured signal and the promoter state depends on the molecular design of the reporting probe. We develop an analysis approach based on tailor made autocorrelation functions that overcomes the short trace problems and quantifies the dynamics of transcription initiation. Based on live imaging data, we identify signatures of bursty transcription initiation from the hunchback promoter. We show that the precision of the expression of the hunchback gene to measure its position along the anterior-posterior axis is low both at the boundary and in the anterior even at cycle 13, suggesting additional post-transcriptional averaging mechanisms to provide the precision observed in fixed embryos. |
2306.12448 | Glen Pridham | Glen Pridham and Andrew D. Rutenberg | Network dynamical stability analysis reveals key "mallostatic" natural
variables that erode homeostasis and drive age-related decline of health | 42 pages including supplmenetal | Sci Rep 13, 22140 (2023) | 10.1038/s41598-023-49129-7 | null | q-bio.OT physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using longitudinal study data, we dynamically model how aging affects
homeostasis in both mice and humans. We operationalize homeostasis as a
multivariate mean-reverting stochastic process. We hypothesize that biomarkers
have stable equilibrium values, but that deviations from equilibrium of each
biomarker affects other biomarkers through an interaction network - this
precludes univariate analysis. We therefore looked for age-related changes to
homeostasis using dynamic network stability analysis, which transforms observed
biomarker data into independent "natural" variables and determines their
associated recovery rates. Most natural variables remained near equilibrium and
were essentially constant in time. A small number of natural variables were
unable to equilibrate due to a gradual drift with age in their homeostatic
equilibrium, i.e. allostasis. This drift caused them to accumulate over the
lifespan course and makes them natural aging variables. Their rate of
accumulation was correlated with risk of adverse outcomes: death or dementia
onset. We call this tendency for aging organisms to drift towards an
equilibrium position of ever-worsening health "mallostasis". We demonstrate
that the effects of mallostasis on observed biomarkers are spread out through
the interaction network. This could provide a redundancy mechanism to preserve
functioning until multi-system dysfunction emerges at advanced ages.
| [
{
"created": "Tue, 20 Jun 2023 19:02:51 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Oct 2023 14:17:52 GMT",
"version": "v2"
}
] | 2023-12-14 | [
[
"Pridham",
"Glen",
""
],
[
"Rutenberg",
"Andrew D.",
""
]
] | Using longitudinal study data, we dynamically model how aging affects homeostasis in both mice and humans. We operationalize homeostasis as a multivariate mean-reverting stochastic process. We hypothesize that biomarkers have stable equilibrium values, but that deviations from equilibrium of each biomarker affects other biomarkers through an interaction network - this precludes univariate analysis. We therefore looked for age-related changes to homeostasis using dynamic network stability analysis, which transforms observed biomarker data into independent "natural" variables and determines their associated recovery rates. Most natural variables remained near equilibrium and were essentially constant in time. A small number of natural variables were unable to equilibrate due to a gradual drift with age in their homeostatic equilibrium, i.e. allostasis. This drift caused them to accumulate over the lifespan course and makes them natural aging variables. Their rate of accumulation was correlated with risk of adverse outcomes: death or dementia onset. We call this tendency for aging organisms to drift towards an equilibrium position of ever-worsening health "mallostasis". We demonstrate that the effects of mallostasis on observed biomarkers are spread out through the interaction network. This could provide a redundancy mechanism to preserve functioning until multi-system dysfunction emerges at advanced ages. |
2103.03722 | Vittorio Lippi | Mustafa Emre Ak\c{c}ay, Vittorio Lippi, Thomas Mergner | Visual Modulation of Human Responses to Support Surface Translation | null | Frontiers in Human Neuroscience 15 (2021) 98 | 10.3389/fnhum.2021.615200 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Vision is known to improve human postural responses to external
perturbations. This study investigates the role of vision for the responses to
continuous pseudorandom support surface translations in the body sagittal plane
in three visual conditions: with the eyes closed (EC), in stroboscopic
illumination (EO/SI; only visual position information) and with eyes open in
continuous illumination (EO/CI; position and velocity information) with the
room as static visual scene (or the interior of a moving cabin, in some of the
trials). In the frequency spectrum of the translation stimulus we distinguished
on the basis of the response patterns between a low-frequency, mid-frequency,
and high-frequency range (LFR: 0.0165-0.14 Hz; MFR: 0.15-0.57 Hz; HFR:
0.58-2.46 Hz). With EC, subjects' mean sway response gain was very low in the
LFR. On average it increased with EO/SI (although not to a significant degree p
= 0.078) and more so with EO/CI (p < 10<sup>-6</sup>). In contrast, the average
gain in the MFR decreased from EC to EO/SI (although not to a significant
degree, p = 0.548) and further to EO/CI (p = 0.0002). In the HFR, all three
visual conditions produced, similarly, high gain levels. A single inverted
pendulum (SIP) model controlling center of mass (COM) balancing about the ankle
joints formally described the EC response as being strongly shaped by a
resonance phenomenon arising primarily from the control's proprioceptive
feedback loop. The effect of adding visual information in these simulations
lies in a reduction of the resonance, similar as in the experiments. Extending
the model to a double inverted pendulum (DIP) suggested in addition a
biomechanical damping effective from trunk sway in the hip joints on the
resonance.
| [
{
"created": "Fri, 5 Mar 2021 14:52:19 GMT",
"version": "v1"
}
] | 2021-03-08 | [
[
"Akçay",
"Mustafa Emre",
""
],
[
"Lippi",
"Vittorio",
""
],
[
"Mergner",
"Thomas",
""
]
] | Vision is known to improve human postural responses to external perturbations. This study investigates the role of vision for the responses to continuous pseudorandom support surface translations in the body sagittal plane in three visual conditions: with the eyes closed (EC), in stroboscopic illumination (EO/SI; only visual position information) and with eyes open in continuous illumination (EO/CI; position and velocity information) with the room as static visual scene (or the interior of a moving cabin, in some of the trials). In the frequency spectrum of the translation stimulus we distinguished on the basis of the response patterns between a low-frequency, mid-frequency, and high-frequency range (LFR: 0.0165-0.14 Hz; MFR: 0.15-0.57 Hz; HFR: 0.58-2.46 Hz). With EC, subjects' mean sway response gain was very low in the LFR. On average it increased with EO/SI (although not to a significant degree p = 0.078) and more so with EO/CI (p < 10<sup>-6</sup>). In contrast, the average gain in the MFR decreased from EC to EO/SI (although not to a significant degree, p = 0.548) and further to EO/CI (p = 0.0002). In the HFR, all three visual conditions produced, similarly, high gain levels. A single inverted pendulum (SIP) model controlling center of mass (COM) balancing about the ankle joints formally described the EC response as being strongly shaped by a resonance phenomenon arising primarily from the control's proprioceptive feedback loop. The effect of adding visual information in these simulations lies in a reduction of the resonance, similar as in the experiments. Extending the model to a double inverted pendulum (DIP) suggested in addition a biomechanical damping effective from trunk sway in the hip joints on the resonance. |
2312.02193 | Petr Slepicka | Jana Pryjmakova, Daniel Grossberger, Anna Kutova, Barbora Vokata,
Miroslav Slouf, Petr Slepicka, Jakub Siegel | A new promising material for biological applications: multi-level
physical modification of AgNPs-decorated PEEK | null | null | null | null | q-bio.OT physics.med-ph | http://creativecommons.org/licenses/by/4.0/ | In the case of polymer medical devices, the surface design plays a crucial
role in contact with human tissue. The use of AgNPs as antibacterial agents is
well known; however, their anchoring into the polymer surface can still be
investigated. This work describes the change in surface morphology and
behaviour in the biological environment of polyetheretherketone (PEEK) with
immobilised AgNPs after different surface modifications. The initial composites
were prepared by immobilisation of silver nanoparticles from a colloid solution
into the upper surface layers of polyetheretherketone (PEEK). The prepared
samples (Ag/PEEK) had a planar morphology and were further modified with a KrF
laser, a GaN laser, and Ar plasma. The samples were studied using the AFM
method to visualise changes in surface morphology and to obtain information on
the height of the structures and other surface parameters. Comparative analysis
of the nanoparticles and polymers was performed using FEG-SEM. The chemical
composition of the surface of the samples and optical activity were studied by
XPS and UV-Vis spectroscopy. Finally, drop plate antibacterial and cytotoxicity
tests were performed to determine the role of Ag nanoparticles after
modification and suitability of the surface, which are important for the use of
the resulting composite in biomedical applications.
| [
{
"created": "Sat, 2 Dec 2023 08:44:13 GMT",
"version": "v1"
}
] | 2023-12-06 | [
[
"Pryjmakova",
"Jana",
""
],
[
"Grossberger",
"Daniel",
""
],
[
"Kutova",
"Anna",
""
],
[
"Vokata",
"Barbora",
""
],
[
"Slouf",
"Miroslav",
""
],
[
"Slepicka",
"Petr",
""
],
[
"Siegel",
"Jakub",
""
]
] | In the case of polymer medical devices, the surface design plays a crucial role in contact with human tissue. The use of AgNPs as antibacterial agents is well known; however, their anchoring into the polymer surface can still be investigated. This work describes the change in surface morphology and behaviour in the biological environment of polyetheretherketone (PEEK) with immobilised AgNPs after different surface modifications. The initial composites were prepared by immobilisation of silver nanoparticles from a colloid solution into the upper surface layers of polyetheretherketone (PEEK). The prepared samples (Ag/PEEK) had a planar morphology and were further modified with a KrF laser, a GaN laser, and Ar plasma. The samples were studied using the AFM method to visualise changes in surface morphology and to obtain information on the height of the structures and other surface parameters. Comparative analysis of the nanoparticles and polymers was performed using FEG-SEM. The chemical composition of the surface of the samples and optical activity were studied by XPS and UV-Vis spectroscopy. Finally, drop plate antibacterial and cytotoxicity tests were performed to determine the role of Ag nanoparticles after modification and suitability of the surface, which are important for the use of the resulting composite in biomedical applications. |
2407.16215 | Tsuyoshi Tatsukawa | Tsuyoshi Tatsukawa and Jun-nosuke Teramae | Energy-information trade-off makes the cortical critical power law the
optimal coding | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stimulus responses of cortical neurons exhibit the critical power law, where
the covariance eigenspectrum follows the power law with the exponent just at
the edge of differentiability of the neural manifold. This criticality is
conjectured to balance the expressivity and robustness of neural codes, because
a non-differential fractal manifold spoils coding reliability. However,
contrary to the conjecture, here we prove that the neural coding is not
degraded even on the non-differentiable fractal manifold, where the coding is
extremely sensitive to perturbations. Rather, we show that the trade-off
between energetic cost and information always makes this critical power-law
response the optimal neural coding. Direct construction of a maximum likelihood
decoder of the power-law coding validates the theoretical prediction. By
revealing the non-trivial nature of high-dimensional coding, the theory
developed here will contribute to a deeper understanding of criticality and
power laws in computation in biological and artificial neural networks.
| [
{
"created": "Tue, 23 Jul 2024 06:44:45 GMT",
"version": "v1"
}
] | 2024-07-24 | [
[
"Tatsukawa",
"Tsuyoshi",
""
],
[
"Teramae",
"Jun-nosuke",
""
]
] | Stimulus responses of cortical neurons exhibit the critical power law, where the covariance eigenspectrum follows the power law with the exponent just at the edge of differentiability of the neural manifold. This criticality is conjectured to balance the expressivity and robustness of neural codes, because a non-differential fractal manifold spoils coding reliability. However, contrary to the conjecture, here we prove that the neural coding is not degraded even on the non-differentiable fractal manifold, where the coding is extremely sensitive to perturbations. Rather, we show that the trade-off between energetic cost and information always makes this critical power-law response the optimal neural coding. Direct construction of a maximum likelihood decoder of the power-law coding validates the theoretical prediction. By revealing the non-trivial nature of high-dimensional coding, the theory developed here will contribute to a deeper understanding of criticality and power laws in computation in biological and artificial neural networks. |
q-bio/0407019 | Reza Ejtehadi | M. R. Ejtehadi (1 and 2), S. P. Avall (1) and S. S. Plotkin (1) ((1)
Univ. of British Columbia, Canada, (2) Sharif Univ. of Tech., Iran) | Three-body Interactions Improve the Prediction of Rate and Mechanism in
Protein Folding Models | 9 pages, 2 tables and 5 figures | null | 10.1073/pnas.0403486101 | null | q-bio.QM cond-mat.soft physics.bio-ph q-bio.BM | null | Here we study the effects of many-body interactions on rate and mechanism in
protein folding, using the results of molecular dynamics simulations on
numerous coarse-grained C-alpha-model single-domain proteins. After adding
three-body interactions explicitly as a perturbation to a Go-like Hamiltonian
with native pair-wise interactions only, we have found 1) a significantly
increased correlation with experimental phi-values and folding rates, 2) a
stronger correlation of folding rate with contact order, matching the
experimental range in rates when the fraction of three-body energy in the
native state is ~ 20%, and 3) a considerably larger amount of 3-body energy
present in Chymotripsin inhibitor than other proteins studied.
| [
{
"created": "Wed, 14 Jul 2004 19:28:38 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Ejtehadi",
"M. R.",
"",
"1 and 2"
],
[
"Avall",
"S. P.",
""
],
[
"Plotkin",
"S. S.",
""
]
] | Here we study the effects of many-body interactions on rate and mechanism in protein folding, using the results of molecular dynamics simulations on numerous coarse-grained C-alpha-model single-domain proteins. After adding three-body interactions explicitly as a perturbation to a Go-like Hamiltonian with native pair-wise interactions only, we have found 1) a significantly increased correlation with experimental phi-values and folding rates, 2) a stronger correlation of folding rate with contact order, matching the experimental range in rates when the fraction of three-body energy in the native state is ~ 20%, and 3) a considerably larger amount of 3-body energy present in Chymotripsin inhibitor than other proteins studied. |
1805.06795 | Igor P. Omelyan | Igor Omelyan and Yuri Kozitsky | Spatially inhomogeneous population dynamics: beyond the mean field
approximation | 7 pages, 4 figures | null | 10.1088/1751-8121/ab2808 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel method for numerical modeling of spatially inhomogeneous
moment dynamics of populations with nonlocal dispersal and competition in
continuous space. It is based on analytically solvable decompositions of the
time evolution operator for a coupled set of master equations. This has allowed
us -- for the first time in the literature -- to perform moment dynamics
simulations of spatially inhomogeneous systems beyond the mean-field approach
and to calculate the inhomogeneous pair correlation function using the Kirkwood
superposition ansatz. As a result, we revealed a number of new subtle effects,
possible in real populations. Namely, for systems with short-range dispersal
and mid-range competition, strong clustering of entities at small distances
followed by their deep disaggregation at larger separations are observed in the
wavefront of density propagation. For populations in which the competition
range is much shorter than that of dispersal, the pair correlation function
exhibits a long-tail behavior. Remarkably, the latter effect takes place only
due to the spatial inhomogeneity and thus was completely unknown before.
Moreover, both effects get stronger in the direction of propagation. All these
types of behavior are interpreted as a trade-off between the dispersal and
competition in the coexistence of reproductive pair correlations and the
inhomogeneity of the density of the system.
| [
{
"created": "Thu, 17 May 2018 14:18:24 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Jul 2018 15:09:06 GMT",
"version": "v2"
},
{
"created": "Mon, 6 Aug 2018 14:41:04 GMT",
"version": "v3"
},
{
"created": "Tue, 4 Dec 2018 13:32:09 GMT",
"version": "v4"
}
] | 2019-07-24 | [
[
"Omelyan",
"Igor",
""
],
[
"Kozitsky",
"Yuri",
""
]
] | We propose a novel method for numerical modeling of spatially inhomogeneous moment dynamics of populations with nonlocal dispersal and competition in continuous space. It is based on analytically solvable decompositions of the time evolution operator for a coupled set of master equations. This has allowed us -- for the first time in the literature -- to perform moment dynamics simulations of spatially inhomogeneous systems beyond the mean-field approach and to calculate the inhomogeneous pair correlation function using the Kirkwood superposition ansatz. As a result, we revealed a number of new subtle effects, possible in real populations. Namely, for systems with short-range dispersal and mid-range competition, strong clustering of entities at small distances followed by their deep disaggregation at larger separations are observed in the wavefront of density propagation. For populations in which the competition range is much shorter than that of dispersal, the pair correlation function exhibits a long-tail behavior. Remarkably, the latter effect takes place only due to the spatial inhomogeneity and thus was completely unknown before. Moreover, both effects get stronger in the direction of propagation. All these types of behavior are interpreted as a trade-off between the dispersal and competition in the coexistence of reproductive pair correlations and the inhomogeneity of the density of the system. |
1310.3011 | Liane Gabora | Liane Gabora and Nancy Holmes | Dark Side of Creativity - Dangling from a Tassel on the Fabric of
Socially Constructed Reality: Reflections on the Creative Writing Process | 22 pages. Gabora, L., & Holmes, N. (2010). In (A. Cropley, D.
Cropley, J. Kaufman, & M. Runco, Eds.) The Dark Side of Creativity (pp.
277-296). Cambridge UK: Cambridge University Press | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This chapter begins with an investigation into experiences of depression,
alienation, and self-abuse amongst the highly creative. After this journey to
the dark side, it may be uplifting to see that Mother Nature may have a few
tricks up her sleeve to minimize the extent to which we succumb to the negative
aspects of creativity while still benefiting from its riches. Finally, we
discuss another sobering aspect of creativity--the fact that many of our
inventions are dangerous to ourselves, our planet, and the other living things
we share it with--and discuss how a creation intimately reflects the structure
of the worldview(s) of its creators. Although the discussion focuses primarily
on creative writers, we believe that it relevant to creativity in other
domains, particularly the arts, and to a lesser extent science, engineering,
and business.
| [
{
"created": "Fri, 11 Oct 2013 03:07:06 GMT",
"version": "v1"
}
] | 2013-10-14 | [
[
"Gabora",
"Liane",
""
],
[
"Holmes",
"Nancy",
""
]
] | This chapter begins with an investigation into experiences of depression, alienation, and self-abuse amongst the highly creative. After this journey to the dark side, it may be uplifting to see that Mother Nature may have a few tricks up her sleeve to minimize the extent to which we succumb to the negative aspects of creativity while still benefiting from its riches. Finally, we discuss another sobering aspect of creativity--the fact that many of our inventions are dangerous to ourselves, our planet, and the other living things we share it with--and discuss how a creation intimately reflects the structure of the worldview(s) of its creators. Although the discussion focuses primarily on creative writers, we believe that it relevant to creativity in other domains, particularly the arts, and to a lesser extent science, engineering, and business. |
2310.12070 | Alexander Lewanski | Alexander L. Lewanski and Michael C. Grundler and Gideon S. Bradburd | The era of the ARG: an empiricist's guide to ancestral recombination
graphs | 34 pages, 3 figures, 3 tables | null | null | null | q-bio.PE q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | In the presence of recombination, the evolutionary relationships between a
set of sampled genomes cannot be described by a single genealogical tree.
Instead, the genomes are related by a complex, interwoven collection of
genealogies formalized in a structure called an ancestral recombination graph
(ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is
replete with valuable information for addressing diverse questions in
evolutionary biology. Despite its potential utility, technological and
methodological limitations, along with a lack of approachable literature, have
severely restricted awareness and application of ARGs in empirical evolution
research. Excitingly, recent progress in ARG reconstruction and simulation have
made ARG-based approaches feasible for many questions and systems. In this
review, we provide an accessible introduction and exploration of ARGs, survey
recent methodological breakthroughs, and describe the potential for ARGs to
further existing goals and open avenues of inquiry that were previously
inaccessible in evolutionary genomics. Through this discussion, we aim to more
widely disseminate the promise of ARGs in evolutionary genomics and encourage
the broader development and adoption of ARG-based inference.
| [
{
"created": "Wed, 18 Oct 2023 16:04:51 GMT",
"version": "v1"
}
] | 2023-10-19 | [
[
"Lewanski",
"Alexander L.",
""
],
[
"Grundler",
"Michael C.",
""
],
[
"Bradburd",
"Gideon S.",
""
]
] | In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is replete with valuable information for addressing diverse questions in evolutionary biology. Despite its potential utility, technological and methodological limitations, along with a lack of approachable literature, have severely restricted awareness and application of ARGs in empirical evolution research. Excitingly, recent progress in ARG reconstruction and simulation have made ARG-based approaches feasible for many questions and systems. In this review, we provide an accessible introduction and exploration of ARGs, survey recent methodological breakthroughs, and describe the potential for ARGs to further existing goals and open avenues of inquiry that were previously inaccessible in evolutionary genomics. Through this discussion, we aim to more widely disseminate the promise of ARGs in evolutionary genomics and encourage the broader development and adoption of ARG-based inference. |
2307.10833 | Vyacheslav Yukalov | V.I. Yukalov | Selected Topics of Social Physics: Nonequilibrium Systems | Review, 60 pages | Physics 5 (2023) 704--751 | null | null | q-bio.PE physics.bio-ph physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | This review article is the second part of the project ``Selected Topics of
Social Physics". The first part has been devoted to equilibrium systems. The
present part considers nonequilibrium systems. The style of the paper combines
the features of a tutorial and a review, which, from one side, makes it easy to
read for nonspecialists aiming at grasping the basics of social physics, and
from the other side, describes several rather recent original models containing
new ideas that could be of interest to experienced researchers in the field.
The present material is based on the lectures that the author had been giving
during several years at the Swiss Federal Institute of Technology in Zurich
(ETH Zurich).
| [
{
"created": "Thu, 20 Jul 2023 12:53:02 GMT",
"version": "v1"
}
] | 2023-07-22 | [
[
"Yukalov",
"V. I.",
""
]
] | This review article is the second part of the project ``Selected Topics of Social Physics". The first part has been devoted to equilibrium systems. The present part considers nonequilibrium systems. The style of the paper combines the features of a tutorial and a review, which, from one side, makes it easy to read for nonspecialists aiming at grasping the basics of social physics, and from the other side, describes several rather recent original models containing new ideas that could be of interest to experienced researchers in the field. The present material is based on the lectures that the author had been giving during several years at the Swiss Federal Institute of Technology in Zurich (ETH Zurich). |
q-bio/0606001 | Georgy Karev | Georgy P. Karev | On Mathematical Theory of Selection: Discrete-Time Models | 8 pages, 3 figures; submitted to International Conference on Complex
Systems 2006 | null | null | null | q-bio.PE q-bio.QM | null | Mathematical theory of selection systems is developed for a wide class of
dynamical models of inhomogeneous populations with discrete time. The Price
equation and its particular case, the Fisher Fundamental theorem of natural
selection (FTNS), are well known general results of the theory. It is known
that the Price equation being a mathematical identity is not dynamically
sufficient, i.e., it does not allow one to predict changes in the mean of a
trait beyond the immediate response if only the value of covariance of the
trait and fitness at this moment is known. We show that the problem of
dynamically insufficiency for the Price equations and for the FTNS can be
correctly overcome if to study these equations subject to given initial
distribution in framework of exact models of the population dynamics. The
knowledge of the entire distribution at any given instant allows making the
exact prediction for indefinite time and this prediction dramatically depends
on the initial distribution. For these models, the current trait distribution
and hence all statistical characteristics of interest, such as mean values of
the fitness or any trait could be computed effectively at any time moment.
| [
{
"created": "Thu, 1 Jun 2006 16:13:28 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Karev",
"Georgy P.",
""
]
] | Mathematical theory of selection systems is developed for a wide class of dynamical models of inhomogeneous populations with discrete time. The Price equation and its particular case, the Fisher Fundamental theorem of natural selection (FTNS), are well known general results of the theory. It is known that the Price equation being a mathematical identity is not dynamically sufficient, i.e., it does not allow one to predict changes in the mean of a trait beyond the immediate response if only the value of covariance of the trait and fitness at this moment is known. We show that the problem of dynamically insufficiency for the Price equations and for the FTNS can be correctly overcome if to study these equations subject to given initial distribution in framework of exact models of the population dynamics. The knowledge of the entire distribution at any given instant allows making the exact prediction for indefinite time and this prediction dramatically depends on the initial distribution. For these models, the current trait distribution and hence all statistical characteristics of interest, such as mean values of the fitness or any trait could be computed effectively at any time moment. |
2207.00821 | Min Li | Huimin Zhu, Renyi Zhou, Jing Tang, Min Li | PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive
Molecular Generation | null | null | null | null | q-bio.BM cs.LG q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rational design of novel molecules with desired bioactivity is a critical
but challenging task in drug discovery, especially when treating a novel target
family or understudied targets. Here, we propose PGMG, a pharmacophore-guided
deep learning approach for bioactivate molecule generation. Through the
guidance of pharmacophore, PGMG provides a flexible strategy to generate
bioactive molecules with structural diversity in various scenarios using a
trained variational autoencoder. We show that PGMG can generate molecules
matching given pharmacophore models while maintaining a high level of validity,
uniqueness, and novelty. In the case studies, we demonstrate the application of
PGMG to generate bioactive molecules in ligand-based and structure-based drug
de novo design, as well as in lead optimization scenarios. Overall, the
flexibility and effectiveness of PGMG make it a useful tool for accelerating
the drug discovery process.
| [
{
"created": "Sat, 2 Jul 2022 12:31:17 GMT",
"version": "v1"
}
] | 2022-07-05 | [
[
"Zhu",
"Huimin",
""
],
[
"Zhou",
"Renyi",
""
],
[
"Tang",
"Jing",
""
],
[
"Li",
"Min",
""
]
] | The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. Here, we propose PGMG, a pharmacophore-guided deep learning approach for bioactivate molecule generation. Through the guidance of pharmacophore, PGMG provides a flexible strategy to generate bioactive molecules with structural diversity in various scenarios using a trained variational autoencoder. We show that PGMG can generate molecules matching given pharmacophore models while maintaining a high level of validity, uniqueness, and novelty. In the case studies, we demonstrate the application of PGMG to generate bioactive molecules in ligand-based and structure-based drug de novo design, as well as in lead optimization scenarios. Overall, the flexibility and effectiveness of PGMG make it a useful tool for accelerating the drug discovery process. |
1410.3301 | Joseph Crawford | Yihan Sun, Joseph Crawford, Jie Tang, Tijana Milenkovi\'c | Simultaneous Optimization of Both Node and Edge Conservation in Network
Alignment via WAVE | 12 pages, 4 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network alignment can be used to transfer functional knowledge between
conserved regions of different networks. Typically, existing methods use a node
cost function (NCF) to compute similarity between nodes in different networks
and an alignment strategy (AS) to find high-scoring alignments with respect to
the total NCF over all aligned nodes (or node conservation). But, they then
evaluate quality of their alignments via some other measure that is different
than the node conservation measure used to guide the alignment construction
process. Typically, one measures the amount of conserved edges, but only after
alignments are produced. Hence, a recent attempt aimed to directly maximize the
amount of conserved edges while constructing alignments, which improved
alignment accuracy. Here, we aim to directly maximize both node and edge
conservation during alignment construction to further improve alignment
accuracy. For this, we design a novel measure of edge conservation that (unlike
existing measures that treat each conserved edge the same) weighs each
conserved edge so that edges with highly NCF-similar end nodes are favored. As
a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can
optimize any measures of node and edge conservation, and which can be used with
any NCF or combination of multiple NCFs. Using WAVE on top of established
state-of-the-art NCFs leads to superior alignments compared to the existing
methods that optimize only node conservation or only edge conservation or that
treat each conserved edge the same. And while we evaluate WAVE in the
computational biology domain, it is easily applicable in any domain.
| [
{
"created": "Mon, 13 Oct 2014 13:38:30 GMT",
"version": "v1"
}
] | 2014-10-14 | [
[
"Sun",
"Yihan",
""
],
[
"Crawford",
"Joseph",
""
],
[
"Tang",
"Jie",
""
],
[
"Milenković",
"Tijana",
""
]
] | Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Typically, existing methods use a node cost function (NCF) to compute similarity between nodes in different networks and an alignment strategy (AS) to find high-scoring alignments with respect to the total NCF over all aligned nodes (or node conservation). But, they then evaluate quality of their alignments via some other measure that is different than the node conservation measure used to guide the alignment construction process. Typically, one measures the amount of conserved edges, but only after alignments are produced. Hence, a recent attempt aimed to directly maximize the amount of conserved edges while constructing alignments, which improved alignment accuracy. Here, we aim to directly maximize both node and edge conservation during alignment construction to further improve alignment accuracy. For this, we design a novel measure of edge conservation that (unlike existing measures that treat each conserved edge the same) weighs each conserved edge so that edges with highly NCF-similar end nodes are favored. As a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can optimize any measures of node and edge conservation, and which can be used with any NCF or combination of multiple NCFs. Using WAVE on top of established state-of-the-art NCFs leads to superior alignments compared to the existing methods that optimize only node conservation or only edge conservation or that treat each conserved edge the same. And while we evaluate WAVE in the computational biology domain, it is easily applicable in any domain. |
2012.10197 | Mihaela Delcea | Ina Buchholz, Felix Nagel, Annelie Klein, Preshit R. Wagh, Ujjwal M.
Mahajan, Andreas Greinacher, Markus M. Lerch, Julia Mayerle, Mihaela Delcea | The impact of physiological stress conditions on protein structure and
trypsin inhibition of serine protease inhibitor Kazal type 1 (SPINK1) and its
N34S variant | null | null | 10.1016/j.bbapap.2019.140281 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most common mutations in the serine protease inhibitor Kazal type
1 (SPINK1) gene is the N34S variant which is strongly associated with chronic
pancreatitis. Although it is assumed that N34S mutation constitutes a high-risk
factor, the underlying pathologic mechanism is still unknown. In the present
study, we investigated the impact of physiological stress factors on SPINK1
protein structure and trypsin inhibitor function using biophysical methods. Our
circular dichroism spectroscopy data revealed differences in the secondary
structure of SPINK1 and N34S mutant suggesting protein structural changes
induced by the mutation as an impairment that could be disease-relevant. We
further confirmed that both SPINK1 (KD of 0.15 +/- 0.06 nM) and its N34S
variant (KD of 0.08 +/- 0.02 nM) have similar binding affinity and inhibitory
effect towards trypsin as shown by surface plasmon resonance and trypsin
inhibition assay studies, respectively. We found that stress conditions such as
altered ion concentrations (i.e. potassium, calcium), temperature shifts, as
well as environmental pH lead to insignificant differences in trypsin
inhibition between SPINK1 and N34S mutant. However, we have shown that the
environmental pH induces structural changes in both SPINK1 constructs in a
different manner. Our findings suggest protein structural changes in the N34S
variant as an impairment of SPINK1 and environmental pH shift as a trigger that
could play a role in disease progression of pancreatitis.
| [
{
"created": "Fri, 18 Dec 2020 12:43:35 GMT",
"version": "v1"
}
] | 2020-12-21 | [
[
"Buchholz",
"Ina",
""
],
[
"Nagel",
"Felix",
""
],
[
"Klein",
"Annelie",
""
],
[
"Wagh",
"Preshit R.",
""
],
[
"Mahajan",
"Ujjwal M.",
""
],
[
"Greinacher",
"Andreas",
""
],
[
"Lerch",
"Markus M.",
""
],
[
"Mayerle",
"Julia",
""
],
[
"Delcea",
"Mihaela",
""
]
] | One of the most common mutations in the serine protease inhibitor Kazal type 1 (SPINK1) gene is the N34S variant which is strongly associated with chronic pancreatitis. Although it is assumed that N34S mutation constitutes a high-risk factor, the underlying pathologic mechanism is still unknown. In the present study, we investigated the impact of physiological stress factors on SPINK1 protein structure and trypsin inhibitor function using biophysical methods. Our circular dichroism spectroscopy data revealed differences in the secondary structure of SPINK1 and N34S mutant suggesting protein structural changes induced by the mutation as an impairment that could be disease-relevant. We further confirmed that both SPINK1 (KD of 0.15 +/- 0.06 nM) and its N34S variant (KD of 0.08 +/- 0.02 nM) have similar binding affinity and inhibitory effect towards trypsin as shown by surface plasmon resonance and trypsin inhibition assay studies, respectively. We found that stress conditions such as altered ion concentrations (i.e. potassium, calcium), temperature shifts, as well as environmental pH lead to insignificant differences in trypsin inhibition between SPINK1 and N34S mutant. However, we have shown that the environmental pH induces structural changes in both SPINK1 constructs in a different manner. Our findings suggest protein structural changes in the N34S variant as an impairment of SPINK1 and environmental pH shift as a trigger that could play a role in disease progression of pancreatitis. |
0907.4907 | Chandrasekar Kuppusamy | Jane H. Sheeba, V. K. Chandrasekar and M. Lakshmanan | Event--related desynchronization in diffusively coupled oscillator
models | Accepted in Physical Review Letters | null | 10.1103/PhysRevLett.103.074101 | null | q-bio.NC nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We seek explanation for the neurophysiological phenomenon of event related
desynchronization (ERD) by using models of diffusively coupled nonlinear
oscillators. We demonstrate that when the strength of the event is sufficient,
ERD is found to emerge and the accomplishment of a behavioral/functional task
is determined by the nature of the desynchronized state. We illustrate the
phenomenon for the case of limit cycle and chaotic systems. We numerically
demonstrate the occurrence of ERD and provide analytical explanation. We also
discuss possible applications of the observed phenomenon in real physical
systems other than the brain.
| [
{
"created": "Tue, 28 Jul 2009 12:59:53 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Sheeba",
"Jane H.",
""
],
[
"Chandrasekar",
"V. K.",
""
],
[
"Lakshmanan",
"M.",
""
]
] | We seek explanation for the neurophysiological phenomenon of event related desynchronization (ERD) by using models of diffusively coupled nonlinear oscillators. We demonstrate that when the strength of the event is sufficient, ERD is found to emerge and the accomplishment of a behavioral/functional task is determined by the nature of the desynchronized state. We illustrate the phenomenon for the case of limit cycle and chaotic systems. We numerically demonstrate the occurrence of ERD and provide analytical explanation. We also discuss possible applications of the observed phenomenon in real physical systems other than the brain. |
2304.02697 | Zehua Zeng | Zehua Zeng and Hongwu Du | Revolutionizing Single Cell Analysis: The Power of Large Language Models
for Cell Type Annotation | 5 pages, 1 figures | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In recent years, single cell RNA sequencing has become a widely used
technique to study cellular diversity and function. However, accurately
annotating cell types from single cell data has been a challenging task, as it
requires extensive knowledge of cell biology and gene function. The emergence
of large language models such as ChatGPT and New Bing in 2023 has
revolutionized this process by integrating the scientific literature and
providing accurate annotations of cell types. This breakthrough enables
researchers to conduct literature reviews more efficiently and accurately, and
can potentially uncover new insights into cell type annotation. By using
ChatGPT to annotate single cell data, we can relate rare cell type to their
function and reveal specific differentiation trajectories of cell subtypes that
were previously overlooked. This can have important applications in
understanding cancer progression, mammalian development, and stem cell
differentiation, and can potentially lead to the discovery of key cells that
interrupt the differentiation pathway and solve key problems in the life
sciences. Overall, the future of cell type annotation in single cell data looks
promising and the Large Language model will be an important milestone in the
history of single cell analysis.
| [
{
"created": "Wed, 5 Apr 2023 18:45:54 GMT",
"version": "v1"
}
] | 2023-04-07 | [
[
"Zeng",
"Zehua",
""
],
[
"Du",
"Hongwu",
""
]
] | In recent years, single cell RNA sequencing has become a widely used technique to study cellular diversity and function. However, accurately annotating cell types from single cell data has been a challenging task, as it requires extensive knowledge of cell biology and gene function. The emergence of large language models such as ChatGPT and New Bing in 2023 has revolutionized this process by integrating the scientific literature and providing accurate annotations of cell types. This breakthrough enables researchers to conduct literature reviews more efficiently and accurately, and can potentially uncover new insights into cell type annotation. By using ChatGPT to annotate single cell data, we can relate rare cell type to their function and reveal specific differentiation trajectories of cell subtypes that were previously overlooked. This can have important applications in understanding cancer progression, mammalian development, and stem cell differentiation, and can potentially lead to the discovery of key cells that interrupt the differentiation pathway and solve key problems in the life sciences. Overall, the future of cell type annotation in single cell data looks promising and the Large Language model will be an important milestone in the history of single cell analysis. |
2102.09729 | Tijl Grootswagers | Tijl Grootswagers, Amanda K Robinson | Overfitting the literature to one set of stimuli and data | null | Front. Hum. Neurosci. 15:682661 (2021) | 10.3389/fnhum.2021.682661 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The fast-growing field of Computational Cognitive Neuroscience is on track to
meet its first crisis. A large number of papers in this nascent field are
developing and testing novel analysis methods using the same stimuli and
neuroimaging datasets. Publication bias and confirmatory exploration will
result in overfitting to the limited available data. The field urgently needs
to collect more good quality open neuroimaging data using a variety of
experimental stimuli, to test the generalisability of current published
results, and allow for more robust results in future work.
| [
{
"created": "Fri, 19 Feb 2021 04:06:39 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Feb 2021 00:44:36 GMT",
"version": "v2"
}
] | 2021-07-09 | [
[
"Grootswagers",
"Tijl",
""
],
[
"Robinson",
"Amanda K",
""
]
] | The fast-growing field of Computational Cognitive Neuroscience is on track to meet its first crisis. A large number of papers in this nascent field are developing and testing novel analysis methods using the same stimuli and neuroimaging datasets. Publication bias and confirmatory exploration will result in overfitting to the limited available data. The field urgently needs to collect more good quality open neuroimaging data using a variety of experimental stimuli, to test the generalisability of current published results, and allow for more robust results in future work. |
1907.09551 | Stuart Newman | Stuart A. Newman | Cell differentiation: what have we learned in 50 years? | null | null | null | null | q-bio.TO q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I revisit two theories of cell differentiation in multicellular organisms
published a half-century ago, Stuart Kauffman's global gene regulatory dynamics
(GGRD) model and Roy Britten's and Eric Davidson's modular gene regulatory
network (MGRN) model, in light of newer knowledge of mechanisms of gene
regulation in the metazoans (animals). The two models continue to inform
hypotheses and computational studies of differentiation of lineage-adjacent
cell types. However, their shared notion (based on bacterial regulatory
systems) of gene switches and networks built from them, have constrained
progress in understanding the dynamics and evolution of differentiation. Recent
work has described unique write-read-rewrite chromatin-based expression
encoding in eukaryotes, as well metazoan-specific processes of gene activation
and silencing in condensed-phase, enhancer-recruiting regulatory hubs,
employing disordered proteins, including transcription factors, with
context-dependent identities. These findings suggest an evolutionary scenario
in which the origination of differentiation in animals, rather than depending
exclusively on adaptive natural selection, emerged as a consequence of a type
of multicellularity in which the novel metazoan gene regulatory apparatus was
readily mobilized to amplify and exaggerate inherent cell functions of
unicellular ancestors. The plausibility of this hypothesis is illustrated by
the evolution of the developmental role of Grainyhead-like in the formation of
epithelium.
| [
{
"created": "Mon, 22 Jul 2019 20:10:19 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Sep 2019 20:08:20 GMT",
"version": "v2"
}
] | 2019-09-10 | [
[
"Newman",
"Stuart A.",
""
]
] | I revisit two theories of cell differentiation in multicellular organisms published a half-century ago, Stuart Kauffman's global gene regulatory dynamics (GGRD) model and Roy Britten's and Eric Davidson's modular gene regulatory network (MGRN) model, in light of newer knowledge of mechanisms of gene regulation in the metazoans (animals). The two models continue to inform hypotheses and computational studies of differentiation of lineage-adjacent cell types. However, their shared notion (based on bacterial regulatory systems) of gene switches and networks built from them, have constrained progress in understanding the dynamics and evolution of differentiation. Recent work has described unique write-read-rewrite chromatin-based expression encoding in eukaryotes, as well metazoan-specific processes of gene activation and silencing in condensed-phase, enhancer-recruiting regulatory hubs, employing disordered proteins, including transcription factors, with context-dependent identities. These findings suggest an evolutionary scenario in which the origination of differentiation in animals, rather than depending exclusively on adaptive natural selection, emerged as a consequence of a type of multicellularity in which the novel metazoan gene regulatory apparatus was readily mobilized to amplify and exaggerate inherent cell functions of unicellular ancestors. The plausibility of this hypothesis is illustrated by the evolution of the developmental role of Grainyhead-like in the formation of epithelium. |
2010.01591 | Richard Betzel | Richard Betzel | Network neuroscience and the connectomics revolution | 24 pages, 5 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Connectomics and network neuroscience offer quantitative scientific
frameworks for modeling and analyzing networks of structurally and functionally
interacting neurons, neuronal populations, and macroscopic brain areas. This
shift in perspective and emphasis on distributed brain function has provided
fundamental insight into the role played by the brain's network architecture in
cognition, disease, development, and aging. In this chapter, we review the core
concepts of human connectomics at the macroscale. From the construction of
networks using functional and diffusion MRI data, to their subsequent analysis
using methods from network neuroscience, this review highlights key findings,
commonly-used methodologies, and discusses several emerging frontiers in
connectomics.
| [
{
"created": "Sun, 4 Oct 2020 14:36:03 GMT",
"version": "v1"
}
] | 2020-10-06 | [
[
"Betzel",
"Richard",
""
]
] | Connectomics and network neuroscience offer quantitative scientific frameworks for modeling and analyzing networks of structurally and functionally interacting neurons, neuronal populations, and macroscopic brain areas. This shift in perspective and emphasis on distributed brain function has provided fundamental insight into the role played by the brain's network architecture in cognition, disease, development, and aging. In this chapter, we review the core concepts of human connectomics at the macroscale. From the construction of networks using functional and diffusion MRI data, to their subsequent analysis using methods from network neuroscience, this review highlights key findings, commonly-used methodologies, and discusses several emerging frontiers in connectomics. |
1602.05887 | Gianna Vivaldo | Gianna Vivaldo, Elisa Masi, Camilla Pandolfi, Stefano Mancuso, Guido
Caldarelli | Networks of plants: how to measure similarity in vegetable species | 18 pages, 6 figures, 7 tables, 1 section of Supplementary Material | null | null | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the common misconception of nearly static organisms, plants do
interact continuously with the environment and with each other. It is fair to
assume that during their evolution they developed particular features to
overcome problems and to exploit possibilities from environment. In this paper
we introduce various quantitative measures based on recent advancements in
complex network theory that allow to measure the effective similarities of
various species. By using this approach on the similarity in fruit-typology
ecological traits we obtain a clear plant classification in a way similar to
traditional taxonomic classification. This result is not trivial, since a
similar analysis done on the basis of diaspore morphological properties do not
provide any clear parameter to classify plants species. Complex network theory
can then be used in order to determine which feature amongst many can be used
to distinguish scope and possibly evolution of plants. Future uses of this
approach range from functional classification to quantitative determination of
plant communities in nature.
| [
{
"created": "Thu, 18 Feb 2016 17:40:48 GMT",
"version": "v1"
}
] | 2016-02-19 | [
[
"Vivaldo",
"Gianna",
""
],
[
"Masi",
"Elisa",
""
],
[
"Pandolfi",
"Camilla",
""
],
[
"Mancuso",
"Stefano",
""
],
[
"Caldarelli",
"Guido",
""
]
] | Despite the common misconception of nearly static organisms, plants do interact continuously with the environment and with each other. It is fair to assume that during their evolution they developed particular features to overcome problems and to exploit possibilities from environment. In this paper we introduce various quantitative measures based on recent advancements in complex network theory that allow to measure the effective similarities of various species. By using this approach on the similarity in fruit-typology ecological traits we obtain a clear plant classification in a way similar to traditional taxonomic classification. This result is not trivial, since a similar analysis done on the basis of diaspore morphological properties do not provide any clear parameter to classify plants species. Complex network theory can then be used in order to determine which feature amongst many can be used to distinguish scope and possibly evolution of plants. Future uses of this approach range from functional classification to quantitative determination of plant communities in nature. |
2004.14829 | Korbinian Schreiber | K. Schreiber, T. C. Wunderlich, C. Pehle, M. A. Petrovici, J.
Schemmel, and K. Meier | Closed-loop experiments on the BrainScaleS-2 architecture | Neuro-inspired Computational Elements Workshop (NICE 2020). arXiv
admin note: text overlap with arXiv:1912.12980 | null | 10.1145/3381755.3381776 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution of biological brains has always been contingent on their
embodiment within their respective environments, in which survival required
appropriate navigation and manipulation skills. Studying such interactions thus
represents an important aspect of computational neuroscience and, by extension,
a topic of interest for neuromorphic engineering. Here, we present three
examples of embodiment on the BrainScaleS-2 architecture, in which dynamical
timescales of both agents and environment are accelerated by several orders of
magnitude with respect to their biological archetypes.
| [
{
"created": "Wed, 29 Apr 2020 12:15:24 GMT",
"version": "v1"
}
] | 2020-05-01 | [
[
"Schreiber",
"K.",
""
],
[
"Wunderlich",
"T. C.",
""
],
[
"Pehle",
"C.",
""
],
[
"Petrovici",
"M. A.",
""
],
[
"Schemmel",
"J.",
""
],
[
"Meier",
"K.",
""
]
] | The evolution of biological brains has always been contingent on their embodiment within their respective environments, in which survival required appropriate navigation and manipulation skills. Studying such interactions thus represents an important aspect of computational neuroscience and, by extension, a topic of interest for neuromorphic engineering. Here, we present three examples of embodiment on the BrainScaleS-2 architecture, in which dynamical timescales of both agents and environment are accelerated by several orders of magnitude with respect to their biological archetypes. |
2303.01579 | Wolfram M\"obius | Thomas Tunstall, Tim Rogers, Wolfram M\"obius | Assisted percolation of slow-spreading mutants in heterogeneous
environments | null | null | null | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Environmental heterogeneity can drive genetic heterogeneity in expanding
populations; mutant strains may emerge that trade overall growth rate for an
improved ability to survive in patches that are hostile to the wild type. This
evolutionary dynamic is of practical importance when seeking to prevent the
emergence of damaging traits. We show that a sub-critical slow-spreading mutant
can attain dominance even when the density of patches is below their
percolation threshold and predict this transition using geometrical arguments.
This work demonstrates a phenomenon of ''assisted percolation'', where one
sub-critical process assists another to achieve super-criticality.
| [
{
"created": "Thu, 2 Mar 2023 21:13:52 GMT",
"version": "v1"
}
] | 2023-03-06 | [
[
"Tunstall",
"Thomas",
""
],
[
"Rogers",
"Tim",
""
],
[
"Möbius",
"Wolfram",
""
]
] | Environmental heterogeneity can drive genetic heterogeneity in expanding populations; mutant strains may emerge that trade overall growth rate for an improved ability to survive in patches that are hostile to the wild type. This evolutionary dynamic is of practical importance when seeking to prevent the emergence of damaging traits. We show that a sub-critical slow-spreading mutant can attain dominance even when the density of patches is below their percolation threshold and predict this transition using geometrical arguments. This work demonstrates a phenomenon of ''assisted percolation'', where one sub-critical process assists another to achieve super-criticality. |
1711.04870 | Adam Noel | Adam Noel and Yuting Fang and Nan Yang and Dimitrios Makrakis and
Andrew W. Eckford | Using Game Theory for Real-Time Behavioural Dynamics in Microscopic
Populations with Noisy Signalling | 10 pages, 10 figures, 1 table. Submitted for publication | null | null | null | q-bio.CB cs.IT math.IT physics.bio-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the application of game theory to understand noisy
real-time signalling and the resulting behavioural dynamics in microscopic
populations such as bacteria and other cells. It presents a bridge between the
fields of molecular communication and microscopic game theory. Molecular
communication uses conventional communication engineering theory and techniques
to study and design systems that use chemical molecules as information
carriers. Microscopic game theory models interactions within and between
populations of cells and microorganisms. Integrating these two fields provides
unique opportunities to understand and control microscopic populations that
have imperfect signal propagation. Two examples, namely bacteria quorum sensing
and tumour cell signalling, are presented with potential games to demonstrate
the application of this approach. Finally, a case study of bacteria resource
sharing demonstrates how noisy signalling can alter the distribution of
behaviour.
| [
{
"created": "Mon, 13 Nov 2017 21:54:49 GMT",
"version": "v1"
},
{
"created": "Wed, 16 May 2018 13:07:20 GMT",
"version": "v2"
},
{
"created": "Mon, 4 Feb 2019 23:09:02 GMT",
"version": "v3"
}
] | 2019-02-06 | [
[
"Noel",
"Adam",
""
],
[
"Fang",
"Yuting",
""
],
[
"Yang",
"Nan",
""
],
[
"Makrakis",
"Dimitrios",
""
],
[
"Eckford",
"Andrew W.",
""
]
] | This paper introduces the application of game theory to understand noisy real-time signalling and the resulting behavioural dynamics in microscopic populations such as bacteria and other cells. It presents a bridge between the fields of molecular communication and microscopic game theory. Molecular communication uses conventional communication engineering theory and techniques to study and design systems that use chemical molecules as information carriers. Microscopic game theory models interactions within and between populations of cells and microorganisms. Integrating these two fields provides unique opportunities to understand and control microscopic populations that have imperfect signal propagation. Two examples, namely bacteria quorum sensing and tumour cell signalling, are presented with potential games to demonstrate the application of this approach. Finally, a case study of bacteria resource sharing demonstrates how noisy signalling can alter the distribution of behaviour. |
0809.1968 | Quan-Xing Liu | Quan-Xing Liu, Rong-Hua Wang and Zhen Jin | Persistence, extinction and spatio-temporal synchronization of SIRS
cellular automata models | 12pages | J. Stat. Mech. (2009) P07007 | 10.1088/1742-5468/2009/07/P07007 | null | q-bio.PE nlin.CG nlin.PS physics.soc-ph q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatially explicit models have been widely used in today's mathematical
ecology and epidemiology to study persistence and extinction of populations as
well as their spatial patterns. Here we extend the earlier work--static
dispersal between neighbouring individuals to mobility of individuals as well
as multi-patches environment. As is commonly found, the basic reproductive
ratio is maximized for the evolutionary stable strategy (ESS) on diseases'
persistence in mean-field theory. This has important implications, as it
implies that for a wide range of parameters that infection rate will tend
maximum. This is opposite with present results obtained in spatial explicit
models that infection rate is limited by upper bound. We observe the emergence
of trade-offs of extinction and persistence on the parameters of the infection
period and infection rate and show the extinction time having a linear
relationship with respect to system size. We further find that the higher
mobility can pronouncedly promote the persistence of spread of epidemics, i.e.,
the phase transition occurs from extinction domain to persistence domain, and
the spirals' wavelength increases as the mobility increasing and ultimately, it
will saturate at a certain value. Furthermore, for multi-patches case, we find
that the lower coupling strength leads to anti-phase oscillation of infected
fraction, while higher coupling strength corresponds to in-phase oscillation.
| [
{
"created": "Thu, 11 Sep 2008 12:05:25 GMT",
"version": "v1"
}
] | 2009-07-01 | [
[
"Liu",
"Quan-Xing",
""
],
[
"Wang",
"Rong-Hua",
""
],
[
"Jin",
"Zhen",
""
]
] | Spatially explicit models have been widely used in today's mathematical ecology and epidemiology to study persistence and extinction of populations as well as their spatial patterns. Here we extend the earlier work--static dispersal between neighbouring individuals to mobility of individuals as well as multi-patches environment. As is commonly found, the basic reproductive ratio is maximized for the evolutionary stable strategy (ESS) on diseases' persistence in mean-field theory. This has important implications, as it implies that for a wide range of parameters that infection rate will tend maximum. This is opposite with present results obtained in spatial explicit models that infection rate is limited by upper bound. We observe the emergence of trade-offs of extinction and persistence on the parameters of the infection period and infection rate and show the extinction time having a linear relationship with respect to system size. We further find that the higher mobility can pronouncedly promote the persistence of spread of epidemics, i.e., the phase transition occurs from extinction domain to persistence domain, and the spirals' wavelength increases as the mobility increasing and ultimately, it will saturate at a certain value. Furthermore, for multi-patches case, we find that the lower coupling strength leads to anti-phase oscillation of infected fraction, while higher coupling strength corresponds to in-phase oscillation. |
0910.3226 | Filippo Posta | Filippo Posta, Tom Chou | A mathematical model of intercellular signaling during epithelial wound
healing | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent experiments in epithelial wound healing have demonstrated the
necessity of Mitogen-activated protein kinase (MAPK) for coordinated cell
movement after damage. This MAPK activity is characterized by two wave-like
phenomena. One MAPK "wave" that originates immediately after injury, propagates
deep into the cell layer, and then rebounds back to the wound interface. After
this initial MAPK activity has largely disappeared, a second MAPK front
propagates slowly from the wound interface and continues into the tissue,
maintaining a sustained level of MAPK activity throughout the cell layer. It
has been suggested that the first wave is initiated by reactive oxygen species
(ROS) generated at the time of injury. In this paper, we develop a minimal
mechanistic diffusion-convection model that reproduces the observed behavior.
The main ingredients of our model are a competition between ligand (e.g.,
Epithelial Growth Factor) and ROS for the activation of Epithelial Growth
Factor Receptor (EGFR) and a second MAPK wave that is sustained by stresses
induced by the slow cell movement that closes the wound. We explore the
mathematical properties of the model in connection with the bistability of the
MAPK cascade and look for traveling wave solutions consistent with the
experimentally observed MAPK activity patterns.
| [
{
"created": "Fri, 16 Oct 2009 20:50:28 GMT",
"version": "v1"
}
] | 2009-10-20 | [
[
"Posta",
"Filippo",
""
],
[
"Chou",
"Tom",
""
]
] | Recent experiments in epithelial wound healing have demonstrated the necessity of Mitogen-activated protein kinase (MAPK) for coordinated cell movement after damage. This MAPK activity is characterized by two wave-like phenomena. One MAPK "wave" that originates immediately after injury, propagates deep into the cell layer, and then rebounds back to the wound interface. After this initial MAPK activity has largely disappeared, a second MAPK front propagates slowly from the wound interface and continues into the tissue, maintaining a sustained level of MAPK activity throughout the cell layer. It has been suggested that the first wave is initiated by reactive oxygen species (ROS) generated at the time of injury. In this paper, we develop a minimal mechanistic diffusion-convection model that reproduces the observed behavior. The main ingredients of our model are a competition between ligand (e.g., Epithelial Growth Factor) and ROS for the activation of Epithelial Growth Factor Receptor (EGFR) and a second MAPK wave that is sustained by stresses induced by the slow cell movement that closes the wound. We explore the mathematical properties of the model in connection with the bistability of the MAPK cascade and look for traveling wave solutions consistent with the experimentally observed MAPK activity patterns. |
2211.08527 | Robin Gutzen | Robin Gutzen, Giulia De Bonis, Chiara De Luca, Elena Pastorelli,
Cristiano Capone, Anna Letizia Allegra Mascaro, Francesco Resta, Arnau
Manasanch, Francesco Saverio Pavone, Maria V. Sanchez-Vives, Maurizio Mattia,
Sonja Gr\"un, Pier Stanislao Paolucci, Michael Denker | Comparing apples to apples -- Using a modular and adaptable analysis
pipeline to compare slow cerebral rhythms across heterogeneous datasets | null | null | 10.1016/j.crmeth.2023.100681 | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Neuroscience is moving towards a more integrative discipline, where
understanding brain function requires consolidating the accumulated evidence
seen across experiments, species, and measurement techniques. A remaining
challenge on that path is integrating such heterogeneous data into analysis
workflows such that consistent and comparable conclusions can be distilled as
an experimental basis for models and theories. Here, we propose a solution in
the context of slow wave activity ($<1$ Hz), which occurs during unconscious
brain states like sleep and general anesthesia, and is observed across diverse
experimental approaches. We address the issue of integrating and comparing
heterogeneous data by conceptualizing a general pipeline design that is
adaptable to a variety of inputs and applications. Furthermore, we present the
Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable
software implementation to perform broad, detailed, and rigorous comparisons of
slow wave characteristics across multiple, openly available ECoG and calcium
imaging datasets.
| [
{
"created": "Tue, 15 Nov 2022 21:54:40 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Feb 2023 17:03:16 GMT",
"version": "v2"
}
] | 2024-01-09 | [
[
"Gutzen",
"Robin",
""
],
[
"De Bonis",
"Giulia",
""
],
[
"De Luca",
"Chiara",
""
],
[
"Pastorelli",
"Elena",
""
],
[
"Capone",
"Cristiano",
""
],
[
"Mascaro",
"Anna Letizia Allegra",
""
],
[
"Resta",
"Francesco",
""
],
[
"Manasanch",
"Arnau",
""
],
[
"Pavone",
"Francesco Saverio",
""
],
[
"Sanchez-Vives",
"Maria V.",
""
],
[
"Mattia",
"Maurizio",
""
],
[
"Grün",
"Sonja",
""
],
[
"Paolucci",
"Pier Stanislao",
""
],
[
"Denker",
"Michael",
""
]
] | Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow wave activity ($<1$ Hz), which occurs during unconscious brain states like sleep and general anesthesia, and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow wave characteristics across multiple, openly available ECoG and calcium imaging datasets. |
1708.08560 | David K. Lubensky | Jeremy Hadidjojo and David K. Lubensky | Spontaneous Chiral Symmetry Breaking in Planar Polarized Epithelia | null | null | null | null | q-bio.TO physics.bio-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most animal body plans have some degree of left-right asymmetry. This
chirality at the tissue and organ level is often assumed to originate from the
intrinsic handedness of biological molecules. How this handedness might be
transferred from molecules to tissues during development, however, is not well
understood. Here we explore an alternative paradigm where tissue chirality
results from spontaneous symmetry breaking at the cellular scale, with
molecular chirality acting only as a weak bias that ensures that one handedness
predominates over the other. Specifically, we show that systems capable of
generating planar polarity, found in many epithelial tissues, can also
generically break left-right symmetry, and we identify the key interaction
parameters that must be varied to access the chiral phase. In addition to a
chiral polar phase corresponding to one found in liquid crystal films, a
two-dimensional chiral nematic phase with no liquid crystal analog is also
possible. Our results have clear implications for the interpretation of mutant
phenotypes, especially in certain Drosophila epithelia.
| [
{
"created": "Mon, 28 Aug 2017 23:38:19 GMT",
"version": "v1"
}
] | 2017-08-30 | [
[
"Hadidjojo",
"Jeremy",
""
],
[
"Lubensky",
"David K.",
""
]
] | Most animal body plans have some degree of left-right asymmetry. This chirality at the tissue and organ level is often assumed to originate from the intrinsic handedness of biological molecules. How this handedness might be transferred from molecules to tissues during development, however, is not well understood. Here we explore an alternative paradigm where tissue chirality results from spontaneous symmetry breaking at the cellular scale, with molecular chirality acting only as a weak bias that ensures that one handedness predominates over the other. Specifically, we show that systems capable of generating planar polarity, found in many epithelial tissues, can also generically break left-right symmetry, and we identify the key interaction parameters that must be varied to access the chiral phase. In addition to a chiral polar phase corresponding to one found in liquid crystal films, a two-dimensional chiral nematic phase with no liquid crystal analog is also possible. Our results have clear implications for the interpretation of mutant phenotypes, especially in certain Drosophila epithelia. |
1808.03359 | Ekkehard Ullner | Antonio Politi, Ekkehard Ullner, and Alessandro Torcini | Collective irregular dynamics in balanced networks of leaky
integrate-and-fire neurons | 12 pages, 13 figures | Eur. Phys. J. Special Topics 227, 1185 (2018) | 10.1140/epjst/e2018-00079-7 | null | q-bio.NC cond-mat.dis-nn nlin.AO nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We extensively explore networks of weakly unbalanced, leaky
integrate-and-fire (LIF) neurons for different coupling strength, connectivity,
and by varying the degree of refractoriness, as well as the delay in the spike
transmission. We find that the neural network does not only exhibit a
microscopic (single-neuron) stochastic-like evolution, but also a collective
irregular dynamics (CID). Our analysis is based on the computation of a
suitable order parameter, typically used to characterize synchronization
phenomena and on a detailed scaling analysis (i.e. simulations of different
network sizes). As a result, we can conclude that CID is a true thermodynamic
phase, intrinsically different from the standard asynchronous regime.
| [
{
"created": "Thu, 9 Aug 2018 22:03:32 GMT",
"version": "v1"
}
] | 2018-12-14 | [
[
"Politi",
"Antonio",
""
],
[
"Ullner",
"Ekkehard",
""
],
[
"Torcini",
"Alessandro",
""
]
] | We extensively explore networks of weakly unbalanced, leaky integrate-and-fire (LIF) neurons for different coupling strength, connectivity, and by varying the degree of refractoriness, as well as the delay in the spike transmission. We find that the neural network does not only exhibit a microscopic (single-neuron) stochastic-like evolution, but also a collective irregular dynamics (CID). Our analysis is based on the computation of a suitable order parameter, typically used to characterize synchronization phenomena and on a detailed scaling analysis (i.e. simulations of different network sizes). As a result, we can conclude that CID is a true thermodynamic phase, intrinsically different from the standard asynchronous regime. |
1510.03194 | Jicun Wang-Michelitsch | Jicun Wang-Michelitsch, Thomas M. Michelitsch | The high osmotic pressure in a lens fiber as a driving force for the
development of senile cortical cataract | 8 pages, figures | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In lens cataract, the clouding change in lens leads to a decline of
transparency of part of the lens. There are three types of senile cataract:
cortical cataract, nuclear cataract, and posterior/anterior sub-capsular
cataract. The most common senile cataract is cortical cataract. For
understanding cortical cataract, the pathology and the causing factors in
cortical cataract are analyzed. The clouding change in senile cortical cataract
begins from the edge of the lens and develops progressively to lens centre. The
pathology of clouding change in cortical cataract is characterized by
disruption of some lens fibers, swelling of some other fibers, and deposition
of water between fibers. Based on the property of a lens fiber, we propose here
a hypothesis on the mechanism of development of senile cortical cataract. I.
Cortical cataract is triggered by disruption of a cortical lens fiber as a
result of injury. The disrupted fiber will release water and crystallin
proteins. II. Neighbor fibers can absorb this water due to high intracellular
osmotic pressure (IOP) and become swollen. Swelling makes a fiber be stiff and
have increased risk to disrupt when it is curved during the accommodation of
the lens. These fibers will release water again when they disrupt, and the
water will make more local fibers swelling. In this way, the local fibers
become swollen and then disrupt successively. III. The successive swellings and
disruptions of local fibers result in enlargement of a clouding change in lens.
Since the fibers on the outer part of lens cortex have higher risk to be
injured than that in the inner part, a clouding change starts from the edge of
the lens. In conclusion, the progressive development of senile cortical
cataract is a result of successive swellings and disruptions of local lens
fibers, and this is driven by the high IOP in a lens fiber.
| [
{
"created": "Mon, 12 Oct 2015 09:23:09 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Feb 2018 11:07:55 GMT",
"version": "v2"
}
] | 2018-02-06 | [
[
"Wang-Michelitsch",
"Jicun",
""
],
[
"Michelitsch",
"Thomas M.",
""
]
] | In lens cataract, the clouding change in lens leads to a decline of transparency of part of the lens. There are three types of senile cataract: cortical cataract, nuclear cataract, and posterior/anterior sub-capsular cataract. The most common senile cataract is cortical cataract. For understanding cortical cataract, the pathology and the causing factors in cortical cataract are analyzed. The clouding change in senile cortical cataract begins from the edge of the lens and develops progressively to lens centre. The pathology of clouding change in cortical cataract is characterized by disruption of some lens fibers, swelling of some other fibers, and deposition of water between fibers. Based on the property of a lens fiber, we propose here a hypothesis on the mechanism of development of senile cortical cataract. I. Cortical cataract is triggered by disruption of a cortical lens fiber as a result of injury. The disrupted fiber will release water and crystallin proteins. II. Neighbor fibers can absorb this water due to high intracellular osmotic pressure (IOP) and become swollen. Swelling makes a fiber be stiff and have increased risk to disrupt when it is curved during the accommodation of the lens. These fibers will release water again when they disrupt, and the water will make more local fibers swelling. In this way, the local fibers become swollen and then disrupt successively. III. The successive swellings and disruptions of local fibers result in enlargement of a clouding change in lens. Since the fibers on the outer part of lens cortex have higher risk to be injured than that in the inner part, a clouding change starts from the edge of the lens. In conclusion, the progressive development of senile cortical cataract is a result of successive swellings and disruptions of local lens fibers, and this is driven by the high IOP in a lens fiber. |
2405.05091 | Rudy Arthur | Rudy Arthur, Arwen E. Nicholson and Nathan J. Mayne | What doesn't kill Gaia makes her stronger | 12 pages, 7 figures | null | null | null | q-bio.PE astro-ph.EP physics.pop-ph | http://creativecommons.org/licenses/by/4.0/ | Life on Earth has experienced numerous upheavals over its approximately 4
billion year history. In previous work we have discussed how interruptions to
stability lead, on average, to increases in habitability over time, a tendency
we called Entropic Gaia. Here we continue this exploration, working with the
Tangled Nature Model of co-evolution, to understand how the evolutionary
history of life is shaped by periods of acute environmental stress. We find
that while these periods of stress pose a risk of complete extinction, they
also create opportunities for evolutionary exploration which would otherwise be
impossible, leading to more populous and stable states among the survivors than
in alternative histories without a stress period. We also study how the
duration, repetition and number of refugia into which life escapes during the
perturbation affects the final outcome. The model results are discussed in
relation to both Earth history and the search for alien life.
| [
{
"created": "Wed, 8 May 2024 14:40:45 GMT",
"version": "v1"
}
] | 2024-05-09 | [
[
"Arthur",
"Rudy",
""
],
[
"Nicholson",
"Arwen E.",
""
],
[
"Mayne",
"Nathan J.",
""
]
] | Life on Earth has experienced numerous upheavals over its approximately 4 billion year history. In previous work we have discussed how interruptions to stability lead, on average, to increases in habitability over time, a tendency we called Entropic Gaia. Here we continue this exploration, working with the Tangled Nature Model of co-evolution, to understand how the evolutionary history of life is shaped by periods of acute environmental stress. We find that while these periods of stress pose a risk of complete extinction, they also create opportunities for evolutionary exploration which would otherwise be impossible, leading to more populous and stable states among the survivors than in alternative histories without a stress period. We also study how the duration, repetition and number of refugia into which life escapes during the perturbation affects the final outcome. The model results are discussed in relation to both Earth history and the search for alien life. |
2305.18370 | Saurabh Sihag | Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro | Explainable Brain Age Prediction using coVariance Neural Networks | Camera ready version for NeurIPS 2023. arXiv admin note: substantial
text overlap with arXiv:2305.01807 | null | null | null | q-bio.QM cs.LG stat.AP | http://creativecommons.org/licenses/by/4.0/ | In computational neuroscience, there has been an increased interest in
developing machine learning algorithms that leverage brain imaging data to
provide estimates of "brain age" for an individual. Importantly, the
discordance between brain age and chronological age (referred to as "brain age
gap") can capture accelerated aging due to adverse health conditions and
therefore, can reflect increased vulnerability towards neurological disease or
cognitive impairments. However, widespread adoption of brain age for clinical
decision support has been hindered due to lack of transparency and
methodological justifications in most existing brain age prediction algorithms.
In this paper, we leverage coVariance neural networks (VNN) to propose an
explanation-driven and anatomically interpretable framework for brain age
prediction using cortical thickness features. Specifically, our brain age
prediction framework extends beyond the coarse metric of brain age gap in
Alzheimer's disease (AD) and we make two important observations: (i) VNNs can
assign anatomical interpretability to elevated brain age gap in AD by
identifying contributing brain regions, (ii) the interpretability offered by
VNNs is contingent on their ability to exploit specific eigenvectors of the
anatomical covariance matrix. Together, these observations facilitate an
explainable and anatomically interpretable perspective to the task of brain age
prediction.
| [
{
"created": "Sat, 27 May 2023 22:28:25 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Sep 2023 23:03:07 GMT",
"version": "v2"
},
{
"created": "Fri, 27 Oct 2023 17:21:37 GMT",
"version": "v3"
}
] | 2023-10-30 | [
[
"Sihag",
"Saurabh",
""
],
[
"Mateos",
"Gonzalo",
""
],
[
"McMillan",
"Corey",
""
],
[
"Ribeiro",
"Alejandro",
""
]
] | In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap") can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) and we make two important observations: (i) VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions, (ii) the interpretability offered by VNNs is contingent on their ability to exploit specific eigenvectors of the anatomical covariance matrix. Together, these observations facilitate an explainable and anatomically interpretable perspective to the task of brain age prediction. |
1610.08227 | Franz Chouly | Marine Bruneau (LMB), Thierry Mottet, Serge Moulin, Ma\"el Kerbiriou
(LMB), Franz Chouly (LMB), St\'ephane Chretien (NPL), Christophe Guyeux | A clustering tool for nucleotide sequences using Laplacian Eigenmaps and
Gaussian Mixture Models | null | null | null | null | q-bio.QM math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new procedure for clustering nucleotide sequences based on the
"Laplacian Eigenmaps" and Gaussian Mixture modelling. This proposal is then
applied to a set of 100 DNA sequences from the mitochondrially encoded NADH
dehydrogenase 3 (ND3) gene of a collection of Platyhelminthes and Nematoda
species. The resulting clusters are then shown to be consistent with the gene
phylogenetic tree computed using a maximum likelihood approach. This comparison
shows in particular that the clustering produced by the methodology combining
Laplacian Eigenmaps with Gaussian Mixture models is coherent with the phylogeny
as well as with the NCBI taxonomy. We also developed a Python package for this
procedure which is available online.
| [
{
"created": "Wed, 26 Oct 2016 08:43:58 GMT",
"version": "v1"
}
] | 2016-10-27 | [
[
"Bruneau",
"Marine",
"",
"LMB"
],
[
"Mottet",
"Thierry",
"",
"LMB"
],
[
"Moulin",
"Serge",
"",
"LMB"
],
[
"Kerbiriou",
"Maël",
"",
"LMB"
],
[
"Chouly",
"Franz",
"",
"LMB"
],
[
"Chretien",
"Stéphane",
"",
"NPL"
],
[
"Guyeux",
"Christophe",
""
]
] | We propose a new procedure for clustering nucleotide sequences based on the "Laplacian Eigenmaps" and Gaussian Mixture modelling. This proposal is then applied to a set of 100 DNA sequences from the mitochondrially encoded NADH dehydrogenase 3 (ND3) gene of a collection of Platyhelminthes and Nematoda species. The resulting clusters are then shown to be consistent with the gene phylogenetic tree computed using a maximum likelihood approach. This comparison shows in particular that the clustering produced by the methodology combining Laplacian Eigenmaps with Gaussian Mixture models is coherent with the phylogeny as well as with the NCBI taxonomy. We also developed a Python package for this procedure which is available online. |
1608.02038 | Eran Elhaik | Ranajit Das, Paul Wexler, Mehdi Pirooznia, and Eran Elhaik | Responding to an enquiry concerning the geographic population structure
(GPS) approach and the origin of Ashkenazic Jews - a reply to Flegontov et al | 32 pages, 2 figures, 2 tables | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recently, we investigated the geographical origins of Ashkenazic Jews (AJs)
and their native language Yiddish by applying a biogeographical tool, the
Geographic Population Structure (GPS), to a cohort of 367 exclusively
Yiddish-speaking and multilingual AJs genotyped on the Genochip microarray. GPS
localized most AJs along major ancient trade routes in northeastern Turkey
adjacent to primeval villages with names that may be derived from the word
"Ashkenaz." These findings were compatible with the hypothesis of an
Irano-Turko-Slavic origin for AJs and a Slavic origin for Yiddish and at odds
with the Rhineland hypothesis advocating a German origin of both. Our approach
has been recently adopted by Flegontov et al. (2016a) to trace the origin of
the Siberian Ket people and their language. Recently, Flegontov et al. (2016b)
have raised several questions concerning the accuracy of the Genochip
microarray and GPS, specifically in relation to AJs and Yiddish. Although many
of these issues have been addressed in our previous papers, we take this
opportunity to clarify the principles of the GPS approach, review the recent
biogeographical and ancient DNA findings regarding AJs, and comment on the
origin of Yiddish.
| [
{
"created": "Fri, 5 Aug 2016 22:54:34 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Aug 2016 12:09:23 GMT",
"version": "v2"
}
] | 2016-08-18 | [
[
"Das",
"Ranajit",
""
],
[
"Wexler",
"Paul",
""
],
[
"Pirooznia",
"Mehdi",
""
],
[
"Elhaik",
"Eran",
""
]
] | Recently, we investigated the geographical origins of Ashkenazic Jews (AJs) and their native language Yiddish by applying a biogeographical tool, the Geographic Population Structure (GPS), to a cohort of 367 exclusively Yiddish-speaking and multilingual AJs genotyped on the Genochip microarray. GPS localized most AJs along major ancient trade routes in northeastern Turkey adjacent to primeval villages with names that may be derived from the word "Ashkenaz." These findings were compatible with the hypothesis of an Irano-Turko-Slavic origin for AJs and a Slavic origin for Yiddish and at odds with the Rhineland hypothesis advocating a German origin of both. Our approach has been recently adopted by Flegontov et al. (2016a) to trace the origin of the Siberian Ket people and their language. Recently, Flegontov et al. (2016b) have raised several questions concerning the accuracy of the Genochip microarray and GPS, specifically in relation to AJs and Yiddish. Although many of these issues have been addressed in our previous papers, we take this opportunity to clarify the principles of the GPS approach, review the recent biogeographical and ancient DNA findings regarding AJs, and comment on the origin of Yiddish. |
2004.13452 | Sebasti\'an Contreras | Sebastian Contreras, H. Andres Villavicencio, David Medina-Ortiz, Juan
Pablo Biron-Lattes, Alvaro Olivera-Nappa | A multi-group SEIRA model for the spread of COVID-19 among heterogeneous
populations | null | Chaos Solitons Fractals 136 (2020) 109925 | 10.1016/j.chaos.2020.109925 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The outbreak and propagation of COVID-19 have posed a considerable challenge
to modern society. In particular, the different restrictive actions taken by
governments to prevent the spread of the virus have changed the way humans
interact and conceive interaction. Due to geographical, behavioral, or economic
factors, different sub-groups among a population are more (or less) likely to
interact, and thus to spread/acquire the virus. In this work, we present a
general multi-group SEIRA model for representing the spread of COVID-19 among a
heterogeneous population and test it in a numerical case of study. By
highlighting its applicability and the ease with which its general formulation
can be adapted to particular studies, we expect our model to lead us to a
better understanding of the evolution of this pandemic and to better
public-health policies to control it.
| [
{
"created": "Tue, 28 Apr 2020 12:11:50 GMT",
"version": "v1"
}
] | 2021-10-05 | [
[
"Contreras",
"Sebastian",
""
],
[
"Villavicencio",
"H. Andres",
""
],
[
"Medina-Ortiz",
"David",
""
],
[
"Biron-Lattes",
"Juan Pablo",
""
],
[
"Olivera-Nappa",
"Alvaro",
""
]
] | The outbreak and propagation of COVID-19 have posed a considerable challenge to modern society. In particular, the different restrictive actions taken by governments to prevent the spread of the virus have changed the way humans interact and conceive interaction. Due to geographical, behavioral, or economic factors, different sub-groups among a population are more (or less) likely to interact, and thus to spread/acquire the virus. In this work, we present a general multi-group SEIRA model for representing the spread of COVID-19 among a heterogeneous population and test it in a numerical case of study. By highlighting its applicability and the ease with which its general formulation can be adapted to particular studies, we expect our model to lead us to a better understanding of the evolution of this pandemic and to better public-health policies to control it. |
1209.1371 | Ralph Brinks | Ralph Brinks | On the age-, time- and migration dependent dynamics of diseases | 13 pages, 4 figures | null | null | null | q-bio.PE q-bio.QM stat.ME stat.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper generalizes a previously published differential equation that
describes the relation between the age-specific incidence, remission, and
mortality of a disease with its prevalence. The underlying model is a simple
compartment model with three states (illness-death model). In contrast to the
former work, migration- and calendar time-effects are included. As an
application of the theoretical findings, a hypothetical example of an
irreversible disease is treated.
| [
{
"created": "Thu, 6 Sep 2012 19:04:35 GMT",
"version": "v1"
}
] | 2012-09-07 | [
[
"Brinks",
"Ralph",
""
]
] | This paper generalizes a previously published differential equation that describes the relation between the age-specific incidence, remission, and mortality of a disease with its prevalence. The underlying model is a simple compartment model with three states (illness-death model). In contrast to the former work, migration- and calendar time-effects are included. As an application of the theoretical findings, a hypothetical example of an irreversible disease is treated. |
2202.01316 | Massimiliano Esposito | Artur Wachtel, Riccardo Rao, Massimiliano Esposito | Free-Energy Transduction in Chemical Reaction Networks: from Enzymes to
Metabolism | 17 pages, 17 figues, (v2: IV.A.3 & IV.C modified, VI expanded) | null | 10.1063/5.0091035 | null | q-bio.MN cond-mat.stat-mech physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | We provide a rigorous definition of free-energy transduction and its
efficiency in arbitrary -- linear or nonlinear -- open chemical reaction
networks (CRNs) operating at steady state. Our method is based on the knowledge
of the stoichiometric matrix and of the chemostatted species (i.e. the species
maintained at constant concentration by the environment) to identify the
fundamental currents and forces contributing to the entropy production.
Transduction occurs when the current of a stoichiometrically balanced process
is driven against its spontaneous direction (set by its force) thanks to other
processes flowing along their spontaneous direction. In these regimes, open
CRNs operate as thermodynamic machines. After exemplifying these general ideas
using toy models, we analyze central energy metabolism. We relate the
fundamental currents to metabolic pathways and discuss the efficiency with
which they are able to transduce free energy.
| [
{
"created": "Wed, 2 Feb 2022 22:41:30 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Jun 2022 21:16:55 GMT",
"version": "v2"
}
] | 2022-06-15 | [
[
"Wachtel",
"Artur",
""
],
[
"Rao",
"Riccardo",
""
],
[
"Esposito",
"Massimiliano",
""
]
] | We provide a rigorous definition of free-energy transduction and its efficiency in arbitrary -- linear or nonlinear -- open chemical reaction networks (CRNs) operating at steady state. Our method is based on the knowledge of the stoichiometric matrix and of the chemostatted species (i.e. the species maintained at constant concentration by the environment) to identify the fundamental currents and forces contributing to the entropy production. Transduction occurs when the current of a stoichiometrically balanced process is driven against its spontaneous direction (set by its force) thanks to other processes flowing along their spontaneous direction. In these regimes, open CRNs operate as thermodynamic machines. After exemplifying these general ideas using toy models, we analyze central energy metabolism. We relate the fundamental currents to metabolic pathways and discuss the efficiency with which they are able to transduce free energy. |
2101.10617 | Lingbin Bian | Lingbin Bian, Tiangang Cui, B.T. Thomas Yeo, Alex Fornito, Adeel Razi
and Jonathan Keith | Identification of brain states, transitions, and communities using
functional MRI | null | null | null | null | q-bio.NC stat.ML | http://creativecommons.org/licenses/by/4.0/ | Brain function relies on a precisely coordinated and dynamic balance between
the functional integration and segregation of distinct neural systems.
Characterizing the way in which neural systems reconfigure their interactions
to give rise to distinct but hidden brain states remains an open challenge. In
this paper, we propose a Bayesian model-based characterization of latent brain
states and showcase a novel method based on posterior predictive discrepancy
using the latent block model to detect transitions between latent brain states
in blood oxygen level-dependent (BOLD) time series. The set of estimated
parameters in the model includes a latent label vector that assigns network
nodes to communities, and also block model parameters that reflect the weighted
connectivity within and between communities. Besides extensive in-silico model
evaluation, we also provide empirical validation (and replication) using the
Human Connectome Project (HCP) dataset of 100 healthy adults. Our results
obtained through an analysis of task-fMRI data during working memory
performance show appropriate lags between external task demands and
change-points between brain states, with distinctive community patterns
distinguishing fixation, low-demand and high-demand task conditions.
| [
{
"created": "Tue, 26 Jan 2021 08:10:00 GMT",
"version": "v1"
}
] | 2021-01-28 | [
[
"Bian",
"Lingbin",
""
],
[
"Cui",
"Tiangang",
""
],
[
"Yeo",
"B. T. Thomas",
""
],
[
"Fornito",
"Alex",
""
],
[
"Razi",
"Adeel",
""
],
[
"Keith",
"Jonathan",
""
]
] | Brain function relies on a precisely coordinated and dynamic balance between the functional integration and segregation of distinct neural systems. Characterizing the way in which neural systems reconfigure their interactions to give rise to distinct but hidden brain states remains an open challenge. In this paper, we propose a Bayesian model-based characterization of latent brain states and showcase a novel method based on posterior predictive discrepancy using the latent block model to detect transitions between latent brain states in blood oxygen level-dependent (BOLD) time series. The set of estimated parameters in the model includes a latent label vector that assigns network nodes to communities, and also block model parameters that reflect the weighted connectivity within and between communities. Besides extensive in-silico model evaluation, we also provide empirical validation (and replication) using the Human Connectome Project (HCP) dataset of 100 healthy adults. Our results obtained through an analysis of task-fMRI data during working memory performance show appropriate lags between external task demands and change-points between brain states, with distinctive community patterns distinguishing fixation, low-demand and high-demand task conditions. |
1404.2487 | Liya Wang | Liya Wang, Lincoln Stein, and Doreen Ware | The relationships among GC content, nucleosome occupancy, and exon size | 26 pages, 9 figures | null | null | null | q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The average size of internal translated exons, ranging from 120 to 165 nt
across metazoans, is approximately the size of the typical mononucleosome (147
nt). Genome-wide study has also shown that nucleosome occupancy is
significantly higher in exons than in introns, which might indicate that the
evolution of exon size is related to its nucleosome occupancy. By grouping
exons by the GC contents of their flanking introns, we show that the average
exon size is positively correlated with its GC content. Using the sequencing
data from direct mapping of Homo sapiens nucleosomes with limited nuclease
digestion, we show that the level of nucleosome occupancy is also positively
correlated with the exon GC content in a similar fashion. We then demonstrated
that exon size is positively correlated with their nucleosome occupancy. The
strong correlation between exon size and the nucleosome occupancy suggests that
chromatin organization may be related to the evolution of exon sizes.
| [
{
"created": "Wed, 9 Apr 2014 13:51:52 GMT",
"version": "v1"
},
{
"created": "Tue, 27 May 2014 20:23:26 GMT",
"version": "v2"
}
] | 2014-05-29 | [
[
"Wang",
"Liya",
""
],
[
"Stein",
"Lincoln",
""
],
[
"Ware",
"Doreen",
""
]
] | The average size of internal translated exons, ranging from 120 to 165 nt across metazoans, is approximately the size of the typical mononucleosome (147 nt). Genome-wide study has also shown that nucleosome occupancy is significantly higher in exons than in introns, which might indicate that the evolution of exon size is related to its nucleosome occupancy. By grouping exons by the GC contents of their flanking introns, we show that the average exon size is positively correlated with its GC content. Using the sequencing data from direct mapping of Homo sapiens nucleosomes with limited nuclease digestion, we show that the level of nucleosome occupancy is also positively correlated with the exon GC content in a similar fashion. We then demonstrated that exon size is positively correlated with their nucleosome occupancy. The strong correlation between exon size and the nucleosome occupancy suggests that chromatin organization may be related to the evolution of exon sizes. |
2407.05226 | Hong Qin | Hong Qin | The Emergent Aging Model: Aging as an Emergent Property of Biological
Systems | 10 pages, 2 figures | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Based on the study of cellular aging using the single-cell model organism of
budding yeast and corroborated by other studies, we propose the Emergent Aging
Model (EAM). EAM hypothesizes that aging is an emergent property of complex
biological systems, exemplified by biological networks such as gene networks.
An emergent property refers to traits that a system has at the system level but
which its low-level components do not. EAM is based on a quantitative
definition of aging using the mortality rate. A biological entity with a
constant mortality rate is considered non-aging which is equivalent to a
first-order chemical reaction. Aging can be quantitatively defined as an
increasing mortality rate over time, corresponding to an organism's increasing
chance of dying over time. EAM posits that biological aging can arise at the
system level of an organism, even if the system is composed of only non-aging
components. EAM is consistent with the observation that aging is largely
stochastic, influenced by numerous genes and epigenetic factors, with no single
gene or factor known as the bona fide cause of aging. A parsimonious version of
EAM can predict the Gompertz model of biological aging, the Strehler-Mildvan
correlation, and the trade-off between initial reproductive fitness (asexual
reproductive fitness) and late-life survival. EAM has been applied to
experimental results of the replicative lifespan of the budding yeast and can
potentially offer new insights into the aging process of other biological
species.
| [
{
"created": "Sun, 7 Jul 2024 01:19:22 GMT",
"version": "v1"
}
] | 2024-07-09 | [
[
"Qin",
"Hong",
""
]
] | Based on the study of cellular aging using the single-cell model organism of budding yeast and corroborated by other studies, we propose the Emergent Aging Model (EAM). EAM hypothesizes that aging is an emergent property of complex biological systems, exemplified by biological networks such as gene networks. An emergent property refers to traits that a system has at the system level but which its low-level components do not. EAM is based on a quantitative definition of aging using the mortality rate. A biological entity with a constant mortality rate is considered non-aging which is equivalent to a first-order chemical reaction. Aging can be quantitatively defined as an increasing mortality rate over time, corresponding to an organism's increasing chance of dying over time. EAM posits that biological aging can arise at the system level of an organism, even if the system is composed of only non-aging components. EAM is consistent with the observation that aging is largely stochastic, influenced by numerous genes and epigenetic factors, with no single gene or factor known as the bona fide cause of aging. A parsimonious version of EAM can predict the Gompertz model of biological aging, the Strehler-Mildvan correlation, and the trade-off between initial reproductive fitness (asexual reproductive fitness) and late-life survival. EAM has been applied to experimental results of the replicative lifespan of the budding yeast and can potentially offer new insights into the aging process of other biological species. |
1411.0431 | Valmir Barbosa | Luciano Dyballa, Valmir C. Barbosa | Further insights into the interareal connectivity of a cortical network | null | Network Science 3 (2015), 526-550 | 10.1017/nws.2015.19 | null | q-bio.NC cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the past years, network science has proven invaluable as a means to
better understand many of the processes taking place in the brain. Recently,
interareal connectivity data of the macaque cortex was made available with
great richness of detail. We explore new aspects of this dataset, such as a
correlation between connection weights and cortical hierarchy. We also look at
the link-community structure that emerges from the data to uncover the major
communication pathways in the network, and moreover investigate its reciprocal
connections, showing that they share similar properties.
| [
{
"created": "Mon, 3 Nov 2014 11:22:27 GMT",
"version": "v1"
}
] | 2022-09-02 | [
[
"Dyballa",
"Luciano",
""
],
[
"Barbosa",
"Valmir C.",
""
]
] | Over the past years, network science has proven invaluable as a means to better understand many of the processes taking place in the brain. Recently, interareal connectivity data of the macaque cortex was made available with great richness of detail. We explore new aspects of this dataset, such as a correlation between connection weights and cortical hierarchy. We also look at the link-community structure that emerges from the data to uncover the major communication pathways in the network, and moreover investigate its reciprocal connections, showing that they share similar properties. |
1807.00701 | Adam Kleczkowski | Adam Kleczkowski, Andrew Bate, Michael Redenti and Nick Hanley | Weakest-link control of invasive species: Impacts of memory, bounded
rationality and network structure in repeated cooperative games | 29 pages, 6 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The nature of dispersal of many invasive pests and pathogens in agricultural
and forestry makes it necessary to consider how the actions of one manager
affect neighbouring properties. In addition to the direct effects of a
potential spread of a pest and the resulting economic loss, there are also
indirect consequences that affect whole regions and that require coordinated
actions to manage and/or to eradicate it (like movement restrictions). In this
paper we address the emergence and stability of cooperation among agents who
respond to a threat of an invasive pest or disease. The model, based on the
weakest-link paradigm, uses repeated multi-participant coordination games where
players' pay-offs depend on management decisions to prevent the invasion on
their own land as well as of their neighbours on a network. We show that for
the basic cooperation game agents select the risk-dominant strategy of a Stag
hunt game over the pay-off dominant strategy of implementing control measures.
However, cooperation can be achieved by the social planner offering a
biosecurity payment. The critical level of this payment depends on the details
of the decision-making process, with higher trust (based on a reputation of
other agents reflecting their past performance) allowing a significant
reduction in necessary payments and slowing down decay in cooperation when the
payment is low. We also find that allowing for uncertainty in decision-making
process can enhance cooperation for low levels of payments. Finally, we show
the importance of industry structure to the emergence of cooperation, with
increase in the average coordination number of network nodes leading to
increase in the critical biosecurity payment.
| [
{
"created": "Fri, 29 Jun 2018 16:22:02 GMT",
"version": "v1"
}
] | 2018-07-03 | [
[
"Kleczkowski",
"Adam",
""
],
[
"Bate",
"Andrew",
""
],
[
"Redenti",
"Michael",
""
],
[
"Hanley",
"Nick",
""
]
] | The nature of dispersal of many invasive pests and pathogens in agricultural and forestry makes it necessary to consider how the actions of one manager affect neighbouring properties. In addition to the direct effects of a potential spread of a pest and the resulting economic loss, there are also indirect consequences that affect whole regions and that require coordinated actions to manage and/or to eradicate it (like movement restrictions). In this paper we address the emergence and stability of cooperation among agents who respond to a threat of an invasive pest or disease. The model, based on the weakest-link paradigm, uses repeated multi-participant coordination games where players' pay-offs depend on management decisions to prevent the invasion on their own land as well as of their neighbours on a network. We show that for the basic cooperation game agents select the risk-dominant strategy of a Stag hunt game over the pay-off dominant strategy of implementing control measures. However, cooperation can be achieved by the social planner offering a biosecurity payment. The critical level of this payment depends on the details of the decision-making process, with higher trust (based on a reputation of other agents reflecting their past performance) allowing a significant reduction in necessary payments and slowing down decay in cooperation when the payment is low. We also find that allowing for uncertainty in decision-making process can enhance cooperation for low levels of payments. Finally, we show the importance of industry structure to the emergence of cooperation, with increase in the average coordination number of network nodes leading to increase in the critical biosecurity payment. |
2001.06093 | Mareike Fischer | Mareike Fischer, Michelle Galla and Kristina Wicke | Non-binary universal tree-based networks | arXiv admin note: text overlap with arXiv:1810.06853 | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A tree-based network $N$ on $X$ is called universal if every phylogenetic
tree on $X$ is a base tree for $N$. Recently, binary universal tree-based
networks have attracted great attention in the literature and their existence
has been analyzed in various studies. In this note, we extend the analysis to
non-binary networks and show that there exist both a rooted and an unrooted
non-binary universal tree-based network with $n$ leaves for all positive
integers $n$.
| [
{
"created": "Tue, 14 Jan 2020 08:34:40 GMT",
"version": "v1"
}
] | 2020-01-20 | [
[
"Fischer",
"Mareike",
""
],
[
"Galla",
"Michelle",
""
],
[
"Wicke",
"Kristina",
""
]
] | A tree-based network $N$ on $X$ is called universal if every phylogenetic tree on $X$ is a base tree for $N$. Recently, binary universal tree-based networks have attracted great attention in the literature and their existence has been analyzed in various studies. In this note, we extend the analysis to non-binary networks and show that there exist both a rooted and an unrooted non-binary universal tree-based network with $n$ leaves for all positive integers $n$. |
1007.0986 | Ilya M. Nemenman | Pradeep Bandaru, Mukesh Bansal, and Ilya Nemenman | Mass Conservation And Inference of Metabolic Networks from
High-throughput Mass Spectrometry Data | 18 pages | J Comput Biol, vol. 18 (2) pp. 147-54, 2011 | 10.1089/cmb.2010.0222 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a step towards the metabolome-wide computational inference of
cellular metabolic reaction networks from metabolic profiling data, such as
mass spectrometry. The reconstruction is based on identification of irreducible
statistical interactions among the metabolite activities using the ARACNE
reverse-engineering algorithm and on constraining possible metabolic
transformations to satisfy the conservation of mass. The resulting algorithms
are validated on synthetic data from an abridged computational model of
Escherichia coli metabolism. Precision rates upwards of 50% are routinely
observed for identification of full metabolic reactions, and recalls upwards of
20% are also seen.
| [
{
"created": "Tue, 6 Jul 2010 19:52:39 GMT",
"version": "v1"
}
] | 2011-06-02 | [
[
"Bandaru",
"Pradeep",
""
],
[
"Bansal",
"Mukesh",
""
],
[
"Nemenman",
"Ilya",
""
]
] | We present a step towards the metabolome-wide computational inference of cellular metabolic reaction networks from metabolic profiling data, such as mass spectrometry. The reconstruction is based on identification of irreducible statistical interactions among the metabolite activities using the ARACNE reverse-engineering algorithm and on constraining possible metabolic transformations to satisfy the conservation of mass. The resulting algorithms are validated on synthetic data from an abridged computational model of Escherichia coli metabolism. Precision rates upwards of 50% are routinely observed for identification of full metabolic reactions, and recalls upwards of 20% are also seen. |
1809.04804 | Peter Kasson | Peter M. Kasson and Shantenu Jha | Adaptive ensemble simulations of biomolecules | Manuscript accepted for publication in Current Opinion in Structural
Biology | Current Opinion in Structural Biology 2018. 52:87-94 | 10.1016/j.sbi.2018.09.005 | null | q-bio.QM physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in both theory and computational power have created
opportunities to simulate biomolecular processes more efficiently using
adaptive ensemble simulations. Ensemble simulations are now widely used to
compute a number of individual simulation trajectories and analyze statistics
across them. Adaptive ensemble simulations offer a further level of
sophistication and flexibility by enabling high-level algorithms to control
simulations based on intermediate results. We review some of the adaptive
ensemble algorithms and software infrastructure currently in use and outline
where the complexities of implementing adaptive simulation have limited
algorithmic innovation to date. We describe an adaptive ensemble API to
overcome some of these barriers and more flexibly and simply express adaptive
simulation algorithms to help realize the power of this type of simulation.
| [
{
"created": "Thu, 13 Sep 2018 07:08:18 GMT",
"version": "v1"
}
] | 2018-09-27 | [
[
"Kasson",
"Peter M.",
""
],
[
"Jha",
"Shantenu",
""
]
] | Recent advances in both theory and computational power have created opportunities to simulate biomolecular processes more efficiently using adaptive ensemble simulations. Ensemble simulations are now widely used to compute a number of individual simulation trajectories and analyze statistics across them. Adaptive ensemble simulations offer a further level of sophistication and flexibility by enabling high-level algorithms to control simulations based on intermediate results. We review some of the adaptive ensemble algorithms and software infrastructure currently in use and outline where the complexities of implementing adaptive simulation have limited algorithmic innovation to date. We describe an adaptive ensemble API to overcome some of these barriers and more flexibly and simply express adaptive simulation algorithms to help realize the power of this type of simulation. |
1305.5413 | Carlos Gershenson | Nelson Fernandez and Carlos Gershenson | Measuring Complexity in an Aquatic Ecosystem | 6 pages, to be published in Proceedings of the CCBCOL 2013, 2nd
Colombian Computational Biology Congress, Springer | null | null | null | q-bio.PE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We apply formal measures of emergence, self-organization, homeostasis,
autopoiesis and complexity to an aquatic ecosystem; in particular to the
physiochemical component of an Arctic lake. These measures are based on
information theory. Variables with an homogeneous distribution have higher
values of emergence, while variables with a more heterogeneous distribution
have a higher self-organization. Variables with a high complexity reflect a
balance between change (emergence) and regularity/order (self-organization). In
addition, homeostasis values coincide with the variation of the winter and
summer seasons. Autopoiesis values show a higher degree of independence of
biological components over their environment. Our approach shows how the
ecological dynamics can be described in terms of information.
| [
{
"created": "Wed, 22 May 2013 03:43:08 GMT",
"version": "v1"
}
] | 2013-05-24 | [
[
"Fernandez",
"Nelson",
""
],
[
"Gershenson",
"Carlos",
""
]
] | We apply formal measures of emergence, self-organization, homeostasis, autopoiesis and complexity to an aquatic ecosystem; in particular to the physiochemical component of an Arctic lake. These measures are based on information theory. Variables with an homogeneous distribution have higher values of emergence, while variables with a more heterogeneous distribution have a higher self-organization. Variables with a high complexity reflect a balance between change (emergence) and regularity/order (self-organization). In addition, homeostasis values coincide with the variation of the winter and summer seasons. Autopoiesis values show a higher degree of independence of biological components over their environment. Our approach shows how the ecological dynamics can be described in terms of information. |
1307.0968 | Jose A. Cuesta | Susanna Manrubia and Jos\'e A. Cuesta | Evolution on neutral networks accelerates the ticking rate of the
molecular clock | 31 pages, 4 figures | Journal of the Royal Society Interface 102, 20141010 (2015) | 10.1098/rsif.2014.1010 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large sets of genotypes give rise to the same phenotype because phenotypic
expression is highly redundant. Accordingly, a population can accept mutations
without altering its phenotype, as long as thegenotype mutates into another one
on the same set. By linking every pair of genotypes that are mutually
accessible through mutation, genotypes organize themselves into neutral
networks (NN). These networks are known to be heterogeneous and assortative,
and these properties affect the evolutionary dynamics of the population. By
studying the dynamics of populations on NN with arbitrary topology we analyze
the effect of assortativity, of NN (phenotype) fitness, and of network size. We
find that the probability that the population leaves the network is smaller the
longer the time spent on it. This progressive "phenotypic entrapment" entails a
systematic increase in the overdispersion of the process with time and an
acceleration in the fixation rate of neutral mutations. We also quantify the
variation of these effects with the size of the phenotype and with its fitness
relative to that of neighbouring alternatives.
| [
{
"created": "Wed, 3 Jul 2013 11:15:09 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Sep 2014 20:47:30 GMT",
"version": "v2"
}
] | 2015-02-18 | [
[
"Manrubia",
"Susanna",
""
],
[
"Cuesta",
"José A.",
""
]
] | Large sets of genotypes give rise to the same phenotype because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as thegenotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NN). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NN with arbitrary topology we analyze the effect of assortativity, of NN (phenotype) fitness, and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive "phenotypic entrapment" entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives. |
2104.08969 | Ethan Moyer | Ethan Moyer, Jeff Winchell, Isamu Isozaki, Yigit Alparslan, Mali
Halac, and Edward Kim | Functional Protein Structure Annotation Using a Deep Convolutional
Generative Adversarial Network | 4 pages, 1 figure, 1 table | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Identifying novel functional protein structures is at the heart of molecular
engineering and molecular biology, requiring an often computationally
exhaustive search. We introduce the use of a Deep Convolutional Generative
Adversarial Network (DCGAN) to classify protein structures based on their
functionality by encoding each sample in a grid object structure using three
features in each object: the generic atom type, the position atom type, and its
occupancy relative to a given atom. We train DCGAN on 3-dimensional (3D) decoy
and native protein structures in order to generate and discriminate 3D protein
structures. At the end of our training, loss converges to a local minimum and
our DCGAN can annotate functional proteins robustly against adversarial protein
samples. In the future we hope to extend the novel structures we found from the
generator in our DCGAN with more samples to explore more granular functionality
with varying functions. We hope that our effort will advance the field of
protein structure prediction.
| [
{
"created": "Sun, 18 Apr 2021 22:18:52 GMT",
"version": "v1"
}
] | 2021-04-20 | [
[
"Moyer",
"Ethan",
""
],
[
"Winchell",
"Jeff",
""
],
[
"Isozaki",
"Isamu",
""
],
[
"Alparslan",
"Yigit",
""
],
[
"Halac",
"Mali",
""
],
[
"Kim",
"Edward",
""
]
] | Identifying novel functional protein structures is at the heart of molecular engineering and molecular biology, requiring an often computationally exhaustive search. We introduce the use of a Deep Convolutional Generative Adversarial Network (DCGAN) to classify protein structures based on their functionality by encoding each sample in a grid object structure using three features in each object: the generic atom type, the position atom type, and its occupancy relative to a given atom. We train DCGAN on 3-dimensional (3D) decoy and native protein structures in order to generate and discriminate 3D protein structures. At the end of our training, loss converges to a local minimum and our DCGAN can annotate functional proteins robustly against adversarial protein samples. In the future we hope to extend the novel structures we found from the generator in our DCGAN with more samples to explore more granular functionality with varying functions. We hope that our effort will advance the field of protein structure prediction. |
1705.07109 | Ya\u{g}mur G\"u\c{c}l\"ut\"urk | Ya\u{g}mur G\"u\c{c}l\"ut\"urk, Umut G\"u\c{c}l\"u, Katja Seeliger,
Sander Bosch, Rob van Lier, Marcel van Gerven | Deep adversarial neural decoding | Added appendix and updated figures | null | null | null | q-bio.NC cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here, we present a novel approach to solve the problem of reconstructing
perceived stimuli from brain responses by combining probabilistic inference
with deep learning. Our approach first inverts the linear transformation from
latent features to brain responses with maximum a posteriori estimation and
then inverts the nonlinear transformation from perceived stimuli to latent
features with adversarial training of convolutional neural networks. We test
our approach with a functional magnetic resonance imaging experiment and show
that it can generate state-of-the-art reconstructions of perceived faces from
brain activations.
| [
{
"created": "Fri, 19 May 2017 17:43:01 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Jun 2017 13:15:25 GMT",
"version": "v2"
},
{
"created": "Thu, 15 Jun 2017 16:56:34 GMT",
"version": "v3"
}
] | 2017-06-16 | [
[
"Güçlütürk",
"Yağmur",
""
],
[
"Güçlü",
"Umut",
""
],
[
"Seeliger",
"Katja",
""
],
[
"Bosch",
"Sander",
""
],
[
"van Lier",
"Rob",
""
],
[
"van Gerven",
"Marcel",
""
]
] | Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approach first inverts the linear transformation from latent features to brain responses with maximum a posteriori estimation and then inverts the nonlinear transformation from perceived stimuli to latent features with adversarial training of convolutional neural networks. We test our approach with a functional magnetic resonance imaging experiment and show that it can generate state-of-the-art reconstructions of perceived faces from brain activations. |
1401.5701 | Fabian Spill | Fabian Spill, Pilar Guerrero, Tomas Alarcon, Philip K. Maini, Helen M.
Byrne | Mesoscopic and continuum modelling of angiogenesis | 48 pages, 13 figures | Journal of Mathematical Biology February 2015, Volume 70, Issue 3,
pp 485-532 | 10.1007/s00285-014-0771-1 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Angiogenesis is the formation of new blood vessels from pre-existing ones in
response to chemical signals secreted by, for example, a wound or a tumour. In
this paper, we propose a mesoscopic lattice-based model of angiogenesis, in
which processes that include proliferation and cell movement are considered as
stochastic events. By studying the dependence of the model on the lattice
spacing and the number of cells involved, we are able to derive the
deterministic continuum limit of our equations and compare it to similar
existing models of angiogenesis. We further identify conditions under which the
use of continuum models is justified, and others for which stochastic or
discrete effects dominate. We also compare different stochastic models for the
movement of endothelial tip cells which have the same macroscopic,
deterministic behaviour, but lead to markedly different behaviour in terms of
production of new vessel cells.
| [
{
"created": "Wed, 22 Jan 2014 15:15:28 GMT",
"version": "v1"
}
] | 2016-03-02 | [
[
"Spill",
"Fabian",
""
],
[
"Guerrero",
"Pilar",
""
],
[
"Alarcon",
"Tomas",
""
],
[
"Maini",
"Philip K.",
""
],
[
"Byrne",
"Helen M.",
""
]
] | Angiogenesis is the formation of new blood vessels from pre-existing ones in response to chemical signals secreted by, for example, a wound or a tumour. In this paper, we propose a mesoscopic lattice-based model of angiogenesis, in which processes that include proliferation and cell movement are considered as stochastic events. By studying the dependence of the model on the lattice spacing and the number of cells involved, we are able to derive the deterministic continuum limit of our equations and compare it to similar existing models of angiogenesis. We further identify conditions under which the use of continuum models is justified, and others for which stochastic or discrete effects dominate. We also compare different stochastic models for the movement of endothelial tip cells which have the same macroscopic, deterministic behaviour, but lead to markedly different behaviour in terms of production of new vessel cells. |
2002.07732 | Breno de Oliveira Ferraz | D. Bazeia, M.V. de Moraes, B.F. de Oliveira | Model for clustering of living species | 7 pages, 10 figures | EPL, 129 (2020) 28002 | 10.1209/0295-5075/129/28002 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clusters appear in nature in a diversity of contexts, involving distances as
long as the cosmological ones, and down to atoms and molecules and the very
small nuclear size. They also appear in several other scenarios, in particular
in biological systems as in ants, bees, birds, fishes, gnus and rats, for
instance. Here we describe a model composed of a set of female and male
individuals that obeys simple rules that rapidly transform an uniform initial
state into a single cluster that evolves in time as a stable dynamical
structure. We show that the center of mass of the structure moves as a random
walk, and that the size of the cluster engenders a power law behavior in terms
of the number of individuals in the system. Moreover, we also examine other
possibilities, in particular the case of two distinct species that can evolve
to form one or two distinct clusters.
| [
{
"created": "Tue, 18 Feb 2020 17:02:06 GMT",
"version": "v1"
}
] | 2020-02-19 | [
[
"Bazeia",
"D.",
""
],
[
"de Moraes",
"M. V.",
""
],
[
"de Oliveira",
"B. F.",
""
]
] | Clusters appear in nature in a diversity of contexts, involving distances as long as the cosmological ones, and down to atoms and molecules and the very small nuclear size. They also appear in several other scenarios, in particular in biological systems as in ants, bees, birds, fishes, gnus and rats, for instance. Here we describe a model composed of a set of female and male individuals that obeys simple rules that rapidly transform an uniform initial state into a single cluster that evolves in time as a stable dynamical structure. We show that the center of mass of the structure moves as a random walk, and that the size of the cluster engenders a power law behavior in terms of the number of individuals in the system. Moreover, we also examine other possibilities, in particular the case of two distinct species that can evolve to form one or two distinct clusters. |
1803.01111 | Michael Assaf | Ohad Vilk and Michael Assaf | Population Extinction under Bursty Reproduction in a Time Modulated
Environment | 11 pages, 8 figures | Phys. Rev. E 97, 062114 (2018) | 10.1103/PhysRevE.97.062114 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years non-demographic variability has been shown to greatly affect
dynamics of stochastic populations. For example, non-demographic noise in the
form of a bursty reproduction process with an a-priori unknown burst size, or
environmental variability in the form of time-varying reaction rates, have been
separately found to dramatically impact the extinction risk of isolated
populations. In this work we investigate the extinction risk of an isolated
population under the combined influence of these two types of non-demographic
variation. Using the so-called momentum-space WKB approach we arrive at a set
of time-dependent Hamilton equations. In order to account for the explicit time
dependence, we find the instanton of the time-perturbed Hamiltonian
numerically, where analytical expressions are presented in particular limits
using various perturbation techniques. We focus on two classes of time-varying
environments: periodically-varying rates corresponding to seasonal effects, and
a sudden decrease in the birth rate corresponding to a catastrophe. All our
theoretical results are tested against numerical Monte Carlo simulations with
time-dependent rates and also against a numerical solution of the corresponding
time-dependent Hamilton equations.
| [
{
"created": "Sat, 3 Mar 2018 06:01:22 GMT",
"version": "v1"
}
] | 2018-06-13 | [
[
"Vilk",
"Ohad",
""
],
[
"Assaf",
"Michael",
""
]
] | In recent years non-demographic variability has been shown to greatly affect dynamics of stochastic populations. For example, non-demographic noise in the form of a bursty reproduction process with an a-priori unknown burst size, or environmental variability in the form of time-varying reaction rates, have been separately found to dramatically impact the extinction risk of isolated populations. In this work we investigate the extinction risk of an isolated population under the combined influence of these two types of non-demographic variation. Using the so-called momentum-space WKB approach we arrive at a set of time-dependent Hamilton equations. In order to account for the explicit time dependence, we find the instanton of the time-perturbed Hamiltonian numerically, where analytical expressions are presented in particular limits using various perturbation techniques. We focus on two classes of time-varying environments: periodically-varying rates corresponding to seasonal effects, and a sudden decrease in the birth rate corresponding to a catastrophe. All our theoretical results are tested against numerical Monte Carlo simulations with time-dependent rates and also against a numerical solution of the corresponding time-dependent Hamilton equations. |
2108.02570 | Minhong Kim | Minhong Kim | Predicting Post-Concussion Syndrome Outcomes with Machine Learning | null | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, machine learning models are used to predict outcomes for
patients with persistent post-concussion syndrome (PCS). Patients had sustained
a concussion at an average of two to three months before the study. By
utilizing assessed data, the machine learning models aimed to predict whether
or not a patient would continue to have PCS after four to five months. The
random forest classifier achieved the highest performance with an 85% accuracy
and an area under the receiver operating characteristic curve (AUC) of 0.94.
Factors found to be predictive of PCS outcome were Post-Traumatic Stress
Disorder (PTSD), perceived injustice, self-rated prognosis, and symptom
severity post-injury. The results of this study demonstrate that machine
learning models can predict PCS outcomes with high accuracy. With further
research, machine learning models may be implemented in healthcare settings to
help patients with persistent PCS.
| [
{
"created": "Wed, 4 Aug 2021 09:04:13 GMT",
"version": "v1"
}
] | 2021-08-06 | [
[
"Kim",
"Minhong",
""
]
] | In this paper, machine learning models are used to predict outcomes for patients with persistent post-concussion syndrome (PCS). Patients had sustained a concussion at an average of two to three months before the study. By utilizing assessed data, the machine learning models aimed to predict whether or not a patient would continue to have PCS after four to five months. The random forest classifier achieved the highest performance with an 85% accuracy and an area under the receiver operating characteristic curve (AUC) of 0.94. Factors found to be predictive of PCS outcome were Post-Traumatic Stress Disorder (PTSD), perceived injustice, self-rated prognosis, and symptom severity post-injury. The results of this study demonstrate that machine learning models can predict PCS outcomes with high accuracy. With further research, machine learning models may be implemented in healthcare settings to help patients with persistent PCS. |
1802.05166 | Alexander Bershadskii | A. Bershadskii | Hamiltonian dynamics and distributed chaos in DNA | extended | null | null | null | q-bio.OT physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is shown that distributed chaos, generated by Hamiltonian DNA dynamics
with spontaneously broken time translational symmetry, imprints itself on the
DNA sequence of Arabidopsis thaliana (a model plant for genetic sequencing and
mapping) and of the NRXN1 and BRCA2 human genes (as an example). The
base-stacking interactions in the DNA duplex and degenerate codon groups have
been discussed in this context.
| [
{
"created": "Wed, 14 Feb 2018 15:47:44 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Feb 2018 17:18:38 GMT",
"version": "v2"
},
{
"created": "Tue, 13 Mar 2018 17:10:45 GMT",
"version": "v3"
}
] | 2018-03-14 | [
[
"Bershadskii",
"A.",
""
]
] | It is shown that distributed chaos, generated by Hamiltonian DNA dynamics with spontaneously broken time translational symmetry, imprints itself on the DNA sequence of Arabidopsis thaliana (a model plant for genetic sequencing and mapping) and of the NRXN1 and BRCA2 human genes (as an example). The base-stacking interactions in the DNA duplex and degenerate codon groups have been discussed in this context. |
2005.14597 | Paulo Protachevicz | P R Protachevicz, M S Santos, E G Seifert, E C Gabrick, F S Borges, R
R Borges, J Trobia, J D Szezech Jr, K C Iarosz, I L Caldas, C G Antonopoulos,
Y Xu, R L Viana, A M Batista | Noise induces continuous and noncontinuous transitions in neuronal
interspike intervals range | null | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Noise appears in the brain due to various sources, such as ionic channel
fluctuations and synaptic events. They affect the activities of the brain and
influence neuron action potentials. Stochastic differential equations have been
used to model firing patterns of neurons subject to noise. In this work, we
consider perturbing noise in the adaptive exponential integrate-and-fire (AEIF)
neuron. The AEIF is a two-dimensional model that describes different neuronal
firing patterns by varying its parameters. Noise is added in the equation
related to the membrane potential. We show that a noise current can induce
continuous and noncontinuous transitions in neuronal interspike intervals.
Moreover, we show that the noncontinuous transition occurs mainly for
parameters close to the border between tonic spiking and burst activities of
the neuron without noise.
| [
{
"created": "Fri, 29 May 2020 14:28:30 GMT",
"version": "v1"
}
] | 2020-06-01 | [
[
"Protachevicz",
"P R",
""
],
[
"Santos",
"M S",
""
],
[
"Seifert",
"E G",
""
],
[
"Gabrick",
"E C",
""
],
[
"Borges",
"F S",
""
],
[
"Borges",
"R R",
""
],
[
"Trobia",
"J",
""
],
[
"Szezech",
"J D",
"Jr"
],
[
"Iarosz",
"K C",
""
],
[
"Caldas",
"I L",
""
],
[
"Antonopoulos",
"C G",
""
],
[
"Xu",
"Y",
""
],
[
"Viana",
"R L",
""
],
[
"Batista",
"A M",
""
]
] | Noise appears in the brain due to various sources, such as ionic channel fluctuations and synaptic events. They affect the activities of the brain and influence neuron action potentials. Stochastic differential equations have been used to model firing patterns of neurons subject to noise. In this work, we consider perturbing noise in the adaptive exponential integrate-and-fire (AEIF) neuron. The AEIF is a two-dimensional model that describes different neuronal firing patterns by varying its parameters. Noise is added in the equation related to the membrane potential. We show that a noise current can induce continuous and noncontinuous transitions in neuronal interspike intervals. Moreover, we show that the noncontinuous transition occurs mainly for parameters close to the border between tonic spiking and burst activities of the neuron without noise. |
2001.07822 | Michael Baker Ph.D. | Michael E. Baker and Yoshinao Katsu | Progesterone: An Enigmatic Ligand for the Mineralocorticoid Receptor | 17 pages, 5 figures | null | null | null | q-bio.MN q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The progesterone receptor (PR) mediates progesterone regulation of female
reproductive physiology, as well as gene transcription in non-reproductive
tissues, such as brain, bone, lung and vasculature, in both women and men. An
unusual property of progesterone is its high affinity for the mineralocorticoid
receptor (MR), which regulates electrolyte transport in the kidney in humans
and other terrestrial vertebrates. In humans, rats, alligators and frogs,
progesterone antagonizes activation of the MR by aldosterone, the physiological
mineralocorticoid in terrestrial vertebrates. In contrast, in elephant shark,
ray-finned fishes and chickens, progesterone activates the MR. Interestingly,
cartilaginous fishes and ray-finned fishes do not synthesize aldosterone,
raising the question of which steroid(s) activate the MR in cartilaginous
fishes and ray-finned fishes. The simpler synthesis of progesterone, compared
to cortisol and other corticosteroids, makes progesterone a candidate
physiological activator of the MR in elephant sharks and ray-finned fishes.
Elephant shark and ray-finned fish MRs are expressed in diverse tissues,
including heart, brain and lung, as well as, ovary and testis, two reproductive
tissues that are targets for progesterone, which together suggests a
multi-faceted physiological role for progesterone activation of the MR in
elephant shark and ray-finned fish. The functional consequences of progesterone
as an antagonist of some terrestrial vertebrate MRs and as an agonist of fish
and chicken MRs are not fully understood. Indeed, little is known of
physiological activities of progesterone via any vertebrate MR.
| [
{
"created": "Wed, 22 Jan 2020 00:08:17 GMT",
"version": "v1"
}
] | 2020-01-23 | [
[
"Baker",
"Michael E.",
""
],
[
"Katsu",
"Yoshinao",
""
]
] | The progesterone receptor (PR) mediates progesterone regulation of female reproductive physiology, as well as gene transcription in non-reproductive tissues, such as brain, bone, lung and vasculature, in both women and men. An unusual property of progesterone is its high affinity for the mineralocorticoid receptor (MR), which regulates electrolyte transport in the kidney in humans and other terrestrial vertebrates. In humans, rats, alligators and frogs, progesterone antagonizes activation of the MR by aldosterone, the physiological mineralocorticoid in terrestrial vertebrates. In contrast, in elephant shark, ray-finned fishes and chickens, progesterone activates the MR. Interestingly, cartilaginous fishes and ray-finned fishes do not synthesize aldosterone, raising the question of which steroid(s) activate the MR in cartilaginous fishes and ray-finned fishes. The simpler synthesis of progesterone, compared to cortisol and other corticosteroids, makes progesterone a candidate physiological activator of the MR in elephant sharks and ray-finned fishes. Elephant shark and ray-finned fish MRs are expressed in diverse tissues, including heart, brain and lung, as well as, ovary and testis, two reproductive tissues that are targets for progesterone, which together suggests a multi-faceted physiological role for progesterone activation of the MR in elephant shark and ray-finned fish. The functional consequences of progesterone as an antagonist of some terrestrial vertebrate MRs and as an agonist of fish and chicken MRs are not fully understood. Indeed, little is known of physiological activities of progesterone via any vertebrate MR. |
1807.00038 | Adilson Enio Motter | Reka Albert, John Baillieul, Adilson E. Motter | Introduction to the Special Issue on Approaches to Control Biological
and Biologically Inspired Networks | null | IEEE Trans. Control Netw. Syst. 5(2), 690-693 (2018) | 10.1109/TCNS.2018.2836303 | null | q-bio.MN cond-mat.dis-nn cs.SY math.OC nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The emerging field at the intersection of quantitative biology, network
modeling, and control theory has enjoyed significant progress in recent years.
This Special Issue brings together a selection of papers on complementary
approaches to observe, identify, and control biological and biologically
inspired networks. These approaches advance the state of the art in the field
by addressing challenges common to many such networks, including high
dimensionality, strong nonlinearity, uncertainty, and limited opportunities for
observation and intervention. Because these challenges are not unique to
biological systems, it is expected that many of the results presented in these
contributions will also find applications in other domains, including physical,
social, and technological networks.
| [
{
"created": "Thu, 28 Jun 2018 05:30:38 GMT",
"version": "v1"
}
] | 2018-07-10 | [
[
"Albert",
"Reka",
""
],
[
"Baillieul",
"John",
""
],
[
"Motter",
"Adilson E.",
""
]
] | The emerging field at the intersection of quantitative biology, network modeling, and control theory has enjoyed significant progress in recent years. This Special Issue brings together a selection of papers on complementary approaches to observe, identify, and control biological and biologically inspired networks. These approaches advance the state of the art in the field by addressing challenges common to many such networks, including high dimensionality, strong nonlinearity, uncertainty, and limited opportunities for observation and intervention. Because these challenges are not unique to biological systems, it is expected that many of the results presented in these contributions will also find applications in other domains, including physical, social, and technological networks. |
1012.5649 | Valmir Barbosa | Andre Nathan, Valmir C. Barbosa | Network algorithmics and the emergence of information integration in
cortical models | null | Physical Review E 84 (2011), 011904 | 10.1103/PhysRevE.84.011904 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An information-theoretic framework known as integrated information theory
(IIT) has been introduced recently for the study of the emergence of
consciousness in the brain [D. Balduzzi and G. Tononi, PLoS Comput. Biol. 4,
e1000091 (2008)]. IIT purports that this phenomenon is to be equated with the
generation of information by the brain surpassing the information which the
brain's constituents already generate independently of one another. IIT is not
fully plausible in its modeling assumptions, nor is it testable due to severe
combinatorial growth embedded in its key definitions. Here we introduce an
alternative to IIT which, while inspired in similar information-theoretic
principles, seeks to address some of IIT's shortcomings to some extent. Our
alternative framework uses the same network-algorithmic cortical model we
introduced earlier [A. Nathan and V. C. Barbosa, Phys. Rev. E 81, 021916
(2010)] and, to allow for somewhat improved testability relative to IIT, adopts
the well-known notions of information gain and total correlation applied to a
set of variables representing the reachability of neurons by messages in the
model's dynamics. We argue that these two quantities relate to each other in
such a way that can be used to quantify the system's efficiency in generating
information beyond that which does not depend on integration, and give
computational results on our cortical model and on variants thereof that are
either structurally random in the sense of an Erdos-Renyi random directed graph
or structurally deterministic. We have found that our cortical model stands out
with respect to the others in the sense that many of its instances are capable
of integrating information more efficiently than most of those others'
instances.
| [
{
"created": "Mon, 27 Dec 2010 19:42:38 GMT",
"version": "v1"
}
] | 2011-07-11 | [
[
"Nathan",
"Andre",
""
],
[
"Barbosa",
"Valmir C.",
""
]
] | An information-theoretic framework known as integrated information theory (IIT) has been introduced recently for the study of the emergence of consciousness in the brain [D. Balduzzi and G. Tononi, PLoS Comput. Biol. 4, e1000091 (2008)]. IIT purports that this phenomenon is to be equated with the generation of information by the brain surpassing the information which the brain's constituents already generate independently of one another. IIT is not fully plausible in its modeling assumptions, nor is it testable due to severe combinatorial growth embedded in its key definitions. Here we introduce an alternative to IIT which, while inspired in similar information-theoretic principles, seeks to address some of IIT's shortcomings to some extent. Our alternative framework uses the same network-algorithmic cortical model we introduced earlier [A. Nathan and V. C. Barbosa, Phys. Rev. E 81, 021916 (2010)] and, to allow for somewhat improved testability relative to IIT, adopts the well-known notions of information gain and total correlation applied to a set of variables representing the reachability of neurons by messages in the model's dynamics. We argue that these two quantities relate to each other in such a way that can be used to quantify the system's efficiency in generating information beyond that which does not depend on integration, and give computational results on our cortical model and on variants thereof that are either structurally random in the sense of an Erdos-Renyi random directed graph or structurally deterministic. We have found that our cortical model stands out with respect to the others in the sense that many of its instances are capable of integrating information more efficiently than most of those others' instances. |
2002.01889 | Tatjana Petrov | Tatjana Petrov and Denis Repin | Automated Deep Abstractions for Stochastic Chemical Reaction Networks | null | null | null | null | q-bio.MN cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting stochastic cellular dynamics as emerging from the mechanistic
models of molecular interactions is a long-standing challenge in systems
biology: low-level chemical reaction network (CRN) models give raise to a
highly-dimensional continuous-time Markov chain (CTMC) which is computationally
demanding and often prohibitive to analyse in practice. A recently proposed
abstraction method uses deep learning to replace this CTMC with a discrete-time
continuous-space process, by training a mixture density deep neural network
with traces sampled at regular time intervals (which can obtained either by
simulating a given CRN or as time-series data from experiment). The major
advantage of such abstraction is that it produces a computational model that is
dramatically cheaper to execute, while preserving the statistical features of
the training data. In general, the abstraction accuracy improves with the
amount of training data. However, depending on a CRN, the overall quality of
the method -- the efficiency gain and abstraction accuracy -- will also depend
on the choice of neural network architecture given by hyper-parameters such as
the layer types and connections between them. As a consequence, in practice,
the modeller would have to take care of finding the suitable architecture
manually, for each given CRN, through a tedious and time-consuming
trial-and-error cycle. In this paper, we propose to further automatise deep
abstractions for stochastic CRNs, through learning the optimal neural network
architecture along with learning the transition kernel of the abstract process.
Automated search of the architecture makes the method applicable directly to
any given CRN, which is time-saving for deep learning experts and crucial for
non-specialists. We implement the method and demonstrate its performance on a
number of representative CRNs with multi-modal emergent phenotypes.
| [
{
"created": "Thu, 30 Jan 2020 13:49:58 GMT",
"version": "v1"
}
] | 2020-02-06 | [
[
"Petrov",
"Tatjana",
""
],
[
"Repin",
"Denis",
""
]
] | Predicting stochastic cellular dynamics as emerging from the mechanistic models of molecular interactions is a long-standing challenge in systems biology: low-level chemical reaction network (CRN) models give raise to a highly-dimensional continuous-time Markov chain (CTMC) which is computationally demanding and often prohibitive to analyse in practice. A recently proposed abstraction method uses deep learning to replace this CTMC with a discrete-time continuous-space process, by training a mixture density deep neural network with traces sampled at regular time intervals (which can obtained either by simulating a given CRN or as time-series data from experiment). The major advantage of such abstraction is that it produces a computational model that is dramatically cheaper to execute, while preserving the statistical features of the training data. In general, the abstraction accuracy improves with the amount of training data. However, depending on a CRN, the overall quality of the method -- the efficiency gain and abstraction accuracy -- will also depend on the choice of neural network architecture given by hyper-parameters such as the layer types and connections between them. As a consequence, in practice, the modeller would have to take care of finding the suitable architecture manually, for each given CRN, through a tedious and time-consuming trial-and-error cycle. In this paper, we propose to further automatise deep abstractions for stochastic CRNs, through learning the optimal neural network architecture along with learning the transition kernel of the abstract process. Automated search of the architecture makes the method applicable directly to any given CRN, which is time-saving for deep learning experts and crucial for non-specialists. We implement the method and demonstrate its performance on a number of representative CRNs with multi-modal emergent phenotypes. |
1905.06973 | Yang Jiao | Yu Zheng and Hanqing Nan and Qihui Fan and Xiaochen Wang and Liyu Liu
and Ruchuan Liu and Fangfu Ye and Bo Sun and Yang Jiao | Modeling cell migration regulated by cell-ECM micromechanical coupling | 11 pages 11 figures | Phys. Rev. E 100, 043303 (2019) | 10.1103/PhysRevE.100.043303 | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell migration in fibreous extracellular matrix (ECM) is crucial to many
physiological and pathological processes such as tissue regeneration, immune
response and cancer progression. During migration, individual cells can
generate active pulling forces via actin filament contraction, which are
transmitted to the ECM fibers through focal adhesion complexes, remodel the
ECM, and eventually propagate to and can be sensed by other cells in the
system. The microstructure and physical properties of the ECM can also
significantly influence cell migration, e.g., via durotaxis and contact
guidance. Here, we develop a computational model for cell migration regulated
by cell-ECM micro-mechanical coupling. Our model explicitly takes into account
a variety of cellular level processes including focal adhesion formation and
disassembly, active traction force generation and cell locomotion due to actin
filament contraction, transmission and propagation of tensile forces in the
ECM, as well as the resulting ECM remodeling. We validate our model by
accurately reproducing single-cell dynamics of MCF-10A breast cancer cells
migrating on collagen gels and show that the durotaxis and contact guidance
effects naturally arise as a consequence of the cell-ECM micro-mechanical
interactions considered in the model. Moreover, our model predicts strongly
correlated multi-cellular migration dynamics, which are resulted from the
ECM-mediated mechanical coupling among the migrating cell and are subsequently
verified in {\it in vitro} experiments using MCF-10A cells. Our computational
model provides a robust tool to investigate emergent collective dynamics of
multi-cellular systems in complex {\it in vivo} micro-environment and can be
utilized to design {\it in vitro} micro-environments to guide collective
behaviors and self-organization of cells.
| [
{
"created": "Thu, 16 May 2019 18:03:48 GMT",
"version": "v1"
}
] | 2019-10-16 | [
[
"Zheng",
"Yu",
""
],
[
"Nan",
"Hanqing",
""
],
[
"Fan",
"Qihui",
""
],
[
"Wang",
"Xiaochen",
""
],
[
"Liu",
"Liyu",
""
],
[
"Liu",
"Ruchuan",
""
],
[
"Ye",
"Fangfu",
""
],
[
"Sun",
"Bo",
""
],
[
"Jiao",
"Yang",
""
]
] | Cell migration in fibreous extracellular matrix (ECM) is crucial to many physiological and pathological processes such as tissue regeneration, immune response and cancer progression. During migration, individual cells can generate active pulling forces via actin filament contraction, which are transmitted to the ECM fibers through focal adhesion complexes, remodel the ECM, and eventually propagate to and can be sensed by other cells in the system. The microstructure and physical properties of the ECM can also significantly influence cell migration, e.g., via durotaxis and contact guidance. Here, we develop a computational model for cell migration regulated by cell-ECM micro-mechanical coupling. Our model explicitly takes into account a variety of cellular level processes including focal adhesion formation and disassembly, active traction force generation and cell locomotion due to actin filament contraction, transmission and propagation of tensile forces in the ECM, as well as the resulting ECM remodeling. We validate our model by accurately reproducing single-cell dynamics of MCF-10A breast cancer cells migrating on collagen gels and show that the durotaxis and contact guidance effects naturally arise as a consequence of the cell-ECM micro-mechanical interactions considered in the model. Moreover, our model predicts strongly correlated multi-cellular migration dynamics, which are resulted from the ECM-mediated mechanical coupling among the migrating cell and are subsequently verified in {\it in vitro} experiments using MCF-10A cells. Our computational model provides a robust tool to investigate emergent collective dynamics of multi-cellular systems in complex {\it in vivo} micro-environment and can be utilized to design {\it in vitro} micro-environments to guide collective behaviors and self-organization of cells. |
1408.3101 | Sergio Verduzco-Flores | Sergio Verduzco-Flores | How stochastic synchrony could work in cerebellar Purkinje cells | 46 pages, 8 figures. Similar to a version submitted to the Journal of
Mathematical Neuroscience | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Simple spike synchrony between Purkinje cells projecting to a common neuron
in the deep cerebellar nucleus is emerging as an important factor in the
encoding of output information from cerebellar cortex. Stochastic
synchronization is a viable mechanism through which this synchrony could be
generated, but it has received scarce attention, perhaps because the presence
of feedforward inhibition in the input to Purkinje cells makes insights
difficult. This paper presents a method to account for feedforward inhibition
so the usual mathematical approaches to stochastic synchronization can be
applied. Three concepts (input correlation, heterogeneity, and PRC shape) are
then introduced to facilitate an intuitive understanding of how different
factors can affect synchronization in Purkinje cells. This is followed by a
discussion of how stochastic synchrony could play a role in the cerebellar
response under different assumptions.
| [
{
"created": "Wed, 13 Aug 2014 19:35:00 GMT",
"version": "v1"
}
] | 2014-08-14 | [
[
"Verduzco-Flores",
"Sergio",
""
]
] | Simple spike synchrony between Purkinje cells projecting to a common neuron in the deep cerebellar nucleus is emerging as an important factor in the encoding of output information from cerebellar cortex. Stochastic synchronization is a viable mechanism through which this synchrony could be generated, but it has received scarce attention, perhaps because the presence of feedforward inhibition in the input to Purkinje cells makes insights difficult. This paper presents a method to account for feedforward inhibition so the usual mathematical approaches to stochastic synchronization can be applied. Three concepts (input correlation, heterogeneity, and PRC shape) are then introduced to facilitate an intuitive understanding of how different factors can affect synchronization in Purkinje cells. This is followed by a discussion of how stochastic synchrony could play a role in the cerebellar response under different assumptions. |
1808.08086 | Alberto Sorrentino | Gianvittorio Luria, Dunja Duran, Elisa Visani, Sara Sommariva, Fabio
Rotondi, Davide Rossi Sebastiano, Ferruccio Panzica, Michele Piana, Alberto
Sorrentino | Bayesian Multi--Dipole Modeling in the Frequency Domain | null | Journal of Neuroscience Methods Volume 312, 15 January 2019, Pages
27-36 | 10.1016/j.jneumeth.2018.11.007 | null | q-bio.QM stat.AP stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Magneto- and Electro-encephalography record the electromagnetic
field generated by neural currents with high temporal frequency and good
spatial resolution, and are therefore well suited for source localization in
the time and in the frequency domain. In particular, localization of the
generators of neural oscillations is very important in the study of cognitive
processes in the healthy and in the pathological brain.
New method: We introduce the use of a Bayesian multi-dipole localization
method in the frequency domain. Given the Fourier Transform of the data at one
or multiple frequencies and/or trials, the algorithm approximates numerically
the posterior distribution with Monte Carlo techniques.
Results: We use synthetic data to show that the proposed method behaves well
under a wide range of experimental conditions, including low signal-to-noise
ratios and correlated sources. We use dipole clusters to mimic the effect of
extended sources. In addition, we test the algorithm on real MEG data to
confirm its feasibility.
Comparison with existing method(s): Throughout the whole study, DICS (Dynamic
Imaging of Coherent Sources) is used systematically as a benchmark. The two
methods provide similar general pictures; the posterior distributions of the
Bayesian approach contain much richer information at the price of a higher
computational cost.
Conclusions: The Bayesian method described in this paper represents a
reliable approach for localization of multiple dipoles in the frequency domain.
| [
{
"created": "Fri, 24 Aug 2018 10:55:07 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Dec 2018 14:21:54 GMT",
"version": "v2"
}
] | 2018-12-14 | [
[
"Luria",
"Gianvittorio",
""
],
[
"Duran",
"Dunja",
""
],
[
"Visani",
"Elisa",
""
],
[
"Sommariva",
"Sara",
""
],
[
"Rotondi",
"Fabio",
""
],
[
"Sebastiano",
"Davide Rossi",
""
],
[
"Panzica",
"Ferruccio",
""
],
[
"Piana",
"Michele",
""
],
[
"Sorrentino",
"Alberto",
""
]
] | Background: Magneto- and Electro-encephalography record the electromagnetic field generated by neural currents with high temporal frequency and good spatial resolution, and are therefore well suited for source localization in the time and in the frequency domain. In particular, localization of the generators of neural oscillations is very important in the study of cognitive processes in the healthy and in the pathological brain. New method: We introduce the use of a Bayesian multi-dipole localization method in the frequency domain. Given the Fourier Transform of the data at one or multiple frequencies and/or trials, the algorithm approximates numerically the posterior distribution with Monte Carlo techniques. Results: We use synthetic data to show that the proposed method behaves well under a wide range of experimental conditions, including low signal-to-noise ratios and correlated sources. We use dipole clusters to mimic the effect of extended sources. In addition, we test the algorithm on real MEG data to confirm its feasibility. Comparison with existing method(s): Throughout the whole study, DICS (Dynamic Imaging of Coherent Sources) is used systematically as a benchmark. The two methods provide similar general pictures; the posterior distributions of the Bayesian approach contain much richer information at the price of a higher computational cost. Conclusions: The Bayesian method described in this paper represents a reliable approach for localization of multiple dipoles in the frequency domain. |
2211.10205 | Georgina Al-Badri Dr | Georgina Al-Badri, James B. Phillips, Rebecca J. Shipley, and Nicholas
C. Ovenden | Formation of vascular-like structures using a chemotaxis-driven
multiphase model | null | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We propose a continuum model for pattern formation, based on the multiphase
model framework, to explore in vitro cell patterning within an extracellular
matrix. We demonstrate that, within this framework, chemotaxis-driven cell
migration can lead to formation of cell clusters and vascular-like structures
in 1D and 2D respectively. The influence on pattern formation of additional
mechanisms commonly included in multiphase tissue models, including cell-matrix
traction, contact inhibition, and cell-cell aggregation, are also investigated.
Using sensitivity analysis, the relative impact of each model parameter on the
simulation outcomes is assessed to identify the key parameters involved.
Chemoattractant-matrix binding is further included, motivated by previous
experimental studies, and to augment the spatial scale of patterning to within
a biologically plausible range. Key findings from the in-depth parameter
analysis of the 1D models, both with and without chemoattractant-matrix
binding, are demonstrated to translate well to the 2D model, obtaining
vascular-like cell patterning for multiple parameter regimes. Overall, we
demonstrate a biologically-motivated multiphase model capable of generating
long-term pattern formation on a biologically plausible spatial scale both in
1D and 2D, with applications for modelling in vitro vascular network formation.
| [
{
"created": "Fri, 18 Nov 2022 12:50:56 GMT",
"version": "v1"
}
] | 2022-11-21 | [
[
"Al-Badri",
"Georgina",
""
],
[
"Phillips",
"James B.",
""
],
[
"Shipley",
"Rebecca J.",
""
],
[
"Ovenden",
"Nicholas C.",
""
]
] | We propose a continuum model for pattern formation, based on the multiphase model framework, to explore in vitro cell patterning within an extracellular matrix. We demonstrate that, within this framework, chemotaxis-driven cell migration can lead to formation of cell clusters and vascular-like structures in 1D and 2D respectively. The influence on pattern formation of additional mechanisms commonly included in multiphase tissue models, including cell-matrix traction, contact inhibition, and cell-cell aggregation, are also investigated. Using sensitivity analysis, the relative impact of each model parameter on the simulation outcomes is assessed to identify the key parameters involved. Chemoattractant-matrix binding is further included, motivated by previous experimental studies, and to augment the spatial scale of patterning to within a biologically plausible range. Key findings from the in-depth parameter analysis of the 1D models, both with and without chemoattractant-matrix binding, are demonstrated to translate well to the 2D model, obtaining vascular-like cell patterning for multiple parameter regimes. Overall, we demonstrate a biologically-motivated multiphase model capable of generating long-term pattern formation on a biologically plausible spatial scale both in 1D and 2D, with applications for modelling in vitro vascular network formation. |
q-bio/0602007 | Mika Yoshida | Mika Yoshida, Kinji Fuchikami and Tatsuya Uezu | Realization of features of immune response by dynamical system models
and a possible mechanism of memory of antigen invasion | 17 pages, 11 figures | null | null | null | q-bio.PE | null | Among features of real immune responses which occur when antigens invade a
body,there are two remarkable features. One is that the amount of antibodies
produced in the secondary invasion by the same antigens is more than 10 times
larger than that in the primary invasion. The other is that more effective
antibodies which can neutralize the antigens more quickly are produced by
somatic hypermutation during the immune response. This phenomenon is named as
'affinity maturation'. In this paper, we try to reproduce these features by
dynamical system models and present possible factors to realize them. Further,
we present a model in which the memory of the invasion by antigens is realized
without immune memory cells.
| [
{
"created": "Tue, 7 Feb 2006 04:38:15 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Yoshida",
"Mika",
""
],
[
"Fuchikami",
"Kinji",
""
],
[
"Uezu",
"Tatsuya",
""
]
] | Among features of real immune responses which occur when antigens invade a body,there are two remarkable features. One is that the amount of antibodies produced in the secondary invasion by the same antigens is more than 10 times larger than that in the primary invasion. The other is that more effective antibodies which can neutralize the antigens more quickly are produced by somatic hypermutation during the immune response. This phenomenon is named as 'affinity maturation'. In this paper, we try to reproduce these features by dynamical system models and present possible factors to realize them. Further, we present a model in which the memory of the invasion by antigens is realized without immune memory cells. |
1711.02754 | Dionisio Bazeia | D. Bazeia, J. Menezes, B.F. de Oliveira, J.G.G.S. Ramos | Hamming distance and mobility behavior in generalized
rock-paper-scissors models | 7 pages, 9 figures. To appear in EPL | EPL 119 (2017) 58003 | 10.1209/0295-5075/119/58003 | null | q-bio.PE cond-mat.stat-mech nlin.CD physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work reports on two related investigations of stochastic simulations
which are widely used to study biodiversity and other related issues. We first
deal with the behavior of the Hamming distance under the increase of the number
of species and the size of the lattice, and then investigate how the mobility
of the species contributes to jeopardize biodiversity. The investigations are
based on the standard rules of reproduction, mobility and predation or
competition, which are described by specific rules, guided by generalization of
the rock-paper-scissors game, valid in the case of three species. The results
on the Hamming distance indicate that it engenders universal behavior,
independently of the number of species and the size of the square lattice. The
results on the mobility confirm the prediction that it may destroy diversity,
if it is increased to higher and higher values.
| [
{
"created": "Tue, 7 Nov 2017 22:34:48 GMT",
"version": "v1"
}
] | 2017-11-29 | [
[
"Bazeia",
"D.",
""
],
[
"Menezes",
"J.",
""
],
[
"de Oliveira",
"B. F.",
""
],
[
"Ramos",
"J. G. G. S.",
""
]
] | This work reports on two related investigations of stochastic simulations which are widely used to study biodiversity and other related issues. We first deal with the behavior of the Hamming distance under the increase of the number of species and the size of the lattice, and then investigate how the mobility of the species contributes to jeopardize biodiversity. The investigations are based on the standard rules of reproduction, mobility and predation or competition, which are described by specific rules, guided by generalization of the rock-paper-scissors game, valid in the case of three species. The results on the Hamming distance indicate that it engenders universal behavior, independently of the number of species and the size of the square lattice. The results on the mobility confirm the prediction that it may destroy diversity, if it is increased to higher and higher values. |
2307.16182 | Chiara Balestra | Chiara Balestra, Carlo Maj, Emmanuel M\"uller, Andreas Mayr | Redundancy-aware unsupervised rankings for collections of gene sets | arXiv admin note: substantial text overlap with arXiv:2207.12184 | null | null | null | q-bio.QM cs.GT cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The biological roles of gene sets are used to group them into collections.
These collections are often characterized by being high-dimensional,
overlapping, and redundant families of sets, thus precluding a straightforward
interpretation and study of their content. Bioinformatics looked for solutions
to reduce their dimension or increase their intepretability. One possibility
lies in aggregating overlapping gene sets to create larger pathways, but the
modified biological pathways are hardly biologically justifiable. We propose to
use importance scores to rank the pathways in the collections studying the
context from a set covering perspective. The proposed Shapley values-based
scores consider the distribution of the singletons and the size of the sets in
the families; Furthermore, a trick allows us to circumvent the usual
exponential complexity of Shapley values' computation. Finally, we address the
challenge of including a redundancy awareness in the obtained rankings where,
in our case, sets are redundant if they show prominent intersections.
The rankings can be used to reduce the dimension of collections of gene sets,
such that they show lower redundancy and still a high coverage of the genes. We
further investigate the impact of our selection on Gene Sets Enrichment
Analysis. The proposed method shows a practical utility in bioinformatics to
increase the interpretability of the collections of gene sets and a step
forward to include redundancy into Shapley values computations.
| [
{
"created": "Sun, 30 Jul 2023 09:39:42 GMT",
"version": "v1"
}
] | 2023-08-01 | [
[
"Balestra",
"Chiara",
""
],
[
"Maj",
"Carlo",
""
],
[
"Müller",
"Emmanuel",
""
],
[
"Mayr",
"Andreas",
""
]
] | The biological roles of gene sets are used to group them into collections. These collections are often characterized by being high-dimensional, overlapping, and redundant families of sets, thus precluding a straightforward interpretation and study of their content. Bioinformatics looked for solutions to reduce their dimension or increase their intepretability. One possibility lies in aggregating overlapping gene sets to create larger pathways, but the modified biological pathways are hardly biologically justifiable. We propose to use importance scores to rank the pathways in the collections studying the context from a set covering perspective. The proposed Shapley values-based scores consider the distribution of the singletons and the size of the sets in the families; Furthermore, a trick allows us to circumvent the usual exponential complexity of Shapley values' computation. Finally, we address the challenge of including a redundancy awareness in the obtained rankings where, in our case, sets are redundant if they show prominent intersections. The rankings can be used to reduce the dimension of collections of gene sets, such that they show lower redundancy and still a high coverage of the genes. We further investigate the impact of our selection on Gene Sets Enrichment Analysis. The proposed method shows a practical utility in bioinformatics to increase the interpretability of the collections of gene sets and a step forward to include redundancy into Shapley values computations. |
1911.04447 | Tanvir Ferdousi | Tanvir Ferdousi, Sifat Afroj Moon, Adrian Self, and Caterina Scoglio | Generation of swine movement network and analysis of efficient
mitigation strategies for African swine fever virus | 19 pages, 8 figures, journal article (under review in PLOS ONE) | null | 10.1371/journal.pone.0225785 | null | q-bio.PE cs.SI q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Animal movement networks are essential in understanding and containing the
spread of infectious diseases in farming industries. Due to its confidential
nature, movement data for the US swine farming population is not readily
available. Hence, we propose a method to generate such networks from limited
data available in the public domain. As a potentially devastating candidate, we
simulate the spread of African swine fever virus (ASFV) in our generated
network and analyze how the network structure affects the disease spread. We
find that high in-degree farm operations (i.e., markets) play critical roles in
the disease spread. We also find that high in-degree based targeted isolation
and hypothetical vaccinations are more effective for disease control compared
to other centrality-based mitigation strategies. The generated networks can be
made more robust by validation with more data whenever more movement data will
be available.
| [
{
"created": "Mon, 11 Nov 2019 18:51:14 GMT",
"version": "v1"
}
] | 2020-07-01 | [
[
"Ferdousi",
"Tanvir",
""
],
[
"Moon",
"Sifat Afroj",
""
],
[
"Self",
"Adrian",
""
],
[
"Scoglio",
"Caterina",
""
]
] | Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available. |
q-bio/0309031 | Jonathan D. Victor | Jonathan D. Victor and Keith P. Purpura | Metric-space analysis of spike trains: theory, algorithms, and
application | 16 Figures (not in this file). Originally submitted to the neuro-sys
archive which was never publicly announced (was 9810001) | Network 8, 127-164 (1997) | null | null | q-bio.NC q-bio.QM | null | We present the mathematical basis of a new approach to the analysis of
temporal coding. The foundation of the approach is the construction of several
families of novel distances (metrics) between neuronal impulse trains. In
contrast to most previous approaches to the analysis of temporal coding, the
present approach does not attempt to embed impulse trains in a vector space,
and does not assume a Euclidean notion of distance. Rather, the proposed
metrics formalize physiologically-based hypotheses for what aspects of the
firing pattern might be stimulus-dependent, and make essential use of the point
process nature of neural discharges. We show that these families of metrics
endow the space of impulse trains with related but inequivalent topological
structures. We show how these metrics can be used to determine whether a set of
observed responses have stimulus-dependent temporal structure without a
vector-space embedding. We show how multidimensional scaling can be used to
assess the similarity of these metrics to Euclidean distances. For two of these
families of metrics (one based on spike times and one based on spike
intervals), we present highly efficient computational algorithms for
calculating the distances. We illustrate these ideas by application to
artificial datasets and to recordings from auditory and visual cortex.
| [
{
"created": "Fri, 30 Oct 1998 22:20:10 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Victor",
"Jonathan D.",
""
],
[
"Purpura",
"Keith P.",
""
]
] | We present the mathematical basis of a new approach to the analysis of temporal coding. The foundation of the approach is the construction of several families of novel distances (metrics) between neuronal impulse trains. In contrast to most previous approaches to the analysis of temporal coding, the present approach does not attempt to embed impulse trains in a vector space, and does not assume a Euclidean notion of distance. Rather, the proposed metrics formalize physiologically-based hypotheses for what aspects of the firing pattern might be stimulus-dependent, and make essential use of the point process nature of neural discharges. We show that these families of metrics endow the space of impulse trains with related but inequivalent topological structures. We show how these metrics can be used to determine whether a set of observed responses have stimulus-dependent temporal structure without a vector-space embedding. We show how multidimensional scaling can be used to assess the similarity of these metrics to Euclidean distances. For two of these families of metrics (one based on spike times and one based on spike intervals), we present highly efficient computational algorithms for calculating the distances. We illustrate these ideas by application to artificial datasets and to recordings from auditory and visual cortex. |
2205.13363 | Santosh Pandey | Upender Kalwa, Christopher Legner, Elizabeth Wlezien, Gregory Tylka,
Santosh Pandey | New methods of removing debris and high-throughput counting of cyst
nematode eggs extracted from field soil | null | Plos One 2019 | 10.1371/journal.pone.0223386 | null | q-bio.QM eess.IV | http://creativecommons.org/licenses/by/4.0/ | The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging
pathogen of soybeans in the United States. To assess the severity of nematode
infestations in the field, SCN egg population densities are determined. Cysts
(dead females) of the nematode must be extracted from soil samples and then
ground to extract the eggs within. Sucrose centrifugation commonly is used to
separate debris from suspensions of extracted nematode eggs. We present a
method using OptiPrep as a density gradient medium with improved separation and
recovery of extracted eggs compared to the sucrose centrifugation technique.
Also, computerized methods were developed to automate the identification and
counting of nematode eggs from the processed samples. In one approach, a
high-resolution scanner was used to take static images of extracted eggs and
debris on filter papers, and a deep learning network was trained to identify
and count the eggs among the debris. In the second approach, a lensless imaging
setup was developed using off-the-shelf components, and the processed egg
samples were passed through a microfluidic flow chip made from double-sided
adhesive tape. Holographic videos were recorded of the passing eggs and debris,
and the videos were reconstructed and processed by custom software program to
obtain egg counts. The performance of the software programs for egg counting
was characterized with SCN-infested soil collected from two farms, and the
results using these methods were compared with those obtained through manual
counting.
| [
{
"created": "Wed, 25 May 2022 01:55:27 GMT",
"version": "v1"
}
] | 2022-05-27 | [
[
"Kalwa",
"Upender",
""
],
[
"Legner",
"Christopher",
""
],
[
"Wlezien",
"Elizabeth",
""
],
[
"Tylka",
"Gregory",
""
],
[
"Pandey",
"Santosh",
""
]
] | The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybeans in the United States. To assess the severity of nematode infestations in the field, SCN egg population densities are determined. Cysts (dead females) of the nematode must be extracted from soil samples and then ground to extract the eggs within. Sucrose centrifugation commonly is used to separate debris from suspensions of extracted nematode eggs. We present a method using OptiPrep as a density gradient medium with improved separation and recovery of extracted eggs compared to the sucrose centrifugation technique. Also, computerized methods were developed to automate the identification and counting of nematode eggs from the processed samples. In one approach, a high-resolution scanner was used to take static images of extracted eggs and debris on filter papers, and a deep learning network was trained to identify and count the eggs among the debris. In the second approach, a lensless imaging setup was developed using off-the-shelf components, and the processed egg samples were passed through a microfluidic flow chip made from double-sided adhesive tape. Holographic videos were recorded of the passing eggs and debris, and the videos were reconstructed and processed by custom software program to obtain egg counts. The performance of the software programs for egg counting was characterized with SCN-infested soil collected from two farms, and the results using these methods were compared with those obtained through manual counting. |
2308.01452 | Phillip Wilson | Elliott Hughes, Miguel Moyers-Gonzalez, Rua Murray, Phillip L. Wilson | A Mathematically Robust Model of Exotic Pine Invasions | 36 pages, 9 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Invasive pine trees pose a threat to biodiversity in a variety of Southern
Hemisphere countries, but understanding of the dynamics of invasions and the
factors that retard or accelerate spread is limited. Here, we consider the past
models of wilding pine spread and develop a new model of pine invasion. We show
that many prior models feature parameter estimates which are not biologically
supported and rely on a conjecture to obtain an asymptotic spread speed of
invasive pine populations, the main output of these models. In contrast to
prior approaches, we use partial differential equations to model an invasion.
We show that invasions are almost static for a significant period of time
before rapidly accelerating to spread at a constant rate, matching observed
behaviour in at least some field sites. Our work suggests that prior methods
for estimating invasion speeds may not accurately predict spread and are
sensitive to assumptions about the distribution of parameters. However, we
present alternative estimation methods and suggest directions for further
research.
| [
{
"created": "Wed, 2 Aug 2023 21:59:55 GMT",
"version": "v1"
}
] | 2023-08-04 | [
[
"Hughes",
"Elliott",
""
],
[
"Moyers-Gonzalez",
"Miguel",
""
],
[
"Murray",
"Rua",
""
],
[
"Wilson",
"Phillip L.",
""
]
] | Invasive pine trees pose a threat to biodiversity in a variety of Southern Hemisphere countries, but understanding of the dynamics of invasions and the factors that retard or accelerate spread is limited. Here, we consider the past models of wilding pine spread and develop a new model of pine invasion. We show that many prior models feature parameter estimates which are not biologically supported and rely on a conjecture to obtain an asymptotic spread speed of invasive pine populations, the main output of these models. In contrast to prior approaches, we use partial differential equations to model an invasion. We show that invasions are almost static for a significant period of time before rapidly accelerating to spread at a constant rate, matching observed behaviour in at least some field sites. Our work suggests that prior methods for estimating invasion speeds may not accurately predict spread and are sensitive to assumptions about the distribution of parameters. However, we present alternative estimation methods and suggest directions for further research. |
0705.0201 | Jesse Bloom | Jesse D Bloom, Philip A Romero, Zhongyi Lu, and Frances H Arnold | Neutral genetic drift can aid functional protein evolution | null | Biology Direct 2:17 (2007) | 10.1186/1745-6150-2-17 | null | q-bio.PE q-bio.BM | null | BACKGROUND: Many of the mutations accumulated by naturally evolving proteins
are neutral in the sense that they do not significantly alter a protein's
ability to perform its primary biological function. However, new protein
functions evolve when selection begins to favor other, "promiscuous" functions
that are incidental to a protein's biological role. If mutations that are
neutral with respect to a protein's primary biological function cause
substantial changes in promiscuous functions, these mutations could enable
future functional evolution.
RESULTS: Here we investigate this possibility experimentally by examining how
cytochrome P450 enzymes that have evolved neutrally with respect to activity on
a single substrate have changed in their abilities to catalyze reactions on
five other substrates. We find that the enzymes have sometimes changed as much
as four-fold in the promiscuous activities. The changes in promiscuous
activities tend to increase with the number of mutations, and can be largely
rationalized in terms of the chemical structures of the substrates. The
activities on chemically similar substrates tend to change in a coordinated
fashion, potentially providing a route for systematically predicting the change
in one function based on the measurement of several others.
CONCLUSIONS: Our work suggests that initially neutral genetic drift can lead
to substantial changes in protein functions that are not currently under
selection, in effect poising the proteins to more readily undergo functional
evolution should selection "ask new questions" in the future.
| [
{
"created": "Wed, 2 May 2007 05:02:10 GMT",
"version": "v1"
}
] | 2007-07-18 | [
[
"Bloom",
"Jesse D",
""
],
[
"Romero",
"Philip A",
""
],
[
"Lu",
"Zhongyi",
""
],
[
"Arnold",
"Frances H",
""
]
] | BACKGROUND: Many of the mutations accumulated by naturally evolving proteins are neutral in the sense that they do not significantly alter a protein's ability to perform its primary biological function. However, new protein functions evolve when selection begins to favor other, "promiscuous" functions that are incidental to a protein's biological role. If mutations that are neutral with respect to a protein's primary biological function cause substantial changes in promiscuous functions, these mutations could enable future functional evolution. RESULTS: Here we investigate this possibility experimentally by examining how cytochrome P450 enzymes that have evolved neutrally with respect to activity on a single substrate have changed in their abilities to catalyze reactions on five other substrates. We find that the enzymes have sometimes changed as much as four-fold in the promiscuous activities. The changes in promiscuous activities tend to increase with the number of mutations, and can be largely rationalized in terms of the chemical structures of the substrates. The activities on chemically similar substrates tend to change in a coordinated fashion, potentially providing a route for systematically predicting the change in one function based on the measurement of several others. CONCLUSIONS: Our work suggests that initially neutral genetic drift can lead to substantial changes in protein functions that are not currently under selection, in effect poising the proteins to more readily undergo functional evolution should selection "ask new questions" in the future. |
1811.02507 | Takashi Morita | Takashi Morita, Hiroki Koda | Superregular grammars do not provide additional explanatory power but
allow for a compact analysis of animal song | Accepted for publication by Royal Society Open Science | null | 10.1098/rsos.190139 | null | q-bio.NC cs.CL | http://creativecommons.org/licenses/by/4.0/ | A pervasive belief with regard to the differences between human language and
animal vocal sequences (song) is that they belong to different classes of
computational complexity, with animal song belonging to regular languages,
whereas human language is superregular. This argument, however, lacks empirical
evidence since superregular analyses of animal song are understudied. The goal
of this paper is to perform a superregular analysis of animal song, using data
from gibbons as a case study, and demonstrate that a superregular analysis can
be effectively used with non-human data. A key finding is that a superregular
analysis does not increase explanatory power but rather provides for compact
analysis: Fewer grammatical rules are necessary once superregularity is
allowed. This pattern is analogous to a previous computational analysis of
human language, and accordingly, the null hypothesis, that human language and
animal song are governed by the same type of grammatical systems, cannot be
rejected.
| [
{
"created": "Mon, 5 Nov 2018 05:07:37 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Jun 2019 12:06:15 GMT",
"version": "v2"
}
] | 2020-11-03 | [
[
"Morita",
"Takashi",
""
],
[
"Koda",
"Hiroki",
""
]
] | A pervasive belief with regard to the differences between human language and animal vocal sequences (song) is that they belong to different classes of computational complexity, with animal song belonging to regular languages, whereas human language is superregular. This argument, however, lacks empirical evidence since superregular analyses of animal song are understudied. The goal of this paper is to perform a superregular analysis of animal song, using data from gibbons as a case study, and demonstrate that a superregular analysis can be effectively used with non-human data. A key finding is that a superregular analysis does not increase explanatory power but rather provides for compact analysis: Fewer grammatical rules are necessary once superregularity is allowed. This pattern is analogous to a previous computational analysis of human language, and accordingly, the null hypothesis, that human language and animal song are governed by the same type of grammatical systems, cannot be rejected. |
1204.3398 | Namiko Mitarai | Namiko Mitarai, Joachim Mathiesen, Kim Sneppen | Emergence of diversity in a model ecosystem | 7 pages, 6 figures. Accepted for publication in PRE. Typos corrected,
Fig.3A and Fig.6 updated | Phys. Rev. E 86, 011929 (2012) | 10.1103/PhysRevE.86.011929 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The biological requirements for an ecosystem to develop and maintain species
diversity are in general unknown. Here we consider a model ecosystem of sessile
and mutually excluding organisms competing for space [Mathiesen et al. Phys.
Rev. Lett. 107, 188101 (2011)]. The competition is controlled by an interaction
network with fixed links chosen by a Bernoulli process. New species are
introduced in the system at a predefined rate. In the limit of small
introduction rates, the system becomes bistable and can undergo a phase
transition from a state of low diversity to high diversity. We suggest that
patches of isolated meta-populations formed by the collapse of cyclic relations
are essential for the transition to the state of high diversity.
| [
{
"created": "Mon, 16 Apr 2012 08:27:03 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Jul 2012 12:46:52 GMT",
"version": "v2"
},
{
"created": "Wed, 25 Jul 2012 07:46:12 GMT",
"version": "v3"
}
] | 2012-09-10 | [
[
"Mitarai",
"Namiko",
""
],
[
"Mathiesen",
"Joachim",
""
],
[
"Sneppen",
"Kim",
""
]
] | The biological requirements for an ecosystem to develop and maintain species diversity are in general unknown. Here we consider a model ecosystem of sessile and mutually excluding organisms competing for space [Mathiesen et al. Phys. Rev. Lett. 107, 188101 (2011)]. The competition is controlled by an interaction network with fixed links chosen by a Bernoulli process. New species are introduced in the system at a predefined rate. In the limit of small introduction rates, the system becomes bistable and can undergo a phase transition from a state of low diversity to high diversity. We suggest that patches of isolated meta-populations formed by the collapse of cyclic relations are essential for the transition to the state of high diversity. |
q-bio/0507042 | Michael Baake | Evelyn Rost (Greifswald), Ralf Geske (Neubrandenburg), Michael Baake
(Bielefeld) | Signal analysis of impulse response functions in MR- and CT-measurements
of cerebral blood flow | 15 pages, 6 figures | J. Theor. Biol. 240 (2006) 451-458 | null | null | q-bio.TO | null | The impulse response function (IRF) of a localized bolus in cerebral blood
flow codes important information on the tissue type. It is indirectly
accessible both from MR- and CT-imaging methods, at least in principle. In
practice, however, noise and limited signal resolution render standard
deconvolution techniques almost useless. Parametric signal descriptions look
more promising, and it is the aim of this contribution to develop some
improvements along this line.
| [
{
"created": "Thu, 28 Jul 2005 13:41:31 GMT",
"version": "v1"
}
] | 2014-09-30 | [
[
"Rost",
"Evelyn",
"",
"Greifswald"
],
[
"Geske",
"Ralf",
"",
"Neubrandenburg"
],
[
"Baake",
"Michael",
"",
"Bielefeld"
]
] | The impulse response function (IRF) of a localized bolus in cerebral blood flow codes important information on the tissue type. It is indirectly accessible both from MR- and CT-imaging methods, at least in principle. In practice, however, noise and limited signal resolution render standard deconvolution techniques almost useless. Parametric signal descriptions look more promising, and it is the aim of this contribution to develop some improvements along this line. |
q-bio/0611013 | Zhao Jing | Zhao Jing, Tao Lin, Yu Hong, Luo Jian-Hua, Z. W. Cao, Li Yixue | Bow-tie topological features of metabolic networks and the functional
significance | 15 pages, 5 figures | Chinese Science Bulletin 2007, 52:1036 - 1045 | null | null | q-bio.MN | null | Exploring the structural topology of genome-based large-scale metabolic
network is essential for investigating possible relations between structure and
functionality. Visualization would be helpful for obtaining immediate
information about structural organization. In this work, metabolic networks of
75 organisms were investigated from a topological point of view. A spread
bow-tie model was proposed to give a clear visualization of the bow-tie
structure for metabolic networks. The revealed topological pattern helps to
design more efficient algorithm specifically for metabolic networks. This
coarse-grained graph also visualizes the vulnerable connections in the network,
and thus could have important implication for disease studies and drug target
identifications. In addition, analysis on the reciprocal links and main cores
in the GSC part of bow-tie also reveals that the bow-tie structure of metabolic
networks has its own intrinsic and significant features which are significantly
different from those of random networks.
| [
{
"created": "Sat, 4 Nov 2006 02:15:44 GMT",
"version": "v1"
}
] | 2007-09-06 | [
[
"Jing",
"Zhao",
""
],
[
"Lin",
"Tao",
""
],
[
"Hong",
"Yu",
""
],
[
"Jian-Hua",
"Luo",
""
],
[
"Cao",
"Z. W.",
""
],
[
"Yixue",
"Li",
""
]
] | Exploring the structural topology of genome-based large-scale metabolic network is essential for investigating possible relations between structure and functionality. Visualization would be helpful for obtaining immediate information about structural organization. In this work, metabolic networks of 75 organisms were investigated from a topological point of view. A spread bow-tie model was proposed to give a clear visualization of the bow-tie structure for metabolic networks. The revealed topological pattern helps to design more efficient algorithm specifically for metabolic networks. This coarse-grained graph also visualizes the vulnerable connections in the network, and thus could have important implication for disease studies and drug target identifications. In addition, analysis on the reciprocal links and main cores in the GSC part of bow-tie also reveals that the bow-tie structure of metabolic networks has its own intrinsic and significant features which are significantly different from those of random networks. |
1408.4782 | Jaewook Joo | Jaewook Joo and Sanjeev Chauhan | Design principles of noise-induced oscillation in biochemical reaction
networks: II. coupled positive and negative feedback loops | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | According to the chemical reaction network theory, the topology of a certain
class of chemical reaction networks, regardless of the kinetic details, sets a
limit on the dynamical properties that a particular network can potentially
admit; the structure of a network predetermines the dynamic capacity of the
network. We note that stochastic fluctuations can possibly confer a new
dynamical capability to a network. Thus, it is of tremendous value to
understand and be able to control the landscape of stochastic dynamical
behaviors of a biochemical reaction network as a function of network
architecture. Here we investigate such a case where stochastic fluctuations can
give rise to the new capability of noise-induced oscillation in a subset of
biochemical reaction networks, the networks with only three biochemical species
whose reactions are governed by mass action kinetics and with the coupling of
positive and negative feedback loops. We model the networks with the master
equations and approximate them, using the linear noise approximation. For each
network, we read the signal-to-noise ratio value, an indicator of amplified and
coherent noise-induced oscillation, off from the analytically derived power
spectra. We classify the networks into three performance groups based on the
average values of the signal-to-noise ratio and the robustness. We identify the
common network architecture among the networks belonging to the same
performance group, from which we learn that the coupling of negative and
positive feedback loops generally enhance the noise-induced oscillation
performance better than the negative feedback loops alone. The performance of
networks also depends on the relative size of the positive and negative
feedback loops; the networks with the bigger positive and smaller negative
feedbacks are much worse oscillators than the networks with only negative
feedback loops.
| [
{
"created": "Wed, 20 Aug 2014 19:49:07 GMT",
"version": "v1"
}
] | 2014-08-21 | [
[
"Joo",
"Jaewook",
""
],
[
"Chauhan",
"Sanjeev",
""
]
] | According to the chemical reaction network theory, the topology of a certain class of chemical reaction networks, regardless of the kinetic details, sets a limit on the dynamical properties that a particular network can potentially admit; the structure of a network predetermines the dynamic capacity of the network. We note that stochastic fluctuations can possibly confer a new dynamical capability to a network. Thus, it is of tremendous value to understand and be able to control the landscape of stochastic dynamical behaviors of a biochemical reaction network as a function of network architecture. Here we investigate such a case where stochastic fluctuations can give rise to the new capability of noise-induced oscillation in a subset of biochemical reaction networks, the networks with only three biochemical species whose reactions are governed by mass action kinetics and with the coupling of positive and negative feedback loops. We model the networks with the master equations and approximate them, using the linear noise approximation. For each network, we read the signal-to-noise ratio value, an indicator of amplified and coherent noise-induced oscillation, off from the analytically derived power spectra. We classify the networks into three performance groups based on the average values of the signal-to-noise ratio and the robustness. We identify the common network architecture among the networks belonging to the same performance group, from which we learn that the coupling of negative and positive feedback loops generally enhance the noise-induced oscillation performance better than the negative feedback loops alone. The performance of networks also depends on the relative size of the positive and negative feedback loops; the networks with the bigger positive and smaller negative feedbacks are much worse oscillators than the networks with only negative feedback loops. |
1809.06632 | William Grant | William P. Grant, Sebastian E. Ahnert | Modular decomposition of protein structure using community detection | 8 figures, 1 table, 11 pages | Journal of Complex Networks (2018), cny014 | 10.1093/comnet/cny014 | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | As the number of solved protein structures increases, the opportunities for
meta-analysis of this dataset increase too. Protein structures are known to be
formed of domains; structural and functional subunits that are often repeated
across sets of proteins. These domains generally form compact, globular
regions, and are therefore often easily identifiable by inspection, yet the
problem of automatically fragmenting the protein into these compact
substructures remains computationally challenging. Existing domain
classification methods focus on finding subregions of protein structure that
are conserved, rather than finding a decomposition which spans the full protein
structure. However, such a decomposition would find ready application in
coarse-graining molecular dynamics, analysing the protein's topology, in de
novo protein design and in fitting electron microscopy maps. Here, we present a
tool for performing this modular decomposition using the Infomap community
detection algorithm. The protein structure is abstracted into a network in
which its amino acids are the nodes, and where the edges are generated using a
simple proximity test. Infomap can then be used to identify highly
intra-connected regions of the protein. We perform this decomposition
systematically across 4000 distinct protein structures, taken from the Protein
Data Bank. The decomposition obtained correlates well with existing PFAM
sequence classifications, but has the advantage of spanning the full protein,
with the potential for novel domains. The coarse-grained network formed by the
communities can also be used as a proxy for protein topology at the
single-chain level; we demonstrate that grouping these proteins by their
coarse-grained network results in a functionally significant classification.
| [
{
"created": "Tue, 18 Sep 2018 10:39:53 GMT",
"version": "v1"
}
] | 2018-09-19 | [
[
"Grant",
"William P.",
""
],
[
"Ahnert",
"Sebastian E.",
""
]
] | As the number of solved protein structures increases, the opportunities for meta-analysis of this dataset increase too. Protein structures are known to be formed of domains; structural and functional subunits that are often repeated across sets of proteins. These domains generally form compact, globular regions, and are therefore often easily identifiable by inspection, yet the problem of automatically fragmenting the protein into these compact substructures remains computationally challenging. Existing domain classification methods focus on finding subregions of protein structure that are conserved, rather than finding a decomposition which spans the full protein structure. However, such a decomposition would find ready application in coarse-graining molecular dynamics, analysing the protein's topology, in de novo protein design and in fitting electron microscopy maps. Here, we present a tool for performing this modular decomposition using the Infomap community detection algorithm. The protein structure is abstracted into a network in which its amino acids are the nodes, and where the edges are generated using a simple proximity test. Infomap can then be used to identify highly intra-connected regions of the protein. We perform this decomposition systematically across 4000 distinct protein structures, taken from the Protein Data Bank. The decomposition obtained correlates well with existing PFAM sequence classifications, but has the advantage of spanning the full protein, with the potential for novel domains. The coarse-grained network formed by the communities can also be used as a proxy for protein topology at the single-chain level; we demonstrate that grouping these proteins by their coarse-grained network results in a functionally significant classification. |
2304.09225 | Zijin Gu | Zijin Gu, Keith Jamison, Mert R. Sabuncu and Amy Kuceyeski | Modulating human brain responses via optimal natural image selection and
synthetic image generation | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Understanding how human brains interpret and process information is
important. Here, we investigated the selectivity and inter-individual
differences in human brain responses to images via functional MRI. In our first
experiment, we found that images predicted to achieve maximal activations using
a group level encoding model evoke higher responses than images predicted to
achieve average activations, and the activation gain is positively associated
with the encoding model accuracy. Furthermore, aTLfaces and FBA1 had higher
activation in response to maximal synthetic images compared to maximal natural
images. In our second experiment, we found that synthetic images derived using
a personalized encoding model elicited higher responses compared to synthetic
images from group-level or other subjects' encoding models. The finding of
aTLfaces favoring synthetic images than natural images was also replicated. Our
results indicate the possibility of using data-driven and generative approaches
to modulate macro-scale brain region responses and probe inter-individual
differences in and functional specialization of the human visual system.
| [
{
"created": "Tue, 18 Apr 2023 18:25:26 GMT",
"version": "v1"
}
] | 2023-04-20 | [
[
"Gu",
"Zijin",
""
],
[
"Jamison",
"Keith",
""
],
[
"Sabuncu",
"Mert R.",
""
],
[
"Kuceyeski",
"Amy",
""
]
] | Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to images via functional MRI. In our first experiment, we found that images predicted to achieve maximal activations using a group level encoding model evoke higher responses than images predicted to achieve average activations, and the activation gain is positively associated with the encoding model accuracy. Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images. In our second experiment, we found that synthetic images derived using a personalized encoding model elicited higher responses compared to synthetic images from group-level or other subjects' encoding models. The finding of aTLfaces favoring synthetic images than natural images was also replicated. Our results indicate the possibility of using data-driven and generative approaches to modulate macro-scale brain region responses and probe inter-individual differences in and functional specialization of the human visual system. |
1206.3003 | Thierry Rabilloud | Thierry Rabilloud (LCBM) | The Whereabouts of 2D Gels in Quantitative Proteomics | null | Methods in Molecular Biology -Clifton then Totowa- 893 (2012)
25-35 | 10.1007/978-1-61779-885-6_2 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two-dimensional gel electrophoresis has been instrumental in the development
of proteomics. Although it is no longer the exclusive scheme used for
proteomics, its unique features make it a still highly valuable tool,
especially when multiple quantitative comparisons of samples must be made, and
even for large samples series. However, quantitative proteomics using 2D gels
is critically dependent on the performances of the protein detection methods
used after the electrophoretic separations. This chapter therefore examines
critically the various detection methods (radioactivity, dyes, fluorescence,
and silver) as well as the data analysis issues that must be taken into account
when quantitative comparative analysis of 2D gels is performed.
| [
{
"created": "Thu, 14 Jun 2012 04:57:18 GMT",
"version": "v1"
}
] | 2012-06-15 | [
[
"Rabilloud",
"Thierry",
"",
"LCBM"
]
] | Two-dimensional gel electrophoresis has been instrumental in the development of proteomics. Although it is no longer the exclusive scheme used for proteomics, its unique features make it a still highly valuable tool, especially when multiple quantitative comparisons of samples must be made, and even for large samples series. However, quantitative proteomics using 2D gels is critically dependent on the performances of the protein detection methods used after the electrophoretic separations. This chapter therefore examines critically the various detection methods (radioactivity, dyes, fluorescence, and silver) as well as the data analysis issues that must be taken into account when quantitative comparative analysis of 2D gels is performed. |
2407.19059 | Andrea Radtke | Andrea J. Radtke (Lymphocyte Biology Section and Center for Advanced
Tissue Imaging, Laboratory of Immune System Biology, National Institute of
Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD,
USA), Ifeanyichukwu Anidi (Critical Care Medicine and Pulmonary Branch,
National Heart, Lung and Blood Institute, National Institutes of Health,
Bethesda, MD, USA), Leanne Arakkal (Lymphocyte Biology Section, Laboratory of
Immune System Biology, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, MD, USA), Armando Arroyo-Mejias
(Lymphocyte Biology Section, Laboratory of Immune System Biology, National
Institute of Allergy and Infectious Diseases, National Institutes of Health,
Bethesda, MD, USA), Rebecca T. Beuschel (Lymphocyte Biology Section,
Laboratory of Immune System Biology, National Institute of Allergy and
Infectious Diseases, National Institutes of Health, Bethesda, MD, USA), Katy
Borner (Department of Intelligent Systems Engineering, Indiana University,
Bloomington, IN, USA), Colin J. Chu (UCL Institute of Ophthalmology and NIHR
Moorfields Biomedical Research Centre, London, UK), Beatrice Clark
(Lymphocyte Biology Section, Laboratory of Immune System Biology, National
Institute of Allergy and Infectious Diseases, National Institutes of Health,
Bethesda, MD, USA), Menna R. Clatworthy (Cambridge Institute for Therapeutic
Immunology and Infectious Diseases, University of Cambridge Department of
Medicine, Molecular Immunity Unit, Laboratory of Molecular Biology,
Cambridge, UK), Jake Colautti (McMaster Immunology Research Centre, Schroeder
Allergy and Immunology Research Institute, Department of Medicine, Faculty of
Health Sciences, McMaster University, Hamilton, ON, Canada), Joshua Croteau
(Department of Business Development, BioLegend Inc., San Diego, CA, USA),
Saven Denha (McMaster Immunology Research Centre, Schroeder Allergy and
Immunology Research Institute, Department of Medicine, Faculty of Health
Sciences, McMaster University, Hamilton, ON, Canada), Rose Dever (Functional
Immunogenomics Unit, National Institute of Arthritis and Musculoskeletal and
Skin Diseases, National Institutes of Health, Bethesda, MD, USA), Walderez O.
Dutra (Laboratory of Cell-Cell Interactions, Department of Morphology,
Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo
Horizonte, MG, Brazil), Sonja Fritzsche (Max-Delbrueck-Center for Molecular
Medicine in the Helmholtz Association), Spencer Fullam (Division of
Rheumatology, Rush University Medical Center, Chicago, IL, USA), Michael Y.
Gerner (Department of Immunology, University of Washington School of
Medicine, Seattle, WA, USA), Anita Gola (Robin Chemers Neustein Laboratory of
Mammalian Cell Biology and Development, The Rockefeller University, New York,
NY, USA), Kenneth J. Gollob (Center for Research in Immuno-oncology (CRIO),
Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil), Jonathan M.
Hernandez (Surgical Oncology Program, National Cancer Institute, National
Institutes of Health, Bethesda, MD, USA), Jyh Liang Hor (Lymphocyte Biology
Section, Laboratory of Immune System Biology, National Institute of Allergy
and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA),
Hiroshi Ichise (Lymphocyte Biology Section, Laboratory of Immune System
Biology, National Institute of Allergy and Infectious Diseases, National
Institutes of Health, Bethesda, MD, USA), Zhixin Jing (Lymphocyte Biology
Section, Laboratory of Immune System Biology, National Institute of Allergy
and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA),
Danny Jonigk (Institute of Pathology, Aachen Medical University, RWTH Aachen,
Aachen, Germany), Evelyn Kandov (Lymphocyte Biology Section, Laboratory of
Immune System Biology, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, MD, USA), Wolfgang Kastenmueller
(Wurzburg Institute of Systems Immunology, Max Planck Research Group at the
Julius-Maximilians-Universitat Wurzburg, Wurzburg, Germany), Joshua F.E.
Koenig (McMaster Immunology Research Centre, Schroeder Allergy and Immunology
Research Institute, Department of Medicine, Faculty of Health Sciences,
McMaster University, Hamilton, ON, Canada), Aanandita Kothurkar (UCL
Institute of Ophthalmology and NIHR Moorfields Biomedical Research Centre,
London, UK), Alexandra Y. Kreins (Infection Immunity and Inflammation
Research and Teaching Department, University College London Great Ormond
Street Institute of Child Health, London, UK), Ian Lamborn (Lymphocyte
Biology Section, Laboratory of Immune System Biology, National Institute of
Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD,
USA), Yuri Lin (Surgical Oncology Program, National Cancer Institute,
National Institutes of Health, Bethesda, MD, USA), Katia Luciano Pereira
Morais (Center for Research in Immuno-oncology (CRIO), Hospital Israelita
Albert Einstein, Sao Paulo, SP, Brazil), Aleksandra Lunich (Critical Care
Medicine and Pulmonary Branch, National Heart, Lung and Blood Institute,
National Institutes of Health, Bethesda, MD, USA), Jean C. S. Luz (Viral
Vector Laboratory, Cancer Institute of Sao Paulo, University of Sao Paulo,
SP, Brazil), Ryan B. MacDonald (UCL Institute of Ophthalmology and NIHR
Moorfields Biomedical Research Centre, London, UK), Chen Makranz
(Neuro-Oncology Branch, National Cancer Institute, National Institutes of
Health, Bethesda, MD, USA), Vivien I. Maltez (Division of Allergy, Immunology
and Rheumatology, Department of Pediatrics, University of California San
Diego, La Jolla, CA, USA), Ryan V. Moriaty (Department of Cellular and
Developmental Biology, Northwestern University, Chicago, IL, USA), Juan M.
Ocampo-Godinez (Laboratorio de Bioingenieria de Tejidos, Departamento de
Estudios de Posgrado e Investigacion, Universidad Nacional Autonoma de
Mexico, Mexico City, Mexico), Vitoria M. Olyntho (McMaster Immunology
Research Centre, Schroeder Allergy and Immunology Research Institute,
Department of Medicine, Faculty of Health Sciences, McMaster University,
Hamilton, ON, Canada), Kartika Padhan (Lymphocyte Biology Section and Center
for Advanced Tissue Imaging, Laboratory of Immune System Biology, National
Institute of Allergy and Infectious Diseases, National Institutes of Health,
Bethesda, MD, USA), Kirsten Remmert (Surgical Oncology Program, National
Cancer Institute, National Institutes of Health, Bethesda, MD, USA), Nathan
Richoz (Cambridge Institute for Therapeutic Immunology and Infectious
Diseases, University of Cambridge Department of Medicine, Molecular Immunity
Unit, Laboratory of Molecular Biology, Cambridge, UK), Edward C. Schrom
(Lymphocyte Biology Section, Laboratory of Immune System Biology, National
Institute of Allergy and Infectious Diseases, National Institutes of Health,
Bethesda, MD, USA), Wanjing Shang (Lymphocyte Biology Section, Laboratory of
Immune System Biology, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, MD, USA), Lihong Shi (Laboratory of
Immune System Biology, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, MD, USA), Rochelle M. Shih
(Lymphocyte Biology Section, Laboratory of Immune System Biology, National
Institute of Allergy and Infectious Diseases, National Institutes of Health,
Bethesda, MD, USA), Emily Speranza (Florida Research and Innovation Center,
Cleveland Clinic Lerner Research Institute, Port Saint Lucie, FL, USA),
Salome Stierli (Institute of Anatomy, University of Zurich, Zurich,
Switzerland), Sarah A. Teichmann (Cambridge Stem Cell Institute, Jeffrey
Cheah Biomedical Centre, Puddicombe Way, Cambridge Biomedical Campus,
Cambridge, UK), Tibor Z. Veres (Lymphocyte Biology Section, Laboratory of
Immune System Biology, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, MD, USA), Megan Vierhout (McMaster
Immunology Research Centre, Schroeder Allergy and Immunology Research
Institute, Department of Medicine, Faculty of Health Sciences, McMaster
University, Hamilton, ON, Canada), Brianna T. Wachter (Laboratory of Clinical
Immunology and Microbiology, National Institute of Allergy and Infectious
Diseases, National Institutes of Health, Bethesda, MD, USA), Adam K.
Wade-Vallance (Lymphocyte Biology Section, Laboratory of Immune System
Biology, National Institute of Allergy and Infectious Diseases, National
Institutes of Health, Bethesda, MD, USA), Margaret Williams (Critical Care
Medicine and Pulmonary Branch, National Heart, Lung and Blood Institute,
National Institutes of Health, Bethesda, MD, USA), Nathan Zangger (Institute
of Microbiology, ETH Zurich, Zurich, Switzerland), Ronald N. Germain
(Lymphocyte Biology Section and Center for Advanced Tissue Imaging,
Laboratory of Immune System Biology, National Institute of Allergy and
Infectious Diseases, National Institutes of Health, Bethesda, MD, USA and
Lymphocyte Biology Section, Laboratory of Immune System Biology, National
Institute of Allergy and Infectious Diseases, National Institutes of Health,
Bethesda, MD, USA), and Ziv Yaniv (Bioinformatics and Computational
Bioscience Branch, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, MD, USA) | The IBEX Knowledge-Base: Achieving more together with open science | 8 pages, 1 figure, 9 references | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for
highly multiplexed imaging of diverse tissues. Based on open science
principles, we created the IBEX Knowledge-Base, a resource for reagents,
protocols and more, to empower innovation.
| [
{
"created": "Fri, 26 Jul 2024 19:35:20 GMT",
"version": "v1"
}
] | 2024-07-30 | [
[
"Radtke",
"Andrea J.",
"",
"Lymphocyte Biology Section and Center for Advanced\n Tissue Imaging, Laboratory of Immune System Biology, National Institute of\n Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD,\n USA"
],
[
"Anidi",
"Ifeanyichukwu",
"",
"Critical Care Medicine and Pulmonary Branch,\n National Heart, Lung and Blood Institute, National Institutes of Health,\n Bethesda, MD, USA"
],
[
"Arakkal",
"Leanne",
"",
"Lymphocyte Biology Section, Laboratory of\n Immune System Biology, National Institute of Allergy and Infectious Diseases,\n National Institutes of Health, Bethesda, MD, USA"
],
[
"Arroyo-Mejias",
"Armando",
"",
"Lymphocyte Biology Section, Laboratory of Immune System Biology, National\n Institute of Allergy and Infectious Diseases, National Institutes of Health,\n Bethesda, MD, USA"
],
[
"Beuschel",
"Rebecca T.",
"",
"Lymphocyte Biology Section,\n Laboratory of Immune System Biology, National Institute of Allergy and\n Infectious Diseases, National Institutes of Health, Bethesda, MD, USA"
],
[
"Borner",
"Katy",
"",
"Department of Intelligent Systems Engineering, Indiana University,\n Bloomington, IN, USA"
],
[
"Chu",
"Colin J.",
"",
"UCL Institute of Ophthalmology and NIHR\n Moorfields Biomedical Research Centre, London, UK"
],
[
"Clark",
"Beatrice",
"",
"Lymphocyte Biology Section, Laboratory of Immune System Biology, National\n Institute of Allergy and Infectious Diseases, National Institutes of Health,\n Bethesda, MD, USA"
],
[
"Clatworthy",
"Menna R.",
"",
"Cambridge Institute for Therapeutic\n Immunology and Infectious Diseases, University of Cambridge Department of\n Medicine, Molecular Immunity Unit, Laboratory of Molecular Biology,\n Cambridge, UK"
],
[
"Colautti",
"Jake",
"",
"McMaster Immunology Research Centre, Schroeder\n Allergy and Immunology Research Institute, Department of Medicine, Faculty of\n Health Sciences, McMaster University, Hamilton, ON, Canada"
],
[
"Croteau",
"Joshua",
"",
"Department of Business Development, BioLegend Inc., San Diego, CA, USA"
],
[
"Denha",
"Saven",
"",
"McMaster Immunology Research Centre, Schroeder Allergy and\n Immunology Research Institute, Department of Medicine, Faculty of Health\n Sciences, McMaster University, Hamilton, ON, Canada"
],
[
"Dever",
"Rose",
"",
"Functional\n Immunogenomics Unit, National Institute of Arthritis and Musculoskeletal and\n Skin Diseases, National Institutes of Health, Bethesda, MD, USA"
],
[
"Dutra",
"Walderez O.",
"",
"Laboratory of Cell-Cell Interactions, Department of Morphology,\n Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo\n Horizonte, MG, Brazil"
],
[
"Fritzsche",
"Sonja",
"",
"Max-Delbrueck-Center for Molecular\n Medicine in the Helmholtz Association"
],
[
"Fullam",
"Spencer",
"",
"Division of\n Rheumatology, Rush University Medical Center, Chicago, IL, USA"
],
[
"Gerner",
"Michael Y.",
"",
"Department of Immunology, University of Washington School of\n Medicine, Seattle, WA, USA"
],
[
"Gola",
"Anita",
"",
"Robin Chemers Neustein Laboratory of\n Mammalian Cell Biology and Development, The Rockefeller University, New York,\n NY, USA"
],
[
"Gollob",
"Kenneth J.",
"",
"Center for Research in Immuno-oncology"
],
[
"Hernandez",
"Jonathan M.",
"",
"Surgical Oncology Program, National Cancer Institute, National\n Institutes of Health, Bethesda, MD, USA"
],
[
"Hor",
"Jyh Liang",
"",
"Lymphocyte Biology\n Section, Laboratory of Immune System Biology, National Institute of Allergy\n and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA"
],
[
"Ichise",
"Hiroshi",
"",
"Lymphocyte Biology Section, Laboratory of Immune System\n Biology, National Institute of Allergy and Infectious Diseases, National\n Institutes of Health, Bethesda, MD, USA"
],
[
"Jing",
"Zhixin",
"",
"Lymphocyte Biology\n Section, Laboratory of Immune System Biology, National Institute of Allergy\n and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA"
],
[
"Jonigk",
"Danny",
"",
"Institute of Pathology, Aachen Medical University, RWTH Aachen,\n Aachen, Germany"
],
[
"Kandov",
"Evelyn",
"",
"Lymphocyte Biology Section, Laboratory of\n Immune System Biology, National Institute of Allergy and Infectious Diseases,\n National Institutes of Health, Bethesda, MD, USA"
],
[
"Kastenmueller",
"Wolfgang",
"",
"Wurzburg Institute of Systems Immunology, Max Planck Research Group at the\n Julius-Maximilians-Universitat Wurzburg, Wurzburg, Germany"
],
[
"Koenig",
"Joshua F. E.",
"",
"McMaster Immunology Research Centre, Schroeder Allergy and Immunology\n Research Institute, Department of Medicine, Faculty of Health Sciences,\n McMaster University, Hamilton, ON, Canada"
],
[
"Kothurkar",
"Aanandita",
"",
"UCL\n Institute of Ophthalmology and NIHR Moorfields Biomedical Research Centre,\n London, UK"
],
[
"Kreins",
"Alexandra Y.",
"",
"Infection Immunity and Inflammation\n Research and Teaching Department, University College London Great Ormond\n Street Institute of Child Health, London, UK"
],
[
"Lamborn",
"Ian",
"",
"Lymphocyte\n Biology Section, Laboratory of Immune System Biology, National Institute of\n Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD,\n USA"
],
[
"Lin",
"Yuri",
"",
"Surgical Oncology Program, National Cancer Institute,\n National Institutes of Health, Bethesda, MD, USA"
],
[
"Morais",
"Katia Luciano Pereira",
"",
"Center for Research in Immuno-oncology"
],
[
"Lunich",
"Aleksandra",
"",
"Critical Care\n Medicine and Pulmonary Branch, National Heart, Lung and Blood Institute,\n National Institutes of Health, Bethesda, MD, USA"
],
[
"Luz",
"Jean C. S.",
"",
"Viral\n Vector Laboratory, Cancer Institute of Sao Paulo, University of Sao Paulo,\n SP, Brazil"
],
[
"MacDonald",
"Ryan B.",
"",
"UCL Institute of Ophthalmology and NIHR\n Moorfields Biomedical Research Centre, London, UK"
],
[
"Makranz",
"Chen",
"",
"Neuro-Oncology Branch, National Cancer Institute, National Institutes of\n Health, Bethesda, MD, USA"
],
[
"Maltez",
"Vivien I.",
"",
"Division of Allergy, Immunology\n and Rheumatology, Department of Pediatrics, University of California San\n Diego, La Jolla, CA, USA"
],
[
"Moriaty",
"Ryan V.",
"",
"Department of Cellular and\n Developmental Biology, Northwestern University, Chicago, IL, USA"
],
[
"Ocampo-Godinez",
"Juan M.",
"",
"Laboratorio de Bioingenieria de Tejidos, Departamento de\n Estudios de Posgrado e Investigacion, Universidad Nacional Autonoma de\n Mexico, Mexico City, Mexico"
],
[
"Olyntho",
"Vitoria M.",
"",
"McMaster Immunology\n Research Centre, Schroeder Allergy and Immunology Research Institute,\n Department of Medicine, Faculty of Health Sciences, McMaster University,\n Hamilton, ON, Canada"
],
[
"Padhan",
"Kartika",
"",
"Lymphocyte Biology Section and Center\n for Advanced Tissue Imaging, Laboratory of Immune System Biology, National\n Institute of Allergy and Infectious Diseases, National Institutes of Health,\n Bethesda, MD, USA"
],
[
"Remmert",
"Kirsten",
"",
"Surgical Oncology Program, National\n Cancer Institute, National Institutes of Health, Bethesda, MD, USA"
],
[
"Richoz",
"Nathan",
"",
"Cambridge Institute for Therapeutic Immunology and Infectious\n Diseases, University of Cambridge Department of Medicine, Molecular Immunity\n Unit, Laboratory of Molecular Biology, Cambridge, UK"
],
[
"Schrom",
"Edward C.",
"",
"Lymphocyte Biology Section, Laboratory of Immune System Biology, National\n Institute of Allergy and Infectious Diseases, National Institutes of Health,\n Bethesda, MD, USA"
],
[
"Shang",
"Wanjing",
"",
"Lymphocyte Biology Section, Laboratory of\n Immune System Biology, National Institute of Allergy and Infectious Diseases,\n National Institutes of Health, Bethesda, MD, USA"
],
[
"Shi",
"Lihong",
"",
"Laboratory of\n Immune System Biology, National Institute of Allergy and Infectious Diseases,\n National Institutes of Health, Bethesda, MD, USA"
],
[
"Shih",
"Rochelle M.",
"",
"Lymphocyte Biology Section, Laboratory of Immune System Biology, National\n Institute of Allergy and Infectious Diseases, National Institutes of Health,\n Bethesda, MD, USA"
],
[
"Speranza",
"Emily",
"",
"Florida Research and Innovation Center,\n Cleveland Clinic Lerner Research Institute, Port Saint Lucie, FL, USA"
],
[
"Stierli",
"Salome",
"",
"Institute of Anatomy, University of Zurich, Zurich,\n Switzerland"
],
[
"Teichmann",
"Sarah A.",
"",
"Cambridge Stem Cell Institute, Jeffrey\n Cheah Biomedical Centre, Puddicombe Way, Cambridge Biomedical Campus,\n Cambridge, UK"
],
[
"Veres",
"Tibor Z.",
"",
"Lymphocyte Biology Section, Laboratory of\n Immune System Biology, National Institute of Allergy and Infectious Diseases,\n National Institutes of Health, Bethesda, MD, USA"
],
[
"Vierhout",
"Megan",
"",
"McMaster\n Immunology Research Centre, Schroeder Allergy and Immunology Research\n Institute, Department of Medicine, Faculty of Health Sciences, McMaster\n University, Hamilton, ON, Canada"
],
[
"Wachter",
"Brianna T.",
"",
"Laboratory of Clinical\n Immunology and Microbiology, National Institute of Allergy and Infectious\n Diseases, National Institutes of Health, Bethesda, MD, USA"
],
[
"Wade-Vallance",
"Adam K.",
"",
"Lymphocyte Biology Section, Laboratory of Immune System\n Biology, National Institute of Allergy and Infectious Diseases, National\n Institutes of Health, Bethesda, MD, USA"
],
[
"Williams",
"Margaret",
"",
"Critical Care\n Medicine and Pulmonary Branch, National Heart, Lung and Blood Institute,\n National Institutes of Health, Bethesda, MD, USA"
],
[
"Zangger",
"Nathan",
"",
"Institute\n of Microbiology, ETH Zurich, Zurich, Switzerland"
],
[
"Germain",
"Ronald N.",
"",
"Lymphocyte Biology Section and Center for Advanced Tissue Imaging,\n Laboratory of Immune System Biology, National Institute of Allergy and\n Infectious Diseases, National Institutes of Health, Bethesda, MD, USA and\n Lymphocyte Biology Section, Laboratory of Immune System Biology, National\n Institute of Allergy and Infectious Diseases, National Institutes of Health,\n Bethesda, MD, USA"
],
[
"Yaniv",
"Ziv",
"",
"Bioinformatics and Computational\n Bioscience Branch, National Institute of Allergy and Infectious Diseases,\n National Institutes of Health, Bethesda, MD, USA"
]
] | Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, protocols and more, to empower innovation. |
1710.06984 | Michael Meehan Dr | Michael T. Meehan, Daniel G. Cocks, Emma S. McBryde | Global stability of the multi-strain Kermack-McKendrick (renewal)
epidemic model | 8 pages | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We extend a recent investigation by Meehan et al. (2019) regarding the global
stability properties of the general Kermack-McKendrick (renewal) model to the
multi-strain case. We demonstrate that the basic reproduction number of each
strain $R_{0j}$ represents a sharp threshold parameter such that when $R_{0j}
\leq 1$ for all $j$ each strain dies out and the infection-free equilibrium is
globally asymptotically stable; whereas for $R_{01} \equiv \mathrm{max}_j\,
R_{0j} > 1$ the endemic equilibrium point $\bar{P}^1$, at which only the
fittest strain (i.e. strain 1) remains in circulation, becomes globally
asymptotically stable.
| [
{
"created": "Thu, 19 Oct 2017 02:03:50 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Jul 2019 04:02:49 GMT",
"version": "v2"
}
] | 2019-07-03 | [
[
"Meehan",
"Michael T.",
""
],
[
"Cocks",
"Daniel G.",
""
],
[
"McBryde",
"Emma S.",
""
]
] | We extend a recent investigation by Meehan et al. (2019) regarding the global stability properties of the general Kermack-McKendrick (renewal) model to the multi-strain case. We demonstrate that the basic reproduction number of each strain $R_{0j}$ represents a sharp threshold parameter such that when $R_{0j} \leq 1$ for all $j$ each strain dies out and the infection-free equilibrium is globally asymptotically stable; whereas for $R_{01} \equiv \mathrm{max}_j\, R_{0j} > 1$ the endemic equilibrium point $\bar{P}^1$, at which only the fittest strain (i.e. strain 1) remains in circulation, becomes globally asymptotically stable. |
2104.02594 | Andrea De Martino | Anna Paola Muntoni, Alfredo Braunstein, Andrea Pagnani, Daniele De
Martino, Andrea De Martino | Relationship between fitness and heterogeneity in exponentially growing
microbial populations | 12+30 pages (includes Supporting Text) | null | 10.1016/j.bpj.2022.04.012 | null | q-bio.MN cond-mat.dis-nn cond-mat.stat-mech physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite major environmental and genetic differences, microbial metabolic
networks are known to generate consistent physiological outcomes across vastly
different organisms. This remarkable robustness suggests that, at least in
bacteria, metabolic activity may be guided by universal principles. The
constrained optimization of evolutionarily-motivated objective functions like
the growth rate has emerged as the key theoretical assumption for the study of
bacterial metabolism. While conceptually and practically useful in many
situations, the idea that certain functions are optimized is hard to validate
in data. Moreover, it is not always clear how optimality can be reconciled with
the high degree of single-cell variability observed in experiments within
microbial populations. To shed light on these issues, we develop an inverse
modeling framework that connects the fitness of a population of cells
(represented by the mean single-cell growth rate) to the underlying metabolic
variability through the Maximum-Entropy inference of the distribution of
metabolic phenotypes from data. While no clear objective function emerges, we
find that, as the medium gets richer, the fitness and inferred variability for
Escherichia coli populations follow and slowly approach the theoretically
optimal bound defined by minimal reduction of variability at given fitness.
These results suggest that bacterial metabolism may be crucially shaped by a
population-level trade-off between growth and heterogeneity.
| [
{
"created": "Tue, 6 Apr 2021 15:31:22 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Apr 2022 08:35:42 GMT",
"version": "v2"
}
] | 2022-05-24 | [
[
"Muntoni",
"Anna Paola",
""
],
[
"Braunstein",
"Alfredo",
""
],
[
"Pagnani",
"Andrea",
""
],
[
"De Martino",
"Daniele",
""
],
[
"De Martino",
"Andrea",
""
]
] | Despite major environmental and genetic differences, microbial metabolic networks are known to generate consistent physiological outcomes across vastly different organisms. This remarkable robustness suggests that, at least in bacteria, metabolic activity may be guided by universal principles. The constrained optimization of evolutionarily-motivated objective functions like the growth rate has emerged as the key theoretical assumption for the study of bacterial metabolism. While conceptually and practically useful in many situations, the idea that certain functions are optimized is hard to validate in data. Moreover, it is not always clear how optimality can be reconciled with the high degree of single-cell variability observed in experiments within microbial populations. To shed light on these issues, we develop an inverse modeling framework that connects the fitness of a population of cells (represented by the mean single-cell growth rate) to the underlying metabolic variability through the Maximum-Entropy inference of the distribution of metabolic phenotypes from data. While no clear objective function emerges, we find that, as the medium gets richer, the fitness and inferred variability for Escherichia coli populations follow and slowly approach the theoretically optimal bound defined by minimal reduction of variability at given fitness. These results suggest that bacterial metabolism may be crucially shaped by a population-level trade-off between growth and heterogeneity. |
2209.10698 | R.K. Brojen Singh | Moirangthem Shubhakanta Singh, Mairembam Kelvin Singh and R.K. Brojen
Singh | Stochastic approach to study the properties of the complex patterns
observed in cytokine and T-cells interaction process | 14 pages, 1 figure | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Patterns in complex systems store hidden information of the system which is
needed to be explored. We present a simple model of cytokine and T-cells
interaction and studied the model within stochastic framework by constructing
Master equation of the system and solving it. The solved probability
distribution function of the model show classical Poisson pattern in the large
population limit $M,Z\rightarrow large$ indicating the system has the tendency
to attract a large number small-scale random processes of the cytokine
population towards the basin of attraction of the system by segregating from
nonrandom processes. Further, in the large $\langle Z\rangle$ limit, the
pattern transform to classical Normal pattern, where, uncorrelated small-scale
fluctuations are wiped out to form a regular but memoryless spatiotemporal
aggregated pattern. The estimated noise using Fano factor shows clearly that
the cytokine dynamics is noise induced process driving the system far away from
equilibrium.
| [
{
"created": "Wed, 21 Sep 2022 22:54:02 GMT",
"version": "v1"
}
] | 2022-09-23 | [
[
"Singh",
"Moirangthem Shubhakanta",
""
],
[
"Singh",
"Mairembam Kelvin",
""
],
[
"Singh",
"R. K. Brojen",
""
]
] | Patterns in complex systems store hidden information of the system which is needed to be explored. We present a simple model of cytokine and T-cells interaction and studied the model within stochastic framework by constructing Master equation of the system and solving it. The solved probability distribution function of the model show classical Poisson pattern in the large population limit $M,Z\rightarrow large$ indicating the system has the tendency to attract a large number small-scale random processes of the cytokine population towards the basin of attraction of the system by segregating from nonrandom processes. Further, in the large $\langle Z\rangle$ limit, the pattern transform to classical Normal pattern, where, uncorrelated small-scale fluctuations are wiped out to form a regular but memoryless spatiotemporal aggregated pattern. The estimated noise using Fano factor shows clearly that the cytokine dynamics is noise induced process driving the system far away from equilibrium. |
1907.12742 | Hilaria Mollica | Hilaria Mollica, Roberto Palomba, Rosita Primavera and Paolo Decuzzi | Two channel compartmentalized microfluidic chip for real time monitoring
of the metastatic cascade | null | null | 10.1021/acsbiomaterials.9b00697 | null | q-bio.CB q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Metastases are the primary cause of death in cancer patients. Small animal
models are helping in dissecting some of key features in the metastatic
cascade. Yet, tools for systematically analyze the contribution of blood flow,
vascular permeability, inflammation, tissue architecture, and biochemical
stimuli are missing. In this work, a microfluidic chip is designed and tested
to replicate in vitro key steps in the metastatic cascade. It comprises two
channels, resting on the same plane, connected via an array of rounded pillars
to form a permeable micro-membrane. One channel acts as a vascular compartment
and is coated by a fully confluent monolayer of endothelial cells, whereas the
other channel is filled with a mixture of matrigel and breast cancer cells
(MDA-MB-231) and reproduces the malignant tissue. The vascular permeability can
be finely modulated by inducing pro-inflammatory conditions in the tissue
compartment, which transiently opens up the tight junctions of endothelial
cells. Fresh medium flowing continuously in the vascular compartment is
sufficient to induce cancer cell intravasation at rates of 8 cells/day with an
average velocity of 0.5 um/min. On the other hand, the vascular adhesion and
extravasation of circulating cancer cells require TNF-a stimulation.
Extravasation occurs at lower rates with 4 cells/day and an average velocity of
0.1 um/min. Finally, the same chip is completely filled with matrigel and the
migration of cancer cells from one channel to the other is monitored over a
region of about 400 um. Invasion rates of 12 cells/day are documented upon
TNF-a stimulation. This work demonstrates that the proposed compartmentalized
microfluidic chip can efficiently replicate in vitro, under controlled
biophysical and biochemical conditions, the multiple key steps in the cancer
metastatic cascade.
| [
{
"created": "Tue, 30 Jul 2019 05:21:26 GMT",
"version": "v1"
}
] | 2019-07-31 | [
[
"Mollica",
"Hilaria",
""
],
[
"Palomba",
"Roberto",
""
],
[
"Primavera",
"Rosita",
""
],
[
"Decuzzi",
"Paolo",
""
]
] | Metastases are the primary cause of death in cancer patients. Small animal models are helping in dissecting some of key features in the metastatic cascade. Yet, tools for systematically analyze the contribution of blood flow, vascular permeability, inflammation, tissue architecture, and biochemical stimuli are missing. In this work, a microfluidic chip is designed and tested to replicate in vitro key steps in the metastatic cascade. It comprises two channels, resting on the same plane, connected via an array of rounded pillars to form a permeable micro-membrane. One channel acts as a vascular compartment and is coated by a fully confluent monolayer of endothelial cells, whereas the other channel is filled with a mixture of matrigel and breast cancer cells (MDA-MB-231) and reproduces the malignant tissue. The vascular permeability can be finely modulated by inducing pro-inflammatory conditions in the tissue compartment, which transiently opens up the tight junctions of endothelial cells. Fresh medium flowing continuously in the vascular compartment is sufficient to induce cancer cell intravasation at rates of 8 cells/day with an average velocity of 0.5 um/min. On the other hand, the vascular adhesion and extravasation of circulating cancer cells require TNF-a stimulation. Extravasation occurs at lower rates with 4 cells/day and an average velocity of 0.1 um/min. Finally, the same chip is completely filled with matrigel and the migration of cancer cells from one channel to the other is monitored over a region of about 400 um. Invasion rates of 12 cells/day are documented upon TNF-a stimulation. This work demonstrates that the proposed compartmentalized microfluidic chip can efficiently replicate in vitro, under controlled biophysical and biochemical conditions, the multiple key steps in the cancer metastatic cascade. |
q-bio/0410004 | Taguchi Y.-H. | Koji Matsumura and Y-h. Taguchi | Can Neural Networks Recognize Parts? | Submitted to J. Phys. Soc. Jpn | null | null | null | q-bio.NC | null | We have demonstrated neural networks can recognize parts by visual images.
Input signals are gray scale photographs of objects consisting of some parts
and output signals are their shapes. By training neural networks by a few set
of images, without any supervision they become to be able to recognize the
boundary between parts.
| [
{
"created": "Mon, 4 Oct 2004 10:29:49 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Matsumura",
"Koji",
""
],
[
"Taguchi",
"Y-h.",
""
]
] | We have demonstrated neural networks can recognize parts by visual images. Input signals are gray scale photographs of objects consisting of some parts and output signals are their shapes. By training neural networks by a few set of images, without any supervision they become to be able to recognize the boundary between parts. |
1803.08440 | David Fisher | David N Fisher, Matthew Brachmann, Joseph B Burant | Complex dynamics and development of behavioural individuality | Version of manuscript following initial rejection and invitation to
resubmit from journal. Further revised version now published, see:
https://www.sciencedirect.com/science/article/pii/S0003347218300654 | null | 10.1016/j.anbehav.2018.02.015 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Behavioural differences may arise in the absence of genetic or environmental
variation. Chaotic dynamics may influence behavioural development, and so this
among-individual variation. We discuss methods and experimental designs to test
this idea. Ultimately, nonlinear and chaotic behavioural development may
explain much of natural variation.
| [
{
"created": "Thu, 22 Mar 2018 16:34:29 GMT",
"version": "v1"
}
] | 2018-03-23 | [
[
"Fisher",
"David N",
""
],
[
"Brachmann",
"Matthew",
""
],
[
"Burant",
"Joseph B",
""
]
] | Behavioural differences may arise in the absence of genetic or environmental variation. Chaotic dynamics may influence behavioural development, and so this among-individual variation. We discuss methods and experimental designs to test this idea. Ultimately, nonlinear and chaotic behavioural development may explain much of natural variation. |
1011.5737 | Juraj Stacho | Michel Habib and Juraj Stacho | Unique perfect phylogeny is NP-hard | null | null | null | null | q-bio.PE cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We answer, in the affirmative, the following question proposed by Mike Steel
as a $100 challenge: "Is the following problem NP-hard? Given a ternary
phylogenetic X-tree T and a collection Q of quartet subtrees on X, is T the
only tree that displays Q ?"
| [
{
"created": "Fri, 26 Nov 2010 09:36:03 GMT",
"version": "v1"
}
] | 2010-11-29 | [
[
"Habib",
"Michel",
""
],
[
"Stacho",
"Juraj",
""
]
] | We answer, in the affirmative, the following question proposed by Mike Steel as a $100 challenge: "Is the following problem NP-hard? Given a ternary phylogenetic X-tree T and a collection Q of quartet subtrees on X, is T the only tree that displays Q ?" |
1503.08992 | Changwang Zhang | Changwang Zhang, Shi Zhou, Elisabetta Groppelli, Pierre Pellegrino,
Ian Williams, Persephone Borrow, Benjamin M. Chain, Clare Jolly | Hybrid spreading mechanisms and T cell activation shape the dynamics of
HIV-1 infection | null | PLOS Computational Biology. 2015 Apr 2;11(4):e1004179 | 10.1371/journal.pcbi.1004179 | null | q-bio.PE cs.AI cs.CE physics.bio-ph q-bio.CB | http://creativecommons.org/licenses/by-nc-sa/3.0/ | HIV-1 can disseminate between susceptible cells by two mechanisms: cell-free
infection following fluid-phase diffusion of virions and by highly-efficient
direct cell-to-cell transmission at immune cell contacts. The contribution of
this hybrid spreading mechanism, which is also a characteristic of some
important computer worm outbreaks, to HIV-1 progression in vivo remains
unknown. Here we present a new mathematical model that explicitly incorporates
the ability of HIV-1 to use hybrid spreading mechanisms and evaluate the
consequences for HIV-1 pathogenenesis. The model captures the major phases of
the HIV-1 infection course of a cohort of treatment naive patients and also
accurately predicts the results of the Short Pulse Anti-Retroviral Therapy at
Seroconversion (SPARTAC) trial. Using this model we find that hybrid spreading
is critical to seed and establish infection, and that cell-to-cell spread and
increased CD4+ T cell activation are important for HIV-1 progression. Notably,
the model predicts that cell-to-cell spread becomes increasingly effective as
infection progresses and thus may present a considerable treatment barrier.
Deriving predictions of various treatments' influence on HIV-1 progression
highlights the importance of earlier intervention and suggests that treatments
effectively targeting cell-to-cell HIV-1 spread can delay progression to AIDS.
This study suggests that hybrid spreading is a fundamental feature of HIV
infection, and provides the mathematical framework incorporating this feature
with which to evaluate future therapeutic strategies.
| [
{
"created": "Tue, 31 Mar 2015 10:14:54 GMT",
"version": "v1"
}
] | 2024-01-02 | [
[
"Zhang",
"Changwang",
""
],
[
"Zhou",
"Shi",
""
],
[
"Groppelli",
"Elisabetta",
""
],
[
"Pellegrino",
"Pierre",
""
],
[
"Williams",
"Ian",
""
],
[
"Borrow",
"Persephone",
""
],
[
"Chain",
"Benjamin M.",
""
],
[
"Jolly",
"Clare",
""
]
] | HIV-1 can disseminate between susceptible cells by two mechanisms: cell-free infection following fluid-phase diffusion of virions and by highly-efficient direct cell-to-cell transmission at immune cell contacts. The contribution of this hybrid spreading mechanism, which is also a characteristic of some important computer worm outbreaks, to HIV-1 progression in vivo remains unknown. Here we present a new mathematical model that explicitly incorporates the ability of HIV-1 to use hybrid spreading mechanisms and evaluate the consequences for HIV-1 pathogenenesis. The model captures the major phases of the HIV-1 infection course of a cohort of treatment naive patients and also accurately predicts the results of the Short Pulse Anti-Retroviral Therapy at Seroconversion (SPARTAC) trial. Using this model we find that hybrid spreading is critical to seed and establish infection, and that cell-to-cell spread and increased CD4+ T cell activation are important for HIV-1 progression. Notably, the model predicts that cell-to-cell spread becomes increasingly effective as infection progresses and thus may present a considerable treatment barrier. Deriving predictions of various treatments' influence on HIV-1 progression highlights the importance of earlier intervention and suggests that treatments effectively targeting cell-to-cell HIV-1 spread can delay progression to AIDS. This study suggests that hybrid spreading is a fundamental feature of HIV infection, and provides the mathematical framework incorporating this feature with which to evaluate future therapeutic strategies. |
1406.4030 | Arne Traulsen | Bin Wu, Benedikt Bauer, Tobias Galla, and Arne Traulsen | When do microscopic assumptions determine the outcome in evolutionary
game dynamics? | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The modelling of evolutionary game dynamics in finite populations requires
microscopic processes that determine how strategies spread. The exact details
of these processes are often chosen without much further consideration.
Different types of microscopic models, including in particular fitness-based
selection rules and imitation-based dynamics, are often used as if they were
interchangeable. We challenge this view and investigate how robust these
choices on the micro-level really are. Focusing on a key macroscopic
observable, the probability for a single mutant to take over a population of
wild-type individuals, we show that there is a unique pair of a fitness-based
process and an imitation process leading to identical outcomes for arbitrary
games and for all intensities of selection. This highlights the perils of
making arbitrary choices at the micro-level without regard of the consequences
at the macro-level.
| [
{
"created": "Mon, 16 Jun 2014 14:32:17 GMT",
"version": "v1"
}
] | 2014-06-17 | [
[
"Wu",
"Bin",
""
],
[
"Bauer",
"Benedikt",
""
],
[
"Galla",
"Tobias",
""
],
[
"Traulsen",
"Arne",
""
]
] | The modelling of evolutionary game dynamics in finite populations requires microscopic processes that determine how strategies spread. The exact details of these processes are often chosen without much further consideration. Different types of microscopic models, including in particular fitness-based selection rules and imitation-based dynamics, are often used as if they were interchangeable. We challenge this view and investigate how robust these choices on the micro-level really are. Focusing on a key macroscopic observable, the probability for a single mutant to take over a population of wild-type individuals, we show that there is a unique pair of a fitness-based process and an imitation process leading to identical outcomes for arbitrary games and for all intensities of selection. This highlights the perils of making arbitrary choices at the micro-level without regard of the consequences at the macro-level. |
1102.2634 | Philippe Desjardins-Proulx | Philippe Desjardins-Proulx and Dominique Gravel | How likely is speciation in neutral ecology ? | 7 pages, 3 figures | The American Naturalist 179(1):137-144, 2012 | 10.1086/663196 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Patterns of biodiversity predicted by the neutral theory rely on a simple
phenomenological model of speciation. To further investigate the effect of
speciation on neutral biodiversity, we analyze a spatially-explicit neutral
model based on population genetics. We define the metacommunity as a system of
populations exchanging migrants and we use this framework to introduce
speciation with little or no gene flow (allopatric and parapatric speciation).
We find that with realistic mutation rates, our metacommunity model driven by
neutral processes cannot support more than a few species. Adding natural
selection in the population genetics of speciation increases the number of
species in the metacommunity but the level of diversity found in Barro Colorado
Island is difficult to reach.
| [
{
"created": "Sun, 13 Feb 2011 20:02:31 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Feb 2011 05:50:16 GMT",
"version": "v2"
},
{
"created": "Thu, 23 Jun 2011 09:21:29 GMT",
"version": "v3"
}
] | 2012-07-10 | [
[
"Desjardins-Proulx",
"Philippe",
""
],
[
"Gravel",
"Dominique",
""
]
] | Patterns of biodiversity predicted by the neutral theory rely on a simple phenomenological model of speciation. To further investigate the effect of speciation on neutral biodiversity, we analyze a spatially-explicit neutral model based on population genetics. We define the metacommunity as a system of populations exchanging migrants and we use this framework to introduce speciation with little or no gene flow (allopatric and parapatric speciation). We find that with realistic mutation rates, our metacommunity model driven by neutral processes cannot support more than a few species. Adding natural selection in the population genetics of speciation increases the number of species in the metacommunity but the level of diversity found in Barro Colorado Island is difficult to reach. |
1910.09600 | Paul Bertin | Mohammad Hashir, Paul Bertin, Martin Weiss, Vincent Frappier, Theodore
J. Perkins, Genevi\`eve Boucher and Joseph Paul Cohen | Is graph-based feature selection of genes better than random? | Accepted to the Machine Learning in Computational Biology (MLCB)
meeting 2019. 7 pages. 4 figures. arXiv admin note: substantial text overlap
with arXiv:1905.02295 | null | null | null | q-bio.GN cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene interaction graphs aim to capture various relationships between genes
and represent decades of biology research. When trying to make predictions from
genomic data, those graphs could be used to overcome the curse of
dimensionality by making machine learning models sparser and more consistent
with biological common knowledge. In this work, we focus on assessing whether
those graphs capture dependencies seen in gene expression data better than
random. We formulate a condition that graphs should satisfy to provide a good
prior knowledge and propose to test it using a `Single Gene Inference' (SGI)
task. We compare random graphs with seven major gene interaction graphs
published by different research groups, aiming to measure the true benefit of
using biologically relevant graphs in this context. Our analysis finds that
dependencies can be captured almost as well at random which suggests that, in
terms of gene expression levels, the relevant information about the state of
the cell is spread across many genes.
| [
{
"created": "Mon, 21 Oct 2019 18:51:25 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Nov 2019 23:35:05 GMT",
"version": "v2"
},
{
"created": "Fri, 27 Dec 2019 17:43:20 GMT",
"version": "v3"
}
] | 2020-01-14 | [
[
"Hashir",
"Mohammad",
""
],
[
"Bertin",
"Paul",
""
],
[
"Weiss",
"Martin",
""
],
[
"Frappier",
"Vincent",
""
],
[
"Perkins",
"Theodore J.",
""
],
[
"Boucher",
"Geneviève",
""
],
[
"Cohen",
"Joseph Paul",
""
]
] | Gene interaction graphs aim to capture various relationships between genes and represent decades of biology research. When trying to make predictions from genomic data, those graphs could be used to overcome the curse of dimensionality by making machine learning models sparser and more consistent with biological common knowledge. In this work, we focus on assessing whether those graphs capture dependencies seen in gene expression data better than random. We formulate a condition that graphs should satisfy to provide a good prior knowledge and propose to test it using a `Single Gene Inference' (SGI) task. We compare random graphs with seven major gene interaction graphs published by different research groups, aiming to measure the true benefit of using biologically relevant graphs in this context. Our analysis finds that dependencies can be captured almost as well at random which suggests that, in terms of gene expression levels, the relevant information about the state of the cell is spread across many genes. |
0811.3407 | Nicholas Chia | Nicholas Chia, Ido Golding, Nigel Goldenfeld | Lambda-prophage induction modeled as a cooperative failure mode of lytic
repression | added reference | null | 10.1103/PhysRevE.80.030901 | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze a system-level model for lytic repression of lambda-phage in E.
coli using reliability theory, showing that the repressor circuit comprises 4
redundant components whose failure mode is prophage induction. Our model
reflects the specific biochemical mechanisms involved in regulation, including
long-range cooperative binding, and its detailed predictions for prophage
induction in E. coli under ultra-violet radiation are in good agreement with
experimental data.
| [
{
"created": "Thu, 20 Nov 2008 20:56:11 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Nov 2008 06:51:37 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Dec 2008 08:07:58 GMT",
"version": "v3"
}
] | 2013-05-29 | [
[
"Chia",
"Nicholas",
""
],
[
"Golding",
"Ido",
""
],
[
"Goldenfeld",
"Nigel",
""
]
] | We analyze a system-level model for lytic repression of lambda-phage in E. coli using reliability theory, showing that the repressor circuit comprises 4 redundant components whose failure mode is prophage induction. Our model reflects the specific biochemical mechanisms involved in regulation, including long-range cooperative binding, and its detailed predictions for prophage induction in E. coli under ultra-violet radiation are in good agreement with experimental data. |
2205.08308 | Alicia Shin | Alicia Shin | The Relationship Between Insulin Resistance Neutrophil to Lymphocyte
Ratio | 12 pages, 6 figures | null | null | null | q-bio.QM | http://creativecommons.org/publicdomain/zero/1.0/ | Aim: There is increasing interest in the role of chronic inflammation on
pathogenesis of various disease, and one of its markers, high NLR is associated
with various mortality and morbidity risk. Insulin resistance (IR) might be one
potential associate factors, as suggested in preclinical studies. However,
epidemiological studies are scarce which investigated the association between
NLR, and insulin resistance (IR) and they included only diabetes mellitus
patients, not the general population. This study aims to determine if there is
a direct correlation between NLR and IR in the US general population. Methods:
The sample consists of 3,307 from general population, provided by National
Health and Nutrition Examination Survey (NHANES). Homeostasis Model Assessment
of Insulin Resistance (HOMA-IR) value was calculated to evaluate insulin
resistance. We investigated the relationship between their NLR and HOMA-IR
values by bivariate and multivariate linear regression analyses. As insulin use
could results in inaccurate HOMA-IR estimation, we excluded them and ran the
analyses in subgroup analyses. Results: There was a relationship shown when
insulin users were included, having a beta coefficient value of 0.010 (95%
confidence interval [CI] of 0.003-0.017). However, when insulin users were
excluded, the beta value decreased to 0.004 (95% CI of -0.006-0.015). The
statistical significance was not reached when age, sex, and body mass index
were adjusted for in the multivariate analyses. Conclusion: There is no visible
relationship between IR and NLR in the general population. IR might not explain
the variation of NLR value in healthy people, and further studies are needed to
reveal the associated factor of high NLR.
| [
{
"created": "Fri, 13 May 2022 20:18:26 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Dec 2022 20:53:02 GMT",
"version": "v2"
}
] | 2022-12-29 | [
[
"Shin",
"Alicia",
""
]
] | Aim: There is increasing interest in the role of chronic inflammation on pathogenesis of various disease, and one of its markers, high NLR is associated with various mortality and morbidity risk. Insulin resistance (IR) might be one potential associate factors, as suggested in preclinical studies. However, epidemiological studies are scarce which investigated the association between NLR, and insulin resistance (IR) and they included only diabetes mellitus patients, not the general population. This study aims to determine if there is a direct correlation between NLR and IR in the US general population. Methods: The sample consists of 3,307 from general population, provided by National Health and Nutrition Examination Survey (NHANES). Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) value was calculated to evaluate insulin resistance. We investigated the relationship between their NLR and HOMA-IR values by bivariate and multivariate linear regression analyses. As insulin use could results in inaccurate HOMA-IR estimation, we excluded them and ran the analyses in subgroup analyses. Results: There was a relationship shown when insulin users were included, having a beta coefficient value of 0.010 (95% confidence interval [CI] of 0.003-0.017). However, when insulin users were excluded, the beta value decreased to 0.004 (95% CI of -0.006-0.015). The statistical significance was not reached when age, sex, and body mass index were adjusted for in the multivariate analyses. Conclusion: There is no visible relationship between IR and NLR in the general population. IR might not explain the variation of NLR value in healthy people, and further studies are needed to reveal the associated factor of high NLR. |
1007.3447 | Stephane Ghozzi | St\'ephane Ghozzi, J\'er\^ome Wong Ng, Didier Chatenay and J\'er\^ome
Robert | Inference of plasmid copy number mean and noise from single cell gene
expression data | 9 pages, 3 figures, 2 tables | Phys. Rev. E 82, 051916 (2010) | 10.1103/PhysRevE.82.051916 | null | q-bio.CB physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Plasmids are extra-chromosomal DNA molecules which code for their own
replication. We previously reported a setup using genes coding for fluorescent
proteins of two colors that allowed us, using a simple model, to extract the
plasmid copy number noise in a monoclonal population of bacteria [J. Wong Ng et
al., Phys. Rev. E, 81, 011909 (2010)]. Here we present a detailed calculation
relating this noise to the measured levels of fluorescence, taking into account
all sources of fluorescence fluctuations: the fluctuation of gene expression as
in the simple model, but also the growth and division of bacteria, the
non-uniform distribution of their ages, the random partition of proteins at
divisions and the replication and partition of plasmids and chromosome. We show
how using the chromosome as a reference helps extracting the plasmid copy
number noise in a self-consistent manner.
| [
{
"created": "Tue, 20 Jul 2010 15:25:12 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Nov 2010 09:55:24 GMT",
"version": "v2"
}
] | 2010-11-17 | [
[
"Ghozzi",
"Stéphane",
""
],
[
"Ng",
"Jérôme Wong",
""
],
[
"Chatenay",
"Didier",
""
],
[
"Robert",
"Jérôme",
""
]
] | Plasmids are extra-chromosomal DNA molecules which code for their own replication. We previously reported a setup using genes coding for fluorescent proteins of two colors that allowed us, using a simple model, to extract the plasmid copy number noise in a monoclonal population of bacteria [J. Wong Ng et al., Phys. Rev. E, 81, 011909 (2010)]. Here we present a detailed calculation relating this noise to the measured levels of fluorescence, taking into account all sources of fluorescence fluctuations: the fluctuation of gene expression as in the simple model, but also the growth and division of bacteria, the non-uniform distribution of their ages, the random partition of proteins at divisions and the replication and partition of plasmids and chromosome. We show how using the chromosome as a reference helps extracting the plasmid copy number noise in a self-consistent manner. |
2402.12383 | Idoia Berges | Idoia Berges, Jes\'us Berm\'udez, Arantza Illarramendi | Binding SNOMED-CT Terms to Archetype Elements: Establishing a Baseline
of Results | This document is the Accepted Manuscript version of a Published Work
that appeared in final form in Methods of Information in Medicine 54(1) :
45-49 (2015), copyright 2015 Schattauer. To access the final edited and
published work see https://doi.org/10.3414/me13-02-0022 | Methods of Information in Medicine 54(1) : 45-49 (2015) | 10.3414/me13-02-0022 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Introduction: This article is part of the Focus Theme of METHODS of
Information in Medicine on "Managing Interoperability and Complexity in Health
Systems". Background: The proliferation of archetypes as a means to represent
information of Electronic Health Records has raised the need of binding
terminological codes - such as SNOMED CT codes - to their elements, in order to
identify them univocally. However, the large size of the terminologies makes it
difficult to perform this task manually. Objectives: To establish a baseline of
results for the aforementioned problem by using off-the-shelf string
comparison-based techniques against which results from more complex techniques
could be evaluated. Methods: Nine Typed Comparison METHODS were evaluated for
binding using a set of 487 archetype elements. Their recall was calculated and
Friedman and Nemenyi tests were applied in order to assess whether any of the
methods outperformed the others. Results: Using the qGrams method along with
the 'Text' information piece of archetype elements outperforms the other
methods if a level of confidence of 90% is considered. A recall of 25.26% is
obtained if just one SNOMED CT term is retrieved for each archetype element.
This recall rises to 50.51% and 75.56% if 10 and 100 elements are retrieved
respectively, that being a reduction of more than 99.99% on the SNOMED CT code
set. Conclusions: The baseline has been established following the
above-mentioned results. Moreover, it has been observed that although string
comparison-based methods do not outperform more sophisticated techniques, they
still can be an alternative for providing a reduced set of candidate terms for
each archetype element from which the ultimate term can be chosen later in the
more-than-likely manual supervision task.
| [
{
"created": "Fri, 26 Jan 2024 15:13:34 GMT",
"version": "v1"
}
] | 2024-02-21 | [
[
"Berges",
"Idoia",
""
],
[
"Bermúdez",
"Jesús",
""
],
[
"Illarramendi",
"Arantza",
""
]
] | Introduction: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". Background: The proliferation of archetypes as a means to represent information of Electronic Health Records has raised the need of binding terminological codes - such as SNOMED CT codes - to their elements, in order to identify them univocally. However, the large size of the terminologies makes it difficult to perform this task manually. Objectives: To establish a baseline of results for the aforementioned problem by using off-the-shelf string comparison-based techniques against which results from more complex techniques could be evaluated. Methods: Nine Typed Comparison METHODS were evaluated for binding using a set of 487 archetype elements. Their recall was calculated and Friedman and Nemenyi tests were applied in order to assess whether any of the methods outperformed the others. Results: Using the qGrams method along with the 'Text' information piece of archetype elements outperforms the other methods if a level of confidence of 90% is considered. A recall of 25.26% is obtained if just one SNOMED CT term is retrieved for each archetype element. This recall rises to 50.51% and 75.56% if 10 and 100 elements are retrieved respectively, that being a reduction of more than 99.99% on the SNOMED CT code set. Conclusions: The baseline has been established following the above-mentioned results. Moreover, it has been observed that although string comparison-based methods do not outperform more sophisticated techniques, they still can be an alternative for providing a reduced set of candidate terms for each archetype element from which the ultimate term can be chosen later in the more-than-likely manual supervision task. |
1304.7109 | Manuel Sch\"olling | Manuel Sch\"olling and Rudolf Hanel | Reconstructing protein binding patterns from ChIP time-series | null | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation Gene transcription requires the orchestrated binding of various
proteins to the promoter of a gene. The binding times and binding order of
proteins allow to draw conclusions about the proteins' exact function in the
recruitment process. Time-resolved ChIP experiments are being used to analyze
the order of protein binding for these processes. However, these ChIP signals
do not represent the exact protein binding patterns. Results We show that for
promoter complexes that follow sequential recruitment dynamics the ChIP signal
can be understood as a convoluted signal and propose the application of
deconvolution methods to recover the protein binding patterns from experimental
ChIP time-series. We analyze the suitability of four deconvolution methods: two
non-blind deconvolution methods, Wiener deconvolution and Lucy-Richardson
deconvolution, and two blind deconvolution methods, blind Lucy-Richardson
deconvolution and binary blind deconvolution. We apply these methods to infer
the protein binding pattern from ChIP time-series for the pS2 gene.
| [
{
"created": "Fri, 26 Apr 2013 09:51:04 GMT",
"version": "v1"
}
] | 2013-04-29 | [
[
"Schölling",
"Manuel",
""
],
[
"Hanel",
"Rudolf",
""
]
] | Motivation Gene transcription requires the orchestrated binding of various proteins to the promoter of a gene. The binding times and binding order of proteins allow to draw conclusions about the proteins' exact function in the recruitment process. Time-resolved ChIP experiments are being used to analyze the order of protein binding for these processes. However, these ChIP signals do not represent the exact protein binding patterns. Results We show that for promoter complexes that follow sequential recruitment dynamics the ChIP signal can be understood as a convoluted signal and propose the application of deconvolution methods to recover the protein binding patterns from experimental ChIP time-series. We analyze the suitability of four deconvolution methods: two non-blind deconvolution methods, Wiener deconvolution and Lucy-Richardson deconvolution, and two blind deconvolution methods, blind Lucy-Richardson deconvolution and binary blind deconvolution. We apply these methods to infer the protein binding pattern from ChIP time-series for the pS2 gene. |
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