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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0603037 | Herculano Martinho | Renata Andrade Bitar, Herculano da Silva Martinho, Carlos Julio Tierra
Criollo, Leandra Naira Zambelli Ramalho, Mario Mourao Netto, Airton Abrahao
Martin | Biochemical analysis of human breast tissues using FT-Raman spectroscopy | 29 pages, 9 figures. accepted Journal of Biomedical Optics | null | 10.1117/1.2363362 | null | q-bio.TO q-bio.QM | null | In this work we employ the Fourier Transform Raman Spectroscopy to study
normal and tumoral human breast tissues, including several subtypes of cancers.
We analyzed 194 Raman spectra from breast tissues that were separated into 9
groups according to their corresponding histopathological diagnosis. The
assignment of the relevant Raman bands enabled us to connect the several kinds
of breast tissues (normal and pathological) to their corresponding biochemical
moieties alterations and distinguish among 7 groups: normal breast, fibrocystic
condition, duct carcinoma-in-situ, duct carcinoma-in-situ with necrosis,
infiltrating duct carcinoma not otherwise specified, colloid infiltrating duct
carcinoma and invasive lobular carcinomas. We were able to establish the
biochemical basis for each spectrum, relating the observed peaks to specific
biomolecules that play special role in the carcinogenesis process. This work is
very useful for the premature optical diagnosis of a broad range of breast
pathologies. We noticed that we were not able to differentiate inflammatory and
medullary duct carcinomas from infiltrating duct carcinoma not otherwise
specified.
| [
{
"created": "Fri, 31 Mar 2006 18:52:11 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Bitar",
"Renata Andrade",
""
],
[
"Martinho",
"Herculano da Silva",
""
],
[
"Criollo",
"Carlos Julio Tierra",
""
],
[
"Ramalho",
"Leandra Naira Zambelli",
""
],
[
"Netto",
"Mario Mourao",
""
],
[
"Martin",
"Airton Abrahao",
""
]
] | In this work we employ the Fourier Transform Raman Spectroscopy to study normal and tumoral human breast tissues, including several subtypes of cancers. We analyzed 194 Raman spectra from breast tissues that were separated into 9 groups according to their corresponding histopathological diagnosis. The assignment of the relevant Raman bands enabled us to connect the several kinds of breast tissues (normal and pathological) to their corresponding biochemical moieties alterations and distinguish among 7 groups: normal breast, fibrocystic condition, duct carcinoma-in-situ, duct carcinoma-in-situ with necrosis, infiltrating duct carcinoma not otherwise specified, colloid infiltrating duct carcinoma and invasive lobular carcinomas. We were able to establish the biochemical basis for each spectrum, relating the observed peaks to specific biomolecules that play special role in the carcinogenesis process. This work is very useful for the premature optical diagnosis of a broad range of breast pathologies. We noticed that we were not able to differentiate inflammatory and medullary duct carcinomas from infiltrating duct carcinoma not otherwise specified. |
1510.08965 | Boya Song | Linda Ma, Boya Song, Thomas Curran, Nhu Phong, Emilie Dressaire and
Marcus Roper | Defining individual size in the model filamentous fungus
$\textit{Neurospora crassa}$ | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is challenging to apply the tenets of individuality to filamentous fungi:
a fungal mycelium can contain millions of genetically diverse but totipotent
nuclei, each capable of founding new mycelia. Moreover a single mycelium can
potentially stretch over kilometers and it is unlikely that its distant parts
share resources or have the same fitness. Here we directly measure how a single
mycelium of the model ascomycete {\it Neurospora crassa} is patterned into
Reproductive Units (RUs); subpopulations of nuclei that propagate together as
spores, and function as reproductive individuals. The density of RUs is
sensitive to the geometry of growth; we detect 50-fold smaller RUs when mycelia
had expanding frontiers than when they were constrained to grow in one
direction only. RUs fragmented further when the mycelial network was perturbed.
In mycelia with expanding frontiers RU composition was strongly influenced by
the distribution of genotypes early in development. Our results provide a
concept of fungal individuality that is directly connected to reproductive
potential, and therefore to theories of how fungal individuals adapt and evolve
over time. Our data show that the size of reproductive individuals is a dynamic
and environment-dependent property, even within apparently totally connected
fungal mycelia.
| [
{
"created": "Fri, 30 Oct 2015 04:00:22 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Jan 2016 01:07:41 GMT",
"version": "v2"
}
] | 2016-01-06 | [
[
"Ma",
"Linda",
""
],
[
"Song",
"Boya",
""
],
[
"Curran",
"Thomas",
""
],
[
"Phong",
"Nhu",
""
],
[
"Dressaire",
"Emilie",
""
],
[
"Roper",
"Marcus",
""
]
] | It is challenging to apply the tenets of individuality to filamentous fungi: a fungal mycelium can contain millions of genetically diverse but totipotent nuclei, each capable of founding new mycelia. Moreover a single mycelium can potentially stretch over kilometers and it is unlikely that its distant parts share resources or have the same fitness. Here we directly measure how a single mycelium of the model ascomycete {\it Neurospora crassa} is patterned into Reproductive Units (RUs); subpopulations of nuclei that propagate together as spores, and function as reproductive individuals. The density of RUs is sensitive to the geometry of growth; we detect 50-fold smaller RUs when mycelia had expanding frontiers than when they were constrained to grow in one direction only. RUs fragmented further when the mycelial network was perturbed. In mycelia with expanding frontiers RU composition was strongly influenced by the distribution of genotypes early in development. Our results provide a concept of fungal individuality that is directly connected to reproductive potential, and therefore to theories of how fungal individuals adapt and evolve over time. Our data show that the size of reproductive individuals is a dynamic and environment-dependent property, even within apparently totally connected fungal mycelia. |
1812.11405 | Andreea Beica | Andreea Beica, J\'er\^ome Feret, Tatjana Petrov | Tropical Abstraction of Biochemical Reaction Networks with Guarantees | null | null | null | null | q-bio.MN cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biochemical molecules interact through modification and binding reactions,
giving raise to a combinatorial number of possible biochemical species. The
time-dependent evolution of concentrations of the species is commonly described
by a system of coupled ordinary differential equations (ODEs). However, the
analysis of such high-dimensional, non-linear system of equations is often
computationally expensive and even prohibitive in practice. The major challenge
towards reducing such models is providing the guarantees as to how the solution
of the reduced model relates to that of the original model, while avoiding to
solve the original model. In this paper, we have designed and tested an
approximation method for ODE models of biochemical reaction systems, in which
the guarantees are our major requirement. Borrowing from tropical analysis
techniques, dominance relations among terms of each species' ODE are exploited
to simplify the original model, by neglecting the dominated terms. As the
dominant subsystems can change during the system's dynamics, depending on which
species dominate the others, several possible modes exist. Thus, simpler models
consisting of only the dominant subsystems can be assembled into hybrid,
piecewise smooth models, which approximate the behavior of the initial system.
By combining the detection of dominated terms with symbolic bounds propagation,
we show how to approximate the original model by an assembly of simpler models,
consisting in ordinary differential equations that provide time-dependent lower
and upper bounds for the concentrations of the initial models species. Our
method provides sound interval bounds for the concentrations of the chemical
species, and hence can serve to evaluate the faithfulness of
tropicalization-based reduction heuristics for ODE models of biochemical
reduction systems. The method is tested on several case studies.
| [
{
"created": "Sat, 29 Dec 2018 17:30:35 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Mar 2019 09:56:13 GMT",
"version": "v2"
}
] | 2019-03-22 | [
[
"Beica",
"Andreea",
""
],
[
"Feret",
"Jérôme",
""
],
[
"Petrov",
"Tatjana",
""
]
] | Biochemical molecules interact through modification and binding reactions, giving raise to a combinatorial number of possible biochemical species. The time-dependent evolution of concentrations of the species is commonly described by a system of coupled ordinary differential equations (ODEs). However, the analysis of such high-dimensional, non-linear system of equations is often computationally expensive and even prohibitive in practice. The major challenge towards reducing such models is providing the guarantees as to how the solution of the reduced model relates to that of the original model, while avoiding to solve the original model. In this paper, we have designed and tested an approximation method for ODE models of biochemical reaction systems, in which the guarantees are our major requirement. Borrowing from tropical analysis techniques, dominance relations among terms of each species' ODE are exploited to simplify the original model, by neglecting the dominated terms. As the dominant subsystems can change during the system's dynamics, depending on which species dominate the others, several possible modes exist. Thus, simpler models consisting of only the dominant subsystems can be assembled into hybrid, piecewise smooth models, which approximate the behavior of the initial system. By combining the detection of dominated terms with symbolic bounds propagation, we show how to approximate the original model by an assembly of simpler models, consisting in ordinary differential equations that provide time-dependent lower and upper bounds for the concentrations of the initial models species. Our method provides sound interval bounds for the concentrations of the chemical species, and hence can serve to evaluate the faithfulness of tropicalization-based reduction heuristics for ODE models of biochemical reduction systems. The method is tested on several case studies. |
2305.13348 | Justin Eilertsen | Justin Eilertsen and Wylie Stroberg | On the role of eigenvalue disparity and coordinate transformations in
the reduction of the linear noise approximation | 9 Figures, 38 pages | null | null | null | q-bio.MN math.DS math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Eigenvalue disparity, also known as timescale separation, permits the
systematic reduction of deterministic models of enzyme kinetics. Geometric
singular perturbation theory, of which eigenvalue disparity is central,
provides a coordinate-free framework for deriving reduced mass action models in
the deterministic realm. Moreover, homologous deterministic reductions are
often employed in stochastic models to reduce the computational complexity
required to simulate reactions with the Gillespie algorithm. Interestingly,
several detailed studies indicate that timescale separation does not always
guarantee the accuracy of reduced stochastic models. In this work, we examine
the roles of timescale separation and coordinate transformations in the
reduction of the Linear Noise Approximation (LNA) and, unlike previous studies,
we do not require the system to be comprised of distinct fast and slow
variables. Instead, we adopt a coordinate-free approach. We demonstrate that
eigenvalue disparity does not guarantee the accuracy of the reduced LNA, known
as the slow scale LNA (ssLNA). However, the inaccuracy of the ssLNA can often
be eliminated with a proper coordinate transformation. For planar systems in
separated (standard) form, we prove that the error between the variances of the
slow variable generated by the LNA and the ssLNA is $\mathcal{O}(\varepsilon)$.
We also address a nilpotent Jacobian scenario and use the blow-up method to
construct a reduced equation that is accurate near the singular limit in the
deterministic regime. However, this reduction in the stochastic regime is far
less accurate, which illustrates that eigenvalue disparity plays a central role
in stochastic model reduction.
| [
{
"created": "Mon, 22 May 2023 16:39:17 GMT",
"version": "v1"
}
] | 2023-05-24 | [
[
"Eilertsen",
"Justin",
""
],
[
"Stroberg",
"Wylie",
""
]
] | Eigenvalue disparity, also known as timescale separation, permits the systematic reduction of deterministic models of enzyme kinetics. Geometric singular perturbation theory, of which eigenvalue disparity is central, provides a coordinate-free framework for deriving reduced mass action models in the deterministic realm. Moreover, homologous deterministic reductions are often employed in stochastic models to reduce the computational complexity required to simulate reactions with the Gillespie algorithm. Interestingly, several detailed studies indicate that timescale separation does not always guarantee the accuracy of reduced stochastic models. In this work, we examine the roles of timescale separation and coordinate transformations in the reduction of the Linear Noise Approximation (LNA) and, unlike previous studies, we do not require the system to be comprised of distinct fast and slow variables. Instead, we adopt a coordinate-free approach. We demonstrate that eigenvalue disparity does not guarantee the accuracy of the reduced LNA, known as the slow scale LNA (ssLNA). However, the inaccuracy of the ssLNA can often be eliminated with a proper coordinate transformation. For planar systems in separated (standard) form, we prove that the error between the variances of the slow variable generated by the LNA and the ssLNA is $\mathcal{O}(\varepsilon)$. We also address a nilpotent Jacobian scenario and use the blow-up method to construct a reduced equation that is accurate near the singular limit in the deterministic regime. However, this reduction in the stochastic regime is far less accurate, which illustrates that eigenvalue disparity plays a central role in stochastic model reduction. |
q-bio/0505010 | Ted Hesselroth | Ted Hesselroth and Klaus Schulten | The Dynamics of Image Processing by Feature Maps in the Primary Visual
Cortex | 44 pages, 16 figures. For archival purposes | null | null | null | q-bio.NC q-bio.QM | null | The operational characteristics of a linear neural network image processing
system based on the brain's vision system are investigated. The final stage of
the network consists of edge detectors of various orienations arranged in a
feature map, corresponding to the primary visual cortex, or V1. The lateral
geniculate nucleus is modeled as a preprocessing stage. Excitatory forward and
inhibitory backward connections exist between the LGN and V1. By a method of
reconstructing the input images in terms of V1 activities, the simulations show
that images can be faithfully represented in V1 by the proposed network. The
signal-to-noise ratio of the image is improved by the representation, and
compression ratios of well over two-hundred are possible. Lateral interacations
between V1 neurons sharpen their orientational tuning. We further study the
dynamics of the processing, showing that the rate of decrease of the error of
the representation is maximized for the receptive fields used, and we develop a
Fokker-Planck equation for a more detailed prediction of the error value vs.
time. Finally, we show how the eigenvalues of the covariance matrix of the
inputs can be employed to predict the rate of error decrease.
| [
{
"created": "Thu, 5 May 2005 16:58:44 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Hesselroth",
"Ted",
""
],
[
"Schulten",
"Klaus",
""
]
] | The operational characteristics of a linear neural network image processing system based on the brain's vision system are investigated. The final stage of the network consists of edge detectors of various orienations arranged in a feature map, corresponding to the primary visual cortex, or V1. The lateral geniculate nucleus is modeled as a preprocessing stage. Excitatory forward and inhibitory backward connections exist between the LGN and V1. By a method of reconstructing the input images in terms of V1 activities, the simulations show that images can be faithfully represented in V1 by the proposed network. The signal-to-noise ratio of the image is improved by the representation, and compression ratios of well over two-hundred are possible. Lateral interacations between V1 neurons sharpen their orientational tuning. We further study the dynamics of the processing, showing that the rate of decrease of the error of the representation is maximized for the receptive fields used, and we develop a Fokker-Planck equation for a more detailed prediction of the error value vs. time. Finally, we show how the eigenvalues of the covariance matrix of the inputs can be employed to predict the rate of error decrease. |
2005.03651 | Alessio Notari | Alessio Notari, Giorgio Torrieri | COVID-19 transmission risk factors | 42 pages, 31 figures, 32 tables. Principal Component Analysis added
in v2 | null | null | null | q-bio.QM physics.soc-ph q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze risk factors correlated with the initial transmission growth rate
of the recent COVID-19 pandemic in different countries. The number of cases
follows in its early stages an almost exponential expansion; we chose as a
starting point in each country the first day $d_i$ with 30 cases and we fitted
for 12 days, capturing thus the early exponential growth. We looked then for
linear correlations of the exponents $\alpha$ with other variables, for a
sample of 126 countries. We find a positive correlation, {\it i.e. faster
spread of COVID-19}, with high confidence level with the following variables,
with respective $p$-value: low Temperature ($4\cdot10^{-7}$), high ratio of old
vs.~working-age people ($3\cdot10^{-6}$), life expectancy ($8\cdot10^{-6}$),
number of international tourists ($1\cdot10^{-5}$), earlier epidemic starting
date $d_i$ ($2\cdot10^{-5}$), high level of physical contact in greeting habits
($6 \cdot 10^{-5}$), lung cancer prevalence ($6 \cdot 10^{-5}$), obesity in
males ($1 \cdot 10^{-4}$), share of population in urban areas
($2\cdot10^{-4}$), cancer prevalence ($3 \cdot 10^{-4}$), alcohol consumption
($0.0019$), daily smoking prevalence ($0.0036$), UV index ($0.004$, 73
countries). We also find a correlation with low Vitamin D levels
($0.002-0.006$, smaller sample, $\sim 50$ countries, to be confirmed on a
larger sample). There is highly significant correlation also with blood types:
positive correlation with types RH- ($3\cdot10^{-5}$) and A+ ($3\cdot10^{-3}$),
negative correlation with B+ ($2\cdot10^{-4}$). Several of the above variables
are intercorrelated and likely to have common interpretations. We performed a
Principal Component Analysis, in order to find their significant independent
linear combinations. We also analyzed a possible bias: countries with low
GDP-per capita might have less testing and we discuss correlation with the
above variables.
| [
{
"created": "Thu, 7 May 2020 17:57:58 GMT",
"version": "v1"
},
{
"created": "Mon, 10 May 2021 22:16:08 GMT",
"version": "v2"
}
] | 2021-05-12 | [
[
"Notari",
"Alessio",
""
],
[
"Torrieri",
"Giorgio",
""
]
] | We analyze risk factors correlated with the initial transmission growth rate of the recent COVID-19 pandemic in different countries. The number of cases follows in its early stages an almost exponential expansion; we chose as a starting point in each country the first day $d_i$ with 30 cases and we fitted for 12 days, capturing thus the early exponential growth. We looked then for linear correlations of the exponents $\alpha$ with other variables, for a sample of 126 countries. We find a positive correlation, {\it i.e. faster spread of COVID-19}, with high confidence level with the following variables, with respective $p$-value: low Temperature ($4\cdot10^{-7}$), high ratio of old vs.~working-age people ($3\cdot10^{-6}$), life expectancy ($8\cdot10^{-6}$), number of international tourists ($1\cdot10^{-5}$), earlier epidemic starting date $d_i$ ($2\cdot10^{-5}$), high level of physical contact in greeting habits ($6 \cdot 10^{-5}$), lung cancer prevalence ($6 \cdot 10^{-5}$), obesity in males ($1 \cdot 10^{-4}$), share of population in urban areas ($2\cdot10^{-4}$), cancer prevalence ($3 \cdot 10^{-4}$), alcohol consumption ($0.0019$), daily smoking prevalence ($0.0036$), UV index ($0.004$, 73 countries). We also find a correlation with low Vitamin D levels ($0.002-0.006$, smaller sample, $\sim 50$ countries, to be confirmed on a larger sample). There is highly significant correlation also with blood types: positive correlation with types RH- ($3\cdot10^{-5}$) and A+ ($3\cdot10^{-3}$), negative correlation with B+ ($2\cdot10^{-4}$). Several of the above variables are intercorrelated and likely to have common interpretations. We performed a Principal Component Analysis, in order to find their significant independent linear combinations. We also analyzed a possible bias: countries with low GDP-per capita might have less testing and we discuss correlation with the above variables. |
1008.2314 | Talitha Washington | Ronald E. Mickens and Talitha M. Washington | A Note on an NSFD Scheme for a Mathematical Model of Respiratory Virus
Transmission | The final version will be published in Journal of Difference
Equations and Applications | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We construct a nonstandard finite difference (NSFD) scheme for an SIRS
mathematical model of respiratory virus transmission. This discretization is in
full compliance with the NSFD methodology as formulated by R. E. Mickens. By
use of an exact conservation law satisfied by the SIRS differential equations,
we are able to determine the corresponding denominator function for the
discrete first-order time derivatives. Our scheme is dynamically consistent
with the SIRS differential equations since the conservation laws are preserved.
Further, the scheme is shown to satisfy a positivity condition for its
solutions for all values of the time step-size.
| [
{
"created": "Fri, 13 Aug 2010 13:36:23 GMT",
"version": "v1"
}
] | 2010-08-16 | [
[
"Mickens",
"Ronald E.",
""
],
[
"Washington",
"Talitha M.",
""
]
] | We construct a nonstandard finite difference (NSFD) scheme for an SIRS mathematical model of respiratory virus transmission. This discretization is in full compliance with the NSFD methodology as formulated by R. E. Mickens. By use of an exact conservation law satisfied by the SIRS differential equations, we are able to determine the corresponding denominator function for the discrete first-order time derivatives. Our scheme is dynamically consistent with the SIRS differential equations since the conservation laws are preserved. Further, the scheme is shown to satisfy a positivity condition for its solutions for all values of the time step-size. |
0902.2120 | Anirban Banerji | Anirban Banerji | An algorithm to relate protein surface roughness with local geometry of
protein exterior shape | null | null | null | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Changes in the extent of local concavity along with changes in surface
roughness of binding sites of proteins have long been considered as useful
markers to identify functional sites of proteins. However, an algorithm that
describes the connection between the simultaneous changes of these important
parameters - eludes the students of structural biology. Here a simple yet
general mathematical scheme is proposed that attempts to achieve the same.
Instead of n-dimensional random vector description, protein surface roughness
is described here as a system of algebraic equations. Such description resulted
in the construction of a generalized index that not only describes the
shape-change-vs-surface-roughness-change process but also reduces the
estimation error in local shape characterization. Suitable algorithmic
implementation of it in context-specific macromolecular recognition can be
attempted easily. Contemporary drug discovery studies will be enormously
benefited from this work because it is the first algorithm that can estimate
the change in protein surface roughness as the local shape of the protein is
changing (and vice-versa).
| [
{
"created": "Thu, 12 Feb 2009 14:27:52 GMT",
"version": "v1"
},
{
"created": "Mon, 28 Nov 2011 05:21:53 GMT",
"version": "v2"
}
] | 2011-11-29 | [
[
"Banerji",
"Anirban",
""
]
] | Changes in the extent of local concavity along with changes in surface roughness of binding sites of proteins have long been considered as useful markers to identify functional sites of proteins. However, an algorithm that describes the connection between the simultaneous changes of these important parameters - eludes the students of structural biology. Here a simple yet general mathematical scheme is proposed that attempts to achieve the same. Instead of n-dimensional random vector description, protein surface roughness is described here as a system of algebraic equations. Such description resulted in the construction of a generalized index that not only describes the shape-change-vs-surface-roughness-change process but also reduces the estimation error in local shape characterization. Suitable algorithmic implementation of it in context-specific macromolecular recognition can be attempted easily. Contemporary drug discovery studies will be enormously benefited from this work because it is the first algorithm that can estimate the change in protein surface roughness as the local shape of the protein is changing (and vice-versa). |
2202.12185 | Attila Szolnoki | Breno F. de Oliveira and Attila Szolnoki | Competition among alliances of different sizes | 9 pages, 7 figures, accepted for publication in Chaos, Solitons and
Fractals | Chaos, Solitons and Fractals 157 (2022) 111940 | 10.1016/j.chaos.2022.111940 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | To understand the biodiversity of an ecosystem cannot be understood by solely
analyzing the pair relations of competing species. Instead, we should consider
multi-point interactions because the presence of a third party could change the
original microscopic outcome significantly. In this way an alliance may emerge
where species, who may have biased relations otherwise, can protect each other
from an external invader. Such an alliance can be formed by two, three or even
more species. By introducing a minimal model where six species compete for
space we here study how the size of an alliance determines the vitality of a
formation. We show that in the majority of parameter space the group of the
smallest size prevails and other solutions can only be observed in a limited
parameter range. These phases are separated by discontinuous phase transitions
which can only be identified by intensive numerical efforts due to serious
finite size effects and long relaxation processes.
| [
{
"created": "Thu, 24 Feb 2022 16:37:44 GMT",
"version": "v1"
}
] | 2022-03-01 | [
[
"de Oliveira",
"Breno F.",
""
],
[
"Szolnoki",
"Attila",
""
]
] | To understand the biodiversity of an ecosystem cannot be understood by solely analyzing the pair relations of competing species. Instead, we should consider multi-point interactions because the presence of a third party could change the original microscopic outcome significantly. In this way an alliance may emerge where species, who may have biased relations otherwise, can protect each other from an external invader. Such an alliance can be formed by two, three or even more species. By introducing a minimal model where six species compete for space we here study how the size of an alliance determines the vitality of a formation. We show that in the majority of parameter space the group of the smallest size prevails and other solutions can only be observed in a limited parameter range. These phases are separated by discontinuous phase transitions which can only be identified by intensive numerical efforts due to serious finite size effects and long relaxation processes. |
2202.07533 | Luis Pedro Garc\'ia-Pintos | Luis Pedro Garc\'ia-Pintos | Limits on the Evolutionary Rates of Biological Traits | Close to the published version | null | 10.1038/s41598-024-61872-z | null | q-bio.PE cond-mat.stat-mech physics.bio-ph q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper focuses on the maximum speed at which biological evolution can
occur. I derive inequalities that limit the rate of evolutionary processes
driven by natural selection, mutations, or genetic drift. These \emph{rate
limits} link the variability in a population to evolutionary rates. In
particular, high variances in the fitness of a population and of a quantitative
trait allow for fast changes in the trait's average. In contrast, low
variability makes a trait less susceptible to random changes due to genetic
drift. The results in this article generalize Fisher's fundamental theorem of
natural selection to dynamics that allow for mutations and genetic drift, via
trade-off relations that constrain the evolutionary rates of arbitrary traits.
The rate limits can be used to probe questions in various evolutionary biology
and ecology settings. They apply, for instance, to trait dynamics within or
across species or to the evolution of bacteria strains. They apply to any
quantitative trait, e.g., from species' weights to the lengths of DNA strands.
| [
{
"created": "Tue, 15 Feb 2022 16:05:37 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Feb 2022 01:27:55 GMT",
"version": "v2"
},
{
"created": "Tue, 18 Jul 2023 15:39:33 GMT",
"version": "v3"
},
{
"created": "Fri, 14 Jun 2024 16:23:15 GMT",
"version": "v4"
}
] | 2024-06-17 | [
[
"García-Pintos",
"Luis Pedro",
""
]
] | This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These \emph{rate limits} link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait's average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher's fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-off relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species' weights to the lengths of DNA strands. |
1709.01116 | Dimitrios Adamos Dr | Fotis Kalaganis (1), Dimitrios A. Adamos (2 and 3), Nikos Laskaris (1
and 3) ((1) AIIA Lab, Department of Informatics, Aristotle University of
Thessaloniki, (2) School of Music Studies, Aristotle University of
Thessaloniki, (3) Neuroinformatics GRoup, Aristotle University of
Thessaloniki) | Musical NeuroPicks: a consumer-grade BCI for on-demand music streaming
services | null | Neurocomputing 2017 | null | null | q-bio.NC cs.CY cs.HC cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigated the possibility of using a machine-learning scheme in
conjunction with commercial wearable EEG-devices for translating listener's
subjective experience of music into scores that can be used in popular
on-demand music streaming services. Our study resulted into two variants,
differing in terms of performance and execution time, and hence, subserving
distinct applications in online streaming music platforms. The first method,
NeuroPicks, is extremely accurate but slower. It is based on the
well-established neuroscientific concepts of brainwave frequency bands,
activation asymmetry index and cross frequency coupling (CFC). The second
method, NeuroPicksVQ, offers prompt predictions of lower credibility and relies
on a custom-built version of vector quantization procedure that facilitates a
novel parameterization of the music-modulated brainwaves. Beyond the feature
engineering step, both methods exploit the inherent efficiency of extreme
learning machines (ELMs) so as to translate, in a personalized fashion, the
derived patterns into a listener's score. NeuroPicks method may find
applications as an integral part of contemporary music recommendation systems,
while NeuroPicksVQ can control the selection of music tracks. Encouraging
experimental results, from a pragmatic use of the systems, are presented.
| [
{
"created": "Mon, 4 Sep 2017 18:55:35 GMT",
"version": "v1"
}
] | 2017-09-06 | [
[
"Kalaganis",
"Fotis",
"",
"2 and 3"
],
[
"Adamos",
"Dimitrios A.",
"",
"2 and 3"
],
[
"Laskaris",
"Nikos",
"",
"1\n and 3"
]
] | We investigated the possibility of using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener's subjective experience of music into scores that can be used in popular on-demand music streaming services. Our study resulted into two variants, differing in terms of performance and execution time, and hence, subserving distinct applications in online streaming music platforms. The first method, NeuroPicks, is extremely accurate but slower. It is based on the well-established neuroscientific concepts of brainwave frequency bands, activation asymmetry index and cross frequency coupling (CFC). The second method, NeuroPicksVQ, offers prompt predictions of lower credibility and relies on a custom-built version of vector quantization procedure that facilitates a novel parameterization of the music-modulated brainwaves. Beyond the feature engineering step, both methods exploit the inherent efficiency of extreme learning machines (ELMs) so as to translate, in a personalized fashion, the derived patterns into a listener's score. NeuroPicks method may find applications as an integral part of contemporary music recommendation systems, while NeuroPicksVQ can control the selection of music tracks. Encouraging experimental results, from a pragmatic use of the systems, are presented. |
0902.1941 | Francesco Piazza | Lorenzo Bongini, Francesco Piazza, Lapo Casetti and Paolo De Los Rios | Vibrational entropy and the structural organization of proteins | 12 pages, 8 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we analyze the vibrational spectra of a large ensemble of
non-homologous protein structures by means of a novel tool, that we coin the
Hierarchical Network Model (HNM). Our coarse-grained scheme accounts for the
intrinsic heterogeneity of force constants displayed by protein arrangements
and also incorporates side-chain degrees of freedom.
Our analysis shows that vibrational entropy per unit residue correlates with
the content of secondary structure. Furthermore, we assess the individual
contribution to vibrational entropy of the novel features of our scheme as
compared with the predictions of state-of-the-art network models. This analysis
highlights the importance of properly accounting for the intrinsic hierarchy in
force strengths typical of the different atomic bonds that build up and
stabilize protein scaffolds.
Finally, we discuss possible implications of our findings in the context of
protein aggregation phenomena.
| [
{
"created": "Wed, 11 Feb 2009 16:56:02 GMT",
"version": "v1"
}
] | 2009-02-12 | [
[
"Bongini",
"Lorenzo",
""
],
[
"Piazza",
"Francesco",
""
],
[
"Casetti",
"Lapo",
""
],
[
"Rios",
"Paolo De Los",
""
]
] | In this paper we analyze the vibrational spectra of a large ensemble of non-homologous protein structures by means of a novel tool, that we coin the Hierarchical Network Model (HNM). Our coarse-grained scheme accounts for the intrinsic heterogeneity of force constants displayed by protein arrangements and also incorporates side-chain degrees of freedom. Our analysis shows that vibrational entropy per unit residue correlates with the content of secondary structure. Furthermore, we assess the individual contribution to vibrational entropy of the novel features of our scheme as compared with the predictions of state-of-the-art network models. This analysis highlights the importance of properly accounting for the intrinsic hierarchy in force strengths typical of the different atomic bonds that build up and stabilize protein scaffolds. Finally, we discuss possible implications of our findings in the context of protein aggregation phenomena. |
1112.2317 | Christopher L. Henley | Christopher L. Henley | Possible origins of macroscopic left-right asymmetry in organisms | 42 pages, 6 figures, resubmitted to J. Stat. Phys. special issue.
Major rewrite, rearranged sections/subsections, new Fig 3 + 6, new physics in
Sec 2.4 and 3.4.1, added Sec 5 and subsections of Sec 4 | null | 10.1007/s10955-012-0520-z | null | q-bio.SC q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I consider the microscopic mechanisms by which a particular left-right (L/R)
asymmetry is generated at the organism level from the microscopic handedness of
cytoskeletal molecules. In light of a fundamental symmetry principle, the
typical pattern-formation mechanisms of diffusion plus regulation cannot
implement the "right-hand rule"; at the microscopic level, the cell's
cytoskeleton of chiral filaments seems always to be involved, usually in
collective states driven by polymerization forces or molecular motors. It seems
particularly easy for handedness to emerge in a shear or rotation in the
background of an effectively two-dimensional system, such as the cell membrane
or a layer of cells, as this requires no pre-existing axis apart from the layer
normal. I detail a scenario involving actin/myosin layers in snails and in C.
elegans, and also one about the microtubule layer in plant cells. I also survey
the other examples that I am aware of, such as the emergence of handedness such
as the emergence of handedness in neurons, in eukaryote cell motility, and in
non-flagellated bacteria.
| [
{
"created": "Sun, 11 Dec 2011 03:57:16 GMT",
"version": "v1"
},
{
"created": "Thu, 3 May 2012 01:36:21 GMT",
"version": "v2"
}
] | 2015-06-03 | [
[
"Henley",
"Christopher L.",
""
]
] | I consider the microscopic mechanisms by which a particular left-right (L/R) asymmetry is generated at the organism level from the microscopic handedness of cytoskeletal molecules. In light of a fundamental symmetry principle, the typical pattern-formation mechanisms of diffusion plus regulation cannot implement the "right-hand rule"; at the microscopic level, the cell's cytoskeleton of chiral filaments seems always to be involved, usually in collective states driven by polymerization forces or molecular motors. It seems particularly easy for handedness to emerge in a shear or rotation in the background of an effectively two-dimensional system, such as the cell membrane or a layer of cells, as this requires no pre-existing axis apart from the layer normal. I detail a scenario involving actin/myosin layers in snails and in C. elegans, and also one about the microtubule layer in plant cells. I also survey the other examples that I am aware of, such as the emergence of handedness such as the emergence of handedness in neurons, in eukaryote cell motility, and in non-flagellated bacteria. |
q-bio/0505016 | Dietrich Stauffer | Klaus Rohde and Dietrich Stauffer | Simulation of geographical trends in Chowdhury ecosystem model | 13 pages including 8 figures | null | null | null | q-bio.PE | null | A computer simulation based on individual births and deaths gives a
biodiversity increasing from cold to warm climates, in agreement with reality.
Complexity of foodwebs increases with time and at a higher rate at low
latitudes, and there is a higher rate of species creation at low latitudes.
Keeping many niches empty makes the results correspond more closely to natural
gradients.
| [
{
"created": "Mon, 9 May 2005 13:57:29 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Rohde",
"Klaus",
""
],
[
"Stauffer",
"Dietrich",
""
]
] | A computer simulation based on individual births and deaths gives a biodiversity increasing from cold to warm climates, in agreement with reality. Complexity of foodwebs increases with time and at a higher rate at low latitudes, and there is a higher rate of species creation at low latitudes. Keeping many niches empty makes the results correspond more closely to natural gradients. |
1909.11943 | Maksims Fiosins | Jelena Fiosina, Maksims Fiosins, Stefan Bonn | Deep Learning and Random Forest-Based Augmentation of sRNA Expression
Profiles | null | Lecture Notes in Computer Science, 11490 (2019) | 10.1007/978-3-030-20242-2_14 | null | q-bio.GN cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The lack of well-structured annotations in a growing amount of RNA expression
data complicates data interoperability and reusability. Commonly - used text
mining methods extract annotations from existing unstructured data descriptions
and often provide inaccurate output that requires manual curation. Automatic
data-based augmentation (generation of annotations on the base of expression
data) can considerably improve the annotation quality and has not been
well-studied. We formulate an automatic augmentation of small RNA-seq
expression data as a classification problem and investigate deep learning (DL)
and random forest (RF) approaches to solve it. We generate tissue and sex
annotations from small RNA-seq expression data for tissues and cell lines of
homo sapiens. We validate our approach on 4243 annotated small RNA-seq samples
from the Small RNA Expression Atlas (SEA) database. The average prediction
accuracy for tissue groups is 98% (DL), for tissues - 96.5% (DL), and for sex -
77% (DL). The "one dataset out" average accuracy for tissue group prediction is
83% (DL) and 59% (RF). On average, DL provides better results as compared to
RF, and considerably improves classification performance for 'unseen' datasets.
| [
{
"created": "Thu, 26 Sep 2019 07:10:03 GMT",
"version": "v1"
}
] | 2019-09-27 | [
[
"Fiosina",
"Jelena",
""
],
[
"Fiosins",
"Maksims",
""
],
[
"Bonn",
"Stefan",
""
]
] | The lack of well-structured annotations in a growing amount of RNA expression data complicates data interoperability and reusability. Commonly - used text mining methods extract annotations from existing unstructured data descriptions and often provide inaccurate output that requires manual curation. Automatic data-based augmentation (generation of annotations on the base of expression data) can considerably improve the annotation quality and has not been well-studied. We formulate an automatic augmentation of small RNA-seq expression data as a classification problem and investigate deep learning (DL) and random forest (RF) approaches to solve it. We generate tissue and sex annotations from small RNA-seq expression data for tissues and cell lines of homo sapiens. We validate our approach on 4243 annotated small RNA-seq samples from the Small RNA Expression Atlas (SEA) database. The average prediction accuracy for tissue groups is 98% (DL), for tissues - 96.5% (DL), and for sex - 77% (DL). The "one dataset out" average accuracy for tissue group prediction is 83% (DL) and 59% (RF). On average, DL provides better results as compared to RF, and considerably improves classification performance for 'unseen' datasets. |
2101.04183 | Peter Thompson | Peter R. Thompson, Andrew E. Derocher, Mark A. Edwards, Mark A. Lewis | Detecting seasonal episodic-like spatiotemporal memory patterns using
animal movement modelling | 23 pages, including title and abstract; 4 tables; 3 figures; one
Appendix containing 2 additional tables and 1 additional figure | null | 10.1111/2041-210X.13743 | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | 1. Spatial memory plays a role in the way animals perceive their
environments, resulting in memory-informed movement patterns that are
observable to ecologists. Developing mathematical techniques to understand how
animals use memory in their environments allows for an increased understanding
of animal cognition. 2. Here we describe a model that accounts for the memory
of seasonal or ephemeral qualities of an animal's environment. The model
captures multiple behaviors at once by allowing for resource selection in the
present time as well as long-distance navigations to previously visited
locations within an animal's home range. 3. We performed a set of analyses on
simulated data to test our model, determining that it can provide informative
results from as little as one year of discrete-time location data. We also show
that the accuracy of model selection and parameter estimation increases with
more location data. 4. This model has potential to identify a specific
mechanism in which animals use memory to optimize their foraging, by revisiting
temporally and predictably variable resources at consistent time lags.
| [
{
"created": "Mon, 11 Jan 2021 20:46:35 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Feb 2022 21:09:01 GMT",
"version": "v2"
}
] | 2022-02-17 | [
[
"Thompson",
"Peter R.",
""
],
[
"Derocher",
"Andrew E.",
""
],
[
"Edwards",
"Mark A.",
""
],
[
"Lewis",
"Mark A.",
""
]
] | 1. Spatial memory plays a role in the way animals perceive their environments, resulting in memory-informed movement patterns that are observable to ecologists. Developing mathematical techniques to understand how animals use memory in their environments allows for an increased understanding of animal cognition. 2. Here we describe a model that accounts for the memory of seasonal or ephemeral qualities of an animal's environment. The model captures multiple behaviors at once by allowing for resource selection in the present time as well as long-distance navigations to previously visited locations within an animal's home range. 3. We performed a set of analyses on simulated data to test our model, determining that it can provide informative results from as little as one year of discrete-time location data. We also show that the accuracy of model selection and parameter estimation increases with more location data. 4. This model has potential to identify a specific mechanism in which animals use memory to optimize their foraging, by revisiting temporally and predictably variable resources at consistent time lags. |
2103.04246 | Alvin Chan | Alvin Chan, Anna Korsakova, Yew-Soon Ong, Fernaldo Richtia Winnerdy,
Kah Wai Lim, Anh Tuan Phan | RNA Alternative Splicing Prediction with Discrete Compositional Energy
Network | ACM CHIL 2021 Camera-Ready | null | null | null | q-bio.GN cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | A single gene can encode for different protein versions through a process
called alternative splicing. Since proteins play major roles in cellular
functions, aberrant splicing profiles can result in a variety of diseases,
including cancers. Alternative splicing is determined by the gene's primary
sequence and other regulatory factors such as RNA-binding protein levels. With
these as input, we formulate the prediction of RNA splicing as a regression
task and build a new training dataset (CAPD) to benchmark learned models. We
propose discrete compositional energy network (DCEN) which leverages the
hierarchical relationships between splice sites, junctions and transcripts to
approach this task. In the case of alternative splicing prediction, DCEN models
mRNA transcript probabilities through its constituent splice junctions' energy
values. These transcript probabilities are subsequently mapped to relative
abundance values of key nucleotides and trained with ground-truth experimental
measurements. Through our experiments on CAPD, we show that DCEN outperforms
baselines and ablation variants.
| [
{
"created": "Sun, 7 Mar 2021 03:15:10 GMT",
"version": "v1"
}
] | 2021-03-09 | [
[
"Chan",
"Alvin",
""
],
[
"Korsakova",
"Anna",
""
],
[
"Ong",
"Yew-Soon",
""
],
[
"Winnerdy",
"Fernaldo Richtia",
""
],
[
"Lim",
"Kah Wai",
""
],
[
"Phan",
"Anh Tuan",
""
]
] | A single gene can encode for different protein versions through a process called alternative splicing. Since proteins play major roles in cellular functions, aberrant splicing profiles can result in a variety of diseases, including cancers. Alternative splicing is determined by the gene's primary sequence and other regulatory factors such as RNA-binding protein levels. With these as input, we formulate the prediction of RNA splicing as a regression task and build a new training dataset (CAPD) to benchmark learned models. We propose discrete compositional energy network (DCEN) which leverages the hierarchical relationships between splice sites, junctions and transcripts to approach this task. In the case of alternative splicing prediction, DCEN models mRNA transcript probabilities through its constituent splice junctions' energy values. These transcript probabilities are subsequently mapped to relative abundance values of key nucleotides and trained with ground-truth experimental measurements. Through our experiments on CAPD, we show that DCEN outperforms baselines and ablation variants. |
1911.08121 | Nina Miolane | Nina Miolane, Fr\'ed\'eric Poitevin, Yee-Ting Li, Susan Holmes | Estimation of Orientation and Camera Parameters from Cryo-Electron
Microscopy Images with Variational Autoencoders and Generative Adversarial
Networks | null | null | null | null | q-bio.QM cs.CV eess.IV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D
images of biomolecules at near-atomic resolution. As such, it represents one of
the most promising imaging techniques in structural biology. However, raw
cryo-EM images are only highly corrupted - noisy and band-pass filtered - 2D
projections of the target 3D biomolecules. Reconstructing the 3D molecular
shape starts with the removal of image outliers, the estimation of the
orientation of the biomolecule that has produced the given 2D image, and the
estimation of camera parameters to correct for intensity defects. Current
techniques performing these tasks are often computationally expensive, while
the dataset sizes keep growing. There is a need for next-generation algorithms
that preserve accuracy while improving speed and scalability. In this paper, we
combine variational autoencoders (VAEs) and generative adversarial networks
(GANs) to learn a low-dimensional latent representation of cryo-EM images. We
perform an exploratory analysis of the obtained latent space, that is shown to
have a structure of "orbits", in the sense of Lie group theory, consistent with
the acquisition procedure of cryo-EM images. This analysis leads us to design
an estimation method for orientation and camera parameters of single-particle
cryo-EM images, together with an outliers detection procedure. As such, it
opens the door to geometric approaches for unsupervised estimations of
orientations and camera parameters, making possible fast cryo-EM biomolecule
reconstruction.
| [
{
"created": "Tue, 19 Nov 2019 07:04:43 GMT",
"version": "v1"
},
{
"created": "Sun, 23 May 2021 19:11:06 GMT",
"version": "v2"
}
] | 2021-05-25 | [
[
"Miolane",
"Nina",
""
],
[
"Poitevin",
"Frédéric",
""
],
[
"Li",
"Yee-Ting",
""
],
[
"Holmes",
"Susan",
""
]
] | Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D images of biomolecules at near-atomic resolution. As such, it represents one of the most promising imaging techniques in structural biology. However, raw cryo-EM images are only highly corrupted - noisy and band-pass filtered - 2D projections of the target 3D biomolecules. Reconstructing the 3D molecular shape starts with the removal of image outliers, the estimation of the orientation of the biomolecule that has produced the given 2D image, and the estimation of camera parameters to correct for intensity defects. Current techniques performing these tasks are often computationally expensive, while the dataset sizes keep growing. There is a need for next-generation algorithms that preserve accuracy while improving speed and scalability. In this paper, we combine variational autoencoders (VAEs) and generative adversarial networks (GANs) to learn a low-dimensional latent representation of cryo-EM images. We perform an exploratory analysis of the obtained latent space, that is shown to have a structure of "orbits", in the sense of Lie group theory, consistent with the acquisition procedure of cryo-EM images. This analysis leads us to design an estimation method for orientation and camera parameters of single-particle cryo-EM images, together with an outliers detection procedure. As such, it opens the door to geometric approaches for unsupervised estimations of orientations and camera parameters, making possible fast cryo-EM biomolecule reconstruction. |
q-bio/0605009 | Stefan Braunewell | Stefan Braunewell and Stefan Bornholdt | Superstability of the yeast cell cycle dynamics: Ensuring causality in
the presence of biochemical stochasticity | 6 pages, 4 figures, 1 table | null | null | null | q-bio.MN | null | Gene regulatory dynamics is governed by molecular processes and therefore
exhibits an inherent stochasticity. However, for the survival of an organism it
is a strict necessity that this intrinsic noise does not prevent robust
functioning of the system. It is still an open question how dynamical stability
is achieved in biological systems despite the omnipresent fluctuations. In this
paper we investigate the cell-cycle of the budding yeast Saccharomyces
cerevisiae as an example of a well-studied organism.
We study a genetic network model of eleven genes that coordinate the
cell-cycle dynamics using a modeling framework which generalizes the concept of
discrete threshold dynamics. By allowing for fluctuations in the
transcription/translation times, we introduce noise in the model, accounting
for the effects of biochemical stochasticity. We study the dynamical attractor
of the cell cycle and find a remarkable robustness against fluctuations of this
kind. We identify mechanisms that ensure reliability in spite of fluctuations:
'Catcher' states and persistence of activity levels contribute significantly to
the stability of the yeast cell cycle despite the inherent stochasticity.
| [
{
"created": "Fri, 5 May 2006 09:33:36 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Braunewell",
"Stefan",
""
],
[
"Bornholdt",
"Stefan",
""
]
] | Gene regulatory dynamics is governed by molecular processes and therefore exhibits an inherent stochasticity. However, for the survival of an organism it is a strict necessity that this intrinsic noise does not prevent robust functioning of the system. It is still an open question how dynamical stability is achieved in biological systems despite the omnipresent fluctuations. In this paper we investigate the cell-cycle of the budding yeast Saccharomyces cerevisiae as an example of a well-studied organism. We study a genetic network model of eleven genes that coordinate the cell-cycle dynamics using a modeling framework which generalizes the concept of discrete threshold dynamics. By allowing for fluctuations in the transcription/translation times, we introduce noise in the model, accounting for the effects of biochemical stochasticity. We study the dynamical attractor of the cell cycle and find a remarkable robustness against fluctuations of this kind. We identify mechanisms that ensure reliability in spite of fluctuations: 'Catcher' states and persistence of activity levels contribute significantly to the stability of the yeast cell cycle despite the inherent stochasticity. |
1905.10916 | Sepehr Ehsani | Sepehr Ehsani | The challenges of purely mechanistic models in biology and the minimum
need for a 'mechanism-plus-X' framework | 38 pages, 3 figures | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Ever since the advent of molecular biology in the 1970s, mechanical models
have become the dogma in the field, where a "true" understanding of any subject
is equated to a mechanistic description. This has been to the detriment of the
biomedical sciences, where, barring some exceptions, notable new feats of
understanding have arguably not been achieved in normal and disease biology,
including neurodegenerative disease and cancer pathobiology. I argue for a
"mechanism-plus-X" paradigm, where mainstay elements of mechanistic models such
as hierarchy and correlation are combined with nomological principles such as
general operative rules and generative principles. Depending on the question at
hand and the nature of the inquiry, X could range from proven physical laws to
speculative biological generalizations, such as the notional principle of
cellular synchrony. I argue that the "mechanism-plus-X" approach should
ultimately aim to move biological inquiries out of the deadlock of
oft-encountered mechanistic pitfalls and reposition biology to its former
capacity of illuminating fundamental truths about the world.
| [
{
"created": "Mon, 27 May 2019 00:56:00 GMT",
"version": "v1"
}
] | 2019-05-28 | [
[
"Ehsani",
"Sepehr",
""
]
] | Ever since the advent of molecular biology in the 1970s, mechanical models have become the dogma in the field, where a "true" understanding of any subject is equated to a mechanistic description. This has been to the detriment of the biomedical sciences, where, barring some exceptions, notable new feats of understanding have arguably not been achieved in normal and disease biology, including neurodegenerative disease and cancer pathobiology. I argue for a "mechanism-plus-X" paradigm, where mainstay elements of mechanistic models such as hierarchy and correlation are combined with nomological principles such as general operative rules and generative principles. Depending on the question at hand and the nature of the inquiry, X could range from proven physical laws to speculative biological generalizations, such as the notional principle of cellular synchrony. I argue that the "mechanism-plus-X" approach should ultimately aim to move biological inquiries out of the deadlock of oft-encountered mechanistic pitfalls and reposition biology to its former capacity of illuminating fundamental truths about the world. |
2403.05044 | Eddy Kwessi | Eddy Kwessi | Information Theory in a Darwinian Evolution Population Dynamics Model | null | null | null | null | q-bio.PE cs.IT math.DS math.IT math.ST stat.TH | http://creativecommons.org/licenses/by/4.0/ | Using information theory, we propose an estimation method for traits
parameters in a Darwinian evolution model for species with on trait or multiple
traits. We use the Fisher's information to obtain the errors on the estimation
for one species with one or multiple traits. We perform simulations to
illustrate the method.
| [
{
"created": "Fri, 8 Mar 2024 04:39:05 GMT",
"version": "v1"
}
] | 2024-03-11 | [
[
"Kwessi",
"Eddy",
""
]
] | Using information theory, we propose an estimation method for traits parameters in a Darwinian evolution model for species with on trait or multiple traits. We use the Fisher's information to obtain the errors on the estimation for one species with one or multiple traits. We perform simulations to illustrate the method. |
2112.01318 | Jiahui Chen | Jiahui Chen, Rui Wang, Nancy Benovich Gilby and Guo-Wei Wei | Omicron (B.1.1.529): Infectivity, vaccine breakthrough, and antibody
resistance | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
variant Omicron (B.1.1.529) has ushered panic responses around the world due to
its contagious and vaccine escape mutations. The essential infectivity and
antibody resistance of the SARS-CoV-2 variant are determined by its mutations
on the spike (S) protein receptor-binding domain (RBD). However, a complete
experimental evaluation of Omicron might take weeks or even months. Here, we
present a comprehensive quantitative analysis of Omicron's infectivity,
vaccine-breakthrough, and antibody resistance. An artificial intelligence (AI)
model, which has been trained with tens of thousands of experimental data
points and extensively validated by experimental data on SARS-CoV-2, reveals
that Omicron may be over ten times more contagious than the original virus or
about twice as infectious as the Delta variant. Based on 132 three-dimensional
(3D) structures of antibody-RBD complexes, we unveil that Omicron may be twice
more likely to escape current vaccines than the Delta variant. The Food and
Drug Administration (FDA)-approved monoclonal antibodies (mAbs) from Eli Lilly
may be seriously compromised. Omicron may also diminish the efficacy of mAbs
from Celltrion and Rockefeller University. However, its impact on Regeneron mAb
cocktail appears to be mild.
| [
{
"created": "Wed, 1 Dec 2021 14:29:48 GMT",
"version": "v1"
}
] | 2021-12-03 | [
[
"Chen",
"Jiahui",
""
],
[
"Wang",
"Rui",
""
],
[
"Gilby",
"Nancy Benovich",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B.1.1.529) has ushered panic responses around the world due to its contagious and vaccine escape mutations. The essential infectivity and antibody resistance of the SARS-CoV-2 variant are determined by its mutations on the spike (S) protein receptor-binding domain (RBD). However, a complete experimental evaluation of Omicron might take weeks or even months. Here, we present a comprehensive quantitative analysis of Omicron's infectivity, vaccine-breakthrough, and antibody resistance. An artificial intelligence (AI) model, which has been trained with tens of thousands of experimental data points and extensively validated by experimental data on SARS-CoV-2, reveals that Omicron may be over ten times more contagious than the original virus or about twice as infectious as the Delta variant. Based on 132 three-dimensional (3D) structures of antibody-RBD complexes, we unveil that Omicron may be twice more likely to escape current vaccines than the Delta variant. The Food and Drug Administration (FDA)-approved monoclonal antibodies (mAbs) from Eli Lilly may be seriously compromised. Omicron may also diminish the efficacy of mAbs from Celltrion and Rockefeller University. However, its impact on Regeneron mAb cocktail appears to be mild. |
2212.12034 | Alexandria Volkening | Electa Cleveland, Angela Zhu, Bjorn Sandstede, and Alexandria
Volkening | Quantifying different modeling frameworks using topological data
analysis: a case study with zebrafish patterns | null | null | null | null | q-bio.CB math.DS | http://creativecommons.org/licenses/by/4.0/ | Mathematical models come in many forms across biological applications. In the
case of complex, spatial dynamics and pattern formation, stochastic models also
face two main challenges: pattern data is largely qualitative, and model
realizations may vary significantly. Together these issues make it difficult to
relate models and empirical data -- or even models and models -- limiting how
different approaches can be combined to offer new insights into biology. These
challenges also raise mathematical questions about how models are related,
since alternative approaches to the same problem -- e.g., cellular Potts
models; off-lattice, agent-based models; on-lattice, cellular automaton models;
and continuum approaches -- treat uncertainty and implement cell behavior in
different ways. To help open the door to future work on questions like these,
here we adapt methods from topological data analysis and computational geometry
to quantitatively relate two different models of the same biological process in
a fair, comparable way. To center our work and illustrate concrete challenges,
we focus on the example of zebrafish-skin pattern formation, and we relate
patterns that arise from agent-based and cellular automaton models.
| [
{
"created": "Thu, 22 Dec 2022 20:54:32 GMT",
"version": "v1"
}
] | 2022-12-26 | [
[
"Cleveland",
"Electa",
""
],
[
"Zhu",
"Angela",
""
],
[
"Sandstede",
"Bjorn",
""
],
[
"Volkening",
"Alexandria",
""
]
] | Mathematical models come in many forms across biological applications. In the case of complex, spatial dynamics and pattern formation, stochastic models also face two main challenges: pattern data is largely qualitative, and model realizations may vary significantly. Together these issues make it difficult to relate models and empirical data -- or even models and models -- limiting how different approaches can be combined to offer new insights into biology. These challenges also raise mathematical questions about how models are related, since alternative approaches to the same problem -- e.g., cellular Potts models; off-lattice, agent-based models; on-lattice, cellular automaton models; and continuum approaches -- treat uncertainty and implement cell behavior in different ways. To help open the door to future work on questions like these, here we adapt methods from topological data analysis and computational geometry to quantitatively relate two different models of the same biological process in a fair, comparable way. To center our work and illustrate concrete challenges, we focus on the example of zebrafish-skin pattern formation, and we relate patterns that arise from agent-based and cellular automaton models. |
2408.07222 | Marek Mutwil | Rohan Shawn Sunil, Shan Chun Lim, Manoj Itharajula, Marek Mutwil | The gene function prediction challenge: large language models and
knowledge graphs to the rescue | null | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Elucidating gene function is one of the ultimate goals of plant science.
Despite this, only ~15% of all genes in the model plant Arabidopsis thaliana
have comprehensively experimentally verified functions. While bioinformatical
gene function prediction approaches can guide biologists in their experimental
efforts, neither the performance of the gene function prediction methods nor
the number of experimental characterisation of genes has increased dramatically
in recent years. In this review, we will discuss the status quo and the
trajectory of gene function elucidation and outline the recent advances in gene
function prediction approaches. We will then discuss how recent artificial
intelligence advances in large language models and knowledge graphs can be
leveraged to accelerate gene function predictions and keep us updated with
scientific literature.
| [
{
"created": "Tue, 13 Aug 2024 22:39:21 GMT",
"version": "v1"
}
] | 2024-08-15 | [
[
"Sunil",
"Rohan Shawn",
""
],
[
"Lim",
"Shan Chun",
""
],
[
"Itharajula",
"Manoj",
""
],
[
"Mutwil",
"Marek",
""
]
] | Elucidating gene function is one of the ultimate goals of plant science. Despite this, only ~15% of all genes in the model plant Arabidopsis thaliana have comprehensively experimentally verified functions. While bioinformatical gene function prediction approaches can guide biologists in their experimental efforts, neither the performance of the gene function prediction methods nor the number of experimental characterisation of genes has increased dramatically in recent years. In this review, we will discuss the status quo and the trajectory of gene function elucidation and outline the recent advances in gene function prediction approaches. We will then discuss how recent artificial intelligence advances in large language models and knowledge graphs can be leveraged to accelerate gene function predictions and keep us updated with scientific literature. |
2111.10530 | Manuel Baltieri Dr | Manuel Baltieri and Takuya Isomura | Kalman filters as the steady-state solution of gradient descent on
variational free energy | null | null | null | null | q-bio.NC cs.SY eess.SY math.OC math.ST stat.TH | http://creativecommons.org/licenses/by-sa/4.0/ | The Kalman filter is an algorithm for the estimation of hidden variables in
dynamical systems under linear Gauss-Markov assumptions with widespread
applications across different fields. Recently, its Bayesian interpretation has
received a growing amount of attention especially in neuroscience, robotics and
machine learning. In neuroscience, in particular, models of perception and
control under the banners of predictive coding, optimal feedback control,
active inference and more generally the so-called Bayesian brain hypothesis,
have all heavily relied on ideas behind the Kalman filter. Active inference, an
algorithmic theory based on the free energy principle, specifically builds on
approximate Bayesian inference methods proposing a variational account of
neural computation and behaviour in terms of gradients of variational free
energy. Using this ambitious framework, several works have discussed different
possible relations between free energy minimisation and standard Kalman
filters. With a few exceptions, however, such relations point at a mere
qualitative resemblance or are built on a set of very diverse comparisons based
on purported differences between free energy minimisation and Kalman filtering.
In this work, we present a straightforward derivation of Kalman filters
consistent with active inference via a variational treatment of free energy
minimisation in terms of gradient descent. The approach considered here offers
a more direct link between models of neural dynamics as gradient descent and
standard accounts of perception and decision making based on probabilistic
inference, further bridging the gap between hypotheses about neural
implementation and computational principles in brain and behavioural sciences.
| [
{
"created": "Sat, 20 Nov 2021 07:23:02 GMT",
"version": "v1"
}
] | 2021-11-23 | [
[
"Baltieri",
"Manuel",
""
],
[
"Isomura",
"Takuya",
""
]
] | The Kalman filter is an algorithm for the estimation of hidden variables in dynamical systems under linear Gauss-Markov assumptions with widespread applications across different fields. Recently, its Bayesian interpretation has received a growing amount of attention especially in neuroscience, robotics and machine learning. In neuroscience, in particular, models of perception and control under the banners of predictive coding, optimal feedback control, active inference and more generally the so-called Bayesian brain hypothesis, have all heavily relied on ideas behind the Kalman filter. Active inference, an algorithmic theory based on the free energy principle, specifically builds on approximate Bayesian inference methods proposing a variational account of neural computation and behaviour in terms of gradients of variational free energy. Using this ambitious framework, several works have discussed different possible relations between free energy minimisation and standard Kalman filters. With a few exceptions, however, such relations point at a mere qualitative resemblance or are built on a set of very diverse comparisons based on purported differences between free energy minimisation and Kalman filtering. In this work, we present a straightforward derivation of Kalman filters consistent with active inference via a variational treatment of free energy minimisation in terms of gradient descent. The approach considered here offers a more direct link between models of neural dynamics as gradient descent and standard accounts of perception and decision making based on probabilistic inference, further bridging the gap between hypotheses about neural implementation and computational principles in brain and behavioural sciences. |
q-bio/0606018 | Wentian Li | Wentian Li, Young Ju Suh, Jingshan Zhang | Does Logarithm Transformation of Microarray Data Affect Ranking Order of
Differentially Expressed Genes? | submitted to IEEE/EMBS Conference'06 | null | 10.1109/IEMBS.2006.260896 | null | q-bio.QM | null | A common practice in microarray analysis is to transform the microarray raw
data (light intensity) by a logarithmic transformation, and the justification
for this transformation is to make the distribution more symmetric and
Gaussian-like. Since this transformation is not universally practiced in all
microarray analysis, we examined whether the discrepancy of this treatment of
raw data affect the "high level" analysis result. In particular, whether the
differentially expressed genes as obtained by $t$-test, regularized t-test, or
logistic regression have altered rank orders due to presence or absence of the
transformation. We show that as much as 20%--40% of significant genes are
"discordant" (significant only in one form of the data and not in both),
depending on the test being used and the threshold value for claiming
significance. The t-test is more likely to be affected by logarithmic
transformation than logistic regression, and regularized $t$-test more affected
than t-test. On the other hand, the very top ranking genes (e.g. up to top
20--50 genes, depending on the test) are not affected by the logarithmic
transformation.
| [
{
"created": "Wed, 14 Jun 2006 20:45:25 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Li",
"Wentian",
""
],
[
"Suh",
"Young Ju",
""
],
[
"Zhang",
"Jingshan",
""
]
] | A common practice in microarray analysis is to transform the microarray raw data (light intensity) by a logarithmic transformation, and the justification for this transformation is to make the distribution more symmetric and Gaussian-like. Since this transformation is not universally practiced in all microarray analysis, we examined whether the discrepancy of this treatment of raw data affect the "high level" analysis result. In particular, whether the differentially expressed genes as obtained by $t$-test, regularized t-test, or logistic regression have altered rank orders due to presence or absence of the transformation. We show that as much as 20%--40% of significant genes are "discordant" (significant only in one form of the data and not in both), depending on the test being used and the threshold value for claiming significance. The t-test is more likely to be affected by logarithmic transformation than logistic regression, and regularized $t$-test more affected than t-test. On the other hand, the very top ranking genes (e.g. up to top 20--50 genes, depending on the test) are not affected by the logarithmic transformation. |
2301.12221 | Luigi Frunzo | F. Russo, A. Tenore, M.R. Mattei, L. Frunzo | Multiscale modelling of heavy metals adsorption on algal-bacterial
photogranules | 43 pages, 18 figures, preprint version | null | null | null | q-bio.CB physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A multiscale mathematical model describing the genesis and ecology of
algal-bacterial photogranules and the metals biosorption on their solid matrix
within a sequencing batch reactor (SBR) is presented. The granular biofilm is
modelled as a spherical free boundary domain with radial symmetry and a
vanishing initial value. The free boundary evolution is governed by an ODE
accounting for microbial growth, attachment and detachment phenomena. The model
is based on systems of PDEs derived from mass conservation principles.
Specifically, two systems of nonlinear hyperbolic PDEs model the growth of
attached species and the dynamics of free adsorption sites; and two systems of
quasi-linear parabolic PDEs govern the diffusive transport and conversion of
nutrients and metals. The model is completed with systems of impulsive ordinary
differential equations (IDEs) describing the evolution of dissolved substrates,
metals, and planktonic and detached biomasses within the granular-based SBR.
All main phenomena involved in the process are considered in the mathematical
model. Moreover, the dual effect of metal presence on the formation process of
photogranules is accounted: metal stimulates the production of EPS by sessile
species and negatively affects the metabolic activities of microbial species.
To describe the effects related to metal presence, a stimulation term for EPS
production and an inhibition term for metal are included in all microbial
kinetics. The model is used to examine the role of the microbial species and
EPS in the adsorption process, and the effect of metal concentration and
adsorption proprieties of biofilm components on the metal removal. Numerical
results show that the model accurately describes the photogranules evolution
and ecology and confirm the applicability of algal-bacterial photogranules
systems for metal-rich wastewater treatment.
| [
{
"created": "Sat, 28 Jan 2023 15:02:17 GMT",
"version": "v1"
}
] | 2023-01-31 | [
[
"Russo",
"F.",
""
],
[
"Tenore",
"A.",
""
],
[
"Mattei",
"M. R.",
""
],
[
"Frunzo",
"L.",
""
]
] | A multiscale mathematical model describing the genesis and ecology of algal-bacterial photogranules and the metals biosorption on their solid matrix within a sequencing batch reactor (SBR) is presented. The granular biofilm is modelled as a spherical free boundary domain with radial symmetry and a vanishing initial value. The free boundary evolution is governed by an ODE accounting for microbial growth, attachment and detachment phenomena. The model is based on systems of PDEs derived from mass conservation principles. Specifically, two systems of nonlinear hyperbolic PDEs model the growth of attached species and the dynamics of free adsorption sites; and two systems of quasi-linear parabolic PDEs govern the diffusive transport and conversion of nutrients and metals. The model is completed with systems of impulsive ordinary differential equations (IDEs) describing the evolution of dissolved substrates, metals, and planktonic and detached biomasses within the granular-based SBR. All main phenomena involved in the process are considered in the mathematical model. Moreover, the dual effect of metal presence on the formation process of photogranules is accounted: metal stimulates the production of EPS by sessile species and negatively affects the metabolic activities of microbial species. To describe the effects related to metal presence, a stimulation term for EPS production and an inhibition term for metal are included in all microbial kinetics. The model is used to examine the role of the microbial species and EPS in the adsorption process, and the effect of metal concentration and adsorption proprieties of biofilm components on the metal removal. Numerical results show that the model accurately describes the photogranules evolution and ecology and confirm the applicability of algal-bacterial photogranules systems for metal-rich wastewater treatment. |
1301.6360 | Tomasz Rutkowski | M. Chang, N. Nishikawa, Z.R. Struzik, K. Mori, S. Makino, D. Mandic,
and T.M. Rutkowski | Comparison of P300 Responses in Auditory, Visual and Audiovisual Spatial
Speller BCI Paradigms | Proceedings of the Fifth International Brain-Computer Interface
Meeting 2013, 2 pages, 1 figure | null | 10.3217/978-3-85125-260-6-156 | null | q-bio.NC cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aim of this study is to provide a comprehensive test of three spatial
speller settings, for the auditory, visual, and audiovisual paradigms. For
rigour, the study is conducted with 16 BCI-na\"ive subjects in an experimental
set-up based on five Japanese hiragana characters. Auditory P300 responses give
encouragingly longer target vs. non-target latencies during the training phase,
however, real-world online BCI experiments in the multimodal setting do not
validate this potential advantage. Our case studies indicate that the auditory
spatial unimodal paradigm needs further development in order to be a viable
alternative to the established visual domain speller applications, as far as
BCI-na\"ive subjects are concerned.
| [
{
"created": "Sun, 27 Jan 2013 14:44:23 GMT",
"version": "v1"
},
{
"created": "Sun, 12 May 2013 06:39:06 GMT",
"version": "v2"
}
] | 2013-05-14 | [
[
"Chang",
"M.",
""
],
[
"Nishikawa",
"N.",
""
],
[
"Struzik",
"Z. R.",
""
],
[
"Mori",
"K.",
""
],
[
"Makino",
"S.",
""
],
[
"Mandic",
"D.",
""
],
[
"Rutkowski",
"T. M.",
""
]
] | The aim of this study is to provide a comprehensive test of three spatial speller settings, for the auditory, visual, and audiovisual paradigms. For rigour, the study is conducted with 16 BCI-na\"ive subjects in an experimental set-up based on five Japanese hiragana characters. Auditory P300 responses give encouragingly longer target vs. non-target latencies during the training phase, however, real-world online BCI experiments in the multimodal setting do not validate this potential advantage. Our case studies indicate that the auditory spatial unimodal paradigm needs further development in order to be a viable alternative to the established visual domain speller applications, as far as BCI-na\"ive subjects are concerned. |
2310.16004 | Frederik Van Daele | Frederik Van Daele, Olivier Honnay, Hanne De Kort | Habitat fragmentation reshapes genomic footprints of selection in a
forest herb | 26 pages, 5 figures | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the combined effects of climate change and habitat
fragmentation on the adaptive potential of plant populations is essential for
devising effective conservation strategies. This is particularly important
where mating system variation impacts the evolutionary consequences of habitat
fragmentation. Here we aimed to reveal how habitat fragmentation and climate
adaptation jointly influence the evolutionary trajectories in Primula elatior,
a heterostylous self-incompatible and dispersal-constrained forest herb. We
quantified the genomic variation and degree of herkogamy, a floral trait
reducing self-pollination, across 60 geographically paired populations of
Primula elatior across Europe, each pair featuring contrasting levels of
habitat fragmentation. Our findings revealed a large and unique set of adaptive
outliers in more fragmented landscapes, compared to high-connectivity ones,
despite the geographic proximity of the sampling pairs. This suggests elevated
selective pressures in fragmented habitats, mirrored by a reduced adaptive
potential to cope with climate change. Finally, a minority of genetic variants
associated with herkogamy were influenced by current levels of habitat
fragmentation and population size, potentially signalling early indicators of
evolutionary mating system changes in response to pollinator limitation.
Because evolutionary trajectories and adaptive potential are expected to be
increasingly affected by habitat fragmentation, our findings underscore the
importance of considering both habitat fragmentation and climate adaptation in
conservation research and planning.
| [
{
"created": "Tue, 24 Oct 2023 16:57:38 GMT",
"version": "v1"
}
] | 2023-10-25 | [
[
"Van Daele",
"Frederik",
""
],
[
"Honnay",
"Olivier",
""
],
[
"De Kort",
"Hanne",
""
]
] | Understanding the combined effects of climate change and habitat fragmentation on the adaptive potential of plant populations is essential for devising effective conservation strategies. This is particularly important where mating system variation impacts the evolutionary consequences of habitat fragmentation. Here we aimed to reveal how habitat fragmentation and climate adaptation jointly influence the evolutionary trajectories in Primula elatior, a heterostylous self-incompatible and dispersal-constrained forest herb. We quantified the genomic variation and degree of herkogamy, a floral trait reducing self-pollination, across 60 geographically paired populations of Primula elatior across Europe, each pair featuring contrasting levels of habitat fragmentation. Our findings revealed a large and unique set of adaptive outliers in more fragmented landscapes, compared to high-connectivity ones, despite the geographic proximity of the sampling pairs. This suggests elevated selective pressures in fragmented habitats, mirrored by a reduced adaptive potential to cope with climate change. Finally, a minority of genetic variants associated with herkogamy were influenced by current levels of habitat fragmentation and population size, potentially signalling early indicators of evolutionary mating system changes in response to pollinator limitation. Because evolutionary trajectories and adaptive potential are expected to be increasingly affected by habitat fragmentation, our findings underscore the importance of considering both habitat fragmentation and climate adaptation in conservation research and planning. |
1510.00696 | Lior Pachter | Harold Pimentel, John G. Conboy, and Lior Pachter | Keep Me Around: Intron Retention Detection and Analysis | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a tool, keep me around (kma), a suite of python scripts and an R
package that finds retained introns in RNA-Seq experiments and incorporates
biological replicates to reduce the number of false positives when detecting
retention events. kma uses the results of existing quantification tools that
probabilistically assign multi-mapping reads, thus interfacing easily with
transcript quantification pipelines. The data is represented in a convenient,
database style format that allows for easy aggregation across introns, genes,
samples, and conditions to allow for further exploratory analysis.
| [
{
"created": "Fri, 2 Oct 2015 19:20:20 GMT",
"version": "v1"
}
] | 2015-10-05 | [
[
"Pimentel",
"Harold",
""
],
[
"Conboy",
"John G.",
""
],
[
"Pachter",
"Lior",
""
]
] | We present a tool, keep me around (kma), a suite of python scripts and an R package that finds retained introns in RNA-Seq experiments and incorporates biological replicates to reduce the number of false positives when detecting retention events. kma uses the results of existing quantification tools that probabilistically assign multi-mapping reads, thus interfacing easily with transcript quantification pipelines. The data is represented in a convenient, database style format that allows for easy aggregation across introns, genes, samples, and conditions to allow for further exploratory analysis. |
0905.4629 | Johan Elf | Paul Sjoberg, Otto G Berg and Johan Elf | Taking the reaction-diffusion master equation to the microscopic limit | null | null | null | null | q-bio.QM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The reaction-diffusion master equation (RDME) is commonly used to model
processes where both the spatial and stochastic nature of chemical reactions
need to be considered. We show that the RDME in many cases is inconsistent with
a microscopic description of diffusion limited chemical reactions and that this
will result in unphysical results. We describe how the inconsistency can be
reconciled if the association and dissociation rates used in the RDME are
derived from the underlying microscopic description. These rate constants will
however necessarily depend on the spatial discretization. At fine spatial
resolution the rates approach the microscopic rate constants defined at the
reaction radius. At low resolution the rates converge to the macroscopic
diffusion limited rate constants in 3D, whereas there is no limiting value in
2D. Our results make it possible to develop spatially discretized
reaction-diffusion models that correspond to a well-defined microscopic
description. We show that this is critical for a correct description of 2D
systems and systems that require high spatial resolution in 3D.
| [
{
"created": "Thu, 28 May 2009 12:18:30 GMT",
"version": "v1"
}
] | 2009-05-29 | [
[
"Sjoberg",
"Paul",
""
],
[
"Berg",
"Otto G",
""
],
[
"Elf",
"Johan",
""
]
] | The reaction-diffusion master equation (RDME) is commonly used to model processes where both the spatial and stochastic nature of chemical reactions need to be considered. We show that the RDME in many cases is inconsistent with a microscopic description of diffusion limited chemical reactions and that this will result in unphysical results. We describe how the inconsistency can be reconciled if the association and dissociation rates used in the RDME are derived from the underlying microscopic description. These rate constants will however necessarily depend on the spatial discretization. At fine spatial resolution the rates approach the microscopic rate constants defined at the reaction radius. At low resolution the rates converge to the macroscopic diffusion limited rate constants in 3D, whereas there is no limiting value in 2D. Our results make it possible to develop spatially discretized reaction-diffusion models that correspond to a well-defined microscopic description. We show that this is critical for a correct description of 2D systems and systems that require high spatial resolution in 3D. |
0810.3280 | Oleksii Kuchaiev | Oleksii Kuchaiev, Tijana Milenkovic, Vesna Memisevic, Wayne Hayes,
Natasa Przulj | Topological network alignment uncovers biological function and phylogeny | Algorithm explained in more details. Additional analysis added | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequence comparison and alignment has had an enormous impact on our
understanding of evolution, biology, and disease. Comparison and alignment of
biological networks will likely have a similar impact. Existing network
alignments use information external to the networks, such as sequence, because
no good algorithm for purely topological alignment has yet been devised. In
this paper, we present a novel algorithm based solely on network topology, that
can be used to align any two networks. We apply it to biological networks to
produce by far the most complete topological alignments of biological networks
to date. We demonstrate that both species phylogeny and detailed biological
function of individual proteins can be extracted from our alignments.
Topology-based alignments have the potential to provide a completely new,
independent source of phylogenetic information. Our alignment of the
protein-protein interaction networks of two very different species--yeast and
human--indicate that even distant species share a surprising amount of network
topology with each other, suggesting broad similarities in internal cellular
wiring across all life on Earth.
| [
{
"created": "Sat, 18 Oct 2008 00:49:44 GMT",
"version": "v1"
},
{
"created": "Sat, 14 Mar 2009 00:00:59 GMT",
"version": "v2"
},
{
"created": "Fri, 14 Aug 2009 20:30:35 GMT",
"version": "v3"
},
{
"created": "Wed, 7 Oct 2009 23:50:54 GMT",
"version": "v4"
}
] | 2009-10-08 | [
[
"Kuchaiev",
"Oleksii",
""
],
[
"Milenkovic",
"Tijana",
""
],
[
"Memisevic",
"Vesna",
""
],
[
"Hayes",
"Wayne",
""
],
[
"Przulj",
"Natasa",
""
]
] | Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology, and disease. Comparison and alignment of biological networks will likely have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological networks to date. We demonstrate that both species phylogeny and detailed biological function of individual proteins can be extracted from our alignments. Topology-based alignments have the potential to provide a completely new, independent source of phylogenetic information. Our alignment of the protein-protein interaction networks of two very different species--yeast and human--indicate that even distant species share a surprising amount of network topology with each other, suggesting broad similarities in internal cellular wiring across all life on Earth. |
2403.12995 | Siyu Long | Kangjie Zheng (equal contribution), Siyu Long (equal contribution),
Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, Hao
Zhou | ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular
Modeling | ICML2024 camera-ready, update some experimental results, add github
url, fix some typos | null | null | null | q-bio.BM cs.CE cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Protein language models have demonstrated significant potential in the field
of protein engineering. However, current protein language models primarily
operate at the residue scale, which limits their ability to provide information
at the atom level. This limitation prevents us from fully exploiting the
capabilities of protein language models for applications involving both
proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom),
a novel approach that enables atom-scale and residue-scale unified molecular
modeling. ESM-AA achieves this by pre-training on multi-scale code-switch
protein sequences and utilizing a multi-scale position encoding to capture
relationships among residues and atoms. Experimental results indicate that
ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the
full utilization of protein language models. Further investigations reveal that
through unified molecular modeling, ESM-AA not only gains molecular knowledge
but also retains its understanding of proteins. The source codes of ESM-AA are
publicly released at https://github.com/zhengkangjie/ESM-AA.
| [
{
"created": "Tue, 5 Mar 2024 13:35:41 GMT",
"version": "v1"
},
{
"created": "Thu, 16 May 2024 08:21:11 GMT",
"version": "v2"
},
{
"created": "Fri, 31 May 2024 07:28:40 GMT",
"version": "v3"
},
{
"created": "Thu, 13 Jun 2024 02:29:34 GMT",
"version": "v4"
}
] | 2024-06-14 | [
[
"Zheng",
"Kangjie",
"",
"equal contribution"
],
[
"Long",
"Siyu",
"",
"equal contribution"
],
[
"Lu",
"Tianyu",
""
],
[
"Yang",
"Junwei",
""
],
[
"Dai",
"Xinyu",
""
],
[
"Zhang",
"Ming",
""
],
[
"Nie",
"Zaiqing",
""
],
[
"Ma",
"Wei-Ying",
""
],
[
"Zhou",
"Hao",
""
]
] | Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pre-training on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins. The source codes of ESM-AA are publicly released at https://github.com/zhengkangjie/ESM-AA. |
1209.4603 | Jianhua Xing | Ping Wang, Chaoming Song, Hang Zhang, Zhanghan Wu, Jianhua Xing | Global Epigenetic State Network Governs Cellular Pluripotent
Reprogramming and Transdifferentiation | 18 pages,4 figures, 2 tables | Interface Focus 4(3):20130068 (2014) | 10.1098/?rsfs.2013.0068 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How do mammalian cells that share the same genome exist in notably distinct
phenotypes, exhibiting differences in morphology, gene expression patterns, and
epigenetic chromatin statuses? Furthermore how do cells of different phenotypes
differentiate reproducibly from a single fertilized egg? These fundamental
questions are closely related to a deeply rooted paradigm in developmental
biology that cell differentiation is irreversible. Yet, recently a growing body
of research suggests the possibility of cell reprogramming, which offers the
potential for us to convert one type of cell into another. Despite the
significance of quantitative understandings of cell reprogramming, theoretical
efforts often suffer from the complexity of large circuits maintaining cell
phenotypes coupled at many different epigenetic and gene regulation levels. To
capture the global architecture of cell phenotypes, we propose an "epigenetic
state network" approach that translates the classical concept of an epigenetic
landscape into a simple-yet-predictive mathematical model. As a testing case,
we apply the approach to the reprogramming of fibroblasts (FB) to
cardiomyocytes (CM). The epigenetic state network for this case predicts three
major pathways of reprogramming. One pathway goes by way of induced pluripotent
stem cells (iPSC) and continues on to the normal pathway of cardiomyocyte
differentiation. The other two pathways involve transdifferentiation (TD)
either indirectly through cardiac progenitor (CP) cells or directly from
fibroblast to cardiomyocyte. Numerous experimental observations support the
predicted states and pathways.
| [
{
"created": "Thu, 20 Sep 2012 18:39:25 GMT",
"version": "v1"
}
] | 2014-04-29 | [
[
"Wang",
"Ping",
""
],
[
"Song",
"Chaoming",
""
],
[
"Zhang",
"Hang",
""
],
[
"Wu",
"Zhanghan",
""
],
[
"Xing",
"Jianhua",
""
]
] | How do mammalian cells that share the same genome exist in notably distinct phenotypes, exhibiting differences in morphology, gene expression patterns, and epigenetic chromatin statuses? Furthermore how do cells of different phenotypes differentiate reproducibly from a single fertilized egg? These fundamental questions are closely related to a deeply rooted paradigm in developmental biology that cell differentiation is irreversible. Yet, recently a growing body of research suggests the possibility of cell reprogramming, which offers the potential for us to convert one type of cell into another. Despite the significance of quantitative understandings of cell reprogramming, theoretical efforts often suffer from the complexity of large circuits maintaining cell phenotypes coupled at many different epigenetic and gene regulation levels. To capture the global architecture of cell phenotypes, we propose an "epigenetic state network" approach that translates the classical concept of an epigenetic landscape into a simple-yet-predictive mathematical model. As a testing case, we apply the approach to the reprogramming of fibroblasts (FB) to cardiomyocytes (CM). The epigenetic state network for this case predicts three major pathways of reprogramming. One pathway goes by way of induced pluripotent stem cells (iPSC) and continues on to the normal pathway of cardiomyocyte differentiation. The other two pathways involve transdifferentiation (TD) either indirectly through cardiac progenitor (CP) cells or directly from fibroblast to cardiomyocyte. Numerous experimental observations support the predicted states and pathways. |
1103.5150 | Mohamed I Shehata | M.I.Shehata | On stability in dynamical Prisoner's dilemma game with non-uniform
interaction rates | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stability of evolutionary dynamics of non-repeated Prisoner's Dilemma game
with non-uniform interaction rates [1], via benefit and cost dilemma is studied
. Moreover, the stability condition (b+c/b-c)2 < r1r3 is derived in case of
coexistence between cooperators and defectors .If r1,r3 -> infinity cooperation
is the dominant strategy and defectors can no longer exploit cooperators.
| [
{
"created": "Sat, 26 Mar 2011 19:48:13 GMT",
"version": "v1"
}
] | 2011-03-29 | [
[
"Shehata",
"M. I.",
""
]
] | Stability of evolutionary dynamics of non-repeated Prisoner's Dilemma game with non-uniform interaction rates [1], via benefit and cost dilemma is studied . Moreover, the stability condition (b+c/b-c)2 < r1r3 is derived in case of coexistence between cooperators and defectors .If r1,r3 -> infinity cooperation is the dominant strategy and defectors can no longer exploit cooperators. |
2004.11325 | Sahar Rahbar | Sahar Rahbar | Mathematical and Preclinical Investigation of Respiratory Sinus
Arrhythmia Effects on Cardiac Output | null | null | null | null | q-bio.TO math.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Respiratory sinus arrhythmia (RSA) is heart rate variability in synchrony
with respiration although its functional significance not clear. The loss of
sinus arrhythmia may indicate underlying heart failure or disease; therefore,
there would be a great advantage of knowing how it works and affects the
cardio-respiratory system, especially by providing a mathematical model. To
this end, Windkessel model and cardiovascular partial differential equations
are used to obtain cardiac output based on the elasticity of left ventricle,
which is related to RSA. By solving the corresponding equations, it would be
possible to propose a new model to predict the RSA effects on cardiac output.
| [
{
"created": "Thu, 23 Apr 2020 17:19:44 GMT",
"version": "v1"
}
] | 2020-04-24 | [
[
"Rahbar",
"Sahar",
""
]
] | Respiratory sinus arrhythmia (RSA) is heart rate variability in synchrony with respiration although its functional significance not clear. The loss of sinus arrhythmia may indicate underlying heart failure or disease; therefore, there would be a great advantage of knowing how it works and affects the cardio-respiratory system, especially by providing a mathematical model. To this end, Windkessel model and cardiovascular partial differential equations are used to obtain cardiac output based on the elasticity of left ventricle, which is related to RSA. By solving the corresponding equations, it would be possible to propose a new model to predict the RSA effects on cardiac output. |
2401.16220 | Alexey Ovchinnikov | Yosef Berman, Joshua Forrest, Matthew Grote, Alexey Ovchinnikov, and
Sonia Rueda | Symbolic-numeric algorithm for parameter estimation in discrete-time
models with $\exp$ | null | null | null | null | q-bio.QM cs.SC cs.SY eess.SY math.AC math.DS | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Determining unknown parameter values in dynamic models is crucial for
accurate analysis of the dynamics across the different scientific disciplines.
Discrete-time dynamic models are widely used to model biological processes, but
it is often difficult to determine these parameters. In this paper, we propose
a robust symbolic-numeric approach for parameter estimation in discrete-time
models that involve non-algebraic functions such as exp. We illustrate the
performance (precision) of our approach by applying our approach to the flour
beetle (LPA) model, an archetypal discrete-time model in biology. Unlike
optimization-based methods, our algorithm guarantees to find all solutions of
the parameter values given time-series data for the measured variables.
| [
{
"created": "Mon, 29 Jan 2024 15:19:16 GMT",
"version": "v1"
}
] | 2024-01-30 | [
[
"Berman",
"Yosef",
""
],
[
"Forrest",
"Joshua",
""
],
[
"Grote",
"Matthew",
""
],
[
"Ovchinnikov",
"Alexey",
""
],
[
"Rueda",
"Sonia",
""
]
] | Determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. Discrete-time dynamic models are widely used to model biological processes, but it is often difficult to determine these parameters. In this paper, we propose a robust symbolic-numeric approach for parameter estimation in discrete-time models that involve non-algebraic functions such as exp. We illustrate the performance (precision) of our approach by applying our approach to the flour beetle (LPA) model, an archetypal discrete-time model in biology. Unlike optimization-based methods, our algorithm guarantees to find all solutions of the parameter values given time-series data for the measured variables. |
1405.1314 | Pooja Chandrashekar | Pooja Chandrashekar | Mathematically Modeling the GPe/STN Neuronal Cluster to Account for
Parkinsonian Tremor and Developing a Novel Method to Accurately Diagnose
Parkinson's Disease Using Speech Measurements and an Artificial Neural
Network | 13 pages | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parkinsons disease (PD) is a debilitating motor system disorder characterized
by progressive loss of movement, tremors, and speech slurring. PD is due to the
loss of dopamine-producing brain cells, and symptoms only worsen over time,
making early detection and diagnosis of the disease key to effective management
and treatment. There is currently no standardized method of diagnosis
available, and instead a combination of the medical history of a patient and
physician judgment is used. In this research, a novel method of accurately
diagnosing PD using an artificial neural network (ANN) and speech measurements
was developed. Using this technology, a patient need only speak into a computer
microphone. Speech data is then analyzed using Praat and inputted into the ANN
to obtain the diagnosis. The ANN, built using MATLAB and trained and tested
with actual patient data, was able to correctly classify 96.55% of test data. A
mathematical model of the GPe/STN neural cluster was then constructed to
account for Parkinsonian tremor. This was done using a two-cell model that
coupled a GPe neuron to a STN neuron and consisted of a system of twelve
ordinary differential equations. The model was structured to account for neuron
bursting behavior, neurotransmitter release, and synaptic connections between
neurons. After comparison to the biological behavior of the cluster and neuron
firing patterns in PD patients, the model was determined to be predictive of
known biological results. This work presents a significant step forward in PD
research and could be successfully implemented into clinical practice.
| [
{
"created": "Tue, 6 May 2014 15:28:38 GMT",
"version": "v1"
}
] | 2014-05-07 | [
[
"Chandrashekar",
"Pooja",
""
]
] | Parkinsons disease (PD) is a debilitating motor system disorder characterized by progressive loss of movement, tremors, and speech slurring. PD is due to the loss of dopamine-producing brain cells, and symptoms only worsen over time, making early detection and diagnosis of the disease key to effective management and treatment. There is currently no standardized method of diagnosis available, and instead a combination of the medical history of a patient and physician judgment is used. In this research, a novel method of accurately diagnosing PD using an artificial neural network (ANN) and speech measurements was developed. Using this technology, a patient need only speak into a computer microphone. Speech data is then analyzed using Praat and inputted into the ANN to obtain the diagnosis. The ANN, built using MATLAB and trained and tested with actual patient data, was able to correctly classify 96.55% of test data. A mathematical model of the GPe/STN neural cluster was then constructed to account for Parkinsonian tremor. This was done using a two-cell model that coupled a GPe neuron to a STN neuron and consisted of a system of twelve ordinary differential equations. The model was structured to account for neuron bursting behavior, neurotransmitter release, and synaptic connections between neurons. After comparison to the biological behavior of the cluster and neuron firing patterns in PD patients, the model was determined to be predictive of known biological results. This work presents a significant step forward in PD research and could be successfully implemented into clinical practice. |
1707.06524 | Denis Horv\'ath | Denis Horvath, Branislav Brutovsky | Toward understanding of the role of reversibility of phenotypic
switching in the evolution of resistance to therapy | 24 pages, 1 table, 8 figures | null | 10.1016/j.physleta.2018.03.052 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reversibility of state transitions is intensively studied topic in many
scientific disciplines over many years. In cell biology, it plays an important
role in epigenetic variation of phenotypes, known as phenotypic plasticity.
More interestingly, the cell state reversibility is probably crucial in the
adaptation of population phenotypic heterogeneity to environmental fluctuations
by evolving bet-hedging strategy, which might confer to cancer cells resistance
to therapy. In this article, we propose a formalization of the evolution of
highly reversible states in the environments of periodic variability. Two
interrelated models of heterogeneous cell populations are proposed and their
behavior is studied. The first model captures selection dynamics of the cell
clones for the respective levels of phenotypic reversibility. The second model
focuses on the interplay between reversibility and drug resistance in the
particular case of cancer. Overall, our results show that the threshold
dependencies are emergent features of the investigated model with eventual
therapeutic relevance. Presented examples demonstrate importance of taking into
account cell to cell heterogeneity within a system of clones with different
reversibility quantified by appropriately chosen genetic and epigenetic entropy
measures.
| [
{
"created": "Thu, 20 Jul 2017 14:14:12 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Nov 2017 06:20:03 GMT",
"version": "v2"
},
{
"created": "Fri, 30 Mar 2018 03:24:38 GMT",
"version": "v3"
}
] | 2018-05-23 | [
[
"Horvath",
"Denis",
""
],
[
"Brutovsky",
"Branislav",
""
]
] | Reversibility of state transitions is intensively studied topic in many scientific disciplines over many years. In cell biology, it plays an important role in epigenetic variation of phenotypes, known as phenotypic plasticity. More interestingly, the cell state reversibility is probably crucial in the adaptation of population phenotypic heterogeneity to environmental fluctuations by evolving bet-hedging strategy, which might confer to cancer cells resistance to therapy. In this article, we propose a formalization of the evolution of highly reversible states in the environments of periodic variability. Two interrelated models of heterogeneous cell populations are proposed and their behavior is studied. The first model captures selection dynamics of the cell clones for the respective levels of phenotypic reversibility. The second model focuses on the interplay between reversibility and drug resistance in the particular case of cancer. Overall, our results show that the threshold dependencies are emergent features of the investigated model with eventual therapeutic relevance. Presented examples demonstrate importance of taking into account cell to cell heterogeneity within a system of clones with different reversibility quantified by appropriately chosen genetic and epigenetic entropy measures. |
2204.02527 | Moo K. Chung | Soumya Das, D. Vijay Anand, Moo K. Chung | Topological Data Analysis of Human Brain Networks Through Order
Statistics | null | null | 10.1371/journal.pone.0276419 | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Understanding the common topological characteristics of the human brain
network across a population is central to understanding brain functions. The
abstraction of human connectome as a graph has been pivotal in gaining insights
on the topological properties of the brain network. The development of
group-level statistical inference procedures in brain graphs while accounting
for the heterogeneity and randomness still remains a difficult task. In this
study, we develop a robust statistical framework based on persistent homology
using the order statistics for analyzing brain networks. The use of order
statistics greatly simplifies the computation of the persistent barcodes. We
validate the proposed methods using comprehensive simulation studies and
subsequently apply to the resting-state functional magnetic resonance images.
We found a statistically significant topological difference between the male
and female brain networks.
| [
{
"created": "Wed, 6 Apr 2022 00:33:45 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Aug 2022 01:24:17 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Oct 2022 06:07:20 GMT",
"version": "v3"
}
] | 2023-04-26 | [
[
"Das",
"Soumya",
""
],
[
"Anand",
"D. Vijay",
""
],
[
"Chung",
"Moo K.",
""
]
] | Understanding the common topological characteristics of the human brain network across a population is central to understanding brain functions. The abstraction of human connectome as a graph has been pivotal in gaining insights on the topological properties of the brain network. The development of group-level statistical inference procedures in brain graphs while accounting for the heterogeneity and randomness still remains a difficult task. In this study, we develop a robust statistical framework based on persistent homology using the order statistics for analyzing brain networks. The use of order statistics greatly simplifies the computation of the persistent barcodes. We validate the proposed methods using comprehensive simulation studies and subsequently apply to the resting-state functional magnetic resonance images. We found a statistically significant topological difference between the male and female brain networks. |
1404.4073 | Ido Kanter | Amir Goldental, Shoshana Guberman, Roni Vardi and Ido Kanter | Computational paradigm for dynamic logic-gates in neuronal activity | 32 pages, 14 figures, 1 table | Front. Comput. Neurosci. 8:52. (2014) | 10.3389/fncom.2014.00052 | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In 1943 McCulloch and Pitts suggested that the brain is composed of reliable
logic-gates similar to the logic at the core of today's computers. This
framework had a limited impact on neuroscience, since neurons exhibit far
richer dynamics. Here we propose a new experimentally corroborated paradigm in
which the truth tables of the brain's logic-gates are time dependent, i.e.
dynamic logicgates (DLGs). The truth tables of the DLGs depend on the history
of their activity and the stimulation frequencies of their input neurons. Our
experimental results are based on a procedure where conditioned stimulations
were enforced on circuits of neurons embedded within a large-scale network of
cortical cells in-vitro. We demonstrate that the underlying biological
mechanism is the unavoidable increase of neuronal response latencies to ongoing
stimulations, which imposes a nonuniform gradual stretching of network delays.
The limited experimental results are confirmed and extended by simulations and
theoretical arguments based on identical neurons with a fixed increase of the
neuronal response latency per evoked spike. We anticipate our results to lead
to better understanding of the suitability of this computational paradigm to
account for the brain's functionalities and will require the development of new
systematic mathematical methods beyond the methods developed for traditional
Boolean algebra.
| [
{
"created": "Tue, 15 Apr 2014 20:41:21 GMT",
"version": "v1"
}
] | 2014-05-06 | [
[
"Goldental",
"Amir",
""
],
[
"Guberman",
"Shoshana",
""
],
[
"Vardi",
"Roni",
""
],
[
"Kanter",
"Ido",
""
]
] | In 1943 McCulloch and Pitts suggested that the brain is composed of reliable logic-gates similar to the logic at the core of today's computers. This framework had a limited impact on neuroscience, since neurons exhibit far richer dynamics. Here we propose a new experimentally corroborated paradigm in which the truth tables of the brain's logic-gates are time dependent, i.e. dynamic logicgates (DLGs). The truth tables of the DLGs depend on the history of their activity and the stimulation frequencies of their input neurons. Our experimental results are based on a procedure where conditioned stimulations were enforced on circuits of neurons embedded within a large-scale network of cortical cells in-vitro. We demonstrate that the underlying biological mechanism is the unavoidable increase of neuronal response latencies to ongoing stimulations, which imposes a nonuniform gradual stretching of network delays. The limited experimental results are confirmed and extended by simulations and theoretical arguments based on identical neurons with a fixed increase of the neuronal response latency per evoked spike. We anticipate our results to lead to better understanding of the suitability of this computational paradigm to account for the brain's functionalities and will require the development of new systematic mathematical methods beyond the methods developed for traditional Boolean algebra. |
2212.00273 | Maywan Hariono | Maywan Hariono, Irwan Hidayat, Ipang Djunarko, Jeffry Julianus, Fadi
G. Saqallah, Muhammad Hidhir Khawory, Nurul Hanim Salin, Habibah Abdul Wahab | Carica papaya Leaf Extract Inhibits SARS-CoV-2 Main Proteases but not
Human TMPRSS2: An In-vitro and In-silico Study | 28 pages, 7 figures, 2 tables | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Carica papaya (CP) leaf is long known for its traditional pharmacological
effects against dengue virus and malaria. Therefore, CP could also be a
potential solution for the treatment of other infectious diseases, such as
coronavirus. In this study, we evaluate the potential effect of the ethanolic
CP leaf extract in inhibiting the enzymatic activity of three protein targets
in SARS-CoV-2 life cycle, which include the 3-chymotrypsin-like protease
(3CLpro), papain-like protease (PLpro) and the human transmembrane protein
serine 2 (TMPRSS2). Results demonstrate that CP leaf extract inhibits 3CLpro
and PLpro significantly with IC50 of 0.02 microgram/mL and 0.06 microgram/mL,
respectively, but it is inactive towards TMPRSS2. Phenol,
2-methyl-5-(1,2,2-trimethylcyclopentyl)-(S)- (17a) and beta-mannofuranoside,
farnesyl- (21a) were identified in the extract using GC-MS. These two compounds
demonstrated a stronger binding affinity towards the main proteases than
TMPRSS2 during the docking simulation, which agrees with the in-vitro study.
Further pharmacophore mapping suggests that 17a has a fit score higher than 21a
to the SARS-CoV-2 3CLpro pharmacophore model concluding that CP leaf extract
has the potential to be developed as a herbal SARS-CoV-2 antiviral agent.
| [
{
"created": "Thu, 1 Dec 2022 04:33:28 GMT",
"version": "v1"
}
] | 2022-12-02 | [
[
"Hariono",
"Maywan",
""
],
[
"Hidayat",
"Irwan",
""
],
[
"Djunarko",
"Ipang",
""
],
[
"Julianus",
"Jeffry",
""
],
[
"Saqallah",
"Fadi G.",
""
],
[
"Khawory",
"Muhammad Hidhir",
""
],
[
"Salin",
"Nurul Hanim",
""
],
[
"Wahab",
"Habibah Abdul",
""
]
] | Carica papaya (CP) leaf is long known for its traditional pharmacological effects against dengue virus and malaria. Therefore, CP could also be a potential solution for the treatment of other infectious diseases, such as coronavirus. In this study, we evaluate the potential effect of the ethanolic CP leaf extract in inhibiting the enzymatic activity of three protein targets in SARS-CoV-2 life cycle, which include the 3-chymotrypsin-like protease (3CLpro), papain-like protease (PLpro) and the human transmembrane protein serine 2 (TMPRSS2). Results demonstrate that CP leaf extract inhibits 3CLpro and PLpro significantly with IC50 of 0.02 microgram/mL and 0.06 microgram/mL, respectively, but it is inactive towards TMPRSS2. Phenol, 2-methyl-5-(1,2,2-trimethylcyclopentyl)-(S)- (17a) and beta-mannofuranoside, farnesyl- (21a) were identified in the extract using GC-MS. These two compounds demonstrated a stronger binding affinity towards the main proteases than TMPRSS2 during the docking simulation, which agrees with the in-vitro study. Further pharmacophore mapping suggests that 17a has a fit score higher than 21a to the SARS-CoV-2 3CLpro pharmacophore model concluding that CP leaf extract has the potential to be developed as a herbal SARS-CoV-2 antiviral agent. |
1808.08842 | Mussa Quareshy | Mussa Quareshy, Justyna Prusinska, Martin Kieffer, Kosuke Fukui,
Alonso J. Pardal, Silke Lehmann, Patrick Schafer, Charo I. del Genio, Stefan
Kepinski, Kenichiro Hayashi, Andrew Marsh, Richard M. Napier | The tetrazole analogue of the auxin indole-3-acetic acid binds
preferentially to TIR1 and not AFB5 | ACS Chemical Biology, 2018 | null | 10.1021/acschembio.8b00527 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Auxin is considered one of the cardinal hormones in plant growth and
development. It regulates a wide range of processes throughout the plant.
Synthetic auxins exploit the auxin-signalling pathway and are valuable as
herbicidal agrochemicals. Currently, despite a diversity of chemical scaffolds
all synthetic auxins have a carboxylic acid as the active core group. By
applying bio-isosteric replacement we discovered that indole-3-tetrazole was
active by surface plasmon resonance (SPR) spectrometry, showing that the
tetrazole could initiate assembly of the TIR1 auxin co-receptor complex. We
then tested the tetrazole's efficacy in a range of whole plant physiological
assays and in protoplast reporter assays which all confirmed auxin activity,
albeit rather weak. We then tested indole-3-tetrazole against the AFB5
homologue of TIR1, finding that binding was selective against TIR1, absent with
AFB5. The kinetics of binding to TIR1 are contrasted to those for the herbicide
picloram, which shows the opposite receptor preference as it binds to AFB5 with
far greater affinity than to TIR1. The basis of the preference of
indole-3-tetrazole for TIR1 was revealed to be a single residue substitution
using molecular docking, and assays using tir1 and afb5 mutant lines confirmed
selectivity in vivo. Given the potential that a TIR1-selective auxin might have
for unmasking receptor-specific actions, we followed a rational design, lead
optimisation campaign and a set of chlorinated indole-3-tetrazoles was
synthesised. Improved affinity for TIR1 and the preference for binding to TIR1
was maintained for 4- and 6-chloroindole-3-tetrazoles, coupled with improved
efficacy in vivo. This work expands the range of auxin chemistry for the design
of receptor-selective synthetic auxins.
| [
{
"created": "Mon, 27 Aug 2018 13:40:56 GMT",
"version": "v1"
}
] | 2018-08-28 | [
[
"Quareshy",
"Mussa",
""
],
[
"Prusinska",
"Justyna",
""
],
[
"Kieffer",
"Martin",
""
],
[
"Fukui",
"Kosuke",
""
],
[
"Pardal",
"Alonso J.",
""
],
[
"Lehmann",
"Silke",
""
],
[
"Schafer",
"Patrick",
""
],
[
"del Genio",
"Charo I.",
""
],
[
"Kepinski",
"Stefan",
""
],
[
"Hayashi",
"Kenichiro",
""
],
[
"Marsh",
"Andrew",
""
],
[
"Napier",
"Richard M.",
""
]
] | Auxin is considered one of the cardinal hormones in plant growth and development. It regulates a wide range of processes throughout the plant. Synthetic auxins exploit the auxin-signalling pathway and are valuable as herbicidal agrochemicals. Currently, despite a diversity of chemical scaffolds all synthetic auxins have a carboxylic acid as the active core group. By applying bio-isosteric replacement we discovered that indole-3-tetrazole was active by surface plasmon resonance (SPR) spectrometry, showing that the tetrazole could initiate assembly of the TIR1 auxin co-receptor complex. We then tested the tetrazole's efficacy in a range of whole plant physiological assays and in protoplast reporter assays which all confirmed auxin activity, albeit rather weak. We then tested indole-3-tetrazole against the AFB5 homologue of TIR1, finding that binding was selective against TIR1, absent with AFB5. The kinetics of binding to TIR1 are contrasted to those for the herbicide picloram, which shows the opposite receptor preference as it binds to AFB5 with far greater affinity than to TIR1. The basis of the preference of indole-3-tetrazole for TIR1 was revealed to be a single residue substitution using molecular docking, and assays using tir1 and afb5 mutant lines confirmed selectivity in vivo. Given the potential that a TIR1-selective auxin might have for unmasking receptor-specific actions, we followed a rational design, lead optimisation campaign and a set of chlorinated indole-3-tetrazoles was synthesised. Improved affinity for TIR1 and the preference for binding to TIR1 was maintained for 4- and 6-chloroindole-3-tetrazoles, coupled with improved efficacy in vivo. This work expands the range of auxin chemistry for the design of receptor-selective synthetic auxins. |
2404.15318 | James Ruffle | James K Ruffle, Samia Mohinta, Kelly Pegoretti Baruteau, Rebekah
Rajiah, Faith Lee, Sebastian Brandner, Parashkev Nachev, Harpreet Hyare | VASARI-auto: equitable, efficient, and economical featurisation of
glioma MRI | 28 pages, 6 figures, 1 table | null | null | null | q-bio.QM cs.CV q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | The VASARI MRI feature set is a quantitative system designed to standardise
glioma imaging descriptions. Though effective, deriving VASARI is
time-consuming and seldom used in clinical practice. This is a problem that
machine learning could plausibly automate. Using glioma data from 1172
patients, we developed VASARI-auto, an automated labelling software applied to
both open-source lesion masks and our openly available tumour segmentation
model. In parallel, two consultant neuroradiologists independently quantified
VASARI features in a subsample of 100 glioblastoma cases. We quantified: 1)
agreement across neuroradiologists and VASARI-auto; 2) calibration of
performance equity; 3) an economic workforce analysis; and 4) fidelity in
predicting patient survival. Tumour segmentation was compatible with the
current state of the art and equally performant regardless of age or sex. A
modest inter-rater variability between in-house neuroradiologists was
comparable to between neuroradiologists and VASARI-auto, with far higher
agreement between VASARI-auto methods. The time taken for neuroradiologists to
derive VASARI was substantially higher than VASARI-auto (mean time per case 317
vs. 3 seconds). A UK hospital workforce analysis forecast that three years of
VASARI featurisation would demand 29,777 consultant neuroradiologist workforce
hours ({\pounds}1,574,935), reducible to 332 hours of computing time (and
{\pounds}146 of power) with VASARI-auto. The best-performing survival model
utilised VASARI-auto features as opposed to those derived by neuroradiologists.
VASARI-auto is a highly efficient automated labelling system with equitable
performance across patient age or sex, a favourable economic profile if used as
a decision support tool, and with non-inferior fidelity in downstream patient
survival prediction. Future work should iterate upon and integrate such tools
to enhance patient care.
| [
{
"created": "Wed, 3 Apr 2024 13:33:07 GMT",
"version": "v1"
}
] | 2024-04-25 | [
[
"Ruffle",
"James K",
""
],
[
"Mohinta",
"Samia",
""
],
[
"Baruteau",
"Kelly Pegoretti",
""
],
[
"Rajiah",
"Rebekah",
""
],
[
"Lee",
"Faith",
""
],
[
"Brandner",
"Sebastian",
""
],
[
"Nachev",
"Parashkev",
""
],
[
"Hyare",
"Harpreet",
""
]
] | The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clinical practice. This is a problem that machine learning could plausibly automate. Using glioma data from 1172 patients, we developed VASARI-auto, an automated labelling software applied to both open-source lesion masks and our openly available tumour segmentation model. In parallel, two consultant neuroradiologists independently quantified VASARI features in a subsample of 100 glioblastoma cases. We quantified: 1) agreement across neuroradiologists and VASARI-auto; 2) calibration of performance equity; 3) an economic workforce analysis; and 4) fidelity in predicting patient survival. Tumour segmentation was compatible with the current state of the art and equally performant regardless of age or sex. A modest inter-rater variability between in-house neuroradiologists was comparable to between neuroradiologists and VASARI-auto, with far higher agreement between VASARI-auto methods. The time taken for neuroradiologists to derive VASARI was substantially higher than VASARI-auto (mean time per case 317 vs. 3 seconds). A UK hospital workforce analysis forecast that three years of VASARI featurisation would demand 29,777 consultant neuroradiologist workforce hours ({\pounds}1,574,935), reducible to 332 hours of computing time (and {\pounds}146 of power) with VASARI-auto. The best-performing survival model utilised VASARI-auto features as opposed to those derived by neuroradiologists. VASARI-auto is a highly efficient automated labelling system with equitable performance across patient age or sex, a favourable economic profile if used as a decision support tool, and with non-inferior fidelity in downstream patient survival prediction. Future work should iterate upon and integrate such tools to enhance patient care. |
1310.3465 | Martin Voracek | Martin Voracek | No effects of androgen receptor gene CAG and GGC repeat polymorphisms on
digit ratio (2D:4D): Meta-analysis | null | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objectives: A series of meta-analyses assessed whether differentially
efficacious variants (CAG and GGC repeat-length polymorphisms) of the human
androgen receptor gene are associated with digit ratio (2D:4D), a widely
investigated putative pointer to prenatal androgen action. Methods: Extensive
literature search strategies identified a maximum of 16 samples (total N =
2157) eligible for meta-analysis. Results: In contrast to a small-sample (N =
50) initial report, widely cited affirmatively in the literature, meta-analysis
of the entire retrievable evidence base did not support associations between
androgen receptor gene efficacy and 2D:4D. Conclusions: These meta-analytical
nil findings, along with several further suggestive strands of evidence
consistent with these, undermine one validity claim for 2D:4D as a
retrospective pointer to prenatal testosterone action.
| [
{
"created": "Sun, 13 Oct 2013 10:35:26 GMT",
"version": "v1"
}
] | 2013-10-15 | [
[
"Voracek",
"Martin",
""
]
] | Objectives: A series of meta-analyses assessed whether differentially efficacious variants (CAG and GGC repeat-length polymorphisms) of the human androgen receptor gene are associated with digit ratio (2D:4D), a widely investigated putative pointer to prenatal androgen action. Methods: Extensive literature search strategies identified a maximum of 16 samples (total N = 2157) eligible for meta-analysis. Results: In contrast to a small-sample (N = 50) initial report, widely cited affirmatively in the literature, meta-analysis of the entire retrievable evidence base did not support associations between androgen receptor gene efficacy and 2D:4D. Conclusions: These meta-analytical nil findings, along with several further suggestive strands of evidence consistent with these, undermine one validity claim for 2D:4D as a retrospective pointer to prenatal testosterone action. |
2308.09941 | Tom Chou | Yue Wang, Blerta Shtylla, Tom Chou | Order-of-mutation effects on cancer progression: models for
myeloproliferative neoplasm | 22 pp, 7 figures. arXiv admin note: text overlap with
arXiv:2302.07955 | null | null | null | q-bio.MN q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We develop a modeling framework for cancer progression that distinguishes the
order of two possible mutations. Recent observations and information on
myeloproliferative neoplasms are analyzed within our framework. In some
patients with myeloproliferative neoplasms, two genetic mutations can be found,
JAK2 V617F and TET2. Whether or not one mutation is present will influence how
the other subsequent mutation affects the regulation of gene expression. When
both mutations are present, the order of their occurrence has been shown to
influence disease progression and prognosis. In this paper, we propose a
nonlinear ordinary differential equation (ODE) and Markov chain models to
explain the non-additive and non-commutative clinical observations with respect
to different orders of mutations: gene expression patterns, proportions of
cells with different mutations, and ages at diagnosis. We also propose
potential experiments measurements that can be used to verify our models.
| [
{
"created": "Sat, 19 Aug 2023 08:21:56 GMT",
"version": "v1"
}
] | 2023-08-22 | [
[
"Wang",
"Yue",
""
],
[
"Shtylla",
"Blerta",
""
],
[
"Chou",
"Tom",
""
]
] | We develop a modeling framework for cancer progression that distinguishes the order of two possible mutations. Recent observations and information on myeloproliferative neoplasms are analyzed within our framework. In some patients with myeloproliferative neoplasms, two genetic mutations can be found, JAK2 V617F and TET2. Whether or not one mutation is present will influence how the other subsequent mutation affects the regulation of gene expression. When both mutations are present, the order of their occurrence has been shown to influence disease progression and prognosis. In this paper, we propose a nonlinear ordinary differential equation (ODE) and Markov chain models to explain the non-additive and non-commutative clinical observations with respect to different orders of mutations: gene expression patterns, proportions of cells with different mutations, and ages at diagnosis. We also propose potential experiments measurements that can be used to verify our models. |
1308.2277 | Simon Childs | S.J. Childs | An Improved Temporal Formulation of Pupal Transpiration in Glossina | 33 pages, 27 figures, 3 tables. arXiv admin note: text overlap with
arXiv:0901.2470 | Mathematical Biosciences, 262: 214-229, 2015 | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The temporal aspect of a model of pupal dehydration is improved upon. The
observed dependence of pupal transpiration on time is attributed to an
alternation between two, essential modes, for which the deposition of a thin,
pupal skin inside the puparium and its subsequent demise are thought to be
responsible. For each mode of transpiration, the results of the Bursell (1958)
investigation into pupal dehydration are used as a rudimentary data set. These
data are generalised to all temperatures and humidities by invoking the
property of multiplicative separability. The problem, then, is that as the
temperature varies with time, so does the metabolism and the developmental
stages to which the model data pertain, must necessarily warp. The
puparial-duration formula of Phelps and Burrows (1969) and Hargrove (2004) is
exploited to facilitate a mapping between the constant-temperature time domain
of the data and that of some, more general case at hand. The resulting,
Glossina morsitans model is extrapolated to other species using their relative
surface areas, their relative protected and unprotected transpiration rates and
their different fourth instar excretions (drawing, to a lesser extent, from the
data of Buxton and Lewis, 1934). In this way the problem of pupal dehydration
is formulated as a series of integrals and the consequent survival can be
predicted. The discovery of a distinct definition for hygrophilic species,
within the formulation, prompts the investigation of the hypothetical effect of
a two-day heat wave on pupae. This leads to the conclusion that the
classification of species as hygrophilic, mesophilic and xerophilic is largely
true only in so much as their third and fourth instars are and, possibly, the
hours shortly before eclosion.
| [
{
"created": "Sat, 10 Aug 2013 05:14:22 GMT",
"version": "v1"
},
{
"created": "Fri, 9 May 2014 19:11:14 GMT",
"version": "v2"
},
{
"created": "Tue, 19 May 2015 14:59:27 GMT",
"version": "v3"
}
] | 2015-05-20 | [
[
"Childs",
"S. J.",
""
]
] | The temporal aspect of a model of pupal dehydration is improved upon. The observed dependence of pupal transpiration on time is attributed to an alternation between two, essential modes, for which the deposition of a thin, pupal skin inside the puparium and its subsequent demise are thought to be responsible. For each mode of transpiration, the results of the Bursell (1958) investigation into pupal dehydration are used as a rudimentary data set. These data are generalised to all temperatures and humidities by invoking the property of multiplicative separability. The problem, then, is that as the temperature varies with time, so does the metabolism and the developmental stages to which the model data pertain, must necessarily warp. The puparial-duration formula of Phelps and Burrows (1969) and Hargrove (2004) is exploited to facilitate a mapping between the constant-temperature time domain of the data and that of some, more general case at hand. The resulting, Glossina morsitans model is extrapolated to other species using their relative surface areas, their relative protected and unprotected transpiration rates and their different fourth instar excretions (drawing, to a lesser extent, from the data of Buxton and Lewis, 1934). In this way the problem of pupal dehydration is formulated as a series of integrals and the consequent survival can be predicted. The discovery of a distinct definition for hygrophilic species, within the formulation, prompts the investigation of the hypothetical effect of a two-day heat wave on pupae. This leads to the conclusion that the classification of species as hygrophilic, mesophilic and xerophilic is largely true only in so much as their third and fourth instars are and, possibly, the hours shortly before eclosion. |
2303.06951 | Frederic Lavancier | Lisa Balsollier (Nantes Univ), Fr\'ed\'eric Lavancier (Nantes Univ),
Jean Salamero, Charles Kervrann | A generative model to synthetize spatio-temporal dynamics of
biomolecules in cells | null | null | null | null | q-bio.SC stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generators of space-time dynamics in bioimaging have become essential to
build ground truth datasets for image processing algorithm evaluation such as
biomolecule detectors and trackers, as well as to generate training datasets
for deep learning algorithms. In this contribution, we leverage a stochastic
model, called birth-death-move (BDM) point process, in order to generate joint
dynamics of biomolecules in cells. This approach is very flexible and allows us
to model a system of particles in motion, possibly in interaction, that can
each possibly switch from a motion regime (e.g. Brownian) to another (e.g. a
directed motion), along with the appearance over time of new trajectories and
their death after some lifetime, all of these features possibly depending on
the current spatial configuration of all existing particles. We explain how to
specify all characteristics of a BDM model, with many practical examples that
are relevant for bioimaging applications. Based on real fluorescence microscopy
datasets, we finally calibrate our model to mimic the joint dynamics of
Langerin and Rab11 proteins near the plasma membrane. We show that the
resulting synthetic sequences exhibit comparable features as those observed in
real microscopy image sequences.
| [
{
"created": "Mon, 13 Mar 2023 09:43:48 GMT",
"version": "v1"
}
] | 2023-03-14 | [
[
"Balsollier",
"Lisa",
"",
"Nantes Univ"
],
[
"Lavancier",
"Frédéric",
"",
"Nantes Univ"
],
[
"Salamero",
"Jean",
""
],
[
"Kervrann",
"Charles",
""
]
] | Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This approach is very flexible and allows us to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g. Brownian) to another (e.g. a directed motion), along with the appearance over time of new trajectories and their death after some lifetime, all of these features possibly depending on the current spatial configuration of all existing particles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. Based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences. |
2109.06691 | Niko Komin | Niko Komin, Alexander Skupin | How to address cellular heterogeneity by distribution biology | null | Current Opinion in Systems Biology, Volume 3, June 2017, Pages
154-160 | 10.1016/j.coisb.2017.05.010 | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Cellular heterogeneity is an immanent property of biological systems that
covers very different aspects of life ranging from genetic diversity to
cell-to-cell variability driven by stochastic molecular interactions, and noise
induced cell differentiation. Here, we review recent developments in
characterizing cellular heterogeneity by distributions and argue that
understanding multicellular life requires the analysis of heterogeneity
dynamics at single cell resolution by integrative approaches that combine
methods from non-equilibrium statistical physics, information theory and omics
biology.
| [
{
"created": "Mon, 13 Sep 2021 16:46:58 GMT",
"version": "v1"
}
] | 2021-09-15 | [
[
"Komin",
"Niko",
""
],
[
"Skupin",
"Alexander",
""
]
] | Cellular heterogeneity is an immanent property of biological systems that covers very different aspects of life ranging from genetic diversity to cell-to-cell variability driven by stochastic molecular interactions, and noise induced cell differentiation. Here, we review recent developments in characterizing cellular heterogeneity by distributions and argue that understanding multicellular life requires the analysis of heterogeneity dynamics at single cell resolution by integrative approaches that combine methods from non-equilibrium statistical physics, information theory and omics biology. |
1708.09264 | Brian Munsky | Zachary Fox and Brian Munsky | Stochasticity or Noise in Biochemical Reactions | Submitted for inclusion in the textbook Quantitative Biology: Theory,
Computational Methods and Examples of Models, edited by Brian Munsky, Lev S.
Tsimring, and William S. Hlavacek, to be published by MIT Press | null | null | null | q-bio.QM q-bio.MN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Heterogeneity in gene expression across isogenic cell populations can give
rise to phenotypic diversity, even when cells are in homogenous environments.
This diversity arises from the discrete, stochastic nature of biochemical
reactions, which naturally arise due to the very small numbers of genes, RNA,
or protein molecules in single cells. Modern measurements of single
biomolecules have created a vast wealth of information about the fluctuations
of these molecules, but a quantitative understanding of these complex,
stochastic systems requires precise computational tools. In this article, we
present modern tools necessary to model variability in biological system and to
compare model results to experimental data. We review the Chemical Master
Equation and approaches to solve for probability distributions of discrete
numbers of biomolecules. We discuss how to fit probability distributions to
single-cell data using likelihood based approaches. Finally, we provide
examples of fitting discrete stochastic models to single-molecule fluorescent
in-situ hybridization data that quantifies RNA levels across populations of
cells in bacteria and yeast.
| [
{
"created": "Wed, 30 Aug 2017 13:38:59 GMT",
"version": "v1"
}
] | 2017-08-31 | [
[
"Fox",
"Zachary",
""
],
[
"Munsky",
"Brian",
""
]
] | Heterogeneity in gene expression across isogenic cell populations can give rise to phenotypic diversity, even when cells are in homogenous environments. This diversity arises from the discrete, stochastic nature of biochemical reactions, which naturally arise due to the very small numbers of genes, RNA, or protein molecules in single cells. Modern measurements of single biomolecules have created a vast wealth of information about the fluctuations of these molecules, but a quantitative understanding of these complex, stochastic systems requires precise computational tools. In this article, we present modern tools necessary to model variability in biological system and to compare model results to experimental data. We review the Chemical Master Equation and approaches to solve for probability distributions of discrete numbers of biomolecules. We discuss how to fit probability distributions to single-cell data using likelihood based approaches. Finally, we provide examples of fitting discrete stochastic models to single-molecule fluorescent in-situ hybridization data that quantifies RNA levels across populations of cells in bacteria and yeast. |
1711.08927 | Tom Michoel | Pau Erola, Eric Bonnet, Tom Michoel | Learning differential module networks across multiple experimental
conditions | Minor revision; 19 pages, 5 figures; chapter for a forthcoming book
on gene regulatory network inference | Methods in Molecular Biology, vol 1883, pp 303-321 (2018) | 10.1007/978-1-4939-8882-2_13 | null | q-bio.QM q-bio.GN q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Module network inference is a statistical method to reconstruct gene
regulatory networks, which uses probabilistic graphical models to learn modules
of coregulated genes and their upstream regulatory programs from genome-wide
gene expression and other omics data. Here we review the basic theory of module
network inference, present protocols for common gene regulatory network
reconstruction scenarios based on the Lemon-Tree software, and show, using
human gene expression data, how the software can also be applied to learn
differential module networks across multiple experimental conditions.
| [
{
"created": "Fri, 24 Nov 2017 11:17:14 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Feb 2018 10:45:57 GMT",
"version": "v2"
}
] | 2019-05-28 | [
[
"Erola",
"Pau",
""
],
[
"Bonnet",
"Eric",
""
],
[
"Michoel",
"Tom",
""
]
] | Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions. |
1309.5337 | Mengjie Chen | Mengjie Chen and Haifan Lin and Hongyu Zhao | Change Point Analysis of Histone Modifications Reveals Epigenetic Blocks
Linking to Physical Domains | 23 pages, 6 figures | null | null | null | q-bio.GN q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Histone modification is a vital epigenetic mechanism for transcriptional
control in eukaryotes. High-throughput techniques have enabled whole-genome
analysis of histone modifications in recent years. However, most studies assume
one combination of histone modification invariantly translates to one
transcriptional output regardless of local chromatin environment. In this study
we hypothesize that, the genome is organized into local domains that manifest
similar enrichment pattern of histone modification, which leads to orchestrated
regulation of expression of genes with relevant bio- logical functions. We
propose a multivariate Bayesian Change Point (BCP) model to segment the
Drosophila melanogaster genome into consecutive blocks on the basis of
combinatorial patterns of histone marks. By modeling the sparse distribution of
histone marks across the chromosome with a zero-inflated Gaussian mixture, our
partitions capture local BLOCKs that manifest relatively homogeneous enrichment
pattern of histone modifications. We further characterized BLOCKs by their
transcription levels, distribution of genes, degree of co-regulation and GO
enrichment. Our results demonstrate that these BLOCKs, although inferred merely
from histone modifications, reveal strong relevance with physical domains,
which suggest their important roles in chromatin organization and coordinated
gene regulation.
| [
{
"created": "Fri, 20 Sep 2013 17:52:18 GMT",
"version": "v1"
},
{
"created": "Fri, 9 May 2014 14:38:44 GMT",
"version": "v2"
}
] | 2014-05-12 | [
[
"Chen",
"Mengjie",
""
],
[
"Lin",
"Haifan",
""
],
[
"Zhao",
"Hongyu",
""
]
] | Histone modification is a vital epigenetic mechanism for transcriptional control in eukaryotes. High-throughput techniques have enabled whole-genome analysis of histone modifications in recent years. However, most studies assume one combination of histone modification invariantly translates to one transcriptional output regardless of local chromatin environment. In this study we hypothesize that, the genome is organized into local domains that manifest similar enrichment pattern of histone modification, which leads to orchestrated regulation of expression of genes with relevant bio- logical functions. We propose a multivariate Bayesian Change Point (BCP) model to segment the Drosophila melanogaster genome into consecutive blocks on the basis of combinatorial patterns of histone marks. By modeling the sparse distribution of histone marks across the chromosome with a zero-inflated Gaussian mixture, our partitions capture local BLOCKs that manifest relatively homogeneous enrichment pattern of histone modifications. We further characterized BLOCKs by their transcription levels, distribution of genes, degree of co-regulation and GO enrichment. Our results demonstrate that these BLOCKs, although inferred merely from histone modifications, reveal strong relevance with physical domains, which suggest their important roles in chromatin organization and coordinated gene regulation. |
2210.00539 | Stephen Turner | Stephen D. Turner | KGP: An R Package with Metadata from the 1000 Genomes Project | 5 pages, 2 figures, 2 tables | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | The 1000 Genomes Project provides sequencing data on 3,202 samples from 26
populations spanning five continental regions with no access or use
restrictions. The kgp R package provides consistent and comprehensive metadata
about samples and populations in the 1000 Genomes Project and other population
sequencing data in the International Genome Sample Resource collection. The kgp
package is distributed via the Comprehensive R Archive Network (CRAN) at
https://cran.r-project.org/package=kgp. Source code is available on GitHub at
https://github.com/stephenturner/kgp. Further documentation is online at
https://stephenturner.github.io/kgp/.
| [
{
"created": "Sun, 2 Oct 2022 14:42:26 GMT",
"version": "v1"
}
] | 2022-10-04 | [
[
"Turner",
"Stephen D.",
""
]
] | The 1000 Genomes Project provides sequencing data on 3,202 samples from 26 populations spanning five continental regions with no access or use restrictions. The kgp R package provides consistent and comprehensive metadata about samples and populations in the 1000 Genomes Project and other population sequencing data in the International Genome Sample Resource collection. The kgp package is distributed via the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/package=kgp. Source code is available on GitHub at https://github.com/stephenturner/kgp. Further documentation is online at https://stephenturner.github.io/kgp/. |
2005.07269 | Ritam Guha Mr. | Ritam Guha, Anik Sengupta, Ankan Dutta | Sewage Pooling Test for SARS-CoV-2 | 8 pages, 4 figures | null | null | null | q-bio.OT cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | CoVID-19 is currently one of the biggest threats to mankind. To date, it is
the reason for infections of over 35 lakhs and the death of over 2 lakh human
beings. We propose a procedure to detect CoVID-19 affected localities using a
sewage mass testing and pooling mechanism which has gained ground in recent
times. The proposed method named Sewage Pooling Algorithm tests wastewater
samples from sewage systems to pinpoint the regions which are affected by
maximum chances of the virus spread. The algorithm also uses a priority-based
backtracking procedure to perform testing in sewage links depending on the
probability of infection in the sub-areas. For places with very rare CoVID
cases, we present a gradient-based search method to prune those areas. The
proposed method has less human intervention and increases the effective
tests/million people over current in-place methods.
| [
{
"created": "Wed, 13 May 2020 16:00:17 GMT",
"version": "v1"
}
] | 2020-05-18 | [
[
"Guha",
"Ritam",
""
],
[
"Sengupta",
"Anik",
""
],
[
"Dutta",
"Ankan",
""
]
] | CoVID-19 is currently one of the biggest threats to mankind. To date, it is the reason for infections of over 35 lakhs and the death of over 2 lakh human beings. We propose a procedure to detect CoVID-19 affected localities using a sewage mass testing and pooling mechanism which has gained ground in recent times. The proposed method named Sewage Pooling Algorithm tests wastewater samples from sewage systems to pinpoint the regions which are affected by maximum chances of the virus spread. The algorithm also uses a priority-based backtracking procedure to perform testing in sewage links depending on the probability of infection in the sub-areas. For places with very rare CoVID cases, we present a gradient-based search method to prune those areas. The proposed method has less human intervention and increases the effective tests/million people over current in-place methods. |
1206.1017 | Mike Steel Prof. | Wim Hordijk and Mike Steel | Autocatalytic Sets Extended: Dynamics, Inhibition, and a Generalization | 24 pages, 6 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Autocatalytic sets are often considered a necessary (but not
sufficient) condition for the origin and early evolution of life. Although the
idea of autocatalytic sets was already conceived of many years ago, only
recently have they gained more interest, following advances in creating them
experimentally in the laboratory. In our own work, we have studied
autocatalytic sets extensively from a computational and theoretical point of
view.
Results: We present results from an initial study of the dynamics of
self-sustaining autocatalytic sets (RAFs). In particular, simulations of
molecular flow on autocatalytic sets are performed, to illustrate the kinds of
dynamics that can occur. Next, we present an extension of our (previously
introduced) algorithm for finding autocatalytic sets in general reaction
networks, which can also handle inhibition. We show that in this case detecting
autocatalytic sets is fixed parameter tractable. Finally, we formulate a
generalized version of the algorithm that can also be applied outside the
context of chemistry and origin of life, which we illustrate with a toy example
from economics.
Conclusions: Having shown theoretically (in previous work) that autocatalytic
sets are highly likely to exist, we conclude here that also in terms of
dynamics such sets are viable and outcompete non-autocatalytic sets.
Furthermore, our dynamical results confirm arguments made earlier about how
autocatalytic subsets can enable their own growth or give rise to other such
subsets coming into existence. Finally, our algorithmic extension and
generalization show that more realistic scenarios (e.g., including inhibition)
can also be dealt with within our framework, and that it can even be applied to
areas outside of chemistry, such as economics.
| [
{
"created": "Tue, 5 Jun 2012 18:28:54 GMT",
"version": "v1"
}
] | 2012-06-06 | [
[
"Hordijk",
"Wim",
""
],
[
"Steel",
"Mike",
""
]
] | Background: Autocatalytic sets are often considered a necessary (but not sufficient) condition for the origin and early evolution of life. Although the idea of autocatalytic sets was already conceived of many years ago, only recently have they gained more interest, following advances in creating them experimentally in the laboratory. In our own work, we have studied autocatalytic sets extensively from a computational and theoretical point of view. Results: We present results from an initial study of the dynamics of self-sustaining autocatalytic sets (RAFs). In particular, simulations of molecular flow on autocatalytic sets are performed, to illustrate the kinds of dynamics that can occur. Next, we present an extension of our (previously introduced) algorithm for finding autocatalytic sets in general reaction networks, which can also handle inhibition. We show that in this case detecting autocatalytic sets is fixed parameter tractable. Finally, we formulate a generalized version of the algorithm that can also be applied outside the context of chemistry and origin of life, which we illustrate with a toy example from economics. Conclusions: Having shown theoretically (in previous work) that autocatalytic sets are highly likely to exist, we conclude here that also in terms of dynamics such sets are viable and outcompete non-autocatalytic sets. Furthermore, our dynamical results confirm arguments made earlier about how autocatalytic subsets can enable their own growth or give rise to other such subsets coming into existence. Finally, our algorithmic extension and generalization show that more realistic scenarios (e.g., including inhibition) can also be dealt with within our framework, and that it can even be applied to areas outside of chemistry, such as economics. |
q-bio/0505007 | Ernesto Estrada | Ernesto Estrada | Virtual Identification of Essential Proteins Within the Protein
Interaction Network of Yeast | 11 pages, 3 figures | null | 10.1002/pmic.200500209 | null | q-bio.MN | null | Topological analysis of large scale protein-protein interaction networks
(PINs) is important for understanding the organisational and functional
principles of individual proteins. The number of interactions that a protein
has in a PIN has been observed to be correlated with its indispensability.
Essential proteins generally have more interactions than the non-essential
ones. We show here that the lethality associated with removal of a protein from
the yeast proteome correlates with different centrality measures of the nodes
in the PIN, such as the closeness of a protein to many other proteins, or the
number of pairs of proteins which need a specific protein as an intermediary in
their communications, or the participation of a protein in different protein
clusters in the PIN. These measures are significantly better than random
selection in identifying essential proteins in a PIN. Centrality measures based
on graph spectral properties of the network, in particular the subgraph
centrality, show the best performance in identifying essential proteins in the
yeast PIN. Subgraph centrality gives important structural information about the
role of individual proteins and permits the selection of possible targets for
rational drug discovery through the identification of essential proteins in the
PIN.
| [
{
"created": "Tue, 3 May 2005 21:38:51 GMT",
"version": "v1"
}
] | 2013-04-02 | [
[
"Estrada",
"Ernesto",
""
]
] | Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organisational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the non-essential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN. |
1609.02141 | Antonino Sciarrino | Diego Cocurullo and Antonino Sciarrino | Correlations in Usage Frequencies and Shannon Entropy for Codons | 42 pages, 12 figures, 33 Tables | null | null | Dipartimento di Scienze Fisiche, Naples,Italy, DSF-Th-2/08-v2 | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The usage frequencies for codons belonging to quartets are analized, over the
whole exonic region, for 92 biological species. Correlation is put into
evidence, between the usage frequencies of synonymous codons with third
nucleotide A and C and between the usage frequencies of non synonymous codons,
belonging to suitable subsets of the quartets, with the same third nucleotide.
A correlation is pointed out between amino acids belonging to subsets of the
set encoded by quartets of codons. It is remarked that the computed Shannon
entropy for quartets is weakly dependent on the biological species. The
observed correlations well fit in the mathematical scheme of the crystal basis
model of the genetic code.
| [
{
"created": "Wed, 7 Sep 2016 19:03:09 GMT",
"version": "v1"
}
] | 2016-09-09 | [
[
"Cocurullo",
"Diego",
""
],
[
"Sciarrino",
"Antonino",
""
]
] | The usage frequencies for codons belonging to quartets are analized, over the whole exonic region, for 92 biological species. Correlation is put into evidence, between the usage frequencies of synonymous codons with third nucleotide A and C and between the usage frequencies of non synonymous codons, belonging to suitable subsets of the quartets, with the same third nucleotide. A correlation is pointed out between amino acids belonging to subsets of the set encoded by quartets of codons. It is remarked that the computed Shannon entropy for quartets is weakly dependent on the biological species. The observed correlations well fit in the mathematical scheme of the crystal basis model of the genetic code. |
1512.07596 | Danielle Bassett | Zitong Zhang, Qawi K. Telesford, Chad Giusti, Kelvin O. Lim, Danielle
S. Bassett | Choosing Wavelet Methods, Filters, and Lengths for Functional Brain
Network Construction | working paper | null | 10.1371/journal.pone.0157243 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into
time series representing neurophysiological activity in fixed frequency bands.
Using these time series, one can estimate frequency-band specific functional
connectivity between sensors or regions of interest, and thereby construct
functional brain networks that can be examined from a graph theoretic
perspective. Despite their common use, however, practical guidelines for the
choice of wavelet method, filter, and length have remained largely
undelineated. Here, we explicitly explore the effects of wavelet method (MODWT
vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least
Asymmetric, and Coiflet families), and wavelet length (2 to 24) - each
essential parameters in wavelet-based methods - on the estimated values of
network diagnostics and in their sensitivity to alterations in psychiatric
disease. We observe that the MODWT method produces less variable estimates than
the DWT method. We also observe that the length of the wavelet filter chosen
has a greater impact on the estimated values of network diagnostics than the
type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of
the method to detect differences between health and disease and tunes
classification accuracy. Collectively, our results suggest that the choice of
wavelet method and length significantly alters the reliability and sensitivity
of these methods in estimating values of network diagnostics drawn from graph
theory. They furthermore demonstrate the importance of reporting the choices
utilized in neuroimaging studies and support the utility of exploring wavelet
parameters to maximize classification accuracy in the development of biomarkers
of psychiatric disease and neurological disorders.
| [
{
"created": "Wed, 23 Dec 2015 19:25:08 GMT",
"version": "v1"
}
] | 2016-09-28 | [
[
"Zhang",
"Zitong",
""
],
[
"Telesford",
"Qawi K.",
""
],
[
"Giusti",
"Chad",
""
],
[
"Lim",
"Kelvin O.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24) - each essential parameters in wavelet-based methods - on the estimated values of network diagnostics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of network diagnostics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of network diagnostics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders. |
1702.03418 | Yanjun Wang Dr. | Zhi-Song lv, Chen-Ping Zhu, Pei Nie, Jing Zhao, Hui-Jie Yang, Yan-Jun
Wang, Chin-Kun Hu | Exponential distance distribution of connected neurons in simulations of
two-dimensional in vitro neural network development | null | null | null | null | q-bio.NC physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The distribution of the geometric distances of connected neurons is a
practical factor underlying neural networks in the brain. It can affect the
brain\'s dynamic properties at the ground level. Karbowski derived a power-law
decay distribution that has not yet been verified by experiment. In this work,
we check its validity using simulations with a phenomenological model. Based on
the in vitro two-dimensional development of neural networks in culture vessels
by Ito, we match the synapse number saturation time to obtain suitable
parameters for the development process, then determine the distribution of
distances between connected neurons under such conditions. Our simulations
obtain a clear exponential distribution instead of a power-law one, which
indicates that Karbowski's conclusion is invalid, at least for the case of in
vitro neural network development in two-dimensional culture vessels.
| [
{
"created": "Sat, 11 Feb 2017 12:26:25 GMT",
"version": "v1"
}
] | 2017-02-14 | [
[
"lv",
"Zhi-Song",
""
],
[
"Zhu",
"Chen-Ping",
""
],
[
"Nie",
"Pei",
""
],
[
"Zhao",
"Jing",
""
],
[
"Yang",
"Hui-Jie",
""
],
[
"Wang",
"Yan-Jun",
""
],
[
"Hu",
"Chin-Kun",
""
]
] | The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain\'s dynamic properties at the ground level. Karbowski derived a power-law decay distribution that has not yet been verified by experiment. In this work, we check its validity using simulations with a phenomenological model. Based on the in vitro two-dimensional development of neural networks in culture vessels by Ito, we match the synapse number saturation time to obtain suitable parameters for the development process, then determine the distribution of distances between connected neurons under such conditions. Our simulations obtain a clear exponential distribution instead of a power-law one, which indicates that Karbowski's conclusion is invalid, at least for the case of in vitro neural network development in two-dimensional culture vessels. |
1509.03192 | J\'ozsef Z. Farkas | Jozsef Z. Farkas, Andrew Yu Morozov, E. G. Arashkevich, A. Nikishina | Revisiting the stability of spatially heterogeneous predator-prey
systems under eutrophication | 2 figures; appendices available on request. To appear in the Bulletin
of Mathematical Biology | Bulletin of Mathematical Biology, 77, (2015) 1886-1908 | 10.1007/s11538-015-0108-2 | null | q-bio.PE math.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We employ partial integro-differential equations to model trophic interaction
in a spatially extended heterogeneous environment. Compared to classical
reaction-diffusion models, this framework allows us to more realistically
describe the situation where movement of individuals occurs on a faster time
scale than the demographic (population) time scale, and we cannot determine
population growth based on local density. However, most of the results reported
so far for such systems have only been verified numerically and for a
particular choice of model functions, which obviously casts doubts about these
findings. In this paper, we analyse a class of integro-differential
predator-prey models with a highly mobile predator in a heterogeneous
environment, and we reveal the main factors stabilizing such systems. In
particular, we explore an ecologically relevant case of interactions in a
highly eutrophic environment, where the prey carrying capacity can be formally
set to 'infinity'. We investigate two main scenarios: (i) the spatial gradient
of the growth rate is due to abiotic factors only, and (ii) the local growth
rate depends on the global density distribution across the environment (e.g.
due to non-local self-shading). For an arbitrary spatial gradient of the prey
growth rate, we analytically investigate the possibility of the predator-prey
equilibrium in such systems and we explore the conditions of stability of this
equilibrium. In particular, we demonstrate that for a Holling type I (linear)
functional response, the predator can stabilize the system at low prey density
even for an 'unlimited' carrying capacity. We conclude that the interplay
between spatial heterogeneity in the prey growth and fast displacement of the
predator across the habitat works as an efficient stabilizing mechanism.
| [
{
"created": "Thu, 10 Sep 2015 15:20:19 GMT",
"version": "v1"
}
] | 2019-03-06 | [
[
"Farkas",
"Jozsef Z.",
""
],
[
"Morozov",
"Andrew Yu",
""
],
[
"Arashkevich",
"E. G.",
""
],
[
"Nikishina",
"A.",
""
]
] | We employ partial integro-differential equations to model trophic interaction in a spatially extended heterogeneous environment. Compared to classical reaction-diffusion models, this framework allows us to more realistically describe the situation where movement of individuals occurs on a faster time scale than the demographic (population) time scale, and we cannot determine population growth based on local density. However, most of the results reported so far for such systems have only been verified numerically and for a particular choice of model functions, which obviously casts doubts about these findings. In this paper, we analyse a class of integro-differential predator-prey models with a highly mobile predator in a heterogeneous environment, and we reveal the main factors stabilizing such systems. In particular, we explore an ecologically relevant case of interactions in a highly eutrophic environment, where the prey carrying capacity can be formally set to 'infinity'. We investigate two main scenarios: (i) the spatial gradient of the growth rate is due to abiotic factors only, and (ii) the local growth rate depends on the global density distribution across the environment (e.g. due to non-local self-shading). For an arbitrary spatial gradient of the prey growth rate, we analytically investigate the possibility of the predator-prey equilibrium in such systems and we explore the conditions of stability of this equilibrium. In particular, we demonstrate that for a Holling type I (linear) functional response, the predator can stabilize the system at low prey density even for an 'unlimited' carrying capacity. We conclude that the interplay between spatial heterogeneity in the prey growth and fast displacement of the predator across the habitat works as an efficient stabilizing mechanism. |
q-bio/0408009 | Ganesh Bagler | Ganesh Bagler and Somdatta Sinha | Network properties of protein structures | 5 pages, 7 postscript figures, Accepted for publication in Physica A | Physica A, 346/1-2 (2005) pp. 27-33 | 10.1016/j.physa.2004.08.046 | null | q-bio.MN q-bio.BM | null | Protein structures can be studied as complex networks of interacting amino
acids. We study proteins of different structural classes from the network
perspective. Our results indicate that proteins, regardless of their structural
class, show small-world network property. Various network parameters offer
insight into the structural organisation of proteins and provide indications of
modularity in protein networks.
| [
{
"created": "Fri, 13 Aug 2004 20:05:39 GMT",
"version": "v1"
}
] | 2007-11-19 | [
[
"Bagler",
"Ganesh",
""
],
[
"Sinha",
"Somdatta",
""
]
] | Protein structures can be studied as complex networks of interacting amino acids. We study proteins of different structural classes from the network perspective. Our results indicate that proteins, regardless of their structural class, show small-world network property. Various network parameters offer insight into the structural organisation of proteins and provide indications of modularity in protein networks. |
2003.01324 | Liaofu Luo | Yang Gao, Tao Li, Liaofu Luo | Phylogenetic Study of 2019-nCoV by Using Alignment Free Method
(Evolutionary Bifurcation of Novel Coronavirus Mutants) | 14 pages;2 figures | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | The phylogenetic tree of SARS-CoV-2 (nCov-19) viruses is reconstructed
according to the similarity of genome sequences. The tree topology of
Betacoronavirus is remarkably consistent with biologist's systematics. Because
the tree construction contains enough information about virus mutants, it is
suitable to study the evolutionary relationship between novel coronavirus
mutants transmitted among humans. The emergences of 14 kinds of main mutants
are studied and these strains can be classified as eight bifurcations of the
phylogenetic tree. It is found that there exist three types of virus mutations,
namely, the mutation among sub-branches of the same branch, the off-root
mutation and the root-oriented mutation between large branches of the tree.
From the point of the relation between viral mutation and host selection we
found that individuals with low immunity provide a special environment for the
positive natural selection of virus evolution. It gives a mechanism to explain
why large mutations between two distant branches generally occur in the nCov-19
phylogenetic tree. The finding is helpful to formulate strategies to control
the spread of COVID-19.
| [
{
"created": "Tue, 3 Mar 2020 04:10:07 GMT",
"version": "v1"
},
{
"created": "Mon, 4 May 2020 03:55:09 GMT",
"version": "v2"
},
{
"created": "Fri, 28 Jan 2022 07:32:01 GMT",
"version": "v3"
}
] | 2022-01-31 | [
[
"Gao",
"Yang",
""
],
[
"Li",
"Tao",
""
],
[
"Luo",
"Liaofu",
""
]
] | The phylogenetic tree of SARS-CoV-2 (nCov-19) viruses is reconstructed according to the similarity of genome sequences. The tree topology of Betacoronavirus is remarkably consistent with biologist's systematics. Because the tree construction contains enough information about virus mutants, it is suitable to study the evolutionary relationship between novel coronavirus mutants transmitted among humans. The emergences of 14 kinds of main mutants are studied and these strains can be classified as eight bifurcations of the phylogenetic tree. It is found that there exist three types of virus mutations, namely, the mutation among sub-branches of the same branch, the off-root mutation and the root-oriented mutation between large branches of the tree. From the point of the relation between viral mutation and host selection we found that individuals with low immunity provide a special environment for the positive natural selection of virus evolution. It gives a mechanism to explain why large mutations between two distant branches generally occur in the nCov-19 phylogenetic tree. The finding is helpful to formulate strategies to control the spread of COVID-19. |
q-bio/0604021 | Jesus M. Cortes | J.J. Torres, J.M. Cortes, J. Marro | Instability of attractors in autoassociative networks with bioinspired
fast synaptic noise | 6 pages, 2 figures | LNCS 3512: 161-167, 2005 | null | null | q-bio.NC | null | We studied autoassociative networks in which synapses are noisy on a time
scale much shorter that the one for the neuron dynamics. In our model a
presynaptic noise causes postsynaptic depression as recently observed in
neurobiological systems. This results in a nonequilibrium condition in which
the network sensitivity to an external stimulus is enhanced. In particular, the
fixed points are qualitatively modified, and the system may easily scape from
the attractors. As a result, in addition to pattern recognition, the model is
useful for class identification and categorization.
| [
{
"created": "Sun, 16 Apr 2006 22:02:28 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Torres",
"J. J.",
""
],
[
"Cortes",
"J. M.",
""
],
[
"Marro",
"J.",
""
]
] | We studied autoassociative networks in which synapses are noisy on a time scale much shorter that the one for the neuron dynamics. In our model a presynaptic noise causes postsynaptic depression as recently observed in neurobiological systems. This results in a nonequilibrium condition in which the network sensitivity to an external stimulus is enhanced. In particular, the fixed points are qualitatively modified, and the system may easily scape from the attractors. As a result, in addition to pattern recognition, the model is useful for class identification and categorization. |
1809.10866 | Philippe Marcq | V. Nier, G. Peyret, J. d'Alessandro, S. Ishihara, B. Ladoux and P.
Marcq | Kalman inversion stress microscopy | 23 pages, 8 pages | Biophysical Journal 115, 1808-1816 (2018) | 10.1016/j.bpj.2018.09.013 | null | q-bio.QM q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although mechanical cues are crucial to tissue morphogenesis and development,
the tissue mechanical stress field remains poorly characterized. Given traction
force timelapse movies, as obtained by traction force microscopy of in vitro
cellular sheets, we show that the tissue stress field can be estimated by
Kalman filtering. After validation using numerical data, we apply Kalman
inversion stress microscopy to experimental data. We combine the inferred
stress field with velocity and cell shape measurements to quantify the rheology
of epithelial cell monolayers in physiological conditions, found to be close to
that of an elastic and active material.
| [
{
"created": "Fri, 28 Sep 2018 06:04:31 GMT",
"version": "v1"
}
] | 2019-04-22 | [
[
"Nier",
"V.",
""
],
[
"Peyret",
"G.",
""
],
[
"d'Alessandro",
"J.",
""
],
[
"Ishihara",
"S.",
""
],
[
"Ladoux",
"B.",
""
],
[
"Marcq",
"P.",
""
]
] | Although mechanical cues are crucial to tissue morphogenesis and development, the tissue mechanical stress field remains poorly characterized. Given traction force timelapse movies, as obtained by traction force microscopy of in vitro cellular sheets, we show that the tissue stress field can be estimated by Kalman filtering. After validation using numerical data, we apply Kalman inversion stress microscopy to experimental data. We combine the inferred stress field with velocity and cell shape measurements to quantify the rheology of epithelial cell monolayers in physiological conditions, found to be close to that of an elastic and active material. |
1003.0199 | Mark Little Mark Peter Little | M.P. Little | Monocyte and T-lymphocyte trans-endothelial migration in relation to
cardiovascular disease: some alternative boundary conditions in a model
recently proposed by Little et al. (PLoS Comput Biol 2009 5(10) e1000539) | null | null | null | null | q-bio.CB q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a slight modification to the monocyte and T-lymphocyte boundary
conditions of Little et al. (PLoS Comput Biol 2009 5(10) e1000539) and derive
alternative parameter estimates. No changes to the results and conclusions of
the paper of Little et al. (PLoS Comput Biol 2009 5(10) e1000539) are implied.
| [
{
"created": "Sun, 28 Feb 2010 17:34:59 GMT",
"version": "v1"
}
] | 2010-03-02 | [
[
"Little",
"M. P.",
""
]
] | We consider a slight modification to the monocyte and T-lymphocyte boundary conditions of Little et al. (PLoS Comput Biol 2009 5(10) e1000539) and derive alternative parameter estimates. No changes to the results and conclusions of the paper of Little et al. (PLoS Comput Biol 2009 5(10) e1000539) are implied. |
2108.00047 | Andrew Tilman | Andrew R. Tilman, V\'itor V. Vasconcelos, Erol Ak\c{c}ay, Joshua B.
Plotkin | The evolution of forecasting for decision making in dynamic environments | revised ms; results unchanged | Collective Intelligence 2:4 (2023) 1-14 | 10.1177/26339137231221726 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Global change is reshaping ecosystems and societies. Strategic choices that
were best yesterday may be sub-optimal tomorrow; and environmental conditions
that were once taken for granted may soon cease to exist. In this setting, how
people choose behavioral strategies has important consequences for
environmental dynamics. Economic and evolutionary theories make similar
predictions for strategic behavior in a static environment, even though one
approach assumes perfect rationality and the other assumes no cognition
whatsoever; but predictions differ in a dynamic environment. Here we explore a
middle ground between economic rationality and evolutionary myopia. Starting
from a population of myopic agents, we study the emergence of a new type that
forms environmental forecasts when making strategic decisions. We show that
forecasting types can have an advantage in changing environments, even when the
act of forecasting is costly. Forecasting types can invade but not overtake the
population, producing a stable coexistence with myopic types. Moreover,
forecasters provide a public good by reducing the amplitude of environmental
oscillations and increasing mean payoff to forecasting and myopic types alike.
We interpret our results for understanding the evolution of different modes of
decision-making. And we discuss implications for the management of
environmental systems of great societal importance.
| [
{
"created": "Fri, 30 Jul 2021 19:12:36 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Aug 2022 17:02:23 GMT",
"version": "v2"
}
] | 2024-04-23 | [
[
"Tilman",
"Andrew R.",
""
],
[
"Vasconcelos",
"Vítor V.",
""
],
[
"Akçay",
"Erol",
""
],
[
"Plotkin",
"Joshua B.",
""
]
] | Global change is reshaping ecosystems and societies. Strategic choices that were best yesterday may be sub-optimal tomorrow; and environmental conditions that were once taken for granted may soon cease to exist. In this setting, how people choose behavioral strategies has important consequences for environmental dynamics. Economic and evolutionary theories make similar predictions for strategic behavior in a static environment, even though one approach assumes perfect rationality and the other assumes no cognition whatsoever; but predictions differ in a dynamic environment. Here we explore a middle ground between economic rationality and evolutionary myopia. Starting from a population of myopic agents, we study the emergence of a new type that forms environmental forecasts when making strategic decisions. We show that forecasting types can have an advantage in changing environments, even when the act of forecasting is costly. Forecasting types can invade but not overtake the population, producing a stable coexistence with myopic types. Moreover, forecasters provide a public good by reducing the amplitude of environmental oscillations and increasing mean payoff to forecasting and myopic types alike. We interpret our results for understanding the evolution of different modes of decision-making. And we discuss implications for the management of environmental systems of great societal importance. |
0809.5225 | Abhinav Singh | Abhinav Singh, Hao Wang, Wendy Morrison and Howard Weiss | Modeling Fish Biomass Structure at Near Pristine Coral Reefs and
Degradation by Fishing | 24 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Until recently, the only examples of inverted biomass pyramids have been in
freshwater and marine planktonic communities. In 2002 and 2008 investigators
documented inverted biomass pyramids for nearly pristine coral reef ecosystems
within the NW Hawaiian islands and the Line Islands, where apex predator
abundance comprises up to 85% of the fish biomass. We build a new refuge based
predator-prey model to study the fish biomass structure at coral reefs and
investigate the effect of fishing on biomass pyramids. Utilizing realistic life
history parameters of coral reef fish, our model exhibits a stable inverted
biomass pyramid. Since the predators and prey are not well mixed, our model
does not incorporate homogeneous mixing and the inverted biomass pyramid is a
consequence of the refuge. Understanding predator-prey dynamics in nearly
pristine conditions provides a more realistic historical framework for
comparison with fished reefs. Finally, we show that fishing transforms the
inverted biomass pyramid to be bottom heavy.
| [
{
"created": "Tue, 30 Sep 2008 16:16:03 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Oct 2008 03:43:39 GMT",
"version": "v2"
},
{
"created": "Thu, 30 Jul 2009 17:14:07 GMT",
"version": "v3"
}
] | 2009-07-30 | [
[
"Singh",
"Abhinav",
""
],
[
"Wang",
"Hao",
""
],
[
"Morrison",
"Wendy",
""
],
[
"Weiss",
"Howard",
""
]
] | Until recently, the only examples of inverted biomass pyramids have been in freshwater and marine planktonic communities. In 2002 and 2008 investigators documented inverted biomass pyramids for nearly pristine coral reef ecosystems within the NW Hawaiian islands and the Line Islands, where apex predator abundance comprises up to 85% of the fish biomass. We build a new refuge based predator-prey model to study the fish biomass structure at coral reefs and investigate the effect of fishing on biomass pyramids. Utilizing realistic life history parameters of coral reef fish, our model exhibits a stable inverted biomass pyramid. Since the predators and prey are not well mixed, our model does not incorporate homogeneous mixing and the inverted biomass pyramid is a consequence of the refuge. Understanding predator-prey dynamics in nearly pristine conditions provides a more realistic historical framework for comparison with fished reefs. Finally, we show that fishing transforms the inverted biomass pyramid to be bottom heavy. |
2105.07087 | Luis G. Morelli | Sol M. Fern\'andez Arancibia and Hern\'an E. Grecco and Luis G.
Morelli | Effective description of bistability and irreversibility in apoptosis | null | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Apoptosis is a mechanism of programmed cell death in which cells engage in a
controlled demolition and prepare to be digested without damaging their
environment. In normal conditions apoptosis is repressed, until it is
irreversibly induced by an appropriate signal. In adult organisms apoptosis is
a natural way to dispose of damaged cells, and its disruption or excess is
associated with cancer and autoimmune diseases. Apoptosis is regulated by a
complex signaling network controlled by caspases, specialized enzymes that
digest essential cellular components and promote the degradation of genomic
DNA. In this work we propose an effective description of the signaling network
focused on caspase-3 as a readout of cell fate. We integrate intermediate
network interactions into a nonlinear feedback function acting on caspase-3 and
introduce the effect of pro-apoptotic stimuli and regulatory elements as a
saturating activation function. We find that the theory has a robust bistable
regime where two possible states coexist, representing survival and cell death
fates. For a broad range of parameters, strong stimuli can induce an
irreversible switch to the death fate. We use the theory to explore dynamical
stimulation conditions and determine how cell fate depends on different stimuli
patterns. This analysis reveals a critical relation between transient stimuli
intensity and duration to trigger irreversible apoptosis.
| [
{
"created": "Fri, 14 May 2021 22:51:51 GMT",
"version": "v1"
}
] | 2021-05-18 | [
[
"Arancibia",
"Sol M. Fernández",
""
],
[
"Grecco",
"Hernán E.",
""
],
[
"Morelli",
"Luis G.",
""
]
] | Apoptosis is a mechanism of programmed cell death in which cells engage in a controlled demolition and prepare to be digested without damaging their environment. In normal conditions apoptosis is repressed, until it is irreversibly induced by an appropriate signal. In adult organisms apoptosis is a natural way to dispose of damaged cells, and its disruption or excess is associated with cancer and autoimmune diseases. Apoptosis is regulated by a complex signaling network controlled by caspases, specialized enzymes that digest essential cellular components and promote the degradation of genomic DNA. In this work we propose an effective description of the signaling network focused on caspase-3 as a readout of cell fate. We integrate intermediate network interactions into a nonlinear feedback function acting on caspase-3 and introduce the effect of pro-apoptotic stimuli and regulatory elements as a saturating activation function. We find that the theory has a robust bistable regime where two possible states coexist, representing survival and cell death fates. For a broad range of parameters, strong stimuli can induce an irreversible switch to the death fate. We use the theory to explore dynamical stimulation conditions and determine how cell fate depends on different stimuli patterns. This analysis reveals a critical relation between transient stimuli intensity and duration to trigger irreversible apoptosis. |
2301.01264 | Oleg Kogan | Niranjan Sarpangala, Brooke Randell, Ajay Gopinathan, Oleg Kogan | Tunable intracellular transport on converging microtubule morphologies | null | null | null | null | q-bio.CB cond-mat.stat-mech q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A common type of cytoskeletal morphology involves multiple converging
microbutubules with their minus ends collected and stabilized by a microtubule
organizing center (MTOC) in the interior of the cell. This arrangement enables
the ballistic transport of cargo bound to microtubules, both dynein mediated
transport towards the MTOC and kinesin mediated transport away from it,
interspersed with diffusion for unbound cargo-motor complexes. Spatial and
temporal positioning of the MTOC allows for bidirectional transport towards and
away from specific organelles and locations within the cell and also the
sequestering and subsequent dispersal of dynein transported cargo. The general
principles governing dynamics, efficiency and tunability of such transport in
the MTOC vicinity is not fully understood. To address this, we develop a
one-dimensional model that includes advective transport towards an attractor
(such as the MTOC), and diffusive transport that allows particles to reach
absorbing boundaries (such as cellular membranes). We calculated the mean first
passage time (MFPT) for cargo to reach the boundaries as a measure of the
effectiveness of sequestering (large MFPT) and diffusive dispersal (low MFPT).
The MFPT experiences a dramatic growth in magnitude, transitioning from a low
to high MFPT regime (dispersal to sequestering) over a window of cargo
attachment/detachment rates that is close to in vivo values. We find that
increasing either the attachment or detachment rate, while fixing the other,
can result in optimal dispersal when the attractor is placed asymmetrically.
Finally, we describe a rare event regime, where the escape location is
exponentially sensitive to the attractor positioning. Our results suggest that
structures such as the MTOC allow for the sensitive control of the spatial and
temporal features of transport and corresponding function under physiological
conditions.
| [
{
"created": "Tue, 3 Jan 2023 18:06:46 GMT",
"version": "v1"
}
] | 2023-01-04 | [
[
"Sarpangala",
"Niranjan",
""
],
[
"Randell",
"Brooke",
""
],
[
"Gopinathan",
"Ajay",
""
],
[
"Kogan",
"Oleg",
""
]
] | A common type of cytoskeletal morphology involves multiple converging microbutubules with their minus ends collected and stabilized by a microtubule organizing center (MTOC) in the interior of the cell. This arrangement enables the ballistic transport of cargo bound to microtubules, both dynein mediated transport towards the MTOC and kinesin mediated transport away from it, interspersed with diffusion for unbound cargo-motor complexes. Spatial and temporal positioning of the MTOC allows for bidirectional transport towards and away from specific organelles and locations within the cell and also the sequestering and subsequent dispersal of dynein transported cargo. The general principles governing dynamics, efficiency and tunability of such transport in the MTOC vicinity is not fully understood. To address this, we develop a one-dimensional model that includes advective transport towards an attractor (such as the MTOC), and diffusive transport that allows particles to reach absorbing boundaries (such as cellular membranes). We calculated the mean first passage time (MFPT) for cargo to reach the boundaries as a measure of the effectiveness of sequestering (large MFPT) and diffusive dispersal (low MFPT). The MFPT experiences a dramatic growth in magnitude, transitioning from a low to high MFPT regime (dispersal to sequestering) over a window of cargo attachment/detachment rates that is close to in vivo values. We find that increasing either the attachment or detachment rate, while fixing the other, can result in optimal dispersal when the attractor is placed asymmetrically. Finally, we describe a rare event regime, where the escape location is exponentially sensitive to the attractor positioning. Our results suggest that structures such as the MTOC allow for the sensitive control of the spatial and temporal features of transport and corresponding function under physiological conditions. |
2310.12979 | Jeffrey Ouyang-Zhang | Jeffrey Ouyang-Zhang, Daniel J. Diaz, Adam R. Klivans, Philipp
Kr\"ahenb\"uhl | Predicting a Protein's Stability under a Million Mutations | NeurIPS 2023. Code available at
https://github.com/jozhang97/MutateEverything | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Stabilizing proteins is a foundational step in protein engineering. However,
the evolutionary pressure of all extant proteins makes identifying the scarce
number of mutations that will improve thermodynamic stability challenging. Deep
learning has recently emerged as a powerful tool for identifying promising
mutations. Existing approaches, however, are computationally expensive, as the
number of model inferences scales with the number of mutations queried. Our
main contribution is a simple, parallel decoding algorithm. Our Mutate
Everything is capable of predicting the effect of all single and double
mutations in one forward pass. It is even versatile enough to predict
higher-order mutations with minimal computational overhead. We build Mutate
Everything on top of ESM2 and AlphaFold, neither of which were trained to
predict thermodynamic stability. We trained on the Mega-Scale cDNA proteolysis
dataset and achieved state-of-the-art performance on single and higher-order
mutations on S669, ProTherm, and ProteinGym datasets. Code is available at
https://github.com/jozhang97/MutateEverything
| [
{
"created": "Thu, 19 Oct 2023 17:59:47 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Oct 2023 19:45:12 GMT",
"version": "v2"
}
] | 2023-11-01 | [
[
"Ouyang-Zhang",
"Jeffrey",
""
],
[
"Diaz",
"Daniel J.",
""
],
[
"Klivans",
"Adam R.",
""
],
[
"Krähenbühl",
"Philipp",
""
]
] | Stabilizing proteins is a foundational step in protein engineering. However, the evolutionary pressure of all extant proteins makes identifying the scarce number of mutations that will improve thermodynamic stability challenging. Deep learning has recently emerged as a powerful tool for identifying promising mutations. Existing approaches, however, are computationally expensive, as the number of model inferences scales with the number of mutations queried. Our main contribution is a simple, parallel decoding algorithm. Our Mutate Everything is capable of predicting the effect of all single and double mutations in one forward pass. It is even versatile enough to predict higher-order mutations with minimal computational overhead. We build Mutate Everything on top of ESM2 and AlphaFold, neither of which were trained to predict thermodynamic stability. We trained on the Mega-Scale cDNA proteolysis dataset and achieved state-of-the-art performance on single and higher-order mutations on S669, ProTherm, and ProteinGym datasets. Code is available at https://github.com/jozhang97/MutateEverything |
1202.6274 | Haidong Feng | Haidong Feng, Jin Wang | A new mechanism of development and differentiation through slow
binding/unbinding of regulatory proteins to the genes | 25 pages, 5 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the differentiation, a biological process from a multipotent
stem or progenitor state to a mature cell is critically important. We develop a
theoretical framework to quantify the underlying potential landscape and
biological paths for cell development and differentiation. We propose a new
mechanism of differentiation and development through binding/unbinding of
regulatory proteins to the gene promoters. We found indeed the differentiated
states can emerge from the slow promoter binding/unbinding processes.
Furthermore, under slow promoter binding/unbinding, we found multiple
meta-stable differentiated states. This can explain the origin of multiple
states observed in the recent experiments. In addition, the kinetic time
quantified by mean first passage transition time for the differentiation and
reprogramming strongly depends on the time scale of the promoter
binding/unbinding processes. We discovered an optimal speed for differentiation
for certain binding/unbinding rates of regulatory proteins to promoters. More
experiments in the future might be able to tell if cells differentiate at at
that optimal speed. In addition, we quantify kinetic pathways for the
differentiation and reprogramming. We found that they are irreversible. This
captures the non-equilibrium dynamics in multipotent stem or progenitor cells.
Such inherent time-asymmetry as a result of irreversibility of state transition
pathways as shown may provide the origin of time arrow for cell development.
| [
{
"created": "Tue, 28 Feb 2012 16:30:52 GMT",
"version": "v1"
}
] | 2012-02-29 | [
[
"Feng",
"Haidong",
""
],
[
"Wang",
"Jin",
""
]
] | Understanding the differentiation, a biological process from a multipotent stem or progenitor state to a mature cell is critically important. We develop a theoretical framework to quantify the underlying potential landscape and biological paths for cell development and differentiation. We propose a new mechanism of differentiation and development through binding/unbinding of regulatory proteins to the gene promoters. We found indeed the differentiated states can emerge from the slow promoter binding/unbinding processes. Furthermore, under slow promoter binding/unbinding, we found multiple meta-stable differentiated states. This can explain the origin of multiple states observed in the recent experiments. In addition, the kinetic time quantified by mean first passage transition time for the differentiation and reprogramming strongly depends on the time scale of the promoter binding/unbinding processes. We discovered an optimal speed for differentiation for certain binding/unbinding rates of regulatory proteins to promoters. More experiments in the future might be able to tell if cells differentiate at at that optimal speed. In addition, we quantify kinetic pathways for the differentiation and reprogramming. We found that they are irreversible. This captures the non-equilibrium dynamics in multipotent stem or progenitor cells. Such inherent time-asymmetry as a result of irreversibility of state transition pathways as shown may provide the origin of time arrow for cell development. |
1002.1736 | Hamid Reza Chitsaz | Hamidreza Chitsaz | Prediction of RNA-RNA interaction structure by centroids in the
Boltzmann ensemble | null | null | null | null | q-bio.BM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | New high-throughput sequencing technologies have made it possible to pursue
the advent of genome-wide transcriptomics. That progress combined with the
recent discovery of regulatory non-coding RNAs (ncRNAs) has necessitated fast
and accurate algorithms to predict RNA-RNA interaction probability and
structure. Although there are algorithms to predict minimum free energy
interaction secondary structure for two nucleic acids, little work has been
done to exploit the information invested in the base pair probabilities to
improve interaction structure prediction. In this paper, we present an
algorithm to predict the Hamming centroid of the Boltzmann ensemble of
interaction structures. We also present an efficient algorithm to sample
interaction structures from the ensemble. Our sampling algorithm uses a
balanced scheme for traversing indices which improves the running time of the
Ding-Lawrence sampling algorithm. The Ding-Lawrence sampling algorithm has
$O(n^2m^2)$ time complexity whereas our algorithm has $O((n+m)^2\log(n+m))$
time complexity, in which $n$ and $m$ are the lengths of input strands. We
implemented our algorithm in a new version of {\tt piRNA} and compared our
structure prediction results with competitors. Our centroid prediction
outperforms competitor minimum-free-energy prediction algorithms on average.
| [
{
"created": "Mon, 8 Feb 2010 23:20:58 GMT",
"version": "v1"
}
] | 2010-02-10 | [
[
"Chitsaz",
"Hamidreza",
""
]
] | New high-throughput sequencing technologies have made it possible to pursue the advent of genome-wide transcriptomics. That progress combined with the recent discovery of regulatory non-coding RNAs (ncRNAs) has necessitated fast and accurate algorithms to predict RNA-RNA interaction probability and structure. Although there are algorithms to predict minimum free energy interaction secondary structure for two nucleic acids, little work has been done to exploit the information invested in the base pair probabilities to improve interaction structure prediction. In this paper, we present an algorithm to predict the Hamming centroid of the Boltzmann ensemble of interaction structures. We also present an efficient algorithm to sample interaction structures from the ensemble. Our sampling algorithm uses a balanced scheme for traversing indices which improves the running time of the Ding-Lawrence sampling algorithm. The Ding-Lawrence sampling algorithm has $O(n^2m^2)$ time complexity whereas our algorithm has $O((n+m)^2\log(n+m))$ time complexity, in which $n$ and $m$ are the lengths of input strands. We implemented our algorithm in a new version of {\tt piRNA} and compared our structure prediction results with competitors. Our centroid prediction outperforms competitor minimum-free-energy prediction algorithms on average. |
q-bio/0512015 | Walton Gutierrez | Walton R. Gutierrez | Distribution networks and the optimal form of the kidney and lung | 5 pages, 4 figures | null | null | null | q-bio.TO q-bio.QM | null | A model is proposed to minimize the total volume of the main distribution
networks of fluids in organs such as the kidney and the lung. A consequence of
the minimization analysis is that the optimal overall form of the organs is a
modified ellipsoid. The variational procedure implementing this minimization is
similar to the traditional isoperimetric theorems of geometry.
| [
{
"created": "Wed, 7 Dec 2005 01:24:00 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Gutierrez",
"Walton R.",
""
]
] | A model is proposed to minimize the total volume of the main distribution networks of fluids in organs such as the kidney and the lung. A consequence of the minimization analysis is that the optimal overall form of the organs is a modified ellipsoid. The variational procedure implementing this minimization is similar to the traditional isoperimetric theorems of geometry. |
1908.11374 | Brian Camley | Melissa H. Mai and Brian A. Camley | Hydrodynamic Effects on the Motility of Crawling Eukaryotic Cells | Note: four supplemental movies are provided | null | null | null | q-bio.CB cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Eukaryotic cell motility is crucial during development, wound healing, the
immune response, and cancer metastasis. Some eukaryotic cells can swim, but
cells more commonly adhere to and crawl along the extracellular matrix. We
study the relationship between hydrodynamics and adhesion that describe whether
a cell is swimming, crawling, or combining these motions. Our simple model of a
cell, based on the three-sphere swimmer, is capable of both swimming and
crawling. As cell-matrix adhesion strength increases, the influence of
hydrodynamics on migration diminish. Cells with significant adhesion can crawl
with speeds much larger than their nonadherent, swimming counterparts. We
predict that, while most eukaryotic cells are in the strong-adhesion limit,
increasing environment viscosity or decreasing cell-matrix adhesion could lead
to significant hydrodynamic effects even in crawling cells. Signatures of
hydrodynamic effects include dependence of cell speed on the medium viscosity
or the presence of a nearby substrate and the presence of interactions between
noncontacting cells. These signatures will be suppressed at large adhesion
strengths, but even strongly adherent cells will generate relevant fluid flows
that will advect nearby passive particles and swimmers.
| [
{
"created": "Thu, 29 Aug 2019 16:04:19 GMT",
"version": "v1"
}
] | 2019-09-02 | [
[
"Mai",
"Melissa H.",
""
],
[
"Camley",
"Brian A.",
""
]
] | Eukaryotic cell motility is crucial during development, wound healing, the immune response, and cancer metastasis. Some eukaryotic cells can swim, but cells more commonly adhere to and crawl along the extracellular matrix. We study the relationship between hydrodynamics and adhesion that describe whether a cell is swimming, crawling, or combining these motions. Our simple model of a cell, based on the three-sphere swimmer, is capable of both swimming and crawling. As cell-matrix adhesion strength increases, the influence of hydrodynamics on migration diminish. Cells with significant adhesion can crawl with speeds much larger than their nonadherent, swimming counterparts. We predict that, while most eukaryotic cells are in the strong-adhesion limit, increasing environment viscosity or decreasing cell-matrix adhesion could lead to significant hydrodynamic effects even in crawling cells. Signatures of hydrodynamic effects include dependence of cell speed on the medium viscosity or the presence of a nearby substrate and the presence of interactions between noncontacting cells. These signatures will be suppressed at large adhesion strengths, but even strongly adherent cells will generate relevant fluid flows that will advect nearby passive particles and swimmers. |
1907.08230 | Thierry Mora | Thierry Mora, Aleksandra M. Walczak | How many different clonotypes do immune repertoires contain? | null | Current Opinion in Systems Biology 18, 104--110 (2019) | 10.1016/j.coisb.2019.10.001 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Immune repertoires rely on diversity of T-cell and B-cell receptors to
protect us against foreign threats. The ability to recognize a wide variety of
pathogens is linked to the number of different clonotypes expressed by an
individual. Out of the estimated $\sim 10^{12}$ different B and T cells in
humans, how many of them express distinct receptors? We review current and past
estimates for these numbers. We point out a fundamental limitation of current
methods, which ignore the tail of small clones in the distribution of clone
sizes. We show that this tail strongly affects the total number of clones, but
it is impractical to access experimentally. We propose that combining
statistical models with mechanistic models of lymphocyte clonal dynamics offers
possible new strategies for estimating the number of clones.
| [
{
"created": "Thu, 18 Jul 2019 18:23:22 GMT",
"version": "v1"
}
] | 2020-11-20 | [
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] | Immune repertoires rely on diversity of T-cell and B-cell receptors to protect us against foreign threats. The ability to recognize a wide variety of pathogens is linked to the number of different clonotypes expressed by an individual. Out of the estimated $\sim 10^{12}$ different B and T cells in humans, how many of them express distinct receptors? We review current and past estimates for these numbers. We point out a fundamental limitation of current methods, which ignore the tail of small clones in the distribution of clone sizes. We show that this tail strongly affects the total number of clones, but it is impractical to access experimentally. We propose that combining statistical models with mechanistic models of lymphocyte clonal dynamics offers possible new strategies for estimating the number of clones. |
2212.02868 | Attila Szolnoki | Attila Szolnoki and Matjaz Perc | Oppressed species can form a winning pair in a multi-species ecosystem | 10 pages, 8 figures | Appl. Math. Comput. 438 (2023) 127568 | 10.1016/j.amc.2022.127568 | null | q-bio.PE cond-mat.stat-mech cs.GT nlin.PS physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | The self-protection of alliances against external invaders is a key concept
behind the maintenance of biodiversity in the face of natural selection. But
since these alliances, which can be formed by different numbers of competitors,
can also compete against each other, it is important to identify their
strengths and weaknesses. Here, we therefore compare the vitalities of two
two-species alliances whose members either beat each other mutually via a
bidirectional invasion or they exchange their positions during an inner
dynamics. The resulting four-species model shows rich behavior in dependence on
the model parameter $p$, which characterizes the inner invasions, and $\beta$,
which determines the intensity of site exchanges. In the low $p$ and the large
$p$ limit, when the inner invasion becomes biased, three-member
rock-scissors-paper-type solutions emerge, where one of the members is
oppressed by having the smallest average concentration due to heterogeneous
inner invasion rates. Interestingly, however, if we allow a more intensive site
exchange between the oppressed species, they can morph into a winning pair and
dominate the full parameter plane. We show that their victory utilizes the
vulnerability of the rival alliance based on cyclic dominance, where a species
can easily fixate a limited-size domain.
| [
{
"created": "Tue, 6 Dec 2022 10:16:46 GMT",
"version": "v1"
}
] | 2022-12-07 | [
[
"Szolnoki",
"Attila",
""
],
[
"Perc",
"Matjaz",
""
]
] | The self-protection of alliances against external invaders is a key concept behind the maintenance of biodiversity in the face of natural selection. But since these alliances, which can be formed by different numbers of competitors, can also compete against each other, it is important to identify their strengths and weaknesses. Here, we therefore compare the vitalities of two two-species alliances whose members either beat each other mutually via a bidirectional invasion or they exchange their positions during an inner dynamics. The resulting four-species model shows rich behavior in dependence on the model parameter $p$, which characterizes the inner invasions, and $\beta$, which determines the intensity of site exchanges. In the low $p$ and the large $p$ limit, when the inner invasion becomes biased, three-member rock-scissors-paper-type solutions emerge, where one of the members is oppressed by having the smallest average concentration due to heterogeneous inner invasion rates. Interestingly, however, if we allow a more intensive site exchange between the oppressed species, they can morph into a winning pair and dominate the full parameter plane. We show that their victory utilizes the vulnerability of the rival alliance based on cyclic dominance, where a species can easily fixate a limited-size domain. |
1305.4264 | Simon Holbek | Simon Holbek, Kristian Moss Bendtsen, Jeppe Juul | Moderate stem cell telomere shortening rate postpones cancer onset in
stochastic model | null | null | 10.1103/PhysRevE.88.042706 | null | q-bio.CB physics.bio-ph q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mammalian cells are restricted from proliferating indefinitely. Telomeres at
the end of each chromosome are shortened at cell division and, when they reach
a critical length, the cell will enter permanent cell cycle arrest - a state
known as senescence. This mechanism is thought to be tumor suppressing, as it
helps prevent precancerous cells from dividing uncontrollably.
Stem cells express the enzyme telomerase, which elongates the telomeres,
thereby postponing senescence. However, unlike germ cells and most types of
cancer cells, stem cells only express telomerase at levels insufficient to
fully maintain the length of their telomeres leading to a slow decline in
proliferation potential. It is not yet fully understood how this decline
influences the risk of cancer and the longevity of the organism. We here
develop a stochastic model to explore the role of telomere dynamics in relation
to both senescence and cancer. The model describes the accumulation of
cancerous mutations in a multicellular organism and creates a coherent
theoretical framework for interpreting the results of several recent
experiments on telomerase regulation. We demonstrate that the longest average
cancer free life span before cancer onset is obtained when stem cells start
with relatively long telomeres that are shortened at a steady rate at cell
division. Furthermore, the risk of cancer early in life can be reduced by
having a short initial telomere length. Finally, our model suggests that
evolution will favour a shorter than optimal average cancer free life span in
order to postpone cancer onset until late in life.
| [
{
"created": "Sat, 18 May 2013 13:23:05 GMT",
"version": "v1"
}
] | 2015-06-16 | [
[
"Holbek",
"Simon",
""
],
[
"Bendtsen",
"Kristian Moss",
""
],
[
"Juul",
"Jeppe",
""
]
] | Mammalian cells are restricted from proliferating indefinitely. Telomeres at the end of each chromosome are shortened at cell division and, when they reach a critical length, the cell will enter permanent cell cycle arrest - a state known as senescence. This mechanism is thought to be tumor suppressing, as it helps prevent precancerous cells from dividing uncontrollably. Stem cells express the enzyme telomerase, which elongates the telomeres, thereby postponing senescence. However, unlike germ cells and most types of cancer cells, stem cells only express telomerase at levels insufficient to fully maintain the length of their telomeres leading to a slow decline in proliferation potential. It is not yet fully understood how this decline influences the risk of cancer and the longevity of the organism. We here develop a stochastic model to explore the role of telomere dynamics in relation to both senescence and cancer. The model describes the accumulation of cancerous mutations in a multicellular organism and creates a coherent theoretical framework for interpreting the results of several recent experiments on telomerase regulation. We demonstrate that the longest average cancer free life span before cancer onset is obtained when stem cells start with relatively long telomeres that are shortened at a steady rate at cell division. Furthermore, the risk of cancer early in life can be reduced by having a short initial telomere length. Finally, our model suggests that evolution will favour a shorter than optimal average cancer free life span in order to postpone cancer onset until late in life. |
q-bio/0505019 | Henrik Jeldtoft Jensen | Daniel Lawson, Henrik Jeldtoft Jensen and Kunihiko Kaneko | Diversity as a product of interspecial interactions | 7 pages, 3 figures | null | null | null | q-bio.PE | null | We demonstrate diversification rather than optimisation for highly
interacting organisms in a well mixed biological system by means of a simple
model and reference to experiment, and find the cause to be the complex network
of interactions formed, allowing species less well adapted to an environment to
flourish by co-interaction over the `best' species. This diversification can be
considered as the construction of many co-evolutionary niches by the network of
interactions between species. Evidence for this comes from work with the
bacteria Escherichia coli, which may coexist with their own mutants under
certain conditions. Diversification only occurs above a certain threshold
interaction strength, below which competitive exclusion occurs.
| [
{
"created": "Tue, 10 May 2005 13:58:37 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Lawson",
"Daniel",
""
],
[
"Jensen",
"Henrik Jeldtoft",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | We demonstrate diversification rather than optimisation for highly interacting organisms in a well mixed biological system by means of a simple model and reference to experiment, and find the cause to be the complex network of interactions formed, allowing species less well adapted to an environment to flourish by co-interaction over the `best' species. This diversification can be considered as the construction of many co-evolutionary niches by the network of interactions between species. Evidence for this comes from work with the bacteria Escherichia coli, which may coexist with their own mutants under certain conditions. Diversification only occurs above a certain threshold interaction strength, below which competitive exclusion occurs. |
2112.08126 | Maja Linke | Maja Linke, Michael Ramscar | Finding Structure in Silence: The Role of Pauses in Aligning Speaker
Expectations | 25 pages, 5 figures | null | null | null | q-bio.NC q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The intelligibility of speech relies on the ability of interlocutors to
dynamically align their expectations about the rates at which informative
changes in signals occur. Exactly how this is achieved remains an open
question. We propose that speaker alignment is supported by the statistical
structure of spoken signals and show how pauses offer a time-invariant template
for structuring speech sequences. Consistent with this, we show that pause
distributions in conversational English and Korean provide a memoryless
information source. We describe how this can facilitate both the initial
structuring and maintenance of predictability in spoken signals over time, and
show how the properties of this signal change predictably with speaker
experience. These results indicate that pauses provide a structuring signal
that interacts with the morphological and rhythmical structure of languages,
allowing speakers at all stages of lifespan development to distinguish signal
from noise and maintain mutual predictability in time.
| [
{
"created": "Wed, 15 Dec 2021 13:56:51 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Mar 2022 07:42:40 GMT",
"version": "v2"
},
{
"created": "Wed, 2 Nov 2022 11:21:57 GMT",
"version": "v3"
}
] | 2022-11-03 | [
[
"Linke",
"Maja",
""
],
[
"Ramscar",
"Michael",
""
]
] | The intelligibility of speech relies on the ability of interlocutors to dynamically align their expectations about the rates at which informative changes in signals occur. Exactly how this is achieved remains an open question. We propose that speaker alignment is supported by the statistical structure of spoken signals and show how pauses offer a time-invariant template for structuring speech sequences. Consistent with this, we show that pause distributions in conversational English and Korean provide a memoryless information source. We describe how this can facilitate both the initial structuring and maintenance of predictability in spoken signals over time, and show how the properties of this signal change predictably with speaker experience. These results indicate that pauses provide a structuring signal that interacts with the morphological and rhythmical structure of languages, allowing speakers at all stages of lifespan development to distinguish signal from noise and maintain mutual predictability in time. |
2212.03290 | Rastine Merat | Rastine Merat | The human antigen R as an actionable super-hub within the network of
cancer cell persistency and plasticity | 12 pages, 3 figures | Translational Oncology, Volume 35, 2023, 101722, ISSN 1936-5233 | 10.1016/j.tranon.2023.101722 | null | q-bio.TO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this perspective article, a clinically inspired phenotype-driven
experimental approach is put forward to address the challenge of the adaptive
response of solid cancers to small-molecule targeted therapies. A list of
conditions is derived, including an experimental quantitative assessment of
cell plasticity and an information theory-based detection of in vivo
dependencies, for the discovery of post-transcriptional druggable mechanisms
capable of preventing at multiple levels the emergence of plastic
dedifferentiated slow-proliferating cells. The approach is illustrated by the
author's own work in the example case of the adaptive response of
BRAFV600-melanoma to BRAF inhibition. A bench-to-bedside and back to bench
effort leads to a therapeutic strategy in which the inhibition of the baseline
activity of the interferon-gamma-activated inhibitor of translation (GAIT)
complex, incriminated in the expression insufficiency of the RNA-binding
protein HuR in a minority of cells, results in the suppression of the plastic,
intermittently slow-proliferating cells involved in the adaptive response. A
similar approach is recommended for the validation of other classes of
mechanisms that we seek to modulate to overcome this complex challenge of
modern cancer therapy.
| [
{
"created": "Tue, 6 Dec 2022 19:46:10 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Jan 2023 11:47:09 GMT",
"version": "v2"
},
{
"created": "Sun, 26 Mar 2023 10:37:09 GMT",
"version": "v3"
},
{
"created": "Wed, 21 Jun 2023 10:03:14 GMT",
"version": "v4"
}
] | 2023-06-22 | [
[
"Merat",
"Rastine",
""
]
] | In this perspective article, a clinically inspired phenotype-driven experimental approach is put forward to address the challenge of the adaptive response of solid cancers to small-molecule targeted therapies. A list of conditions is derived, including an experimental quantitative assessment of cell plasticity and an information theory-based detection of in vivo dependencies, for the discovery of post-transcriptional druggable mechanisms capable of preventing at multiple levels the emergence of plastic dedifferentiated slow-proliferating cells. The approach is illustrated by the author's own work in the example case of the adaptive response of BRAFV600-melanoma to BRAF inhibition. A bench-to-bedside and back to bench effort leads to a therapeutic strategy in which the inhibition of the baseline activity of the interferon-gamma-activated inhibitor of translation (GAIT) complex, incriminated in the expression insufficiency of the RNA-binding protein HuR in a minority of cells, results in the suppression of the plastic, intermittently slow-proliferating cells involved in the adaptive response. A similar approach is recommended for the validation of other classes of mechanisms that we seek to modulate to overcome this complex challenge of modern cancer therapy. |
1803.09231 | Peter Gawthrop | Peter J. Gawthrop and Edmund J. Crampin | Biomolecular System Energetics | Accepted for 13th International Conference on Bond Graph Modeling
(ICBGM 18), July 9-12, 2018, Bordeaux, France | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Efficient energy transduction is one driver of evolution; and thus
understanding biomolecular energy transduction is crucial to understanding
living organisms. As an energy-orientated modelling methodology, bond graphs
provide a useful approach to describing and modelling the efficiency of living
systems. This paper gives some new results on the efficiency of metabolism
based on bond graph models of the key metabolic processes: glycolysis.
| [
{
"created": "Sun, 25 Mar 2018 11:02:14 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Jun 2018 07:31:13 GMT",
"version": "v2"
}
] | 2018-06-11 | [
[
"Gawthrop",
"Peter J.",
""
],
[
"Crampin",
"Edmund J.",
""
]
] | Efficient energy transduction is one driver of evolution; and thus understanding biomolecular energy transduction is crucial to understanding living organisms. As an energy-orientated modelling methodology, bond graphs provide a useful approach to describing and modelling the efficiency of living systems. This paper gives some new results on the efficiency of metabolism based on bond graph models of the key metabolic processes: glycolysis. |
1110.5091 | Debora Marks | Debora S. Marks, Lucy J. Colwell, Robert Sheridan, Thomas A. Hopf,
Andrea Pagnani, Riccardo Zecchina, Chris Sander | 3D Protein Structure Predicted from Sequence | Debora S Marks and Lucy J Colwell are joint first authors. Supplement
and Appendices at: http://cbio.mskcc.org/foldingproteins. Updated version
25-Oct-2011 with '3D' added to the title and corrections of details in the
methods section to make it compatible with derivation of equations in the
main text and in the supplement | null | null | null | q-bio.BM cs.CE physics.bio-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolutionary trajectory of a protein through sequence space is
constrained by function and three-dimensional (3D) structure. Residues in
spatial proximity tend to co-evolve, yet attempts to invert the evolutionary
record to identify these constraints and use them to computationally fold
proteins have so far been unsuccessful. Here, we show that co-variation of
residue pairs, observed in a large protein family, provides sufficient
information to determine 3D protein structure. Using a data-constrained maximum
entropy model of the multiple sequence alignment, we identify pairs of
statistically coupled residue positions which are expected to be close in the
protein fold, termed contacts inferred from evolutionary information (EICs). To
assess the amount of information about the protein fold contained in these
coupled pairs, we evaluate the accuracy of predicted 3D structures for proteins
of 50-260 residues, from 15 diverse protein families, including a G-protein
coupled receptor. These structure predictions are de novo, i.e., they do not
use homology modeling or sequence-similar fragments from known structures. The
resulting low C{\alpha}-RMSD error range of 2.7-5.1{\AA}, over at least 75% of
the protein, indicates the potential for predicting essentially correct 3D
structures for the thousands of protein families that have no known structure,
provided they include a sufficiently large number of divergent sample
sequences. With the current enormous growth in sequence information based on
new sequencing technology, this opens the door to a comprehensive survey of
protein 3D structures, including many not currently accessible to the
experimental methods of structural genomics. This advance has potential
applications in many biological contexts, such as synthetic biology,
identification of functional sites in proteins and interpretation of the
functional impact of genetic variants.
| [
{
"created": "Sun, 23 Oct 2011 22:02:12 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Oct 2011 18:59:33 GMT",
"version": "v2"
}
] | 2015-03-13 | [
[
"Marks",
"Debora S.",
""
],
[
"Colwell",
"Lucy J.",
""
],
[
"Sheridan",
"Robert",
""
],
[
"Hopf",
"Thomas A.",
""
],
[
"Pagnani",
"Andrea",
""
],
[
"Zecchina",
"Riccardo",
""
],
[
"Sander",
"Chris",
""
]
] | The evolutionary trajectory of a protein through sequence space is constrained by function and three-dimensional (3D) structure. Residues in spatial proximity tend to co-evolve, yet attempts to invert the evolutionary record to identify these constraints and use them to computationally fold proteins have so far been unsuccessful. Here, we show that co-variation of residue pairs, observed in a large protein family, provides sufficient information to determine 3D protein structure. Using a data-constrained maximum entropy model of the multiple sequence alignment, we identify pairs of statistically coupled residue positions which are expected to be close in the protein fold, termed contacts inferred from evolutionary information (EICs). To assess the amount of information about the protein fold contained in these coupled pairs, we evaluate the accuracy of predicted 3D structures for proteins of 50-260 residues, from 15 diverse protein families, including a G-protein coupled receptor. These structure predictions are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The resulting low C{\alpha}-RMSD error range of 2.7-5.1{\AA}, over at least 75% of the protein, indicates the potential for predicting essentially correct 3D structures for the thousands of protein families that have no known structure, provided they include a sufficiently large number of divergent sample sequences. With the current enormous growth in sequence information based on new sequencing technology, this opens the door to a comprehensive survey of protein 3D structures, including many not currently accessible to the experimental methods of structural genomics. This advance has potential applications in many biological contexts, such as synthetic biology, identification of functional sites in proteins and interpretation of the functional impact of genetic variants. |
1909.11211 | I\~nigo Urteaga | Kathy Li, I\~nigo Urteaga, Chris H. Wiggins, Anna Druet, Amanda Shea,
Virginia J. Vitzthum, No\'emie Elhadad | Characterizing physiological and symptomatic variation in menstrual
cycles using self-tracked mobile health data | The Supplementary Information for this work, as well as the code
required for data pre-processing and producing results is available in
https://github.com/iurteaga/menstrual_cycle_analysis | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The menstrual cycle is a key indicator of overall health for women of
reproductive age. Previously, menstruation was primarily studied through survey
results; however, as menstrual tracking mobile apps become more widely adopted,
they provide an increasingly large, content-rich source of menstrual health
experiences and behaviors over time. By exploring a database of user-tracked
observations from the Clue app by BioWink of over 378,000 users and 4.9 million
natural cycles, we show that self-reported menstrual tracker data can reveal
statistically significant relationships between per-person cycle length
variability and self-reported qualitative symptoms. A concern for self-tracked
data is that they reflect not only physiological behaviors, but also the
engagement dynamics of app users. To mitigate such potential artifacts, we
develop a procedure to exclude cycles lacking user engagement, thereby allowing
us to better distinguish true menstrual patterns from tracking anomalies. We
uncover that women located at different ends of the menstrual variability
spectrum, based on the consistency of their cycle length statistics, exhibit
statistically significant differences in their cycle characteristics and
symptom tracking patterns. We also find that cycle and period length statistics
are stationary over the app usage timeline across the variability spectrum. The
symptoms that we identify as showing statistically significant association with
timing data can be useful to clinicians and users for predicting cycle
variability from symptoms or as potential health indicators for conditions like
endometriosis. Our findings showcase the potential of longitudinal,
high-resolution self-tracked data to improve understanding of menstruation and
women's health as a whole.
| [
{
"created": "Tue, 24 Sep 2019 22:21:10 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Apr 2020 23:41:29 GMT",
"version": "v2"
},
{
"created": "Thu, 14 May 2020 22:57:02 GMT",
"version": "v3"
}
] | 2020-05-18 | [
[
"Li",
"Kathy",
""
],
[
"Urteaga",
"Iñigo",
""
],
[
"Wiggins",
"Chris H.",
""
],
[
"Druet",
"Anna",
""
],
[
"Shea",
"Amanda",
""
],
[
"Vitzthum",
"Virginia J.",
""
],
[
"Elhadad",
"Noémie",
""
]
] | The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts, we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum, based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women's health as a whole. |
1310.7662 | Emmanuelle Tognoli | Emmanuelle Tognoli and J. A. Scott Kelso | Spectral dissociation of lateralized pairs of brain rhythms | 6 pages, 2 figures | null | 10.1016/j.neures.2019.12.006 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using high resolution spectral methods to uncover neuromarkers of social,
cognitive and behavioral function, we have found that hemi-lateralized pairs of
oscillations such as left and right occipital alpha, or left and right rolandic
mu dissociate spectrally. That is, they show a shifted frequency distribution,
with one member of the pair peaking at a slightly lower frequency than the
other. To illustrate the phenomenon, we analyze EEG spatio-spectral patterns
providing examples of dissociations in the 10Hz frequency band. Our
observations suggest that homologous pairs of neuromarkers have distinct
intrinsic frequencies and only transiently coordinate their oscillations into
synchronous ensembles. On occasion, hemi-lateralized pairs are observed to
blend into medial aggregates, leading to strongly coherent dynamics. We
hypothesize that spectral dissociation plays a role in the balance of
integration and segregation in the brain: separation of oscillations from
homologous cortical areas allows them to function independently under certain
circumstances, while preserving a potential for stronger interactions supported
by structural and functional symmetries. As a method, spectral dissociation may
be harnessed to better track the individual power of each member of a
hemi-lateralized pair, and their respective time-courses. Resulting insights
may shed light on the functional interaction between homologous cortices in
studies of attention (alpha), e.g. during perceptual and social interaction
tasks, and in studies of somatomotor processing (mu), e.g. in bimanual
coordination and brain computer interfaces.
| [
{
"created": "Tue, 29 Oct 2013 02:07:17 GMT",
"version": "v1"
}
] | 2020-05-11 | [
[
"Tognoli",
"Emmanuelle",
""
],
[
"Kelso",
"J. A. Scott",
""
]
] | Using high resolution spectral methods to uncover neuromarkers of social, cognitive and behavioral function, we have found that hemi-lateralized pairs of oscillations such as left and right occipital alpha, or left and right rolandic mu dissociate spectrally. That is, they show a shifted frequency distribution, with one member of the pair peaking at a slightly lower frequency than the other. To illustrate the phenomenon, we analyze EEG spatio-spectral patterns providing examples of dissociations in the 10Hz frequency band. Our observations suggest that homologous pairs of neuromarkers have distinct intrinsic frequencies and only transiently coordinate their oscillations into synchronous ensembles. On occasion, hemi-lateralized pairs are observed to blend into medial aggregates, leading to strongly coherent dynamics. We hypothesize that spectral dissociation plays a role in the balance of integration and segregation in the brain: separation of oscillations from homologous cortical areas allows them to function independently under certain circumstances, while preserving a potential for stronger interactions supported by structural and functional symmetries. As a method, spectral dissociation may be harnessed to better track the individual power of each member of a hemi-lateralized pair, and their respective time-courses. Resulting insights may shed light on the functional interaction between homologous cortices in studies of attention (alpha), e.g. during perceptual and social interaction tasks, and in studies of somatomotor processing (mu), e.g. in bimanual coordination and brain computer interfaces. |
2110.07873 | Lokesh Boominathan | Lokesh Boominathan, Xaq Pitkow | Phase transitions in when feedback is useful | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Sensory observations about the world are invariably ambiguous. Inference
about the world's latent variables is thus an important computation for the
brain. However, computational constraints limit the performance of these
computations. These constraints include energetic costs for neural activity and
noise on every channel. Efficient coding is one prominent theory that describes
how such limited resources can best be used. In one incarnation, this leads to
a theory of predictive coding, where predictions are subtracted from signals,
reducing the cost of sending something that is already known. This theory does
not, however, account for the costs or noise associated with those predictions.
Here we offer a theory that accounts for both feedforward and feedback costs,
and noise in all computations. We formulate this inference problem as
message-passing on a graph whereby feedback serves as an internal control
signal aiming to maximize how well an inference tracks a target state while
minimizing the costs of computation. We apply this novel formulation of
inference as control to the canonical problem of inferring the hidden scalar
state of a linear dynamical system with Gaussian variability. The best solution
depends on architectural constraints, such as Dale's law, the ubiquitous law
that each neuron makes solely excitatory or inhibitory postsynaptic
connections. This biological structure can create asymmetric costs for
feedforward and feedback channels. Under such conditions, our theory predicts
the gain of optimal predictive feedback and how it is incorporated into the
inference computation. We show that there is a non-monotonic dependence of
optimal feedback gain as a function of both the computational parameters and
the world dynamics, leading to phase transitions in whether feedback provides
any utility in optimal inference under computational constraints.
| [
{
"created": "Fri, 15 Oct 2021 05:50:16 GMT",
"version": "v1"
},
{
"created": "Wed, 25 May 2022 17:25:12 GMT",
"version": "v2"
},
{
"created": "Tue, 11 Oct 2022 21:16:16 GMT",
"version": "v3"
}
] | 2022-10-13 | [
[
"Boominathan",
"Lokesh",
""
],
[
"Pitkow",
"Xaq",
""
]
] | Sensory observations about the world are invariably ambiguous. Inference about the world's latent variables is thus an important computation for the brain. However, computational constraints limit the performance of these computations. These constraints include energetic costs for neural activity and noise on every channel. Efficient coding is one prominent theory that describes how such limited resources can best be used. In one incarnation, this leads to a theory of predictive coding, where predictions are subtracted from signals, reducing the cost of sending something that is already known. This theory does not, however, account for the costs or noise associated with those predictions. Here we offer a theory that accounts for both feedforward and feedback costs, and noise in all computations. We formulate this inference problem as message-passing on a graph whereby feedback serves as an internal control signal aiming to maximize how well an inference tracks a target state while minimizing the costs of computation. We apply this novel formulation of inference as control to the canonical problem of inferring the hidden scalar state of a linear dynamical system with Gaussian variability. The best solution depends on architectural constraints, such as Dale's law, the ubiquitous law that each neuron makes solely excitatory or inhibitory postsynaptic connections. This biological structure can create asymmetric costs for feedforward and feedback channels. Under such conditions, our theory predicts the gain of optimal predictive feedback and how it is incorporated into the inference computation. We show that there is a non-monotonic dependence of optimal feedback gain as a function of both the computational parameters and the world dynamics, leading to phase transitions in whether feedback provides any utility in optimal inference under computational constraints. |
2009.03272 | Vivek Subramanian | Vivek Subramanian, Joshua Khani | Graph Convolutional Networks Reveal Neural Connections Encoding
Prosthetic Sensation | null | null | null | null | q-bio.NC cs.LG q-bio.QM stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Extracting stimulus features from neuronal ensembles is of great interest to
the development of neuroprosthetics that project sensory information directly
to the brain via electrical stimulation. Machine learning strategies that
optimize stimulation parameters as the subject learns to interpret the
artificial input could improve device efficacy, increase prosthetic
performance, ensure stability of evoked sensations, and improve power
consumption by eliminating extraneous input. Recent advances extending deep
learning techniques to non-Euclidean graph data provide a novel approach to
interpreting neuronal spiking activity. For this study, we apply graph
convolutional networks (GCNs) to infer the underlying functional relationship
between neurons that are involved in the processing of artificial sensory
information. Data was collected from a freely behaving rat using a four
infrared (IR) sensor, ICMS-based neuroprosthesis to localize IR light sources.
We use GCNs to predict the stimulation frequency across four stimulating
channels in the prosthesis, which encode relative distance and directional
information to an IR-emitting reward port. Our GCN model is able to achieve a
peak performance of 73.5% on a modified ordinal regression performance metric
in a multiclass classification problem consisting of 7 classes, where chance is
14.3%. Additionally, the inferred adjacency matrix provides a adequate
representation of the underlying neural circuitry encoding the artificial
sensation.
| [
{
"created": "Sun, 23 Aug 2020 01:43:46 GMT",
"version": "v1"
}
] | 2020-09-08 | [
[
"Subramanian",
"Vivek",
""
],
[
"Khani",
"Joshua",
""
]
] | Extracting stimulus features from neuronal ensembles is of great interest to the development of neuroprosthetics that project sensory information directly to the brain via electrical stimulation. Machine learning strategies that optimize stimulation parameters as the subject learns to interpret the artificial input could improve device efficacy, increase prosthetic performance, ensure stability of evoked sensations, and improve power consumption by eliminating extraneous input. Recent advances extending deep learning techniques to non-Euclidean graph data provide a novel approach to interpreting neuronal spiking activity. For this study, we apply graph convolutional networks (GCNs) to infer the underlying functional relationship between neurons that are involved in the processing of artificial sensory information. Data was collected from a freely behaving rat using a four infrared (IR) sensor, ICMS-based neuroprosthesis to localize IR light sources. We use GCNs to predict the stimulation frequency across four stimulating channels in the prosthesis, which encode relative distance and directional information to an IR-emitting reward port. Our GCN model is able to achieve a peak performance of 73.5% on a modified ordinal regression performance metric in a multiclass classification problem consisting of 7 classes, where chance is 14.3%. Additionally, the inferred adjacency matrix provides a adequate representation of the underlying neural circuitry encoding the artificial sensation. |
0907.4935 | Simone Bianco | Simone Bianco, Leah B. Shaw | Asymmetry in the presence of migration stabilizes multistrain disease
outbreaks | 17 pages, 5 figures, 3 appendices. Submitted version | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the effect of migration between coupled populations, or patches, on
the stability properties of multistrain disease dynamics. The epidemic model
used in this work displays a Hopf bifurcation to oscillations in a single well
mixed population. It is shown numerically that migration between two
non-identical patches stabilizes the endemic steady state, delaying the onset
of large amplitude outbreaks and reducing the total number of infections. This
result is motivated by analyzing generic Hopf bifurcations with different
frequencies and with diffusive coupling between them. Stabilization of the
steady state is again seen, indicating that our observation in the full
multistrain model is based on qualitative characteristics of the dynamics
rather than on details of the disease model.
| [
{
"created": "Tue, 28 Jul 2009 15:25:40 GMT",
"version": "v1"
}
] | 2009-07-29 | [
[
"Bianco",
"Simone",
""
],
[
"Shaw",
"Leah B.",
""
]
] | We study the effect of migration between coupled populations, or patches, on the stability properties of multistrain disease dynamics. The epidemic model used in this work displays a Hopf bifurcation to oscillations in a single well mixed population. It is shown numerically that migration between two non-identical patches stabilizes the endemic steady state, delaying the onset of large amplitude outbreaks and reducing the total number of infections. This result is motivated by analyzing generic Hopf bifurcations with different frequencies and with diffusive coupling between them. Stabilization of the steady state is again seen, indicating that our observation in the full multistrain model is based on qualitative characteristics of the dynamics rather than on details of the disease model. |
2203.11920 | Pavle Goldstein | Braslav Rabar, Keti Ni\v{z}eti\'c and Pavle Goldstein | A Clique-Based Method for Improving Motif Scanning Accuracy | 14 pages, plenty of figures | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | We present a new approach for improving motif scanning accuracy, based on
analysis of in-between similarity. Given a set of motifs obtained from a
scanning process, we construct an associated weighted graph. We also compute
the expected weight of an edge in such a graph. It turns out that restricting
results to the maximal clique in the graph, computed with respect to the
expected weight, greatly increases precision, hence improves accuracy of the
scan. We tested the method on an ungapped motif-characterized protein family
from five plant proteomes. The method was applied to three iterative motif
scanners - PSI-BLAST, JackHMMer and IGLOSS - with very good results
| [
{
"created": "Tue, 22 Mar 2022 17:41:08 GMT",
"version": "v1"
}
] | 2022-03-23 | [
[
"Rabar",
"Braslav",
""
],
[
"Nižetić",
"Keti",
""
],
[
"Goldstein",
"Pavle",
""
]
] | We present a new approach for improving motif scanning accuracy, based on analysis of in-between similarity. Given a set of motifs obtained from a scanning process, we construct an associated weighted graph. We also compute the expected weight of an edge in such a graph. It turns out that restricting results to the maximal clique in the graph, computed with respect to the expected weight, greatly increases precision, hence improves accuracy of the scan. We tested the method on an ungapped motif-characterized protein family from five plant proteomes. The method was applied to three iterative motif scanners - PSI-BLAST, JackHMMer and IGLOSS - with very good results |
2206.03364 | Han Li | Han Li, Dan Zhao and Jianyang Zeng | KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular
Property Prediction | 11 pages, to appear in KDD 2022 research track | null | 10.1145/3534678.3539426 | null | q-bio.BM cs.AI cs.LG physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Designing accurate deep learning models for molecular property prediction
plays an increasingly essential role in drug and material discovery. Recently,
due to the scarcity of labeled molecules, self-supervised learning methods for
learning generalizable and transferable representations of molecular graphs
have attracted lots of attention. In this paper, we argue that there exist two
major issues hindering current self-supervised learning methods from obtaining
desired performance on molecular property prediction, that is, the ill-defined
pre-training tasks and the limited model capacity. To this end, we introduce
Knowledge-guided Pre-training of Graph Transformer (KPGT), a novel
self-supervised learning framework for molecular graph representation learning,
to alleviate the aforementioned issues and improve the performance on the
downstream molecular property prediction tasks. More specifically, we first
introduce a high-capacity model, named Line Graph Transformer (LiGhT), which
emphasizes the importance of chemical bonds and is mainly designed to model the
structural information of molecular graphs. Then, a knowledge-guided
pre-training strategy is proposed to exploit the additional knowledge of
molecules to guide the model to capture the abundant structural and semantic
information from large-scale unlabeled molecular graphs. Extensive
computational tests demonstrated that KPGT can offer superior performance over
current state-of-the-art methods on several molecular property prediction
tasks.
| [
{
"created": "Thu, 2 Jun 2022 08:22:14 GMT",
"version": "v1"
}
] | 2022-06-08 | [
[
"Li",
"Han",
""
],
[
"Zhao",
"Dan",
""
],
[
"Zeng",
"Jianyang",
""
]
] | Designing accurate deep learning models for molecular property prediction plays an increasingly essential role in drug and material discovery. Recently, due to the scarcity of labeled molecules, self-supervised learning methods for learning generalizable and transferable representations of molecular graphs have attracted lots of attention. In this paper, we argue that there exist two major issues hindering current self-supervised learning methods from obtaining desired performance on molecular property prediction, that is, the ill-defined pre-training tasks and the limited model capacity. To this end, we introduce Knowledge-guided Pre-training of Graph Transformer (KPGT), a novel self-supervised learning framework for molecular graph representation learning, to alleviate the aforementioned issues and improve the performance on the downstream molecular property prediction tasks. More specifically, we first introduce a high-capacity model, named Line Graph Transformer (LiGhT), which emphasizes the importance of chemical bonds and is mainly designed to model the structural information of molecular graphs. Then, a knowledge-guided pre-training strategy is proposed to exploit the additional knowledge of molecules to guide the model to capture the abundant structural and semantic information from large-scale unlabeled molecular graphs. Extensive computational tests demonstrated that KPGT can offer superior performance over current state-of-the-art methods on several molecular property prediction tasks. |
1812.02478 | Jochen Braun | Stepan Aleshin, Gergo Ziman, Ilona Kovacs, Jochen Braun | Perceptual reversals in binocular rivalry: improved detection from OKN | 37 pages, 9 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When binocular rivalry is induced by opponent motion displays, perceptual
reversals are often associated with changed oculomotor behaviour (Frassle et
al., 2014; Fujiwara et al., 2017). Specifically, the direction of smooth
pursuit phases in optokinetic nystagmus (OKN) typically corresponds to the
direction of motion that dominates perceptual appearance at any given time.
Here we report an improved analysis that continuously estimates perceived
motion in terms of `cumulative smooth pursuit'. In essence, smooth pursuit
segments are identified, interpolated where necessary, and joined
probabilistically into a continuous record of `cumulative smooth pursuit'
(i.e., a probability of eye position disregarding blinks, saccades, signal
losses, and artefacts). The analysis is fully automated and robust in healthy,
developmental, and patient populations. To validate reliability, we compare
volitional reports of perceptual reversals in rivalry displays, and of physical
reversals in non-rivalrous control displays. `Cumulative smooth pursuit'
detects physical reversals and estimates eye velocity more accurately than
existing methods do (Frassle et al., 2014). It also appears to distinguish
dominant and transitional perceptual states, detecting changes with a precision
of $\pm100\,\mathit{ms}$. We conclude that `cumulative smooth pursuit'
significantly improves the monitoring of binocular rivalry by means of
recording OKN.
| [
{
"created": "Thu, 6 Dec 2018 11:45:27 GMT",
"version": "v1"
}
] | 2018-12-07 | [
[
"Aleshin",
"Stepan",
""
],
[
"Ziman",
"Gergo",
""
],
[
"Kovacs",
"Ilona",
""
],
[
"Braun",
"Jochen",
""
]
] | When binocular rivalry is induced by opponent motion displays, perceptual reversals are often associated with changed oculomotor behaviour (Frassle et al., 2014; Fujiwara et al., 2017). Specifically, the direction of smooth pursuit phases in optokinetic nystagmus (OKN) typically corresponds to the direction of motion that dominates perceptual appearance at any given time. Here we report an improved analysis that continuously estimates perceived motion in terms of `cumulative smooth pursuit'. In essence, smooth pursuit segments are identified, interpolated where necessary, and joined probabilistically into a continuous record of `cumulative smooth pursuit' (i.e., a probability of eye position disregarding blinks, saccades, signal losses, and artefacts). The analysis is fully automated and robust in healthy, developmental, and patient populations. To validate reliability, we compare volitional reports of perceptual reversals in rivalry displays, and of physical reversals in non-rivalrous control displays. `Cumulative smooth pursuit' detects physical reversals and estimates eye velocity more accurately than existing methods do (Frassle et al., 2014). It also appears to distinguish dominant and transitional perceptual states, detecting changes with a precision of $\pm100\,\mathit{ms}$. We conclude that `cumulative smooth pursuit' significantly improves the monitoring of binocular rivalry by means of recording OKN. |
1207.7196 | Jo\~ao Sacramento | Jo\~ao Sacramento and Andreas Wichert | Binary Willshaw learning yields high synaptic capacity for long-term
familiarity memory | 20 pages, 4 figures | Biological Cybernetics, vol. 106, no. 2, pp. 123-133, 2012 | 10.1007/s00422-012-0488-4 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate from a computational perspective the efficiency of the
Willshaw synaptic update rule in the context of familiarity discrimination, a
binary-answer, memory-related task that has been linked through psychophysical
experiments with modified neural activity patterns in the prefrontal and
perirhinal cortex regions. Our motivation for recovering this well-known
learning prescription is two-fold: first, the switch-like nature of the induced
synaptic bonds, as there is evidence that biological synaptic transitions might
occur in a discrete stepwise fashion. Second, the possibility that in the
mammalian brain, unused, silent synapses might be pruned in the long-term.
Besides the usual pattern and network capacities, we calculate the synaptic
capacity of the model, a recently proposed measure where only the functional
subset of synapses is taken into account. We find that in terms of network
capacity, Willshaw learning is strongly affected by the pattern coding rates,
which have to be kept fixed and very low at any time to achieve a non-zero
capacity in the large network limit. The information carried per functional
synapse, however, diverges and is comparable to that of the pattern association
case, even for more realistic moderately low activity levels that are a
function of network size.
| [
{
"created": "Tue, 31 Jul 2012 09:42:31 GMT",
"version": "v1"
}
] | 2012-08-01 | [
[
"Sacramento",
"João",
""
],
[
"Wichert",
"Andreas",
""
]
] | We investigate from a computational perspective the efficiency of the Willshaw synaptic update rule in the context of familiarity discrimination, a binary-answer, memory-related task that has been linked through psychophysical experiments with modified neural activity patterns in the prefrontal and perirhinal cortex regions. Our motivation for recovering this well-known learning prescription is two-fold: first, the switch-like nature of the induced synaptic bonds, as there is evidence that biological synaptic transitions might occur in a discrete stepwise fashion. Second, the possibility that in the mammalian brain, unused, silent synapses might be pruned in the long-term. Besides the usual pattern and network capacities, we calculate the synaptic capacity of the model, a recently proposed measure where only the functional subset of synapses is taken into account. We find that in terms of network capacity, Willshaw learning is strongly affected by the pattern coding rates, which have to be kept fixed and very low at any time to achieve a non-zero capacity in the large network limit. The information carried per functional synapse, however, diverges and is comparable to that of the pattern association case, even for more realistic moderately low activity levels that are a function of network size. |
1906.10538 | Jared Vasil | Jared Vasil, Paul B. Badcock, Axel Constant, Karl Friston, Maxwell J.
D. Ramstead | A World unto Itself: Human Communication as Active Inference | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Work in developmental psychology suggests that humans are predisposed to
align their mental states with other individuals. This manifests principally in
cooperative communication, that is, intentional communication geared towards
aligning mental states. This viewpoint has received ample empirical support.
However, this view lacks a formal grounding, and provides no precise
neuroscientific hypotheses. To remedy this, we suggest an active inference
approach to cooperative communication. We suggest that humans appear to possess
an evolved adaptive prior belief that their mental states are aligned with
those of conspecifics. Cooperative communication emerges as the principal means
to gather evidence for this belief. Our approach has implications for the study
of the usage, ontogeny, and cultural evolution of human communication.
| [
{
"created": "Tue, 25 Jun 2019 13:58:36 GMT",
"version": "v1"
},
{
"created": "Sun, 23 Feb 2020 01:09:33 GMT",
"version": "v2"
}
] | 2020-02-25 | [
[
"Vasil",
"Jared",
""
],
[
"Badcock",
"Paul B.",
""
],
[
"Constant",
"Axel",
""
],
[
"Friston",
"Karl",
""
],
[
"Ramstead",
"Maxwell J. D.",
""
]
] | Work in developmental psychology suggests that humans are predisposed to align their mental states with other individuals. This manifests principally in cooperative communication, that is, intentional communication geared towards aligning mental states. This viewpoint has received ample empirical support. However, this view lacks a formal grounding, and provides no precise neuroscientific hypotheses. To remedy this, we suggest an active inference approach to cooperative communication. We suggest that humans appear to possess an evolved adaptive prior belief that their mental states are aligned with those of conspecifics. Cooperative communication emerges as the principal means to gather evidence for this belief. Our approach has implications for the study of the usage, ontogeny, and cultural evolution of human communication. |
1308.4917 | Chad M. Topaz | Christa Nilsen, John Paige, Olivia Warner, Benjamin Mayhew, Ryan
Sutley, Matthew Lam, Andrew J. Bernoff, and Chad M. Topaz | Social aggregation in pea aphids: Experimental measurement and
stochastic modeling | 19 pages, 6 figures | null | 10.1371/journal.pone.0083343 | null | q-bio.QM nlin.AO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An ongoing challenge in the mathematical modeling of biological aggregations
is to strengthen the connection between models and biological data by
quantifying the rules that individuals follow. We model aggregation of the pea
aphid, Acyrthosiphon pisum. Specifically, we conduct experiments to track the
motion of aphids walking in a featureless circular arena in order to deduce
individual-level rules. We observe that each aphid transitions stochastically
between a moving and a stationary state. Moving aphids follow a correlated
random walk. The probabilities of motion state transitions, as well as the
random walk parameters, depend strongly on distance to an aphid's nearest
neighbor. For large nearest neighbor distances, when an aphid is essentially
isolated, its motion is ballistic with aphids moving faster, turning less, and
being less likely to stop. In contrast, for short nearest neighbor distances,
aphids move more slowly, turn more, and are more likely to become stationary;
this behavior constitutes an aggregation mechanism. From the experimental data,
we estimate the state transition probabilities and correlated random walk
parameters as a function of nearest neighbor distance. With the
individual-level model established, we assess whether it reproduces the
macroscopic patterns of movement at the group level. To do so, we consider
three distributions, namely distance to nearest neighbor, angle to nearest
neighbor, and percentage of population moving at any given time. For each of
these three distributions, we compare our experimental data to the output of
numerical simulations of our nearest neighbor model, and of a control model in
which aphids do not interact socially. Our stochastic, social nearest neighbor
model reproduces salient features of the experimental data that are not
captured by the control.
| [
{
"created": "Fri, 16 Aug 2013 19:18:25 GMT",
"version": "v1"
}
] | 2014-03-05 | [
[
"Nilsen",
"Christa",
""
],
[
"Paige",
"John",
""
],
[
"Warner",
"Olivia",
""
],
[
"Mayhew",
"Benjamin",
""
],
[
"Sutley",
"Ryan",
""
],
[
"Lam",
"Matthew",
""
],
[
"Bernoff",
"Andrew J.",
""
],
[
"Topaz",
"Chad M.",
""
]
] | An ongoing challenge in the mathematical modeling of biological aggregations is to strengthen the connection between models and biological data by quantifying the rules that individuals follow. We model aggregation of the pea aphid, Acyrthosiphon pisum. Specifically, we conduct experiments to track the motion of aphids walking in a featureless circular arena in order to deduce individual-level rules. We observe that each aphid transitions stochastically between a moving and a stationary state. Moving aphids follow a correlated random walk. The probabilities of motion state transitions, as well as the random walk parameters, depend strongly on distance to an aphid's nearest neighbor. For large nearest neighbor distances, when an aphid is essentially isolated, its motion is ballistic with aphids moving faster, turning less, and being less likely to stop. In contrast, for short nearest neighbor distances, aphids move more slowly, turn more, and are more likely to become stationary; this behavior constitutes an aggregation mechanism. From the experimental data, we estimate the state transition probabilities and correlated random walk parameters as a function of nearest neighbor distance. With the individual-level model established, we assess whether it reproduces the macroscopic patterns of movement at the group level. To do so, we consider three distributions, namely distance to nearest neighbor, angle to nearest neighbor, and percentage of population moving at any given time. For each of these three distributions, we compare our experimental data to the output of numerical simulations of our nearest neighbor model, and of a control model in which aphids do not interact socially. Our stochastic, social nearest neighbor model reproduces salient features of the experimental data that are not captured by the control. |
2406.02618 | Mika\"el Simard | Mika\"el Simard, Zhuoyan Shen, Maria A. Hawkins, Charles-Antoine
Collins-Fekete | Immunocto: a massive immune cell database auto-generated for
histopathology | null | null | null | null | q-bio.QM cs.AI eess.IV | http://creativecommons.org/licenses/by/4.0/ | With the advent of novel cancer treatment options such as immunotherapy,
studying the tumour immune micro-environment is crucial to inform on prognosis
and understand response to therapeutic agents. A key approach to characterising
the tumour immune micro-environment may be through combining (1) digitised
microscopic high-resolution optical images of hematoxylin and eosin (H&E)
stained tissue sections obtained in routine histopathology examinations with
(2) automated immune cell detection and classification methods. However,
current individual immune cell classification models for digital pathology
present relatively poor performance. This is mainly due to the limited size of
currently available datasets of individual immune cells, a consequence of the
time-consuming and difficult problem of manually annotating immune cells on
digitised H&E whole slide images. In that context, we introduce Immunocto, a
massive, multi-million automatically generated database of 6,848,454 human
cells, including 2,282,818 immune cells distributed across 4 subtypes: CD4$^+$
T cell lymphocytes, CD8$^+$ T cell lymphocytes, B cell lymphocytes, and
macrophages. For each cell, we provide a 64$\times$64 pixels H&E image at
$\mathbf{40}\times$ magnification, along with a binary mask of the nucleus and
a label. To create Immunocto, we combined open-source models and data to
automatically generate the majority of contours and labels. The cells are
obtained from a matched H&E and immunofluorescence colorectal dataset from the
Orion platform, while contours are obtained using the Segment Anything Model. A
classifier trained on H&E images from Immunocto produces an average F1 score of
0.74 to differentiate the 4 immune cell subtypes and other cells. Immunocto can
be downloaded at: https://zenodo.org/uploads/11073373.
| [
{
"created": "Mon, 3 Jun 2024 17:03:58 GMT",
"version": "v1"
}
] | 2024-06-06 | [
[
"Simard",
"Mikaël",
""
],
[
"Shen",
"Zhuoyan",
""
],
[
"Hawkins",
"Maria A.",
""
],
[
"Collins-Fekete",
"Charles-Antoine",
""
]
] | With the advent of novel cancer treatment options such as immunotherapy, studying the tumour immune micro-environment is crucial to inform on prognosis and understand response to therapeutic agents. A key approach to characterising the tumour immune micro-environment may be through combining (1) digitised microscopic high-resolution optical images of hematoxylin and eosin (H&E) stained tissue sections obtained in routine histopathology examinations with (2) automated immune cell detection and classification methods. However, current individual immune cell classification models for digital pathology present relatively poor performance. This is mainly due to the limited size of currently available datasets of individual immune cells, a consequence of the time-consuming and difficult problem of manually annotating immune cells on digitised H&E whole slide images. In that context, we introduce Immunocto, a massive, multi-million automatically generated database of 6,848,454 human cells, including 2,282,818 immune cells distributed across 4 subtypes: CD4$^+$ T cell lymphocytes, CD8$^+$ T cell lymphocytes, B cell lymphocytes, and macrophages. For each cell, we provide a 64$\times$64 pixels H&E image at $\mathbf{40}\times$ magnification, along with a binary mask of the nucleus and a label. To create Immunocto, we combined open-source models and data to automatically generate the majority of contours and labels. The cells are obtained from a matched H&E and immunofluorescence colorectal dataset from the Orion platform, while contours are obtained using the Segment Anything Model. A classifier trained on H&E images from Immunocto produces an average F1 score of 0.74 to differentiate the 4 immune cell subtypes and other cells. Immunocto can be downloaded at: https://zenodo.org/uploads/11073373. |
2112.10048 | Linxing Preston Jiang | Linxing Preston Jiang, Rajesh P. N. Rao | Predictive Coding Theories of Cortical Function | In Oxford Research Encyclopedia of Neuroscience (2022) | null | 10.1093/acrefore/9780190264086.013.328 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Predictive coding is a unifying framework for understanding perception,
action and neocortical organization. In predictive coding, different areas of
the neocortex implement a hierarchical generative model of the world that is
learned from sensory inputs. Cortical circuits are hypothesized to perform
Bayesian inference based on this generative model. Specifically, the
Rao-Ballard hierarchical predictive coding model assumes that the top-down
feedback connections from higher to lower order cortical areas convey
predictions of lower-level activities. The bottom-up, feedforward connections
in turn convey the errors between top-down predictions and actual activities.
These errors are used to correct current estimates of the state of the world
and generate new predictions. Through the objective of minimizing prediction
errors, predictive coding provides a functional explanation for a wide range of
neural responses and many aspects of brain organization.
| [
{
"created": "Sun, 19 Dec 2021 03:14:38 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Apr 2023 01:45:33 GMT",
"version": "v2"
},
{
"created": "Fri, 19 May 2023 00:08:31 GMT",
"version": "v3"
}
] | 2023-05-22 | [
[
"Jiang",
"Linxing Preston",
""
],
[
"Rao",
"Rajesh P. N.",
""
]
] | Predictive coding is a unifying framework for understanding perception, action and neocortical organization. In predictive coding, different areas of the neocortex implement a hierarchical generative model of the world that is learned from sensory inputs. Cortical circuits are hypothesized to perform Bayesian inference based on this generative model. Specifically, the Rao-Ballard hierarchical predictive coding model assumes that the top-down feedback connections from higher to lower order cortical areas convey predictions of lower-level activities. The bottom-up, feedforward connections in turn convey the errors between top-down predictions and actual activities. These errors are used to correct current estimates of the state of the world and generate new predictions. Through the objective of minimizing prediction errors, predictive coding provides a functional explanation for a wide range of neural responses and many aspects of brain organization. |
2402.06772 | Zixun Lan | Zixun Lan, Binjie Hong, Jiajun Zhu, Zuo Zeng, Zhenfu Liu, Limin Yu,
Fei Ma | Retrosynthesis Prediction via Search in (Hyper) Graph | null | null | null | null | q-bio.QM cs.AI cs.CE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting reactants from a specified core product stands as a fundamental
challenge within organic synthesis, termed retrosynthesis prediction. Recently,
semi-template-based methods and graph-edits-based methods have achieved good
performance in terms of both interpretability and accuracy. However, due to
their mechanisms these methods cannot predict complex reactions, e.g.,
reactions with multiple reaction center or attaching the same leaving group to
more than one atom. In this study we propose a semi-template-based method, the
\textbf{Retro}synthesis via \textbf{S}earch \textbf{i}n (Hyper) \textbf{G}raph
(RetroSiG) framework to alleviate these limitations. In the proposed method, we
turn the reaction center identification and the leaving group completion tasks
as tasks of searching in the product molecular graph and leaving group
hypergraph respectively. As a semi-template-based method RetroSiG has several
advantages. First, RetroSiG is able to handle the complex reactions mentioned
above by its novel search mechanism. Second, RetroSiG naturally exploits the
hypergraph to model the implicit dependencies between leaving groups. Third,
RetroSiG makes full use of the prior, i.e., one-hop constraint. It reduces the
search space and enhances overall performance. Comprehensive experiments
demonstrated that RetroSiG achieved competitive results. Furthermore, we
conducted experiments to show the capability of RetroSiG in predicting complex
reactions. Ablation experiments verified the efficacy of specific elements,
such as the one-hop constraint and the leaving group hypergraph.
| [
{
"created": "Fri, 9 Feb 2024 20:25:45 GMT",
"version": "v1"
}
] | 2024-02-13 | [
[
"Lan",
"Zixun",
""
],
[
"Hong",
"Binjie",
""
],
[
"Zhu",
"Jiajun",
""
],
[
"Zeng",
"Zuo",
""
],
[
"Liu",
"Zhenfu",
""
],
[
"Yu",
"Limin",
""
],
[
"Ma",
"Fei",
""
]
] | Predicting reactants from a specified core product stands as a fundamental challenge within organic synthesis, termed retrosynthesis prediction. Recently, semi-template-based methods and graph-edits-based methods have achieved good performance in terms of both interpretability and accuracy. However, due to their mechanisms these methods cannot predict complex reactions, e.g., reactions with multiple reaction center or attaching the same leaving group to more than one atom. In this study we propose a semi-template-based method, the \textbf{Retro}synthesis via \textbf{S}earch \textbf{i}n (Hyper) \textbf{G}raph (RetroSiG) framework to alleviate these limitations. In the proposed method, we turn the reaction center identification and the leaving group completion tasks as tasks of searching in the product molecular graph and leaving group hypergraph respectively. As a semi-template-based method RetroSiG has several advantages. First, RetroSiG is able to handle the complex reactions mentioned above by its novel search mechanism. Second, RetroSiG naturally exploits the hypergraph to model the implicit dependencies between leaving groups. Third, RetroSiG makes full use of the prior, i.e., one-hop constraint. It reduces the search space and enhances overall performance. Comprehensive experiments demonstrated that RetroSiG achieved competitive results. Furthermore, we conducted experiments to show the capability of RetroSiG in predicting complex reactions. Ablation experiments verified the efficacy of specific elements, such as the one-hop constraint and the leaving group hypergraph. |
2404.04299 | Anindita Nath Nath | Anindita Nath (1), Savannah Mwesigwa (1), Yulin Dai (1), Xiaoqian
Jiang (2) and Zhongming Zhao (1 and 3) ((1) Center for Precision Health,
McWilliams School of Biomedical Informatics, UT Health Houston, TX (2)
Department of Health Data Science and Artificial Intelligence, McWilliams
School of Biomedical Informatics, UT Health Houston, TX, (3) MD Anderson
Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX) | GENEVIC: GENetic data Exploration and Visualization via Intelligent
interactive Console | null | null | null | null | q-bio.QM cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Summary: The vast generation of genetic data poses a significant challenge in
efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven
chat framework that tackles this challenge by bridging the gap between genetic
data generation and biomedical knowledge discovery. Leveraging generative AI,
notably ChatGPT, it serves as a biologist's 'copilot'. It automates the
analysis, retrieval, and visualization of customized domain-specific genetic
information, and integrates functionalities to generate protein interaction
networks, enrich gene sets, and search scientific literature from PubMed,
Google Scholar, and arXiv, making it a comprehensive tool for biomedical
research. In its pilot phase, GENEVIC is assessed using a curated database that
ranks genetic variants associated with Alzheimer's disease, schizophrenia, and
cognition, based on their effect weights from the Polygenic Score Catalog, thus
enabling researchers to prioritize genetic variants in complex diseases.
GENEVIC's operation is user-friendly, accessible without any specialized
training, secured by Azure OpenAI's HIPAA-compliant infrastructure, and
evaluated for its efficacy through real-time query testing. As a prototype,
GENEVIC is set to advance genetic research, enabling informed biomedical
decisions.
Availability and implementation: GENEVIC is publicly accessible at
https://genevic-anath2024.streamlit.app. The underlying code is open-source and
available via GitHub at https://github.com/anath2110/GENEVIC.git.
| [
{
"created": "Thu, 4 Apr 2024 20:53:30 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Nath",
"Anindita",
"",
"1 and 3"
],
[
"Mwesigwa",
"Savannah",
"",
"1 and 3"
],
[
"Dai",
"Yulin",
"",
"1 and 3"
],
[
"Jiang",
"Xiaoqian",
"",
"1 and 3"
],
[
"Zhao",
"Zhongming",
"",
"1 and 3"
]
] | Summary: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's 'copilot'. It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer's disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. GENEVIC's operation is user-friendly, accessible without any specialized training, secured by Azure OpenAI's HIPAA-compliant infrastructure, and evaluated for its efficacy through real-time query testing. As a prototype, GENEVIC is set to advance genetic research, enabling informed biomedical decisions. Availability and implementation: GENEVIC is publicly accessible at https://genevic-anath2024.streamlit.app. The underlying code is open-source and available via GitHub at https://github.com/anath2110/GENEVIC.git. |
1605.07266 | Pengcheng Zhou | Pengcheng Zhou, Shanna L. Resendez, Jose Rodriguez-Romaguera, Jessica
C. Jimenez, Shay Q. Neufeld, Garret D. Stuber, Rene Hen, Mazen A. Kheirbek,
Bernardo L. Sabatini, Robert E. Kass, Liam Paninski | Efficient and accurate extraction of in vivo calcium signals from
microendoscopic video data | The image has been compressed for meeting arXiv requirements. A pdf
version with higher resolution image can be downloaded here
https://zhoupc.github.io/data/zhou2016.pdf | null | null | null | q-bio.NC q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In vivo calcium imaging through microscopes has enabled deep brain imaging of
previously inaccessible neuronal populations within the brains of freely moving
subjects. However, microendoscopic data suffer from high levels of background
fluorescence as well as an increased potential for overlapping neuronal
signals. Previous methods fail in identifying neurons and demixing their
temporal activity because the cellular signals are often submerged in the large
fluctuating background. Here we develop an efficient method to extract cellular
signals with minimal influence from the background. We model the background
with two realistic components: (1) one models the constant baseline and slow
trends of each pixel, and (2) the other models the fast fluctuations from
out-of-focus signals and is therefore constrained to have low spatial-frequency
structure. This decomposition avoids cellular signals being absorbed into the
background term. After subtracting the background approximated with this model,
we use Constrained Nonnegative Matrix Factorization (CNMF, Pnevmatikakis et al.
(2016)) to better demix neural signals and get their denoised and deconvolved
temporal activity. We validate our method on simulated and experimental data,
where it shows fast, reliable, and high quality signal extraction under a wide
variety of imaging parameters.
| [
{
"created": "Tue, 24 May 2016 02:35:09 GMT",
"version": "v1"
},
{
"created": "Thu, 25 May 2017 20:22:54 GMT",
"version": "v2"
}
] | 2017-05-29 | [
[
"Zhou",
"Pengcheng",
""
],
[
"Resendez",
"Shanna L.",
""
],
[
"Rodriguez-Romaguera",
"Jose",
""
],
[
"Jimenez",
"Jessica C.",
""
],
[
"Neufeld",
"Shay Q.",
""
],
[
"Stuber",
"Garret D.",
""
],
[
"Hen",
"Rene",
""
],
[
"Kheirbek",
"Mazen A.",
""
],
[
"Sabatini",
"Bernardo L.",
""
],
[
"Kass",
"Robert E.",
""
],
[
"Paninski",
"Liam",
""
]
] | In vivo calcium imaging through microscopes has enabled deep brain imaging of previously inaccessible neuronal populations within the brains of freely moving subjects. However, microendoscopic data suffer from high levels of background fluorescence as well as an increased potential for overlapping neuronal signals. Previous methods fail in identifying neurons and demixing their temporal activity because the cellular signals are often submerged in the large fluctuating background. Here we develop an efficient method to extract cellular signals with minimal influence from the background. We model the background with two realistic components: (1) one models the constant baseline and slow trends of each pixel, and (2) the other models the fast fluctuations from out-of-focus signals and is therefore constrained to have low spatial-frequency structure. This decomposition avoids cellular signals being absorbed into the background term. After subtracting the background approximated with this model, we use Constrained Nonnegative Matrix Factorization (CNMF, Pnevmatikakis et al. (2016)) to better demix neural signals and get their denoised and deconvolved temporal activity. We validate our method on simulated and experimental data, where it shows fast, reliable, and high quality signal extraction under a wide variety of imaging parameters. |
2305.05093 | Wang-Yu Tong | Wang-Yu Tong, De-Xiang Yong, Xin Xu, Cai-Hua Qiu, Yan Zhang, Xing-Wang
Yang, Ting-Ting Xia, Qing-Yang Liu, Su-Li Cao, Yan Sun and Xue Li | Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas
system | 113 pages, 10 figures, and 6 tables | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Type I CRISPR-Cas systems are the most common among six types of CRISPR-Cas
systems, however, non-self-targeting genome editing based on a single Cas3 of
type I CRISPR-Cas systems has not been reported. Here, we present the subtype
I-B-Svi CRISPR-Cas system (with three confirmed CRISPRs and a cas gene cluster)
and genome editing based on this system found in Streptomyces virginiae IBL14.
Importantly, like the animal-derived bacterial protein SpCas9 (1368
amino-acids), the single, compact, non-animal-derived bacterial protein SviCas3
(771 amino-acids) can also direct template-based microbial genome editing
through the target cell's own homology-directed repair system, which breaks the
view that the genome editing based on type I CRISPR-Cas systems requires a full
Cascade. Notably, no off-target changes or indel-formation were detected in the
analysis of potential off-target sites. This discovery broadens our
understanding of the diversity of type I CRISPR-Cas systems and will facilitate
new developments in genome editing tools.
| [
{
"created": "Mon, 8 May 2023 23:32:47 GMT",
"version": "v1"
}
] | 2023-05-10 | [
[
"Tong",
"Wang-Yu",
""
],
[
"Yong",
"De-Xiang",
""
],
[
"Xu",
"Xin",
""
],
[
"Qiu",
"Cai-Hua",
""
],
[
"Zhang",
"Yan",
""
],
[
"Yang",
"Xing-Wang",
""
],
[
"Xia",
"Ting-Ting",
""
],
[
"Liu",
"Qing-Yang",
""
],
[
"Cao",
"Su-Li",
""
],
[
"Sun",
"Yan",
""
],
[
"Li",
"Xue",
""
]
] | Type I CRISPR-Cas systems are the most common among six types of CRISPR-Cas systems, however, non-self-targeting genome editing based on a single Cas3 of type I CRISPR-Cas systems has not been reported. Here, we present the subtype I-B-Svi CRISPR-Cas system (with three confirmed CRISPRs and a cas gene cluster) and genome editing based on this system found in Streptomyces virginiae IBL14. Importantly, like the animal-derived bacterial protein SpCas9 (1368 amino-acids), the single, compact, non-animal-derived bacterial protein SviCas3 (771 amino-acids) can also direct template-based microbial genome editing through the target cell's own homology-directed repair system, which breaks the view that the genome editing based on type I CRISPR-Cas systems requires a full Cascade. Notably, no off-target changes or indel-formation were detected in the analysis of potential off-target sites. This discovery broadens our understanding of the diversity of type I CRISPR-Cas systems and will facilitate new developments in genome editing tools. |
1208.2981 | Sivan Leviyang | Sivan Leviyang | Computational Inference Methods for HIV-1 Selective Sweeps Shaped by
Early Cytotoxic T-Lymphocyte Response | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we develop a stochastic model of acute HIV infection, based on
the well-known standard model, that allows us to simulate the complex mutation
pathways of HIV escape from multiple CTL responses. Under this model, we
describe two computational inference methods. In one, we use a Bayesian
approach to construct posteriors for the parameters of our model. In the
second, we use hypothesis testing to determine the fit of the model to data.
The methods are applied to two CHAVI datasets, demonstrating the importance of
accounting for the interaction of multiple mutant variants and
multi-directional selection in analysing HIV dynamics under CTL response.
| [
{
"created": "Tue, 14 Aug 2012 21:46:30 GMT",
"version": "v1"
}
] | 2012-08-16 | [
[
"Leviyang",
"Sivan",
""
]
] | In this work we develop a stochastic model of acute HIV infection, based on the well-known standard model, that allows us to simulate the complex mutation pathways of HIV escape from multiple CTL responses. Under this model, we describe two computational inference methods. In one, we use a Bayesian approach to construct posteriors for the parameters of our model. In the second, we use hypothesis testing to determine the fit of the model to data. The methods are applied to two CHAVI datasets, demonstrating the importance of accounting for the interaction of multiple mutant variants and multi-directional selection in analysing HIV dynamics under CTL response. |
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