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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0411050
|
Suan Li Mai
|
Mai Suan Li, D.K. Klimov, D. Thirumalai
|
Finite size effects on thermal denaturation of globular proteins
|
3 figures. Physical Review Letters (in press)
| null |
10.1103/PhysRevLett.93.268107
| null |
q-bio.BM q-bio.QM
| null |
Finite size effects on the cooperative thermal denaturation of proteins are
considered. A dimensionless measure of cooperativity, Omega, scales as N^zeta,
where N is the number of amino acids. Surprisingly, we find that zeta is
universal with zeta = 1 + gamma, where the exponent gamma characterizes the
divergence of the susceptibility for a self-avoiding walk. Our lattice model
simulations and experimental data are consistent with the theory. Our finding
rationalizes the marginal stability of proteins and substantiates the earlier
predictions that the efficient folding of two-state proteins requires the
folding transition temperature to be close to the collapse temperature.
|
[
{
"created": "Sun, 28 Nov 2004 15:44:42 GMT",
"version": "v1"
}
] |
2009-11-10
|
[
[
"Li",
"Mai Suan",
""
],
[
"Klimov",
"D. K.",
""
],
[
"Thirumalai",
"D.",
""
]
] |
Finite size effects on the cooperative thermal denaturation of proteins are considered. A dimensionless measure of cooperativity, Omega, scales as N^zeta, where N is the number of amino acids. Surprisingly, we find that zeta is universal with zeta = 1 + gamma, where the exponent gamma characterizes the divergence of the susceptibility for a self-avoiding walk. Our lattice model simulations and experimental data are consistent with the theory. Our finding rationalizes the marginal stability of proteins and substantiates the earlier predictions that the efficient folding of two-state proteins requires the folding transition temperature to be close to the collapse temperature.
|
1805.04616
|
Maxwell Zimmerman
|
Maxwell I. Zimmerman, Justin R. Porter, Xianqiang Sun, Roseane R.
Silva, and Gregory R. Bowman
|
Choice of adaptive sampling strategy impacts state discovery, transition
probabilities, and the apparent mechanism of conformational changes
| null | null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Interest in equilibrium-based sampling methods has grown with recent advances
in computational hardware and Markov state modeling (MSM) methods, yet
outstanding questions remain that hinder widespread adoption. Namely, how do
sampling strategies explore conformational space and how might this influence
predictions? Here, we seek to answer these questions for four commonly used
sampling methods: 1) a long simulation, 2) many short simulations, 3) adaptive
sampling, and 4) FAST. We first develop a theoretical framework for
analytically calculating the probability of discovering states and uncover the
drastic effects of varying the number and length of simulations. We then use
kinetic Monte Carlo simulations on a variety of physically inspired landscapes
to characterize state discovery and transition pathways. Consistently, we find
that FAST simulations discover target states with the highest probability and
traverse realistic pathways. Furthermore, we uncover the pathology that short
parallel simulations sometimes predict an incorrect transition pathway by
crossing large energy barriers that long simulations would typically
circumnavigate, which we refer to as pathway tunneling. To protect against
tunneling, we introduce FAST-string, which samples along the highest-flux
transition paths to refine an MSMs transition probabilities and discriminate
between competing pathways. Additionally, we compare MSM estimators in
describing thermodynamics and kinetics. For adaptive sampling, we recommend
normalizing the transition counts out of each state after adding pseudo-counts
to avoid creating sources or sinks. Lastly, we evaluate our insights from
simple landscapes with all-atom molecular dynamics simulations of the folding
of the {\lambda}-repressor protein. We find that FAST-contacts predicts the
same folding pathway as long simulations but with orders of magnitude less
simulation time.
|
[
{
"created": "Fri, 11 May 2018 23:12:11 GMT",
"version": "v1"
}
] |
2018-05-15
|
[
[
"Zimmerman",
"Maxwell I.",
""
],
[
"Porter",
"Justin R.",
""
],
[
"Sun",
"Xianqiang",
""
],
[
"Silva",
"Roseane R.",
""
],
[
"Bowman",
"Gregory R.",
""
]
] |
Interest in equilibrium-based sampling methods has grown with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder widespread adoption. Namely, how do sampling strategies explore conformational space and how might this influence predictions? Here, we seek to answer these questions for four commonly used sampling methods: 1) a long simulation, 2) many short simulations, 3) adaptive sampling, and 4) FAST. We first develop a theoretical framework for analytically calculating the probability of discovering states and uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize state discovery and transition pathways. Consistently, we find that FAST simulations discover target states with the highest probability and traverse realistic pathways. Furthermore, we uncover the pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate, which we refer to as pathway tunneling. To protect against tunneling, we introduce FAST-string, which samples along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare MSM estimators in describing thermodynamics and kinetics. For adaptive sampling, we recommend normalizing the transition counts out of each state after adding pseudo-counts to avoid creating sources or sinks. Lastly, we evaluate our insights from simple landscapes with all-atom molecular dynamics simulations of the folding of the {\lambda}-repressor protein. We find that FAST-contacts predicts the same folding pathway as long simulations but with orders of magnitude less simulation time.
|
1711.07473
|
Kye-Ryong Sin
|
Hyon-Hui Ri, Yong-A Choe, Jong-Ho Yun, Kye-Ryong Sin
|
Determination of carbohydrates in infant milk powders by
ultra-performance liquid chromatography with evaporative light scattering
detector and BEH HILIC column
| null | null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The possibility of carbohydrate separation in BEH HILIC (Ethylene Bridged
Hybride, Hydrophilic Interaction Liquid Chromatography) column was studied by
ultra-performance liquid chromatography (UPLC) with evaporative light
scattering detector (ELSD) and mobile phase containing amine compounds as
modifiers. The chromatography conditions and ELSD parameters were optimized to
separate five typical carbohydrates and applied to analysis of four infant milk
powders. The linear ranges of carbohydrate determination were 20-300mg/L for
fructose and glucose, 20-250mg/L for sucrose and lactose, and 35-180mg/L for
fructo-oligosaccharide. The LODs were 16.4mg/L for fructose and glucose,
17.3mg/L for sucrose, 20.0mg/L for lactose, and 46.7mg/L for
fructo-oligosaccharide. Relative standard deviations (RSDs) ranged between
3.45-4.23%, 1.46-4.17%, 4.14-5.60%, 1.39-4.09%, and 2.49-3.61% for fructose,
glucose, sucrose, lactose, and fructo-oilgosaccharide, respectively and
recoveries ranged between 95.0 and 105.4%
|
[
{
"created": "Mon, 20 Nov 2017 08:23:41 GMT",
"version": "v1"
}
] |
2017-11-22
|
[
[
"Ri",
"Hyon-Hui",
""
],
[
"Choe",
"Yong-A",
""
],
[
"Yun",
"Jong-Ho",
""
],
[
"Sin",
"Kye-Ryong",
""
]
] |
The possibility of carbohydrate separation in BEH HILIC (Ethylene Bridged Hybride, Hydrophilic Interaction Liquid Chromatography) column was studied by ultra-performance liquid chromatography (UPLC) with evaporative light scattering detector (ELSD) and mobile phase containing amine compounds as modifiers. The chromatography conditions and ELSD parameters were optimized to separate five typical carbohydrates and applied to analysis of four infant milk powders. The linear ranges of carbohydrate determination were 20-300mg/L for fructose and glucose, 20-250mg/L for sucrose and lactose, and 35-180mg/L for fructo-oligosaccharide. The LODs were 16.4mg/L for fructose and glucose, 17.3mg/L for sucrose, 20.0mg/L for lactose, and 46.7mg/L for fructo-oligosaccharide. Relative standard deviations (RSDs) ranged between 3.45-4.23%, 1.46-4.17%, 4.14-5.60%, 1.39-4.09%, and 2.49-3.61% for fructose, glucose, sucrose, lactose, and fructo-oilgosaccharide, respectively and recoveries ranged between 95.0 and 105.4%
|
1701.07379
|
Jose Vanterler Da Costa Sousa
|
Jos\'e Vanterler da Costa Sousa, Edmundo Capelas de Oliveira and Luiz
Alberto Magna
|
Fractional Calculus and the ESR test
|
19 pages and 2 figures
| null | null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider a partial differential equation associated with a mathematical
model describing the concentration of nutrients in blood which interferes
directly on the erythrocyte sedimentation rate in the case of an average fluid
velocity equal to zero. Introducing the fractional derivative in the Caputo
sense, we propose a time-fractional mathematical model which contains, as a
particular case, the model proposed by Sharma et al. Our main purpose is to
obtain an analytic solution of this time-fractional partial differential
equation in terms of the Mittag-Leffler function and Wright function.
|
[
{
"created": "Thu, 29 Dec 2016 13:52:46 GMT",
"version": "v1"
}
] |
2017-01-27
|
[
[
"Sousa",
"José Vanterler da Costa",
""
],
[
"de Oliveira",
"Edmundo Capelas",
""
],
[
"Magna",
"Luiz Alberto",
""
]
] |
We consider a partial differential equation associated with a mathematical model describing the concentration of nutrients in blood which interferes directly on the erythrocyte sedimentation rate in the case of an average fluid velocity equal to zero. Introducing the fractional derivative in the Caputo sense, we propose a time-fractional mathematical model which contains, as a particular case, the model proposed by Sharma et al. Our main purpose is to obtain an analytic solution of this time-fractional partial differential equation in terms of the Mittag-Leffler function and Wright function.
|
1810.11818
|
Konstantin Blyuss
|
F. Fatehi, Y.N. Kyrychko, R. Molchanov, K.B. Blyuss
|
Bifurcations and multi-stability in a model of cytokine-mediated
autoimmunity
|
26 pages, 10 figures, accepted for publication in Int. J. Bif. Chaos
| null |
10.1142/S0218127419500342
| null |
q-bio.QM nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper investigates the dynamics of immune response and autoimmunity with
particular emphasis on the role of regulatory T cells (Tregs), T cells with
different activation thresholds, and cytokines in mediating T cell activity.
Analysis of the steady states yields parameter regions corresponding to regimes
of normal clearance of viral infection, chronic infection, or autoimmune
behaviour, and the boundaries of stability and bifurcations of relevant steady
states are found in terms of system parameters. Numerical simulations are
performed to illustrate different dynamical scenarios, and to identify basins
of attraction of different steady states and periodic solutions, highlighting
the important role played by the initial conditions in determining the outcome
of immune interactions.
|
[
{
"created": "Sun, 28 Oct 2018 14:28:22 GMT",
"version": "v1"
}
] |
2019-05-01
|
[
[
"Fatehi",
"F.",
""
],
[
"Kyrychko",
"Y. N.",
""
],
[
"Molchanov",
"R.",
""
],
[
"Blyuss",
"K. B.",
""
]
] |
This paper investigates the dynamics of immune response and autoimmunity with particular emphasis on the role of regulatory T cells (Tregs), T cells with different activation thresholds, and cytokines in mediating T cell activity. Analysis of the steady states yields parameter regions corresponding to regimes of normal clearance of viral infection, chronic infection, or autoimmune behaviour, and the boundaries of stability and bifurcations of relevant steady states are found in terms of system parameters. Numerical simulations are performed to illustrate different dynamical scenarios, and to identify basins of attraction of different steady states and periodic solutions, highlighting the important role played by the initial conditions in determining the outcome of immune interactions.
|
1302.5484
|
Marisa Eisenberg
|
Marisa Eisenberg
|
Input-output equivalence and identifiability: some simple
generalizations of the differential algebra approach
|
Notes on the relationship between input-output equivalence and
structural identifiability
| null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we give an overview of the differential algebra approach to
identifiability, and then note a very simple observation about input-output
equivalence and identifiability, that describes the identifiability equivalence
between input-output equivalent models. We then give several simple
consequences of this observation that can be useful in showing identifiability,
including examining non-first order ODE models, nondimensionalization and
rescaling, model reducibility, and a modular approach to evaluating
identifiability. We also examine how input-output equivalence can allow us to
generate input output equations in the differential algebra approach through a
wider range of methods (e.g. substitution and differential or standard Groebner
basis approaches).
|
[
{
"created": "Fri, 22 Feb 2013 05:01:53 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Aug 2019 05:43:42 GMT",
"version": "v2"
}
] |
2019-08-20
|
[
[
"Eisenberg",
"Marisa",
""
]
] |
In this paper, we give an overview of the differential algebra approach to identifiability, and then note a very simple observation about input-output equivalence and identifiability, that describes the identifiability equivalence between input-output equivalent models. We then give several simple consequences of this observation that can be useful in showing identifiability, including examining non-first order ODE models, nondimensionalization and rescaling, model reducibility, and a modular approach to evaluating identifiability. We also examine how input-output equivalence can allow us to generate input output equations in the differential algebra approach through a wider range of methods (e.g. substitution and differential or standard Groebner basis approaches).
|
1410.8564
|
John Drake
|
John M. Drake, RajReni B. Kaul, Laura Alexander, Suzanne M. O'Regan,
Andrew M. Kramer, J. Tomlin Pulliam, Matthew J. Ferrari, and Andrew W. Park
|
Ebola cases and health system demand in Liberia
|
Includes supplement
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In 2014, a major epidemic of human Ebola virus disease emerged in West
Africa, where human-to-human transmission has now been been sustained for
greater than 10 months. In the summer of 2014, there was great uncertainty
about the answers to several key policy questions concerning the path to
containment. In recent years, epidemic models have been used to guide public
health interventions. But, model-based policy relies on high quality causal
understanding of transmission, including the availability of appropriate
dynamic transmission models and reliable reporting about the sequence of case
incidence for model fitting, which were lacking for this epidemic. To
investigate the range of potential transmission scenarios, we developed a
multi-type branching process model that incorporates key heterogeneities and
time-varying parameters to reflect changing human behavior and deliberate
interventions. Ensembles of this model were evaluated at a set of parameters
that were both epidemiologically plausible and capable of reproducing the
observed trajectory. Results suggest that epidemic outcome depends on both
hospital capacity and individual behavior. The model predicts that if hospital
capacity is not increased soon, then transmission may outpace the rate of
isolation and the ability to provide care for the ill, infectious, and dying.
Similarly, containment will probably require individuals to adopt behaviors
that increase the rates of case identification and isolation and secure burial
of the deceased. Given current knowledge, it is uncertain that this epidemic
will be contained even with 99% hospitalization rate at the currently projected
hospital capacity.
|
[
{
"created": "Thu, 30 Oct 2014 21:29:17 GMT",
"version": "v1"
}
] |
2014-11-03
|
[
[
"Drake",
"John M.",
""
],
[
"Kaul",
"RajReni B.",
""
],
[
"Alexander",
"Laura",
""
],
[
"O'Regan",
"Suzanne M.",
""
],
[
"Kramer",
"Andrew M.",
""
],
[
"Pulliam",
"J. Tomlin",
""
],
[
"Ferrari",
"Matthew J.",
""
],
[
"Park",
"Andrew W.",
""
]
] |
In 2014, a major epidemic of human Ebola virus disease emerged in West Africa, where human-to-human transmission has now been been sustained for greater than 10 months. In the summer of 2014, there was great uncertainty about the answers to several key policy questions concerning the path to containment. In recent years, epidemic models have been used to guide public health interventions. But, model-based policy relies on high quality causal understanding of transmission, including the availability of appropriate dynamic transmission models and reliable reporting about the sequence of case incidence for model fitting, which were lacking for this epidemic. To investigate the range of potential transmission scenarios, we developed a multi-type branching process model that incorporates key heterogeneities and time-varying parameters to reflect changing human behavior and deliberate interventions. Ensembles of this model were evaluated at a set of parameters that were both epidemiologically plausible and capable of reproducing the observed trajectory. Results suggest that epidemic outcome depends on both hospital capacity and individual behavior. The model predicts that if hospital capacity is not increased soon, then transmission may outpace the rate of isolation and the ability to provide care for the ill, infectious, and dying. Similarly, containment will probably require individuals to adopt behaviors that increase the rates of case identification and isolation and secure burial of the deceased. Given current knowledge, it is uncertain that this epidemic will be contained even with 99% hospitalization rate at the currently projected hospital capacity.
|
2105.08944
|
Jacob Russin
|
Jacob Russin, Maryam Zolfaghar, Seongmin A. Park, Erie Boorman,
Randall C. O'Reilly
|
Complementary Structure-Learning Neural Networks for Relational
Reasoning
|
7 pages, 4 figures, Accepted to CogSci 2021 for poster presentation
| null | null | null |
q-bio.NC cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The neural mechanisms supporting flexible relational inferences, especially
in novel situations, are a major focus of current research. In the
complementary learning systems framework, pattern separation in the hippocampus
allows rapid learning in novel environments, while slower learning in neocortex
accumulates small weight changes to extract systematic structure from
well-learned environments. In this work, we adapt this framework to a task from
a recent fMRI experiment where novel transitive inferences must be made
according to implicit relational structure. We show that computational models
capturing the basic cognitive properties of these two systems can explain
relational transitive inferences in both familiar and novel environments, and
reproduce key phenomena observed in the fMRI experiment.
|
[
{
"created": "Wed, 19 May 2021 06:25:21 GMT",
"version": "v1"
}
] |
2021-05-20
|
[
[
"Russin",
"Jacob",
""
],
[
"Zolfaghar",
"Maryam",
""
],
[
"Park",
"Seongmin A.",
""
],
[
"Boorman",
"Erie",
""
],
[
"O'Reilly",
"Randall C.",
""
]
] |
The neural mechanisms supporting flexible relational inferences, especially in novel situations, are a major focus of current research. In the complementary learning systems framework, pattern separation in the hippocampus allows rapid learning in novel environments, while slower learning in neocortex accumulates small weight changes to extract systematic structure from well-learned environments. In this work, we adapt this framework to a task from a recent fMRI experiment where novel transitive inferences must be made according to implicit relational structure. We show that computational models capturing the basic cognitive properties of these two systems can explain relational transitive inferences in both familiar and novel environments, and reproduce key phenomena observed in the fMRI experiment.
|
1501.03463
|
Andrew Leifer
|
Jeffrey P. Nguyen and Frederick B. Shipley and Ashley N. Linder and
George S. Plummer and Joshua W. Shaevitz and Andrew M. Leifer
|
Whole-brain calcium imaging with cellular resolution in freely behaving
C. elegans
|
33 pages, 6 main figures, 7 supplementary figures
| null |
10.1073/pnas.1507110112
| null |
q-bio.NC physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability to acquire large-scale recordings of neuronal activity in awake
and unrestrained animals poses a major challenge for studying neural coding of
animal behavior. We present a new instrument capable of recording intracellular
calcium transients from every neuron in the head of a freely behaving C.
elegans with cellular resolution while simultaneously recording the animal's
position, posture and locomotion. We employ spinning-disk confocal microscopy
to capture 3D volumetric fluorescent images of neurons expressing the calcium
indicator GCaMP6s at 5 head-volumes per second. Two cameras simultaneously
monitor the animal's position and orientation. Custom software tracks the 3D
position of the animal's head in real-time and adjusts a motorized stage to
keep it within the field of view as the animal roams freely. We observe calcium
transients from 78 neurons and correlate this activity with the animal's
behavior. Across worms, multiple neurons show significant correlations with
modes of behavior corresponding to forward, backward, and turning locomotion.
By comparing the 3D positions of these neurons with a known atlas, our results
are consistent with previous single-neuron studies and demonstrate the
existence of new candidate neurons for behavioral circuits.
|
[
{
"created": "Wed, 14 Jan 2015 20:07:59 GMT",
"version": "v1"
}
] |
2015-12-31
|
[
[
"Nguyen",
"Jeffrey P.",
""
],
[
"Shipley",
"Frederick B.",
""
],
[
"Linder",
"Ashley N.",
""
],
[
"Plummer",
"George S.",
""
],
[
"Shaevitz",
"Joshua W.",
""
],
[
"Leifer",
"Andrew M.",
""
]
] |
The ability to acquire large-scale recordings of neuronal activity in awake and unrestrained animals poses a major challenge for studying neural coding of animal behavior. We present a new instrument capable of recording intracellular calcium transients from every neuron in the head of a freely behaving C. elegans with cellular resolution while simultaneously recording the animal's position, posture and locomotion. We employ spinning-disk confocal microscopy to capture 3D volumetric fluorescent images of neurons expressing the calcium indicator GCaMP6s at 5 head-volumes per second. Two cameras simultaneously monitor the animal's position and orientation. Custom software tracks the 3D position of the animal's head in real-time and adjusts a motorized stage to keep it within the field of view as the animal roams freely. We observe calcium transients from 78 neurons and correlate this activity with the animal's behavior. Across worms, multiple neurons show significant correlations with modes of behavior corresponding to forward, backward, and turning locomotion. By comparing the 3D positions of these neurons with a known atlas, our results are consistent with previous single-neuron studies and demonstrate the existence of new candidate neurons for behavioral circuits.
|
1906.03660
|
Margaret Cheung
|
Andrei G. Gasic, Mayank M. Boob, Maxim B. Prigozhin, Dirar Homouz,
Caleb M. Daugherty, Martin Gruebele, Margaret S. Cheung
|
Critical phenomena in the temperature-pressure-crowding phase diagram of
a protein
|
5 figures
|
Phys. Rev. X 9, 041035 (2019)
|
10.1103/PhysRevX.9.041035
| null |
q-bio.BM physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the cell, proteins fold and perform complex functions through global
structural rearrangements. Function requires a protein to be at the brink of
stability to be susceptible to small environmental fluctuations, yet stable
enough to maintain structural integrity. These apparently conflicting behaviors
are exhibited by systems near a critical point, where distinct phases merge $-$
a concept beyond previous studies indicating proteins have a well-defined
folded/unfolded phase boundary in the pressure-temperature plane. Here, by
modeling the protein phosphoglycerate kinase (PGK) on the temperature (T),
pressure (P), and crowding volume-fraction ($\phi$) phase diagram, we
demonstrate a critical transition where phases merge, and PGK exhibits large
structural fluctuations. Above the critical temperature (Tc), the difference
between the intermediate and unfolded phases disappears. When $\phi$ increases,
the Tc moves to a lower T. We verify the calculations with experiments mapping
the T-P-$\phi$ space, which likewise reveal a critical point at 305 K and 170
MPa that moves to a lower T as $\phi$ increases. Crowding places PGK near a
critical line in its natural parameter space, where large conformational
changes can occur without costly free energy barriers. Specific structures are
proposed for each phase based on simulation.
|
[
{
"created": "Sun, 9 Jun 2019 15:37:04 GMT",
"version": "v1"
}
] |
2020-11-17
|
[
[
"Gasic",
"Andrei G.",
""
],
[
"Boob",
"Mayank M.",
""
],
[
"Prigozhin",
"Maxim B.",
""
],
[
"Homouz",
"Dirar",
""
],
[
"Daugherty",
"Caleb M.",
""
],
[
"Gruebele",
"Martin",
""
],
[
"Cheung",
"Margaret S.",
""
]
] |
In the cell, proteins fold and perform complex functions through global structural rearrangements. Function requires a protein to be at the brink of stability to be susceptible to small environmental fluctuations, yet stable enough to maintain structural integrity. These apparently conflicting behaviors are exhibited by systems near a critical point, where distinct phases merge $-$ a concept beyond previous studies indicating proteins have a well-defined folded/unfolded phase boundary in the pressure-temperature plane. Here, by modeling the protein phosphoglycerate kinase (PGK) on the temperature (T), pressure (P), and crowding volume-fraction ($\phi$) phase diagram, we demonstrate a critical transition where phases merge, and PGK exhibits large structural fluctuations. Above the critical temperature (Tc), the difference between the intermediate and unfolded phases disappears. When $\phi$ increases, the Tc moves to a lower T. We verify the calculations with experiments mapping the T-P-$\phi$ space, which likewise reveal a critical point at 305 K and 170 MPa that moves to a lower T as $\phi$ increases. Crowding places PGK near a critical line in its natural parameter space, where large conformational changes can occur without costly free energy barriers. Specific structures are proposed for each phase based on simulation.
|
0904.1959
|
Emilio Hernandez-Garcia
|
E. Tew Kai (1), V. Rossi (2), J. Sudre (2), H. Weimerskirch (3), C.
Lopez (4), E. Hernandez-Garcia (4), F. Marsac (1), and V. Garcon (2) ((1)
THETIS, Sete; (2) LEGOS, Toulouse; (3) C.E.B. Chize; (4) IFISC, Palma de
Mallorca)
|
Top marine predators track Lagrangian coherent structures
| null |
Proceedings of the National Academy of Sciences of the USA (PNAS)
106, 8245-8250 (2009)
|
10.1073/pnas.0811034106
| null |
q-bio.PE nlin.CD physics.ao-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Meso- and submesoscales (fronts, eddies, filaments) in surface ocean flow
have a crucial influence on marine ecosystems. Their dynamics partly control
the foraging behaviour and the displacement of marine top predators (tuna,
birds, turtles, and cetaceans). In this work we focus on the role of
submesoscale structures in the Mozambique Channel on the distribution of a
marine predator, the Great Frigatebird. Using a newly developed dynamical
concept, namely the Finite-Size Lyapunov Exponent (FSLE), we have identified
Lagrangian coherent structures (LCSs) present in the surface flow in the
Channel over a 2-month observation period (August and September 2003). By
comparing seabirds' satellite positions with LCSs locations, we demonstrate
that frigatebirds track precisely these structures in the Mozambique Channel,
providing the first evidence that a top predator is able to track these FSLE
ridges to locate food patches. After comparing bird positions during long and
short trips, and different parts of these trips, we propose several hypotheses
to understand how frigatebirds can follow these LCSs. The birds might use
visual and/or olfactory cues and/or atmospheric current changes over the
structures to move along these biological corridors. The birds being often
associated to tuna schools around foraging areas, a thorough comprehension of
their foraging behaviour and movement during the breeding season is crucial not
only to seabirds' ecology but also to an appropriate ecosystemic approach of
fisheries in the Channel.
|
[
{
"created": "Mon, 13 Apr 2009 15:52:30 GMT",
"version": "v1"
}
] |
2009-05-25
|
[
[
"Kai",
"E. Tew",
""
],
[
"Rossi",
"V.",
""
],
[
"Sudre",
"J.",
""
],
[
"Weimerskirch",
"H.",
""
],
[
"Lopez",
"C.",
""
],
[
"Hernandez-Garcia",
"E.",
""
],
[
"Marsac",
"F.",
""
],
[
"Garcon",
"V.",
""
]
] |
Meso- and submesoscales (fronts, eddies, filaments) in surface ocean flow have a crucial influence on marine ecosystems. Their dynamics partly control the foraging behaviour and the displacement of marine top predators (tuna, birds, turtles, and cetaceans). In this work we focus on the role of submesoscale structures in the Mozambique Channel on the distribution of a marine predator, the Great Frigatebird. Using a newly developed dynamical concept, namely the Finite-Size Lyapunov Exponent (FSLE), we have identified Lagrangian coherent structures (LCSs) present in the surface flow in the Channel over a 2-month observation period (August and September 2003). By comparing seabirds' satellite positions with LCSs locations, we demonstrate that frigatebirds track precisely these structures in the Mozambique Channel, providing the first evidence that a top predator is able to track these FSLE ridges to locate food patches. After comparing bird positions during long and short trips, and different parts of these trips, we propose several hypotheses to understand how frigatebirds can follow these LCSs. The birds might use visual and/or olfactory cues and/or atmospheric current changes over the structures to move along these biological corridors. The birds being often associated to tuna schools around foraging areas, a thorough comprehension of their foraging behaviour and movement during the breeding season is crucial not only to seabirds' ecology but also to an appropriate ecosystemic approach of fisheries in the Channel.
|
1308.5548
|
Pierre Sens
|
Serge Dmitrieff, and Pierre Sens
|
Transient domain formation in membrane-bound organelles undergoing
maturation
|
7 pages, 6 figures
| null |
10.1103/PhysRevE.88.062704
| null |
q-bio.SC cond-mat.soft physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The membrane components of cellular organelles have been shown to segregate
into domains as the result of biochemical maturation. We propose that the
dynamical competition between maturation and lateral segregation of membrane
components regulates domain formation. We study a two- component fluid membrane
in which enzymatic reaction irreversibly converts one component into another,
and phase separation triggers the formation of transient membrane domains. The
maximum domains size is shown to depend on the maturation rate as a power-law
similar to the one observed for domain growth with time in the absence of
maturation, despite this time dependence not being verified in the case of
irreversible maturation. This control of domain size by enzymatic activity
could play a critical role in intra-organelle dynamics.
|
[
{
"created": "Mon, 26 Aug 2013 11:53:39 GMT",
"version": "v1"
}
] |
2015-06-17
|
[
[
"Dmitrieff",
"Serge",
""
],
[
"Sens",
"Pierre",
""
]
] |
The membrane components of cellular organelles have been shown to segregate into domains as the result of biochemical maturation. We propose that the dynamical competition between maturation and lateral segregation of membrane components regulates domain formation. We study a two- component fluid membrane in which enzymatic reaction irreversibly converts one component into another, and phase separation triggers the formation of transient membrane domains. The maximum domains size is shown to depend on the maturation rate as a power-law similar to the one observed for domain growth with time in the absence of maturation, despite this time dependence not being verified in the case of irreversible maturation. This control of domain size by enzymatic activity could play a critical role in intra-organelle dynamics.
|
2011.05304
|
Ishan Barman
|
Santosh Kumar Paidi, Joel Rodriguez Troncoso, Piyush Raj, Paola
Monterroso Diaz, David E. Lee, Narasimhan Rajaram, Ishan Barman
|
Label-free Raman spectroscopy and machine learning enables sensitive
evaluation of differential response to immunotherapy
| null | null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Cancer immunotherapy provides durable clinical benefit in only a small
fraction of patients, particularly due to a lack of reliable biomarkers for
accurate prediction of treatment outcomes and evaluation of response. Here, we
demonstrate the first application of label-free Raman spectroscopy for
elucidating biochemical changes induced by immunotherapy in the tumor
microenvironment. We used CT26 murine colorectal cancer cells to grow tumor
xenografts and subjected them to treatment with anti-CTLA-4 and anti-PD-L1
antibodies. Multivariate curve resolution - alternating least squares (MCR-ALS)
decomposition of Raman spectral dataset obtained from the treated and control
tumors revealed subtle differences in lipid, nucleic acid, and collagen content
due to therapy. Our supervised classification analysis using support vector
machines and random forests provided excellent prediction accuracies for both
immune checkpoint inhibitors and delineated important spectral markers specific
to each therapy, consistent with their differential mechanisms of action. Our
findings pave the way for in vivo studies of response to immunotherapy in
clinical patients using label-free Raman spectroscopy and machine learning.
|
[
{
"created": "Tue, 10 Nov 2020 18:39:14 GMT",
"version": "v1"
}
] |
2020-11-11
|
[
[
"Paidi",
"Santosh Kumar",
""
],
[
"Troncoso",
"Joel Rodriguez",
""
],
[
"Raj",
"Piyush",
""
],
[
"Diaz",
"Paola Monterroso",
""
],
[
"Lee",
"David E.",
""
],
[
"Rajaram",
"Narasimhan",
""
],
[
"Barman",
"Ishan",
""
]
] |
Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, particularly due to a lack of reliable biomarkers for accurate prediction of treatment outcomes and evaluation of response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biochemical changes induced by immunotherapy in the tumor microenvironment. We used CT26 murine colorectal cancer cells to grow tumor xenografts and subjected them to treatment with anti-CTLA-4 and anti-PD-L1 antibodies. Multivariate curve resolution - alternating least squares (MCR-ALS) decomposition of Raman spectral dataset obtained from the treated and control tumors revealed subtle differences in lipid, nucleic acid, and collagen content due to therapy. Our supervised classification analysis using support vector machines and random forests provided excellent prediction accuracies for both immune checkpoint inhibitors and delineated important spectral markers specific to each therapy, consistent with their differential mechanisms of action. Our findings pave the way for in vivo studies of response to immunotherapy in clinical patients using label-free Raman spectroscopy and machine learning.
|
1204.5517
|
William Holmes
|
Benjamin Lin and William R. Holmes and ChiaoChun Wang and Tasuku Ueno
and Andrew Harwell and Leah Edelstein-Keshet and Takanari Inoue and Andre
Levchenko and
|
Synthetic spatially graded Rac activation drives directed cell
polarization and locomotion
| null | null |
10.1073/pnas.1210295109
| null |
q-bio.MN q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Migrating cells possess intracellular gradients of Rho GTPases, but it is
unknown whether these shallow gradients themselves can induce motility. Here we
describe a new method to present cells with induced linear gradients of active,
endogenous Rac without receptor activation. Gradients as low as 15% were
sufficient to not only trigger cell migration up the synthetic gradient, but
also to induce both cell polarization and repolarization. Response kinetics
were inversely proportional to Rac gradient values, in agreement with a new
mathematical model, suggesting a role for natural input gradient amplification
upstream of Rac. Increases in Rac levels beyond a well-defined threshold
dramatically augmented polarization and decreased sensitivity to the gradient
value. The threshold was governed by initial cell polarity and PI3K activity,
supporting a role for both in defining responsiveness to natural or synthetic
Rac activation. Our methodology suggests a general way to investigate processes
regulated by intracellular signaling gradients.
|
[
{
"created": "Wed, 25 Apr 2012 00:17:58 GMT",
"version": "v1"
}
] |
2015-06-04
|
[
[
"Lin",
"Benjamin",
""
],
[
"Holmes",
"William R.",
""
],
[
"Wang",
"ChiaoChun",
""
],
[
"Ueno",
"Tasuku",
""
],
[
"Harwell",
"Andrew",
""
],
[
"Edelstein-Keshet",
"Leah",
""
],
[
"Inoue",
"Takanari",
""
],
[
"and",
"Andre Levchenko",
""
]
] |
Migrating cells possess intracellular gradients of Rho GTPases, but it is unknown whether these shallow gradients themselves can induce motility. Here we describe a new method to present cells with induced linear gradients of active, endogenous Rac without receptor activation. Gradients as low as 15% were sufficient to not only trigger cell migration up the synthetic gradient, but also to induce both cell polarization and repolarization. Response kinetics were inversely proportional to Rac gradient values, in agreement with a new mathematical model, suggesting a role for natural input gradient amplification upstream of Rac. Increases in Rac levels beyond a well-defined threshold dramatically augmented polarization and decreased sensitivity to the gradient value. The threshold was governed by initial cell polarity and PI3K activity, supporting a role for both in defining responsiveness to natural or synthetic Rac activation. Our methodology suggests a general way to investigate processes regulated by intracellular signaling gradients.
|
1610.03986
|
Wolfgang Halter
|
Wolfgang Halter, Jan Maximilian Montenbruck and Frank Allg\"ower
|
Geometric stability considerations of the ribosome flow model with pool
|
Published in the Proceedings of the 22nd International Symposium on
the Mathematical Theory of Networks and Systems, 2016,
http://hdl.handle.net/11299/181518
| null | null | null |
q-bio.SC math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In order to better understand the process of gene translation, the ribosome
flow model (RFM) with pool was introduced recently. This model describes the
movement of several ribosomes along an mRNA template and simultaneously
captures the dynamics of the finite pool of ribosomes. Studying this system
with respect to the number and stability of its equilibria was so far based on
monotone systems theory (Margaliot and Tuller, 2012). We extend the results
obtained therein by using a geometric approach, showing that the equilibria of
the system constitute a normally hyperbolic invariant submanifold.
Subsequently, we analyze the Jacobi linearization of the system evaluated at
the equilibria in order to show that the equilibria are asymptotically stable
relative to certain affine subspaces. As this approach does not require any
monotonicity features of the system, it may also be applied for more complex
systems of the same kind such as bi-directional ribosome flows or time-varying
template numbers.
|
[
{
"created": "Thu, 13 Oct 2016 09:13:17 GMT",
"version": "v1"
}
] |
2016-10-14
|
[
[
"Halter",
"Wolfgang",
""
],
[
"Montenbruck",
"Jan Maximilian",
""
],
[
"Allgöwer",
"Frank",
""
]
] |
In order to better understand the process of gene translation, the ribosome flow model (RFM) with pool was introduced recently. This model describes the movement of several ribosomes along an mRNA template and simultaneously captures the dynamics of the finite pool of ribosomes. Studying this system with respect to the number and stability of its equilibria was so far based on monotone systems theory (Margaliot and Tuller, 2012). We extend the results obtained therein by using a geometric approach, showing that the equilibria of the system constitute a normally hyperbolic invariant submanifold. Subsequently, we analyze the Jacobi linearization of the system evaluated at the equilibria in order to show that the equilibria are asymptotically stable relative to certain affine subspaces. As this approach does not require any monotonicity features of the system, it may also be applied for more complex systems of the same kind such as bi-directional ribosome flows or time-varying template numbers.
|
q-bio/0502026
|
Mazza
|
Martin Gander, Christian Mazza, Hansklaus Rummler
|
Stochastic gene expression in switching environments
|
14 pages, 5 figures
| null | null | null |
q-bio.PE q-bio.CB
| null |
We study a stochastic model proposed recently in the genetic literature to
explain the heterogeneity of cell populations or of gene products. Cells are
located in two colonies, whose sizes fluctuate as birth and migration processes
in switching environments. We prove that there is a range of parameters where
heterogeneity induces a larger mean fitness
|
[
{
"created": "Tue, 22 Feb 2005 13:56:05 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Gander",
"Martin",
""
],
[
"Mazza",
"Christian",
""
],
[
"Rummler",
"Hansklaus",
""
]
] |
We study a stochastic model proposed recently in the genetic literature to explain the heterogeneity of cell populations or of gene products. Cells are located in two colonies, whose sizes fluctuate as birth and migration processes in switching environments. We prove that there is a range of parameters where heterogeneity induces a larger mean fitness
|
2110.13909
|
Ghazal ArabiDarrehDor
|
Ghazal ArabiDarrehDor, Ali Tivay, Chris Meador, George C. Kramer,
Jin-Oh Hahn, and Jose Salinas
|
Mathematical Modeling, In-Human Evaluation and Analysis of Volume
Kinetics and Kidney Function after Burn Injury and Resuscitation
|
24 pages The final version of this paper has been accepted for
publication in the IEEE Journal of Transactions on Biomedical Engineering
(2021) and is available for early access at
https://ieeexplore.ieee.org/document/9478222
| null |
10.1109/TBME.2021.3094515
| null |
q-bio.TO
|
http://creativecommons.org/licenses/by/4.0/
|
Existing burn resuscitation protocols exhibit large variability in treatment
efficacy. Hence, they must be further optimized based on comprehensive
knowledge of burn pathophysiology. A physics-based mathematical model that can
replicate physiological responses in diverse burn patients can serve as an
attractive basis to perform non-clinical testing of burn resuscitation
protocols and to expand knowledge on burn pathophysiology. We intend to
develop, optimize, validate, and analyze a mathematical model to replicate
physiological responses in burn patients. Using clinical datasets collected
from 233 burn patients receiving burn resuscitation, we developed and validated
a mathematical model applicable to computer-aided in-human burn resuscitation
trial and knowledge expansion. Using the validated mathematical model, we
examined possible physiological mechanisms responsible for the cohort-dependent
differences in burn pathophysiology between younger versus older patients,
female versus male patients, and patients with versus without inhalational
injury. We demonstrated that the mathematical model could replicate
physiological responses in burn patients associated with wide demographic
characteristics and injury severity and that an increased inflammatory response
to injury may be a key contributing factor in increasing the mortality risk of
older patients and patients with inhalation injury via an increase in the fluid
retention. The mathematical model may provide an attractive platform to conduct
non-clinical testing of burn resuscitation protocols and test new hypotheses on
burn pathophysiology.
|
[
{
"created": "Sun, 24 Oct 2021 21:40:21 GMT",
"version": "v1"
}
] |
2021-10-28
|
[
[
"ArabiDarrehDor",
"Ghazal",
""
],
[
"Tivay",
"Ali",
""
],
[
"Meador",
"Chris",
""
],
[
"Kramer",
"George C.",
""
],
[
"Hahn",
"Jin-Oh",
""
],
[
"Salinas",
"Jose",
""
]
] |
Existing burn resuscitation protocols exhibit large variability in treatment efficacy. Hence, they must be further optimized based on comprehensive knowledge of burn pathophysiology. A physics-based mathematical model that can replicate physiological responses in diverse burn patients can serve as an attractive basis to perform non-clinical testing of burn resuscitation protocols and to expand knowledge on burn pathophysiology. We intend to develop, optimize, validate, and analyze a mathematical model to replicate physiological responses in burn patients. Using clinical datasets collected from 233 burn patients receiving burn resuscitation, we developed and validated a mathematical model applicable to computer-aided in-human burn resuscitation trial and knowledge expansion. Using the validated mathematical model, we examined possible physiological mechanisms responsible for the cohort-dependent differences in burn pathophysiology between younger versus older patients, female versus male patients, and patients with versus without inhalational injury. We demonstrated that the mathematical model could replicate physiological responses in burn patients associated with wide demographic characteristics and injury severity and that an increased inflammatory response to injury may be a key contributing factor in increasing the mortality risk of older patients and patients with inhalation injury via an increase in the fluid retention. The mathematical model may provide an attractive platform to conduct non-clinical testing of burn resuscitation protocols and test new hypotheses on burn pathophysiology.
|
1906.01094
|
Cecilia Jarne Dr
|
Cecilia Jarne and Rodrigo Laje
|
Exploring weight initialization, diversity of solutions, and degradation
in recurrent neural networks trained for temporal and decision-making tasks
| null | null | null | null |
q-bio.NC cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recurrent Neural Networks (RNNs) are frequently used to model aspects of
brain function and structure. In this work, we trained small fully-connected
RNNs to perform temporal and flow control tasks with time-varying stimuli. Our
results show that different RNNs can solve the same task by converging to
different underlying dynamics and also how the performance gracefully degrades
as either network size is decreased, interval duration is increased, or
connectivity damage is increased. For the considered tasks, we explored how
robust the network obtained after training can be according to task
parameterization. In the process, we developed a framework that can be useful
to parameterize other tasks of interest in computational neuroscience. Our
results are useful to quantify different aspects of the models, which are
normally used as black boxes and need to be understood in order to model the
biological response of cerebral cortex areas.
|
[
{
"created": "Mon, 3 Jun 2019 21:56:48 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Sep 2019 16:23:44 GMT",
"version": "v2"
},
{
"created": "Tue, 1 Oct 2019 14:18:27 GMT",
"version": "v3"
},
{
"created": "Mon, 21 Dec 2020 14:04:01 GMT",
"version": "v4"
},
{
"created": "Mon, 6 Dec 2021 13:41:03 GMT",
"version": "v5"
},
{
"created": "Wed, 28 Jun 2023 07:52:10 GMT",
"version": "v6"
}
] |
2023-06-29
|
[
[
"Jarne",
"Cecilia",
""
],
[
"Laje",
"Rodrigo",
""
]
] |
Recurrent Neural Networks (RNNs) are frequently used to model aspects of brain function and structure. In this work, we trained small fully-connected RNNs to perform temporal and flow control tasks with time-varying stimuli. Our results show that different RNNs can solve the same task by converging to different underlying dynamics and also how the performance gracefully degrades as either network size is decreased, interval duration is increased, or connectivity damage is increased. For the considered tasks, we explored how robust the network obtained after training can be according to task parameterization. In the process, we developed a framework that can be useful to parameterize other tasks of interest in computational neuroscience. Our results are useful to quantify different aspects of the models, which are normally used as black boxes and need to be understood in order to model the biological response of cerebral cortex areas.
|
1607.04064
|
Michael Margaliot
|
Yoram Zarai and Michael Margaliot and Tamir Tuller
|
On the Ribosomal Density that Maximizes Protein Translation Rate
| null | null |
10.1371/journal.pone.0166481
| null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
During mRNA translation, several ribosomes attach to the same mRNA molecule
simultaneously translating it into a protein. This pipelining increases the
protein production rate. A natural and important question is what ribosomal
density maximizes the protein production rate. Using mathematical models of
ribosome flow along both a linear and a circular mRNA molecule we prove that
typically the steady-state production rate is maximized when the ribosomal
density is one half of the maximal possible density. We discuss the
implications of our results to endogenous genes under natural cellular
conditions and also to synthetic biology.
|
[
{
"created": "Thu, 14 Jul 2016 10:13:44 GMT",
"version": "v1"
}
] |
2017-02-08
|
[
[
"Zarai",
"Yoram",
""
],
[
"Margaliot",
"Michael",
""
],
[
"Tuller",
"Tamir",
""
]
] |
During mRNA translation, several ribosomes attach to the same mRNA molecule simultaneously translating it into a protein. This pipelining increases the protein production rate. A natural and important question is what ribosomal density maximizes the protein production rate. Using mathematical models of ribosome flow along both a linear and a circular mRNA molecule we prove that typically the steady-state production rate is maximized when the ribosomal density is one half of the maximal possible density. We discuss the implications of our results to endogenous genes under natural cellular conditions and also to synthetic biology.
|
2012.09723
|
Hsin-Yu Lai
|
Hsin-Yu Lai, Gladynel Saavedra-Pena, Charles G. Sodini, Thomas Heldt,
Vivienne Sze
|
App-based saccade latency and error determination across the adult age
spectrum
|
11 pages, 16 figures, journal
| null | null | null |
q-bio.NC eess.IV eess.SP q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
We aid in neurocognitive monitoring outside the hospital environment by
enabling app-based measurements of visual reaction time (saccade latency) and
error rate in a cohort of subjects spanning the adult age spectrum. Methods: We
developed an iOS app to record subjects with the frontal camera during pro- and
anti-saccade tasks. We further developed automated algorithms for measuring
saccade latency and error rate that take into account the possibility that it
might not always be possible to determine the eye movement from app-based
recordings. Results: To measure saccade latency on a tablet, we ensured that
the absolute timing error between on-screen task presentation and the camera
recording is within 5 ms. We collected over 235,000 eye movements in 80
subjects ranging in age from 20 to 92 years, with 96% of recorded eye movements
either declared good or directional errors. Our error detection code achieved a
sensitivity of 0.97 and a specificity of 0.97. Confirming prior reports, we
observed a positive correlation between saccade latency and age while the
relationship between error rate and age was not significant. Finally, we
observed significant intra- and inter-subject variations in saccade latency and
error rate distributions, which highlights the importance of individualized
tracking of these visual digital biomarkers. Conclusion and Significance: Our
system and algorithms allow ubiquitous tracking of saccade latency and error
rate, which opens up the possibility of quantifying patient state on a finer
timescale in a broader population than previously possible.
|
[
{
"created": "Mon, 14 Dec 2020 02:02:02 GMT",
"version": "v1"
}
] |
2020-12-18
|
[
[
"Lai",
"Hsin-Yu",
""
],
[
"Saavedra-Pena",
"Gladynel",
""
],
[
"Sodini",
"Charles G.",
""
],
[
"Heldt",
"Thomas",
""
],
[
"Sze",
"Vivienne",
""
]
] |
We aid in neurocognitive monitoring outside the hospital environment by enabling app-based measurements of visual reaction time (saccade latency) and error rate in a cohort of subjects spanning the adult age spectrum. Methods: We developed an iOS app to record subjects with the frontal camera during pro- and anti-saccade tasks. We further developed automated algorithms for measuring saccade latency and error rate that take into account the possibility that it might not always be possible to determine the eye movement from app-based recordings. Results: To measure saccade latency on a tablet, we ensured that the absolute timing error between on-screen task presentation and the camera recording is within 5 ms. We collected over 235,000 eye movements in 80 subjects ranging in age from 20 to 92 years, with 96% of recorded eye movements either declared good or directional errors. Our error detection code achieved a sensitivity of 0.97 and a specificity of 0.97. Confirming prior reports, we observed a positive correlation between saccade latency and age while the relationship between error rate and age was not significant. Finally, we observed significant intra- and inter-subject variations in saccade latency and error rate distributions, which highlights the importance of individualized tracking of these visual digital biomarkers. Conclusion and Significance: Our system and algorithms allow ubiquitous tracking of saccade latency and error rate, which opens up the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.
|
2310.07442
|
Andrea Radici
|
Andrea Radici, Daniele Bevacqua, Leonardo Miele, Davide Martinetti
|
Network-thinking to optimize surveillance and control of crop parasites.
A review
|
21 pages, 3 figures
| null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
Increasing cultivated lands, crop homogenization and global food trade have
fostered the spread of crop pests and diseases. Optimizing crop protection is
urgently needed to ensure food safety. One aspect of crop protection is
surveillance, which focuses on the early detection of a parasite, and control,
aiming to fight and possibly eradicate it. Network theory has been widely used
to model the spread of human and animal infectious diseases in systems
described through nodes and edges. It has been successfully used to optimize
monitoring and immunization campaigns. In crop protection, there is a growing
literature using this theory to describe parasites spread and to conceive
protection strategies. Here we review the use of network theory in crop
protection, from the more descriptive to the more applied approaches aimed to
optimize crop protection. We retrace the logical process that has led
epidemiological models to rely on network theory, and we provide examples of
how the spread of crop parasites has been represented via a network
description. We define the objectives of surveillance and control, and we show
how these have been declined in the network-based epidemiological sphere and
then adapted in the agricultural context. We eventually discuss the discrepancy
between the application of network theory in surveillance and control to
identify culprits and solutions. We find that: i) scientists have successfully
interpreted very different modes of parasitic transmission under the lens of
network theory; ii) while network-based surveillance has progressively
clarified its objectives and sound tools have been proposed, network-based
control has been less studied and applied; iii) network-thinking must address
how to properly define edges and nodes at different geographic scale to broad
its application in crop protection.
|
[
{
"created": "Wed, 11 Oct 2023 12:47:29 GMT",
"version": "v1"
}
] |
2023-10-12
|
[
[
"Radici",
"Andrea",
""
],
[
"Bevacqua",
"Daniele",
""
],
[
"Miele",
"Leonardo",
""
],
[
"Martinetti",
"Davide",
""
]
] |
Increasing cultivated lands, crop homogenization and global food trade have fostered the spread of crop pests and diseases. Optimizing crop protection is urgently needed to ensure food safety. One aspect of crop protection is surveillance, which focuses on the early detection of a parasite, and control, aiming to fight and possibly eradicate it. Network theory has been widely used to model the spread of human and animal infectious diseases in systems described through nodes and edges. It has been successfully used to optimize monitoring and immunization campaigns. In crop protection, there is a growing literature using this theory to describe parasites spread and to conceive protection strategies. Here we review the use of network theory in crop protection, from the more descriptive to the more applied approaches aimed to optimize crop protection. We retrace the logical process that has led epidemiological models to rely on network theory, and we provide examples of how the spread of crop parasites has been represented via a network description. We define the objectives of surveillance and control, and we show how these have been declined in the network-based epidemiological sphere and then adapted in the agricultural context. We eventually discuss the discrepancy between the application of network theory in surveillance and control to identify culprits and solutions. We find that: i) scientists have successfully interpreted very different modes of parasitic transmission under the lens of network theory; ii) while network-based surveillance has progressively clarified its objectives and sound tools have been proposed, network-based control has been less studied and applied; iii) network-thinking must address how to properly define edges and nodes at different geographic scale to broad its application in crop protection.
|
1403.4479
|
Claus Bornemann PhD
|
Claus Bornemann
|
Virus-Encoded Ribonucleotide Reductases
|
3 pages, 39 references
| null | null | null |
q-bio.BM q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Ribonucleotide reductases are encoded by many viruses, but without other
enzymes of nucleotide metabolism of no obvious use. A look at the enzymes'
molecular properties and their possible mutator action may give clues.
|
[
{
"created": "Tue, 18 Mar 2014 14:41:26 GMT",
"version": "v1"
}
] |
2014-03-19
|
[
[
"Bornemann",
"Claus",
""
]
] |
Ribonucleotide reductases are encoded by many viruses, but without other enzymes of nucleotide metabolism of no obvious use. A look at the enzymes' molecular properties and their possible mutator action may give clues.
|
2408.05420
|
J Gregory Caporaso
|
Isaiah Raspet, Elizabeth Gehret, Chloe Herman, Jeff Meilander, Andrew
Manley, Anthony Simard, Evan Bolyen, J. Gregory Caporaso
|
Facilitating bootstrapped and rarefaction-based microbiome diversity
analysis with q2-boots
|
5 pages, 1 figure, 1 supplementary table
| null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Background: We present q2-boots, a QIIME 2 plugin that facilitates
bootstrapped and rarefaction-based microbiome diversity analysis. This plugin
provides eight new actions that allow users to apply any of thirty different
alpha diversity metrics and twenty-two beta diversity metrics to bootstrapped
or rarefied feature tables, using a single QIIME 2 Pipeline command, or more
granular QIIME 2 Action commands.
Results: Given a feature table, an even sampling depth, and the number of
iterations to perform (n), the command qiime boots core-metrics will resample
the feature table n times and compute alpha and beta diversity metrics on each
resampled table. The results will be integrated in summary data artifacts that
are identical in structure and type to results that would be generated by
applying diversity metrics to a single table. This enables all of the same
downstream analytic tools to be applied to these tables, and ensures that all
collected data is considered when computing microbiome diversity metrics.
Conclusions: A challenge of this work was deciding how to integrate distance
matrices that were computed on n resampled feature tables, as a simple average
of pairwise distances (median or mean) does not account for the structure of
distance matrices. q2-boots provides three options, and we show here that the
results of these approaches are highly correlated. q2-boots is free and open
source. Source code, installation instructions, and a tutorial can be found at
https://github.com/caporaso-lab/q2-boots.
|
[
{
"created": "Sat, 10 Aug 2024 03:12:15 GMT",
"version": "v1"
}
] |
2024-08-13
|
[
[
"Raspet",
"Isaiah",
""
],
[
"Gehret",
"Elizabeth",
""
],
[
"Herman",
"Chloe",
""
],
[
"Meilander",
"Jeff",
""
],
[
"Manley",
"Andrew",
""
],
[
"Simard",
"Anthony",
""
],
[
"Bolyen",
"Evan",
""
],
[
"Caporaso",
"J. Gregory",
""
]
] |
Background: We present q2-boots, a QIIME 2 plugin that facilitates bootstrapped and rarefaction-based microbiome diversity analysis. This plugin provides eight new actions that allow users to apply any of thirty different alpha diversity metrics and twenty-two beta diversity metrics to bootstrapped or rarefied feature tables, using a single QIIME 2 Pipeline command, or more granular QIIME 2 Action commands. Results: Given a feature table, an even sampling depth, and the number of iterations to perform (n), the command qiime boots core-metrics will resample the feature table n times and compute alpha and beta diversity metrics on each resampled table. The results will be integrated in summary data artifacts that are identical in structure and type to results that would be generated by applying diversity metrics to a single table. This enables all of the same downstream analytic tools to be applied to these tables, and ensures that all collected data is considered when computing microbiome diversity metrics. Conclusions: A challenge of this work was deciding how to integrate distance matrices that were computed on n resampled feature tables, as a simple average of pairwise distances (median or mean) does not account for the structure of distance matrices. q2-boots provides three options, and we show here that the results of these approaches are highly correlated. q2-boots is free and open source. Source code, installation instructions, and a tutorial can be found at https://github.com/caporaso-lab/q2-boots.
|
1502.03201
|
Yann Ponty
|
Jozef Hale\v{s}, J\'an Ma\v{n}uch (UBC-Computer Science), Yann Ponty
(LIX, AMIB), Ladislav Stacho
|
Combinatorial RNA Design: Designability and Structure-Approximating
Algorithm
|
CPM - 26th Annual Symposium on Combinatorial Pattern Matching, Jun
2015, Ischia Island, Italy. LNCS, 2015
| null | null | null |
q-bio.QM cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we consider the Combinatorial RNA Design problem, a minimal
instance of the RNA design problem which aims at finding a sequence that admits
a given target as its unique base pair maximizing structure. We provide
complete characterizations for the structures that can be designed using
restricted alphabets. Under a classic four-letter alphabet, we provide a
complete characterization of designable structures without unpaired bases. When
unpaired bases are allowed, we provide partial characterizations for classes of
designable/undesignable structures, and show that the class of designable
structures is closed under the stutter operation. Membership of a given
structure to any of the classes can be tested in linear time and, for positive
instances, a solution can be found in linear time. Finally, we consider a
structure-approximating version of the problem that allows to extend bands
(helices) and, assuming that the input structure avoids two motifs, we provide
a linear-time algorithm that produces a designable structure with at most twice
more base pairs than the input structure.
|
[
{
"created": "Wed, 11 Feb 2015 06:47:23 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Jun 2015 08:34:03 GMT",
"version": "v2"
}
] |
2015-06-22
|
[
[
"Haleš",
"Jozef",
"",
"UBC-Computer Science"
],
[
"Maňuch",
"Ján",
"",
"UBC-Computer Science"
],
[
"Ponty",
"Yann",
"",
"LIX, AMIB"
],
[
"Stacho",
"Ladislav",
""
]
] |
In this work, we consider the Combinatorial RNA Design problem, a minimal instance of the RNA design problem which aims at finding a sequence that admits a given target as its unique base pair maximizing structure. We provide complete characterizations for the structures that can be designed using restricted alphabets. Under a classic four-letter alphabet, we provide a complete characterization of designable structures without unpaired bases. When unpaired bases are allowed, we provide partial characterizations for classes of designable/undesignable structures, and show that the class of designable structures is closed under the stutter operation. Membership of a given structure to any of the classes can be tested in linear time and, for positive instances, a solution can be found in linear time. Finally, we consider a structure-approximating version of the problem that allows to extend bands (helices) and, assuming that the input structure avoids two motifs, we provide a linear-time algorithm that produces a designable structure with at most twice more base pairs than the input structure.
|
2303.10724
|
Josinaldo Menezes
|
J. Menezes
|
Mobility restrictions in response to local epidemic outbreaks in
rock-paper-scissors models
|
11 pages, 12 figures
|
Journal of Physics: Complex. 5, 015018 (2024)
|
10.1088/2632-072X/ad2d5b
| null |
q-bio.PE nlin.AO nlin.PS physics.bio-ph q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study a three-species cyclic model whose organisms are vulnerable to
contamination with an infectious disease which propagates person-to-person. We
consider that individuals of one species perform an evolutionary
self-preservation strategy by reducing the mobility rate to minimise infection
risk whenever an epidemic outbreak reaches the neighbourhood. Running
stochastic simulations, we quantify the changes in spatial patterns induced by
unevenness in the cyclic game introduced by the mobility restriction strategy
of organisms of one out of the species. Our findings show that variations in
disease virulence impact the benefits of dispersal limitation reaction, with
the relative reduction of the organisms' infection risk accentuating in surges
of less contagious or deadlier diseases. The effectiveness of the mobility
restriction tactic depends on the tolerable fraction of infected neighbours
used as a trigger of the defensive strategy and the deceleration level. If each
organism promptly reacts to the arrival of the first viral vectors in its
surroundings with strict mobility reduction, contamination risk decreases
significantly. Our conclusions may help biologists understand the impact of
evolutionary defensive strategies in ecosystems during an epidemic.
|
[
{
"created": "Sun, 19 Mar 2023 17:43:28 GMT",
"version": "v1"
}
] |
2024-06-05
|
[
[
"Menezes",
"J.",
""
]
] |
We study a three-species cyclic model whose organisms are vulnerable to contamination with an infectious disease which propagates person-to-person. We consider that individuals of one species perform an evolutionary self-preservation strategy by reducing the mobility rate to minimise infection risk whenever an epidemic outbreak reaches the neighbourhood. Running stochastic simulations, we quantify the changes in spatial patterns induced by unevenness in the cyclic game introduced by the mobility restriction strategy of organisms of one out of the species. Our findings show that variations in disease virulence impact the benefits of dispersal limitation reaction, with the relative reduction of the organisms' infection risk accentuating in surges of less contagious or deadlier diseases. The effectiveness of the mobility restriction tactic depends on the tolerable fraction of infected neighbours used as a trigger of the defensive strategy and the deceleration level. If each organism promptly reacts to the arrival of the first viral vectors in its surroundings with strict mobility reduction, contamination risk decreases significantly. Our conclusions may help biologists understand the impact of evolutionary defensive strategies in ecosystems during an epidemic.
|
2401.14665
|
Yipin Lei
|
Yipin Lei, Xu Wang, Meng Fang, Han Li, Xiang Li, Jianyang Zeng
|
PepGB: Facilitating peptide drug discovery via graph neural networks
| null | null | null | null |
q-bio.BM cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Peptides offer great biomedical potential and serve as promising drug
candidates. Currently, the majority of approved peptide drugs are directly
derived from well-explored natural human peptides. It is quite necessary to
utilize advanced deep learning techniques to identify novel peptide drugs in
the vast, unexplored biochemical space. Despite various in silico methods
having been developed to accelerate peptide early drug discovery, existing
models face challenges of overfitting and lacking generalizability due to the
limited size, imbalanced distribution and inconsistent quality of experimental
data. In this study, we propose PepGB, a deep learning framework to facilitate
peptide early drug discovery by predicting peptide-protein interactions
(PepPIs). Employing graph neural networks, PepGB incorporates a fine-grained
perturbation module and a dual-view objective with contrastive learning-based
peptide pre-trained representation to predict PepPIs. Through rigorous
evaluations, we demonstrated that PepGB greatly outperforms baselines and can
accurately identify PepPIs for novel targets and peptide hits, thereby
contributing to the target identification and hit discovery processes. Next, we
derive an extended version, diPepGB, to tackle the bottleneck of modeling
highly imbalanced data prevalent in lead generation and optimization processes.
Utilizing directed edges to represent relative binding strength between two
peptide nodes, diPepGB achieves superior performance in real-world assays. In
summary, our proposed frameworks can serve as potent tools to facilitate
peptide early drug discovery.
|
[
{
"created": "Fri, 26 Jan 2024 06:13:09 GMT",
"version": "v1"
}
] |
2024-01-29
|
[
[
"Lei",
"Yipin",
""
],
[
"Wang",
"Xu",
""
],
[
"Fang",
"Meng",
""
],
[
"Li",
"Han",
""
],
[
"Li",
"Xiang",
""
],
[
"Zeng",
"Jianyang",
""
]
] |
Peptides offer great biomedical potential and serve as promising drug candidates. Currently, the majority of approved peptide drugs are directly derived from well-explored natural human peptides. It is quite necessary to utilize advanced deep learning techniques to identify novel peptide drugs in the vast, unexplored biochemical space. Despite various in silico methods having been developed to accelerate peptide early drug discovery, existing models face challenges of overfitting and lacking generalizability due to the limited size, imbalanced distribution and inconsistent quality of experimental data. In this study, we propose PepGB, a deep learning framework to facilitate peptide early drug discovery by predicting peptide-protein interactions (PepPIs). Employing graph neural networks, PepGB incorporates a fine-grained perturbation module and a dual-view objective with contrastive learning-based peptide pre-trained representation to predict PepPIs. Through rigorous evaluations, we demonstrated that PepGB greatly outperforms baselines and can accurately identify PepPIs for novel targets and peptide hits, thereby contributing to the target identification and hit discovery processes. Next, we derive an extended version, diPepGB, to tackle the bottleneck of modeling highly imbalanced data prevalent in lead generation and optimization processes. Utilizing directed edges to represent relative binding strength between two peptide nodes, diPepGB achieves superior performance in real-world assays. In summary, our proposed frameworks can serve as potent tools to facilitate peptide early drug discovery.
|
2101.08731
|
Tomas Barta
|
Tomas Barta and Lubomir Kostal
|
Regular spiking in high conductance states: the essential role of
inhibition
| null |
Phys. Rev. E 103, 022408 (2021)
|
10.1103/PhysRevE.103.022408
| null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Strong inhibitory input to neurons, which occurs in balanced states of neural
networks, increases synaptic current fluctuations. This has led to the
assumption that inhibition contributes to the high spike-firing irregularity
observed in vivo. We used single compartment neuronal models with
time-correlated (due to synaptic filtering) and state-dependent (due to
reversal potentials) input to demonstrate that inhibitory input acts to
decrease membrane potential fluctuations, a result that cannot be achieved with
simplified neural input models. To clarify the effects on spike-firing
regularity, we used models with different spike-firing adaptation mechanisms
and observed that the addition of inhibition increased firing regularity in
models with dynamic firing thresholds and decreased firing regularity if
spike-firing adaptation was implemented through ionic currents or not at all.
This novel fluctuation-stabilization mechanism provides a new perspective on
the importance of strong inhibitory inputs observed in balanced states of
neural networks and highlights the key roles of biologically plausible inputs
and specific adaptation mechanisms in neuronal modeling.
|
[
{
"created": "Thu, 21 Jan 2021 17:14:25 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Feb 2021 14:50:11 GMT",
"version": "v2"
},
{
"created": "Thu, 18 Feb 2021 16:30:18 GMT",
"version": "v3"
}
] |
2021-02-19
|
[
[
"Barta",
"Tomas",
""
],
[
"Kostal",
"Lubomir",
""
]
] |
Strong inhibitory input to neurons, which occurs in balanced states of neural networks, increases synaptic current fluctuations. This has led to the assumption that inhibition contributes to the high spike-firing irregularity observed in vivo. We used single compartment neuronal models with time-correlated (due to synaptic filtering) and state-dependent (due to reversal potentials) input to demonstrate that inhibitory input acts to decrease membrane potential fluctuations, a result that cannot be achieved with simplified neural input models. To clarify the effects on spike-firing regularity, we used models with different spike-firing adaptation mechanisms and observed that the addition of inhibition increased firing regularity in models with dynamic firing thresholds and decreased firing regularity if spike-firing adaptation was implemented through ionic currents or not at all. This novel fluctuation-stabilization mechanism provides a new perspective on the importance of strong inhibitory inputs observed in balanced states of neural networks and highlights the key roles of biologically plausible inputs and specific adaptation mechanisms in neuronal modeling.
|
2106.09444
|
Birgitta Dresp-Langley
|
Birgitta Dresp-Langley, JM Wandeto
|
Unsupervised classification of cell imaging data using the quantization
error in a Self Organizing Map
|
arXiv admin note: substantial text overlap with arXiv:2011.05209,
arXiv:2011.03970
|
In HR Arabnia, K Ferens, D de la Fuente, EB Kozerenko, JA Olivas
Varela, FG Tinetti (Eds.), Transactions on Computational Science and
Computational Intelligence. Springer-Nature, 2021, pp. 201-210
|
10.1007/978-3-030-70296-0_16
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This study exploits previously demonstrated properties such as sensitivity to
the spatial extent and the intensity of local image contrast of the
quantization error in the output of a Self Organizing Map (SOM QE). Here, the
SOM QE is applied to double color staining based cell viability data in 96
image simulations. The results show that the SOM QE consistently and in only a
few seconds detects fine regular spatial increases in relative amounts of RED
or GREEN pixel staining across the test images, reflecting small, systematic
increases or decreases in the percentage of theoretical cell viability below
the critical threshold. Such small changes may carry clinical significance, but
are almost impossible to detect by human vision. Moreover, we demonstrate a
clear sensitivity of the SOM QE to differences in the relative physical
luminance (Y) of the colors, which here translates into a RED GREEN color
selectivity. Across differences in relative luminance, the SOM QE exhibits
consistently greater sensitivity to the smallest spatial increases in RED image
pixels compared with smallest increases of identical spatial extents in GREEN
image pixels. Further selective color contrast studies on simulations of
biological imaging data will allow generating increasingly larger benchmark
datasets and, ultimately, unravel the full potential of fast, economic, and
unprecedentedly precise biological data analysis using the SOM QE.
|
[
{
"created": "Thu, 17 Jun 2021 12:53:55 GMT",
"version": "v1"
}
] |
2021-10-18
|
[
[
"Dresp-Langley",
"Birgitta",
""
],
[
"Wandeto",
"JM",
""
]
] |
This study exploits previously demonstrated properties such as sensitivity to the spatial extent and the intensity of local image contrast of the quantization error in the output of a Self Organizing Map (SOM QE). Here, the SOM QE is applied to double color staining based cell viability data in 96 image simulations. The results show that the SOM QE consistently and in only a few seconds detects fine regular spatial increases in relative amounts of RED or GREEN pixel staining across the test images, reflecting small, systematic increases or decreases in the percentage of theoretical cell viability below the critical threshold. Such small changes may carry clinical significance, but are almost impossible to detect by human vision. Moreover, we demonstrate a clear sensitivity of the SOM QE to differences in the relative physical luminance (Y) of the colors, which here translates into a RED GREEN color selectivity. Across differences in relative luminance, the SOM QE exhibits consistently greater sensitivity to the smallest spatial increases in RED image pixels compared with smallest increases of identical spatial extents in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise biological data analysis using the SOM QE.
|
1702.00094
|
Masaki Ogura Dr.
|
Masaki Ogura, Masashi Wakaiki, Harvey Rubin, Victor M. Preciado
|
Delayed Bet-Hedging Resilience Strategies Under Environmental
Fluctuations
| null | null |
10.1103/PhysRevE.95.052404
| null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many biological populations, such as bacterial colonies, have developed
through evolution a protection mechanism, called bet-hedging, to increase their
probability of survival under stressful environmental fluctutation. In this
context, the concept of preadaptation refers to a common type of bet-hedging
protection strategy in which a relatively small number of individuals in a
population stochastically switch their phenotypes to a `dormant' metabolic
state in which they increase their probability of survival against potential
environmental shocks. Hence, if an environmental shock took place at some point
in time, preadapted organisms would be better adapted to survive and
proliferate once the shock is over. In many biological populations, the
mechanisms of preadaptation and proliferation present delays whose influence in
the fitness of the population are not well-understood. In this paper, we
propose a rigorous mathematical framework to analyze the role of delays in both
preadaptation and proliferation mechanisms in the survival of biological
populations, with an emphasis on bacterial colonies. Our theoretical framework
allows us to analytically quantify the average growth rate of a bet-hedging
bacterial colony with stochastically delayed reactions with arbitrary
precision. We verify the accuracy of the proposed method by numerical
simulations and conclude that the growth rate of a bet-hedging population shows
a non-trivial dependency on their preadaptation and proliferation delays.
Contrary to the current belief, our results show that faster reactions do not,
in general, increase the overall fitness of a biological population.
|
[
{
"created": "Wed, 1 Feb 2017 00:41:23 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Apr 2017 01:57:52 GMT",
"version": "v2"
}
] |
2017-05-24
|
[
[
"Ogura",
"Masaki",
""
],
[
"Wakaiki",
"Masashi",
""
],
[
"Rubin",
"Harvey",
""
],
[
"Preciado",
"Victor M.",
""
]
] |
Many biological populations, such as bacterial colonies, have developed through evolution a protection mechanism, called bet-hedging, to increase their probability of survival under stressful environmental fluctutation. In this context, the concept of preadaptation refers to a common type of bet-hedging protection strategy in which a relatively small number of individuals in a population stochastically switch their phenotypes to a `dormant' metabolic state in which they increase their probability of survival against potential environmental shocks. Hence, if an environmental shock took place at some point in time, preadapted organisms would be better adapted to survive and proliferate once the shock is over. In many biological populations, the mechanisms of preadaptation and proliferation present delays whose influence in the fitness of the population are not well-understood. In this paper, we propose a rigorous mathematical framework to analyze the role of delays in both preadaptation and proliferation mechanisms in the survival of biological populations, with an emphasis on bacterial colonies. Our theoretical framework allows us to analytically quantify the average growth rate of a bet-hedging bacterial colony with stochastically delayed reactions with arbitrary precision. We verify the accuracy of the proposed method by numerical simulations and conclude that the growth rate of a bet-hedging population shows a non-trivial dependency on their preadaptation and proliferation delays. Contrary to the current belief, our results show that faster reactions do not, in general, increase the overall fitness of a biological population.
|
1607.02581
|
Raul Isea
|
Raul Isea
|
A Potential Correlation Between the Temperature of the Pacific Ocean and
Data from Google Trends May Yield a Warning Sign for the Outbreak of Zika
|
3 pages, 2 figures, Open Access
|
American Journal of Social Sciences (2016), Vol. 4, No. 2, pp.
12-14
| null | null |
q-bio.PE
|
http://creativecommons.org/publicdomain/zero/1.0/
|
There has been a large number of reported cases of the occurrence of Zika in
different countries in 2016 and it is necessary to develop an early warning
system to initiate preventive campaigns against the disease. A potential early
warning system based on the rise in ocean temperature of the Pacific Ni\~no
Index is proposed. The efficacy is verified using data for the outbreak in
Colombia as obtained from Google Trends.
|
[
{
"created": "Sat, 9 Jul 2016 07:58:46 GMT",
"version": "v1"
}
] |
2016-07-12
|
[
[
"Isea",
"Raul",
""
]
] |
There has been a large number of reported cases of the occurrence of Zika in different countries in 2016 and it is necessary to develop an early warning system to initiate preventive campaigns against the disease. A potential early warning system based on the rise in ocean temperature of the Pacific Ni\~no Index is proposed. The efficacy is verified using data for the outbreak in Colombia as obtained from Google Trends.
|
2004.06021
|
Ivan Cherednik
|
Ivan Cherednik
|
Momentum managing epidemic spread and Bessel functions
|
54 pages, 29 figures, v5: the two-phase formula for the spread was
added covering practically the whole periods of Covid-19 in many countries;
v6: the second waves were considered, editing; v7: some data refreshed,
Journal reference added
|
Chaos, Solitons & Fractals, Volume 139, October 2020, 110234
|
10.1016/j.chaos.2020.110234
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Starting with the power law for the total number of detected infections, we
propose differential equations describing the effect of momentum epidemic
management. Our 2-phase formula matches very well the curves of the total
numbers of the Covid-19 infections in many countries; the first phase is
described by Bessel functions. It provides projections for the saturation,
assuming that the management is steady. We discuss Austria, Brazil, Germany,
Japan, India, Israel, Italy, the Netherlands, Sweden, Switzerland, UK, and the
USA, including some analysis of the second waves.
|
[
{
"created": "Mon, 13 Apr 2020 15:54:31 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Apr 2020 16:19:53 GMT",
"version": "v2"
},
{
"created": "Mon, 20 Apr 2020 15:36:56 GMT",
"version": "v3"
},
{
"created": "Thu, 7 May 2020 10:18:50 GMT",
"version": "v4"
},
{
"created": "Thu, 28 May 2020 11:20:34 GMT",
"version": "v5"
},
{
"created": "Fri, 28 Aug 2020 15:57:38 GMT",
"version": "v6"
},
{
"created": "Thu, 8 Oct 2020 21:06:04 GMT",
"version": "v7"
}
] |
2020-10-12
|
[
[
"Cherednik",
"Ivan",
""
]
] |
Starting with the power law for the total number of detected infections, we propose differential equations describing the effect of momentum epidemic management. Our 2-phase formula matches very well the curves of the total numbers of the Covid-19 infections in many countries; the first phase is described by Bessel functions. It provides projections for the saturation, assuming that the management is steady. We discuss Austria, Brazil, Germany, Japan, India, Israel, Italy, the Netherlands, Sweden, Switzerland, UK, and the USA, including some analysis of the second waves.
|
2311.16784
|
Giuseppe Tronci
|
Charles Brooker, Giuseppe Tronci
|
Infection-responsivity of Commercial Dressings Through Halochromic
Drop-casting
|
6 pages, 3 figures, 2 tables. Accepted in AIP conference proceedings
| null | null | null |
q-bio.TO
|
http://creativecommons.org/licenses/by/4.0/
|
Infection control remains one of the most challenging tasks in wound care,
due to growing antimicrobial resistance and ineffective infection diagnostic
tools at the point-of-care. To integrate therapeutic wound dressings with wound
monitoring capability at the point-of-care to enable informed clinical
decision-making, we investigate the encapsulation of a halochromic dye, i.e.
bromothymol blue (BTB), onto two commercial dressings, i.e. Aquacel Extra and
Promogran, through a simple drop-casting method. Our concept leverages the
infection-associated rise in wound pH, on the one hand, and BTB's colour change
capability in the pH range of healing (pH: 5-6) and infected wounds (pH > 7),
on the other hand. BTB-encapsulated samples show a prompt colour switch
(yellow/orange --> blue) following 1-hour incubation at pH 8. The effect of
swelling ratio, chemical composition and microstructure is then explored to
draw relationships between colour change capability and dressing dye retention.
|
[
{
"created": "Tue, 28 Nov 2023 13:47:22 GMT",
"version": "v1"
}
] |
2023-11-29
|
[
[
"Brooker",
"Charles",
""
],
[
"Tronci",
"Giuseppe",
""
]
] |
Infection control remains one of the most challenging tasks in wound care, due to growing antimicrobial resistance and ineffective infection diagnostic tools at the point-of-care. To integrate therapeutic wound dressings with wound monitoring capability at the point-of-care to enable informed clinical decision-making, we investigate the encapsulation of a halochromic dye, i.e. bromothymol blue (BTB), onto two commercial dressings, i.e. Aquacel Extra and Promogran, through a simple drop-casting method. Our concept leverages the infection-associated rise in wound pH, on the one hand, and BTB's colour change capability in the pH range of healing (pH: 5-6) and infected wounds (pH > 7), on the other hand. BTB-encapsulated samples show a prompt colour switch (yellow/orange --> blue) following 1-hour incubation at pH 8. The effect of swelling ratio, chemical composition and microstructure is then explored to draw relationships between colour change capability and dressing dye retention.
|
q-bio/0502006
|
Ophir Flomenbom
|
Ophir Flomenbom, Joseph Klafter, and Attila Szabo
|
What can one learn from two-state single molecule trajectories?
|
Biophys. J., in press (2005)
|
Biophys. J. 88, 3780-3783 (2005)
|
10.1529/biophysj.104.055905
| null |
q-bio.SC q-bio.QM
| null |
A time trajectory of an observable that fluctuates between two values (say,
on and off), stemming from some unknown multi-substate kinetic scheme, is the
output of many single molecule experiments. Here we show that when all
successive waiting times along the trajectory are uncorrelated the on and the
off waiting time probability density functions (PDFs) contain all the
information. By relating the lack of correlation in the trajectory to the
topology of kinetic schemes, we can immediately specify those kinetic schemes
that are equally consistent with experiment, which means that it is impossible
to differentiate between them by any sophisticated analyses of the trajectory.
Correlated trajectories, however, contain additional information about the
underlying kinetic scheme, and we consider the strategy that one should use to
extract it. An example is given on correlations in the activity of individual
lipase molecules.
|
[
{
"created": "Tue, 8 Feb 2005 00:56:08 GMT",
"version": "v1"
}
] |
2010-08-16
|
[
[
"Flomenbom",
"Ophir",
""
],
[
"Klafter",
"Joseph",
""
],
[
"Szabo",
"Attila",
""
]
] |
A time trajectory of an observable that fluctuates between two values (say, on and off), stemming from some unknown multi-substate kinetic scheme, is the output of many single molecule experiments. Here we show that when all successive waiting times along the trajectory are uncorrelated the on and the off waiting time probability density functions (PDFs) contain all the information. By relating the lack of correlation in the trajectory to the topology of kinetic schemes, we can immediately specify those kinetic schemes that are equally consistent with experiment, which means that it is impossible to differentiate between them by any sophisticated analyses of the trajectory. Correlated trajectories, however, contain additional information about the underlying kinetic scheme, and we consider the strategy that one should use to extract it. An example is given on correlations in the activity of individual lipase molecules.
|
2108.02810
|
Sujit Datta
|
Daniel B. Amchin, Jenna A. Ott, Tapomoy Bhattacharjee, Sujit S. Datta
|
Influence of confinement on the spreading of bacterial populations
| null |
PLoS Computational Biology, 18, e1010063 (2022)
|
10.1371/journal.pcbi.1010063
| null |
q-bio.PE cond-mat.soft nlin.PS physics.bio-ph q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The spreading of bacterial populations is central to processes in
agriculture, the environment, and medicine. However, existing models of
spreading typically focus on cells in unconfined settings--despite the fact
that many bacteria inhabit complex and crowded environments, such as soils,
sediments, and biological tissues/gels, in which solid obstacles confine the
cells and thereby strongly regulate population spreading. Here, we develop an
extended version of the classic Keller-Segel model of bacterial spreading that
incorporates the influence of confinement in promoting both cell-solid and
cell-cell collisions. Numerical simulations of this extended model demonstrate
how confinement fundamentally alters the dynamics and morphology of spreading
bacterial populations, in good agreement with recent experimental results. In
particular, with increasing confinement, we find that cell-cell collisions
increasingly hinder the initial formation and the long-time propagation speed
of chemotactic pulses. Moreover, also with increasing confinement, we find that
cellular growth and division plays an increasingly dominant role in driving
population spreading--eventually leading to a transition from chemotactic
spreading to growth-driven spreading via a slower, jammed front. This work thus
provides a theoretical foundation for further investigations of the influence
of confinement on bacterial spreading. More broadly, these results help to
provide a framework to predict and control the dynamics of bacterial
populations in complex and crowded environments.
|
[
{
"created": "Thu, 5 Aug 2021 18:53:51 GMT",
"version": "v1"
}
] |
2022-06-07
|
[
[
"Amchin",
"Daniel B.",
""
],
[
"Ott",
"Jenna A.",
""
],
[
"Bhattacharjee",
"Tapomoy",
""
],
[
"Datta",
"Sujit S.",
""
]
] |
The spreading of bacterial populations is central to processes in agriculture, the environment, and medicine. However, existing models of spreading typically focus on cells in unconfined settings--despite the fact that many bacteria inhabit complex and crowded environments, such as soils, sediments, and biological tissues/gels, in which solid obstacles confine the cells and thereby strongly regulate population spreading. Here, we develop an extended version of the classic Keller-Segel model of bacterial spreading that incorporates the influence of confinement in promoting both cell-solid and cell-cell collisions. Numerical simulations of this extended model demonstrate how confinement fundamentally alters the dynamics and morphology of spreading bacterial populations, in good agreement with recent experimental results. In particular, with increasing confinement, we find that cell-cell collisions increasingly hinder the initial formation and the long-time propagation speed of chemotactic pulses. Moreover, also with increasing confinement, we find that cellular growth and division plays an increasingly dominant role in driving population spreading--eventually leading to a transition from chemotactic spreading to growth-driven spreading via a slower, jammed front. This work thus provides a theoretical foundation for further investigations of the influence of confinement on bacterial spreading. More broadly, these results help to provide a framework to predict and control the dynamics of bacterial populations in complex and crowded environments.
|
2401.08585
|
Stephen Clark
|
Sean Tull, Razin A. Shaikh, Sara Sabrina Zemljic and Stephen Clark
|
From Conceptual Spaces to Quantum Concepts: Formalising and Learning
Structured Conceptual Models
|
This article consolidates our previous reports on concept
formalisation and learning: arXiv:2302.14822 and arXiv:2203.11216
| null | null | null |
q-bio.NC cs.AI quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In this article we present a new modelling framework for structured concepts
using a category-theoretic generalisation of conceptual spaces, and show how
the conceptual representations can be learned automatically from data, using
two very different instantiations: one classical and one quantum. A
contribution of the work is a thorough category-theoretic formalisation of our
framework. We claim that the use of category theory, and in particular the use
of string diagrams to describe quantum processes, helps elucidate some of the
most important features of our approach. We build upon Gardenfors' classical
framework of conceptual spaces, in which cognition is modelled geometrically
through the use of convex spaces, which in turn factorise in terms of simpler
spaces called domains. We show how concepts from the domains of shape, colour,
size and position can be learned from images of simple shapes, where concepts
are represented as Gaussians in the classical implementation, and quantum
effects in the quantum one. In the classical case we develop a new model which
is inspired by the Beta-VAE model of concepts, but is designed to be more
closely connected with language, so that the names of concepts form part of the
graphical model. In the quantum case, concepts are learned by a hybrid
classical-quantum network trained to perform concept classification, where the
classical image processing is carried out by a convolutional neural network and
the quantum representations are produced by a parameterised quantum circuit.
Finally, we consider the question of whether our quantum models of concepts can
be considered conceptual spaces in the Gardenfors sense.
|
[
{
"created": "Mon, 6 Nov 2023 15:08:22 GMT",
"version": "v1"
}
] |
2024-01-18
|
[
[
"Tull",
"Sean",
""
],
[
"Shaikh",
"Razin A.",
""
],
[
"Zemljic",
"Sara Sabrina",
""
],
[
"Clark",
"Stephen",
""
]
] |
In this article we present a new modelling framework for structured concepts using a category-theoretic generalisation of conceptual spaces, and show how the conceptual representations can be learned automatically from data, using two very different instantiations: one classical and one quantum. A contribution of the work is a thorough category-theoretic formalisation of our framework. We claim that the use of category theory, and in particular the use of string diagrams to describe quantum processes, helps elucidate some of the most important features of our approach. We build upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains. We show how concepts from the domains of shape, colour, size and position can be learned from images of simple shapes, where concepts are represented as Gaussians in the classical implementation, and quantum effects in the quantum one. In the classical case we develop a new model which is inspired by the Beta-VAE model of concepts, but is designed to be more closely connected with language, so that the names of concepts form part of the graphical model. In the quantum case, concepts are learned by a hybrid classical-quantum network trained to perform concept classification, where the classical image processing is carried out by a convolutional neural network and the quantum representations are produced by a parameterised quantum circuit. Finally, we consider the question of whether our quantum models of concepts can be considered conceptual spaces in the Gardenfors sense.
|
1310.1453
|
Yoshinori Takano
|
Yoshinori Takano, Yoshito Chikaraishi, Naohiko Ohkouchi
|
Enantiomer-specific isotope analysis of D- and L-alanine: Nitrogen
isotopic hetero- and homogeneity in microbial and chemical processes
|
ISBN: 9784876989607
|
Earth, Life, and Isotopes (Kyoto University Press), pp. 387-402
(2010)
| null | null |
q-bio.BM astro-ph.EP
|
http://creativecommons.org/licenses/publicdomain/
|
Nitrogen isotopic hetero- and homogeneity of D-{\alpha}-alanine and
L-{\alpha}-alanine were investigated in microbial processes in the domain
Bacteria and in chemical processes in symmetric organic synthesis. D-alanine is
an enantiomer that is physiologically essential for microbial growth and
metabolic maintenance. The nitrogen isotopic difference {\Delta}15ND-L (defined
as {\delta}15ND-Ala - {\delta}15NL-Ala) in peptidoglycan amino acids in
bacteria such as the representative gram-positive phyla Firmicutes and
Actinobacteria (Enterococcus faecalis, Staphylococcus aureus, Staphylococcus
staphylolyticus, Lactobacillus acidophilus, Bacillus subtilis, Micrococcus
luteus, and Streptomyces sp.) tended to be 15N-depleted in D-alanine
({\Delta}15ND-L < -2.0 permil). These results suggest that the composition of
isotopically heterogeneous components in these bacteria is primarily controlled
by enzymatic pathways prior to formation of the bacterial cell wall. In
contrast, the {\Delta}15ND-L of racemic alanine in the chemical pathway during
the nucleophilic substitution reaction (SN1 type) between 2-bromopropionic acid
and ammonia identified fully homogeneous components for each enantiomer. The
novel enantiomer-specific isotopic analysis (ESIA) method is useful in
determining the origins of chirality in biogenic and abiogenic processes and is
applicable to enantiomer studies.
-- Keywords: D-alanine, microbial process, chemical process, nitrogen
isotopic composition, enantiomer-specific isotope analysis (ESIA)
|
[
{
"created": "Sat, 5 Oct 2013 07:52:01 GMT",
"version": "v1"
}
] |
2013-10-08
|
[
[
"Takano",
"Yoshinori",
""
],
[
"Chikaraishi",
"Yoshito",
""
],
[
"Ohkouchi",
"Naohiko",
""
]
] |
Nitrogen isotopic hetero- and homogeneity of D-{\alpha}-alanine and L-{\alpha}-alanine were investigated in microbial processes in the domain Bacteria and in chemical processes in symmetric organic synthesis. D-alanine is an enantiomer that is physiologically essential for microbial growth and metabolic maintenance. The nitrogen isotopic difference {\Delta}15ND-L (defined as {\delta}15ND-Ala - {\delta}15NL-Ala) in peptidoglycan amino acids in bacteria such as the representative gram-positive phyla Firmicutes and Actinobacteria (Enterococcus faecalis, Staphylococcus aureus, Staphylococcus staphylolyticus, Lactobacillus acidophilus, Bacillus subtilis, Micrococcus luteus, and Streptomyces sp.) tended to be 15N-depleted in D-alanine ({\Delta}15ND-L < -2.0 permil). These results suggest that the composition of isotopically heterogeneous components in these bacteria is primarily controlled by enzymatic pathways prior to formation of the bacterial cell wall. In contrast, the {\Delta}15ND-L of racemic alanine in the chemical pathway during the nucleophilic substitution reaction (SN1 type) between 2-bromopropionic acid and ammonia identified fully homogeneous components for each enantiomer. The novel enantiomer-specific isotopic analysis (ESIA) method is useful in determining the origins of chirality in biogenic and abiogenic processes and is applicable to enantiomer studies. -- Keywords: D-alanine, microbial process, chemical process, nitrogen isotopic composition, enantiomer-specific isotope analysis (ESIA)
|
1511.07959
|
Christoph Adami
|
Thomas LaBar, Arend Hintze, and Christoph Adami
|
Evolvability tradeoffs in emergent digital replicators
|
26 pages, 5 figures. Revised version, title changed
|
Artificial Life 22 (2016) 483-498
|
10.1162/ARTL_a_00214
| null |
q-bio.PE nlin.AO q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The role of historical contingency in the origin of life is one of the great
unknowns in modern science. Only one example of life exists--one that proceeded
from a single self-replicating organism (or a set of replicating hyper-cycles)
to the vast complexity we see today in Earth's biosphere. We know that emergent
life has the potential to evolve great increases in complexity, but it is
unknown if evolvability is automatic given any self-replicating organism. At
the same time, it is difficult to test such questions in biochemical systems.
Laboratory studies with RNA replicators have had some success with exploring
the capacities of simple self-replicators, but these experiments are still
limited in both capabilities and scope. Here, we use the digital evolution
system Avida to explore the interaction between emergent replicators (rare
randomly-assembled self-replicators) and evolvability. We find that we can
classify fixed-length emergent replicators in Avida into two classes based on
functional analysis. One class is more evolvable in the sense of optimizing
their replication abilities. However, the other class is more evolvable in the
sense of acquiring evolutionary innovations. We tie this trade-off in
evolvability to the structure of the respective classes' replication machinery,
and speculate on the relevance of these results to biochemical replicators.
|
[
{
"created": "Wed, 25 Nov 2015 06:04:05 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Apr 2016 14:05:46 GMT",
"version": "v2"
}
] |
2016-12-07
|
[
[
"LaBar",
"Thomas",
""
],
[
"Hintze",
"Arend",
""
],
[
"Adami",
"Christoph",
""
]
] |
The role of historical contingency in the origin of life is one of the great unknowns in modern science. Only one example of life exists--one that proceeded from a single self-replicating organism (or a set of replicating hyper-cycles) to the vast complexity we see today in Earth's biosphere. We know that emergent life has the potential to evolve great increases in complexity, but it is unknown if evolvability is automatic given any self-replicating organism. At the same time, it is difficult to test such questions in biochemical systems. Laboratory studies with RNA replicators have had some success with exploring the capacities of simple self-replicators, but these experiments are still limited in both capabilities and scope. Here, we use the digital evolution system Avida to explore the interaction between emergent replicators (rare randomly-assembled self-replicators) and evolvability. We find that we can classify fixed-length emergent replicators in Avida into two classes based on functional analysis. One class is more evolvable in the sense of optimizing their replication abilities. However, the other class is more evolvable in the sense of acquiring evolutionary innovations. We tie this trade-off in evolvability to the structure of the respective classes' replication machinery, and speculate on the relevance of these results to biochemical replicators.
|
2103.02417
|
Bharath Ambale Venkatesh
|
Vivek P. Jani, Nadjia Kachenoura, Alban Redheuil, Gisela Teixido-Tura,
Kevin Bouaou, Emilie Bollache, Elie Mousseaux, Alain De Cesare, Shelby Kutty,
Colin O. Wu, David A. Bluemke, Joao A. C. Lima, Bharath Ambale-Venkatesh
|
Deep Learning-based Automated Aortic Area and Distensibility Assessment:
The Multi-Ethnic Study of Atherosclerosis (MESA)
|
25 pages, 5 figures
| null | null | null |
q-bio.QM eess.IV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This study applies convolutional neural network (CNN)-based automatic
segmentation and distensibility measurement of the ascending and descending
aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI)
within the large MESA cohort with subsequent assessment on an external cohort
of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the
ascending and descending aorta at the pulmonary artery bifurcation from the
MESA study were included. Train, validation, and internal test sets consisted
of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies
(8069 images), respectively. An external test set of TAAs consisted of 37
studies (3224 images). A U-Net based CNN was constructed, and performance was
evaluated utilizing dice coefficient (for segmentation) and concordance
correlation coefficients (CCC) of aortic geometric parameters by comparing to
manual segmentation and parameter estimation. Dice coefficients for aorta
segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the
internal and external test of TAAs, respectively. CCC for comparison of manual
and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95,
respectively, on the internal test set and 0.997 and 0.995, respectively, for
the external test. CCCs for maximum and minimum descending aortic areas were
0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93,
respectively, on the external test set. We successfully developed and validated
a U-Net based ascending and descending aortic segmentation and distensibility
quantification model in a large multi-ethnic database and in an external cohort
of TAA patients.
|
[
{
"created": "Wed, 3 Mar 2021 14:11:24 GMT",
"version": "v1"
}
] |
2021-03-04
|
[
[
"Jani",
"Vivek P.",
""
],
[
"Kachenoura",
"Nadjia",
""
],
[
"Redheuil",
"Alban",
""
],
[
"Teixido-Tura",
"Gisela",
""
],
[
"Bouaou",
"Kevin",
""
],
[
"Bollache",
"Emilie",
""
],
[
"Mousseaux",
"Elie",
""
],
[
"De Cesare",
"Alain",
""
],
[
"Kutty",
"Shelby",
""
],
[
"Wu",
"Colin O.",
""
],
[
"Bluemke",
"David A.",
""
],
[
"Lima",
"Joao A. C.",
""
],
[
"Ambale-Venkatesh",
"Bharath",
""
]
] |
This study applies convolutional neural network (CNN)-based automatic segmentation and distensibility measurement of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI) within the large MESA cohort with subsequent assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study were included. Train, validation, and internal test sets consisted of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. An external test set of TAAs consisted of 37 studies (3224 images). A U-Net based CNN was constructed, and performance was evaluated utilizing dice coefficient (for segmentation) and concordance correlation coefficients (CCC) of aortic geometric parameters by comparing to manual segmentation and parameter estimation. Dice coefficients for aorta segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the internal and external test of TAAs, respectively. CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. CCCs for maximum and minimum descending aortic areas were 0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93, respectively, on the external test set. We successfully developed and validated a U-Net based ascending and descending aortic segmentation and distensibility quantification model in a large multi-ethnic database and in an external cohort of TAA patients.
|
q-bio/0508044
|
William Bialek
|
William Bialek
|
Should you believe that this coin is fair?
| null | null | null | null |
q-bio.NC q-bio.QM
| null |
Faced with a sequence of N binary events, such as coin flips (or Ising
spins), it is natural to ask whether these events reflect some underlying
dynamic signals or are just random. Plausible models for the dynamics of hidden
biases lead to surprisingly high probabilities of misidentifying random
sequences as biased. In particular, this probability decays as N^(-1/4), so
that no reasonable amount of data would be sufficient to induce the concept of
a fair coin with high probability. I suggest that these theoretical results may
be relevant to understanding experiments on the apparent misperception of
random sequences by human observers.
|
[
{
"created": "Tue, 30 Aug 2005 03:28:36 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Bialek",
"William",
""
]
] |
Faced with a sequence of N binary events, such as coin flips (or Ising spins), it is natural to ask whether these events reflect some underlying dynamic signals or are just random. Plausible models for the dynamics of hidden biases lead to surprisingly high probabilities of misidentifying random sequences as biased. In particular, this probability decays as N^(-1/4), so that no reasonable amount of data would be sufficient to induce the concept of a fair coin with high probability. I suggest that these theoretical results may be relevant to understanding experiments on the apparent misperception of random sequences by human observers.
|
2002.02425
|
Marco Alberto Javarone
|
Marco Alberto Javarone, Olivia Gosseries, Daniele Marinazzo, Quentin
Noirhomme, Vincent Bonhomme, Steven Laureys, Srivas Chennu
|
A mean field approach to model levels of consciousness from EEG
recordings
|
23 pages, 6 figures. Accepted for publication in Journal of
Statistical Mechanics: Theory and Experiment
| null | null | null |
q-bio.NC cond-mat.dis-nn cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a mean-field model for analysing the dynamics of human
consciousness. In particular, inspired by the Giulio Tononi's Integrated
Information Theory and by the Max Tegmark's representation of consciousness, we
study order-disorder phase transitions on Curie-Weiss models generated by
processing EEG signals. The latter have been recorded on healthy individuals
undergoing deep sedation. Then, we implement a machine learning tool for
classifying mental states using, as input, the critical temperatures computed
in the Curie-Weiss models. Results show that, by the proposed method, it is
possible to discriminate between states of awareness and states of deep
sedation. Besides, we identify a state space for representing the path between
mental states, whose dimensions correspond to critical temperatures computed
over different frequency bands of the EEG signal. Beyond possible theoretical
implications in the study of human consciousness, resulting from our model, we
deem relevant to emphasise that the proposed method could be exploited for
clinical applications.
|
[
{
"created": "Thu, 6 Feb 2020 18:09:37 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Aug 2020 16:03:08 GMT",
"version": "v2"
}
] |
2020-08-05
|
[
[
"Javarone",
"Marco Alberto",
""
],
[
"Gosseries",
"Olivia",
""
],
[
"Marinazzo",
"Daniele",
""
],
[
"Noirhomme",
"Quentin",
""
],
[
"Bonhomme",
"Vincent",
""
],
[
"Laureys",
"Steven",
""
],
[
"Chennu",
"Srivas",
""
]
] |
We introduce a mean-field model for analysing the dynamics of human consciousness. In particular, inspired by the Giulio Tononi's Integrated Information Theory and by the Max Tegmark's representation of consciousness, we study order-disorder phase transitions on Curie-Weiss models generated by processing EEG signals. The latter have been recorded on healthy individuals undergoing deep sedation. Then, we implement a machine learning tool for classifying mental states using, as input, the critical temperatures computed in the Curie-Weiss models. Results show that, by the proposed method, it is possible to discriminate between states of awareness and states of deep sedation. Besides, we identify a state space for representing the path between mental states, whose dimensions correspond to critical temperatures computed over different frequency bands of the EEG signal. Beyond possible theoretical implications in the study of human consciousness, resulting from our model, we deem relevant to emphasise that the proposed method could be exploited for clinical applications.
|
2210.01537
|
Andreas Christ S{\o}lvsten J{\o}rgensen
|
Andreas Christ S{\o}lvsten J{\o}rgensen, Ciaran Scott Hill, Marc
Sturrock, Wenhao Tang, Saketh R. Karamched, Dunja Gorup, Mark F. Lythgoe,
Simona Parrinello, Samuel Marguerat, Vahid Shahrezaei
|
Data-driven spatio-temporal modelling of glioblastoma
|
30 pages, 3 figures, 3 tables
| null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Mathematical oncology provides unique and invaluable insights into tumour
growth on both the microscopic and macroscopic levels. This review presents
state-of-the-art modelling techniques and focuses on their role in
understanding glioblastoma, a malignant form of brain cancer. For each
approach, we summarise the scope, drawbacks, and assets. We highlight the
potential clinical applications of each modelling technique and discuss the
connections between the mathematical models and the molecular and imaging data
used to inform them. By doing so, we aim to prime cancer researchers with
current and emerging computational tools for understanding tumour progression.
Finally, by providing an in-depth picture of the different modelling
techniques, we also aim to assist researchers who seek to build and develop
their own models and the associated inference frameworks.
|
[
{
"created": "Tue, 4 Oct 2022 11:43:34 GMT",
"version": "v1"
}
] |
2022-10-05
|
[
[
"Jørgensen",
"Andreas Christ Sølvsten",
""
],
[
"Hill",
"Ciaran Scott",
""
],
[
"Sturrock",
"Marc",
""
],
[
"Tang",
"Wenhao",
""
],
[
"Karamched",
"Saketh R.",
""
],
[
"Gorup",
"Dunja",
""
],
[
"Lythgoe",
"Mark F.",
""
],
[
"Parrinello",
"Simona",
""
],
[
"Marguerat",
"Samuel",
""
],
[
"Shahrezaei",
"Vahid",
""
]
] |
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarise the scope, drawbacks, and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. Finally, by providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks.
|
1312.4590
|
John Bechhoefer
|
A. Baker and J. Bechhoefer
|
Inferring the spatiotemporal DNA replication program from noisy data
|
14 pages, 7 figures
|
Phys. Rev. E 89, 032703 (2014)
|
10.1103/PhysRevE.89.032703
| null |
q-bio.QM cond-mat.stat-mech physics.data-an
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We generalize a stochastic model of DNA replication to the case where
replication-origin-initiation rates vary locally along the genome and with
time. Using this generalized model, we address the inverse problem of inferring
initiation rates from experimental data concerning replication in cell
populations. Previous work based on curve fitting depended on arbitrarily
chosen functional forms for the initiation rate, with free parameters that were
constrained by the data. We introduce a non-parametric method of inference that
is based on Gaussian process regression. The method replaces specific
assumptions about the functional form of initiation rate with more general
prior expectations about the smoothness of variation of this rate, along the
genome and in time. Using this inference method, we recover, with high
precision, simulated replication schemes from noisy data that are typical of
current experiments.
|
[
{
"created": "Mon, 16 Dec 2013 23:12:51 GMT",
"version": "v1"
}
] |
2015-04-02
|
[
[
"Baker",
"A.",
""
],
[
"Bechhoefer",
"J.",
""
]
] |
We generalize a stochastic model of DNA replication to the case where replication-origin-initiation rates vary locally along the genome and with time. Using this generalized model, we address the inverse problem of inferring initiation rates from experimental data concerning replication in cell populations. Previous work based on curve fitting depended on arbitrarily chosen functional forms for the initiation rate, with free parameters that were constrained by the data. We introduce a non-parametric method of inference that is based on Gaussian process regression. The method replaces specific assumptions about the functional form of initiation rate with more general prior expectations about the smoothness of variation of this rate, along the genome and in time. Using this inference method, we recover, with high precision, simulated replication schemes from noisy data that are typical of current experiments.
|
1010.1743
|
Jaewook Joo
|
Jaewook Joo
|
Linear noise approximation of noise-induced oscillation in NF-{\kappa}B
signaling network
|
19 pages, 5 figures
| null | null | null |
q-bio.MN q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
NF-{\kappa}B, one of key regulators of inflammation, apoptosis, and
differentiation, was found to have noisy oscillatory shuttling between the
nucleus and the cytoplasm in single cells when cells are stimulated by cytokine
TNF{\alpha}. We present the analytical analysis which uncovers the underlying
physical mechanisms of this spectacular noise-induced transition in biological
networks. Starting with the master equation describing both signaling and
transcription events in NF-{\kappa}B signaling network, we derived the
macroscopic and the Fokker-Planck equations by using van Kampen's sysem size
expansion. Using the noise-induced oscillatory signatures present in the power
spectrum, we constructed the two-dimensional phase diagram where the
noise-induced oscillation emerges in the dynamically stable parameter space.
|
[
{
"created": "Fri, 8 Oct 2010 17:26:42 GMT",
"version": "v1"
}
] |
2010-10-11
|
[
[
"Joo",
"Jaewook",
""
]
] |
NF-{\kappa}B, one of key regulators of inflammation, apoptosis, and differentiation, was found to have noisy oscillatory shuttling between the nucleus and the cytoplasm in single cells when cells are stimulated by cytokine TNF{\alpha}. We present the analytical analysis which uncovers the underlying physical mechanisms of this spectacular noise-induced transition in biological networks. Starting with the master equation describing both signaling and transcription events in NF-{\kappa}B signaling network, we derived the macroscopic and the Fokker-Planck equations by using van Kampen's sysem size expansion. Using the noise-induced oscillatory signatures present in the power spectrum, we constructed the two-dimensional phase diagram where the noise-induced oscillation emerges in the dynamically stable parameter space.
|
1304.2018
|
Romann Weber
|
Romann M. Weber
|
Beyond Spikes: Neural Codes and the Chemical Vocabulary of Cognition
|
19 pages, 2 figures; revised version
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, I examine what I refer to as the spike doctrine, which is the
generally held belief in neuroscience that information in the brain is encoded
by sequences of neural action potentials. I present the argument that specific
neurochemicals, and not spikes, are the elementary units of information in the
brain. I outline several predictions that arise from this interpretation,
relate them to results in the current research literature, and show how they
address some open questions.
|
[
{
"created": "Sun, 7 Apr 2013 16:23:50 GMT",
"version": "v1"
},
{
"created": "Sun, 2 Jun 2013 13:08:56 GMT",
"version": "v2"
}
] |
2013-06-04
|
[
[
"Weber",
"Romann M.",
""
]
] |
In this paper, I examine what I refer to as the spike doctrine, which is the generally held belief in neuroscience that information in the brain is encoded by sequences of neural action potentials. I present the argument that specific neurochemicals, and not spikes, are the elementary units of information in the brain. I outline several predictions that arise from this interpretation, relate them to results in the current research literature, and show how they address some open questions.
|
2003.12233
|
Tom Chou
|
Mingtao Xia, Chris D. Greenman, and Tom Chou
|
PDE models of adder mechanisms in cellular proliferation
|
23 pages, 8 figures
| null | null | null |
q-bio.CB math.AP
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Cell division is a process that involves many biochemical steps and complex
biophysical mechanisms. To simplify the understanding of what triggers cell
division, three basic models that subsume more microscopic cellular processes
associated with cell division have been proposed. Cells can divide based on the
time elapsed since their birth, their size, and/or the volume added since their
birth -- the timer, sizer, and adder models, respectively. Here, we propose
unified adder-sizer models and investigate some of the properties of different
adder processes arising in cellular proliferation. Although the adder-sizer
model provides a direct way to model cell population structure, we illustrate
how it is mathematically related to the well-known model in which cell division
depends on age and size. Existence and uniqueness of weak solutions to our
2+1-dimensional PDE model are proved, leading to the convergence of the
discretized numerical solutions and allowing us to numerically compute the
dynamics of cell population densities. We then generalize our PDE model to
incorporate recent experimental findings of a system exhibiting mother-daughter
correlations in cellular growth rates. Numerical experiments illustrating
possible average cell volume blowup and the dynamical behavior of cell
populations with mother-daughter correlated growth rates are carried out.
Finally, motivated by new experimental findings, we extend our adder model
cases where the controlling variable is the added size between DNA replication
initiation points in the cell cycle.
|
[
{
"created": "Fri, 27 Mar 2020 04:50:45 GMT",
"version": "v1"
}
] |
2020-03-30
|
[
[
"Xia",
"Mingtao",
""
],
[
"Greenman",
"Chris D.",
""
],
[
"Chou",
"Tom",
""
]
] |
Cell division is a process that involves many biochemical steps and complex biophysical mechanisms. To simplify the understanding of what triggers cell division, three basic models that subsume more microscopic cellular processes associated with cell division have been proposed. Cells can divide based on the time elapsed since their birth, their size, and/or the volume added since their birth -- the timer, sizer, and adder models, respectively. Here, we propose unified adder-sizer models and investigate some of the properties of different adder processes arising in cellular proliferation. Although the adder-sizer model provides a direct way to model cell population structure, we illustrate how it is mathematically related to the well-known model in which cell division depends on age and size. Existence and uniqueness of weak solutions to our 2+1-dimensional PDE model are proved, leading to the convergence of the discretized numerical solutions and allowing us to numerically compute the dynamics of cell population densities. We then generalize our PDE model to incorporate recent experimental findings of a system exhibiting mother-daughter correlations in cellular growth rates. Numerical experiments illustrating possible average cell volume blowup and the dynamical behavior of cell populations with mother-daughter correlated growth rates are carried out. Finally, motivated by new experimental findings, we extend our adder model cases where the controlling variable is the added size between DNA replication initiation points in the cell cycle.
|
1112.3663
|
Lorenzo Pareschi
|
A. Boccabella, R. Natalini, L. Pareschi
|
On a continuous mixed strategies model for evolutionary game theory
| null |
Kinetic and Related Models 4, 187-213 (2011)
| null | null |
q-bio.PE cond-mat.stat-mech math.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider an integro-differential model for evolutionary game theory which
describes the evolution of a population adopting mixed strategies. Using a
reformulation based on the first moments of the solution, we prove some
analytical properties of the model and global estimates. The asymptotic
behavior and the stability of solutions in the case of two strategies is
analyzed in details. Numerical schemes for two and three strategies which are
able to capture the correct equilibrium states are also proposed together with
several numerical examples.
|
[
{
"created": "Wed, 14 Dec 2011 17:45:16 GMT",
"version": "v1"
}
] |
2011-12-19
|
[
[
"Boccabella",
"A.",
""
],
[
"Natalini",
"R.",
""
],
[
"Pareschi",
"L.",
""
]
] |
We consider an integro-differential model for evolutionary game theory which describes the evolution of a population adopting mixed strategies. Using a reformulation based on the first moments of the solution, we prove some analytical properties of the model and global estimates. The asymptotic behavior and the stability of solutions in the case of two strategies is analyzed in details. Numerical schemes for two and three strategies which are able to capture the correct equilibrium states are also proposed together with several numerical examples.
|
1407.5856
|
Alejandro F Villaverde
|
Alejandro F Villaverde, David Henriques, Kieran Smallbone, Sophia
Bongard, Joachim Schmid, Damjan Cicin-Sain, Anton Crombach, Julio
Saez-Rodriguez, Klaus Mauch, Eva Balsa-Canto, Pedro Mendes, Johannes Jaeger,
and Julio R Banga
|
BioPreDyn-bench: benchmark problems for kinetic modelling in systems
biology
| null |
BMC Systems Biology 2015 9:8
|
10.1186/s12918-015-0144-
| null |
q-bio.QM cs.CE q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dynamic modelling is one of the cornerstones of systems biology. Many
research efforts are currently being invested in the development and
exploitation of large-scale kinetic models. The associated problems of
parameter estimation (model calibration) and optimal experimental design are
particularly challenging. The community has already developed many methods and
software packages which aim to facilitate these tasks. However, there is a lack
of suitable benchmark problems which allow a fair and systematic evaluation and
comparison of these contributions. Here we present BioPreDyn-bench, a set of
challenging parameter estimation problems which aspire to serve as reference
test cases in this area. This set comprises six problems including medium and
large-scale kinetic models of the bacterium E. coli, baker's yeast S.
cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a
generic signal transduction network. The level of description includes
metabolism, transcription, signal transduction, and development. For each
problem we provide (i) a basic description and formulation, (ii)
implementations ready-to-run in several formats, (iii) computational results
obtained with specific solvers, (iv) a basic analysis and interpretation. This
suite of benchmark problems can be readily used to evaluate and compare
parameter estimation methods. Further, it can also be used to build test
problems for sensitivity and identifiability analysis, model reduction and
optimal experimental design methods. The suite, including codes and
documentation, can be freely downloaded from
http://www.iim.csic.es/%7egingproc/biopredynbench/.
|
[
{
"created": "Tue, 22 Jul 2014 13:31:05 GMT",
"version": "v1"
}
] |
2017-03-22
|
[
[
"Villaverde",
"Alejandro F",
""
],
[
"Henriques",
"David",
""
],
[
"Smallbone",
"Kieran",
""
],
[
"Bongard",
"Sophia",
""
],
[
"Schmid",
"Joachim",
""
],
[
"Cicin-Sain",
"Damjan",
""
],
[
"Crombach",
"Anton",
""
],
[
"Saez-Rodriguez",
"Julio",
""
],
[
"Mauch",
"Klaus",
""
],
[
"Balsa-Canto",
"Eva",
""
],
[
"Mendes",
"Pedro",
""
],
[
"Jaeger",
"Johannes",
""
],
[
"Banga",
"Julio R",
""
]
] |
Dynamic modelling is one of the cornerstones of systems biology. Many research efforts are currently being invested in the development and exploitation of large-scale kinetic models. The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging. The community has already developed many methods and software packages which aim to facilitate these tasks. However, there is a lack of suitable benchmark problems which allow a fair and systematic evaluation and comparison of these contributions. Here we present BioPreDyn-bench, a set of challenging parameter estimation problems which aspire to serve as reference test cases in this area. This set comprises six problems including medium and large-scale kinetic models of the bacterium E. coli, baker's yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The level of description includes metabolism, transcription, signal transduction, and development. For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation. This suite of benchmark problems can be readily used to evaluate and compare parameter estimation methods. Further, it can also be used to build test problems for sensitivity and identifiability analysis, model reduction and optimal experimental design methods. The suite, including codes and documentation, can be freely downloaded from http://www.iim.csic.es/%7egingproc/biopredynbench/.
|
1701.02908
|
Ricardo Martinez-Garcia
|
Ricardo Martinez-Garcia
|
Nonequilibrium Statistical Physics in Ecology: Vegetation Patterns,
Animal Mobility and Temporal Fluctuations
|
PhD Thesis
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This thesis focuses on the applications of mathematical tools and concepts
brought from nonequilibrium statistical physics to the modeling of ecological
problems.
The first part provides a short introduction where the theoretical concepts
and mathematical tools that are going to be used in subsequent chapters are
presented. Firstly, the different levels of description usually employed in the
models are explained. Secondly, the mathematical relationships among them are
presented. Finally, the notation and terminology that will be used later on are
explained.
The second part is devoted to studying vegetation pattern formation in
regions where precipitations are not frequent and resources for plant growth
are scarce. This part comprises two chapters.
The third part of the thesis develops a series of mathematical models
describing the collective movement and behavior of some animal species. Its
primary objective is to investigate the effect that communication among
foragers has on searching times and the formation of groups. It consists of two
chapters.
The fourth part covers the effect of stochastic temporal disorder, mimicking
climate and environmental variability, on systems formed by many interacting
particles. These models may serve as an example of ecosystems. The thesis ends
with a summary and devising future research lines.
|
[
{
"created": "Wed, 11 Jan 2017 09:56:45 GMT",
"version": "v1"
}
] |
2017-01-12
|
[
[
"Martinez-Garcia",
"Ricardo",
""
]
] |
This thesis focuses on the applications of mathematical tools and concepts brought from nonequilibrium statistical physics to the modeling of ecological problems. The first part provides a short introduction where the theoretical concepts and mathematical tools that are going to be used in subsequent chapters are presented. Firstly, the different levels of description usually employed in the models are explained. Secondly, the mathematical relationships among them are presented. Finally, the notation and terminology that will be used later on are explained. The second part is devoted to studying vegetation pattern formation in regions where precipitations are not frequent and resources for plant growth are scarce. This part comprises two chapters. The third part of the thesis develops a series of mathematical models describing the collective movement and behavior of some animal species. Its primary objective is to investigate the effect that communication among foragers has on searching times and the formation of groups. It consists of two chapters. The fourth part covers the effect of stochastic temporal disorder, mimicking climate and environmental variability, on systems formed by many interacting particles. These models may serve as an example of ecosystems. The thesis ends with a summary and devising future research lines.
|
1411.4121
|
Zuo-Bing Wu
|
Zuo-Bing Wu
|
Analysis of correlation structures in the Synechocystis PCC6803 genome
|
29 pages, 6 figures
|
Computational Biology and Chemistry, Vol. 53, pp. 49-58 (2014)
| null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transfer of nucleotide strings in the Synechocystis sp. PCC6803 genome is
investigated to exhibit periodic and non-periodic correlation structures by
using the recurrence plot method and the phase space reconstruction technique.
The periodic correlation structures are generated by periodic transfer of
several substrings in long periodic or non-periodic nucleotide strings embedded
in the coding regions of genes. The non-periodic correlation structures are
generated by non-periodic transfer of several substrings covering or
overlapping with the coding regions of genes. In the periodic and non-periodic
transfer, some gaps divide the long nucleotide strings into the substrings and
prevent their global transfer. Most of the gaps are either the replacement of
one base or the insertion/reduction of one base. In the reconstructed phase
space, the points generated from two or three steps for the continuous
iterative transfer via the second maximal distance can be fitted by two lines.
It partly reveals an intrinsic dynamics in the transfer of nucleotide strings.
Due to the comparison of the relative positions and lengths, the substrings
concerned with the non-periodic correlation structures are almost identical to
the mobile elements annotated in the genome. The mobile elements are thus
endowed with the basic results on the correlation structures.
|
[
{
"created": "Sat, 15 Nov 2014 07:40:04 GMT",
"version": "v1"
}
] |
2014-11-18
|
[
[
"Wu",
"Zuo-Bing",
""
]
] |
Transfer of nucleotide strings in the Synechocystis sp. PCC6803 genome is investigated to exhibit periodic and non-periodic correlation structures by using the recurrence plot method and the phase space reconstruction technique. The periodic correlation structures are generated by periodic transfer of several substrings in long periodic or non-periodic nucleotide strings embedded in the coding regions of genes. The non-periodic correlation structures are generated by non-periodic transfer of several substrings covering or overlapping with the coding regions of genes. In the periodic and non-periodic transfer, some gaps divide the long nucleotide strings into the substrings and prevent their global transfer. Most of the gaps are either the replacement of one base or the insertion/reduction of one base. In the reconstructed phase space, the points generated from two or three steps for the continuous iterative transfer via the second maximal distance can be fitted by two lines. It partly reveals an intrinsic dynamics in the transfer of nucleotide strings. Due to the comparison of the relative positions and lengths, the substrings concerned with the non-periodic correlation structures are almost identical to the mobile elements annotated in the genome. The mobile elements are thus endowed with the basic results on the correlation structures.
|
1909.11597
|
Abbas Saberi Abbas Ali Saberi
|
Youness Azimzade, and Abbas Ali Saberi
|
Short-Range Migration Can Alter Evolutionary Dynamics in Solid Tumors
|
8 pages, 6 figures (to appear in JSTAT)
|
J. Stat. Mech.(JSTAT) (2019) 103502
|
10.1088/1742-5468/ab4983
| null |
q-bio.PE cond-mat.soft physics.bio-ph q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Here, we investigate how competition in the Eden model is affected by short
range dispersal and the requirement that site updates occur only after several
updates of the same site have been attempted previously. The latter models the
effect of tissue or media resistance to invasion. We found that the existence
of tissue intensifies 'Natural Selection' and de-accelerating 'Genetic Drift',
both to a limited extent. More interestingly, our results show that short-range
migration can eliminate Genetic demixing and conceal Natural Selection.
|
[
{
"created": "Wed, 25 Sep 2019 16:34:07 GMT",
"version": "v1"
}
] |
2019-12-05
|
[
[
"Azimzade",
"Youness",
""
],
[
"Saberi",
"Abbas Ali",
""
]
] |
Here, we investigate how competition in the Eden model is affected by short range dispersal and the requirement that site updates occur only after several updates of the same site have been attempted previously. The latter models the effect of tissue or media resistance to invasion. We found that the existence of tissue intensifies 'Natural Selection' and de-accelerating 'Genetic Drift', both to a limited extent. More interestingly, our results show that short-range migration can eliminate Genetic demixing and conceal Natural Selection.
|
1101.5865
|
Peter Csermely
|
Peter Csermely, Kuljeet Singh Sandhu, Eszter Hazai, Zsolt Hoksza, Huba
J.M. Kiss, Federico Miozzo, Daniel V. Veres, Francesco Piazza and Ruth
Nussinov
|
Disordered proteins and network disorder in network descriptions of
protein structure, dynamics and function. Hypotheses and a comprehensive
review
|
27 pages, 2 Tables, 5 Figures and 161 references
|
Current Protein and Peptide Science 13, 19-33 (2012)
| null | null |
q-bio.MN cond-mat.dis-nn physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
During the last decade, network approaches became a powerful tool to describe
protein structure and dynamics. Here we review the links between disordered
proteins and the associated networks, and describe the consequences of local,
mesoscopic and global network disorder on changes in protein structure and
dynamics. We introduce a new classification of protein networks into
cumulus-type, i.e., those similar to puffy (white) clouds, and stratus-type,
i.e., those similar to flat, dense (dark) low-lying clouds, and relate these
network types to protein disorder dynamics and to differences in energy
transmission processes. In the first class, there is limited overlap between
the modules, which implies higher rigidity of the individual units; there the
conformational changes can be described by an energy transfer mechanism. In the
second class, the topology presents a compact structure with significant
overlap between the modules; there the conformational changes can be described
by multi-trajectories; that is, multiple highly populated pathways. We further
propose that disordered protein regions evolved to help other protein segments
reach rarely visited but functionally-related states. We also show the role of
disorder in spatial games of amino acids; highlight the effects of
intrinsically disordered proteins (IDPs) on cellular networks and list some
possible studies linking protein disorder and protein structure networks.
|
[
{
"created": "Mon, 31 Jan 2011 07:55:53 GMT",
"version": "v1"
},
{
"created": "Sun, 6 Feb 2011 08:27:53 GMT",
"version": "v2"
},
{
"created": "Fri, 2 Mar 2012 15:09:49 GMT",
"version": "v3"
}
] |
2012-03-05
|
[
[
"Csermely",
"Peter",
""
],
[
"Sandhu",
"Kuljeet Singh",
""
],
[
"Hazai",
"Eszter",
""
],
[
"Hoksza",
"Zsolt",
""
],
[
"Kiss",
"Huba J. M.",
""
],
[
"Miozzo",
"Federico",
""
],
[
"Veres",
"Daniel V.",
""
],
[
"Piazza",
"Francesco",
""
],
[
"Nussinov",
"Ruth",
""
]
] |
During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into cumulus-type, i.e., those similar to puffy (white) clouds, and stratus-type, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an energy transfer mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by multi-trajectories; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach rarely visited but functionally-related states. We also show the role of disorder in spatial games of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks.
|
1910.05332
|
Wolfram M\"obius
|
Wolfram M\"obius, Francesca Tesser, Kim M. J. Alards, Roberto Benzi,
David R. Nelson, Federico Toschi
|
The collective effect of finite-sized inhomogeneities on the spatial
spread of populations in two dimensions
|
Authors Wolfram M\"obius and Francesca Tesser contributed equally
| null | null | null |
q-bio.PE cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The dynamics of a population expanding into unoccupied habitat has been
primarily studied for situations in which growth and dispersal parameters are
uniform in space or vary in one dimension. Here we study the influence of
finite-sized individual inhomogeneities and their collective effect on front
speed if randomly placed in a two-dimensional habitat. We use an
individual-based model to investigate the front dynamics for a region in which
dispersal or growth of individuals is reduced to zero (obstacles) or increased
above the background (hotspots), respectively. In a regime where front dynamics
is determined by a local front speed only, a principle of least time can be
employed to predict front speed and shape. The resulting analytical solutions
motivate an event-based algorithm illustrating the effects of several obstacles
or hotspots. We finally apply the principle of least time to large
heterogeneous environments by solving the Eikonal equation numerically.
Obstacles lead to a slow-down that is dominated by the number density and width
of obstacles, but not by their precise shape. Hotspots result in a speedup,
which we characterise as function of hotspot strength and density. Our findings
emphasise the importance of taking the dimensionality of the environment into
account.
|
[
{
"created": "Fri, 11 Oct 2019 17:54:18 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Sep 2021 07:52:01 GMT",
"version": "v2"
}
] |
2021-09-21
|
[
[
"Möbius",
"Wolfram",
""
],
[
"Tesser",
"Francesca",
""
],
[
"Alards",
"Kim M. J.",
""
],
[
"Benzi",
"Roberto",
""
],
[
"Nelson",
"David R.",
""
],
[
"Toschi",
"Federico",
""
]
] |
The dynamics of a population expanding into unoccupied habitat has been primarily studied for situations in which growth and dispersal parameters are uniform in space or vary in one dimension. Here we study the influence of finite-sized individual inhomogeneities and their collective effect on front speed if randomly placed in a two-dimensional habitat. We use an individual-based model to investigate the front dynamics for a region in which dispersal or growth of individuals is reduced to zero (obstacles) or increased above the background (hotspots), respectively. In a regime where front dynamics is determined by a local front speed only, a principle of least time can be employed to predict front speed and shape. The resulting analytical solutions motivate an event-based algorithm illustrating the effects of several obstacles or hotspots. We finally apply the principle of least time to large heterogeneous environments by solving the Eikonal equation numerically. Obstacles lead to a slow-down that is dominated by the number density and width of obstacles, but not by their precise shape. Hotspots result in a speedup, which we characterise as function of hotspot strength and density. Our findings emphasise the importance of taking the dimensionality of the environment into account.
|
2301.03878
|
Gabriel Cia
|
Gabriel Cia, Fabrizio Pucci, Marianne Rooman
|
Critical review of conformational B-cell epitope prediction methods
|
19 pages, 2 figures, 3 tables
|
Briefings in Bioinformatics, bbac567, 1-9, 2023
|
10.1093/bib/bbac567
| null |
q-bio.BM
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate in-silico prediction of conformational B-cell epitopes would lead to
major improvements in disease diagnostics, drug design and vaccine development.
A variety of computational methods, mainly based on machine learning
approaches, have been developed in the last decades to tackle this challenging
problem. Here, we rigorously benchmarked nine state-of-the-art conformational
B-cell epitope prediction webservers, including generic and antibody-specific
methods, on a dataset of over 250 antibody-antigen structures. The results of
our assessment and statistical analyses show that all the methods achieve very
low performances, and some do not perform better than randomly generated
patches of surface residues. In addition, we also found that commonly used
consensus strategies that combine the results from multiple webservers are at
best only marginally better than random. Finally, we applied all the predictors
to the SARS-CoV-2 spike protein as an independent case study, and showed that
they perform poorly in general, which largely recapitulates our benchmarking
conclusions. We hope that these results will lead to greater caution when using
these tools until the biases and issues that limit current methods have been
addressed, promote the use of state-of-the-art evaluation methodologies in
future publications, and suggest new strategies to improve the performance of
conformational B-cell epitope prediction methods.
|
[
{
"created": "Tue, 10 Jan 2023 10:01:16 GMT",
"version": "v1"
}
] |
2023-01-11
|
[
[
"Cia",
"Gabriel",
""
],
[
"Pucci",
"Fabrizio",
""
],
[
"Rooman",
"Marianne",
""
]
] |
Accurate in-silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications, and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.
|
2205.07656
|
Hilla De-Leon
|
Hilla De-Leon and Francesco Pederiva
|
Using a physical model and aggregate data to estimate the spreading of
Covid-19 in Israel in the presence of waning immunity and competing variants
|
9 pages 8 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In more than two years since the COVID-19 virus was first detected in China,
hundreds of millions of individuals have been infected, and millions have died.
Aside from the immediate need for medical solutions (such as vaccines and
medications) to treat the epidemic, the Corona pandemic has strengthened the
demand for mathematical models that can predict the spread of the pandemic in
an ever-changing reality. Here, we present a novel, dynamic particle model
based on the basic principles of statistical physics that enables the
prediction of the spreading of Covid-19 in the presence of effective vaccines.
This particle model enables us to accurately examine the effects of the vaccine
on different subgroups of the vaccinated population and the entire population
and to identify the vaccine waning. Furthermore, a particle model can predict
the prevalence of two competing variants over time and their associated
morbidity.
|
[
{
"created": "Mon, 16 May 2022 13:19:52 GMT",
"version": "v1"
}
] |
2022-05-17
|
[
[
"De-Leon",
"Hilla",
""
],
[
"Pederiva",
"Francesco",
""
]
] |
In more than two years since the COVID-19 virus was first detected in China, hundreds of millions of individuals have been infected, and millions have died. Aside from the immediate need for medical solutions (such as vaccines and medications) to treat the epidemic, the Corona pandemic has strengthened the demand for mathematical models that can predict the spread of the pandemic in an ever-changing reality. Here, we present a novel, dynamic particle model based on the basic principles of statistical physics that enables the prediction of the spreading of Covid-19 in the presence of effective vaccines. This particle model enables us to accurately examine the effects of the vaccine on different subgroups of the vaccinated population and the entire population and to identify the vaccine waning. Furthermore, a particle model can predict the prevalence of two competing variants over time and their associated morbidity.
|
1111.7183
|
Arry Yanuar
|
Arry Yanuar, Abdul Mun'im, Akma Bertha Aprima Lagho, Rezi Riadhi
Syahdi, Marjuqi Rahmat, Heru Suhartanto
|
Medicinal Plants Database and Three Dimensional Structure of the
Chemical Compounds from Medicinal Plants in Indonesia
|
4 pages, 3 figures
|
Int. J. Comp. Sci. Issue (2011), Vol. 8, Issue 5, No. 1, p180 -
183
| null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
During this era of new drug designing, medicinal plants had become a very
interesting object of further research. Pharmacology screening of active
compound of medicinal plants would be time consuming and costly. Molecular
docking is one of the in silico method which is more efficient compare to in
vitro or in vivo method for its capability of finding the active compound in
medicinal plants. In this method, three-dimensional structure becomes very
important in the molecular docking methods, so we need a database that provides
information on three-dimensional structures of chemical compounds from
medicinal plants in Indonesia. Therefore, this study will prepare a database
which provides information of the three dimensional structures of chemical
compounds of medicinal plants. The database will be prepared by using MySQL
format and is designed to be placed in http://herbaldb.farmasi.ui.ac.id website
so that eventually this database can be accessed quickly and easily by users
via the Internet.
|
[
{
"created": "Tue, 29 Nov 2011 01:30:06 GMT",
"version": "v1"
}
] |
2012-10-17
|
[
[
"Yanuar",
"Arry",
""
],
[
"Mun'im",
"Abdul",
""
],
[
"Lagho",
"Akma Bertha Aprima",
""
],
[
"Syahdi",
"Rezi Riadhi",
""
],
[
"Rahmat",
"Marjuqi",
""
],
[
"Suhartanto",
"Heru",
""
]
] |
During this era of new drug designing, medicinal plants had become a very interesting object of further research. Pharmacology screening of active compound of medicinal plants would be time consuming and costly. Molecular docking is one of the in silico method which is more efficient compare to in vitro or in vivo method for its capability of finding the active compound in medicinal plants. In this method, three-dimensional structure becomes very important in the molecular docking methods, so we need a database that provides information on three-dimensional structures of chemical compounds from medicinal plants in Indonesia. Therefore, this study will prepare a database which provides information of the three dimensional structures of chemical compounds of medicinal plants. The database will be prepared by using MySQL format and is designed to be placed in http://herbaldb.farmasi.ui.ac.id website so that eventually this database can be accessed quickly and easily by users via the Internet.
|
2306.03987
|
Mary Lisa Manning
|
Elizabeth Lawson-Keister, Tao Zhang, M. Lisa Manning
|
Differences in boundary behavior in the 3D vertex and Voronoi models
|
10 pages, 5 figures
| null | null | null |
q-bio.TO cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An important open question in the modeling of biological tissues is how to
identify the right scale for coarse-graining, or equivalently, the right number
of degrees of freedom. For confluent biological tissues, both vertex and
Voronoi models, which differ only in their representation of the degrees of
freedom, have effectively been used to predict behavior, including fluid-solid
transitions and cell tissue compartmentalization, which are important for
biological function. However, recent work in 2D has hinted that there may be
differences between the two models in systems with heterotypic interfaces
between two tissue types, and there is a burgeoning interest in 3D tissue
models. Therefore, we compare the geometric structure and dynamic sorting
behavior in mixtures of two cell types in both 3D vertex and Voronoi models. We
find that while the cell shape indices exhibit similar trends in both models,
the registration between cell centers and cell orientation at the boundary are
significantly different between the two models. We demonstrate that these
macroscopic differences are caused by changes to the cusp-like restoring forces
introduced by the different representations of the degrees of freedom at the
boundary, and that the Voronoi model is more strongly constrained by forces
that are an artifact of the way the degrees of freedom are represented. This
suggests that vertex models may be more appropriate for 3D simulations of
tissues with heterotypic contacts.
|
[
{
"created": "Tue, 6 Jun 2023 19:48:41 GMT",
"version": "v1"
}
] |
2023-06-08
|
[
[
"Lawson-Keister",
"Elizabeth",
""
],
[
"Zhang",
"Tao",
""
],
[
"Manning",
"M. Lisa",
""
]
] |
An important open question in the modeling of biological tissues is how to identify the right scale for coarse-graining, or equivalently, the right number of degrees of freedom. For confluent biological tissues, both vertex and Voronoi models, which differ only in their representation of the degrees of freedom, have effectively been used to predict behavior, including fluid-solid transitions and cell tissue compartmentalization, which are important for biological function. However, recent work in 2D has hinted that there may be differences between the two models in systems with heterotypic interfaces between two tissue types, and there is a burgeoning interest in 3D tissue models. Therefore, we compare the geometric structure and dynamic sorting behavior in mixtures of two cell types in both 3D vertex and Voronoi models. We find that while the cell shape indices exhibit similar trends in both models, the registration between cell centers and cell orientation at the boundary are significantly different between the two models. We demonstrate that these macroscopic differences are caused by changes to the cusp-like restoring forces introduced by the different representations of the degrees of freedom at the boundary, and that the Voronoi model is more strongly constrained by forces that are an artifact of the way the degrees of freedom are represented. This suggests that vertex models may be more appropriate for 3D simulations of tissues with heterotypic contacts.
|
2009.08309
|
Charis Mesaritakis
|
Menelaos Skontranis, George Sarantoglou, Stavros Deligiannidis, Adonis
Bogris, Charis Mesaritakis
|
Unsupervised Image Classification Through Time-Multiplexed Photonic
Multi-Layer Spiking Convolutional Neural Network
|
4 pages, 3 figures
| null |
10.1109/ECOC48923.2020.9333320
| null |
q-bio.NC eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present results of a deep photonic spiking convolutional neural network,
based on two-section VCSELs, targeting image classification. Training is based
on unsupervised spike-timing dependent plasticity, whereas neuron
time-multiplexing and ultra-fast response are exploited towards a a reduction
of the physical neuron count by 90%
|
[
{
"created": "Wed, 16 Sep 2020 07:52:32 GMT",
"version": "v1"
}
] |
2021-03-30
|
[
[
"Skontranis",
"Menelaos",
""
],
[
"Sarantoglou",
"George",
""
],
[
"Deligiannidis",
"Stavros",
""
],
[
"Bogris",
"Adonis",
""
],
[
"Mesaritakis",
"Charis",
""
]
] |
We present results of a deep photonic spiking convolutional neural network, based on two-section VCSELs, targeting image classification. Training is based on unsupervised spike-timing dependent plasticity, whereas neuron time-multiplexing and ultra-fast response are exploited towards a a reduction of the physical neuron count by 90%
|
2302.09151
|
Jin Xu
|
Jin Xu, Gary Geng, Nhan D. Nguyen, Carmen Perena-Cortes, Claire
Samuels, Herbert M. Sauro
|
SBcoyote: An Extensible Python-Based Reaction Editor and Viewer
| null | null |
10.1016/j.biosystems.2023.105001
| null |
q-bio.QM q-bio.MN
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
SBcoyote is an open-source cross-platform biochemical reaction viewer and
editor released under the liberal MIT license. It is written in Python and uses
wxPython to implement the GUI and the drawing canvas. It supports the
visualization and editing of compartments, species, and reactions. It includes
many options to stylize each of these components. For instance, species can be
in different colors and shapes. Other core features include the ability to
create alias nodes, alignment of groups of nodes, network zooming, as well as
an interactive bird-eye view of the network to allow easy navigation on large
networks. A unique feature of the tool is the extensive Python plugin API,
where third-party developers can include new functionality. To assist
third-party plugin developers, we provide a variety of sample plugins,
including, random network generation, a simple auto layout tool, export to
Antimony, export SBML, import SBML, etc. Of particular interest are the export
and import SBML plugins since these support the SBML level 3 layout and render
standard, which is exchangeable with other software packages. Plugins are
stored in a GitHub repository, and an included plugin manager can retrieve and
install new plugins from the repository on demand. Plugins have version
metadata associated with them to make it install plugin updates. Availability:
https://github.com/sys-bio/SBcoyote.
|
[
{
"created": "Fri, 17 Feb 2023 21:24:44 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Aug 2023 21:43:47 GMT",
"version": "v2"
}
] |
2024-02-14
|
[
[
"Xu",
"Jin",
""
],
[
"Geng",
"Gary",
""
],
[
"Nguyen",
"Nhan D.",
""
],
[
"Perena-Cortes",
"Carmen",
""
],
[
"Samuels",
"Claire",
""
],
[
"Sauro",
"Herbert M.",
""
]
] |
SBcoyote is an open-source cross-platform biochemical reaction viewer and editor released under the liberal MIT license. It is written in Python and uses wxPython to implement the GUI and the drawing canvas. It supports the visualization and editing of compartments, species, and reactions. It includes many options to stylize each of these components. For instance, species can be in different colors and shapes. Other core features include the ability to create alias nodes, alignment of groups of nodes, network zooming, as well as an interactive bird-eye view of the network to allow easy navigation on large networks. A unique feature of the tool is the extensive Python plugin API, where third-party developers can include new functionality. To assist third-party plugin developers, we provide a variety of sample plugins, including, random network generation, a simple auto layout tool, export to Antimony, export SBML, import SBML, etc. Of particular interest are the export and import SBML plugins since these support the SBML level 3 layout and render standard, which is exchangeable with other software packages. Plugins are stored in a GitHub repository, and an included plugin manager can retrieve and install new plugins from the repository on demand. Plugins have version metadata associated with them to make it install plugin updates. Availability: https://github.com/sys-bio/SBcoyote.
|
2212.02504
|
Jonas Ditz
|
Jonas C. Ditz, Bernhard Reuter, Nico Pfeifer
|
COmic: Convolutional Kernel Networks for Interpretable End-to-End
Learning on (Multi-)Omics Data
| null | null | null | null |
q-bio.QM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Motivation: The size of available omics datasets is steadily increasing with
technological advancement in recent years. While this increase in sample size
can be used to improve the performance of relevant prediction tasks in
healthcare, models that are optimized for large datasets usually operate as
black boxes. In high stakes scenarios, like healthcare, using a black-box model
poses safety and security issues. Without an explanation about molecular
factors and phenotypes that affected the prediction, healthcare providers are
left with no choice but to blindly trust the models. We propose a new type of
artificial neural network, named Convolutional Omics Kernel Network (COmic). By
combining convolutional kernel networks with pathway-induced kernels, our
method enables robust and interpretable end-to-end learning on omics datasets
ranging in size from a few hundred to several hundreds of thousands of samples.
Furthermore, COmic can be easily adapted to utilize multi-omics data.
Results: We evaluated the performance capabilities of COmic on six different
breast cancer cohorts. Additionally, we trained COmic models on multi-omics
data using the METABRIC cohort. Our models performed either better or similar
to competitors on both tasks. We show how the use of pathway-induced Laplacian
kernels opens the black-box nature of neural networks and results in
intrinsically interpretable models that eliminate the need for post-hoc
explanation models.
|
[
{
"created": "Fri, 2 Dec 2022 12:54:27 GMT",
"version": "v1"
},
{
"created": "Wed, 3 May 2023 11:51:34 GMT",
"version": "v2"
}
] |
2023-05-04
|
[
[
"Ditz",
"Jonas C.",
""
],
[
"Reuter",
"Bernhard",
""
],
[
"Pfeifer",
"Nico",
""
]
] |
Motivation: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that are optimized for large datasets usually operate as black boxes. In high stakes scenarios, like healthcare, using a black-box model poses safety and security issues. Without an explanation about molecular factors and phenotypes that affected the prediction, healthcare providers are left with no choice but to blindly trust the models. We propose a new type of artificial neural network, named Convolutional Omics Kernel Network (COmic). By combining convolutional kernel networks with pathway-induced kernels, our method enables robust and interpretable end-to-end learning on omics datasets ranging in size from a few hundred to several hundreds of thousands of samples. Furthermore, COmic can be easily adapted to utilize multi-omics data. Results: We evaluated the performance capabilities of COmic on six different breast cancer cohorts. Additionally, we trained COmic models on multi-omics data using the METABRIC cohort. Our models performed either better or similar to competitors on both tasks. We show how the use of pathway-induced Laplacian kernels opens the black-box nature of neural networks and results in intrinsically interpretable models that eliminate the need for post-hoc explanation models.
|
1504.00959
|
Jie Ren
|
Jie Ren, Kai Song, Minghua Deng, Gesine Reinert, Charles H. Cannon and
Fengzhu Sun
|
Inference of Markovian Properties of Molecular Sequences from NGS Data
and Applications to Comparative Genomics
|
accepted by RECOMB-SEQ 2015
| null | null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Next Generation Sequencing (NGS) technologies generate large amounts of short
read data for many different organisms. The fact that NGS reads are generally
short makes it challenging to assemble the reads and reconstruct the original
genome sequence. For clustering genomes using such NGS data, word-count based
alignment-free sequence comparison is a promising approach, but for this
approach, the underlying expected word counts are essential.
A plausible model for this underlying distribution of word counts is given
through modelling the DNA sequence as a Markov chain (MC). For single long
sequences, efficient statistics are available to estimate the order of MCs and
the transition probability matrix for the sequences. As NGS data do not provide
a single long sequence, inference methods on Markovian properties of sequences
based on single long sequences cannot be directly used for NGS short read data.
Here we derive a normal approximation for such word counts. We also show that
the traditional Chi-square statistic has an approximate gamma distribution,
using the Lander-Waterman model for physical mapping. We propose several
methods to estimate the order of the MC based on NGS reads and evaluate them
using simulations. We illustrate the applications of our results by clustering
genomic sequences of several vertebrate and tree species based on NGS reads
using alignment-free sequence dissimilarity measures. We find that the
estimated order of the MC has a considerable effect on the clustering results,
and that the clustering results that use a MC of the estimated order give a
plausible clustering of the species.
|
[
{
"created": "Sat, 4 Apr 2015 00:05:38 GMT",
"version": "v1"
}
] |
2015-04-07
|
[
[
"Ren",
"Jie",
""
],
[
"Song",
"Kai",
""
],
[
"Deng",
"Minghua",
""
],
[
"Reinert",
"Gesine",
""
],
[
"Cannon",
"Charles H.",
""
],
[
"Sun",
"Fengzhu",
""
]
] |
Next Generation Sequencing (NGS) technologies generate large amounts of short read data for many different organisms. The fact that NGS reads are generally short makes it challenging to assemble the reads and reconstruct the original genome sequence. For clustering genomes using such NGS data, word-count based alignment-free sequence comparison is a promising approach, but for this approach, the underlying expected word counts are essential. A plausible model for this underlying distribution of word counts is given through modelling the DNA sequence as a Markov chain (MC). For single long sequences, efficient statistics are available to estimate the order of MCs and the transition probability matrix for the sequences. As NGS data do not provide a single long sequence, inference methods on Markovian properties of sequences based on single long sequences cannot be directly used for NGS short read data. Here we derive a normal approximation for such word counts. We also show that the traditional Chi-square statistic has an approximate gamma distribution, using the Lander-Waterman model for physical mapping. We propose several methods to estimate the order of the MC based on NGS reads and evaluate them using simulations. We illustrate the applications of our results by clustering genomic sequences of several vertebrate and tree species based on NGS reads using alignment-free sequence dissimilarity measures. We find that the estimated order of the MC has a considerable effect on the clustering results, and that the clustering results that use a MC of the estimated order give a plausible clustering of the species.
|
2010.02993
|
Erik Fagerholm
|
Erik D. Fagerholm, Karl J. Friston, Rosalyn J. Moran, Robert Leech
|
The principle of stationary action in neural systems
| null | null | null | null |
q-bio.NC physics.class-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The principle of stationary action is a cornerstone of modern physics,
providing a powerful framework for investigating dynamical systems found in
classical mechanics through to quantum field theory. However, computational
neuroscience, despite its heavy reliance on concepts in physics, is anomalous
in this regard as its main equations of motion are not compatible with a
Lagrangian formulation and hence with the principle of stationary action.
Taking the Dynamic Causal Modelling neuronal state equation, Hodgkin-Huxley
model, and the Leaky Integrate-and-Fire model as examples, we show that it is
possible to write complex oscillatory forms of these equations in terms of a
single Lagrangian. We therefore bring mathematical descriptions in
computational neuroscience under the remit of the principle of stationary
action and use this reformulation to express symmetries and associated
conservation laws arising in neural systems.
|
[
{
"created": "Tue, 6 Oct 2020 19:43:55 GMT",
"version": "v1"
}
] |
2020-10-08
|
[
[
"Fagerholm",
"Erik D.",
""
],
[
"Friston",
"Karl J.",
""
],
[
"Moran",
"Rosalyn J.",
""
],
[
"Leech",
"Robert",
""
]
] |
The principle of stationary action is a cornerstone of modern physics, providing a powerful framework for investigating dynamical systems found in classical mechanics through to quantum field theory. However, computational neuroscience, despite its heavy reliance on concepts in physics, is anomalous in this regard as its main equations of motion are not compatible with a Lagrangian formulation and hence with the principle of stationary action. Taking the Dynamic Causal Modelling neuronal state equation, Hodgkin-Huxley model, and the Leaky Integrate-and-Fire model as examples, we show that it is possible to write complex oscillatory forms of these equations in terms of a single Lagrangian. We therefore bring mathematical descriptions in computational neuroscience under the remit of the principle of stationary action and use this reformulation to express symmetries and associated conservation laws arising in neural systems.
|
2303.16558
|
Manh Hong Duong
|
L. Chen, C. Deng, M. H. Duong and T. A. Han
|
On the number of equilibria of the replicator-mutator dynamics for noisy
social dilemmas
| null | null | null | null |
q-bio.PE math.DS
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we consider the replicator-mutator dynamics for pairwise
social dilemmas where the payoff entries are random variables. The randomness
is incorporated to take into account the uncertainty, which is inevitable in
practical applications and may arise from different sources such as lack of
data for measuring the outcomes, noisy and rapidly changing environments, as
well as unavoidable human estimate errors. We analytically and numerically
compute the probability that the replicator-mutator dynamics has a given number
of equilibria for four classes of pairwise social dilemmas (Prisoner's Dilemma,
Snow-Drift Game, Stag-Hunt Game and Harmony Game). As a result, we characterise
the qualitative behaviour of such probabilities as a function of the mutation
rate. Our results clearly show the influence of the mutation rate and the
uncertainty in the payoff matrix definition on the number of equilibria in
these games. Overall, our analysis has provided novel theoretical contributions
to the understanding of the impact of uncertainty on the behavioural diversity
in a complex dynamical system.
|
[
{
"created": "Wed, 29 Mar 2023 09:27:43 GMT",
"version": "v1"
}
] |
2023-03-30
|
[
[
"Chen",
"L.",
""
],
[
"Deng",
"C.",
""
],
[
"Duong",
"M. H.",
""
],
[
"Han",
"T. A.",
""
]
] |
In this paper, we consider the replicator-mutator dynamics for pairwise social dilemmas where the payoff entries are random variables. The randomness is incorporated to take into account the uncertainty, which is inevitable in practical applications and may arise from different sources such as lack of data for measuring the outcomes, noisy and rapidly changing environments, as well as unavoidable human estimate errors. We analytically and numerically compute the probability that the replicator-mutator dynamics has a given number of equilibria for four classes of pairwise social dilemmas (Prisoner's Dilemma, Snow-Drift Game, Stag-Hunt Game and Harmony Game). As a result, we characterise the qualitative behaviour of such probabilities as a function of the mutation rate. Our results clearly show the influence of the mutation rate and the uncertainty in the payoff matrix definition on the number of equilibria in these games. Overall, our analysis has provided novel theoretical contributions to the understanding of the impact of uncertainty on the behavioural diversity in a complex dynamical system.
|
2101.08111
|
Manuel Marques-Pita PhD
|
Santosh Manicka, Manuel Marques-Pita, Luis M. Rocha
|
Effective connectivity determines the critical dynamics of biochemical
networks
|
22 pages, 7 figures
| null | null | null |
q-bio.MN nlin.AO
|
http://creativecommons.org/licenses/by/4.0/
|
Living systems operate in a critical dynamical regime -- between order and
chaos -- where they are both resilient to perturbation, and flexible enough to
evolve. To characterize such critical dynamics, the established 'structural
theory' of criticality uses automata network connectivity and node bias (to be
on or off) as tuning parameters. This parsimony in the number of parameters
needed sometimes leads to uncertain predictions about the dynamical regime of
both random and systems biology models of biochemical regulation. We derive a
more accurate theory of criticality by accounting for canalization, the
existence of redundancy that buffers automata response to inputs -- a known
mechanism that buffers the expression of traits, keeping them close to optimal
states despite genetic and environmental perturbations. The new 'canalization
theory' of criticality is based on a measure of effective connectivity. It
contributes to resolving the problem of finding precise ways to design or
control network models of biochemical regulation for desired dynamical
behavior. Our analyses reveal that effective connectivity significantly
improves the prediction of critical behavior in random automata network
ensembles. We also show that the average effective connectivity of a large
battery of systems biology models is much lower than the connectivity of their
original interaction structure. This suggests that canalization has been
selected to dynamically reduce and homogenize the seemingly heterogeneous
connectivity of biochemical networks.
|
[
{
"created": "Wed, 20 Jan 2021 13:16:39 GMT",
"version": "v1"
}
] |
2022-01-28
|
[
[
"Manicka",
"Santosh",
""
],
[
"Marques-Pita",
"Manuel",
""
],
[
"Rocha",
"Luis M.",
""
]
] |
Living systems operate in a critical dynamical regime -- between order and chaos -- where they are both resilient to perturbation, and flexible enough to evolve. To characterize such critical dynamics, the established 'structural theory' of criticality uses automata network connectivity and node bias (to be on or off) as tuning parameters. This parsimony in the number of parameters needed sometimes leads to uncertain predictions about the dynamical regime of both random and systems biology models of biochemical regulation. We derive a more accurate theory of criticality by accounting for canalization, the existence of redundancy that buffers automata response to inputs -- a known mechanism that buffers the expression of traits, keeping them close to optimal states despite genetic and environmental perturbations. The new 'canalization theory' of criticality is based on a measure of effective connectivity. It contributes to resolving the problem of finding precise ways to design or control network models of biochemical regulation for desired dynamical behavior. Our analyses reveal that effective connectivity significantly improves the prediction of critical behavior in random automata network ensembles. We also show that the average effective connectivity of a large battery of systems biology models is much lower than the connectivity of their original interaction structure. This suggests that canalization has been selected to dynamically reduce and homogenize the seemingly heterogeneous connectivity of biochemical networks.
|
1903.09567
|
Owen Gilbert
|
Owen M. Gilbert
|
Natural reward as the fundamental macroevolutionary force
|
21 pages, 9 figures, 2 tables. Keywords: invention, entrepreneurship,
innovation, success, progress, advancement
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Darwin's theory of evolution by natural selection does not predict long-term
progress or advancement, nor does it provide a useful way to define or
understand these concepts. Nevertheless, the history of life is marked by major
trends that appear progressive, and seemingly more advanced forms of life have
appeared. To reconcile theory and fact, evolutionists have proposed novel
theories that extend natural selection to levels and time frames not justified
by the original structure of Darwin's theory. To extend evolutionary theory
without violating the most basic tenets of Darwinism, I here identify a
separate struggle and an alternative evolutionary force. Owing to the abundant
free energy in our universe, there is a struggle for supremacy that naturally
rewards those that are first to invent novelties that allow exploitation of
untapped resources. This natural reward comes in form of a temporary monopoly,
which is granted to those who win a competitive race to innovate. By analogy to
human economies, natural selection plays the role of nature's inventor,
gradually fashioning inventions to the situation at hand, while natural reward
plays the role of nature's entrepreneur, choosing which inventions to first
disseminate to large markets. Natural reward leads to progress through a
process of invention-conquest macroevolution, in which the dual forces of
natural selection and natural reward create and disseminate major innovations.
Over vast time frames, natural reward drives the advancement of life by a
process of extinction-replacement megaevolution that releases constraints on
progress and increases the innovativeness of life.
|
[
{
"created": "Fri, 22 Mar 2019 15:50:13 GMT",
"version": "v1"
}
] |
2019-03-25
|
[
[
"Gilbert",
"Owen M.",
""
]
] |
Darwin's theory of evolution by natural selection does not predict long-term progress or advancement, nor does it provide a useful way to define or understand these concepts. Nevertheless, the history of life is marked by major trends that appear progressive, and seemingly more advanced forms of life have appeared. To reconcile theory and fact, evolutionists have proposed novel theories that extend natural selection to levels and time frames not justified by the original structure of Darwin's theory. To extend evolutionary theory without violating the most basic tenets of Darwinism, I here identify a separate struggle and an alternative evolutionary force. Owing to the abundant free energy in our universe, there is a struggle for supremacy that naturally rewards those that are first to invent novelties that allow exploitation of untapped resources. This natural reward comes in form of a temporary monopoly, which is granted to those who win a competitive race to innovate. By analogy to human economies, natural selection plays the role of nature's inventor, gradually fashioning inventions to the situation at hand, while natural reward plays the role of nature's entrepreneur, choosing which inventions to first disseminate to large markets. Natural reward leads to progress through a process of invention-conquest macroevolution, in which the dual forces of natural selection and natural reward create and disseminate major innovations. Over vast time frames, natural reward drives the advancement of life by a process of extinction-replacement megaevolution that releases constraints on progress and increases the innovativeness of life.
|
1708.03025
|
Zhuolin Qu
|
Zhuolin Qu, Ling Xue, and James M. Hyman
|
Modeling the transmission of Wolbachia in mosquitoes for controlling
mosquito-borne diseases
|
27 pages, 14 figure; submitted to SIAP
|
SIAM Journal on Applied Mathematics, 2018
|
10.1137/17M1130800
| null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We develop and analyze an ordinary differential equation model to assess the
potential effectiveness of infecting mosquitoes with the Wolbachia bacteria to
control the ongoing mosquito-borne epidemics, such as dengue fever,
chikungunya, and Zika. Wolbachia is a natural parasitic microbe that stops the
proliferation of the harmful viruses inside the mosquito and reduces disease
transmission. It is difficult to sustain an infection of the maternal
transmitted Wolbachia in a wild mosquito population because of the reduced
fitness of the Wolbachia-infected mosquitoes and cytoplasmic incompatibility
limiting maternal transmission. The infection will only persist if the fraction
of the infected mosquitoes exceeds a minimum threshold. Our two-sex mosquito
model captures the complex transmission-cycle by accounting for heterosexual
transmission, multiple pregnant states for female mosquitoes, and the
aquatic-life stage. We identify important dimensionless numbers and analyze the
critical threshold condition for obtaining a sustained Wolbachia infection in
the natural population. This threshold effect is characterized by a backward
bifurcation with three coexisting equilibria of the system of differential
equations: a stable disease-free equilibrium, an unstable
intermediate-infection endemic equilibrium and a stable high-infection endemic
equilibrium. We perform sensitivity analysis on epidemiological and
environmental parameters to determine their relative importance to Wolbachia
transmission and prevalence. We also compare the effectiveness of different
integrated mitigation strategies and observe that the most efficient approach
to establish the Wolbachia infection is to first reduce the natural mosquitoes
and then release both infected males and pregnant females. The initial
reduction of natural population could be accomplished by either residual
spraying or ovitraps.
|
[
{
"created": "Wed, 9 Aug 2017 22:06:03 GMT",
"version": "v1"
}
] |
2020-08-14
|
[
[
"Qu",
"Zhuolin",
""
],
[
"Xue",
"Ling",
""
],
[
"Hyman",
"James M.",
""
]
] |
We develop and analyze an ordinary differential equation model to assess the potential effectiveness of infecting mosquitoes with the Wolbachia bacteria to control the ongoing mosquito-borne epidemics, such as dengue fever, chikungunya, and Zika. Wolbachia is a natural parasitic microbe that stops the proliferation of the harmful viruses inside the mosquito and reduces disease transmission. It is difficult to sustain an infection of the maternal transmitted Wolbachia in a wild mosquito population because of the reduced fitness of the Wolbachia-infected mosquitoes and cytoplasmic incompatibility limiting maternal transmission. The infection will only persist if the fraction of the infected mosquitoes exceeds a minimum threshold. Our two-sex mosquito model captures the complex transmission-cycle by accounting for heterosexual transmission, multiple pregnant states for female mosquitoes, and the aquatic-life stage. We identify important dimensionless numbers and analyze the critical threshold condition for obtaining a sustained Wolbachia infection in the natural population. This threshold effect is characterized by a backward bifurcation with three coexisting equilibria of the system of differential equations: a stable disease-free equilibrium, an unstable intermediate-infection endemic equilibrium and a stable high-infection endemic equilibrium. We perform sensitivity analysis on epidemiological and environmental parameters to determine their relative importance to Wolbachia transmission and prevalence. We also compare the effectiveness of different integrated mitigation strategies and observe that the most efficient approach to establish the Wolbachia infection is to first reduce the natural mosquitoes and then release both infected males and pregnant females. The initial reduction of natural population could be accomplished by either residual spraying or ovitraps.
|
0711.4937
|
Azi Lipshtat
|
Azi Lipshtat, Sudarshan P. Purushothaman, Ravi Iyengar, Avi Ma'ayan
|
Functions of Bifans in Context of Multiple Regulatory Motifs in
Signaling Networks
|
Accepted for publication in the Biophysical Journal
| null |
10.1529/biophysj.107.116673
| null |
q-bio.MN q-bio.QM q-bio.SC
| null |
Representation of intracellular signaling networks as directed graphs allows
for the identification of regulatory motifs. Regulatory motifs are groups of
nodes with the same connectivity structure, capable of processing information.
The bifan motif, made of two source nodes directly cross-regulating two target
nodes, is an over-represented motif in a mammalian cell signaling network and
in transcriptional networks. One example of a bifan is the two MAP-kinases, p38
and JNK that phosphorylate and activate the two transcription factors ATF2 and
Elk-1. We have used a system of coupled ordinary differential equations to
analyze the regulatory capability of this bifan motif by itself, and when it
interacts with other motifs such as positive and negative feedback loops. Our
results indicate that bifans provide temporal regulation of signal propagation
and act as signal sorters, filters, and synchronizers. Bifans that have OR gate
configurations show rapid responses while AND gate bifans can introduce delays
and allow prolongation of signal outputs. Bifans that are AND gates can filter
noisy signal inputs. The p38/JNK-ATF2/Elk-1bifan synchronizes the output of
activated transcription factors. Synchronization is a robust property of bifans
and is exhibited even when the bifan is adjacent to a positive feedback loop.
The presence of the bifan promotes the transcription and translation of the
dual specificity protein phosphatase MKP-1 that inhibits p38 and JNK thus
enabling a negative feedback loop. These results indicate that bifan motifs in
cell signaling networks can contribute to signal processing capability both
intrinsically and by enabling the functions of other regulatory motifs.
|
[
{
"created": "Fri, 30 Nov 2007 14:15:37 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Lipshtat",
"Azi",
""
],
[
"Purushothaman",
"Sudarshan P.",
""
],
[
"Iyengar",
"Ravi",
""
],
[
"Ma'ayan",
"Avi",
""
]
] |
Representation of intracellular signaling networks as directed graphs allows for the identification of regulatory motifs. Regulatory motifs are groups of nodes with the same connectivity structure, capable of processing information. The bifan motif, made of two source nodes directly cross-regulating two target nodes, is an over-represented motif in a mammalian cell signaling network and in transcriptional networks. One example of a bifan is the two MAP-kinases, p38 and JNK that phosphorylate and activate the two transcription factors ATF2 and Elk-1. We have used a system of coupled ordinary differential equations to analyze the regulatory capability of this bifan motif by itself, and when it interacts with other motifs such as positive and negative feedback loops. Our results indicate that bifans provide temporal regulation of signal propagation and act as signal sorters, filters, and synchronizers. Bifans that have OR gate configurations show rapid responses while AND gate bifans can introduce delays and allow prolongation of signal outputs. Bifans that are AND gates can filter noisy signal inputs. The p38/JNK-ATF2/Elk-1bifan synchronizes the output of activated transcription factors. Synchronization is a robust property of bifans and is exhibited even when the bifan is adjacent to a positive feedback loop. The presence of the bifan promotes the transcription and translation of the dual specificity protein phosphatase MKP-1 that inhibits p38 and JNK thus enabling a negative feedback loop. These results indicate that bifan motifs in cell signaling networks can contribute to signal processing capability both intrinsically and by enabling the functions of other regulatory motifs.
|
2401.14651
|
Swadesh Pal
|
Swadesh Pal and Roderick Melnik
|
Nonlocal Models in Biology and Life Sciences: Sources, Developments, and
Applications
|
71 pages
| null | null | null |
q-bio.QM math-ph math.DS math.MP
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Nonlocality is important in realistic mathematical models of physical and
biological systems at small-length scales. It characterizes the properties of
two individuals located in different locations. This review illustrates
different nonlocal mathematical models applied to biology and life sciences.
The major focus has been given to sources, developments, and applications of
such models. Among other things, a systematic discussion has been provided for
the conditions of pattern formations in biological systems of population
dynamics. Special attention has also been given to nonlocal interactions on
networks, network coupling and integration, including models for brain dynamics
that provide us with an important tool to better understand neurodegenerative
diseases. In addition, we have discussed nonlocal modelling approaches for
cancer stem cells and tumor cells that are widely applied in the cell migration
processes, growth, and avascular tumors in any organ. Furthermore, the
discussed nonlocal continuum models can go sufficiently smaller scales applied
to nanotechnology to build biosensors to sense biomaterial and its
concentration. Piezoelectric and other smart materials are among them, and
these devices are becoming increasingly important in the digital and physical
world that is intrinsically interconnected with biological systems.
Additionally, we have reviewed a nonlocal theory of peridynamics, which deals
with continuous and discrete media and applies to model the relationship
between fracture and healing in cortical bone, tissue growth and shrinkage, and
other areas increasingly important in biomedical and bioengineering
applications. Finally, we provided a comprehensive summary of emerging trends
and highlighted future directions in this rapidly expanding field.
|
[
{
"created": "Fri, 26 Jan 2024 05:17:48 GMT",
"version": "v1"
}
] |
2024-01-29
|
[
[
"Pal",
"Swadesh",
""
],
[
"Melnik",
"Roderick",
""
]
] |
Nonlocality is important in realistic mathematical models of physical and biological systems at small-length scales. It characterizes the properties of two individuals located in different locations. This review illustrates different nonlocal mathematical models applied to biology and life sciences. The major focus has been given to sources, developments, and applications of such models. Among other things, a systematic discussion has been provided for the conditions of pattern formations in biological systems of population dynamics. Special attention has also been given to nonlocal interactions on networks, network coupling and integration, including models for brain dynamics that provide us with an important tool to better understand neurodegenerative diseases. In addition, we have discussed nonlocal modelling approaches for cancer stem cells and tumor cells that are widely applied in the cell migration processes, growth, and avascular tumors in any organ. Furthermore, the discussed nonlocal continuum models can go sufficiently smaller scales applied to nanotechnology to build biosensors to sense biomaterial and its concentration. Piezoelectric and other smart materials are among them, and these devices are becoming increasingly important in the digital and physical world that is intrinsically interconnected with biological systems. Additionally, we have reviewed a nonlocal theory of peridynamics, which deals with continuous and discrete media and applies to model the relationship between fracture and healing in cortical bone, tissue growth and shrinkage, and other areas increasingly important in biomedical and bioengineering applications. Finally, we provided a comprehensive summary of emerging trends and highlighted future directions in this rapidly expanding field.
|
1502.07077
|
Flavio Coelho
|
Joyce de Figueir\'o Santos, Fl\'avio Code\c{c}o Coelho, Pierre
Alexandre Bliman
|
Behavioral modulation of the coexistence between Apis melifera and
Varroa destructor: A defense against colony colapse disorder?
| null | null |
10.1371/journal.pone.0160465
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Colony Collapse Disorder has become a global problem for beekeepers and for
the crops which depend on bee polination. Multiple factors are known to
increase the risk of colony colapse, and the ectoparasitic mite Varroa
destructor that parasitizes honey bees is among the main threats to colony
health. Although this mite is unlikely to, by itself, cause the collapse of
hives, it plays an important role as it is a vector for many viral diseases.
Such diseases are among the likely causes for Colony Collapse Disorder.
The effects of V. destructor infestation are disparate in different parts of
the world. Greater morbidity - in the form of colony losses - has been reported
in colonies of European honey bees (EHB) in Europe, Asia and North America.
However, this mite has been present in Brasil for many years and yet there are
no reports of Africanized honey bee (AHB) colonies losses.
Studies carried out in Mexico showed that some resistance behaviors to the
mite - especially grooming and hygienic behavior - appear to be different in
each subspecies. Could those difference in behaviors explain why the AHB are
less susceptible to Colony Collapse Disorder?
In order to answer this question, we propose a mathematical model of the
coexistence dynamics of these two species, the bee and the mite, to analyze the
role of resistance behaviors in the overall health of the colony, and, as a
consequence, its ability to face epidemiological challenges.
|
[
{
"created": "Wed, 25 Feb 2015 08:17:20 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Feb 2015 09:42:26 GMT",
"version": "v2"
}
] |
2020-10-02
|
[
[
"Santos",
"Joyce de Figueiró",
""
],
[
"Coelho",
"Flávio Codeço",
""
],
[
"Bliman",
"Pierre Alexandre",
""
]
] |
Colony Collapse Disorder has become a global problem for beekeepers and for the crops which depend on bee polination. Multiple factors are known to increase the risk of colony colapse, and the ectoparasitic mite Varroa destructor that parasitizes honey bees is among the main threats to colony health. Although this mite is unlikely to, by itself, cause the collapse of hives, it plays an important role as it is a vector for many viral diseases. Such diseases are among the likely causes for Colony Collapse Disorder. The effects of V. destructor infestation are disparate in different parts of the world. Greater morbidity - in the form of colony losses - has been reported in colonies of European honey bees (EHB) in Europe, Asia and North America. However, this mite has been present in Brasil for many years and yet there are no reports of Africanized honey bee (AHB) colonies losses. Studies carried out in Mexico showed that some resistance behaviors to the mite - especially grooming and hygienic behavior - appear to be different in each subspecies. Could those difference in behaviors explain why the AHB are less susceptible to Colony Collapse Disorder? In order to answer this question, we propose a mathematical model of the coexistence dynamics of these two species, the bee and the mite, to analyze the role of resistance behaviors in the overall health of the colony, and, as a consequence, its ability to face epidemiological challenges.
|
1802.09131
|
Tristan Sharp
|
Tristan A. Sharp, Matthias Merkel, M. Lisa Manning, Andrea J. Liu
|
Statistical properties of 3D cell geometry from 2D slices
| null | null |
10.1371/journal.pone.0209892
| null |
q-bio.QM physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although cell shape can reflect the mechanical and biochemical properties of
the cell and its environment, quantification of 3D cell shapes within 3D
tissues remains difficult, typically requiring digital reconstruction from a
stack of 2D images. We investigate a simple alternative technique to extract
information about the 3D shapes of cells in a tissue; this technique connects
the ensemble of 3D shapes in the tissue with the distribution of 2D shapes
observed in independent 2D slices. Using cell vertex model geometries, we find
that the distribution of 2D shapes allows clear determination of the mean value
of a 3D shape index. We analyze the errors that may arise in practice in the
estimation of the mean 3D shape index from 2D imagery and find that typically
only a few dozen cells in 2D imagery are required to reduce uncertainty below
2\%. This framework could be naturally extended to estimate additional 3D
geometric features and quantify their uncertainty in other materials.
|
[
{
"created": "Mon, 26 Feb 2018 02:12:39 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Jun 2018 02:04:58 GMT",
"version": "v2"
}
] |
2019-03-06
|
[
[
"Sharp",
"Tristan A.",
""
],
[
"Merkel",
"Matthias",
""
],
[
"Manning",
"M. Lisa",
""
],
[
"Liu",
"Andrea J.",
""
]
] |
Although cell shape can reflect the mechanical and biochemical properties of the cell and its environment, quantification of 3D cell shapes within 3D tissues remains difficult, typically requiring digital reconstruction from a stack of 2D images. We investigate a simple alternative technique to extract information about the 3D shapes of cells in a tissue; this technique connects the ensemble of 3D shapes in the tissue with the distribution of 2D shapes observed in independent 2D slices. Using cell vertex model geometries, we find that the distribution of 2D shapes allows clear determination of the mean value of a 3D shape index. We analyze the errors that may arise in practice in the estimation of the mean 3D shape index from 2D imagery and find that typically only a few dozen cells in 2D imagery are required to reduce uncertainty below 2\%. This framework could be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.
|
1901.00782
|
Julien Renoult
|
Julien P. Renoult and Tamra C. Mendelson
|
Processing Bias: Extending Sensory Drive to Include Efficacy and
Efficiency in Information Processing
| null | null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
Communication signals often comprise an array of colors, lines, spots, notes
or odors that are arranged in complex patterns, melodies or blends. Receiver
perception is assumed to influence preference and thus the evolution of signal
design, but evolutionary biologists still struggle to understand how
perception, preference, and signal design are mechanistically linked. In
parallel, the field of empirical aesthetics aims to understand why people like
some designs more than others. The model of processing bias discussed here is
rooted in empirical aesthetics, which posits that preferences are influenced by
the emotional system as it monitors the dynamics of information processing, and
that attractive signals have either effective designs that maximize information
transmission, efficient designs that allow information processing at low
metabolic cost, or both. We refer to the causal link between preference and the
emotionally rewarding experience of effective and efficient information
processing as the processing bias, and we apply it to the evolutionary model of
sensory drive. A sensory drive model that incorporates processing bias
hypothesizes a causal chain of relationships between the environment,
perception, pleasure, preference, and ultimately the evolution of signal
design, from simple to complex.
|
[
{
"created": "Thu, 3 Jan 2019 15:08:55 GMT",
"version": "v1"
}
] |
2019-01-04
|
[
[
"Renoult",
"Julien P.",
""
],
[
"Mendelson",
"Tamra C.",
""
]
] |
Communication signals often comprise an array of colors, lines, spots, notes or odors that are arranged in complex patterns, melodies or blends. Receiver perception is assumed to influence preference and thus the evolution of signal design, but evolutionary biologists still struggle to understand how perception, preference, and signal design are mechanistically linked. In parallel, the field of empirical aesthetics aims to understand why people like some designs more than others. The model of processing bias discussed here is rooted in empirical aesthetics, which posits that preferences are influenced by the emotional system as it monitors the dynamics of information processing, and that attractive signals have either effective designs that maximize information transmission, efficient designs that allow information processing at low metabolic cost, or both. We refer to the causal link between preference and the emotionally rewarding experience of effective and efficient information processing as the processing bias, and we apply it to the evolutionary model of sensory drive. A sensory drive model that incorporates processing bias hypothesizes a causal chain of relationships between the environment, perception, pleasure, preference, and ultimately the evolution of signal design, from simple to complex.
|
1202.4335
|
Andr\'es Chavarr\'ia-Krauser
|
Juliane Claus and Andr\'es Chavarr\'ia-Krauser
|
Modeling Regulation of Zinc Uptake via ZIP Transporters in Yeast and
Plant Roots
|
23 pages including 2 tables and 7 figures
| null |
10.1371/journal.pone.0037193
| null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In yeast (Saccharomyces cerevisiae) and plant roots (Arabidopsis thaliana)
zinc enters the cells via influx transporters of the ZIP family. Since zinc is
both essential for cell function and toxic at high concentrations, tight
regulation is essential for cell viability. We provide new insight into the
underlying mechanisms, starting from a general model based on ordinary
differential equations and adapting it to the specific cases of yeast and plant
root cells. In yeast, zinc is transported by the transporters ZRT1 and ZRT2,
which are both regulated by the zinc-responsive transcription factor ZAP1.
Using biological data, parameters were estimated and analyzed, confirming the
different affinities of ZRT1 and ZRT2 reported in the literature. Furthermore,
our model suggests that the positive feedback in ZAP1 production has a
stabilizing function at high influx rates. In plant roots, various ZIP
transporters are involved in zinc uptake. Their regulation is largely unknown,
but bZIP transcription factors are thought to be involved. We set up three
putative models: activator only, activator with dimerization and
activator/inhibitor. These were fitted to measurements and analyzed.
Simulations show that the activator/inhibitor model outperforms the other two
in providing robust and stable homeostasis at reasonable parameter ranges.
|
[
{
"created": "Mon, 20 Feb 2012 14:26:48 GMT",
"version": "v1"
}
] |
2015-06-04
|
[
[
"Claus",
"Juliane",
""
],
[
"Chavarría-Krauser",
"Andrés",
""
]
] |
In yeast (Saccharomyces cerevisiae) and plant roots (Arabidopsis thaliana) zinc enters the cells via influx transporters of the ZIP family. Since zinc is both essential for cell function and toxic at high concentrations, tight regulation is essential for cell viability. We provide new insight into the underlying mechanisms, starting from a general model based on ordinary differential equations and adapting it to the specific cases of yeast and plant root cells. In yeast, zinc is transported by the transporters ZRT1 and ZRT2, which are both regulated by the zinc-responsive transcription factor ZAP1. Using biological data, parameters were estimated and analyzed, confirming the different affinities of ZRT1 and ZRT2 reported in the literature. Furthermore, our model suggests that the positive feedback in ZAP1 production has a stabilizing function at high influx rates. In plant roots, various ZIP transporters are involved in zinc uptake. Their regulation is largely unknown, but bZIP transcription factors are thought to be involved. We set up three putative models: activator only, activator with dimerization and activator/inhibitor. These were fitted to measurements and analyzed. Simulations show that the activator/inhibitor model outperforms the other two in providing robust and stable homeostasis at reasonable parameter ranges.
|
2405.04734
|
Erin Gill
|
Erin E. Gill, Baofeng Jia, Carmen Lia Murall, Rapha\"el Poujol,
Muhammad Zohaib Anwar, Nithu Sara John, Justin Richardsson, Ashley Hobb,
Abayomi S. Olabode, Alexandru Lepsa, Ana T. Duggan, Andrea D. Tyler, Arnaud
N'Guessan, Atul Kachru, Brandon Chan, Catherine Yoshida, Christina K. Yung,
David Bujold, Dusan Andric, Edmund Su, Emma J. Griffiths, Gary Van Domselaar,
Gordon W. Jolly, Heather K.E. Ward, Henrich Feher, Jared Baker, Jared T.
Simpson, Jaser Uddin, Jiannis Ragoussis, Jon Eubank, J\"org H. Fritz, Jos\'e
H\'ector G\'alvez, Karen Fang, Kim Cullion, Leonardo Rivera, Linda Xiang,
Matthew A. Croxen, Mitchell Shiell, Natalie Prystajecky, Pierre-Olivier
Quirion, Rosita Bajari, Samantha Rich, Samira Mubareka, Sandrine Moreira,
Scott Cain, Steven G. Sutcliffe, Susanne A. Kraemer, Yann Joly, Yelizar
Alturmessov, CPHLN consortium, CanCOGeN consortium, VirusSeq Data Portal
Academic and Health network, Marc Fiume, Terrance P. Snutch, Cindy Bell,
Catalina Lopez-Correa, Julie G. Hussin, Jeffrey B. Joy, Caroline Colijn, Paul
M.K. Gordon, William W.L. Hsiao, Art F.Y. Poon, Natalie C. Knox, M\'elanie
Courtot, Lincoln Stein, Sarah P. Otto, Guillaume Bourque, B. Jesse Shapiro,
Fiona S.L. Brinkman
|
The Canadian VirusSeq Data Portal & Duotang: open resources for
SARS-CoV-2 viral sequences and genomic epidemiology
| null | null | null | null |
q-bio.GN
|
http://creativecommons.org/licenses/by/4.0/
|
The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2
genomes from patient samples to track viral evolution and inform public health
response. Millions of SARS-CoV-2 genome sequences have been deposited in global
public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN -
VirusSeq), a consortium tasked with coordinating expanded sequencing of
SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian
VirusSeq Data Portal, with associated data pipelines and procedures, to support
these efforts. The goal of VirusSeq was to allow open access to Canadian
SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that
were unavailable in other repositories and that meet FAIR standards (Findable,
Accessible, Interoperable and Reusable). The Portal data submission pipeline
contains data quality checking procedures and appropriate acknowledgement of
data generators that encourages collaboration. Here we also highlight Duotang,
a web platform that presents genomic epidemiology and modeling analyses on
circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents
dynamic changes in variant composition of SARS-CoV-2 in Canada and by province,
estimates variant growth, and displays complementary interactive
visualizations, with a text overview of the current situation. The VirusSeq
Data Portal and Duotang resources, alongside additional analyses and resources
computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely
available. Together, they provide an updated picture of SARS-CoV-2 evolution to
spur scientific discussions, inform public discourse, and support communication
with and within public health authorities. They also serve as a framework for
other jurisdictions interested in open, collaborative sequence data sharing and
analyses.
|
[
{
"created": "Wed, 8 May 2024 00:50:35 GMT",
"version": "v1"
}
] |
2024-05-09
|
[
[
"Gill",
"Erin E.",
""
],
[
"Jia",
"Baofeng",
""
],
[
"Murall",
"Carmen Lia",
""
],
[
"Poujol",
"Raphaël",
""
],
[
"Anwar",
"Muhammad Zohaib",
""
],
[
"John",
"Nithu Sara",
""
],
[
"Richardsson",
"Justin",
""
],
[
"Hobb",
"Ashley",
""
],
[
"Olabode",
"Abayomi S.",
""
],
[
"Lepsa",
"Alexandru",
""
],
[
"Duggan",
"Ana T.",
""
],
[
"Tyler",
"Andrea D.",
""
],
[
"N'Guessan",
"Arnaud",
""
],
[
"Kachru",
"Atul",
""
],
[
"Chan",
"Brandon",
""
],
[
"Yoshida",
"Catherine",
""
],
[
"Yung",
"Christina K.",
""
],
[
"Bujold",
"David",
""
],
[
"Andric",
"Dusan",
""
],
[
"Su",
"Edmund",
""
],
[
"Griffiths",
"Emma J.",
""
],
[
"Van Domselaar",
"Gary",
""
],
[
"Jolly",
"Gordon W.",
""
],
[
"Ward",
"Heather K. E.",
""
],
[
"Feher",
"Henrich",
""
],
[
"Baker",
"Jared",
""
],
[
"Simpson",
"Jared T.",
""
],
[
"Uddin",
"Jaser",
""
],
[
"Ragoussis",
"Jiannis",
""
],
[
"Eubank",
"Jon",
""
],
[
"Fritz",
"Jörg H.",
""
],
[
"Gálvez",
"José Héctor",
""
],
[
"Fang",
"Karen",
""
],
[
"Cullion",
"Kim",
""
],
[
"Rivera",
"Leonardo",
""
],
[
"Xiang",
"Linda",
""
],
[
"Croxen",
"Matthew A.",
""
],
[
"Shiell",
"Mitchell",
""
],
[
"Prystajecky",
"Natalie",
""
],
[
"Quirion",
"Pierre-Olivier",
""
],
[
"Bajari",
"Rosita",
""
],
[
"Rich",
"Samantha",
""
],
[
"Mubareka",
"Samira",
""
],
[
"Moreira",
"Sandrine",
""
],
[
"Cain",
"Scott",
""
],
[
"Sutcliffe",
"Steven G.",
""
],
[
"Kraemer",
"Susanne A.",
""
],
[
"Joly",
"Yann",
""
],
[
"Alturmessov",
"Yelizar",
""
],
[
"consortium",
"CPHLN",
""
],
[
"consortium",
"CanCOGeN",
""
],
[
"Academic",
"VirusSeq Data Portal",
""
],
[
"network",
"Health",
""
],
[
"Fiume",
"Marc",
""
],
[
"Snutch",
"Terrance P.",
""
],
[
"Bell",
"Cindy",
""
],
[
"Lopez-Correa",
"Catalina",
""
],
[
"Hussin",
"Julie G.",
""
],
[
"Joy",
"Jeffrey B.",
""
],
[
"Colijn",
"Caroline",
""
],
[
"Gordon",
"Paul M. K.",
""
],
[
"Hsiao",
"William W. L.",
""
],
[
"Poon",
"Art F. Y.",
""
],
[
"Knox",
"Natalie C.",
""
],
[
"Courtot",
"Mélanie",
""
],
[
"Stein",
"Lincoln",
""
],
[
"Otto",
"Sarah P.",
""
],
[
"Bourque",
"Guillaume",
""
],
[
"Shapiro",
"B. Jesse",
""
],
[
"Brinkman",
"Fiona S. L.",
""
]
] |
The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.
|
2408.07110
|
Julian Suk
|
Julian Suk, Dieuwertje Alblas, Barbara A. Hutten, Albert Wiegman,
Christoph Brune, Pim van Ooij and Jelmer M. Wolterink
|
Physics-informed graph neural networks for flow field estimation in
carotid arteries
|
Preprint. Under Review
| null | null | null |
q-bio.QM cs.LG physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
Hemodynamic quantities are valuable biomedical risk factors for
cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo
measurement of these quantities can only be performed using a select number of
modalities that are not widely available, such as 4D flow magnetic resonance
imaging (MRI). In this work, we create a surrogate model for hemodynamic flow
field estimation, powered by machine learning. We train graph neural networks
that include priors about the underlying symmetries and physics, limiting the
amount of data required for training. This allows us to train the model using
moderately-sized, in-vivo 4D flow MRI datasets, instead of large in-silico
datasets obtained by computational fluid dynamics (CFD), as is the current
standard. We create an efficient, equivariant neural network by combining the
popular PointNet++ architecture with group-steerable layers. To incorporate the
physics-informed priors, we derive an efficient discretisation scheme for the
involved differential operators. We perform extensive experiments in carotid
arteries and show that our model can accurately estimate low-noise hemodynamic
flow fields in the carotid artery. Moreover, we show how the learned relation
between geometry and hemodynamic quantities transfers to 3D vascular models
obtained using a different imaging modality than the training data. This shows
that physics-informed graph neural networks can be trained using 4D flow MRI
data to estimate blood flow in unseen carotid artery geometries.
|
[
{
"created": "Tue, 13 Aug 2024 13:09:28 GMT",
"version": "v1"
}
] |
2024-08-15
|
[
[
"Suk",
"Julian",
""
],
[
"Alblas",
"Dieuwertje",
""
],
[
"Hutten",
"Barbara A.",
""
],
[
"Wiegman",
"Albert",
""
],
[
"Brune",
"Christoph",
""
],
[
"van Ooij",
"Pim",
""
],
[
"Wolterink",
"Jelmer M.",
""
]
] |
Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be performed using a select number of modalities that are not widely available, such as 4D flow magnetic resonance imaging (MRI). In this work, we create a surrogate model for hemodynamic flow field estimation, powered by machine learning. We train graph neural networks that include priors about the underlying symmetries and physics, limiting the amount of data required for training. This allows us to train the model using moderately-sized, in-vivo 4D flow MRI datasets, instead of large in-silico datasets obtained by computational fluid dynamics (CFD), as is the current standard. We create an efficient, equivariant neural network by combining the popular PointNet++ architecture with group-steerable layers. To incorporate the physics-informed priors, we derive an efficient discretisation scheme for the involved differential operators. We perform extensive experiments in carotid arteries and show that our model can accurately estimate low-noise hemodynamic flow fields in the carotid artery. Moreover, we show how the learned relation between geometry and hemodynamic quantities transfers to 3D vascular models obtained using a different imaging modality than the training data. This shows that physics-informed graph neural networks can be trained using 4D flow MRI data to estimate blood flow in unseen carotid artery geometries.
|
0805.3588
|
Vladimir Ivancevic
|
Vladimir G. Ivancevic
|
Human-Robot Biodynamics
|
20 pages, 11 figures, Latex
| null | null | null |
q-bio.OT q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents the scientific body of knowledge behind the Human
Biodynamics Engine (HBE), a human motion simulator developed on the concept of
Euclidean motion group SE(3), with 270 active degrees of freedom,
force-velocity-time muscular mechanics and two-level neural control -
formulated in the fashion of nonlinear humanoid robotics. The following aspects
of the HBE development are described: geometrical, dynamical, control,
physiological, AI, behavioral and complexity, together with several simulation
examples.
Keywords: Human Biodynamics Engine, Euclidean SE(3)-group,
Lagrangian/Hamiltonian biodynamics, Lie-derivative control, muscular mechanics,
fuzzy-topological coordination, biodynamical complexity, validation,
application
|
[
{
"created": "Fri, 23 May 2008 06:55:30 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Jun 2008 02:51:28 GMT",
"version": "v2"
},
{
"created": "Fri, 18 Jul 2008 05:05:47 GMT",
"version": "v3"
}
] |
2008-07-18
|
[
[
"Ivancevic",
"Vladimir G.",
""
]
] |
This paper presents the scientific body of knowledge behind the Human Biodynamics Engine (HBE), a human motion simulator developed on the concept of Euclidean motion group SE(3), with 270 active degrees of freedom, force-velocity-time muscular mechanics and two-level neural control - formulated in the fashion of nonlinear humanoid robotics. The following aspects of the HBE development are described: geometrical, dynamical, control, physiological, AI, behavioral and complexity, together with several simulation examples. Keywords: Human Biodynamics Engine, Euclidean SE(3)-group, Lagrangian/Hamiltonian biodynamics, Lie-derivative control, muscular mechanics, fuzzy-topological coordination, biodynamical complexity, validation, application
|
q-bio/0506042
|
Van M. Savage jr
|
Van M. Savage and Geoffrey B. West
|
Towards a Quantitative, Metabolic Theory for Mammalian Sleep
|
20 pages, 4 figures
| null | null | null |
q-bio.OT
| null |
Sleep is one of the most noticeable and widespread phenomena occurring in
multicellular animals. Nevertheless, no consensus for a theory of its origins
has emerged. In particular, no explicit, quantitative theory exists that
elucidates or distinguishes between the myriad hypotheses proposed for sleep.
Here, we develop a general, quantitative theory for mammalian sleep that
relates many of its fundamental parameters to metabolic rate and body size.
Most mechanisms suggested for the function of sleep can be placed in this
framework, e.g., cellular repair of damage caused by metabolic processes and
cortical reorganization to process sensory input. Our theory leads to
predictions for sleep time, sleep cycle time, and REM (rapid-eye-movement) time
as functions of body and brain mass, and explains, for example, why mice sleep
\~14 hours per day relative to the 3.5 hours per day that elephants sleep. Data
for 96 species of mammals, spanning six orders of magnitude in body size, are
consistent with these predictions and provide strong evidence that time scales
for sleep are set by the brain's, not the whole-body, metabolic rate.
|
[
{
"created": "Thu, 30 Jun 2005 04:34:16 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Savage",
"Van M.",
""
],
[
"West",
"Geoffrey B.",
""
]
] |
Sleep is one of the most noticeable and widespread phenomena occurring in multicellular animals. Nevertheless, no consensus for a theory of its origins has emerged. In particular, no explicit, quantitative theory exists that elucidates or distinguishes between the myriad hypotheses proposed for sleep. Here, we develop a general, quantitative theory for mammalian sleep that relates many of its fundamental parameters to metabolic rate and body size. Most mechanisms suggested for the function of sleep can be placed in this framework, e.g., cellular repair of damage caused by metabolic processes and cortical reorganization to process sensory input. Our theory leads to predictions for sleep time, sleep cycle time, and REM (rapid-eye-movement) time as functions of body and brain mass, and explains, for example, why mice sleep \~14 hours per day relative to the 3.5 hours per day that elephants sleep. Data for 96 species of mammals, spanning six orders of magnitude in body size, are consistent with these predictions and provide strong evidence that time scales for sleep are set by the brain's, not the whole-body, metabolic rate.
|
1801.01856
|
Glenn Webb Dr
|
Pierre Magal, Glenn F. Webb, and Yixiang Wu
|
Spatial Spread of Epidemic Diseases in Geographical Settings: Seasonal
Influenza Epidemics in Puerto Rico
|
18 pages, 13 figures
| null | null | null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deterministic models are developed for the spatial spread of epidemic
diseases in geographical settings. The models are focused on outbreaks that
arise from a small number of infected hosts imported into sub-regions of the
geographical settings. The goal is to understand how spatial heterogeneity
influences the transmission dynamics of the susceptible and infected
populations. The models consist of systems of partial differential equations
with diffusion terms describing the spatial spread of the underlying microbial
infectious agents. The model is compared with real data from seasonal influenza
epidemics in Puerto Rico.
|
[
{
"created": "Fri, 5 Jan 2018 18:05:14 GMT",
"version": "v1"
}
] |
2018-01-08
|
[
[
"Magal",
"Pierre",
""
],
[
"Webb",
"Glenn F.",
""
],
[
"Wu",
"Yixiang",
""
]
] |
Deterministic models are developed for the spatial spread of epidemic diseases in geographical settings. The models are focused on outbreaks that arise from a small number of infected hosts imported into sub-regions of the geographical settings. The goal is to understand how spatial heterogeneity influences the transmission dynamics of the susceptible and infected populations. The models consist of systems of partial differential equations with diffusion terms describing the spatial spread of the underlying microbial infectious agents. The model is compared with real data from seasonal influenza epidemics in Puerto Rico.
|
2102.04301
|
Khaled Khleifat Dr
|
H.A Qaralleh, M.O. Al-Limoun, A. Khlaifat, K.M. Khleifat, N.
Al-Tawarah, K.Y. Alsharafa, H.A. Abu-Harirah
|
Antibacterial and antibiofilm activities of a traditional herbal formula
against respiratory infection causing bacteria
|
8 pages, 4 tables, 2 figures
|
Antibacterial and Antibiofilm Activities of a Traditional Herbal
Formula against Respiratory Infection Causing Bacteria. Trop J Nat Prod Res.
2020; 4(9):527-534
|
10.26538/tjnpr/v4i9.6
| null |
q-bio.OT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The plants, Althaea officinalis, Tilia cordata and Psidium guaja have been
used traditionally to treat respiratory infection symptoms. Flowers of A.
officinalis and leaves of T. cordata and P. guaja have been used to treat
cough, sore throat, catarrh, oral and pharyngeal mucosa irritation. Therefore,
this study was designed to examine the antibacterial and antibiofilm effects of
these plants individually as well as in combination, as a formula against
respiratory infections causing pathogens. The tested pathogens were Extended
Spectrum Beta-Lactamase producing Escherichia coli (ESBL), Beta-Lactamase
producing Escherichia coli (BL), Beta-Lactamase producing Klebsiella pneumoniae
(BL), Beta-Lactamase producing Pseudomonas aeruginosa (BL), Enterobacter
cloacae, and Beta-Lactamase producing Staphylococcus aureus (BL). The tested
plants were extracted using ethanol and then fractionated using different
polarity solvents (hexane, ethyl acetate and water). Disc diffusion and
microdilution (Minimum Inhibitory Concentration) methods were used to evaluate
the antibacterial activity while the antibiofilm activity was tested using
crystal violet assay. The results showed that A. officinalis and T. cordata
extracts and fractions exhibited weak antibacterial activity having MIC values
ranged from 6.25 to 12.5 mg/mL. P. guaja exhibited moderate antibacterial
activity with MIC values ranged from 6.25 to 1.56 mg/mL. Combination between
these plants extracts and fractions in equal proportion provides stronger
antibacterial (with MIC values ranged from 6.25 to 0.8 mg/mL) and antibiofilm
activities (MBIC50 was 0.2 mg/mL). Therefore, this study provides a valuable
scientific knowledge to support the use of plants in combination rather than
individually.
|
[
{
"created": "Tue, 19 Jan 2021 20:34:55 GMT",
"version": "v1"
}
] |
2021-02-09
|
[
[
"Qaralleh",
"H. A",
""
],
[
"Al-Limoun",
"M. O.",
""
],
[
"Khlaifat",
"A.",
""
],
[
"Khleifat",
"K. M.",
""
],
[
"Al-Tawarah",
"N.",
""
],
[
"Alsharafa",
"K. Y.",
""
],
[
"Abu-Harirah",
"H. A.",
""
]
] |
The plants, Althaea officinalis, Tilia cordata and Psidium guaja have been used traditionally to treat respiratory infection symptoms. Flowers of A. officinalis and leaves of T. cordata and P. guaja have been used to treat cough, sore throat, catarrh, oral and pharyngeal mucosa irritation. Therefore, this study was designed to examine the antibacterial and antibiofilm effects of these plants individually as well as in combination, as a formula against respiratory infections causing pathogens. The tested pathogens were Extended Spectrum Beta-Lactamase producing Escherichia coli (ESBL), Beta-Lactamase producing Escherichia coli (BL), Beta-Lactamase producing Klebsiella pneumoniae (BL), Beta-Lactamase producing Pseudomonas aeruginosa (BL), Enterobacter cloacae, and Beta-Lactamase producing Staphylococcus aureus (BL). The tested plants were extracted using ethanol and then fractionated using different polarity solvents (hexane, ethyl acetate and water). Disc diffusion and microdilution (Minimum Inhibitory Concentration) methods were used to evaluate the antibacterial activity while the antibiofilm activity was tested using crystal violet assay. The results showed that A. officinalis and T. cordata extracts and fractions exhibited weak antibacterial activity having MIC values ranged from 6.25 to 12.5 mg/mL. P. guaja exhibited moderate antibacterial activity with MIC values ranged from 6.25 to 1.56 mg/mL. Combination between these plants extracts and fractions in equal proportion provides stronger antibacterial (with MIC values ranged from 6.25 to 0.8 mg/mL) and antibiofilm activities (MBIC50 was 0.2 mg/mL). Therefore, this study provides a valuable scientific knowledge to support the use of plants in combination rather than individually.
|
1603.03752
|
Luca Ferreri
|
Luca Ferreri and Paolo Bajardi and Mario Giacobini
|
Non-systemic transmission of tick-borne diseases: a network approach
|
15 pages, 4 figures, to be published in Communications in Nonlinear
Science and Numerical Simulation
|
Communications in Nonlinear Science and Numerical Simulations,
Volume 39, October 2016, pages 149-155
|
10.1016/j.cnsns.2016.02.034
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tick-Borne diseases can be transmitted via non-systemic (NS) transmission.
This occurs when tick gets the infection by co-feeding with infected ticks on
the same host resulting in a direct pathogen transmission between the vectors,
without infecting the host. This transmission is peculiar, as it does not
require any systemic infection of the host. The NS transmission is the main
efficient transmission for the persistence of the Tick-Borne Encephalitis virus
in nature. By describing the heterogeneous ticks aggregation on hosts through a
\hyphenation{dynamical} bipartite graphs representation, we are able to
mathematically define the NS transmission and to depict the epidemiological
conditions for the pathogen persistence. Despite the fact that the underlying
network is largely fragmented, analytical and computational results show that
the larger is the variability of the aggregation, and the easier is for the
pathogen to persist in the population.
|
[
{
"created": "Fri, 11 Mar 2016 20:38:14 GMT",
"version": "v1"
}
] |
2017-07-11
|
[
[
"Ferreri",
"Luca",
""
],
[
"Bajardi",
"Paolo",
""
],
[
"Giacobini",
"Mario",
""
]
] |
Tick-Borne diseases can be transmitted via non-systemic (NS) transmission. This occurs when tick gets the infection by co-feeding with infected ticks on the same host resulting in a direct pathogen transmission between the vectors, without infecting the host. This transmission is peculiar, as it does not require any systemic infection of the host. The NS transmission is the main efficient transmission for the persistence of the Tick-Borne Encephalitis virus in nature. By describing the heterogeneous ticks aggregation on hosts through a \hyphenation{dynamical} bipartite graphs representation, we are able to mathematically define the NS transmission and to depict the epidemiological conditions for the pathogen persistence. Despite the fact that the underlying network is largely fragmented, analytical and computational results show that the larger is the variability of the aggregation, and the easier is for the pathogen to persist in the population.
|
1503.02117
|
Tatiana Tatarinova
|
Konstantin Kozlov, Dmitry Chebotarov, Mehedi Hassan, Martin Triska,
Petr Triska, Pavel Flegontov, Tatiana Tatarinova
|
Differential Evolution Approach to Detect Recent Admixture
|
presented at ISMB 2014, VariSIG
|
BMC Genomics 2015, 16(Suppl 8):S9
|
10.1186/1471-2164-16-S8-S9
| null |
q-bio.GN q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The genetic structure of human populations is extraordinarily complex and of
fundamental importance to studies of anthropology, evolution, and medicine.
As increasingly many individuals are of mixed origin, there is an unmet need
for tools that can infer multiple origins. Misclassification of such
individuals can lead to incorrect and costly misinterpretations of genomic
data, primarily in disease studies and drug trials.
We present an advanced tool to infer ancestry that can identify the
biogeographic origins of highly mixed individuals.
reAdmix is an online tool available at
http://chcb.saban-chla.usc.edu/reAdmix/.
|
[
{
"created": "Sat, 7 Mar 2015 01:43:14 GMT",
"version": "v1"
}
] |
2015-08-26
|
[
[
"Kozlov",
"Konstantin",
""
],
[
"Chebotarov",
"Dmitry",
""
],
[
"Hassan",
"Mehedi",
""
],
[
"Triska",
"Martin",
""
],
[
"Triska",
"Petr",
""
],
[
"Flegontov",
"Pavel",
""
],
[
"Tatarinova",
"Tatiana",
""
]
] |
The genetic structure of human populations is extraordinarily complex and of fundamental importance to studies of anthropology, evolution, and medicine. As increasingly many individuals are of mixed origin, there is an unmet need for tools that can infer multiple origins. Misclassification of such individuals can lead to incorrect and costly misinterpretations of genomic data, primarily in disease studies and drug trials. We present an advanced tool to infer ancestry that can identify the biogeographic origins of highly mixed individuals. reAdmix is an online tool available at http://chcb.saban-chla.usc.edu/reAdmix/.
|
0904.1365
|
Simon Mitternacht
|
Anders Irb\"ack, Simon Mitternacht, Sandipan Mohanty
|
An effective all-atom potential for proteins
| null |
PMC Biophysics 2009, 2:2
| null | null |
q-bio.BM cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We describe and test an implicit solvent all-atom potential for simulations
of protein folding and aggregation. The potential is developed through studies
of structural and thermodynamic properties of 17 peptides with diverse
secondary structure. Results obtained using the final form of the potential are
presented for all these peptides. The same model, with unchanged parameters, is
furthermore applied to a heterodimeric coiled-coil system, a mixed alpha/beta
protein and a three-helix-bundle protein, with very good results. The
computational efficiency of the potential makes it possible to investigate the
free-energy landscape of these 49--67-residue systems with high statistical
accuracy, using only modest computational resources by today's standards.
|
[
{
"created": "Wed, 8 Apr 2009 16:05:27 GMT",
"version": "v1"
}
] |
2009-04-09
|
[
[
"Irbäck",
"Anders",
""
],
[
"Mitternacht",
"Simon",
""
],
[
"Mohanty",
"Sandipan",
""
]
] |
We describe and test an implicit solvent all-atom potential for simulations of protein folding and aggregation. The potential is developed through studies of structural and thermodynamic properties of 17 peptides with diverse secondary structure. Results obtained using the final form of the potential are presented for all these peptides. The same model, with unchanged parameters, is furthermore applied to a heterodimeric coiled-coil system, a mixed alpha/beta protein and a three-helix-bundle protein, with very good results. The computational efficiency of the potential makes it possible to investigate the free-energy landscape of these 49--67-residue systems with high statistical accuracy, using only modest computational resources by today's standards.
|
2303.11833
|
Hyunseung Kim
|
Hyunseung Kim (1), Haeyeon Choi (2), Dongju Kang (1), Won Bo Lee (1),
Jonggeol Na (2) ((1) Seoul National University, (2) Ewha Womans University)
|
Materials Discovery with Extreme Properties via Reinforcement
Learning-Guided Combinatorial Chemistry
|
18 pages, 8 figures
|
Chemical Science, 2024
|
10.1039/D3SC05281H
| null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The goal of most materials discovery is to discover materials that are
superior to those currently known. Fundamentally, this is close to
extrapolation, which is a weak point for most machine learning models that
learn the probability distribution of data. Herein, we develop reinforcement
learning-guided combinatorial chemistry, which is a rule-based molecular
designer driven by trained policy for selecting subsequent molecular fragments
to get a target molecule. Since our model has the potential to generate all
possible molecular structures that can be obtained from combinations of
molecular fragments, unknown molecules with superior properties can be
discovered. We theoretically and empirically demonstrate that our model is more
suitable for discovering better compounds than probability
distribution-learning models. In an experiment aimed at discovering molecules
that hit seven extreme target properties, our model discovered 1,315 of all
target-hitting molecules and 7,629 of five target-hitting molecules out of
100,000 trials, whereas the probability distribution-learning models failed.
Moreover, it has been confirmed that every molecule generated under the binding
rules of molecular fragments is 100% chemically valid. To illustrate the
performance in actual problems, we also demonstrate that our models work well
on two practical applications: discovering protein docking molecules and HIV
inhibitors.
|
[
{
"created": "Tue, 21 Mar 2023 13:21:43 GMT",
"version": "v1"
},
{
"created": "Tue, 7 May 2024 15:07:34 GMT",
"version": "v2"
}
] |
2024-05-08
|
[
[
"Kim",
"Hyunseung",
"",
"Seoul National University"
],
[
"Choi",
"Haeyeon",
"",
"Ewha Womans University"
],
[
"Kang",
"Dongju",
"",
"Seoul National University"
],
[
"Lee",
"Won Bo",
"",
"Seoul National University"
],
[
"Na",
"Jonggeol",
"",
"Ewha Womans University"
]
] |
The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop reinforcement learning-guided combinatorial chemistry, which is a rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown molecules with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better compounds than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1,315 of all target-hitting molecules and 7,629 of five target-hitting molecules out of 100,000 trials, whereas the probability distribution-learning models failed. Moreover, it has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking molecules and HIV inhibitors.
|
1206.0959
|
Mary Ann Blaetke
|
Mary Ann Bl\"atke, Anna Dittrich, Christian Rohr, Monika Heiner, Fred
Schaper, Wolfgang Marwan
|
JAK/STAT signalling - an executable model assembled from
molecule-centred modules demonstrating a module-oriented database concept for
systems- and synthetic biology
|
54 pages, 12 figures, 2 tables
| null | null | null |
q-bio.MN
|
http://creativecommons.org/licenses/by/3.0/
|
We describe a molecule-oriented modelling approach based on a collection of
Petri net models organized in the form of modules into a prototype database
accessible through a web interface. The JAK/STAT signalling pathway with the
extensive cross-talk of its components is selected as case study. Each Petri
net module represents the reactions of an individual protein with its specific
interaction partners. These Petri net modules are graphically displayed, can be
executed individually, and allow the automatic composition into coherent models
containing an arbitrary number of molecular species chosen ad hoc by the user.
Each module contains metadata for documentation purposes and can be extended to
a wiki-like minireview. The database can manage multiple versions of each
module. It supports the curation, documentation, version control, and update of
individual modules and the subsequent automatic composition of complex models,
without requiring mathematical skills. Modules can be (semi-) automatically
recombined according to user defined scenarios e.g. gene expression patterns in
given cell types, under certain physiological conditions, or states of disease.
Adding a localisation component to the module database would allow to simulate
models with spatial resolution in the form of coloured Petri nets. As synthetic
biology application we propose the fully automated generation of synthetic or
synthetically rewired network models by composition of metadata-guided
automatically modified modules representing altered protein binding sites.
Petri nets composed from modules can be executed as ODE system, stochastic,
hybrid, or merely qualitative models and exported in SMBL format.
|
[
{
"created": "Tue, 5 Jun 2012 15:21:49 GMT",
"version": "v1"
}
] |
2012-06-06
|
[
[
"Blätke",
"Mary Ann",
""
],
[
"Dittrich",
"Anna",
""
],
[
"Rohr",
"Christian",
""
],
[
"Heiner",
"Monika",
""
],
[
"Schaper",
"Fred",
""
],
[
"Marwan",
"Wolfgang",
""
]
] |
We describe a molecule-oriented modelling approach based on a collection of Petri net models organized in the form of modules into a prototype database accessible through a web interface. The JAK/STAT signalling pathway with the extensive cross-talk of its components is selected as case study. Each Petri net module represents the reactions of an individual protein with its specific interaction partners. These Petri net modules are graphically displayed, can be executed individually, and allow the automatic composition into coherent models containing an arbitrary number of molecular species chosen ad hoc by the user. Each module contains metadata for documentation purposes and can be extended to a wiki-like minireview. The database can manage multiple versions of each module. It supports the curation, documentation, version control, and update of individual modules and the subsequent automatic composition of complex models, without requiring mathematical skills. Modules can be (semi-) automatically recombined according to user defined scenarios e.g. gene expression patterns in given cell types, under certain physiological conditions, or states of disease. Adding a localisation component to the module database would allow to simulate models with spatial resolution in the form of coloured Petri nets. As synthetic biology application we propose the fully automated generation of synthetic or synthetically rewired network models by composition of metadata-guided automatically modified modules representing altered protein binding sites. Petri nets composed from modules can be executed as ODE system, stochastic, hybrid, or merely qualitative models and exported in SMBL format.
|
1901.11246
|
Michael B\"orsch
|
Anika Westphal, Hendrik Sielaff, Stefanie Reuter, Thomas Heitkamp,
Ralf Mrowka, Michael B\"orsch
|
Ligand-induced oligomerization of the human GPCR neurotensin receptor 1
monitored in living HEK293T cells
|
12 pages, 5 figures
| null | null | null |
q-bio.BM physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The human neurotensin receptor 1 (NTSR1) is a G protein-coupled receptor that
can be expressed in HEK293T cells by stable transfection. Its ligand is a
13-amino-acid peptide that binds with nanomolar affinity from the extracellular
side to NTSR1. Ligand binding induces conformational changes that trigger the
intracellular signaling processes. Recent single-molecule studies revealed a
dynamic monomer - dimer equilibrium of the receptor in vitro. Here we report on
the oligomerization state of the human NTSR1 in the plasma membrane of HEK293T
cells in vivo. We fused different fluorescent marker proteins mRuby3 or
mNeonGreen to the C-terminus of NTSR1 and mutated a tetracysteine motif into
intracellular loop 3 (ICL3) for subsequent FlAsH labeling. Oligomerization of
NTSR1 was monitored before and after stimulation of the receptor with its
ligand by FLIM and homoFRET microscopy (i.e. Forster resonance energy transfer
between identical fluorophores detected by fluorescence anisotropy), by
colocalization microscopy and by time-lapse imaging using structured
illumination microscopy (SIM).
|
[
{
"created": "Thu, 31 Jan 2019 07:38:07 GMT",
"version": "v1"
}
] |
2019-02-01
|
[
[
"Westphal",
"Anika",
""
],
[
"Sielaff",
"Hendrik",
""
],
[
"Reuter",
"Stefanie",
""
],
[
"Heitkamp",
"Thomas",
""
],
[
"Mrowka",
"Ralf",
""
],
[
"Börsch",
"Michael",
""
]
] |
The human neurotensin receptor 1 (NTSR1) is a G protein-coupled receptor that can be expressed in HEK293T cells by stable transfection. Its ligand is a 13-amino-acid peptide that binds with nanomolar affinity from the extracellular side to NTSR1. Ligand binding induces conformational changes that trigger the intracellular signaling processes. Recent single-molecule studies revealed a dynamic monomer - dimer equilibrium of the receptor in vitro. Here we report on the oligomerization state of the human NTSR1 in the plasma membrane of HEK293T cells in vivo. We fused different fluorescent marker proteins mRuby3 or mNeonGreen to the C-terminus of NTSR1 and mutated a tetracysteine motif into intracellular loop 3 (ICL3) for subsequent FlAsH labeling. Oligomerization of NTSR1 was monitored before and after stimulation of the receptor with its ligand by FLIM and homoFRET microscopy (i.e. Forster resonance energy transfer between identical fluorophores detected by fluorescence anisotropy), by colocalization microscopy and by time-lapse imaging using structured illumination microscopy (SIM).
|
1706.07789
|
Peter Hufton
|
Peter G. Hufton, Yen Ting Lin, and Tobias Galla
|
Phenotypic switching of populations of cells in a stochastic environment
|
17 pages, 6 figures
| null |
10.1088/1742-5468/aaa78e
| null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In biology phenotypic switching is a common bet-hedging strategy in the face
of uncertain environmental conditions. Existing mathematical models often focus
on periodically changing environments to determine the optimal phenotypic
response. We focus on the case in which the environment switches randomly
between discrete states. Starting from an individual-based model we derive
stochastic differential equations to describe the dynamics, and obtain
analytical expressions for the mean instantaneous growth rates based on the
theory of piecewise deterministic Markov processes. We show that optimal
phenotypic responses are non-trivial for slow and intermediate environmental
processes, and systematically compare the cases of periodic and random
environments. The best response to random switching is more likely to be
heterogeneity than in the case of deterministic periodic environments, net
growth rates tend to be higher under stochastic environmental dynamics. The
combined system of environment and population of cells can be interpreted as
host-pathogen interaction, in which the host tries to choose environmental
switching so as to minimise growth of the pathogen, and in which the pathogen
employs a phenotypic switching optimised to increase its growth rate. We
discuss the existence of Nash-like mutual best-response scenarios for such
host-pathogen games.
|
[
{
"created": "Fri, 23 Jun 2017 17:52:32 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Jan 2018 17:42:23 GMT",
"version": "v2"
}
] |
2018-03-14
|
[
[
"Hufton",
"Peter G.",
""
],
[
"Lin",
"Yen Ting",
""
],
[
"Galla",
"Tobias",
""
]
] |
In biology phenotypic switching is a common bet-hedging strategy in the face of uncertain environmental conditions. Existing mathematical models often focus on periodically changing environments to determine the optimal phenotypic response. We focus on the case in which the environment switches randomly between discrete states. Starting from an individual-based model we derive stochastic differential equations to describe the dynamics, and obtain analytical expressions for the mean instantaneous growth rates based on the theory of piecewise deterministic Markov processes. We show that optimal phenotypic responses are non-trivial for slow and intermediate environmental processes, and systematically compare the cases of periodic and random environments. The best response to random switching is more likely to be heterogeneity than in the case of deterministic periodic environments, net growth rates tend to be higher under stochastic environmental dynamics. The combined system of environment and population of cells can be interpreted as host-pathogen interaction, in which the host tries to choose environmental switching so as to minimise growth of the pathogen, and in which the pathogen employs a phenotypic switching optimised to increase its growth rate. We discuss the existence of Nash-like mutual best-response scenarios for such host-pathogen games.
|
1801.08085
|
Meysam Golmohammadi
|
Vinit Shah, Eva von Weltin, Silvia Lopez, James Riley McHugh, Lily
Veloso, Meysam Golmohammadi, Iyad Obeid and Joseph Picone
|
The Temple University Hospital Seizure Detection Corpus
|
Under review in Frontiers in Neuroscience
| null | null | null |
q-bio.QM eess.SP q-bio.NC stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce the TUH EEG Seizure Corpus (TUSZ), which is the largest open
source corpus of its type, and represents an accurate characterization of
clinical conditions. In this paper, we describe the techniques used to develop
TUSZ, evaluate their effectiveness, and present some descriptive statistics on
the resulting corpus.
|
[
{
"created": "Wed, 3 Jan 2018 01:16:26 GMT",
"version": "v1"
}
] |
2018-01-25
|
[
[
"Shah",
"Vinit",
""
],
[
"von Weltin",
"Eva",
""
],
[
"Lopez",
"Silvia",
""
],
[
"McHugh",
"James Riley",
""
],
[
"Veloso",
"Lily",
""
],
[
"Golmohammadi",
"Meysam",
""
],
[
"Obeid",
"Iyad",
""
],
[
"Picone",
"Joseph",
""
]
] |
We introduce the TUH EEG Seizure Corpus (TUSZ), which is the largest open source corpus of its type, and represents an accurate characterization of clinical conditions. In this paper, we describe the techniques used to develop TUSZ, evaluate their effectiveness, and present some descriptive statistics on the resulting corpus.
|
q-bio/0411043
|
Lucilla de Arcangelis
|
Lucilla de Arcangelis, Hans J. Herrmann and Carla Perrone-Capano
|
Activity-dependent brain model explaining EEG spectra
| null | null | null | null |
q-bio.NC
| null |
Most brain models focus on associative memory or calculation capability,
experimentally inaccessible using physiological methods. Here we present a
model explaining a basic feature of electroencephalograms (EEG). Our model is
based on an electrical network with threshold firing and plasticity of synapses
that reproduces very robustly the measured exponent 0.8 of the medical EEG
spectra, a solid evidence for self-organized criticality. Our result are also
valid on small-world lattices. We propose that an universal scaling behaviour
characterizes many physiological signal spectra for brain controlled
activities.
|
[
{
"created": "Tue, 23 Nov 2004 17:56:16 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"de Arcangelis",
"Lucilla",
""
],
[
"Herrmann",
"Hans J.",
""
],
[
"Perrone-Capano",
"Carla",
""
]
] |
Most brain models focus on associative memory or calculation capability, experimentally inaccessible using physiological methods. Here we present a model explaining a basic feature of electroencephalograms (EEG). Our model is based on an electrical network with threshold firing and plasticity of synapses that reproduces very robustly the measured exponent 0.8 of the medical EEG spectra, a solid evidence for self-organized criticality. Our result are also valid on small-world lattices. We propose that an universal scaling behaviour characterizes many physiological signal spectra for brain controlled activities.
|
q-bio/0412049
|
Carlos Escudero
|
Carlos Escudero
|
Particle Statistics and Population Dynamics
| null | null |
10.1016/j.physa.2005.02.021
| null |
q-bio.PE cond-mat.stat-mech q-bio.QM
| null |
We study a master equation system modelling a population dynamics problem in
a lattice. The problem is the calculation of the minimum size of a refuge that
can protect a population from hostile external conditions, the so called
critical patch size problem. We analize both cases in which the particles are
considered fermions and bosons and show using exact analitical methods that,
while the Fermi-Dirac statistics leads to certain extinction for any refuge
size, the Bose-Eistein statistics allows survival even for the minimal refuge.
|
[
{
"created": "Thu, 30 Dec 2004 11:15:37 GMT",
"version": "v1"
}
] |
2009-11-10
|
[
[
"Escudero",
"Carlos",
""
]
] |
We study a master equation system modelling a population dynamics problem in a lattice. The problem is the calculation of the minimum size of a refuge that can protect a population from hostile external conditions, the so called critical patch size problem. We analize both cases in which the particles are considered fermions and bosons and show using exact analitical methods that, while the Fermi-Dirac statistics leads to certain extinction for any refuge size, the Bose-Eistein statistics allows survival even for the minimal refuge.
|
0807.4040
|
Florian Hartig
|
Florian Hartig and Martin Drechsler
|
The time horizon and its role in multiple species conservation planning
|
8 pages, 5 figures. Minor changes to v1. This version corredponds to
the final publication in Biological Conservation
|
Biological Conservation, 2008, 141, 2625-2631
|
10.1016/j.biocon.2008.07.028
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Survival probability within a certain time horizon T is a common measure of
population viability. The choice of T implicitly involves a time preference,
similar to economic discounting: Conservation success is evaluated at the time
horizon T, while all effects that occur later than T are not considered.
Despite the obvious relevance of the time horizon, ecological studies seldom
analyze its impact on the evaluation of conservation options. In this paper, we
show that, while the choice of T does not change the ranking of conservation
options for single species under stationary conditions, it may substantially
change conservation decisions for multiple species. We conclude that it is of
crucial importance to investigate the sensitivity of model results to the
choice of the time horizon or other measures of time preference when
prioritizing biodiversity conservation efforts.
|
[
{
"created": "Fri, 25 Jul 2008 10:07:52 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Sep 2008 14:50:13 GMT",
"version": "v2"
}
] |
2008-09-26
|
[
[
"Hartig",
"Florian",
""
],
[
"Drechsler",
"Martin",
""
]
] |
Survival probability within a certain time horizon T is a common measure of population viability. The choice of T implicitly involves a time preference, similar to economic discounting: Conservation success is evaluated at the time horizon T, while all effects that occur later than T are not considered. Despite the obvious relevance of the time horizon, ecological studies seldom analyze its impact on the evaluation of conservation options. In this paper, we show that, while the choice of T does not change the ranking of conservation options for single species under stationary conditions, it may substantially change conservation decisions for multiple species. We conclude that it is of crucial importance to investigate the sensitivity of model results to the choice of the time horizon or other measures of time preference when prioritizing biodiversity conservation efforts.
|
1001.0653
|
Enrico Carlon
|
J. Hooyberghs, P. Van Hummelen, E. Carlon
|
The effects of mismatches on hybridization in DNA microarrays:
determination of nearest neighbor parameters
|
31 pages, 5 figures
|
Nucleic Acid Research 37, e53 (2009)
|
10.1093/nar/gkp109
| null |
q-bio.BM q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantifying interactions in DNA microarrays is of central importance for a
better understanding of their functioning. Hybridization thermodynamics for
nucleic acid strands in aqueous solution can be described by the so-called
nearest-neighbor model, which estimates the hybridization free energy of a
given sequence as a sum of dinucleotide terms. Compared with its solution
counterparts, hybridization in DNA microarrays may be hindered due to the
presence of a solid surface and of a high density of DNA strands. We present
here a study aimed at the determination of hybridization free energies in DNA
microarrays. Experiments are performed on custom Agilent slides. The solution
contains a single oligonucleotide. The microarray contains spots with a perfect
matching complementary sequence and other spots with one or two mismatches: in
total 1006 different probe spots, each replicated 15 times per microarray. The
free energy parameters are directly fitted from microarray data. The
experiments demonstrate a clear correlation between hybridization free energies
in the microarray and in solution. The experiments are fully consistent with
the Langmuir model at low intensities, but show a clear deviation at
intermediate (non-saturating) intensities. These results provide new
interesting insights for the quantification of molecular interactions in DNA
microarrays.
|
[
{
"created": "Tue, 5 Jan 2010 09:09:18 GMT",
"version": "v1"
}
] |
2010-01-06
|
[
[
"Hooyberghs",
"J.",
""
],
[
"Van Hummelen",
"P.",
""
],
[
"Carlon",
"E.",
""
]
] |
Quantifying interactions in DNA microarrays is of central importance for a better understanding of their functioning. Hybridization thermodynamics for nucleic acid strands in aqueous solution can be described by the so-called nearest-neighbor model, which estimates the hybridization free energy of a given sequence as a sum of dinucleotide terms. Compared with its solution counterparts, hybridization in DNA microarrays may be hindered due to the presence of a solid surface and of a high density of DNA strands. We present here a study aimed at the determination of hybridization free energies in DNA microarrays. Experiments are performed on custom Agilent slides. The solution contains a single oligonucleotide. The microarray contains spots with a perfect matching complementary sequence and other spots with one or two mismatches: in total 1006 different probe spots, each replicated 15 times per microarray. The free energy parameters are directly fitted from microarray data. The experiments demonstrate a clear correlation between hybridization free energies in the microarray and in solution. The experiments are fully consistent with the Langmuir model at low intensities, but show a clear deviation at intermediate (non-saturating) intensities. These results provide new interesting insights for the quantification of molecular interactions in DNA microarrays.
|
2401.08004
|
Tinghe Zhang
|
Ting-He Zhang, Sumin Jo, Michelle Zhang, Kai Wang, Shou-Jiang Gao and
Yufei Huang
|
Understanding YTHDF2-mediated mRNA Degradation By m6A-BERT-Deg
| null | null | null | null |
q-bio.MN
|
http://creativecommons.org/licenses/by/4.0/
|
N6-methyladenosine (m6A) is the most abundant mRNA modification within
mammalian cells, holding pivotal significance in the regulation of mRNA
stability, translation, and splicing. Furthermore, it plays a critical role in
the regulation of RNA degradation by primarily recruiting the YTHDF2 reader
protein. However, the selective regulation of mRNA decay of the m6A-methylated
mRNA through YTHDF2 binding is poorly understood. To improve our understanding,
we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated
degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality
training dataset by integrating multiple data sources for the HeLa cell line.
To overcome the limitation of small training samples, we employed a
pre-training-fine-tuning strategy by first performing a self-supervised
pre-training of the model on 427,760 unlabeled m6A site sequences. The test
results demonstrated the importance of this pre-training strategy in enabling
m6A-BERT-Deg to outperform other benchmark models. We further conducted a
comprehensive model interpretation and revealed a surprising finding that the
presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated
mRNA degradation, subsequently enhancing mRNA stability. We also extended our
analyses to the HEK293 cell line, shedding light on the context-dependent
YTHDF2-mediated mRNA degradation.
|
[
{
"created": "Mon, 15 Jan 2024 23:15:55 GMT",
"version": "v1"
}
] |
2024-01-17
|
[
[
"Zhang",
"Ting-He",
""
],
[
"Jo",
"Sumin",
""
],
[
"Zhang",
"Michelle",
""
],
[
"Wang",
"Kai",
""
],
[
"Gao",
"Shou-Jiang",
""
],
[
"Huang",
"Yufei",
""
]
] |
N6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation, and splicing. Furthermore, it plays a critical role in the regulation of RNA degradation by primarily recruiting the YTHDF2 reader protein. However, the selective regulation of mRNA decay of the m6A-methylated mRNA through YTHDF2 binding is poorly understood. To improve our understanding, we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality training dataset by integrating multiple data sources for the HeLa cell line. To overcome the limitation of small training samples, we employed a pre-training-fine-tuning strategy by first performing a self-supervised pre-training of the model on 427,760 unlabeled m6A site sequences. The test results demonstrated the importance of this pre-training strategy in enabling m6A-BERT-Deg to outperform other benchmark models. We further conducted a comprehensive model interpretation and revealed a surprising finding that the presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated mRNA degradation, subsequently enhancing mRNA stability. We also extended our analyses to the HEK293 cell line, shedding light on the context-dependent YTHDF2-mediated mRNA degradation.
|
1409.6384
|
Alejandro Ochoa
|
Alejandro Ochoa, John D. Storey, Manuel Llin\'as, and Mona Singh
|
Beyond the E-value: stratified statistics for protein domain prediction
|
31 pages, 8 figures, does not include supplementary files
| null |
10.1371/journal.pcbi.1004509
| null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
E-values have been the dominant statistic for protein sequence analysis for
the past two decades: from identifying statistically significant local sequence
alignments to evaluating matches to hidden Markov models describing protein
domain families. Here we formally show that for "stratified" multiple
hypothesis testing problems, controlling the local False Discovery Rate (lFDR)
per stratum, or partition, yields the most predictions across the data at any
given threshold on the FDR or E-value over all strata combined. For the
important problem of protein domain prediction, a key step in characterizing
protein structure, function and evolution, we show that stratifying statistical
tests by domain family yields excellent results. We develop the first
FDR-estimating algorithms for domain prediction, and evaluate how well
thresholds based on q-values, E-values and lFDRs perform in domain prediction
using five complementary approaches for estimating empirical FDRs in this
context. We show that stratified q-value thresholds substantially outperform
E-values. Contradicting our theoretical results, q-values also outperform
lFDRs; however, our tests reveal a small but coherent subset of domain
families, biased towards models for specific repetitive patterns, for which
FDRs are greatly underestimated due to weaknesses in random sequence models.
Usage of lFDR thresholds outperform q-values for the remaining families, which
have as-expected noise, suggesting that further improvements in domain
predictions can be achieved with improved modeling of random sequences.
Overall, our theoretical and empirical findings suggest that the use of
stratified q-values and lFDRs could result in improvements in a host of
structured multiple hypothesis testing problems arising in bioinformatics,
including genome-wide association studies, orthology prediction, motif
scanning, and multi-microarray analyses.
|
[
{
"created": "Tue, 23 Sep 2014 01:27:56 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Mar 2015 19:57:37 GMT",
"version": "v2"
}
] |
2016-02-17
|
[
[
"Ochoa",
"Alejandro",
""
],
[
"Storey",
"John D.",
""
],
[
"Llinás",
"Manuel",
""
],
[
"Singh",
"Mona",
""
]
] |
E-values have been the dominant statistic for protein sequence analysis for the past two decades: from identifying statistically significant local sequence alignments to evaluating matches to hidden Markov models describing protein domain families. Here we formally show that for "stratified" multiple hypothesis testing problems, controlling the local False Discovery Rate (lFDR) per stratum, or partition, yields the most predictions across the data at any given threshold on the FDR or E-value over all strata combined. For the important problem of protein domain prediction, a key step in characterizing protein structure, function and evolution, we show that stratifying statistical tests by domain family yields excellent results. We develop the first FDR-estimating algorithms for domain prediction, and evaluate how well thresholds based on q-values, E-values and lFDRs perform in domain prediction using five complementary approaches for estimating empirical FDRs in this context. We show that stratified q-value thresholds substantially outperform E-values. Contradicting our theoretical results, q-values also outperform lFDRs; however, our tests reveal a small but coherent subset of domain families, biased towards models for specific repetitive patterns, for which FDRs are greatly underestimated due to weaknesses in random sequence models. Usage of lFDR thresholds outperform q-values for the remaining families, which have as-expected noise, suggesting that further improvements in domain predictions can be achieved with improved modeling of random sequences. Overall, our theoretical and empirical findings suggest that the use of stratified q-values and lFDRs could result in improvements in a host of structured multiple hypothesis testing problems arising in bioinformatics, including genome-wide association studies, orthology prediction, motif scanning, and multi-microarray analyses.
|
1006.0480
|
Vern Williams MR
|
Vernon Williams
|
Mathematical Demonstration of Darwinian Theory of Evolution
|
Keywords: eye evolution, computer eye simulation, simulation of
Darwin's Theory
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Darwin's book, Origin of the Species has been a source of public controversy
for more than hundred and fifty years. Court cases and mountains of words have
not dispelled this controversy. In this paper, a quantitative approach using
simple mathematics shows that the concept of evolution by natural selection
using only random choice of variables does work. The procedure applied to the
optical equations forming the phenotype of a spider eye produces an eye design
modeled after the measurements by Land and Barth.
|
[
{
"created": "Thu, 3 Jun 2010 10:32:07 GMT",
"version": "v1"
}
] |
2010-06-04
|
[
[
"Williams",
"Vernon",
""
]
] |
Darwin's book, Origin of the Species has been a source of public controversy for more than hundred and fifty years. Court cases and mountains of words have not dispelled this controversy. In this paper, a quantitative approach using simple mathematics shows that the concept of evolution by natural selection using only random choice of variables does work. The procedure applied to the optical equations forming the phenotype of a spider eye produces an eye design modeled after the measurements by Land and Barth.
|
0911.2021
|
Stefano Allesina
|
Stefano Allesina
|
Predicting trophic relations in ecological networks: a test of the
Allometric Diet Breadth Model
|
28 pages, 3 figures, 4 tables
| null | null | null |
q-bio.PE q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Few of food web theory hypotheses/predictions can be readily tested using
empirical data. An exception is represented by simple probabilistic models for
food web structure, for which the likelihood has been derived. Here I test the
performance of a more complex model for food web structure that is grounded in
the allometric scaling of interactions with body size and the theory of optimal
foraging (Allometric Diet Breadth Model - ADBM). This deterministic model has
been evaluated measuring the fraction of trophic relations correctly predicted.
I contrast this value with that produced by simpler models based on body sizes
and find that the data does not favor the more complex model: the information
on allometric scaling and optimal foraging does not significantly increase the
fit to the data. Also, I take a different approach and compute the p-value for
the fraction of trophic interactions correctly predicted by ADBM with respect
to three probabilistic null models. I find that the ADBM is clearly better at
predicting links than random graphs, but other models can do even better.
Although optimal foraging and allometric scaling could improve our
understanding of food webs, the models need to be ameliorated to find support
in the data.
|
[
{
"created": "Tue, 10 Nov 2009 22:29:43 GMT",
"version": "v1"
}
] |
2009-11-12
|
[
[
"Allesina",
"Stefano",
""
]
] |
Few of food web theory hypotheses/predictions can be readily tested using empirical data. An exception is represented by simple probabilistic models for food web structure, for which the likelihood has been derived. Here I test the performance of a more complex model for food web structure that is grounded in the allometric scaling of interactions with body size and the theory of optimal foraging (Allometric Diet Breadth Model - ADBM). This deterministic model has been evaluated measuring the fraction of trophic relations correctly predicted. I contrast this value with that produced by simpler models based on body sizes and find that the data does not favor the more complex model: the information on allometric scaling and optimal foraging does not significantly increase the fit to the data. Also, I take a different approach and compute the p-value for the fraction of trophic interactions correctly predicted by ADBM with respect to three probabilistic null models. I find that the ADBM is clearly better at predicting links than random graphs, but other models can do even better. Although optimal foraging and allometric scaling could improve our understanding of food webs, the models need to be ameliorated to find support in the data.
|
2109.14751
|
Young Joon Suh
|
Young Joon Suh (1), Mrinal Pandey (1), Jeffrey E Segall (2), Mingming
Wu (1) ((1) Department of Biological and Environmental Engineering, Cornell
University, Ithaca, NY, USA (2) Anatomy and Structural Biology, Albert
Einstein College of Medicine, Bronx, NY, USA)
|
Tumor Spheroid Chemotaxis in Epidermal Growth Factor Gradients Revealed
by a 3D Microfluidic Device
|
12 pages, 4 figures, and 3 supplementary figures
| null |
10.1088/1478-3975/ac54c7
| null |
q-bio.CB
|
http://creativecommons.org/licenses/by/4.0/
|
Epidermal growth factor (EGF), a potent cytokine, is known to promote tumor
invasion both in vivo and in vitro. Previously, we observed that single breast
tumor cells (MDA-MB-231 cell line) embedded within a 3D collagen matrix
displayed enhanced motility but no discernible chemotaxis in the presence of
linear EGF gradients using a microfluidic platform. Inspired by a recent
theoretical development that clustered mammalian cells respond differently to
chemical gradients than single cells, we studied tumor spheroid invasion within
a 3D extracellular matrix (ECM) in the presence of EGF gradients. We found that
EGF gradients promoted tumor cell detachment from the spheroid core, and the
position of the tumor spheroid core showed a mild chemotactic response towards
the EGF gradients. For those tumor cells detached from the spheroids, they
showed an enhanced chemokinesis response in contrast to previous experimental
results using single cells embedded within an ECM. No discernible chemotactic
response towards the EGF gradients was found for the cells outside the spheroid
core. This work demonstrates that a cluster of tumor cells responds differently
than single tumor cells towards EGF gradients and highlights the importance of
a tumor spheroid platform for chemotaxis studies.
|
[
{
"created": "Wed, 29 Sep 2021 22:53:21 GMT",
"version": "v1"
}
] |
2022-03-23
|
[
[
"Suh",
"Young Joon",
""
],
[
"Pandey",
"Mrinal",
""
],
[
"Segall",
"Jeffrey E",
""
],
[
"Wu",
"Mingming",
""
]
] |
Epidermal growth factor (EGF), a potent cytokine, is known to promote tumor invasion both in vivo and in vitro. Previously, we observed that single breast tumor cells (MDA-MB-231 cell line) embedded within a 3D collagen matrix displayed enhanced motility but no discernible chemotaxis in the presence of linear EGF gradients using a microfluidic platform. Inspired by a recent theoretical development that clustered mammalian cells respond differently to chemical gradients than single cells, we studied tumor spheroid invasion within a 3D extracellular matrix (ECM) in the presence of EGF gradients. We found that EGF gradients promoted tumor cell detachment from the spheroid core, and the position of the tumor spheroid core showed a mild chemotactic response towards the EGF gradients. For those tumor cells detached from the spheroids, they showed an enhanced chemokinesis response in contrast to previous experimental results using single cells embedded within an ECM. No discernible chemotactic response towards the EGF gradients was found for the cells outside the spheroid core. This work demonstrates that a cluster of tumor cells responds differently than single tumor cells towards EGF gradients and highlights the importance of a tumor spheroid platform for chemotaxis studies.
|
1911.03221
|
Valentin Thorey
|
Antoine Guillot, Fabien Sauvet, Emmanuel H During and Valentin Thorey
|
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and
Automated sleep staging
|
10 pages, journal submitted
| null | null | null |
q-bio.QM cs.LG eess.SP stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sleep stage classification constitutes an important element of sleep disorder
diagnosis. It relies on the visual inspection of polysomnography records by
trained sleep technologists. Automated approaches have been designed to
alleviate this resource-intensive task. However, such approaches are usually
compared to a single human scorer annotation despite an inter-rater agreement
of about 85 % only. The present study introduces two publicly-available
datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients
suffering from obstructive sleep apnea (OSA). Both datasets have been scored by
5 sleep technologists from different sleep centers. We developed a framework to
compare automated approaches to a consensus of multiple human scorers. Using
this framework, we benchmarked and compared the main literature approaches. We
also developed and benchmarked a new deep learning method, SimpleSleepNet,
inspired by current state-of-the-art. We demonstrated that many methods can
reach human-level performance on both datasets. SimpleSleepNet achieved an F1
of 89.9 % vs 86.8 % on average for human scorers on DOD-H, and an F1 of 88.3 %
vs 84.8 % on DOD-O. Our study highlights that using state-of-the-art automated
sleep staging outperforms human scorers performance for healthy volunteers and
patients suffering from OSA. Consideration could be made to use automated
approaches in the clinical setting.
|
[
{
"created": "Thu, 31 Oct 2019 16:12:43 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Nov 2019 17:21:23 GMT",
"version": "v2"
},
{
"created": "Mon, 2 Mar 2020 16:53:12 GMT",
"version": "v3"
},
{
"created": "Mon, 27 Apr 2020 09:45:45 GMT",
"version": "v4"
}
] |
2020-04-28
|
[
[
"Guillot",
"Antoine",
""
],
[
"Sauvet",
"Fabien",
""
],
[
"During",
"Emmanuel H",
""
],
[
"Thorey",
"Valentin",
""
]
] |
Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a single human scorer annotation despite an inter-rater agreement of about 85 % only. The present study introduces two publicly-available datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients suffering from obstructive sleep apnea (OSA). Both datasets have been scored by 5 sleep technologists from different sleep centers. We developed a framework to compare automated approaches to a consensus of multiple human scorers. Using this framework, we benchmarked and compared the main literature approaches. We also developed and benchmarked a new deep learning method, SimpleSleepNet, inspired by current state-of-the-art. We demonstrated that many methods can reach human-level performance on both datasets. SimpleSleepNet achieved an F1 of 89.9 % vs 86.8 % on average for human scorers on DOD-H, and an F1 of 88.3 % vs 84.8 % on DOD-O. Our study highlights that using state-of-the-art automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from OSA. Consideration could be made to use automated approaches in the clinical setting.
|
2402.02520
|
Linn\'ea Gyllingberg
|
Linn\'ea Gyllingberg, Yu Tian, and David J.T. Sumpter
|
A minimal model of cognition based on oscillatory and reinforcement
processes
| null | null | null | null |
q-bio.NC cs.SI math.DS nlin.AO physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Building mathematical models of brains is difficult because of the sheer
complexity of the problem. One potential starting point is through basal
cognition, which give abstract representation of a range of organisms without
central nervous systems, including fungi, slime moulds and bacteria. We propose
one such model, demonstrating how a combination of oscillatory and
current-based reinforcement processes can be used to couple resources in an
efficient manner, mimicking the way these organisms function. A key ingredient
in our model, not found in previous basal cognition models, is that we
explicitly model oscillations in the number of particles (i.e. the nutrients,
chemical signals or similar, which make up the biological system) and the flow
of these particles within the modelled organisms. Using this approach, we find
that our model builds efficient solutions, provided the environmental
oscillations are sufficiently out of phase. We further demonstrate that
amplitude differences can promote efficient solutions and that the system is
robust to frequency differences. In the context of these findings, we discuss
connections between our model and basal cognition in biological systems and
slime moulds, in particular, how oscillations might contribute to
self-organised problem-solving by these organisms.
|
[
{
"created": "Sun, 4 Feb 2024 15:08:02 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Jun 2024 14:45:47 GMT",
"version": "v2"
}
] |
2024-06-14
|
[
[
"Gyllingberg",
"Linnéa",
""
],
[
"Tian",
"Yu",
""
],
[
"Sumpter",
"David J. T.",
""
]
] |
Building mathematical models of brains is difficult because of the sheer complexity of the problem. One potential starting point is through basal cognition, which give abstract representation of a range of organisms without central nervous systems, including fungi, slime moulds and bacteria. We propose one such model, demonstrating how a combination of oscillatory and current-based reinforcement processes can be used to couple resources in an efficient manner, mimicking the way these organisms function. A key ingredient in our model, not found in previous basal cognition models, is that we explicitly model oscillations in the number of particles (i.e. the nutrients, chemical signals or similar, which make up the biological system) and the flow of these particles within the modelled organisms. Using this approach, we find that our model builds efficient solutions, provided the environmental oscillations are sufficiently out of phase. We further demonstrate that amplitude differences can promote efficient solutions and that the system is robust to frequency differences. In the context of these findings, we discuss connections between our model and basal cognition in biological systems and slime moulds, in particular, how oscillations might contribute to self-organised problem-solving by these organisms.
|
1906.05556
|
Sungchan Kim
|
Sungchan Kim, Jong Hyuk Byun and Il Hyo Jung
|
Global Stability of an SEIR Epidemic Model where Empirical Distribution
of Incubation Period has Approximated by Coxian Distribution
| null |
Adv Differ Equ (2019) 2019: 469
|
10.1186/s13662-019-2405-9
| null |
q-bio.PE math.DS physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we have developed a Coxian distributed SEIR model in
incorporating an empirical incubation period. We show that the global dynamics
are completely determined by a basic reproduction number. An application of the
Coxian distributed SEIR model using data of an empirical incubation period is
explored. The model may be useful for resolving causing the realistic intrinsic
parts in classical epidemic models since Coxian distribution approximately
converges to any distribution.
|
[
{
"created": "Thu, 13 Jun 2019 09:10:29 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Aug 2019 11:54:48 GMT",
"version": "v2"
}
] |
2019-11-20
|
[
[
"Kim",
"Sungchan",
""
],
[
"Byun",
"Jong Hyuk",
""
],
[
"Jung",
"Il Hyo",
""
]
] |
In this work, we have developed a Coxian distributed SEIR model in incorporating an empirical incubation period. We show that the global dynamics are completely determined by a basic reproduction number. An application of the Coxian distributed SEIR model using data of an empirical incubation period is explored. The model may be useful for resolving causing the realistic intrinsic parts in classical epidemic models since Coxian distribution approximately converges to any distribution.
|
1505.07170
|
Cesar F. Caiafa
|
Cesar F. Caiafa and Franco Pestilli
|
Sparse multiway decomposition for analysis and modeling of diffusion
imaging and tractography
|
19 pages, 1 table, 9 figures
| null | null | null |
q-bio.QM math.NA stat.ME
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The number of neuroimaging data sets publicly available is growing at fast
rate. The increase in availability and resolution of neuroimaging data requires
modern approaches to signal processing for data analysis and results
validation. We introduce the application of sparse multiway decomposition
methods (Caiafa and Cichocki, 2012) to linearized neuroimaging models. We show
that decomposed models are more compact but as accurate as full models and can
be successfully used for fast data analysis. We focus as example on a recent
model for the evaluation of white matter connectomes (Pestilli et al, 2014). We
show that the multiway decomposed model achieves accuracy comparable to the
full model, while requiring only a small fraction of the memory and compute
time. The approach has implications for a majority of neuroimaging methods
using linear approximations to measured signals.
|
[
{
"created": "Wed, 27 May 2015 01:21:37 GMT",
"version": "v1"
}
] |
2015-05-28
|
[
[
"Caiafa",
"Cesar F.",
""
],
[
"Pestilli",
"Franco",
""
]
] |
The number of neuroimaging data sets publicly available is growing at fast rate. The increase in availability and resolution of neuroimaging data requires modern approaches to signal processing for data analysis and results validation. We introduce the application of sparse multiway decomposition methods (Caiafa and Cichocki, 2012) to linearized neuroimaging models. We show that decomposed models are more compact but as accurate as full models and can be successfully used for fast data analysis. We focus as example on a recent model for the evaluation of white matter connectomes (Pestilli et al, 2014). We show that the multiway decomposed model achieves accuracy comparable to the full model, while requiring only a small fraction of the memory and compute time. The approach has implications for a majority of neuroimaging methods using linear approximations to measured signals.
|
2011.00485
|
Marco Wiering
|
Xiangxie Zhang, Ben Beinke, Berlian Al Kindhi and Marco Wiering
|
Comparing Machine Learning Algorithms with or without Feature Extraction
for DNA Classification
|
17 pages
| null | null | null |
q-bio.OT cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The classification of DNA sequences is a key research area in bioinformatics
as it enables researchers to conduct genomic analysis and detect possible
diseases. In this paper, three state-of-the-art algorithms, namely
Convolutional Neural Networks, Deep Neural Networks, and N-gram Probabilistic
Models, are used for the task of DNA classification. Furthermore, we introduce
a novel feature extraction method based on the Levenshtein distance and
randomly generated DNA sub-sequences to compute information-rich features from
the DNA sequences. We also use an existing feature extraction method based on
3-grams to represent amino acids and combine both feature extraction methods
with a multitude of machine learning algorithms. Four different data sets, each
concerning viral diseases such as Covid-19, AIDS, Influenza, and Hepatitis C,
are used for evaluating the different approaches. The results of the
experiments show that all methods obtain high accuracies on the different DNA
datasets. Furthermore, the domain-specific 3-gram feature extraction method
leads in general to the best results in the experiments, while the newly
proposed technique outperforms all other methods on the smallest Covid-19
dataset
|
[
{
"created": "Sun, 1 Nov 2020 12:04:54 GMT",
"version": "v1"
}
] |
2020-11-03
|
[
[
"Zhang",
"Xiangxie",
""
],
[
"Beinke",
"Ben",
""
],
[
"Kindhi",
"Berlian Al",
""
],
[
"Wiering",
"Marco",
""
]
] |
The classification of DNA sequences is a key research area in bioinformatics as it enables researchers to conduct genomic analysis and detect possible diseases. In this paper, three state-of-the-art algorithms, namely Convolutional Neural Networks, Deep Neural Networks, and N-gram Probabilistic Models, are used for the task of DNA classification. Furthermore, we introduce a novel feature extraction method based on the Levenshtein distance and randomly generated DNA sub-sequences to compute information-rich features from the DNA sequences. We also use an existing feature extraction method based on 3-grams to represent amino acids and combine both feature extraction methods with a multitude of machine learning algorithms. Four different data sets, each concerning viral diseases such as Covid-19, AIDS, Influenza, and Hepatitis C, are used for evaluating the different approaches. The results of the experiments show that all methods obtain high accuracies on the different DNA datasets. Furthermore, the domain-specific 3-gram feature extraction method leads in general to the best results in the experiments, while the newly proposed technique outperforms all other methods on the smallest Covid-19 dataset
|
1010.1191
|
Thomas Adams
|
Thomas Adams \ast (1), Graeme Ackland (1), Glenn Marion (2) and Colin
Edwards (3) ((1) School of Physics and Astronomy, The University of
Edinburgh, (2) Biomathematics and Statistics Scotland,(3) Forest Research,
Northern Research Station)
|
Effects of local interaction and dispersal on the dynamics of
size-structured populations
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Traditional approaches to ecosystem modelling have relied on spatially
homogeneous approximations to interaction, growth and death. More recently,
spatial interaction and dispersal have also been considered. While these leads
to certain changes in community dynamics, their effect is sometimes fairly
minimal, and demographic scenarios in which this difference is important have
not been systematically investigated.
We take a simple mean-field model which simulates birth, growth and death
processes, and rewrite it with spatially distributed discrete individuals. Each
individual's growth and mortality is determined by a competition measure which
captures the effects of neighbours in a way which retains the conceptual
simplicity of a generic, analytically-solvable model. Although the model is
generic, we here parameterise it using data from Caledonian Scots Pine stands.
The dynamics of simulated populations, starting from a plantation lattice
configuration, mirror those of well-established qualitative descriptions of
natural forest stand behaviour; an analogy which assists in understanding the
transition from artificial to old-growth structure.
When parameterised for Scots Pine populations, the signature of spatial
processes is evident, but they do not have a large effect on first-order
statistics such as density and biomass. The sensitivity of this result to
variation in each individual rate parameter is investigated; distinct
differences between spatial and mean-field models are seen only upon alteration
of the interaction strength parameters, and in low density populations. Under
the Scots Pine parameterisation, dispersal also has an effect of spatial
structure, but not first-order properties. Only in more intense competitive
scenarios does altering the relative scales of dispersal and interaction lead
to a clear signal in first order behaviour.
|
[
{
"created": "Wed, 6 Oct 2010 16:28:11 GMT",
"version": "v1"
}
] |
2010-10-07
|
[
[
"\\ast",
"Thomas Adams",
""
],
[
"Ackland",
"Graeme",
""
],
[
"Marion",
"Glenn",
""
],
[
"Edwards",
"Colin",
""
]
] |
Traditional approaches to ecosystem modelling have relied on spatially homogeneous approximations to interaction, growth and death. More recently, spatial interaction and dispersal have also been considered. While these leads to certain changes in community dynamics, their effect is sometimes fairly minimal, and demographic scenarios in which this difference is important have not been systematically investigated. We take a simple mean-field model which simulates birth, growth and death processes, and rewrite it with spatially distributed discrete individuals. Each individual's growth and mortality is determined by a competition measure which captures the effects of neighbours in a way which retains the conceptual simplicity of a generic, analytically-solvable model. Although the model is generic, we here parameterise it using data from Caledonian Scots Pine stands. The dynamics of simulated populations, starting from a plantation lattice configuration, mirror those of well-established qualitative descriptions of natural forest stand behaviour; an analogy which assists in understanding the transition from artificial to old-growth structure. When parameterised for Scots Pine populations, the signature of spatial processes is evident, but they do not have a large effect on first-order statistics such as density and biomass. The sensitivity of this result to variation in each individual rate parameter is investigated; distinct differences between spatial and mean-field models are seen only upon alteration of the interaction strength parameters, and in low density populations. Under the Scots Pine parameterisation, dispersal also has an effect of spatial structure, but not first-order properties. Only in more intense competitive scenarios does altering the relative scales of dispersal and interaction lead to a clear signal in first order behaviour.
|
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