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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0610004 | Edward R. Abraham | Edward R Abraham | Sea urchin feeding fronts | submitted to Ecological Complexity | null | null | null | q-bio.PE | null | Sea urchin feeding fronts are a striking example of spatial pattern formation
in an ecological system. If it is assumed that urchins are asocial, and that
they move randomly, then the formation of these dense fronts is an apparent
paradox. The key lies in observations that urchins move further in areas where
their algal food is less plentiful. This naturally leads to the accumulation of
urchins in areas with abundant algae. If urchin movement is represented as a
random walk, with a step size that depends on algal concentration, then their
movement may be described by a Fokker-Planck diffusion equation. For certain
combinations of algal growth and urchin grazing, travelling wave solutions are
obtained. Two dimensional simulations of urchin algal dynamics show that an
initially uniformly distributed urchin population, grazing on an alga with a
smoothly varying density, may form a propagating front separating two sharply
delineated regions. On one side of the front algal density is uniformly low,
and on the other side of the front algal density is uniformly high. Bounds on
when stable fronts will form are obtained in terms of urchin density and
grazing, and algal growth.
| [
{
"created": "Mon, 2 Oct 2006 01:38:27 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Abraham",
"Edward R",
""
]
] | Sea urchin feeding fronts are a striking example of spatial pattern formation in an ecological system. If it is assumed that urchins are asocial, and that they move randomly, then the formation of these dense fronts is an apparent paradox. The key lies in observations that urchins move further in areas where their algal food is less plentiful. This naturally leads to the accumulation of urchins in areas with abundant algae. If urchin movement is represented as a random walk, with a step size that depends on algal concentration, then their movement may be described by a Fokker-Planck diffusion equation. For certain combinations of algal growth and urchin grazing, travelling wave solutions are obtained. Two dimensional simulations of urchin algal dynamics show that an initially uniformly distributed urchin population, grazing on an alga with a smoothly varying density, may form a propagating front separating two sharply delineated regions. On one side of the front algal density is uniformly low, and on the other side of the front algal density is uniformly high. Bounds on when stable fronts will form are obtained in terms of urchin density and grazing, and algal growth. |
1105.5581 | Oskar Hallatschek | Oskar Hallatschek | The noisy edge of traveling waves | For supplementary material and published open access article, see
http://www.pnas.org/content/108/5/1783.abstract?sid=693e63f3-fd1a-407a-983e-c521efc6c8c5
See also Commentary Article by D. S. Fisher,
http://www.pnas.org/content/108/7/2633.extract | Proc Natl Acad Sci USA 108:1783-1787 | 10.1073/pnas.1013529108 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traveling waves are ubiquitous in nature and control the speed of many
important dynamical processes, including chemical reactions, epidemic
outbreaks, and biological evolution. Despite their fundamental role in complex
systems, traveling waves remain elusive because they are often dominated by
rare fluctuations in the wave tip, which have defied any rigorous analysis so
far. Here, we show that by adjusting nonlinear model details, noisy traveling
waves can be solved exactly. The moment equations of these tuned models are
closed and have a simple analytical structure resembling the deterministic
approximation supplemented by a nonlocal cutoff term. The peculiar form of the
cutoff shapes the noisy edge of traveling waves and is critical for the correct
prediction of the wave speed and its fluctuations. Our approach is illustrated
and benchmarked using the example of fitness waves arising in simple models of
microbial evolution, which are highly sensitive to number fluctuations. We
demonstrate explicitly how these models can be tuned to account for finite
population sizes and determine how quickly populations adapt as a function of
population size and mutation rates. More generally, our method is shown to
apply to a broad class of models, in which number fluctuations are generated by
branching processes. Because of this versatility, the method of model tuning
may serve as a promising route toward unraveling universal properties of
complex discrete particle systems.
| [
{
"created": "Fri, 27 May 2011 15:17:03 GMT",
"version": "v1"
}
] | 2011-05-30 | [
[
"Hallatschek",
"Oskar",
""
]
] | Traveling waves are ubiquitous in nature and control the speed of many important dynamical processes, including chemical reactions, epidemic outbreaks, and biological evolution. Despite their fundamental role in complex systems, traveling waves remain elusive because they are often dominated by rare fluctuations in the wave tip, which have defied any rigorous analysis so far. Here, we show that by adjusting nonlinear model details, noisy traveling waves can be solved exactly. The moment equations of these tuned models are closed and have a simple analytical structure resembling the deterministic approximation supplemented by a nonlocal cutoff term. The peculiar form of the cutoff shapes the noisy edge of traveling waves and is critical for the correct prediction of the wave speed and its fluctuations. Our approach is illustrated and benchmarked using the example of fitness waves arising in simple models of microbial evolution, which are highly sensitive to number fluctuations. We demonstrate explicitly how these models can be tuned to account for finite population sizes and determine how quickly populations adapt as a function of population size and mutation rates. More generally, our method is shown to apply to a broad class of models, in which number fluctuations are generated by branching processes. Because of this versatility, the method of model tuning may serve as a promising route toward unraveling universal properties of complex discrete particle systems. |
1203.3367 | Jens Christian Claussen | Arne Traulsen, Jens Christian Claussen, and Christoph Hauert | Stochastic differential equations for evolutionary dynamics with
demographic noise and mutations | 8 pages, 2 figures, accepted for publication in Physical Review E | Phys. Rev. E 85, 041901 (2012) | 10.1103/PhysRevE.85.041901 | null | q-bio.PE cond-mat.stat-mech physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a general framework to describe the evolutionary dynamics of an
arbitrary number of types in finite populations based on stochastic
differential equations (SDE). For large, but finite populations this allows to
include demographic noise without requiring explicit simulations. Instead, the
population size only rescales the amplitude of the noise. Moreover, this
framework admits the inclusion of mutations between different types, provided
that mutation rates, $\mu$, are not too small compared to the inverse
population size 1/N. This ensures that all types are almost always represented
in the population and that the occasional extinction of one type does not
result in an extended absence of that type. For $\mu N\ll1$ this limits the use
of SDE's, but in this case there are well established alternative
approximations based on time scale separation. We illustrate our approach by a
Rock-Scissors-Paper game with mutations, where we demonstrate excellent
agreement with simulation based results for sufficiently large populations. In
the absence of mutations the excellent agreement extends to small population
sizes.
| [
{
"created": "Thu, 15 Mar 2012 14:24:43 GMT",
"version": "v1"
}
] | 2012-06-13 | [
[
"Traulsen",
"Arne",
""
],
[
"Claussen",
"Jens Christian",
""
],
[
"Hauert",
"Christoph",
""
]
] | We present a general framework to describe the evolutionary dynamics of an arbitrary number of types in finite populations based on stochastic differential equations (SDE). For large, but finite populations this allows to include demographic noise without requiring explicit simulations. Instead, the population size only rescales the amplitude of the noise. Moreover, this framework admits the inclusion of mutations between different types, provided that mutation rates, $\mu$, are not too small compared to the inverse population size 1/N. This ensures that all types are almost always represented in the population and that the occasional extinction of one type does not result in an extended absence of that type. For $\mu N\ll1$ this limits the use of SDE's, but in this case there are well established alternative approximations based on time scale separation. We illustrate our approach by a Rock-Scissors-Paper game with mutations, where we demonstrate excellent agreement with simulation based results for sufficiently large populations. In the absence of mutations the excellent agreement extends to small population sizes. |
2110.12058 | Satoshi Kume | Satoshi Kume, Hiroshi Masuya, Mitsuyo Maeda, Mitsuo Suga, Yosky
Kataoka, Norio Kobayashi | Development of Semantic Web-based Imaging Database for Biological
Morphome | null | JIST 2017: Semantic Technology | 10.1007/978-3-319-70682-5_19 | null | q-bio.QM cs.AI cs.CV cs.DB eess.IV | http://creativecommons.org/licenses/by/4.0/ | We introduce the RIKEN Microstructural Imaging Metadatabase, a semantic
web-based imaging database in which image metadata are described using the
Resource Description Framework (RDF) and detailed biological properties
observed in the images can be represented as Linked Open Data. The metadata are
used to develop a large-scale imaging viewer that provides a straightforward
graphical user interface to visualise a large microstructural tiling image at
the gigabyte level. We applied the database to accumulate comprehensive
microstructural imaging data produced by automated scanning electron
microscopy. As a result, we have successfully managed vast numbers of images
and their metadata, including the interpretation of morphological phenotypes
occurring in sub-cellular components and biosamples captured in the images. We
also discuss advanced utilisation of morphological imaging data that can be
promoted by this database.
| [
{
"created": "Wed, 20 Oct 2021 15:59:35 GMT",
"version": "v1"
}
] | 2021-11-23 | [
[
"Kume",
"Satoshi",
""
],
[
"Masuya",
"Hiroshi",
""
],
[
"Maeda",
"Mitsuyo",
""
],
[
"Suga",
"Mitsuo",
""
],
[
"Kataoka",
"Yosky",
""
],
[
"Kobayashi",
"Norio",
""
]
] | We introduce the RIKEN Microstructural Imaging Metadatabase, a semantic web-based imaging database in which image metadata are described using the Resource Description Framework (RDF) and detailed biological properties observed in the images can be represented as Linked Open Data. The metadata are used to develop a large-scale imaging viewer that provides a straightforward graphical user interface to visualise a large microstructural tiling image at the gigabyte level. We applied the database to accumulate comprehensive microstructural imaging data produced by automated scanning electron microscopy. As a result, we have successfully managed vast numbers of images and their metadata, including the interpretation of morphological phenotypes occurring in sub-cellular components and biosamples captured in the images. We also discuss advanced utilisation of morphological imaging data that can be promoted by this database. |
2011.05861 | Jing Mu | Jing Mu, Ying Tan, David B. Grayden, and Denny Oetomo | Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised
Decoding Algorithm for Multi-Frequency SSVEP | 4 pages, 6 figures. This work has been accepted for publication in
the 2021 IEEE EMBC | null | null | null | q-bio.NC cs.HC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stimulation methods that utilise more than one stimulation frequency have
been developed for steady-state visual evoked potential (SSVEP) brain-computer
interfaces (BCIs) with the purpose of increasing the number of targets that can
be presented simultaneously. However, there is no unified decoding algorithm
that can be used without training for each individual users or cases, and
applied to a large class of multi-frequency stimulated SSVEP settings. This
paper extends the widely used canonical correlation analysis (CCA) decoder to
explicitly accommodate multi-frequency SSVEP by exploiting the interactions
between the multiple stimulation frequencies. A concept of order, defined as
the sum of absolute value of the coefficients in the linear combination of the
input frequencies, was introduced to assist the design of Multi-Frequency CCA
(MFCCA). The probability distribution of the order in the resulting SSVEP
response was then used to improve decoding accuracy. Results show that,
compared to the standard CCA formulation, the proposed MFCCA has a 20%
improvement in decoding accuracy on average at order 2, while keeping its
generality and training-free characteristics.
| [
{
"created": "Tue, 27 Oct 2020 09:02:03 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Aug 2021 08:02:23 GMT",
"version": "v2"
}
] | 2021-08-12 | [
[
"Mu",
"Jing",
""
],
[
"Tan",
"Ying",
""
],
[
"Grayden",
"David B.",
""
],
[
"Oetomo",
"Denny",
""
]
] | Stimulation methods that utilise more than one stimulation frequency have been developed for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) with the purpose of increasing the number of targets that can be presented simultaneously. However, there is no unified decoding algorithm that can be used without training for each individual users or cases, and applied to a large class of multi-frequency stimulated SSVEP settings. This paper extends the widely used canonical correlation analysis (CCA) decoder to explicitly accommodate multi-frequency SSVEP by exploiting the interactions between the multiple stimulation frequencies. A concept of order, defined as the sum of absolute value of the coefficients in the linear combination of the input frequencies, was introduced to assist the design of Multi-Frequency CCA (MFCCA). The probability distribution of the order in the resulting SSVEP response was then used to improve decoding accuracy. Results show that, compared to the standard CCA formulation, the proposed MFCCA has a 20% improvement in decoding accuracy on average at order 2, while keeping its generality and training-free characteristics. |
2405.06651 | Arnav Swaroop | Arnav Swaroop | Using GANs for De Novo Protein Design Targeting Microglial IL-3R$\alpha$
to Inhibit Alzheimer's Progression | 9 pages total | null | null | null | q-bio.BM cs.LG q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | IL-3 is a hemopoietic growth factor that usually targets blood cell
precursors; IL-3R is a cytokine receptor that binds to IL-3. However, IL-3
takes on a different role in the context of glial cells in the nervous system,
where studies show that the protein IL-3 protects against Alzheimer's disease
by activating microglia at their IL-3R receptors, causing the microglia to
clear out the tangles caused by the build-up of misfolded Tau proteins. In this
study, we seek to ascertain what role the secondary structure of IL-3 plays in
its binding with the receptor. The motivation behind this study is to learn
more about the mechanism and identify possible drugs that might be able to
activate it, in hopes of inhibiting the spread of Alzheimer's Disease. From a
preliminary analysis of complexes containing IL-3 and IL-3R, we hypothesized
that the binding is largely due to the interactions of three alpha helix
structures stretching towards the active site on the receptor. The original
Il-3 protein serves as the control in this experiment; the other proteins being
tested are generated through several types of computational de novo protein
design, where machine learning allows for the production of entirely novel
structures. The efficacy of the generated proteins is assessed through docking
simulations with the IL-3R receptor, and the binding poses are also
qualitatively examined to gain insight into the function of the binding. From
the docking data and poses, the most successful proteins were those with
similar secondary structure to IL-3.
| [
{
"created": "Fri, 5 Apr 2024 03:16:11 GMT",
"version": "v1"
}
] | 2024-05-14 | [
[
"Swaroop",
"Arnav",
""
]
] | IL-3 is a hemopoietic growth factor that usually targets blood cell precursors; IL-3R is a cytokine receptor that binds to IL-3. However, IL-3 takes on a different role in the context of glial cells in the nervous system, where studies show that the protein IL-3 protects against Alzheimer's disease by activating microglia at their IL-3R receptors, causing the microglia to clear out the tangles caused by the build-up of misfolded Tau proteins. In this study, we seek to ascertain what role the secondary structure of IL-3 plays in its binding with the receptor. The motivation behind this study is to learn more about the mechanism and identify possible drugs that might be able to activate it, in hopes of inhibiting the spread of Alzheimer's Disease. From a preliminary analysis of complexes containing IL-3 and IL-3R, we hypothesized that the binding is largely due to the interactions of three alpha helix structures stretching towards the active site on the receptor. The original Il-3 protein serves as the control in this experiment; the other proteins being tested are generated through several types of computational de novo protein design, where machine learning allows for the production of entirely novel structures. The efficacy of the generated proteins is assessed through docking simulations with the IL-3R receptor, and the binding poses are also qualitatively examined to gain insight into the function of the binding. From the docking data and poses, the most successful proteins were those with similar secondary structure to IL-3. |
2208.02845 | Qinhua Sun | Qinhua Jenny Sun, Khuong Vo, Kitty Lui, Michael Nunez, Joachim
Vandekerckhove, Ramesh Srinivasan | Decision SincNet: Neurocognitive models of decision making that predict
cognitive processes from neural signals | This paper was accepted as an oral presentation at IEEE WCCI 2022
(IJCNN 2022), under the session Neurodynamics and computational Neuroscience.
This paper is published in International Joint Conference on Neural Networks
(IJCNN) Proceedings 2022 | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Human decision making behavior is observed with choice-response time data
during psychological experiments. Drift-diffusion models of this data consist
of a Wiener first-passage time (WFPT) distribution and are described by
cognitive parameters: drift rate, boundary separation, and starting point.
These estimated parameters are of interest to neuroscientists as they can be
mapped to features of cognitive processes of decision making (such as speed,
caution, and bias) and related to brain activity. The observed patterns of RT
also reflect the variability of cognitive processes from trial to trial
mediated by neural dynamics. We adapted a SincNet-based shallow neural network
architecture to fit the Drift-Diffusion model using EEG signals on every
experimental trial. The model consists of a SincNet layer, a depthwise spatial
convolution layer, and two separate FC layers that predict drift rate and
boundary for each trial in-parallel. The SincNet layer parametrized the kernels
in order to directly learn the low and high cutoff frequencies of bandpass
filters that are applied to the EEG data to predict drift and boundary
parameters. During training, model parameters were updated by minimizing the
negative log likelihood function of WFPT distribution given trial RT. We
developed separate decision SincNet models for each participant performing a
two-alternative forced-choice task. Our results showed that single-trial
estimates of drift and boundary performed better at predicting RTs than the
median estimates in both training and test data sets, suggesting that our model
can successfully use EEG features to estimate meaningful single-trial Diffusion
model parameters. Furthermore, the shallow SincNet architecture identified time
windows of information processing related to evidence accumulation and caution
and the EEG frequency bands that reflect these processes within each
participant.
| [
{
"created": "Thu, 4 Aug 2022 18:51:29 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Aug 2022 00:45:42 GMT",
"version": "v2"
}
] | 2022-08-18 | [
[
"Sun",
"Qinhua Jenny",
""
],
[
"Vo",
"Khuong",
""
],
[
"Lui",
"Kitty",
""
],
[
"Nunez",
"Michael",
""
],
[
"Vandekerckhove",
"Joachim",
""
],
[
"Srinivasan",
"Ramesh",
""
]
] | Human decision making behavior is observed with choice-response time data during psychological experiments. Drift-diffusion models of this data consist of a Wiener first-passage time (WFPT) distribution and are described by cognitive parameters: drift rate, boundary separation, and starting point. These estimated parameters are of interest to neuroscientists as they can be mapped to features of cognitive processes of decision making (such as speed, caution, and bias) and related to brain activity. The observed patterns of RT also reflect the variability of cognitive processes from trial to trial mediated by neural dynamics. We adapted a SincNet-based shallow neural network architecture to fit the Drift-Diffusion model using EEG signals on every experimental trial. The model consists of a SincNet layer, a depthwise spatial convolution layer, and two separate FC layers that predict drift rate and boundary for each trial in-parallel. The SincNet layer parametrized the kernels in order to directly learn the low and high cutoff frequencies of bandpass filters that are applied to the EEG data to predict drift and boundary parameters. During training, model parameters were updated by minimizing the negative log likelihood function of WFPT distribution given trial RT. We developed separate decision SincNet models for each participant performing a two-alternative forced-choice task. Our results showed that single-trial estimates of drift and boundary performed better at predicting RTs than the median estimates in both training and test data sets, suggesting that our model can successfully use EEG features to estimate meaningful single-trial Diffusion model parameters. Furthermore, the shallow SincNet architecture identified time windows of information processing related to evidence accumulation and caution and the EEG frequency bands that reflect these processes within each participant. |
2001.05044 | Tanner Sorensen | Tanner Sorensen, Adam Lammert, Louis Goldstein, Shrikanth Narayanan | Derivation of Fitts' law from the Task Dynamics model of speech
production | version before journal submission; 5 pages, 2 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fitts' law is a linear equation relating movement time to an index of
movement difficulty. The recent finding that Fitts' law applies to voluntary
movement of the vocal tract raises the question of whether the theory of speech
production implies Fitts' law. The present letter establishes a theoretical
connection between Fitts' law and the Task Dynamics model of speech production.
We derive a variant of Fitts' law where the intercept and slope are functions
of the parameters of the Task Dynamics model and the index of difficulty is a
product logarithm, or Lambert W function, rather than a logarithm.
| [
{
"created": "Tue, 14 Jan 2020 20:50:22 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Jan 2020 03:36:22 GMT",
"version": "v2"
},
{
"created": "Tue, 17 Mar 2020 15:46:13 GMT",
"version": "v3"
}
] | 2020-03-18 | [
[
"Sorensen",
"Tanner",
""
],
[
"Lammert",
"Adam",
""
],
[
"Goldstein",
"Louis",
""
],
[
"Narayanan",
"Shrikanth",
""
]
] | Fitts' law is a linear equation relating movement time to an index of movement difficulty. The recent finding that Fitts' law applies to voluntary movement of the vocal tract raises the question of whether the theory of speech production implies Fitts' law. The present letter establishes a theoretical connection between Fitts' law and the Task Dynamics model of speech production. We derive a variant of Fitts' law where the intercept and slope are functions of the parameters of the Task Dynamics model and the index of difficulty is a product logarithm, or Lambert W function, rather than a logarithm. |
1405.3958 | Mohammad Soltani | Mohammad Soltani, Cesar Vargas, Niraj Kumar, Rahul Kulkarni, Abhyudai
Singh | Moment Closure Approximations in a Genetic Negative Feedback Circuit | 6 pages, 2 figures, conference on decision and control | null | null | null | q-bio.SC q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Auto-regulation, a process wherein a protein negatively regulates its own
production, is a common motif in gene expression networks. Negative feedback in
gene expression plays a critical role in buffering intracellular fluctuations
in protein concentrations around optimal value. Due to the nonlinearities
present in these feedbacks, moment dynamics are typically not closed, in the
sense that the time derivative of the lower-order statistical moments of the
protein copy number depends on high-order moments. Moment equations are closed
by expressing higher-order moments as nonlinear functions of lower-order
moments, a technique commonly referred to as moment closure. Here, we compare
the performance of different moment closure techniques. Our results show that
the commonly used closure method, which assumes a priori that the protein
population counts are normally distributed, performs poorly. In contrast,
conditional derivative matching, a novel closure scheme proposed here provides
a good approximation to the exact moments across different parameter regimes.
In summary our study provides a new moment closure method for studying
stochastic dynamics of genetic negative feedback circuits, and can be extended
to probe noise in more complex gene networks.
| [
{
"created": "Thu, 15 May 2014 19:30:16 GMT",
"version": "v1"
}
] | 2014-05-16 | [
[
"Soltani",
"Mohammad",
""
],
[
"Vargas",
"Cesar",
""
],
[
"Kumar",
"Niraj",
""
],
[
"Kulkarni",
"Rahul",
""
],
[
"Singh",
"Abhyudai",
""
]
] | Auto-regulation, a process wherein a protein negatively regulates its own production, is a common motif in gene expression networks. Negative feedback in gene expression plays a critical role in buffering intracellular fluctuations in protein concentrations around optimal value. Due to the nonlinearities present in these feedbacks, moment dynamics are typically not closed, in the sense that the time derivative of the lower-order statistical moments of the protein copy number depends on high-order moments. Moment equations are closed by expressing higher-order moments as nonlinear functions of lower-order moments, a technique commonly referred to as moment closure. Here, we compare the performance of different moment closure techniques. Our results show that the commonly used closure method, which assumes a priori that the protein population counts are normally distributed, performs poorly. In contrast, conditional derivative matching, a novel closure scheme proposed here provides a good approximation to the exact moments across different parameter regimes. In summary our study provides a new moment closure method for studying stochastic dynamics of genetic negative feedback circuits, and can be extended to probe noise in more complex gene networks. |
2401.04400 | Bruno. Cessac | Evgenia Kartsaki, Gerrit Hilgen, Evelyne Sernagor, and Bruno Cessac | How does the inner retinal network shape the ganglion cells receptive
field : a computational study | 65 pages, 7 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | We consider a model of basic inner retinal connectivity where bipolar and
amacrine cells interconnect, and both cell types project onto ganglion cells,
modulating their response output to the brain visual areas. We derive an
analytical formula for the spatio-temporal response of retinal ganglion cells
to stimuli taking into account the effects of amacrine cells inhibition. This
analysis reveals two important functional parameters of the network: (i) the
intensity of the interactions between bipolar and amacrine cells, and, (ii) the
characteristic time scale of these responses. Both parameters have a profound
combined impact on the spatiotemporal features of RGC responses to light. %We
show that, depending on these parameters value, retinal ganglion cells can
change their spatio-temporal response (e.g. from monophasic to biphasic). The
validity of the model is confirmed by faithfully reproducing pharmacogenetic
experimental results obtained by stimulating excitatory DREADDs (Designer
Receptors Exclusively Activated by Designer Drugs) expressed on ganglion cells
and amacrine cells subclasses, thereby modifying the inner retinal network
activity to visual stimuli in a complex, entangled manner. Our mathematical
model allows us to explore and decipher these complex effects in a manner that
would not be feasible experimentally and provides novel insights in retinal
dynamics.
| [
{
"created": "Tue, 9 Jan 2024 07:52:37 GMT",
"version": "v1"
}
] | 2024-01-10 | [
[
"Kartsaki",
"Evgenia",
""
],
[
"Hilgen",
"Gerrit",
""
],
[
"Sernagor",
"Evelyne",
""
],
[
"Cessac",
"Bruno",
""
]
] | We consider a model of basic inner retinal connectivity where bipolar and amacrine cells interconnect, and both cell types project onto ganglion cells, modulating their response output to the brain visual areas. We derive an analytical formula for the spatio-temporal response of retinal ganglion cells to stimuli taking into account the effects of amacrine cells inhibition. This analysis reveals two important functional parameters of the network: (i) the intensity of the interactions between bipolar and amacrine cells, and, (ii) the characteristic time scale of these responses. Both parameters have a profound combined impact on the spatiotemporal features of RGC responses to light. %We show that, depending on these parameters value, retinal ganglion cells can change their spatio-temporal response (e.g. from monophasic to biphasic). The validity of the model is confirmed by faithfully reproducing pharmacogenetic experimental results obtained by stimulating excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) expressed on ganglion cells and amacrine cells subclasses, thereby modifying the inner retinal network activity to visual stimuli in a complex, entangled manner. Our mathematical model allows us to explore and decipher these complex effects in a manner that would not be feasible experimentally and provides novel insights in retinal dynamics. |
1508.00429 | Alireza Alemi | Alireza Alemi, Carlo Baldassi, Nicolas Brunel, Riccardo Zecchina | A three-threshold learning rule approaches the maximal capacity of
recurrent neural networks | 24 pages, 10 figures, to be published in PLOS Computational Biology | null | 10.1371/journal.pcbi.1004439 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the theoretical foundations of how memories are encoded and
retrieved in neural populations is a central challenge in neuroscience. A
popular theoretical scenario for modeling memory function is the attractor
neural network scenario, whose prototype is the Hopfield model. The model has a
poor storage capacity, compared with the capacity achieved with perceptron
learning algorithms. Here, by transforming the perceptron learning rule, we
present an online learning rule for a recurrent neural network that achieves
near-maximal storage capacity without an explicit supervisory error signal,
relying only upon locally accessible information. The fully-connected network
consists of excitatory binary neurons with plastic recurrent connections and
non-plastic inhibitory feedback stabilizing the network dynamics; the memory
patterns are presented online as strong afferent currents, producing a bimodal
distribution for the neuron synaptic inputs. Synapses corresponding to active
inputs are modified as a function of the value of the local fields with respect
to three thresholds. Above the highest threshold, and below the lowest
threshold, no plasticity occurs. In between these two thresholds,
potentiation/depression occurs when the local field is above/below an
intermediate threshold. We simulated and analyzed a network of binary neurons
implementing this rule and measured its storage capacity for different sizes of
the basins of attraction. The storage capacity obtained through numerical
simulations is shown to be close to the value predicted by analytical
calculations. We also measured the dependence of capacity on the strength of
external inputs. Finally, we quantified the statistics of the resulting
synaptic connectivity matrix, and found that both the fraction of zero weight
synapses and the degree of symmetry of the weight matrix increase with the
number of stored patterns.
| [
{
"created": "Mon, 3 Aug 2015 14:22:52 GMT",
"version": "v1"
}
] | 2016-02-17 | [
[
"Alemi",
"Alireza",
""
],
[
"Baldassi",
"Carlo",
""
],
[
"Brunel",
"Nicolas",
""
],
[
"Zecchina",
"Riccardo",
""
]
] | Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model has a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns. |
1710.01262 | Hossein Babashah | Fereshte Mozafari, Hossein Babashah, Somayyeh Koohi, Zahra Kavehvash | DNA Sequence Alignment by Window based Optical Correlator | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In genomics, pattern matching against a sequence of nucleotides plays a
pivotal role for DNA sequence alignment and comparing genomes. This helps
tackling some diseases, such as cancer in humans. The complexity of searching
biological sequences in big databases has transformed sequence alignment
problem into a challenging field of research in bioinformatics. A large number
of research has been carried to solve this problem based on electronic
computers. The required extensive amount of computations for handling this huge
database in electronic computers leads to vast amounts of energy consumption
for electrical processing and cooling. On the other hand, optical processing
due to its parallel nature is much faster than electrical counterpart at a
fraction of energy consumption level and cost. In this paper, an algorithm
based on optical parallel processing is proposed that not only locate
similarity between sequences but also determines the exact location of edits.
The proposed algorithm is based on partitioning the read sequence into some
parts, namely, windows, then, computing their correlation with reference
sequence in parallel. Multiple metamaterial based optical correlators are used
in parallel to optically implement the architecture. Design limitations and
challenges of the architecture are also discussed in details. The simulation
results, comparing with the well-known BLAST algorithm, demonstrate superior
speed, accuracy, and much lower power consumption.
| [
{
"created": "Tue, 3 Oct 2017 16:22:35 GMT",
"version": "v1"
}
] | 2017-10-04 | [
[
"Mozafari",
"Fereshte",
""
],
[
"Babashah",
"Hossein",
""
],
[
"Koohi",
"Somayyeh",
""
],
[
"Kavehvash",
"Zahra",
""
]
] | In genomics, pattern matching against a sequence of nucleotides plays a pivotal role for DNA sequence alignment and comparing genomes. This helps tackling some diseases, such as cancer in humans. The complexity of searching biological sequences in big databases has transformed sequence alignment problem into a challenging field of research in bioinformatics. A large number of research has been carried to solve this problem based on electronic computers. The required extensive amount of computations for handling this huge database in electronic computers leads to vast amounts of energy consumption for electrical processing and cooling. On the other hand, optical processing due to its parallel nature is much faster than electrical counterpart at a fraction of energy consumption level and cost. In this paper, an algorithm based on optical parallel processing is proposed that not only locate similarity between sequences but also determines the exact location of edits. The proposed algorithm is based on partitioning the read sequence into some parts, namely, windows, then, computing their correlation with reference sequence in parallel. Multiple metamaterial based optical correlators are used in parallel to optically implement the architecture. Design limitations and challenges of the architecture are also discussed in details. The simulation results, comparing with the well-known BLAST algorithm, demonstrate superior speed, accuracy, and much lower power consumption. |
1704.04199 | Madhavun Candadai Vasu | Madhavun Candadai Vasu, Eduardo J. Izquierdo | Evolution and Analysis of Embodied Spiking Neural Networks Reveals
Task-Specific Clusters of Effective Networks | Camera ready version of accepted for GECCO'17 | null | 10.1145/3071178.3071336 | null | q-bio.NC cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Elucidating principles that underlie computation in neural networks is
currently a major research topic of interest in neuroscience. Transfer Entropy
(TE) is increasingly used as a tool to bridge the gap between network
structure, function, and behavior in fMRI studies. Computational models allow
us to bridge the gap even further by directly associating individual neuron
activity with behavior. However, most computational models that have analyzed
embodied behaviors have employed non-spiking neurons. On the other hand,
computational models that employ spiking neural networks tend to be restricted
to disembodied tasks. We show for the first time the artificial evolution and
TE-analysis of embodied spiking neural networks to perform a
cognitively-interesting behavior. Specifically, we evolved an agent controlled
by an Izhikevich neural network to perform a visual categorization task. The
smallest networks capable of performing the task were found by repeating
evolutionary runs with different network sizes. Informational analysis of the
best solution revealed task-specific TE-network clusters, suggesting that
within-task homogeneity and across-task heterogeneity were key to behavioral
success. Moreover, analysis of the ensemble of solutions revealed that
task-specificity of TE-network clusters correlated with fitness. This provides
an empirically testable hypothesis that links network structure to behavior.
| [
{
"created": "Thu, 13 Apr 2017 16:23:19 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Apr 2017 00:53:16 GMT",
"version": "v2"
}
] | 2017-06-08 | [
[
"Vasu",
"Madhavun Candadai",
""
],
[
"Izquierdo",
"Eduardo J.",
""
]
] | Elucidating principles that underlie computation in neural networks is currently a major research topic of interest in neuroscience. Transfer Entropy (TE) is increasingly used as a tool to bridge the gap between network structure, function, and behavior in fMRI studies. Computational models allow us to bridge the gap even further by directly associating individual neuron activity with behavior. However, most computational models that have analyzed embodied behaviors have employed non-spiking neurons. On the other hand, computational models that employ spiking neural networks tend to be restricted to disembodied tasks. We show for the first time the artificial evolution and TE-analysis of embodied spiking neural networks to perform a cognitively-interesting behavior. Specifically, we evolved an agent controlled by an Izhikevich neural network to perform a visual categorization task. The smallest networks capable of performing the task were found by repeating evolutionary runs with different network sizes. Informational analysis of the best solution revealed task-specific TE-network clusters, suggesting that within-task homogeneity and across-task heterogeneity were key to behavioral success. Moreover, analysis of the ensemble of solutions revealed that task-specificity of TE-network clusters correlated with fitness. This provides an empirically testable hypothesis that links network structure to behavior. |
1810.04280 | Bo Deng | Bo Deng | Is Neuron Made from Mathematics? | 8 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is to derive a mathematical model for neuron by imposing only a
principle of symmetry that two modelers must come up with the same model when
one is approaching the problem by modeling the conductances of ion channels and
the other by the channel resistances.
| [
{
"created": "Tue, 9 Oct 2018 22:16:42 GMT",
"version": "v1"
}
] | 2018-10-11 | [
[
"Deng",
"Bo",
""
]
] | This paper is to derive a mathematical model for neuron by imposing only a principle of symmetry that two modelers must come up with the same model when one is approaching the problem by modeling the conductances of ion channels and the other by the channel resistances. |
1004.1028 | Albert Erives | Albert Erives and Justin Crocker | Dynamic evolution of precise regulatory encodings creates the clustered
signature of developmental enhancers | 33 pages, 16 figures, 3 Tables. First presented at the 51st Annual
Drosophila Research Conference (DRC), Washington D.C., U.S.A. on April 8th,
2010. | Nat Commun. (2010) 1:99 | 10.1038/ncomms1102 | VCID-1415 | q-bio.PE q-bio.CB q-bio.GN q-bio.MN | http://creativecommons.org/licenses/by/3.0/ | A morphogenic protein known as Dorsal patterns the embryonic dorsoventral
body axis of Drosophila by binding to transcriptional enhancers across the
genome. Each such enhancer activates a neighboring gene at a unique threshold
concentration of Dorsal. The presence of Dorsal binding site clusters in these
enhancers and of similar clusters in other enhancers has motivated models of
threshold-encoding in site density. However, we found that the precise length
of a spacer separating a pair of specialized Dorsal and Twist binding sites
determines the threshold-response. Despite this result, the functional range
determined by this spacer element as well as the role and origin of its
surrounding Dorsal site cluster remained completely unknown. Here, we
experiment with enhancers from diverse Drosophila genomes, including the large
uncompacted genomes from ananassae and willistoni, and report three major
interdependent results. First, we map the functional range of the
threshold-encoding spacer variable. Second, we show that the majority of sites
at the cluster are non-functional divergent elements that have been separated
beyond the encoding's functional range. Third, we verify an evolutionary model
involving the frequent replacement of a threshold encoding, whose precision is
easily outdated by shifting accuracy. The process by which encodings are
replaced by newer ones is facilitated by the palindromic nature of the Dorsal
and Twist binding motifs and by intrinsic repeat-instability in the specialized
Twist binding site, which critically impacts the length of the spacer linking
it to Dorsal. Over time, the dynamic process of selective deprecation and
replacement of encodings adds to a growing cluster of deadened elements, or
necro-elements, and strongly biases local sequence composition. ... [ABSTRACT
TRUNCATED]
| [
{
"created": "Wed, 7 Apr 2010 08:49:46 GMT",
"version": "v1"
}
] | 2013-01-16 | [
[
"Erives",
"Albert",
""
],
[
"Crocker",
"Justin",
""
]
] | A morphogenic protein known as Dorsal patterns the embryonic dorsoventral body axis of Drosophila by binding to transcriptional enhancers across the genome. Each such enhancer activates a neighboring gene at a unique threshold concentration of Dorsal. The presence of Dorsal binding site clusters in these enhancers and of similar clusters in other enhancers has motivated models of threshold-encoding in site density. However, we found that the precise length of a spacer separating a pair of specialized Dorsal and Twist binding sites determines the threshold-response. Despite this result, the functional range determined by this spacer element as well as the role and origin of its surrounding Dorsal site cluster remained completely unknown. Here, we experiment with enhancers from diverse Drosophila genomes, including the large uncompacted genomes from ananassae and willistoni, and report three major interdependent results. First, we map the functional range of the threshold-encoding spacer variable. Second, we show that the majority of sites at the cluster are non-functional divergent elements that have been separated beyond the encoding's functional range. Third, we verify an evolutionary model involving the frequent replacement of a threshold encoding, whose precision is easily outdated by shifting accuracy. The process by which encodings are replaced by newer ones is facilitated by the palindromic nature of the Dorsal and Twist binding motifs and by intrinsic repeat-instability in the specialized Twist binding site, which critically impacts the length of the spacer linking it to Dorsal. Over time, the dynamic process of selective deprecation and replacement of encodings adds to a growing cluster of deadened elements, or necro-elements, and strongly biases local sequence composition. ... [ABSTRACT TRUNCATED] |
1904.00662 | Cameron Smith | Cameron A. Smith, C\'ecile Mailler, Christian A. Yates | Unbiased on-lattice domain growth | 23 pages, 10 figures | Phys. Rev. E 100, 063307 (2019) | 10.1103/PhysRevE.100.063307 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain growth is a key process in many areas of biology, including embryonic
development, the growth of tissue, and limb regeneration. As a result,
mechanisms for incorporating it into traditional models for cell movement,
interaction, and proliferation are of great importance. A previously well-used
method in order to incorporate domain growth into on-lattice reaction-diffusion
models causes a build up of particles on the boundaries of the domain, which is
particularly evident when diffusion is low in comparison to the rate of domain
growth. Here, we present a new method which addresses this unphysical build up
of particles at the boundaries, and demonstrate that it is accurate even for
scenarios in which the previous method fails. Further, we discuss for which
parameter regimes it is feasible to continue using the original method due to
diffusion dominating the domain growth mechanism.
| [
{
"created": "Mon, 1 Apr 2019 09:40:20 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Jul 2019 12:37:23 GMT",
"version": "v2"
},
{
"created": "Mon, 7 Oct 2019 13:50:07 GMT",
"version": "v3"
}
] | 2019-12-25 | [
[
"Smith",
"Cameron A.",
""
],
[
"Mailler",
"Cécile",
""
],
[
"Yates",
"Christian A.",
""
]
] | Domain growth is a key process in many areas of biology, including embryonic development, the growth of tissue, and limb regeneration. As a result, mechanisms for incorporating it into traditional models for cell movement, interaction, and proliferation are of great importance. A previously well-used method in order to incorporate domain growth into on-lattice reaction-diffusion models causes a build up of particles on the boundaries of the domain, which is particularly evident when diffusion is low in comparison to the rate of domain growth. Here, we present a new method which addresses this unphysical build up of particles at the boundaries, and demonstrate that it is accurate even for scenarios in which the previous method fails. Further, we discuss for which parameter regimes it is feasible to continue using the original method due to diffusion dominating the domain growth mechanism. |
1304.6319 | Thomas Adams | Thomas P. Adams, Dmitry Aleynik, Michael Burrows | Larval dispersal of intertidal organisms and the influence of coastline
geography | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The dispersal stages of organisms with sessile adults must be able to select
habitats with suitable conditions for establishment and survival, and must also
be able to reach those locations. For marine planktonic larvae, movement due to
currents is often orders of magnitude greater than movement due to swimming
behaviour, so transport is largely passive.
Current patterns are determined by the interaction of geography with tidal
forces, modified by meteorological conditions. These linkages impose an
area-specific focus to connectivity studies. Yet, how geographical features and
meteorological forcing combine to produce specific current patterns and
resultant connectivity among populations remains unclear.
In this study, we followed tracks of generic particles driven by modelled
hydrodynamic currents to investigate how connectivity between evenly spaced
habitat sites varies in relation to coastal topography. Larvae released from
regions of high current velocity, open coastline and low local habitat
availability travelled furthest but were less likely to disperse successfully.
Extensive natal habitat in the vicinity of a site generally had a positive
impact on the number of larvae arriving, as did low current velocities.
However, relationships between numbers of arriving larvae and local
geographical indices were complex, particularly at longer larval durations.
Local geography alone explained up to 50% of the variance in success of
larvae released and around 10% of the variation in the number of larvae
arriving at each site, but coastline properties fall short of predicting
dispersal measures for particular locations. The study shows that
meteorological variation and broad scale current patterns interact strongly
with local geography to determine connectivity in coastal areas.
| [
{
"created": "Tue, 23 Apr 2013 15:23:04 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Jul 2013 11:23:55 GMT",
"version": "v2"
}
] | 2013-07-02 | [
[
"Adams",
"Thomas P.",
""
],
[
"Aleynik",
"Dmitry",
""
],
[
"Burrows",
"Michael",
""
]
] | The dispersal stages of organisms with sessile adults must be able to select habitats with suitable conditions for establishment and survival, and must also be able to reach those locations. For marine planktonic larvae, movement due to currents is often orders of magnitude greater than movement due to swimming behaviour, so transport is largely passive. Current patterns are determined by the interaction of geography with tidal forces, modified by meteorological conditions. These linkages impose an area-specific focus to connectivity studies. Yet, how geographical features and meteorological forcing combine to produce specific current patterns and resultant connectivity among populations remains unclear. In this study, we followed tracks of generic particles driven by modelled hydrodynamic currents to investigate how connectivity between evenly spaced habitat sites varies in relation to coastal topography. Larvae released from regions of high current velocity, open coastline and low local habitat availability travelled furthest but were less likely to disperse successfully. Extensive natal habitat in the vicinity of a site generally had a positive impact on the number of larvae arriving, as did low current velocities. However, relationships between numbers of arriving larvae and local geographical indices were complex, particularly at longer larval durations. Local geography alone explained up to 50% of the variance in success of larvae released and around 10% of the variation in the number of larvae arriving at each site, but coastline properties fall short of predicting dispersal measures for particular locations. The study shows that meteorological variation and broad scale current patterns interact strongly with local geography to determine connectivity in coastal areas. |
1502.05827 | Aristides Moustakas | Matthew R. Evans, Aristides Moustakas, Gregory Carey, Yadvinder Malhi,
Nathalie Butt, Sue Benham, Denise Pallett and Stefanie Schaefer | Allometry and growth of eight tree taxa in United Kingdom woodlands | To appear (in press). Scientific Data 2015 | null | null | null | q-bio.PE q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Allometry and growth rates of 8 forest species in the UK. The data were
collected from two United Kingdom woodlands - Wytham Woods and Alice Holt. Here
we present data from 582 individual trees of eight taxa in the form of summary
variables. In addition the raw data files containing the variables from which
the summary data were obtained. Large sample sizes with longitudinal data
spanning 22 years make these datasets useful for future studies concerned with
the way trees change in size and shape over their life-span. The allometric
relationships include (1) trunk diameter, (2) height, (3) crown height, (4)
crown radius and (5) trunk radial growth rate to (A) the light environment of
each tree and (B) diameter at breast height.
| [
{
"created": "Fri, 20 Feb 2015 11:04:15 GMT",
"version": "v1"
}
] | 2015-02-23 | [
[
"Evans",
"Matthew R.",
""
],
[
"Moustakas",
"Aristides",
""
],
[
"Carey",
"Gregory",
""
],
[
"Malhi",
"Yadvinder",
""
],
[
"Butt",
"Nathalie",
""
],
[
"Benham",
"Sue",
""
],
[
"Pallett",
"Denise",
""
],
... | Allometry and growth rates of 8 forest species in the UK. The data were collected from two United Kingdom woodlands - Wytham Woods and Alice Holt. Here we present data from 582 individual trees of eight taxa in the form of summary variables. In addition the raw data files containing the variables from which the summary data were obtained. Large sample sizes with longitudinal data spanning 22 years make these datasets useful for future studies concerned with the way trees change in size and shape over their life-span. The allometric relationships include (1) trunk diameter, (2) height, (3) crown height, (4) crown radius and (5) trunk radial growth rate to (A) the light environment of each tree and (B) diameter at breast height. |
1012.0606 | Dionysios Barmpoutis | Dionysios Barmpoutis, Richard M. Murray | Quantification and Minimization of Crosstalk Sensitivity in Networks | null | null | null | null | q-bio.MN cond-mat.dis-nn cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Crosstalk is defined as the set of unwanted interactions among the different
entities of a network. Crosstalk is present in various degrees in every system
where information is transmitted through a means that is accessible by all the
individual units of the network. Using concepts from graph theory, we introduce
a quantifiable measure for sensitivity to crosstalk, and analytically derive
the structure of the networks in which it is minimized. It is shown that
networks with an inhomogeneous degree distribution are more robust to crosstalk
than corresponding homogeneous networks. We provide a method to construct the
graph with the minimum possible sensitivity to crosstalk, given its order and
size. Finally, for networks with a fixed degree sequence, we present an
algorithm to find the optimal interconnection structure among their vertices.
| [
{
"created": "Thu, 2 Dec 2010 23:46:58 GMT",
"version": "v1"
}
] | 2010-12-08 | [
[
"Barmpoutis",
"Dionysios",
""
],
[
"Murray",
"Richard M.",
""
]
] | Crosstalk is defined as the set of unwanted interactions among the different entities of a network. Crosstalk is present in various degrees in every system where information is transmitted through a means that is accessible by all the individual units of the network. Using concepts from graph theory, we introduce a quantifiable measure for sensitivity to crosstalk, and analytically derive the structure of the networks in which it is minimized. It is shown that networks with an inhomogeneous degree distribution are more robust to crosstalk than corresponding homogeneous networks. We provide a method to construct the graph with the minimum possible sensitivity to crosstalk, given its order and size. Finally, for networks with a fixed degree sequence, we present an algorithm to find the optimal interconnection structure among their vertices. |
1502.04728 | Chun-Chung Chen | Ying-Jen Yang, Chun-Chung Chen, Pik-Yin Lai, C. K. Chan | Adaptive Synchronization and Anticipatory Dynamical System | 4 pages, 3 figures | Phys. Rev. E 92, 030701 (2015) | 10.1103/PhysRevE.92.030701 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many biological systems can sense periodical variations in a stimulus input
and produce well-timed, anticipatory responses after the input is removed. Such
systems show memory effects for retaining timing information in the stimulus
and cannot be understood from traditional synchronization consideration of
passive oscillatory systems. To understand this anticipatory phenomena, we
consider oscillators built from excitable systems with the addition of an
adaptive dynamics. With such systems, well-timed post-stimulus responses
similar to those from experiments can be obtained. Furthermore, a well-known
model of working memory is shown to possess similar anticipatory dynamics when
the adaptive mechanism is identified with synaptic facilitation. The last
finding suggests that this type of oscillators can be common in neuronal
systems with plasticity.
| [
{
"created": "Mon, 16 Feb 2015 21:24:59 GMT",
"version": "v1"
},
{
"created": "Sat, 15 Aug 2015 03:27:22 GMT",
"version": "v2"
}
] | 2015-09-09 | [
[
"Yang",
"Ying-Jen",
""
],
[
"Chen",
"Chun-Chung",
""
],
[
"Lai",
"Pik-Yin",
""
],
[
"Chan",
"C. K.",
""
]
] | Many biological systems can sense periodical variations in a stimulus input and produce well-timed, anticipatory responses after the input is removed. Such systems show memory effects for retaining timing information in the stimulus and cannot be understood from traditional synchronization consideration of passive oscillatory systems. To understand this anticipatory phenomena, we consider oscillators built from excitable systems with the addition of an adaptive dynamics. With such systems, well-timed post-stimulus responses similar to those from experiments can be obtained. Furthermore, a well-known model of working memory is shown to possess similar anticipatory dynamics when the adaptive mechanism is identified with synaptic facilitation. The last finding suggests that this type of oscillators can be common in neuronal systems with plasticity. |
0809.1833 | Mircea Andrecut Dr | M. Andrecut | Fast GPU Implementation of Sparse Signal Recovery from Random
Projections | accepted for publication in Engineering Letters, 8 pages, code
included, references added | null | null | null | q-bio.QM physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of sparse signal recovery from a small number of
random projections (measurements). This is a well known NP-hard to solve
combinatorial optimization problem. A frequently used approach is based on
greedy iterative procedures, such as the Matching Pursuit (MP) algorithm. Here,
we discuss a fast GPU implementation of the MP algorithm, based on the recently
released NVIDIA CUDA API and CUBLAS library. The results show that the GPU
version is substantially faster (up to 31 times) than the highly optimized CPU
version based on CBLAS (GNU Scientific Library).
| [
{
"created": "Wed, 10 Sep 2008 16:10:03 GMT",
"version": "v1"
},
{
"created": "Sun, 25 Jan 2009 01:18:57 GMT",
"version": "v2"
}
] | 2009-01-25 | [
[
"Andrecut",
"M.",
""
]
] | We consider the problem of sparse signal recovery from a small number of random projections (measurements). This is a well known NP-hard to solve combinatorial optimization problem. A frequently used approach is based on greedy iterative procedures, such as the Matching Pursuit (MP) algorithm. Here, we discuss a fast GPU implementation of the MP algorithm, based on the recently released NVIDIA CUDA API and CUBLAS library. The results show that the GPU version is substantially faster (up to 31 times) than the highly optimized CPU version based on CBLAS (GNU Scientific Library). |
1512.04247 | Gabriele Lohmann | Gabriele Lohmann, Johannes Stelzer, Verena Zuber, Tilo Buschmann,
Daniel Margulies, Andreas Bartels, Klaus Scheffler | Task-related edge density (TED) - a new method for revealing large-scale
network formation in fMRI data of the human brain | 21 pages, 11 figures | PLoS ONE 11(6): e0158185 (2016) | 10.1371/journal.pone.0158185 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The formation of transient networks in response to external stimuli or as a
reflection of internal cognitive processes is a hallmark of human brain
function. However, its identification in fMRI data of the human brain is
notoriously difficult. Here we propose a new method of fMRI data analysis that
tackles this problem by considering large-scale, task-related synchronisation
networks. Networks consist of nodes and edges connecting them, where nodes
correspond to voxels in fMRI data, and the weight of an edge is determined via
task-related changes in dynamic synchronisation between their respective times
series. Based on these definitions, we developed a new data analysis algorithm
that identifies edges in a brain network that differentially respond in unison
to a task onset and that occur in dense packs with similar characteristics.
Hence, we call this approach "Task-related Edge Density" (TED). TED proved to
be a very strong marker for dynamic network formation that easily lends itself
to statistical analysis using large scale statistical inference. A major
advantage of TED compared to other methods is that it does not depend on any
specific hemodynamic response model, and it also does not require a
presegmentation of the data for dimensionality reduction as it can handle large
networks consisting of tens of thousands of voxels. We applied TED to fMRI data
of a fingertapping task provided by the Human Connectome Project. TED revealed
network-based involvement of a large number of brain areas that evaded
detection using traditional GLM-based analysis. We show that our proposed
method provides an entirely new window into the immense complexity of human
brain function.
| [
{
"created": "Mon, 14 Dec 2015 10:31:50 GMT",
"version": "v1"
}
] | 2016-12-05 | [
[
"Lohmann",
"Gabriele",
""
],
[
"Stelzer",
"Johannes",
""
],
[
"Zuber",
"Verena",
""
],
[
"Buschmann",
"Tilo",
""
],
[
"Margulies",
"Daniel",
""
],
[
"Bartels",
"Andreas",
""
],
[
"Scheffler",
"Klaus",
""
... | The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges in a brain network that differentially respond in unison to a task onset and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. |
2101.01699 | Zhuo-Cheng Xiao | Yuhang Cai, Tianyi Wu, Louis Tao, Zhuo-Cheng Xiao | Model Reduction Captures Stochastic Gamma Oscillations on
Low-Dimensional Manifolds | null | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Gamma frequency oscillations (25-140 Hz), observed in the neural activities
within many brain regions, have long been regarded as a physiological basis
underlying many brain functions, such as memory and attention. Among numerous
theoretical and computational modeling studies, gamma oscillations have been
found in biologically realistic spiking network models of the primary visual
cortex. However, due to its high dimensionality and strong nonlinearity, it is
generally difficult to perform detailed theoretical analysis of the emergent
gamma dynamics. Here we propose a suite of Markovian model reduction methods
with varying levels of complexity and applied it to spiking network models
exhibiting heterogeneous dynamical regimes, ranging from homogeneous firing to
strong synchrony in the gamma band. The reduced models not only successfully
reproduce gamma band oscillations in the full model, but also exhibit the same
dynamical features as we vary parameters. Most remarkably, the invariant
measure of the coarse-grained Markov process reveals a two-dimensional surface
in state space upon which the gamma dynamics mainly resides. Our results
suggest that the statistical features of gamma oscillations strongly depend on
the subthreshold neuronal distributions. Because of the generality of the
Markovian assumptions, our dimensional reduction methods offer a powerful
toolbox for theoretical examinations of many other complex cortical
spatio-temporal behaviors observed in both neurophysiological experiments and
numerical simulations.
| [
{
"created": "Tue, 5 Jan 2021 18:44:00 GMT",
"version": "v1"
},
{
"created": "Sat, 23 Jan 2021 03:26:39 GMT",
"version": "v2"
}
] | 2021-01-26 | [
[
"Cai",
"Yuhang",
""
],
[
"Wu",
"Tianyi",
""
],
[
"Tao",
"Louis",
""
],
[
"Xiao",
"Zhuo-Cheng",
""
]
] | Gamma frequency oscillations (25-140 Hz), observed in the neural activities within many brain regions, have long been regarded as a physiological basis underlying many brain functions, such as memory and attention. Among numerous theoretical and computational modeling studies, gamma oscillations have been found in biologically realistic spiking network models of the primary visual cortex. However, due to its high dimensionality and strong nonlinearity, it is generally difficult to perform detailed theoretical analysis of the emergent gamma dynamics. Here we propose a suite of Markovian model reduction methods with varying levels of complexity and applied it to spiking network models exhibiting heterogeneous dynamical regimes, ranging from homogeneous firing to strong synchrony in the gamma band. The reduced models not only successfully reproduce gamma band oscillations in the full model, but also exhibit the same dynamical features as we vary parameters. Most remarkably, the invariant measure of the coarse-grained Markov process reveals a two-dimensional surface in state space upon which the gamma dynamics mainly resides. Our results suggest that the statistical features of gamma oscillations strongly depend on the subthreshold neuronal distributions. Because of the generality of the Markovian assumptions, our dimensional reduction methods offer a powerful toolbox for theoretical examinations of many other complex cortical spatio-temporal behaviors observed in both neurophysiological experiments and numerical simulations. |
1308.0170 | Reto Burri | Marta Promerov\'a, Reto Burri, Luca Fumagalli | PCR-based isolation of multigene families: Lessons from the avian MHC
class IIB | 24 pages, 2 Tables, 2 Figures | null | 10.1111/1755-0998.12234 | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The amount of sequence data available today highly facilitates the access to
genes from many gene families. Universal primers amplifying the desired genes
over a range of species are readily obtained by aligning conserved gene
regions, and laborious gene isolation procedures can often be replaced by
quicker PCR-based approaches. However, in case of multigene families, PCR-based
approaches bear the risk of incomplete isolation of family members. This
problem is most prominent in gene families with highly variable and thus
unpredictable number of gene copies among species, such as in the major
histocompatibility complex (MHC). In the present study we (i) report new
primers for the isolation of the MHC class IIB (MHCIIB) gene family in birds,
and (ii) share our experience with isolating MHCIIB genes from an unprecedented
number of avian species from all over the avian phylogeny. We report important
and usually underappreciated problems encountered during PCR-based multigene
family isolation, and provide a collection of measures that may help to
significantly improve the chance of successfully isolating complete multigene
families using PCR-based approaches.
| [
{
"created": "Thu, 1 Aug 2013 12:23:30 GMT",
"version": "v1"
}
] | 2014-06-17 | [
[
"Promerová",
"Marta",
""
],
[
"Burri",
"Reto",
""
],
[
"Fumagalli",
"Luca",
""
]
] | The amount of sequence data available today highly facilitates the access to genes from many gene families. Universal primers amplifying the desired genes over a range of species are readily obtained by aligning conserved gene regions, and laborious gene isolation procedures can often be replaced by quicker PCR-based approaches. However, in case of multigene families, PCR-based approaches bear the risk of incomplete isolation of family members. This problem is most prominent in gene families with highly variable and thus unpredictable number of gene copies among species, such as in the major histocompatibility complex (MHC). In the present study we (i) report new primers for the isolation of the MHC class IIB (MHCIIB) gene family in birds, and (ii) share our experience with isolating MHCIIB genes from an unprecedented number of avian species from all over the avian phylogeny. We report important and usually underappreciated problems encountered during PCR-based multigene family isolation, and provide a collection of measures that may help to significantly improve the chance of successfully isolating complete multigene families using PCR-based approaches. |
1706.00715 | Paul M\"uller | Mirjam Sch\"urmann, Gheorghe Cojoc, Salvatore Girardo, Elke Ulbricht,
Jochen Guck, Paul M\"uller | 3D correlative single-cell imaging utilizing fluorescence and refractive
index tomography | 15 pages, 5 figures | null | 10.1002/jbio.201700145 | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells alter the path of light, a fact that leads to well-known aberrations in
single cell or tissue imaging. Optical diffraction tomography (ODT) measures
the biophysical property that causes these aberrations, the refractive index
(RI). ODT is complementary to fluorescence imaging and does not require any
markers. The present study introduces RI and fluorescence tomography with
optofluidic rotation (RAFTOR) of suspended cells, quantifying the intracellular
RI distribution and colocalizing it with fluorescence in 3D. The technique is
validated with cell phantoms and used to confirm a lower nuclear RI for HL60
cells. Furthermore, the nuclear inversion of adult mouse photoreceptor cells is
observed in the RI distribution. The applications shown confirm predictions of
previous studies and illustrate the potential of RAFTOR to improve our
understanding of cells and tissues.
| [
{
"created": "Fri, 2 Jun 2017 15:08:56 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Jun 2017 08:08:26 GMT",
"version": "v2"
},
{
"created": "Fri, 11 Aug 2017 08:28:16 GMT",
"version": "v3"
}
] | 2017-08-14 | [
[
"Schürmann",
"Mirjam",
""
],
[
"Cojoc",
"Gheorghe",
""
],
[
"Girardo",
"Salvatore",
""
],
[
"Ulbricht",
"Elke",
""
],
[
"Guck",
"Jochen",
""
],
[
"Müller",
"Paul",
""
]
] | Cells alter the path of light, a fact that leads to well-known aberrations in single cell or tissue imaging. Optical diffraction tomography (ODT) measures the biophysical property that causes these aberrations, the refractive index (RI). ODT is complementary to fluorescence imaging and does not require any markers. The present study introduces RI and fluorescence tomography with optofluidic rotation (RAFTOR) of suspended cells, quantifying the intracellular RI distribution and colocalizing it with fluorescence in 3D. The technique is validated with cell phantoms and used to confirm a lower nuclear RI for HL60 cells. Furthermore, the nuclear inversion of adult mouse photoreceptor cells is observed in the RI distribution. The applications shown confirm predictions of previous studies and illustrate the potential of RAFTOR to improve our understanding of cells and tissues. |
1805.08646 | Fanny Grosselin | Fanny Grosselin, Xavier Navarro-Sune, Mathieu Raux, Thomas Similowski,
Mario Chavez | CARE-rCortex: a Matlab toolbox for the analysis of
CArdio-REspiratory-related activity in the Cortex | This manuscript contains 13 pages including 7 color figures | J Neurosci Methods. 2018 Aug 13. pii: S0165-0270(18)30247-4. doi:
10.1016/j.jneumeth.2018.08.011 | 10.1016/j.jneumeth.2018.08.011 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Although cardio-respiratory (CR) system is generally controlled
by the autonomic nervous system, interactions between the cortex and these
primary functions are receiving an increasing interest in neurosciences. New
method: In general, the timing of such internally paced events (e.g. heartbeats
or respiratory cycles) may display a large variability. For the analysis of
such CR event-related EEG potentials, a baseline must be correctly associated
to each cycle of detected events. The open-source toolbox CARE-rCortex provides
an easy-to-use interface to detect CR events, define baselines, and analyse in
time-frequency (TF) domain the CR-based EEG potentials. Results: CARE-rCortex
provides some practical tools to detect and validate these CR events. Users can
define baselines time-locked to a phase of respiratory or heart cycle. A
statistical test has also been integrated to highlight significant points of
the TF maps with respect to the baseline. We illustrate the use of CARE-rCortex
with the analysis of two real cardio-respiratory datasets. Comparison with
existing methods: Compared to other open-source toolboxes, CARE-rCortex allows
users to automatically detect CR events, to define and check baselines for each
detected event. Different baseline normalizations can be used in the TF
analysis of EEG epochs. Conclusions: The analysis of CR-related EEG activities
could provide valuable information about cognitive or pathological brain
states. CARE-rCortex runs in Matlab as a plug-in of the EEGLAB software, and it
is publicly available at https://github.com/FannyGrosselin/CARE-rCortex.
| [
{
"created": "Tue, 22 May 2018 14:54:03 GMT",
"version": "v1"
},
{
"created": "Wed, 23 May 2018 10:52:01 GMT",
"version": "v2"
},
{
"created": "Thu, 24 May 2018 09:08:41 GMT",
"version": "v3"
},
{
"created": "Fri, 12 Oct 2018 13:01:49 GMT",
"version": "v4"
}
] | 2018-10-15 | [
[
"Grosselin",
"Fanny",
""
],
[
"Navarro-Sune",
"Xavier",
""
],
[
"Raux",
"Mathieu",
""
],
[
"Similowski",
"Thomas",
""
],
[
"Chavez",
"Mario",
""
]
] | Background: Although cardio-respiratory (CR) system is generally controlled by the autonomic nervous system, interactions between the cortex and these primary functions are receiving an increasing interest in neurosciences. New method: In general, the timing of such internally paced events (e.g. heartbeats or respiratory cycles) may display a large variability. For the analysis of such CR event-related EEG potentials, a baseline must be correctly associated to each cycle of detected events. The open-source toolbox CARE-rCortex provides an easy-to-use interface to detect CR events, define baselines, and analyse in time-frequency (TF) domain the CR-based EEG potentials. Results: CARE-rCortex provides some practical tools to detect and validate these CR events. Users can define baselines time-locked to a phase of respiratory or heart cycle. A statistical test has also been integrated to highlight significant points of the TF maps with respect to the baseline. We illustrate the use of CARE-rCortex with the analysis of two real cardio-respiratory datasets. Comparison with existing methods: Compared to other open-source toolboxes, CARE-rCortex allows users to automatically detect CR events, to define and check baselines for each detected event. Different baseline normalizations can be used in the TF analysis of EEG epochs. Conclusions: The analysis of CR-related EEG activities could provide valuable information about cognitive or pathological brain states. CARE-rCortex runs in Matlab as a plug-in of the EEGLAB software, and it is publicly available at https://github.com/FannyGrosselin/CARE-rCortex. |
1701.09157 | Delfim F. M. Torres | Eugenio M. Rocha, Cristiana J. Silva, Delfim F. M. Torres | The effect of immigrant communities coming from higher incidence
tuberculosis regions to a host country | This is a preprint of a paper whose final and definite form is with
'Ricerche di Matematica', ISSN 0035-5038 (Print) 1827-3491 (Online),
available at [http://link.springer.com/journal/11587]. Paper Submitted
10-Feb-2016; Revised and Accepted 31-Jan-2017 | Ric. Mat. 67 (2018), no. 1, 89--112 | 10.1007/s11587-017-0350-z | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new tuberculosis (TB) mathematical model, with $25$
state-space variables where $15$ are evolution disease states (EDSs), which
generalises previous models and takes into account the (seasonal) flux of
populations between a high incidence TB country (A) and a host country (B) with
low TB incidence, where (B) is divided into a community (G) with high
percentage of people from (A) plus the rest of the population (C). Contrary to
some beliefs, related to the fact that agglomerations of individuals increase
proportionally to the disease spread, analysis of the model shows that the
existence of semi-closed communities are beneficial for the TB control from a
global viewpoint. The model and techniques proposed are applied to a case-study
with concrete parameters, which model the situation of Angola (A) and Portugal
(B), in order to show its relevance and meaningfulness. Simulations show that
variations of the transmission coefficient on the origin country has a big
influence on the number of infected (and infectious) individuals on the
community and the host country. Moreover, there is an optimal ratio for the
distribution of individuals in (C) versus (G), which minimizes the reproduction
number $R_0$. Such value does not give the minimal total number of infected
individuals in all (B), since such is attained when the community (G) is
completely isolated (theoretical scenario). Sensitivity analysis and curve
fitting on $R_0$ and on EDSs are pursuit in order to understand the TB effects
in the global statistics, by measuring the variability of the relevant
parameters. We also show that the TB transmission rate $\beta$ does not act
linearly on $R_0$, as is common in compartment models where system feedback or
group interactions do not occur. Further, we find the most important parameters
for the increase of each EDS.
| [
{
"created": "Tue, 31 Jan 2017 18:06:31 GMT",
"version": "v1"
}
] | 2018-05-25 | [
[
"Rocha",
"Eugenio M.",
""
],
[
"Silva",
"Cristiana J.",
""
],
[
"Torres",
"Delfim F. M.",
""
]
] | We introduce a new tuberculosis (TB) mathematical model, with $25$ state-space variables where $15$ are evolution disease states (EDSs), which generalises previous models and takes into account the (seasonal) flux of populations between a high incidence TB country (A) and a host country (B) with low TB incidence, where (B) is divided into a community (G) with high percentage of people from (A) plus the rest of the population (C). Contrary to some beliefs, related to the fact that agglomerations of individuals increase proportionally to the disease spread, analysis of the model shows that the existence of semi-closed communities are beneficial for the TB control from a global viewpoint. The model and techniques proposed are applied to a case-study with concrete parameters, which model the situation of Angola (A) and Portugal (B), in order to show its relevance and meaningfulness. Simulations show that variations of the transmission coefficient on the origin country has a big influence on the number of infected (and infectious) individuals on the community and the host country. Moreover, there is an optimal ratio for the distribution of individuals in (C) versus (G), which minimizes the reproduction number $R_0$. Such value does not give the minimal total number of infected individuals in all (B), since such is attained when the community (G) is completely isolated (theoretical scenario). Sensitivity analysis and curve fitting on $R_0$ and on EDSs are pursuit in order to understand the TB effects in the global statistics, by measuring the variability of the relevant parameters. We also show that the TB transmission rate $\beta$ does not act linearly on $R_0$, as is common in compartment models where system feedback or group interactions do not occur. Further, we find the most important parameters for the increase of each EDS. |
1401.4026 | Christoph Zechner | Christoph Zechner, Federico Wadehn, Heinz Koeppl | Sparse Learning of Markovian Population Models in Random Environments | 6 pages, 5 figures, under review | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Markovian population models are suitable abstractions to describe well-mixed
interacting particle systems in situation where stochastic fluctuations are
significant due to the involvement of low copy particles. In molecular biology,
measurements on the single-cell level attest to this stochasticity and one is
tempted to interpret such measurements across an isogenic cell population as
different sample paths of one and the same Markov model. Over recent years
evidence built up against this interpretation due to the presence of
cell-to-cell variability stemming from factors other than intrinsic
fluctuations. To account for this extrinsic variability, Markovian models in
random environments need to be considered and a key emerging question is how to
perform inference for such models. We model extrinsic variability by a random
parametrization of all propensity functions. To detect which of those
propensities have significant variability, we lay out a sparse learning
procedure captured by a hierarchical Bayesian model whose evidence function is
iteratively maximized using a variational Bayesian expectation-maximization
algorithm.
| [
{
"created": "Thu, 16 Jan 2014 13:48:07 GMT",
"version": "v1"
}
] | 2014-01-17 | [
[
"Zechner",
"Christoph",
""
],
[
"Wadehn",
"Federico",
""
],
[
"Koeppl",
"Heinz",
""
]
] | Markovian population models are suitable abstractions to describe well-mixed interacting particle systems in situation where stochastic fluctuations are significant due to the involvement of low copy particles. In molecular biology, measurements on the single-cell level attest to this stochasticity and one is tempted to interpret such measurements across an isogenic cell population as different sample paths of one and the same Markov model. Over recent years evidence built up against this interpretation due to the presence of cell-to-cell variability stemming from factors other than intrinsic fluctuations. To account for this extrinsic variability, Markovian models in random environments need to be considered and a key emerging question is how to perform inference for such models. We model extrinsic variability by a random parametrization of all propensity functions. To detect which of those propensities have significant variability, we lay out a sparse learning procedure captured by a hierarchical Bayesian model whose evidence function is iteratively maximized using a variational Bayesian expectation-maximization algorithm. |
2004.05104 | Breno de Oliveira Ferraz | P.P. Avelino, B.F. de Oliveira, R.S. Trintin | Performance of weak species in the simplest generalization of the
rock-paper-scissors model to four species | 7 pages, 6 figures. arXiv admin note: text overlap with
arXiv:1907.11280 | Phys. Rev. E 101, 062312 (2020) | 10.1103/PhysRevE.101.062312 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the problem of the predominance and survival of "weak" species
in the context of the simplest generalization of the spatial stochastic
rock-paper-scissors model to four species by considering models in which one,
two, or three species have a reduced predation probability. We show, using
lattice based spatial stochastic simulations with random initial conditions,
that if only one of the four species has its probability reduced then the most
abundant species is the prey of the "weakest" (assuming that the simulations
are large enough for coexistence to prevail). Also, among the remaining cases,
we present examples in which "weak" and "strong" species have similar average
abundances and others in which either of them dominates -- the most abundant
species being always a prey of a "weak" species with which it maintains a
unidirectional predator-prey interaction. However, in contrast to the
three-species model, we find no systematic difference in the global performance
of "weak" and "strong" species, and we conjecture that the same result will
hold if the number of species is further increased. We also determine the
probability of single species survival and coexistence as a function of the
lattice size, discussing its dependence on initial conditions and on the change
to the dynamics of the model which results from the extinction of one of the
species.
| [
{
"created": "Wed, 8 Apr 2020 21:41:55 GMT",
"version": "v1"
}
] | 2020-07-01 | [
[
"Avelino",
"P. P.",
""
],
[
"de Oliveira",
"B. F.",
""
],
[
"Trintin",
"R. S.",
""
]
] | We investigate the problem of the predominance and survival of "weak" species in the context of the simplest generalization of the spatial stochastic rock-paper-scissors model to four species by considering models in which one, two, or three species have a reduced predation probability. We show, using lattice based spatial stochastic simulations with random initial conditions, that if only one of the four species has its probability reduced then the most abundant species is the prey of the "weakest" (assuming that the simulations are large enough for coexistence to prevail). Also, among the remaining cases, we present examples in which "weak" and "strong" species have similar average abundances and others in which either of them dominates -- the most abundant species being always a prey of a "weak" species with which it maintains a unidirectional predator-prey interaction. However, in contrast to the three-species model, we find no systematic difference in the global performance of "weak" and "strong" species, and we conjecture that the same result will hold if the number of species is further increased. We also determine the probability of single species survival and coexistence as a function of the lattice size, discussing its dependence on initial conditions and on the change to the dynamics of the model which results from the extinction of one of the species. |
1306.5058 | Charalambos Neophytou | Charalambos Neophytou, Aikaterini Dounavi, Filippos A. Aravanopoulos | Conservation of nuclear SSR loci reveals high affinity of Quercus
infectoria ssp. veneris A. Kern (Fagaceae) to section Robur | 18 pages, 2 figures, author's accepted version | null | 10.1007/s11105-008-0025-8 | Plant Molecular Biology Reporter (2008), Vol. 26 (2): pp. 133-149 | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conservation of 16 nuclear microsatellite loci, originally developed for
Quercus macrocarpa (section Albae), Q. petraea, Q. robur (section Robur) and Q.
myrsinifolia, (subgenus Cyclobalanopsis) was tested in a Q. infectoria ssp.
veneris population from Cyprus. All loci could be amplified successfully and
displayed allele size and diversity patterns that match those of oak species
belonging to the section Robur. At least in one case, limited amplification and
high levels of homozygosity support the occurrence of 'null alleles', caused by
a possible mutation in the highly conserved primer areas, thus hindering PCR.
The sampled population exhibited high levels of diversity despite the very
limited distribution of this species in Cyprus and extended population
fragmentation. Allele sizes of Q. infectoria at locus QpZAG9 partially match
those of Q. alnifolia and Q. coccifera from neighboring populations. However,
sequencing showed homoplasy, excluding a case of interspecific introgression
with the latter, phylogenetically remote species. Q. infectoria ssp. veneris
sequences at this locus were concordant to those of other species of section
Robur, while sequences of Quercus alnifolia and Quercus coccifera were almost
identical to Q. cerris.
| [
{
"created": "Fri, 21 Jun 2013 06:43:48 GMT",
"version": "v1"
}
] | 2013-06-24 | [
[
"Neophytou",
"Charalambos",
""
],
[
"Dounavi",
"Aikaterini",
""
],
[
"Aravanopoulos",
"Filippos A.",
""
]
] | Conservation of 16 nuclear microsatellite loci, originally developed for Quercus macrocarpa (section Albae), Q. petraea, Q. robur (section Robur) and Q. myrsinifolia, (subgenus Cyclobalanopsis) was tested in a Q. infectoria ssp. veneris population from Cyprus. All loci could be amplified successfully and displayed allele size and diversity patterns that match those of oak species belonging to the section Robur. At least in one case, limited amplification and high levels of homozygosity support the occurrence of 'null alleles', caused by a possible mutation in the highly conserved primer areas, thus hindering PCR. The sampled population exhibited high levels of diversity despite the very limited distribution of this species in Cyprus and extended population fragmentation. Allele sizes of Q. infectoria at locus QpZAG9 partially match those of Q. alnifolia and Q. coccifera from neighboring populations. However, sequencing showed homoplasy, excluding a case of interspecific introgression with the latter, phylogenetically remote species. Q. infectoria ssp. veneris sequences at this locus were concordant to those of other species of section Robur, while sequences of Quercus alnifolia and Quercus coccifera were almost identical to Q. cerris. |
0705.2646 | Martin Weigt | Michele Leone, Sumedha, Martin Weigt | Clustering by soft-constraint affinity propagation: Applications to
gene-expression data | 11 pages, supplementary material:
http://isiosf.isi.it/~weigt/scap_supplement.pdf | Bioinformatics 23, 2708 (2007) | 10.1093/bioinformatics/btm414 | null | q-bio.QM cond-mat.stat-mech physics.data-an | null | Motivation: Similarity-measure based clustering is a crucial problem
appearing throughout scientific data analysis. Recently, a powerful new
algorithm called Affinity Propagation (AP) based on message-passing techniques
was proposed by Frey and Dueck \cite{Frey07}. In AP, each cluster is identified
by a common exemplar all other data points of the same cluster refer to, and
exemplars have to refer to themselves. Albeit its proved power, AP in its
present form suffers from a number of drawbacks. The hard constraint of having
exactly one exemplar per cluster restricts AP to classes of regularly shaped
clusters, and leads to suboptimal performance, {\it e.g.}, in analyzing gene
expression data. Results: This limitation can be overcome by relaxing the AP
hard constraints. A new parameter controls the importance of the constraints
compared to the aim of maximizing the overall similarity, and allows to
interpolate between the simple case where each data point selects its closest
neighbor as an exemplar and the original AP. The resulting soft-constraint
affinity propagation (SCAP) becomes more informative, accurate and leads to
more stable clustering. Even though a new {\it a priori} free-parameter is
introduced, the overall dependence of the algorithm on external tuning is
reduced, as robustness is increased and an optimal strategy for parameter
selection emerges more naturally. SCAP is tested on biological benchmark data,
including in particular microarray data related to various cancer types. We
show that the algorithm efficiently unveils the hierarchical cluster structure
present in the data sets. Further on, it allows to extract sparse gene
expression signatures for each cluster.
| [
{
"created": "Fri, 18 May 2007 08:22:05 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Nov 2007 16:40:18 GMT",
"version": "v2"
}
] | 2007-11-29 | [
[
"Leone",
"Michele",
""
],
[
"Sumedha",
"",
""
],
[
"Weigt",
"Martin",
""
]
] | Motivation: Similarity-measure based clustering is a crucial problem appearing throughout scientific data analysis. Recently, a powerful new algorithm called Affinity Propagation (AP) based on message-passing techniques was proposed by Frey and Dueck \cite{Frey07}. In AP, each cluster is identified by a common exemplar all other data points of the same cluster refer to, and exemplars have to refer to themselves. Albeit its proved power, AP in its present form suffers from a number of drawbacks. The hard constraint of having exactly one exemplar per cluster restricts AP to classes of regularly shaped clusters, and leads to suboptimal performance, {\it e.g.}, in analyzing gene expression data. Results: This limitation can be overcome by relaxing the AP hard constraints. A new parameter controls the importance of the constraints compared to the aim of maximizing the overall similarity, and allows to interpolate between the simple case where each data point selects its closest neighbor as an exemplar and the original AP. The resulting soft-constraint affinity propagation (SCAP) becomes more informative, accurate and leads to more stable clustering. Even though a new {\it a priori} free-parameter is introduced, the overall dependence of the algorithm on external tuning is reduced, as robustness is increased and an optimal strategy for parameter selection emerges more naturally. SCAP is tested on biological benchmark data, including in particular microarray data related to various cancer types. We show that the algorithm efficiently unveils the hierarchical cluster structure present in the data sets. Further on, it allows to extract sparse gene expression signatures for each cluster. |
1011.6136 | Chrysline Margus Pinol | Chrysline Margus Pinol and Ronald Banzon | The effect of limiting resources in aging populations | null | null | null | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The concept of a carrying capacity is essential in most models to prevent
unlimited growth. Despite the large amount of deaths it introduces, the actual
influence of the Verhulst term in simulations is often times not accounted for.
Generally, it is treated merely as a scaling parameter that functions to keep
simulated populations within computer limits. Here, we compare two different
implementations of the concept in the Penna model - Vehulst applied to all
individuals (VA) and to newborns only (VB). We observe variations in certain
model features when random Verhulst deaths are restricted to a single age
group.
| [
{
"created": "Mon, 29 Nov 2010 06:19:19 GMT",
"version": "v1"
}
] | 2010-11-30 | [
[
"Pinol",
"Chrysline Margus",
""
],
[
"Banzon",
"Ronald",
""
]
] | The concept of a carrying capacity is essential in most models to prevent unlimited growth. Despite the large amount of deaths it introduces, the actual influence of the Verhulst term in simulations is often times not accounted for. Generally, it is treated merely as a scaling parameter that functions to keep simulated populations within computer limits. Here, we compare two different implementations of the concept in the Penna model - Vehulst applied to all individuals (VA) and to newborns only (VB). We observe variations in certain model features when random Verhulst deaths are restricted to a single age group. |
2405.17433 | Xiaoxia Liu | Xiaoxia Liu, Robert R Butler III, Prashnna K Gyawali, Frank M Longo,
Zihuai He | ScAtt: an Attention based architecture to analyze Alzheimer's disease at
cell type level from single-cell RNA-sequencing data | null | null | null | null | q-bio.MN q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that leads
to memory and behavior impairment severe enough to interfere with daily life
activities. Understanding this disease pathogenesis can drive the development
of new targets and strategies to prevent and treat AD. Recent advances in
high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled
the generation of massive amounts of transcriptomic data at the single-cell
level provided remarkable insights into understanding the molecular
pathogenesis of Alzheimer's disease. In this study, we introduce ScAtt, an
innovative Attention-based architecture, devised specifically for the
concurrent identification of cell-type specific AD-related genes and their
associated gene regulatory network. ScAtt incorporates a flexible model capable
of capturing nonlinear effects, leading to the detection of AD-associated genes
that might be overlooked by traditional differentially expressed gene (DEG)
analyses. Moreover, ScAtt effectively infers a gene regulatory network
depicting the combined influences of genes on the targeted disease, as opposed
to examining correlations among genes in conventional gene co-expression
networks. In an application to 95,186 single-nucleus transcriptomes from 17
hippocampus samples, ScAtt shows substantially better performance in modeling
quantitative changes in expression levels between AD and healthy controls.
Consequently, ScAtt performs better than existing methods in the identification
of AD-related genes, with more unique discoveries and less overlap between cell
types. Functional enrichments of the corresponding gene modules detected from
gene regulatory network show significant enrichment of biologically meaningful
AD-related pathways across different cell types.
| [
{
"created": "Tue, 12 Mar 2024 21:29:30 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Liu",
"Xiaoxia",
""
],
[
"Butler",
"Robert R",
"III"
],
[
"Gyawali",
"Prashnna K",
""
],
[
"Longo",
"Frank M",
""
],
[
"He",
"Zihuai",
""
]
] | Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that leads to memory and behavior impairment severe enough to interfere with daily life activities. Understanding this disease pathogenesis can drive the development of new targets and strategies to prevent and treat AD. Recent advances in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of massive amounts of transcriptomic data at the single-cell level provided remarkable insights into understanding the molecular pathogenesis of Alzheimer's disease. In this study, we introduce ScAtt, an innovative Attention-based architecture, devised specifically for the concurrent identification of cell-type specific AD-related genes and their associated gene regulatory network. ScAtt incorporates a flexible model capable of capturing nonlinear effects, leading to the detection of AD-associated genes that might be overlooked by traditional differentially expressed gene (DEG) analyses. Moreover, ScAtt effectively infers a gene regulatory network depicting the combined influences of genes on the targeted disease, as opposed to examining correlations among genes in conventional gene co-expression networks. In an application to 95,186 single-nucleus transcriptomes from 17 hippocampus samples, ScAtt shows substantially better performance in modeling quantitative changes in expression levels between AD and healthy controls. Consequently, ScAtt performs better than existing methods in the identification of AD-related genes, with more unique discoveries and less overlap between cell types. Functional enrichments of the corresponding gene modules detected from gene regulatory network show significant enrichment of biologically meaningful AD-related pathways across different cell types. |
2406.17116 | Florian Anderl | Florian Anderl, Gabriela Salvadori, Mladen Veletic, Fernanda Cristina
Petersen and Ilangko Balasingham | Quantitative Aspects, Engineering and Optimization of Bacterial Sensor
Systems | null | null | null | null | q-bio.MN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Bacterial sensor systems can be used for the detection and measurement of
molecular signal concentrations. The dynamics of the sensor directly depend on
the biological properties of the bacterial sensor cells; manipulation of these
features in the wet lab enables the engineering and optimization of the
bacterial sensor kinetics. This necessitates the development of biologically
meaningful computational models for bacterial sensors comprising a variety of
different molecular mechanisms, which further facilitates a systematic and
quantitative evaluation of optimization strategies. In this work, we dissect
the detection chain of bacterial sensors from a mathematical perspective from
which we derive, supported by wet-lab data, a complete computational model for
a Streptococcus mutans-based bacterial sensor as a case example. We address the
engineering of bacterial sensors by investigating the impact of altered
bacterial cell properties on the sensor response characteristics, specifically
sensor sensitivity and response signal intensity. This is achieved through a
sensitivity analysis targeting both the steady-state and transient sensor
response characteristics. Alongside the demonstration of suitability of our
methodological approach, our analysis shows that an increase of sensor
sensitivity, through a targeted manipulation of bacterial physiology, often
comes at the cost of generally diminished sensor response intensity.
| [
{
"created": "Mon, 24 Jun 2024 20:08:50 GMT",
"version": "v1"
}
] | 2024-06-26 | [
[
"Anderl",
"Florian",
""
],
[
"Salvadori",
"Gabriela",
""
],
[
"Veletic",
"Mladen",
""
],
[
"Petersen",
"Fernanda Cristina",
""
],
[
"Balasingham",
"Ilangko",
""
]
] | Bacterial sensor systems can be used for the detection and measurement of molecular signal concentrations. The dynamics of the sensor directly depend on the biological properties of the bacterial sensor cells; manipulation of these features in the wet lab enables the engineering and optimization of the bacterial sensor kinetics. This necessitates the development of biologically meaningful computational models for bacterial sensors comprising a variety of different molecular mechanisms, which further facilitates a systematic and quantitative evaluation of optimization strategies. In this work, we dissect the detection chain of bacterial sensors from a mathematical perspective from which we derive, supported by wet-lab data, a complete computational model for a Streptococcus mutans-based bacterial sensor as a case example. We address the engineering of bacterial sensors by investigating the impact of altered bacterial cell properties on the sensor response characteristics, specifically sensor sensitivity and response signal intensity. This is achieved through a sensitivity analysis targeting both the steady-state and transient sensor response characteristics. Alongside the demonstration of suitability of our methodological approach, our analysis shows that an increase of sensor sensitivity, through a targeted manipulation of bacterial physiology, often comes at the cost of generally diminished sensor response intensity. |
1910.12293 | Marzio Pennisi | Marzio Pennisi, Miguel A. Juarez, Giulia Russo, Marco Viceconti,
Francesco Pappalardo | Generation of digital patients for the simulation of tuberculosis with
UISS-TB | 5 pages | null | null | null | q-bio.QM cs.MA | http://creativecommons.org/licenses/by-nc-sa/4.0/ | EC funded STriTuVaD project aims to test, through a phase IIb clinical trial,
two of the most advanced therapeutic vaccines against tuberculosis. In
parallel, we have extended the Universal Immune System Simulator to include all
relevant determinants of such clinical trial, to establish its predictive
accuracy against the individual patients recruited in the trial, to use it to
generate digital patients and predict their response to the HRT being tested,
and to combine them to the observations made on physical patients using a new
in silico-augmented clinical trial approach that uses a Bayesian adaptive
design. This approach, where found effective could drastically reduce the cost
of innovation in this critical sector of public healthcare. One of the most
challenging task is to develop a methodology to reproduce biological diversity
of the subjects that have to be simulated, i.e., provide an appropriate
strategy for the generation of libraries of digital patients. This has been
achieved through the the creation of the initial immune system repertoire in a
stochastic way, and though the identification of a "vector of features" that
combines both biological and pathophysiological parameters that personalize the
digital patient to reproduce the physiology and the pathophysiology of the
subject.
| [
{
"created": "Sun, 27 Oct 2019 16:09:43 GMT",
"version": "v1"
}
] | 2019-10-29 | [
[
"Pennisi",
"Marzio",
""
],
[
"Juarez",
"Miguel A.",
""
],
[
"Russo",
"Giulia",
""
],
[
"Viceconti",
"Marco",
""
],
[
"Pappalardo",
"Francesco",
""
]
] | EC funded STriTuVaD project aims to test, through a phase IIb clinical trial, two of the most advanced therapeutic vaccines against tuberculosis. In parallel, we have extended the Universal Immune System Simulator to include all relevant determinants of such clinical trial, to establish its predictive accuracy against the individual patients recruited in the trial, to use it to generate digital patients and predict their response to the HRT being tested, and to combine them to the observations made on physical patients using a new in silico-augmented clinical trial approach that uses a Bayesian adaptive design. This approach, where found effective could drastically reduce the cost of innovation in this critical sector of public healthcare. One of the most challenging task is to develop a methodology to reproduce biological diversity of the subjects that have to be simulated, i.e., provide an appropriate strategy for the generation of libraries of digital patients. This has been achieved through the the creation of the initial immune system repertoire in a stochastic way, and though the identification of a "vector of features" that combines both biological and pathophysiological parameters that personalize the digital patient to reproduce the physiology and the pathophysiology of the subject. |
1805.07343 | Rommel Salas | Cesar Rommel Salas | The impact of binaural white noise with oscillations of 100 to 750hz in
the short-term visual working memory and the reactivity of alpha and beta
cerebral waves | in Spanish | null | null | null | q-bio.NC cs.HC | http://creativecommons.org/licenses/by/4.0/ | According to some researchers, noise is typically conceived as a detrimental
factor in cognitive performance affecting perception, decision making, and
motor function. However, in recent studies it is associated with white noise
with concentration and calm, therefore, this research seeks to establish the
impact of binaural white noise on the performance of short-term visual and
working memory, the alpha - beta brain activity, attention - meditation,
through the use of two auditory stimuli with frequency ranges of (100 to 450hz)
and (100 to 750hz). This study was conducted in the city of Montes Claros, the
Republic of Brazil, where seven participants were evaluated (n = 7) with an
average age of 36.71, and two age groups (GP1) 21 to 30 and (GP2) 41 50, with
university studies. Within the experimental process, the short-term visual
memory tests were performed using the cognitive assessment battery CAB of
CogniFit, and the recording of brain activities through the use of monopolar
electroencephalogram and the eSense algorithms. With the results obtained and
through the use of statistical tests, we can infer that the binaural white
noise with oscillations of 100 to 750 Hz contributed to the performance of
visual work memory in the short term
| [
{
"created": "Sat, 5 May 2018 13:57:41 GMT",
"version": "v1"
}
] | 2018-05-21 | [
[
"Salas",
"Cesar Rommel",
""
]
] | According to some researchers, noise is typically conceived as a detrimental factor in cognitive performance affecting perception, decision making, and motor function. However, in recent studies it is associated with white noise with concentration and calm, therefore, this research seeks to establish the impact of binaural white noise on the performance of short-term visual and working memory, the alpha - beta brain activity, attention - meditation, through the use of two auditory stimuli with frequency ranges of (100 to 450hz) and (100 to 750hz). This study was conducted in the city of Montes Claros, the Republic of Brazil, where seven participants were evaluated (n = 7) with an average age of 36.71, and two age groups (GP1) 21 to 30 and (GP2) 41 50, with university studies. Within the experimental process, the short-term visual memory tests were performed using the cognitive assessment battery CAB of CogniFit, and the recording of brain activities through the use of monopolar electroencephalogram and the eSense algorithms. With the results obtained and through the use of statistical tests, we can infer that the binaural white noise with oscillations of 100 to 750 Hz contributed to the performance of visual work memory in the short term |
1407.0666 | Luca Giomi | H. S. Fisher, L. Giomi, H. E. Hoekstra, L. Mahadevan | The dynamics of sperm cooperation in a competitive environment | 21 pages, 7 figures | null | null | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sperm cooperation has evolved in a variety of taxa and is often considered a
response to sperm competition, yet the benefit of this form of collective
movement remains unclear. Here we use fine-scale imaging and a minimal
mathematical model to study sperm aggregation in the rodent genus $Peromyscus$.
We demonstrate that as the number of sperm cells in an aggregate increase, the
group moves with more persistent linearity but without increasing speed; this
benefit, however, is offset in larger aggregates as the geometry of the group
forces sperm to swim against one another. The result is a non-monotonic
relationship between aggregate size and average velocity with both a
theoretically predicted and empirically observed optimum of 6-7
sperm/aggregate. To understand the role of sexual selection in driving these
sperm group dynamics, we compared two sister-species with divergent mating
systems and find that sperm of $P.\,maniculatus$ (highly promiscuous), which
have evolved under intense competition, form optimal-sized aggregates more
often than sperm of $P.\,polionotus$ (strictly monogamous), which lack
competition. Our combined mathematical and experimental study of coordinated
sperm movement reveals the importance of geometry, motion and group size on
sperm velocity and suggests how these physical variables interact with
evolutionary selective pressures to regulate cooperation in competitive
environments.
| [
{
"created": "Wed, 2 Jul 2014 18:07:32 GMT",
"version": "v1"
}
] | 2014-07-03 | [
[
"Fisher",
"H. S.",
""
],
[
"Giomi",
"L.",
""
],
[
"Hoekstra",
"H. E.",
""
],
[
"Mahadevan",
"L.",
""
]
] | Sperm cooperation has evolved in a variety of taxa and is often considered a response to sperm competition, yet the benefit of this form of collective movement remains unclear. Here we use fine-scale imaging and a minimal mathematical model to study sperm aggregation in the rodent genus $Peromyscus$. We demonstrate that as the number of sperm cells in an aggregate increase, the group moves with more persistent linearity but without increasing speed; this benefit, however, is offset in larger aggregates as the geometry of the group forces sperm to swim against one another. The result is a non-monotonic relationship between aggregate size and average velocity with both a theoretically predicted and empirically observed optimum of 6-7 sperm/aggregate. To understand the role of sexual selection in driving these sperm group dynamics, we compared two sister-species with divergent mating systems and find that sperm of $P.\,maniculatus$ (highly promiscuous), which have evolved under intense competition, form optimal-sized aggregates more often than sperm of $P.\,polionotus$ (strictly monogamous), which lack competition. Our combined mathematical and experimental study of coordinated sperm movement reveals the importance of geometry, motion and group size on sperm velocity and suggests how these physical variables interact with evolutionary selective pressures to regulate cooperation in competitive environments. |
1307.4276 | Marco Galardini | Marco Galardini, Alessio Mengoni, Emanuele G. Biondi, Roberto
Semeraro, Alessandro Florio, Marco Bazzicalupo, Anna Benedetti, Stefano
Mocali | DuctApe: a suite for the analysis and correlation of genomic and
OmnilogTM Phenotype Microarray data | null | null | null | null | q-bio.GN q-bio.MN | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Addressing the functionality of genomes is one of the most important and
challenging tasks of today's biology. In particular the ability to link
genotypes to corresponding phenotypes is of interest in the reconstruction and
biotechnological manipulation of metabolic pathways. Over the last years, the
OmniLogTM Phenotype Microarray (PM) technology has been used to address many
specific issues related to the metabolic functionality of microorganisms.
However, computational tools that could directly link PM data with the gene(s)
of interest followed by the extraction of information on genephenotype
correlation are still missing. Here we present DuctApe, a suite that allows the
analysis of both genomic sequences and PM data, to find metabolic differences
among PM experiments and to correlate them with KEGG pathways and gene
presence/absence patterns. As example, an application of the program to four
bacterial datasets is presented. The source code and tutorials are available at
http://combogenomics.github.io/DuctApe/.
| [
{
"created": "Tue, 16 Jul 2013 13:48:07 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Dec 2013 15:14:45 GMT",
"version": "v2"
},
{
"created": "Fri, 13 Dec 2013 09:28:22 GMT",
"version": "v3"
}
] | 2013-12-16 | [
[
"Galardini",
"Marco",
""
],
[
"Mengoni",
"Alessio",
""
],
[
"Biondi",
"Emanuele G.",
""
],
[
"Semeraro",
"Roberto",
""
],
[
"Florio",
"Alessandro",
""
],
[
"Bazzicalupo",
"Marco",
""
],
[
"Benedetti",
"Anna",
... | Addressing the functionality of genomes is one of the most important and challenging tasks of today's biology. In particular the ability to link genotypes to corresponding phenotypes is of interest in the reconstruction and biotechnological manipulation of metabolic pathways. Over the last years, the OmniLogTM Phenotype Microarray (PM) technology has been used to address many specific issues related to the metabolic functionality of microorganisms. However, computational tools that could directly link PM data with the gene(s) of interest followed by the extraction of information on genephenotype correlation are still missing. Here we present DuctApe, a suite that allows the analysis of both genomic sequences and PM data, to find metabolic differences among PM experiments and to correlate them with KEGG pathways and gene presence/absence patterns. As example, an application of the program to four bacterial datasets is presented. The source code and tutorials are available at http://combogenomics.github.io/DuctApe/. |
1910.09117 | John Vastola | John J. Vastola | The chemical birth-death process with additive noise | 24 pages | null | null | null | q-bio.QM cond-mat.stat-mech physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The chemical birth-death process, whose chemical master equation (CME) is
exactly solvable, is a paradigmatic toy problem often used to get intuition for
how stochasticity affects chemical kinetics. In a certain limit, it can be
approximated by an Ornstein-Uhlenbeck-like process which is also exactly
solvable. In this paper, we use this system to showcase eight qualitatively
different ways to exactly solve continuous stochastic systems: (i) integrating
the stochastic differential equation; (ii) computing the characteristic
function; (iii) eigenfunction expansion; (iv) using ladder operators; (v) the
Martin-Siggia-Rose-Janssen-De Dominicis path integral; (vi) the Onsager-Machlup
path integral; (vii) semiclassically approximating the Onsager-Machlup path
integral; and (viii) approximating the solution to the corresponding CME.
| [
{
"created": "Mon, 21 Oct 2019 02:25:12 GMT",
"version": "v1"
}
] | 2019-10-22 | [
[
"Vastola",
"John J.",
""
]
] | The chemical birth-death process, whose chemical master equation (CME) is exactly solvable, is a paradigmatic toy problem often used to get intuition for how stochasticity affects chemical kinetics. In a certain limit, it can be approximated by an Ornstein-Uhlenbeck-like process which is also exactly solvable. In this paper, we use this system to showcase eight qualitatively different ways to exactly solve continuous stochastic systems: (i) integrating the stochastic differential equation; (ii) computing the characteristic function; (iii) eigenfunction expansion; (iv) using ladder operators; (v) the Martin-Siggia-Rose-Janssen-De Dominicis path integral; (vi) the Onsager-Machlup path integral; (vii) semiclassically approximating the Onsager-Machlup path integral; and (viii) approximating the solution to the corresponding CME. |
0712.2398 | Antonio Trovato | Jayanth R. Banavar, Trinh X. Hoang, John H. Maddocks, Amos Maritan,
Chiara Poletto, Andrzej Stasiak, Antonio Trovato | Structural motifs of biomolecules | 13 pages, 5 figures | Proc. Natl. Acad. Sci. USA 104: 17283-17286 (2007) | 10.1073/pnas.0704594104 | null | q-bio.BM | null | Biomolecular structures are assemblies of emergent anisotropic building
modules such as uniaxial helices or biaxial strands. We provide an approach to
understanding a marginally compact phase of matter that is occupied by proteins
and DNA. This phase, which is in some respects analogous to the liquid crystal
phase for chain molecules, stabilizes a range of shapes that can be obtained by
sequence-independent interactions occurring intra- and intermolecularly between
polymeric molecules. We present a singularityfree self-interaction for a tube
in the continuum limit and show that this results in the tube being positioned
in the marginally compact phase. Our work provides a unified framework for
understanding the building blocks of biomolecules.
| [
{
"created": "Fri, 14 Dec 2007 17:23:58 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Banavar",
"Jayanth R.",
""
],
[
"Hoang",
"Trinh X.",
""
],
[
"Maddocks",
"John H.",
""
],
[
"Maritan",
"Amos",
""
],
[
"Poletto",
"Chiara",
""
],
[
"Stasiak",
"Andrzej",
""
],
[
"Trovato",
"Antonio",
""
... | Biomolecular structures are assemblies of emergent anisotropic building modules such as uniaxial helices or biaxial strands. We provide an approach to understanding a marginally compact phase of matter that is occupied by proteins and DNA. This phase, which is in some respects analogous to the liquid crystal phase for chain molecules, stabilizes a range of shapes that can be obtained by sequence-independent interactions occurring intra- and intermolecularly between polymeric molecules. We present a singularityfree self-interaction for a tube in the continuum limit and show that this results in the tube being positioned in the marginally compact phase. Our work provides a unified framework for understanding the building blocks of biomolecules. |
1407.3887 | Andrew Black | Andrew J. Black and J. V. Ross | Computation of epidemic final size distributions | final published version | Journal of Theoretical Biology, 367, 159-165 (2015) | 10.1016/j.jtbi.2014.11.029 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a new methodology for the efficient computation of epidemic final
size distributions for a broad class of Markovian models. We exploit a
particular representation of the stochastic epidemic process to derive a method
which is both computationally efficient and numerically stable. The algorithms
we present are also physically transparent and so allow us to extend this
method from the basic SIR model to a model with a phase-type infectious period
and another with waning immunity. The underlying theory is applicable to many
Markovian models where we wish to efficiently calculate hitting probabilities.
| [
{
"created": "Tue, 15 Jul 2014 06:04:46 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Aug 2014 04:37:26 GMT",
"version": "v2"
},
{
"created": "Mon, 5 Jan 2015 00:04:08 GMT",
"version": "v3"
}
] | 2015-01-06 | [
[
"Black",
"Andrew J.",
""
],
[
"Ross",
"J. V.",
""
]
] | We develop a new methodology for the efficient computation of epidemic final size distributions for a broad class of Markovian models. We exploit a particular representation of the stochastic epidemic process to derive a method which is both computationally efficient and numerically stable. The algorithms we present are also physically transparent and so allow us to extend this method from the basic SIR model to a model with a phase-type infectious period and another with waning immunity. The underlying theory is applicable to many Markovian models where we wish to efficiently calculate hitting probabilities. |
2403.17687 | Jasper Albers | Jasper Albers, Anno C. Kurth, Robin Gutzen, Aitor Morales-Gregorio,
Michael Denker, Sonja Gr\"un, Sacha J. van Albada, Markus Diesmann | Assessing the similarity of real matrices with arbitrary shape | 12 pages, 6 figures | null | null | null | q-bio.NC physics.data-an q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Assessing the similarity of matrices is valuable for analyzing the extent to
which data sets exhibit common features in tasks such as data clustering,
dimensionality reduction, pattern recognition, group comparison, and graph
analysis. Methods proposed for comparing vectors, such as cosine similarity,
can be readily generalized to matrices. However, this approach usually neglects
the inherent two-dimensional structure of matrices. Here, we propose singular
angle similarity (SAS), a measure for evaluating the structural similarity
between two arbitrary, real matrices of the same shape based on singular value
decomposition. After introducing the measure, we compare SAS with standard
measures for matrix comparison and show that only SAS captures the
two-dimensional structure of matrices. Further, we characterize the behavior of
SAS in the presence of noise and as a function of matrix dimensionality.
Finally, we apply SAS to two use cases: square non-symmetric matrices of
probabilistic network connectivity, and non-square matrices representing neural
brain activity. For synthetic data of network connectivity, SAS matches
intuitive expectations and allows for a robust assessment of similarities and
differences. For experimental data of brain activity, SAS captures differences
in the structure of high-dimensional responses to different stimuli. We
conclude that SAS is a suitable measure for quantifying the shared structure of
matrices with arbitrary shape.
| [
{
"created": "Tue, 26 Mar 2024 13:24:52 GMT",
"version": "v1"
}
] | 2024-03-27 | [
[
"Albers",
"Jasper",
""
],
[
"Kurth",
"Anno C.",
""
],
[
"Gutzen",
"Robin",
""
],
[
"Morales-Gregorio",
"Aitor",
""
],
[
"Denker",
"Michael",
""
],
[
"Grün",
"Sonja",
""
],
[
"van Albada",
"Sacha J.",
""
]... | Assessing the similarity of matrices is valuable for analyzing the extent to which data sets exhibit common features in tasks such as data clustering, dimensionality reduction, pattern recognition, group comparison, and graph analysis. Methods proposed for comparing vectors, such as cosine similarity, can be readily generalized to matrices. However, this approach usually neglects the inherent two-dimensional structure of matrices. Here, we propose singular angle similarity (SAS), a measure for evaluating the structural similarity between two arbitrary, real matrices of the same shape based on singular value decomposition. After introducing the measure, we compare SAS with standard measures for matrix comparison and show that only SAS captures the two-dimensional structure of matrices. Further, we characterize the behavior of SAS in the presence of noise and as a function of matrix dimensionality. Finally, we apply SAS to two use cases: square non-symmetric matrices of probabilistic network connectivity, and non-square matrices representing neural brain activity. For synthetic data of network connectivity, SAS matches intuitive expectations and allows for a robust assessment of similarities and differences. For experimental data of brain activity, SAS captures differences in the structure of high-dimensional responses to different stimuli. We conclude that SAS is a suitable measure for quantifying the shared structure of matrices with arbitrary shape. |
2006.07352 | Eli Shlizerman | Jimin Kim, Eli Shlizerman | Deep Reinforcement Learning for Neural Control | Please see the associated Video at: https://youtu.be/ixsUMfb9m_U | null | null | null | q-bio.NC cs.AI cs.LG cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel methodology for control of neural circuits based on deep
reinforcement learning. Our approach achieves aimed behavior by generating
external continuous stimulation of existing neural circuits (neuromodulation
control) or modulations of neural circuits architecture (connectome control).
Both forms of control are challenging due to nonlinear and recurrent complexity
of neural activity. To infer candidate control policies, our approach maps
neural circuits and their connectome into a grid-world like setting and infers
the actions needed to achieve aimed behavior. The actions are inferred by
adaptation of deep Q-learning methods known for their robust performance in
navigating grid-worlds. We apply our approach to the model of \textit{C.
elegans} which simulates the full somatic nervous system with muscles and body.
Our framework successfully infers neuropeptidic currents and synaptic
architectures for control of chemotaxis. Our findings are consistent with in
vivo measurements and provide additional insights into neural control of
chemotaxis. We further demonstrate the generality and scalability of our
methods by inferring chemotactic neural circuits from scratch.
| [
{
"created": "Fri, 12 Jun 2020 17:41:12 GMT",
"version": "v1"
}
] | 2020-06-15 | [
[
"Kim",
"Jimin",
""
],
[
"Shlizerman",
"Eli",
""
]
] | We present a novel methodology for control of neural circuits based on deep reinforcement learning. Our approach achieves aimed behavior by generating external continuous stimulation of existing neural circuits (neuromodulation control) or modulations of neural circuits architecture (connectome control). Both forms of control are challenging due to nonlinear and recurrent complexity of neural activity. To infer candidate control policies, our approach maps neural circuits and their connectome into a grid-world like setting and infers the actions needed to achieve aimed behavior. The actions are inferred by adaptation of deep Q-learning methods known for their robust performance in navigating grid-worlds. We apply our approach to the model of \textit{C. elegans} which simulates the full somatic nervous system with muscles and body. Our framework successfully infers neuropeptidic currents and synaptic architectures for control of chemotaxis. Our findings are consistent with in vivo measurements and provide additional insights into neural control of chemotaxis. We further demonstrate the generality and scalability of our methods by inferring chemotactic neural circuits from scratch. |
2109.00809 | Chao Yang | Chao Yang, Debajyoti Chowdhury, Zhenmiao Zhang, William K. Cheung,
Aiping Lu, Zhao Xiang Bian, Lu Zhang | A review of computational tools for generating metagenome-assembled
genomes from metagenomic sequencing data | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Microbes are essentially yet convolutedly linked with human lives on the
earth. They critically interfere in different physiological processes and thus
influence overall health status. Studying microbial species is used to be
constrained to those that can be cultured in the lab. But it excluded a huge
portion of the microbiome that could not survive on lab conditions. In the past
few years, the culture-independent metagenomic sequencing enabled us to explore
the complex microbial community coexisting within and on us. Metagenomics has
equipped us with new avenues of investigating the microbiome, from studying a
single species to a complex community in a dynamic ecosystem. Thus, identifying
the involved microbes and their genomes becomes one of the core tasks in
metagenomic sequencing. Metagenome-assembled genomes are groups of contigs with
similar sequence characteristics from de novo assembly and could represent the
microbial genomes from metagenomic sequencing. In this paper, we reviewed a
spectrum of tools for producing and annotating metagenome-assembled genomes
from metagenomic sequencing data and discussed their technical and biological
perspectives.
| [
{
"created": "Thu, 2 Sep 2021 09:39:06 GMT",
"version": "v1"
}
] | 2021-09-03 | [
[
"Yang",
"Chao",
""
],
[
"Chowdhury",
"Debajyoti",
""
],
[
"Zhang",
"Zhenmiao",
""
],
[
"Cheung",
"William K.",
""
],
[
"Lu",
"Aiping",
""
],
[
"Bian",
"Zhao Xiang",
""
],
[
"Zhang",
"Lu",
""
]
] | Microbes are essentially yet convolutedly linked with human lives on the earth. They critically interfere in different physiological processes and thus influence overall health status. Studying microbial species is used to be constrained to those that can be cultured in the lab. But it excluded a huge portion of the microbiome that could not survive on lab conditions. In the past few years, the culture-independent metagenomic sequencing enabled us to explore the complex microbial community coexisting within and on us. Metagenomics has equipped us with new avenues of investigating the microbiome, from studying a single species to a complex community in a dynamic ecosystem. Thus, identifying the involved microbes and their genomes becomes one of the core tasks in metagenomic sequencing. Metagenome-assembled genomes are groups of contigs with similar sequence characteristics from de novo assembly and could represent the microbial genomes from metagenomic sequencing. In this paper, we reviewed a spectrum of tools for producing and annotating metagenome-assembled genomes from metagenomic sequencing data and discussed their technical and biological perspectives. |
0904.1298 | Tom Michoel | Tom Michoel, Riet De Smet, Anagha Joshi, Kathleen Marchal, Yves Van de
Peer | Reverse-engineering transcriptional modules from gene expression data | 5 pages REVTeX, 4 figures | Ann. N. Y. Acad. of Sci. 1158, 36 - 43 (2009) | 10.1111/j.1749-6632.2008.03943.x | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | "Module networks" are a framework to learn gene regulatory networks from
expression data using a probabilistic model in which coregulated genes share
the same parameters and conditional distributions. We present a method to infer
ensembles of such networks and an averaging procedure to extract the
statistically most significant modules and their regulators. We show that the
inferred probabilistic models extend beyond the data set used to learn the
models.
| [
{
"created": "Wed, 8 Apr 2009 10:02:41 GMT",
"version": "v1"
}
] | 2009-04-09 | [
[
"Michoel",
"Tom",
""
],
[
"De Smet",
"Riet",
""
],
[
"Joshi",
"Anagha",
""
],
[
"Marchal",
"Kathleen",
""
],
[
"Van de Peer",
"Yves",
""
]
] | "Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the data set used to learn the models. |
1705.06848 | Lucian Ilie | Yiwei Li, Lucian Ilie | SPRINT: Ultrafast protein-protein interaction prediction of the entire
human interactome | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proteins perform their functions usually by interacting with other proteins.
Predicting which proteins interact is a fundamental problem. Experimental
methods are slow, expensive, and have a high rate of error. Many computational
methods have been proposed among which sequence-based ones are very promising.
However, so far no such method is able to predict effectively the entire human
interactome: they require too much time or memory. We present SPRINT (Scoring
PRotein INTeractions), a new sequence-based algorithm and tool for predicting
protein-protein interactions. We comprehensively compare SPRINT with
state-of-the-art programs on seven most reliable human PPI datasets and show
that it is more accurate while running orders of magnitude faster and using
very little memory. SPRINT is the only program that can predict the entire
human interactome. Our goal is to transform the very challenging problem of
predicting the entire human interactome into a routine task. The source code of
SPRINT is freely available from github.com/lucian-ilie/SPRINT/ and the datasets
and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/.
| [
{
"created": "Fri, 19 May 2017 02:08:58 GMT",
"version": "v1"
}
] | 2017-05-22 | [
[
"Li",
"Yiwei",
""
],
[
"Ilie",
"Lucian",
""
]
] | Proteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory. We present SPRINT (Scoring PRotein INTeractions), a new sequence-based algorithm and tool for predicting protein-protein interactions. We comprehensively compare SPRINT with state-of-the-art programs on seven most reliable human PPI datasets and show that it is more accurate while running orders of magnitude faster and using very little memory. SPRINT is the only program that can predict the entire human interactome. Our goal is to transform the very challenging problem of predicting the entire human interactome into a routine task. The source code of SPRINT is freely available from github.com/lucian-ilie/SPRINT/ and the datasets and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/. |
1907.02557 | Sudhakar Mishra | Sudhakar Mishra and U.S.Tiwary | A Cognition-Affect Integrated Model of Emotion | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The focus of the efforts for defining and modelling emotion is broadly
shifting from classical definite marker theory to statistically context
situated conceptual theory. However, the role of context processing and its
interaction with the affect is still not comprehensively explored and modelled.
With the help of neural decoding of functional networks, we have decoded
cognitive functions for 12 different basic and complex emotion conditions.
Using transfer learning in deep neural architecture, we arrived at the
conclusion that the core affect is unable to provide varieties of emotions
unless coupled with cortical cognitive functions such as autobiographical
memory, dmn, self-referential, social, tom and salient event detection.
Following our results, in this article, we present a 'cognition-affect
integrated model of emotion' which includes many cortical and subcortical
regions and their interactions. Our model suggests three testable hypotheses.
First, affect and physiological sensations alone are inconsequential in
defining or classifying emotions until integrated with the domain-general
cognitive systems. Second, cognition and affect modulate each other throughout
the generation of meaningful instance which is situated in the current context.
And, finally, the structural and temporal hierarchies in the brain's
organization and anatomical projections play an important role in emotion
responses in terms of hierarchical activities and their durations. The model,
along with the analytical and anatomical support, is presented. The article
concludes with the future research questions.
| [
{
"created": "Thu, 4 Jul 2019 18:24:10 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Sep 2019 17:14:18 GMT",
"version": "v2"
},
{
"created": "Tue, 28 Apr 2020 09:53:44 GMT",
"version": "v3"
}
] | 2020-05-05 | [
[
"Mishra",
"Sudhakar",
""
],
[
"Tiwary",
"U. S.",
""
]
] | The focus of the efforts for defining and modelling emotion is broadly shifting from classical definite marker theory to statistically context situated conceptual theory. However, the role of context processing and its interaction with the affect is still not comprehensively explored and modelled. With the help of neural decoding of functional networks, we have decoded cognitive functions for 12 different basic and complex emotion conditions. Using transfer learning in deep neural architecture, we arrived at the conclusion that the core affect is unable to provide varieties of emotions unless coupled with cortical cognitive functions such as autobiographical memory, dmn, self-referential, social, tom and salient event detection. Following our results, in this article, we present a 'cognition-affect integrated model of emotion' which includes many cortical and subcortical regions and their interactions. Our model suggests three testable hypotheses. First, affect and physiological sensations alone are inconsequential in defining or classifying emotions until integrated with the domain-general cognitive systems. Second, cognition and affect modulate each other throughout the generation of meaningful instance which is situated in the current context. And, finally, the structural and temporal hierarchies in the brain's organization and anatomical projections play an important role in emotion responses in terms of hierarchical activities and their durations. The model, along with the analytical and anatomical support, is presented. The article concludes with the future research questions. |
2006.02342 | Christoph Bandt | Christoph Bandt | A reproduction rate which perfectly fits Covid-19 | null | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a simple technique to compare the development of the Covid-19
epidemic in different regions, based only on the time series of confirmed
cases. Weekly new infections, taken for every day, are interpreted as infection
potential of Covid-19. We derive a robust time-varying reproduction rate for
the infection potential, including asymptomatic cases, which does not depend on
death rate or testing intensity. It requires few assumptions and shows a more
plausible time course than official reproduction rates in several countries.
| [
{
"created": "Wed, 27 May 2020 15:13:50 GMT",
"version": "v1"
}
] | 2020-06-04 | [
[
"Bandt",
"Christoph",
""
]
] | We present a simple technique to compare the development of the Covid-19 epidemic in different regions, based only on the time series of confirmed cases. Weekly new infections, taken for every day, are interpreted as infection potential of Covid-19. We derive a robust time-varying reproduction rate for the infection potential, including asymptomatic cases, which does not depend on death rate or testing intensity. It requires few assumptions and shows a more plausible time course than official reproduction rates in several countries. |
1506.04301 | Feraz Azhar | Feraz Azhar, William S. Anderson | Predicting single-neuron activity in locally connected networks | 29 pages, 11 figures | Neural Computation, 24(10), 2655-2677, (2012) | 10.1162/NECO_a_00343 | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The characterization of coordinated activity in neuronal populations has
received renewed interest in the light of advancing experimental techniques
which allow recordings from multiple units simultaneously. Across both in vitro
and in vivo preparations, nearby neurons show coordinated responses when
spontaneously active, and when subject to external stimuli. Recent work
(Truccolo, Hochberg, & Donoghue, 2010) has connected these coordinated
responses to behavior, showing that small ensembles of neurons in arm related
areas of sensorimotor cortex can reliably predict single-neuron spikes in
behaving monkeys and humans. We investigate this phenomenon utilizing an
analogous point process model, showing that in the case of a computational
model of cortex responding to random background inputs, one is similarly able
to predict the future state of a single neuron by considering its own spiking
history, together with the spiking histories of randomly sampled ensembles of
nearby neurons. This model exhibits realistic cortical architecture and
displays bursting episodes in the two distinct connectivity schemes studied. We
conjecture that the baseline predictability we find in these instances is
characteristic of locally connected networks more broadly considered.
| [
{
"created": "Sat, 13 Jun 2015 17:38:29 GMT",
"version": "v1"
}
] | 2015-06-16 | [
[
"Azhar",
"Feraz",
""
],
[
"Anderson",
"William S.",
""
]
] | The characterization of coordinated activity in neuronal populations has received renewed interest in the light of advancing experimental techniques which allow recordings from multiple units simultaneously. Across both in vitro and in vivo preparations, nearby neurons show coordinated responses when spontaneously active, and when subject to external stimuli. Recent work (Truccolo, Hochberg, & Donoghue, 2010) has connected these coordinated responses to behavior, showing that small ensembles of neurons in arm related areas of sensorimotor cortex can reliably predict single-neuron spikes in behaving monkeys and humans. We investigate this phenomenon utilizing an analogous point process model, showing that in the case of a computational model of cortex responding to random background inputs, one is similarly able to predict the future state of a single neuron by considering its own spiking history, together with the spiking histories of randomly sampled ensembles of nearby neurons. This model exhibits realistic cortical architecture and displays bursting episodes in the two distinct connectivity schemes studied. We conjecture that the baseline predictability we find in these instances is characteristic of locally connected networks more broadly considered. |
2110.04873 | Renata Rychtarikova | Kirill Lonhus, Dalibor Stys, Renata Rychtarikova | Quantification of collective behaviour via causality analysis | 18 pages, 4 figures | Complex & Intelligent Systems, 2023 | 10.1007/s40747-023-01057-9 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Terms such as leader, mediator, and follower sound equal in the description
of a pack of wolves, a street protest crowd, or a business team and have very
similar meanings. This indicates the presence of some general law or structure
that governs collective behaviour. To reveal this, we selected the most common
parameter for most levels of the organisation -- motion. A causality analysis
of distance correlations was performed to obtain follow-up networks that show
who follows whom and how strongly. These networks characterise an observed
system in general and work as an automation bridge between the biological
experiment and the broad field of network analysis. The proposed method was
tested on 3D image data from a controlled experiment on a 6-member school of
aquarium fish of Tiger Barb. The network patterns can be easily ethologically
interpreted and agreed with expected behaviour.
| [
{
"created": "Sun, 10 Oct 2021 18:21:02 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Oct 2021 10:26:52 GMT",
"version": "v2"
},
{
"created": "Thu, 25 Nov 2021 11:52:57 GMT",
"version": "v3"
},
{
"created": "Thu, 16 Dec 2021 15:49:11 GMT",
"version": "v4"
},
{
"c... | 2023-04-13 | [
[
"Lonhus",
"Kirill",
""
],
[
"Stys",
"Dalibor",
""
],
[
"Rychtarikova",
"Renata",
""
]
] | Terms such as leader, mediator, and follower sound equal in the description of a pack of wolves, a street protest crowd, or a business team and have very similar meanings. This indicates the presence of some general law or structure that governs collective behaviour. To reveal this, we selected the most common parameter for most levels of the organisation -- motion. A causality analysis of distance correlations was performed to obtain follow-up networks that show who follows whom and how strongly. These networks characterise an observed system in general and work as an automation bridge between the biological experiment and the broad field of network analysis. The proposed method was tested on 3D image data from a controlled experiment on a 6-member school of aquarium fish of Tiger Barb. The network patterns can be easily ethologically interpreted and agreed with expected behaviour. |
2004.12994 | Keith Burghardt | Keith Burghardt and Kristina Lerman | Unequal Impact and Spatial Aggregation Distort COVID-19 Growth Rates | 14 pages, 6 figures | null | null | null | q-bio.QM cs.NA math.NA physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The COVID-19 pandemic has emerged as a global public health crisis. To make
decisions about mitigation strategies and to understand the disease dynamics,
policy makers and epidemiologists must know how the disease is spreading in
their communities. We analyze confirmed infections and deaths over multiple
geographic scales to show that COVID-19's impact is highly unequal: many
subregions have nearly zero infections, and others are hot spots. We attribute
the effect to a Reed-Hughes-like mechanism in which disease arrives at
different times and grows exponentially. Hot spots, however, appear to grow
faster than neighboring subregions and dominate spatially aggregated
statistics, thereby amplifying growth rates. The staggered spread of COVID-19
can also make aggregated growth rates appear higher even when subregions grow
at the same rate. Public policy, economic analysis and epidemic modeling need
to account for potential distortions introduced by spatial aggregation.
| [
{
"created": "Mon, 27 Apr 2020 17:59:01 GMT",
"version": "v1"
},
{
"created": "Fri, 15 May 2020 17:55:04 GMT",
"version": "v2"
}
] | 2020-05-18 | [
[
"Burghardt",
"Keith",
""
],
[
"Lerman",
"Kristina",
""
]
] | The COVID-19 pandemic has emerged as a global public health crisis. To make decisions about mitigation strategies and to understand the disease dynamics, policy makers and epidemiologists must know how the disease is spreading in their communities. We analyze confirmed infections and deaths over multiple geographic scales to show that COVID-19's impact is highly unequal: many subregions have nearly zero infections, and others are hot spots. We attribute the effect to a Reed-Hughes-like mechanism in which disease arrives at different times and grows exponentially. Hot spots, however, appear to grow faster than neighboring subregions and dominate spatially aggregated statistics, thereby amplifying growth rates. The staggered spread of COVID-19 can also make aggregated growth rates appear higher even when subregions grow at the same rate. Public policy, economic analysis and epidemic modeling need to account for potential distortions introduced by spatial aggregation. |
1610.09990 | Jannis Schuecker | Jan Hahne, David Dahmen, Jannis Schuecker, Andreas Frommer, Matthias
Bolten, Moritz Helias and Markus Diesmann | Integration of continuous-time dynamics in a spiking neural network
simulator | null | null | 10.3389/fninf.2017.00034 | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Contemporary modeling approaches to the dynamics of neural networks consider
two main classes of models: biologically grounded spiking neurons and
functionally inspired rate-based units. The unified simulation framework
presented here supports the combination of the two for multi-scale modeling
approaches, the quantitative validation of mean-field approaches by spiking
network simulations, and an increase in reliability by usage of the same
simulation code and the same network model specifications for both model
classes. While most efficient spiking simulations rely on the communication of
discrete events, rate models require time-continuous interactions between
neurons. Exploiting the conceptual similarity to the inclusion of gap junctions
in spiking network simulations, we arrive at a reference implementation of
instantaneous and delayed interactions between rate-based models in a spiking
network simulator. The separation of rate dynamics from the general connection
and communication infrastructure ensures flexibility of the framework. We
further demonstrate the broad applicability of the framework by considering
various examples from the literature ranging from random networks to neural
field models. The study provides the prerequisite for interactions between
rate-based and spiking models in a joint simulation.
| [
{
"created": "Mon, 31 Oct 2016 16:02:23 GMT",
"version": "v1"
}
] | 2017-11-27 | [
[
"Hahne",
"Jan",
""
],
[
"Dahmen",
"David",
""
],
[
"Schuecker",
"Jannis",
""
],
[
"Frommer",
"Andreas",
""
],
[
"Bolten",
"Matthias",
""
],
[
"Helias",
"Moritz",
""
],
[
"Diesmann",
"Markus",
""
]
] | Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units. The unified simulation framework presented here supports the combination of the two for multi-scale modeling approaches, the quantitative validation of mean-field approaches by spiking network simulations, and an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most efficient spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. We further demonstrate the broad applicability of the framework by considering various examples from the literature ranging from random networks to neural field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation. |
1206.1621 | Ruth Davidson | Ruth Davidson and Seth Sullivant | Polyhedral Combinatorics of UPGMA Cones | null | null | null | null | q-bio.PE math.CO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distance-based methods such as UPGMA (Unweighted Pair Group Method with
Arithmetic Mean) continue to play a significant role in phylogenetic research.
We use polyhedral combinatorics to analyze the natural subdivision of the
positive orthant induced by classifying the input vectors according to tree
topologies returned by the algorithm. The partition lattice informs the study
of UPGMA trees. We give a closed form for the extreme rays of UPGMA cones on n
taxa, and compute the normalized volumes of the UPGMA cones for small n.
Keywords: phylogenetic trees, polyhedral combinatorics, partition lattice
| [
{
"created": "Thu, 7 Jun 2012 21:28:58 GMT",
"version": "v1"
}
] | 2012-06-11 | [
[
"Davidson",
"Ruth",
""
],
[
"Sullivant",
"Seth",
""
]
] | Distance-based methods such as UPGMA (Unweighted Pair Group Method with Arithmetic Mean) continue to play a significant role in phylogenetic research. We use polyhedral combinatorics to analyze the natural subdivision of the positive orthant induced by classifying the input vectors according to tree topologies returned by the algorithm. The partition lattice informs the study of UPGMA trees. We give a closed form for the extreme rays of UPGMA cones on n taxa, and compute the normalized volumes of the UPGMA cones for small n. Keywords: phylogenetic trees, polyhedral combinatorics, partition lattice |
1903.02771 | Viktoria Dotz | Viktoria Dotz and Manfred Wuhrer | Histo-blood group glycans in the context of personalized medicine | null | Biochimica et Biophysica Acta 1860 (2016) 1596-1607 | 10.1016/j.bbagen.2015.12.026 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: A subset of histo-blood group antigens including ABO and Lewis
are oligosaccharide structures which may be conjugated to lipids or proteins.
They are known to be important recognition motifs not only in the context of
blood transfusions, but also in infection and cancer development. Scope of
review: Current knowledge on the molecular background and the implication of
histo-blood group glycans in the prevention and therapy of infectious and
non-communicable diseases, such as cancer and cardiovascular disease, is
presented. Major conclusions: Glycan-based histo-blood groups are associated
with intestinal microbiota com-position, the risk of various diseases as well
as therapeutic success of, e.g., vaccination. Their potential as prebiotic or
anti-microbial agents, as disease biomarkers and vaccine targets should be
further investigated in future studies. For this, recent and future
technological advancements will be of particular importance, especially with
regard to the unambiguous structural characterization of the glycan portion in
combination with information on the protein and lipid carriers of histo-blood
group-active glycans in large cohorts. General significance: Histo-blood group
glycans have a unique linking position in the complex net-work of genes,
oncodevelopmental biological processes, and disease mechanisms. Thus, they are
highly promising targets for novel approaches in the field of personalized
medicine.
| [
{
"created": "Thu, 7 Mar 2019 08:47:22 GMT",
"version": "v1"
}
] | 2019-03-08 | [
[
"Dotz",
"Viktoria",
""
],
[
"Wuhrer",
"Manfred",
""
]
] | Background: A subset of histo-blood group antigens including ABO and Lewis are oligosaccharide structures which may be conjugated to lipids or proteins. They are known to be important recognition motifs not only in the context of blood transfusions, but also in infection and cancer development. Scope of review: Current knowledge on the molecular background and the implication of histo-blood group glycans in the prevention and therapy of infectious and non-communicable diseases, such as cancer and cardiovascular disease, is presented. Major conclusions: Glycan-based histo-blood groups are associated with intestinal microbiota com-position, the risk of various diseases as well as therapeutic success of, e.g., vaccination. Their potential as prebiotic or anti-microbial agents, as disease biomarkers and vaccine targets should be further investigated in future studies. For this, recent and future technological advancements will be of particular importance, especially with regard to the unambiguous structural characterization of the glycan portion in combination with information on the protein and lipid carriers of histo-blood group-active glycans in large cohorts. General significance: Histo-blood group glycans have a unique linking position in the complex net-work of genes, oncodevelopmental biological processes, and disease mechanisms. Thus, they are highly promising targets for novel approaches in the field of personalized medicine. |
0902.1506 | Rosemary Braun | Rosemary Braun, William Rowe, Carl Schaefer, Jinghui Zhang, and
Kenneth Buetow | Needles in the Haystack: Identifying Individuals Present in Pooled
Genomic Data | 32 pages including 7 figures. Versions: V3 contains major revision of
content, including a new derivation in the Results, rewritten appendix,
additional figures and tables; V2 corrects minor typo from v1 (transposition
of 1145/1142 and 1045/1042) | Braun R, Rowe W, Schaefer C, Zhang J, Buetow K (2009) Needles in
the Haystack: Identifying Individuals Present in Pooled Genomic Data. PLoS
Genet 5(10): e1000668 | 10.1371/journal.pgen.1000668 | null | q-bio.GN q-bio.QM stat.AP stat.CO | http://creativecommons.org/licenses/by/3.0/ | Recent publications have described and applied a novel metric that quantifies
the genetic distance of an individual with respect to two population samples,
and have suggested that the metric makes it possible to infer the presence of
an individual of known genotype in a sample for which only the marginal allele
frequencies are known. However, the assumptions, limitations, and utility of
this metric remained incompletely characterized. Here we present an exploration
of the strengths and limitations of that method. In addition to analytical
investigations of the underlying assumptions, we use both real and simulated
genotypes to test empirically the method's accuracy. The results reveal that,
when used as a means by which to identify individuals as members of a
population sample, the specificity is low in several circumstances. We find
that the misclassifications stem from violations of assumptions that are
crucial to the technique yet hard to control in practice, and we explore the
feasibility of several methods to improve the sensitivity. Additionally, we
find that the specificity may still be lower than expected even in ideal
circumstances. However, despite the metric's inadequacies for identifying the
presence of an individual in a sample, our results suggest potential avenues
for future research on tuning this method to problems of ancestry inference or
disease prediction. By revealing both the strengths and limitations of the
proposed method, we hope to elucidate situations in which this distance metric
may be used in an appropriate manner. We also discuss the implications of our
findings in forensics applications and in the protection of GWAS participant
privacy.
| [
{
"created": "Mon, 9 Feb 2009 20:18:03 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Apr 2009 03:44:42 GMT",
"version": "v2"
},
{
"created": "Wed, 20 May 2009 23:59:21 GMT",
"version": "v3"
}
] | 2015-09-24 | [
[
"Braun",
"Rosemary",
""
],
[
"Rowe",
"William",
""
],
[
"Schaefer",
"Carl",
""
],
[
"Zhang",
"Jinghui",
""
],
[
"Buetow",
"Kenneth",
""
]
] | Recent publications have described and applied a novel metric that quantifies the genetic distance of an individual with respect to two population samples, and have suggested that the metric makes it possible to infer the presence of an individual of known genotype in a sample for which only the marginal allele frequencies are known. However, the assumptions, limitations, and utility of this metric remained incompletely characterized. Here we present an exploration of the strengths and limitations of that method. In addition to analytical investigations of the underlying assumptions, we use both real and simulated genotypes to test empirically the method's accuracy. The results reveal that, when used as a means by which to identify individuals as members of a population sample, the specificity is low in several circumstances. We find that the misclassifications stem from violations of assumptions that are crucial to the technique yet hard to control in practice, and we explore the feasibility of several methods to improve the sensitivity. Additionally, we find that the specificity may still be lower than expected even in ideal circumstances. However, despite the metric's inadequacies for identifying the presence of an individual in a sample, our results suggest potential avenues for future research on tuning this method to problems of ancestry inference or disease prediction. By revealing both the strengths and limitations of the proposed method, we hope to elucidate situations in which this distance metric may be used in an appropriate manner. We also discuss the implications of our findings in forensics applications and in the protection of GWAS participant privacy. |
2010.01047 | Beren Millidge Mr | Beren Millidge, Alexander Tschantz, Anil Seth, Christopher L Buckley | Relaxing the Constraints on Predictive Coding Models | 02/10/20 initial upload; 10/10/20 minor fixes | null | null | null | q-bio.NC cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predictive coding is an influential theory of cortical function which posits
that the principal computation the brain performs, which underlies both
perception and learning, is the minimization of prediction errors. While
motivated by high-level notions of variational inference, detailed
neurophysiological models of cortical microcircuits which can implements its
computations have been developed. Moreover, under certain conditions,
predictive coding has been shown to approximate the backpropagation of error
algorithm, and thus provides a relatively biologically plausible
credit-assignment mechanism for training deep networks. However, standard
implementations of the algorithm still involve potentially neurally implausible
features such as identical forward and backward weights, backward nonlinear
derivatives, and 1-1 error unit connectivity. In this paper, we show that these
features are not integral to the algorithm and can be removed either directly
or through learning additional sets of parameters with Hebbian update rules
without noticeable harm to learning performance. Our work thus relaxes current
constraints on potential microcircuit designs and hopefully opens up new
regions of the design-space for neuromorphic implementations of predictive
coding.
| [
{
"created": "Fri, 2 Oct 2020 15:21:37 GMT",
"version": "v1"
},
{
"created": "Sat, 10 Oct 2020 14:09:12 GMT",
"version": "v2"
}
] | 2020-10-13 | [
[
"Millidge",
"Beren",
""
],
[
"Tschantz",
"Alexander",
""
],
[
"Seth",
"Anil",
""
],
[
"Buckley",
"Christopher L",
""
]
] | Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the minimization of prediction errors. While motivated by high-level notions of variational inference, detailed neurophysiological models of cortical microcircuits which can implements its computations have been developed. Moreover, under certain conditions, predictive coding has been shown to approximate the backpropagation of error algorithm, and thus provides a relatively biologically plausible credit-assignment mechanism for training deep networks. However, standard implementations of the algorithm still involve potentially neurally implausible features such as identical forward and backward weights, backward nonlinear derivatives, and 1-1 error unit connectivity. In this paper, we show that these features are not integral to the algorithm and can be removed either directly or through learning additional sets of parameters with Hebbian update rules without noticeable harm to learning performance. Our work thus relaxes current constraints on potential microcircuit designs and hopefully opens up new regions of the design-space for neuromorphic implementations of predictive coding. |
0711.4843 | Eduardo D. Sontag | Eduardo D. Sontag | Network reconstruction based on quasi-steady state data | Related material can be found in
http://www.math.rutgers.edu/~sontag/PUBDIR/index.html | null | null | null | q-bio.QM | null | This note discusses a theoretical issue regarding the application of the
"Modular Response Analysis" method to quasi-steady state (rather than
steady-state) data.
| [
{
"created": "Thu, 29 Nov 2007 22:39:57 GMT",
"version": "v1"
}
] | 2007-12-03 | [
[
"Sontag",
"Eduardo D.",
""
]
] | This note discusses a theoretical issue regarding the application of the "Modular Response Analysis" method to quasi-steady state (rather than steady-state) data. |
1106.5678 | Tobias Potjans | Tobias C. Potjans and Markus Diesmann | The cell-type specific connectivity of the local cortical network
explains prominent features of neuronal activity | 57 pages (including main text and supplemental material), 12 figures,
8 supplemental figures, 5 tables, 2 supplemental tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the past decade, the cell-type specific connectivity and activity of local
cortical networks have been characterized experimentally to some detail. In
parallel, modeling has been established as a tool to relate network structure
to activity dynamics. While the available connectivity maps have been used in
various computational studies, prominent features of the simulated activity
such as the spontaneous firing rates do not match the experimental findings.
Here, we show that the inconsistency arises from the incompleteness of the
connectivity maps. Our comparison of the most comprehensive maps (Thomson et
al., 2002; Binzegger et al., 2004) reveals their main discrepancies: the
lateral sampling range and the specific selection of target cells. Taking them
into account, we compile an integrated connectivity map and analyze the unified
map by simulations of a full scale model of the local layered cortical network.
The simulated spontaneous activity is asynchronous irregular and the cell-type
specific spontaneous firing rates are in agreement with in vivo recordings in
awake animals, including the low rate of layer 2/3 excitatory cells. Similarly,
the activation patterns evoked by transient thalamic inputs reproduce recent in
vivo measurements. The correspondence of simulation results and experiments
rests on the consideration of specific target type selection and thereby on the
integration of a large body of the available connectivity data. The cell-type
specific hierarchical input structure and the combination of feed-forward and
feedback connections reveal how the interplay of excitation and inhibition
shapes the spontaneous and evoked activity of the local cortical network.
| [
{
"created": "Tue, 28 Jun 2011 14:16:30 GMT",
"version": "v1"
}
] | 2011-06-29 | [
[
"Potjans",
"Tobias C.",
""
],
[
"Diesmann",
"Markus",
""
]
] | In the past decade, the cell-type specific connectivity and activity of local cortical networks have been characterized experimentally to some detail. In parallel, modeling has been established as a tool to relate network structure to activity dynamics. While the available connectivity maps have been used in various computational studies, prominent features of the simulated activity such as the spontaneous firing rates do not match the experimental findings. Here, we show that the inconsistency arises from the incompleteness of the connectivity maps. Our comparison of the most comprehensive maps (Thomson et al., 2002; Binzegger et al., 2004) reveals their main discrepancies: the lateral sampling range and the specific selection of target cells. Taking them into account, we compile an integrated connectivity map and analyze the unified map by simulations of a full scale model of the local layered cortical network. The simulated spontaneous activity is asynchronous irregular and the cell-type specific spontaneous firing rates are in agreement with in vivo recordings in awake animals, including the low rate of layer 2/3 excitatory cells. Similarly, the activation patterns evoked by transient thalamic inputs reproduce recent in vivo measurements. The correspondence of simulation results and experiments rests on the consideration of specific target type selection and thereby on the integration of a large body of the available connectivity data. The cell-type specific hierarchical input structure and the combination of feed-forward and feedback connections reveal how the interplay of excitation and inhibition shapes the spontaneous and evoked activity of the local cortical network. |
2105.08126 | Uwe C. T\"auber | Shannon R. Serrao, Uwe C. T\"auber (Virginia Tech) | Stabilizing spiral structures in the asymmetric May-Leonard model | 16 pages, 10 figures; to appear in Eur. Phys. J. B (2021) | Eur. Phys. J. B 94 (2021) 175 | 10.1140/epjb/s10051-021-00168-x | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the induction and stabilization of spiral structures for the cyclic
three-species stochastic May-Leonard model with asymmetric predation rates on a
spatially inhomogeneous two-dimensional toroidal lattice using Monte Carlo
simulations. In an isolated setting, strongly asymmetric predation rates lead
to rapid extinction from coexistence of all three species to a single surviving
population. Even for weakly asymmetric predation rates, only a fraction of
ecologies in a statistical ensemble manages to maintain full three-species
coexistence. However, when the asymmetric competing system is coupled via
diffusive proliferation to a fully symmetric May-Leonard patch, the stable
spiral patterns from this region induce transient plane-wave fronts and
ultimately quasi-stationary spiral patterns in the vulnerable asymmetric
region. Thus the endangered ecological subsystem may effectively become
stabilized through immigration from even a much smaller stable region. To
describe the stabilization of spiral population structures in the asymmetric
region, we compare the increase in the robustness of these topological defects
at extreme values of the asymmetric predation rates in the spatially coupled
system with the corresponding asymmetric May-Leonard model in isolation. We
delineate the quasi-stationary nature of coexistence induced in the asymmetric
subsystem by its diffusive coupling to a symmetric May-Leonard patch, and
propose a (semi-)quantitative criterion for the spiral oscillations to be
sustained in the asymmetric region.
| [
{
"created": "Mon, 17 May 2021 19:30:19 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Jul 2021 14:02:23 GMT",
"version": "v2"
}
] | 2021-08-30 | [
[
"Serrao",
"Shannon R.",
"",
"Virginia Tech"
],
[
"Täuber",
"Uwe C.",
"",
"Virginia Tech"
]
] | We study the induction and stabilization of spiral structures for the cyclic three-species stochastic May-Leonard model with asymmetric predation rates on a spatially inhomogeneous two-dimensional toroidal lattice using Monte Carlo simulations. In an isolated setting, strongly asymmetric predation rates lead to rapid extinction from coexistence of all three species to a single surviving population. Even for weakly asymmetric predation rates, only a fraction of ecologies in a statistical ensemble manages to maintain full three-species coexistence. However, when the asymmetric competing system is coupled via diffusive proliferation to a fully symmetric May-Leonard patch, the stable spiral patterns from this region induce transient plane-wave fronts and ultimately quasi-stationary spiral patterns in the vulnerable asymmetric region. Thus the endangered ecological subsystem may effectively become stabilized through immigration from even a much smaller stable region. To describe the stabilization of spiral population structures in the asymmetric region, we compare the increase in the robustness of these topological defects at extreme values of the asymmetric predation rates in the spatially coupled system with the corresponding asymmetric May-Leonard model in isolation. We delineate the quasi-stationary nature of coexistence induced in the asymmetric subsystem by its diffusive coupling to a symmetric May-Leonard patch, and propose a (semi-)quantitative criterion for the spiral oscillations to be sustained in the asymmetric region. |
1909.07932 | Peter Taylor | Yujiang Wang, Nishant Sinha, Gabrielle M. Schroeder, Sriharsha
Ramaraju, Andrew W. McEvoy, Anna Miserocchi, Jane de Tisi, Fahmida A.
Chowdhury, Beate Diehl, John S. Duncan, Peter N. Taylor | Interictal intracranial EEG for predicting surgical success: the
importance of space and time | null | Epilepsia 61 (2020) 1417-1426 | 10.1111/epi.16580 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Predicting post-operative seizure freedom using functional correlation
networks derived from interictal intracranial EEG has shown some success.
However, there are important challenges to consider. 1: electrodes physically
closer to each other naturally tend to be more correlated causing a spatial
bias. 2: implantation location and number of electrodes differ between
patients, making cross-subject comparisons difficult. 3: functional correlation
networks can vary over time but are currently assumed as static. In this study
we address these three substantial challenges using intracranial EEG data from
55 patients with intractable focal epilepsy. Patients additionally underwent
preoperative MR imaging, intra-operative CT, and post-operative MRI allowing
accurate localisation of electrodes and delineation of removed tissue. We show
that normalising for spatial proximity between nearby electrodes improves
prediction of post-surgery seizure outcomes. Moreover, patients with more
extensive electrode coverage were more likely to have their outcome predicted
correctly (ROC-AUC >0.9, p<<0.05), but not necessarily more likely to have a
better outcome. Finally, our predictions are robust regardless of the time
segment. Future studies should account for the spatial proximity of electrodes
in functional network construction to improve prediction of post-surgical
seizure outcomes. Greater coverage of both removed and spared tissue allows for
predictions with higher accuracy.
| [
{
"created": "Tue, 17 Sep 2019 17:00:18 GMT",
"version": "v1"
}
] | 2020-09-30 | [
[
"Wang",
"Yujiang",
""
],
[
"Sinha",
"Nishant",
""
],
[
"Schroeder",
"Gabrielle M.",
""
],
[
"Ramaraju",
"Sriharsha",
""
],
[
"McEvoy",
"Andrew W.",
""
],
[
"Miserocchi",
"Anna",
""
],
[
"de Tisi",
"Jane",
"... | Predicting post-operative seizure freedom using functional correlation networks derived from interictal intracranial EEG has shown some success. However, there are important challenges to consider. 1: electrodes physically closer to each other naturally tend to be more correlated causing a spatial bias. 2: implantation location and number of electrodes differ between patients, making cross-subject comparisons difficult. 3: functional correlation networks can vary over time but are currently assumed as static. In this study we address these three substantial challenges using intracranial EEG data from 55 patients with intractable focal epilepsy. Patients additionally underwent preoperative MR imaging, intra-operative CT, and post-operative MRI allowing accurate localisation of electrodes and delineation of removed tissue. We show that normalising for spatial proximity between nearby electrodes improves prediction of post-surgery seizure outcomes. Moreover, patients with more extensive electrode coverage were more likely to have their outcome predicted correctly (ROC-AUC >0.9, p<<0.05), but not necessarily more likely to have a better outcome. Finally, our predictions are robust regardless of the time segment. Future studies should account for the spatial proximity of electrodes in functional network construction to improve prediction of post-surgical seizure outcomes. Greater coverage of both removed and spared tissue allows for predictions with higher accuracy. |
q-bio/0601007 | Wu Zhi-Xi | Sheng-Jun Wang, Xin-Jian Xu, Ying-Hai Wang | Waveform sample method of excitable sensory neuron | 4 pages, 4 figures | null | null | null | q-bio.NC | null | We present a new interpretation for encoding information of the period of
input signals into spike-trains in individual sensory neuronal systems. The
spike-train could be described as the waveform sample of the input signal which
locks sample points to wave crests with randomness. Based on simulations of the
Hodgkin-Huxley (HH) neuron responding to periodic inputs, we demonstrate that
the random sampling is a proper encoding method in medium frequency region
since power spectra of the reconstructed spike-trains are identical to that of
neural signals.
| [
{
"created": "Fri, 6 Jan 2006 19:26:13 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Jan 2006 03:40:49 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Wang",
"Sheng-Jun",
""
],
[
"Xu",
"Xin-Jian",
""
],
[
"Wang",
"Ying-Hai",
""
]
] | We present a new interpretation for encoding information of the period of input signals into spike-trains in individual sensory neuronal systems. The spike-train could be described as the waveform sample of the input signal which locks sample points to wave crests with randomness. Based on simulations of the Hodgkin-Huxley (HH) neuron responding to periodic inputs, we demonstrate that the random sampling is a proper encoding method in medium frequency region since power spectra of the reconstructed spike-trains are identical to that of neural signals. |
2005.12678 | Daniel Schindler | Daniel Schindler, Ted Moldenhawer, Maike Stange, Valentino Lepro,
Carsten Beta, Matthias Holschneider, Wilhelm Huisinga | Analysis of protrusion dynamics in amoeboid cell motility by means of
regularized contour flows | 31 pages, 12 figures, updated version after publication (see
10.1371/journal.pcbi.1009268), for supporting information, see
10.5281/zenodo.3984179, for software and data publication, see
10.5281/zenodo.3982371 and 10.5061/dryad.b5mkkwhbd | PLoS Comput Biol (2021) 17(8): e1009268 | 10.1371/journal.pcbi.1009268 | null | q-bio.CB q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Amoeboid cell motility is essential for a wide range of biological processes
including wound healing, embryonic morphogenesis, and cancer metastasis. It
relies on complex dynamical patterns of cell shape changes that pose
long-standing challenges to mathematical modeling and raise a need for
automated and reproducible approaches to extract quantitative morphological
features from image sequences. Here, we introduce a theoretical framework and a
computational method for obtaining smooth representations of the spatiotemporal
contour dynamics from stacks of segmented microscopy images. Based on a
Gaussian process regression we propose a one-parameter family of regularized
contour flows that allows us to continuously track reference points (virtual
markers) between successive cell contours. We use this approach to define a
coordinate system on the moving cell boundary and to represent different local
geometric quantities in this frame of reference. In particular, we introduce
the local marker dispersion as a measure to identify localized membrane
expansions and provide a fully automated way to extract the properties of such
expansions, including their area and growth time. The methods are available as
an open-source software package called AmoePy, a Python-based toolbox for
analyzing amoeboid cell motility (based on time-lapse microscopy data),
including a graphical user interface and detailed documentation. Due to the
mathematical rigor of our framework, we envision it to be of use for the
development of novel cell motility models. We mainly use experimental data of
the social amoeba Dictyostelium discoideum to illustrate and validate our
approach.
| [
{
"created": "Tue, 26 May 2020 13:02:42 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Aug 2020 13:03:31 GMT",
"version": "v2"
},
{
"created": "Wed, 8 Sep 2021 15:11:44 GMT",
"version": "v3"
}
] | 2021-09-09 | [
[
"Schindler",
"Daniel",
""
],
[
"Moldenhawer",
"Ted",
""
],
[
"Stange",
"Maike",
""
],
[
"Lepro",
"Valentino",
""
],
[
"Beta",
"Carsten",
""
],
[
"Holschneider",
"Matthias",
""
],
[
"Huisinga",
"Wilhelm",
""... | Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach. |
1702.08509 | Yonghong Chen | YongHong Chen | The Main Cognitive Model of Visual Recognition: Contour Recognition | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we will study the following pattern recognition problem: Every
pattern is a 3-dimensional graph, its surface can be split up into some
regions, every region is composed of the pixels with the approximately same
colour value and the approximately same depth value that is distance to eyes,
and there may also be some contours, e.g., literal contours, on a surface of
every pattern. For this problem we reveal the inherent laws. Moreover, we
establish a cognitive model to reflect the essential characteristics of the
recognition of this type of patterns. In [1], a coarser model or a basicer one
is described. In this paper, some important errors are revised, some key things
are added, at last, a complete model is described.
| [
{
"created": "Fri, 24 Feb 2017 02:28:25 GMT",
"version": "v1"
},
{
"created": "Sat, 4 Mar 2017 05:37:20 GMT",
"version": "v2"
}
] | 2017-03-07 | [
[
"Chen",
"YongHong",
""
]
] | In this paper, we will study the following pattern recognition problem: Every pattern is a 3-dimensional graph, its surface can be split up into some regions, every region is composed of the pixels with the approximately same colour value and the approximately same depth value that is distance to eyes, and there may also be some contours, e.g., literal contours, on a surface of every pattern. For this problem we reveal the inherent laws. Moreover, we establish a cognitive model to reflect the essential characteristics of the recognition of this type of patterns. In [1], a coarser model or a basicer one is described. In this paper, some important errors are revised, some key things are added, at last, a complete model is described. |
1009.3156 | Georg Fritz | Judith A. Megerle, Georg Fritz, Ulrich Gerland, Kirsten Jung, Joachim
O. R\"adler | Timing and dynamics of single cell gene expression in the arabinose
utilization system | 13 pages, 8 figures; supplementary material available upon request
from the authors | Biophys. J. 95:2103-2115 (2008) | 10.1529/biophysj.107.127191 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The arabinose utilization system of E. coli displays a stochastic "all or
nothing" response at intermediate levels of arabinose, where the population
divides into a fraction catabolizing the sugar at a high rate (ON state) and a
fraction not utilizing arabinose (OFF state). Here we study this decision
process in individual cells, focusing on the dynamics of the transition from
the OFF to the ON state. Using quantitative time-lapse microscopy, we determine
the time delay between inducer addition and fluorescence onset of a GFP
reporter. Through independent characterization of the GFP maturation process,
we can separate the lag time caused by the reporter from the intrinsic
activation time of the arabinose system. The resulting distribution of
intrinsic time delays scales inversely with the external arabinose
concentration, and is compatible with a simple stochastic model for arabinose
uptake. Our findings support the idea that the heterogeneous timing of gene
induction is causally related to a broad distribution of uptake proteins at the
time of sugar addition.
| [
{
"created": "Thu, 16 Sep 2010 12:16:14 GMT",
"version": "v1"
}
] | 2010-09-17 | [
[
"Megerle",
"Judith A.",
""
],
[
"Fritz",
"Georg",
""
],
[
"Gerland",
"Ulrich",
""
],
[
"Jung",
"Kirsten",
""
],
[
"Rädler",
"Joachim O.",
""
]
] | The arabinose utilization system of E. coli displays a stochastic "all or nothing" response at intermediate levels of arabinose, where the population divides into a fraction catabolizing the sugar at a high rate (ON state) and a fraction not utilizing arabinose (OFF state). Here we study this decision process in individual cells, focusing on the dynamics of the transition from the OFF to the ON state. Using quantitative time-lapse microscopy, we determine the time delay between inducer addition and fluorescence onset of a GFP reporter. Through independent characterization of the GFP maturation process, we can separate the lag time caused by the reporter from the intrinsic activation time of the arabinose system. The resulting distribution of intrinsic time delays scales inversely with the external arabinose concentration, and is compatible with a simple stochastic model for arabinose uptake. Our findings support the idea that the heterogeneous timing of gene induction is causally related to a broad distribution of uptake proteins at the time of sugar addition. |
1504.03954 | Anna Cattani | Anna Cattani and Gaute T. Einevoll and Stefano Panzeri | Phase-of-firing code | In press, Encyclopedia of Computational Neuroscience, 2015 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Definition. The phase-of-firing code is a neural coding scheme whereby
neurons encode information using the time at which they fire spikes within a
cycle of the ongoing oscillatory pattern of network activity. This coding
scheme may allow neurons to use their temporal pattern of spikes to encode
information that is not encoded in their firing rate.
| [
{
"created": "Wed, 15 Apr 2015 15:49:56 GMT",
"version": "v1"
}
] | 2015-04-16 | [
[
"Cattani",
"Anna",
""
],
[
"Einevoll",
"Gaute T.",
""
],
[
"Panzeri",
"Stefano",
""
]
] | Definition. The phase-of-firing code is a neural coding scheme whereby neurons encode information using the time at which they fire spikes within a cycle of the ongoing oscillatory pattern of network activity. This coding scheme may allow neurons to use their temporal pattern of spikes to encode information that is not encoded in their firing rate. |
1509.05483 | Alkan Kabak\c{c}io\u{g}lu | B. Kav, M. Ozturk and A. Kabakcioglu | Function changing mutations in glucocorticoid receptor evolution
correlate with their relevance to mode coupling | 8 pages, 6 figures | null | 10.1002/prot.25014 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nonlinear effects in protein dynamics are expected to play role in function,
particularly of allosteric nature, by facilitating energy transfer between
vibrational modes. A recently proposed method focusing on the non-Gaussian
shape of the population near equilibrium projects this information onto real
space in order to identify the aminoacids relevant to function. We here apply
this method to three ancestral proteins in glucocorticoid receptor (GR) family
and show that the mutations that restrict functional activity during GR
evolution correlate significantly with locations that are highlighted by the
nonlinear contribution to the near-native configurational distribution. Our
findings demonstrate that nonlinear effects are not only indispensible for
understanding functionality in proteins, but they can also be harnessed into a
predictive tool for functional site determination.
| [
{
"created": "Fri, 18 Sep 2015 01:40:41 GMT",
"version": "v1"
}
] | 2022-03-23 | [
[
"Kav",
"B.",
""
],
[
"Ozturk",
"M.",
""
],
[
"Kabakcioglu",
"A.",
""
]
] | Nonlinear effects in protein dynamics are expected to play role in function, particularly of allosteric nature, by facilitating energy transfer between vibrational modes. A recently proposed method focusing on the non-Gaussian shape of the population near equilibrium projects this information onto real space in order to identify the aminoacids relevant to function. We here apply this method to three ancestral proteins in glucocorticoid receptor (GR) family and show that the mutations that restrict functional activity during GR evolution correlate significantly with locations that are highlighted by the nonlinear contribution to the near-native configurational distribution. Our findings demonstrate that nonlinear effects are not only indispensible for understanding functionality in proteins, but they can also be harnessed into a predictive tool for functional site determination. |
2406.13489 | Daniele Proverbio | Uros Sutulovic, Daniele Proverbio, Rami Katz, Giulia Giordano | Efficient gPC-based quantification of probabilistic robustness for
systems in neuroscience | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We introduce and analyze generalised polynomial chaos (gPC), considering both
intrusive and non-intrusive approaches, as an uncertainty quantification method
in studies of probabilistic robustness. The considered gPC methods are
complementary to Monte Carlo (MC) methods and are shown to be fast and
scalable, allowing for comprehensive and efficient exploration of parameter
spaces. These properties enable robustness analysis of a wider set of models,
compared to computationally expensive MC methods, while retaining desired
levels of accuracy. We discuss the application of gPC methods to systems in
biology and neuroscience, notably subject to multiple parametric uncertainties,
and we examine a well-known model of neural dynamics as a case study.
| [
{
"created": "Wed, 19 Jun 2024 12:19:03 GMT",
"version": "v1"
}
] | 2024-06-21 | [
[
"Sutulovic",
"Uros",
""
],
[
"Proverbio",
"Daniele",
""
],
[
"Katz",
"Rami",
""
],
[
"Giordano",
"Giulia",
""
]
] | We introduce and analyze generalised polynomial chaos (gPC), considering both intrusive and non-intrusive approaches, as an uncertainty quantification method in studies of probabilistic robustness. The considered gPC methods are complementary to Monte Carlo (MC) methods and are shown to be fast and scalable, allowing for comprehensive and efficient exploration of parameter spaces. These properties enable robustness analysis of a wider set of models, compared to computationally expensive MC methods, while retaining desired levels of accuracy. We discuss the application of gPC methods to systems in biology and neuroscience, notably subject to multiple parametric uncertainties, and we examine a well-known model of neural dynamics as a case study. |
1901.06864 | Antti Niemi | Jiaojiao Liu, Jin Dai, Jianfeng He, Xubiao Peng, Antti J. Niemi | Can all-atom protein dynamics be reconstructed from the knowledge of
C-alpha time evolution? | 24 figures | null | null | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We inquire to what extent protein peptide plane and side chain dynamics can
be reconstructed from knowledge of C-alpha dynamics. Due to lack of
experimental data we analyze all atom molecular dynamics trajectories from
Anton supercomputer, and for clarity we limit our attention to the peptide
plane O atoms and side chain C-beta atoms. We try and reconstruct their
dynamics using four different approaches. Three of these are the publicly
available reconstruction programs Pulchra, Remo Scwrl4. The fourth, Statistical
Method, builds entirely on statistical analysis of Protein Data Bank (PDB)
structures. All four methods place the O and C-beta atoms accurately along the
Anton trajectories. However, the Statistical Method performs best. The results
suggest that under physiological conditions, the all atom dynamics is slaved to
that of C-alpha atoms. The results can help improve all atom force fields, and
advance reconstruction and refinement methods for reduced protein structures.
The results provide impetus for development of effective coarse grained force
fields in terms of reduced coordinates.
| [
{
"created": "Mon, 21 Jan 2019 10:39:27 GMT",
"version": "v1"
}
] | 2019-01-23 | [
[
"Liu",
"Jiaojiao",
""
],
[
"Dai",
"Jin",
""
],
[
"He",
"Jianfeng",
""
],
[
"Peng",
"Xubiao",
""
],
[
"Niemi",
"Antti J.",
""
]
] | We inquire to what extent protein peptide plane and side chain dynamics can be reconstructed from knowledge of C-alpha dynamics. Due to lack of experimental data we analyze all atom molecular dynamics trajectories from Anton supercomputer, and for clarity we limit our attention to the peptide plane O atoms and side chain C-beta atoms. We try and reconstruct their dynamics using four different approaches. Three of these are the publicly available reconstruction programs Pulchra, Remo Scwrl4. The fourth, Statistical Method, builds entirely on statistical analysis of Protein Data Bank (PDB) structures. All four methods place the O and C-beta atoms accurately along the Anton trajectories. However, the Statistical Method performs best. The results suggest that under physiological conditions, the all atom dynamics is slaved to that of C-alpha atoms. The results can help improve all atom force fields, and advance reconstruction and refinement methods for reduced protein structures. The results provide impetus for development of effective coarse grained force fields in terms of reduced coordinates. |
1302.4772 | Francesca Mancini | Francesca Mancini, Chris H. Wiggins, Matteo Marsili, Aleksandra M.
Walczak | Time-dependent information transmission in a model regulatory circuit | 14 pages, 8 figures | Phys.Rev.E88:022708,2013 | 10.1103/PhysRevE.88.022708 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many biological regulatory systems process signals out of steady state and
respond with a physiological delay. A simple model of regulation which respects
these features shows how the ability of a delayed output to transmit
information is limited: at short times by the timescale of the dynamic input,
at long times by that of the dynamic output. We find that topologies of
maximally informative networks correspond to commonly occurring biological
circuits linked to stress response and that circuits functioning out of steady
state may exploit absorbing states to transmit information optimally.
| [
{
"created": "Tue, 19 Feb 2013 22:53:58 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Aug 2013 16:41:05 GMT",
"version": "v2"
}
] | 2013-08-13 | [
[
"Mancini",
"Francesca",
""
],
[
"Wiggins",
"Chris H.",
""
],
[
"Marsili",
"Matteo",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] | Many biological regulatory systems process signals out of steady state and respond with a physiological delay. A simple model of regulation which respects these features shows how the ability of a delayed output to transmit information is limited: at short times by the timescale of the dynamic input, at long times by that of the dynamic output. We find that topologies of maximally informative networks correspond to commonly occurring biological circuits linked to stress response and that circuits functioning out of steady state may exploit absorbing states to transmit information optimally. |
0811.2441 | Craig Powell | Craig R. Powell | An agent-based approach to food web assembly | 10 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An agent-based model of population dynamics is presented. The model has as
its expected behaviour the population dynamics of the equation-based Webworld
model, within which large communities of species can be grown on evolutionary
time scales. Such communities can be used in the agent-based model without
disrupting the food web structure, and hence a unified model of evolutionary
time and individual-based dynamics can be realised. Individuals encounter
potential prey, and optimal foraging strategies are arrived at through natural
selection.
| [
{
"created": "Fri, 14 Nov 2008 23:32:21 GMT",
"version": "v1"
}
] | 2008-11-18 | [
[
"Powell",
"Craig R.",
""
]
] | An agent-based model of population dynamics is presented. The model has as its expected behaviour the population dynamics of the equation-based Webworld model, within which large communities of species can be grown on evolutionary time scales. Such communities can be used in the agent-based model without disrupting the food web structure, and hence a unified model of evolutionary time and individual-based dynamics can be realised. Individuals encounter potential prey, and optimal foraging strategies are arrived at through natural selection. |
2210.07144 | Igor Melnyk | Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit
Dhurandhar, Inkit Padhi, Devleena Das | Reprogramming Pretrained Language Models for Antibody Sequence Infilling | ICML 2023 | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Antibodies comprise the most versatile class of binding molecules, with
numerous applications in biomedicine. Computational design of antibodies
involves generating novel and diverse sequences, while maintaining structural
consistency. Unique to antibodies, designing the complementarity-determining
region (CDR), which determines the antigen binding affinity and specificity,
creates its own unique challenges. Recent deep learning models have shown
impressive results, however the limited number of known antibody
sequence/structure pairs frequently leads to degraded performance, particularly
lacking diversity in the generated sequences. In our work we address this
challenge by leveraging Model Reprogramming (MR), which repurposes pretrained
models on a source language to adapt to the tasks that are in a different
language and have scarce data - where it may be difficult to train a
high-performing model from scratch or effectively fine-tune an existing
pre-trained model on the specific task. Specifically, we introduce ReprogBert
in which a pretrained English language model is repurposed for protein sequence
infilling - thus considers cross-language adaptation using less data. Results
on antibody design benchmarks show that our model on low-resourced antibody
sequence dataset provides highly diverse CDR sequences, up to more than a
two-fold increase of diversity over the baselines, without losing structural
integrity and naturalness. The generated sequences also demonstrate enhanced
antigen binding specificity and virus neutralization ability. Code is available
at https://github.com/IBM/ReprogBERT
| [
{
"created": "Wed, 5 Oct 2022 20:44:55 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Jun 2023 21:42:43 GMT",
"version": "v2"
}
] | 2023-06-21 | [
[
"Melnyk",
"Igor",
""
],
[
"Chenthamarakshan",
"Vijil",
""
],
[
"Chen",
"Pin-Yu",
""
],
[
"Das",
"Payel",
""
],
[
"Dhurandhar",
"Amit",
""
],
[
"Padhi",
"Inkit",
""
],
[
"Das",
"Devleena",
""
]
] | Antibodies comprise the most versatile class of binding molecules, with numerous applications in biomedicine. Computational design of antibodies involves generating novel and diverse sequences, while maintaining structural consistency. Unique to antibodies, designing the complementarity-determining region (CDR), which determines the antigen binding affinity and specificity, creates its own unique challenges. Recent deep learning models have shown impressive results, however the limited number of known antibody sequence/structure pairs frequently leads to degraded performance, particularly lacking diversity in the generated sequences. In our work we address this challenge by leveraging Model Reprogramming (MR), which repurposes pretrained models on a source language to adapt to the tasks that are in a different language and have scarce data - where it may be difficult to train a high-performing model from scratch or effectively fine-tune an existing pre-trained model on the specific task. Specifically, we introduce ReprogBert in which a pretrained English language model is repurposed for protein sequence infilling - thus considers cross-language adaptation using less data. Results on antibody design benchmarks show that our model on low-resourced antibody sequence dataset provides highly diverse CDR sequences, up to more than a two-fold increase of diversity over the baselines, without losing structural integrity and naturalness. The generated sequences also demonstrate enhanced antigen binding specificity and virus neutralization ability. Code is available at https://github.com/IBM/ReprogBERT |
1907.03891 | Thierry Mora | Gr\'egoire Altan-Bonnet, Thierry Mora, Aleksandra M. Walczak | Quantitative Immunology for Physicists | 78 page review | Physics Reports 849, 1--83 (2020) | 10.1016/j.physrep.2020.01.001 | null | q-bio.QM q-bio.GN q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The adaptive immune system is a dynamical, self-organized multiscale system
that protects vertebrates from both pathogens and internal irregularities, such
as tumours. For these reason it fascinates physicists, yet the multitude of
different cells, molecules and sub-systems is often also petrifying. Despite
this complexity, as experiments on different scales of the adaptive immune
system become more quantitative, many physicists have made both theoretical and
experimental contributions that help predict the behaviour of ensembles of
cells and molecules that participate in an immune response. Here we review some
recent contributions with an emphasis on quantitative questions and
methodologies. We also provide a more general methods section that presents
some of the wide array of theoretical tools used in the field.
| [
{
"created": "Mon, 8 Jul 2019 22:06:12 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Jul 2019 12:40:48 GMT",
"version": "v2"
}
] | 2020-11-20 | [
[
"Altan-Bonnet",
"Grégoire",
""
],
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] | The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. |
1906.06979 | Steven Frank | Steven A. Frank and Jordi Bascompte | Invariance in ecological pattern | Version 2: Revised throughout for clarity, additional explanation of
derivations and interpretations. Added Appendix to clarify interpretation of
scale in a common way between discrete and continuous probability
distributions | null | null | null | q-bio.PE cond-mat.stat-mech | http://creativecommons.org/licenses/by/4.0/ | The abundance of different species in a community often follows the log
series distribution. Other ecological patterns also have simple forms. Why does
the complexity and variability of ecological systems reduce to such simplicity?
Common answers include maximum entropy, neutrality, and convergent outcome from
different underlying biological processes. This article proposes a more general
answer based on the concept of invariance, the property by which a pattern
remains the same after transformation. Invariance has a long tradition in
physics. For example, general relativity emphasizes the need for the equations
describing the laws of physics to have the same form in all frames of
reference. By bringing this unifying invariance approach into ecology, we show
that the log series pattern dominates when the consequences of processes acting
on abundance are invariant to the addition or multiplication of abundance by a
constant. The lognormal pattern dominates when the processes acting on net
species growth rate obey rotational invariance (symmetry) with respect to the
summing up of the individual component processes. Recognizing how these
invariances connect pattern to process leads to a synthesis of previous
approaches. First, invariance provides a simpler and more fundamental maximum
entropy derivation of the log series distribution. Second, invariance provides
a simple derivation of the key result from neutral theory: the log series at
the metacommunity scale and a clearer form of the skewed lognormal at the local
community scale. The invariance expressions are easy to understand because they
uniquely describe the basic underlying components that shape pattern.
| [
{
"created": "Mon, 17 Jun 2019 12:07:02 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Aug 2019 07:21:52 GMT",
"version": "v2"
}
] | 2019-08-21 | [
[
"Frank",
"Steven A.",
""
],
[
"Bascompte",
"Jordi",
""
]
] | The abundance of different species in a community often follows the log series distribution. Other ecological patterns also have simple forms. Why does the complexity and variability of ecological systems reduce to such simplicity? Common answers include maximum entropy, neutrality, and convergent outcome from different underlying biological processes. This article proposes a more general answer based on the concept of invariance, the property by which a pattern remains the same after transformation. Invariance has a long tradition in physics. For example, general relativity emphasizes the need for the equations describing the laws of physics to have the same form in all frames of reference. By bringing this unifying invariance approach into ecology, we show that the log series pattern dominates when the consequences of processes acting on abundance are invariant to the addition or multiplication of abundance by a constant. The lognormal pattern dominates when the processes acting on net species growth rate obey rotational invariance (symmetry) with respect to the summing up of the individual component processes. Recognizing how these invariances connect pattern to process leads to a synthesis of previous approaches. First, invariance provides a simpler and more fundamental maximum entropy derivation of the log series distribution. Second, invariance provides a simple derivation of the key result from neutral theory: the log series at the metacommunity scale and a clearer form of the skewed lognormal at the local community scale. The invariance expressions are easy to understand because they uniquely describe the basic underlying components that shape pattern. |
1309.3332 | Alberto d'Onofrio | Alberto d'Onofrio and Ian P.M. Tomlinson | A non-linear mathematical model of cell turnover, differentiation and
tumorigenesis in the intestinal crypt | 12 pages, 6 figures | Journal of Theoretical Biology (2007) 244(3):367-377 | 10.1016/j.jtbi.2006.08.022 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a development of a model of the relationship between cells in
three compartments of the intestinal crypt: stem cells, semi-differentiated
cells and fully differentiated cells. Stem and semi-differentiated cells may
divide to self-renew, undergo programmed death or progress to
semi-differentiated and fully differentiated cells respectively. The
probabilities of each of these events provide the most important parameters of
the model. Fully differentiated cells do not divide, but a proportion undergoes
programmed death in each generation. Our previous models showed that failure of
programmed death - for example, in tumorigenesis - could lead either to
exponential growth in cell numbers or to growth to some plateau. Our new models
incorporate plausible fluctuation in the parameters of the model and introduce
non-linearity by assuming that the parameters depend on the numbers of cells in
each state of differentiation. We find that the model is characterized by
bifurcation between increase in cell numbers to stable equilibrium or
'explosive' exponential growth; in a restricted number of cases, there may be
multiple stable equilibria. Fluctuation in cell numbers undergoing programmed
death, for example caused by tissue damage, generally makes exponential growth
more likely, as long as the size of the fluctuation exceeds a certain critical
value for a sufficiently long period of time. In most cases, once exponential
growth has started, this process is irreversible. In some circumstances,
exponential growth is preceded by a long plateau phase, of variable duration,
mimicking equilibrium: thus apparently self-limiting lesions may not be so in
practice.
| [
{
"created": "Thu, 12 Sep 2013 23:36:29 GMT",
"version": "v1"
}
] | 2013-09-16 | [
[
"d'Onofrio",
"Alberto",
""
],
[
"Tomlinson",
"Ian P. M.",
""
]
] | We present a development of a model of the relationship between cells in three compartments of the intestinal crypt: stem cells, semi-differentiated cells and fully differentiated cells. Stem and semi-differentiated cells may divide to self-renew, undergo programmed death or progress to semi-differentiated and fully differentiated cells respectively. The probabilities of each of these events provide the most important parameters of the model. Fully differentiated cells do not divide, but a proportion undergoes programmed death in each generation. Our previous models showed that failure of programmed death - for example, in tumorigenesis - could lead either to exponential growth in cell numbers or to growth to some plateau. Our new models incorporate plausible fluctuation in the parameters of the model and introduce non-linearity by assuming that the parameters depend on the numbers of cells in each state of differentiation. We find that the model is characterized by bifurcation between increase in cell numbers to stable equilibrium or 'explosive' exponential growth; in a restricted number of cases, there may be multiple stable equilibria. Fluctuation in cell numbers undergoing programmed death, for example caused by tissue damage, generally makes exponential growth more likely, as long as the size of the fluctuation exceeds a certain critical value for a sufficiently long period of time. In most cases, once exponential growth has started, this process is irreversible. In some circumstances, exponential growth is preceded by a long plateau phase, of variable duration, mimicking equilibrium: thus apparently self-limiting lesions may not be so in practice. |
2407.05995 | Kamaran Salh Rasul Dr. | Mihraban Sharif Maeruf, Djshwar Dhahir Lateef, Kamil Mahmood Mustafa,
Hero Fatih Hamakareem, Shang Hasseb Abdalqadir, Dastan Ahmad Ahmad, Shokhan
Mahmood Sleman, Kamaran Salh Rasul | Analysis of genetic diversity among some Iraqi durum wheat cultivars
revealed by different molecular markers | 15 | null | 10.58928/KU24.15228 | null | q-bio.PE q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Durum wheat has been cultivated since the beginning of crop domestication,
occupying now the tenth ranking among the global most significant cultivated
crops. Despite the fact that, the extent of the crop genetic diversity has not
yet fully incorporated into modern varieties through breeding programs. In this
study, a total of 35 markers (11 RAPD, 12 ISSR, and 12 CDDP) were utilized to
assess the genetic variability and population structure of sixteen different
cultivars of Iraqi durum wheat. Out of 294 bands obtained, 171 were identified
as polymorphic: 47.00 polymorphic alleles from 98 RAPD bands, 53 polymorphic
alleles from a total of 89 ISSR bands, and 71 alleles from 107 CDDP bands. The
average number of observed alleles (Na), effective number of alleles (Ne),
Shannon's information index (I), expected heterozygosity or gene diversity
(He), unbiased expected heterozygosity (uHe), and polymorphic information
content (PIC) (1.45, 1.38, 0.32, 0.22, 0.24, and 0.28, respectively) were
obtained for RAPDs , (1.63, 1.45, 0.40, 0.27, 0.29, and 0.32, respectively)
ISSRs and (1.35, 1.35, 0.31, 0.21, 0.23, and 0.30, respectively) for the CDDP
markers. A dendrogram of two main clades (unweighted pair group method with
arithmetic mean; UPGMA) and three populations of structure analysis, were
obtained based on the three markers data. The analysis of molecular variance
indicated 97.00%, 97.00%, and 90.00% variability within populations, applying
RAPD, ISSR, and CDDP markers, respectively. The highest diversity indices were
revealed in population 2 under the RAPD and CDDP markers, whereas population 1
had the highest values of these indices according to the ISSR markers. The
results provide greater knowledge on the genetic makeup of Iraqi durum wheat
cultivars, that facilitate future breeding programs of this crop.
| [
{
"created": "Mon, 8 Jul 2024 14:44:33 GMT",
"version": "v1"
}
] | 2024-07-09 | [
[
"Maeruf",
"Mihraban Sharif",
""
],
[
"Lateef",
"Djshwar Dhahir",
""
],
[
"Mustafa",
"Kamil Mahmood",
""
],
[
"Hamakareem",
"Hero Fatih",
""
],
[
"Abdalqadir",
"Shang Hasseb",
""
],
[
"Ahmad",
"Dastan Ahmad",
""
],
[
... | Durum wheat has been cultivated since the beginning of crop domestication, occupying now the tenth ranking among the global most significant cultivated crops. Despite the fact that, the extent of the crop genetic diversity has not yet fully incorporated into modern varieties through breeding programs. In this study, a total of 35 markers (11 RAPD, 12 ISSR, and 12 CDDP) were utilized to assess the genetic variability and population structure of sixteen different cultivars of Iraqi durum wheat. Out of 294 bands obtained, 171 were identified as polymorphic: 47.00 polymorphic alleles from 98 RAPD bands, 53 polymorphic alleles from a total of 89 ISSR bands, and 71 alleles from 107 CDDP bands. The average number of observed alleles (Na), effective number of alleles (Ne), Shannon's information index (I), expected heterozygosity or gene diversity (He), unbiased expected heterozygosity (uHe), and polymorphic information content (PIC) (1.45, 1.38, 0.32, 0.22, 0.24, and 0.28, respectively) were obtained for RAPDs , (1.63, 1.45, 0.40, 0.27, 0.29, and 0.32, respectively) ISSRs and (1.35, 1.35, 0.31, 0.21, 0.23, and 0.30, respectively) for the CDDP markers. A dendrogram of two main clades (unweighted pair group method with arithmetic mean; UPGMA) and three populations of structure analysis, were obtained based on the three markers data. The analysis of molecular variance indicated 97.00%, 97.00%, and 90.00% variability within populations, applying RAPD, ISSR, and CDDP markers, respectively. The highest diversity indices were revealed in population 2 under the RAPD and CDDP markers, whereas population 1 had the highest values of these indices according to the ISSR markers. The results provide greater knowledge on the genetic makeup of Iraqi durum wheat cultivars, that facilitate future breeding programs of this crop. |
1711.11070 | Victor Greiff | Enkelejda Miho, Alexander Yermanos, C\'edric R. Weber, Christoph T.
Berger, Sai T. Reddy, Victor Greiff | Computational strategies for dissecting the high-dimensional complexity
of adaptive immune repertoires | 27 pages, 2 figures | null | 10.3389/fimmu.2018.00224 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The adaptive immune system recognizes antigens via an immense array of
antigen-binding antibodies and T-cell receptors, the immune repertoire. The
interrogation of immune repertoires is of high relevance for understanding the
adaptive immune response in disease and infection (e.g., autoimmunity, cancer,
HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the
quantitative and molecular-level profiling of immune repertoires thereby
revealing the high-dimensional complexity of the immune receptor sequence
landscape. Several methods for the computational and statistical analysis of
large-scale AIRR-seq data have been developed to resolve immune repertoire
complexity in order to understand the dynamics of adaptive immunity. Here, we
review the current research on (i) diversity, (ii) clustering and network,
(iii) phylogenetic and (iv) machine learning methods applied to dissect,
quantify and compare the architecture, evolution, and specificity of immune
repertoires. We summarize outstanding questions in computational immunology and
propose future directions for systems immunology towards coupling AIRR-seq with
the computational discovery of immunotherapeutics, vaccines, and
immunodiagnostics.
| [
{
"created": "Wed, 29 Nov 2017 19:28:57 GMT",
"version": "v1"
}
] | 2018-03-02 | [
[
"Miho",
"Enkelejda",
""
],
[
"Yermanos",
"Alexander",
""
],
[
"Weber",
"Cédric R.",
""
],
[
"Berger",
"Christoph T.",
""
],
[
"Reddy",
"Sai T.",
""
],
[
"Greiff",
"Victor",
""
]
] | The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics. |
2006.01363 | Maytee Cruz-Aponte | Mayte\'e Cruz-Aponte and Jos\'e Caraballo-Cueto | Balancing Fiscal and Mortality Impact of SARS-CoV-2 Mitigation
Measurements | https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/457 | Letters in Biomathematics, 2021 | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | An epidemic carries human and fiscal costs. In the case of imported
pandemics, the first-best solution is to restrict national borders to identify
and isolate infected individuals. However, when that opportunity is not fully
seized and there is no preventative intervention available, second-best options
must be chosen. In this article we develop a system of differential equations
that simulate both the fiscal and human costs associated to different
mitigation measurements. After simulating several scenarios, we conclude that
herd immunity (or unleashing the pandemic) is the worst policy in terms of both
human and fiscal cost. We found that the second-best policy would be a strict
policy (e.g. physical distancing with massive testing) established under the
first 20 days after the pandemic, that lowers the probability of infection by
80%. In the case of the US, this strict policy would save more than 239
thousands lives and almost $170.8 billion to taxpayers when compared to the
herd immunity case.
| [
{
"created": "Tue, 2 Jun 2020 03:05:54 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Mar 2021 21:10:41 GMT",
"version": "v2"
}
] | 2022-03-31 | [
[
"Cruz-Aponte",
"Mayteé",
""
],
[
"Caraballo-Cueto",
"José",
""
]
] | An epidemic carries human and fiscal costs. In the case of imported pandemics, the first-best solution is to restrict national borders to identify and isolate infected individuals. However, when that opportunity is not fully seized and there is no preventative intervention available, second-best options must be chosen. In this article we develop a system of differential equations that simulate both the fiscal and human costs associated to different mitigation measurements. After simulating several scenarios, we conclude that herd immunity (or unleashing the pandemic) is the worst policy in terms of both human and fiscal cost. We found that the second-best policy would be a strict policy (e.g. physical distancing with massive testing) established under the first 20 days after the pandemic, that lowers the probability of infection by 80%. In the case of the US, this strict policy would save more than 239 thousands lives and almost $170.8 billion to taxpayers when compared to the herd immunity case. |
1703.10982 | Zixuan Cang | Zixuan Cang and Guo-Wei Wei | Topological fingerprints reveal protein-ligand binding mechanism | 11 pages, 7 figures | null | null | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein-ligand binding is a fundamental biological process that is paramount
to many other biological processes, such as signal transduction, metabolic
pathways, enzyme construction, cell secretion, gene expression, etc. Accurate
prediction of protein-ligand binding affinities is vital to rational drug
design and the understanding of protein-ligand binding and binding induced
function. Existing binding affinity prediction methods are inundated with
geometric detail and involve excessively high dimensions, which undermines
their predictive power for massive binding data. Topology provides an ultimate
level of abstraction and thus incurs too much reduction in geometric
information. Persistent homology embeds geometric information into topological
invariants and bridges the gap between complex geometry and abstract topology.
However, it over simplifies biological information. This work introduces
element specific persistent homology (ESPH) to retain crucial biological
information during topological simplification. The combination of ESPH and
machine learning gives rise to one of the most efficient and powerful tools for
revealing protein-ligand binding mechanism and for predicting binding
affinities.
| [
{
"created": "Fri, 31 Mar 2017 16:59:53 GMT",
"version": "v1"
}
] | 2017-04-03 | [
[
"Cang",
"Zixuan",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, gene expression, etc. Accurate prediction of protein-ligand binding affinities is vital to rational drug design and the understanding of protein-ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides an ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it over simplifies biological information. This work introduces element specific persistent homology (ESPH) to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to one of the most efficient and powerful tools for revealing protein-ligand binding mechanism and for predicting binding affinities. |
2403.17889 | Fr\'ed\'eric Dreyer | Henry Kenlay, Fr\'ed\'eric A. Dreyer, Aleksandr Kovaltsuk, Dom Miketa,
Douglas Pires, Charlotte M. Deane | Large scale paired antibody language models | 14 pages, 2 figures, 6 tables, model weights available at
https://zenodo.org/doi/10.5281/zenodo.10876908 | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Antibodies are proteins produced by the immune system that can identify and
neutralise a wide variety of antigens with high specificity and affinity, and
constitute the most successful class of biotherapeutics. With the advent of
next-generation sequencing, billions of antibody sequences have been collected
in recent years, though their application in the design of better therapeutics
has been constrained by the sheer volume and complexity of the data. To address
this challenge, we present IgBert and IgT5, the best performing
antibody-specific language models developed to date which can consistently
handle both paired and unpaired variable region sequences as input. These
models are trained comprehensively using the more than two billion unpaired
sequences and two million paired sequences of light and heavy chains present in
the Observed Antibody Space dataset. We show that our models outperform
existing antibody and protein language models on a diverse range of design and
regression tasks relevant to antibody engineering. This advancement marks a
significant leap forward in leveraging machine learning, large scale data sets
and high-performance computing for enhancing antibody design for therapeutic
development.
| [
{
"created": "Tue, 26 Mar 2024 17:21:54 GMT",
"version": "v1"
}
] | 2024-03-27 | [
[
"Kenlay",
"Henry",
""
],
[
"Dreyer",
"Frédéric A.",
""
],
[
"Kovaltsuk",
"Aleksandr",
""
],
[
"Miketa",
"Dom",
""
],
[
"Pires",
"Douglas",
""
],
[
"Deane",
"Charlotte M.",
""
]
] | Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics. With the advent of next-generation sequencing, billions of antibody sequences have been collected in recent years, though their application in the design of better therapeutics has been constrained by the sheer volume and complexity of the data. To address this challenge, we present IgBert and IgT5, the best performing antibody-specific language models developed to date which can consistently handle both paired and unpaired variable region sequences as input. These models are trained comprehensively using the more than two billion unpaired sequences and two million paired sequences of light and heavy chains present in the Observed Antibody Space dataset. We show that our models outperform existing antibody and protein language models on a diverse range of design and regression tasks relevant to antibody engineering. This advancement marks a significant leap forward in leveraging machine learning, large scale data sets and high-performance computing for enhancing antibody design for therapeutic development. |
1807.08353 | Donald Forsdyke Dr. | Donald R. Forsdyke | Shift in Nomenclature not Thesis: Innate Immune Memory, Pathogen Dose
and Opsonins | 5 pages, 1 figure | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent writings on "innate immune memory" or "trained immunity," and on
"hormetic responses," herald reinvigoration of an important research area with
early 20th century roots. However, it is questionable that the thesis of a
"major importance of the sensed dose of pathogen for the development of innate
immune system-mediated responses," should be labelled a "paradigm shift." The
works of Almroth Wright (1861-1947) should be taken into account.
| [
{
"created": "Sun, 22 Jul 2018 19:32:40 GMT",
"version": "v1"
}
] | 2018-07-24 | [
[
"Forsdyke",
"Donald R.",
""
]
] | Recent writings on "innate immune memory" or "trained immunity," and on "hormetic responses," herald reinvigoration of an important research area with early 20th century roots. However, it is questionable that the thesis of a "major importance of the sensed dose of pathogen for the development of innate immune system-mediated responses," should be labelled a "paradigm shift." The works of Almroth Wright (1861-1947) should be taken into account. |
2302.08815 | Wilhelm Huisinga | Jane Kn\"ochel, Charlotte Kloft, Wilhelm Huisinga | Index analysis: an approach to understand signal transduction with
application to the EGFR signalling pathway | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In systems biology and pharmacology, large-scale kinetic models are used to
study the dynamic response of a system to a specific input or stimulus. While
in many applications, a deeper understanding of the input-response behaviour is
highly desirable, it is often hindered by the large number of molecular species
and the complexity of the interactions. An approach that identifies key
molecular species for a given input-response relationship and characterises
dynamic properties of states is therefore highly desirable. We introduce the
concept of index analysis; it is based on different time- and state-dependent
quantities (indices) to identify important dynamic characteristics of molecular
species. All indices are defined for a specific pair of input and response
variables as well as for a specific magnitude of the input. In application to a
large-scale kinetic model of the EGFR signalling cascade, we identified
different phases of signal transduction, the peculiar role of Phosphatase3
during signal activation and Ras recycling during signal onset. In addition, we
discuss the challenges and pitfalls of interpreting the relevance of molecular
species based on knock-out simulation studies, and provide an alternative view
on conflicting results on the importance of parallel EGFR downstream pathways.
We envision that index analysis will be beneficial in comparing different model
scenarios (e.g., healthy and diseased conditions), in designing more informed
model reduction approaches, and in translating large-scale systems biology
models from early to late phase in drug discovery and development.
| [
{
"created": "Fri, 17 Feb 2023 11:19:02 GMT",
"version": "v1"
}
] | 2023-02-20 | [
[
"Knöchel",
"Jane",
""
],
[
"Kloft",
"Charlotte",
""
],
[
"Huisinga",
"Wilhelm",
""
]
] | In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. We envision that index analysis will be beneficial in comparing different model scenarios (e.g., healthy and diseased conditions), in designing more informed model reduction approaches, and in translating large-scale systems biology models from early to late phase in drug discovery and development. |
1601.07062 | Filippo Biscarini | Filippo Biscarini, Stefano Biffani, Nicola Morandi, Ezequiel L.
Nicolazzi, Alessandra Stella | Using runs of homozygosity to detect genomic regions associated with
susceptibility to infectious and metabolic diseases in dairy cows under
intensive farming conditions | null | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Runs of homozygosity (ROH) are contiguous stretches of homozygous genome
which likely reflect transmission from common ances- tors and can be used to
track the inheritance of haplotypes of interest. In the present paper, ROH were
extracted from 50K SNPs and used to detect regions of the genome associated
with susceptibility to diseases in a population of 468 Holstein-Frisian cows.
Diagnosed diseases were categorised as infectious diseases, metabolic
syndromes, mastitis, reproductive diseases and locomotive disorders. ROH
associated with infectious diseases, mastitis and locomotive disorders were
found on BTA 12. A long region of homozygosity linked with metabolic syndromes,
infectious and reproductive diseases was detected on BTA 15, disclosing complex
relationships between immunity, metabolism and functional disorders. ROH
associated with infectious and reproductive diseases, mastitis and metabolic
syndromes were observed on chromosomes 3, 5, 7, 13 and 18. Previous studies
reported QTLs for milk production traits on all of these regions, thus
substantiating the known negative relationship between selection for milk
production and health in dairy cattle.
| [
{
"created": "Tue, 26 Jan 2016 15:10:17 GMT",
"version": "v1"
}
] | 2016-01-27 | [
[
"Biscarini",
"Filippo",
""
],
[
"Biffani",
"Stefano",
""
],
[
"Morandi",
"Nicola",
""
],
[
"Nicolazzi",
"Ezequiel L.",
""
],
[
"Stella",
"Alessandra",
""
]
] | Runs of homozygosity (ROH) are contiguous stretches of homozygous genome which likely reflect transmission from common ances- tors and can be used to track the inheritance of haplotypes of interest. In the present paper, ROH were extracted from 50K SNPs and used to detect regions of the genome associated with susceptibility to diseases in a population of 468 Holstein-Frisian cows. Diagnosed diseases were categorised as infectious diseases, metabolic syndromes, mastitis, reproductive diseases and locomotive disorders. ROH associated with infectious diseases, mastitis and locomotive disorders were found on BTA 12. A long region of homozygosity linked with metabolic syndromes, infectious and reproductive diseases was detected on BTA 15, disclosing complex relationships between immunity, metabolism and functional disorders. ROH associated with infectious and reproductive diseases, mastitis and metabolic syndromes were observed on chromosomes 3, 5, 7, 13 and 18. Previous studies reported QTLs for milk production traits on all of these regions, thus substantiating the known negative relationship between selection for milk production and health in dairy cattle. |
2004.13216 | Elisa Franco | Elisa Franco | A feedback SIR (fSIR) model highlights advantages and limitations of
infection-dependent mitigation strategies | Updated literature, amended mistakes in the previous version | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transmission rates in epidemic outbreaks may vary over time depending on the
societal response. Non-pharmacological mitigation strategies such as social
distancing and the adoption of protective equipment aim precisely at reducing
transmission rates by reducing infectious contacts. To investigate the effects
of mitigation strategies on the evolution of epidemics, nonlinear transmission
rates that are influenced by the levels of infections, deaths or recoveries
have been included in many variants of the classical SIR model. This class of
models is particularly relevant to the COVID-19 epidemic, in which the
population behavior has been affected by the unprecedented abundance and rapid
distribution of global infection and death data through online platforms. This
manuscript revisits a SIR model in which the reduction of transmission rate is
due to knowledge of infections. Through a mean field approach that assumes
individuals behave like molecules in a well-mixed solution, one derives a
time-varying reproduction number that depends on infection information through
a negative feedback term that is equivalent to Holling type II functions in
ecology and Michaelis-Menten functions in chemistry and molecular biology. A
step-by-step derivation of the model is provided, together with an overview of
methods for its qualitative analysis, showing that negative feedback
structurally reduces the peak of infections. At the same time, feedback may
substantially extend the duration of an epidemic. Computational simulations
agree with the analytical predictions, and further suggest that infection peak
reduction persists even in the presence of information delays. If the
mitigation strategy is linearly proportional to infections, a single parameter
is added to the SIR model, making it useful to illustrate the effects of
infection-dependent social distancing.
| [
{
"created": "Tue, 28 Apr 2020 00:13:33 GMT",
"version": "v1"
},
{
"created": "Mon, 4 May 2020 07:59:50 GMT",
"version": "v2"
},
{
"created": "Wed, 23 Dec 2020 01:36:46 GMT",
"version": "v3"
}
] | 2020-12-24 | [
[
"Franco",
"Elisa",
""
]
] | Transmission rates in epidemic outbreaks may vary over time depending on the societal response. Non-pharmacological mitigation strategies such as social distancing and the adoption of protective equipment aim precisely at reducing transmission rates by reducing infectious contacts. To investigate the effects of mitigation strategies on the evolution of epidemics, nonlinear transmission rates that are influenced by the levels of infections, deaths or recoveries have been included in many variants of the classical SIR model. This class of models is particularly relevant to the COVID-19 epidemic, in which the population behavior has been affected by the unprecedented abundance and rapid distribution of global infection and death data through online platforms. This manuscript revisits a SIR model in which the reduction of transmission rate is due to knowledge of infections. Through a mean field approach that assumes individuals behave like molecules in a well-mixed solution, one derives a time-varying reproduction number that depends on infection information through a negative feedback term that is equivalent to Holling type II functions in ecology and Michaelis-Menten functions in chemistry and molecular biology. A step-by-step derivation of the model is provided, together with an overview of methods for its qualitative analysis, showing that negative feedback structurally reduces the peak of infections. At the same time, feedback may substantially extend the duration of an epidemic. Computational simulations agree with the analytical predictions, and further suggest that infection peak reduction persists even in the presence of information delays. If the mitigation strategy is linearly proportional to infections, a single parameter is added to the SIR model, making it useful to illustrate the effects of infection-dependent social distancing. |
1205.4632 | Mary Ann Blaetke | Mary Ann Bl\"atke, Monika Heiner and Wolfgang Marwan | Predicting Phenotype from Genotype Through Automatically Composed Petri
Nets | 21 pages, 9 Figures, submitted to "The 10th Conference on
Computational Methods in Systems Biology (CMSB 2012)" | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a modular modelling approach permitting curation, updating, and
distributed development of modules through joined community effort overcoming
the problem of keeping a combinatorially exploding number of monolithic models
up to date. For this purpose, the effects of genes and their mutated alleles on
downstream components are modeled by composable, metadata-containing Petri net
models organized in a database with version control, accessible through a web
interface. Gene modules can be coupled to protein modules through mRNA modules
by specific interfaces designed for the automatic, database-assisted
composition. Automatically assembled executable models may then consider cell
type-specific gene expression patterns and the resulting protein
concentrations. Gene modules and allelic interference modules may represent
effects of gene mutation and predict their pleiotropic consequences or uncover
complex genotype/phenotype relationships. Forward and reverse engineered
modules are fully compatible.
| [
{
"created": "Mon, 21 May 2012 15:12:42 GMT",
"version": "v1"
}
] | 2012-05-22 | [
[
"Blätke",
"Mary Ann",
""
],
[
"Heiner",
"Monika",
""
],
[
"Marwan",
"Wolfgang",
""
]
] | We describe a modular modelling approach permitting curation, updating, and distributed development of modules through joined community effort overcoming the problem of keeping a combinatorially exploding number of monolithic models up to date. For this purpose, the effects of genes and their mutated alleles on downstream components are modeled by composable, metadata-containing Petri net models organized in a database with version control, accessible through a web interface. Gene modules can be coupled to protein modules through mRNA modules by specific interfaces designed for the automatic, database-assisted composition. Automatically assembled executable models may then consider cell type-specific gene expression patterns and the resulting protein concentrations. Gene modules and allelic interference modules may represent effects of gene mutation and predict their pleiotropic consequences or uncover complex genotype/phenotype relationships. Forward and reverse engineered modules are fully compatible. |
1906.01586 | Colby Long | Elizabeth Gross, Colby Long, and Joseph Rusinko | Phylogenetic Networks | 26 pages, 8 figures. Chapter submitted for "Foundations for
Undergraduate Research in Mathematics" edited by Pamela Harris, Erik Insko,
and Aaron Wooton | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetics is the study of the evolutionary relationships between
organisms. One of the main challenges in the field is to take biological data
for a group of organisms and to infer an evolutionary tree, a graph that
represents these relationships. Developing practical and efficient methods for
inferring phylogenetic trees has lead to a number of interesting mathematical
questions across a variety of fields. However, due to hybridization and gene
flow, a phylogenetic network may be a better representation of the evolutionary
history of some groups of organisms. In this chapter, we introduce some of the
basic concepts in phylogenetics, and present related undergraduate research
projects on phylogenetic networks that touch on areas of graph theory and
abstract algebra. In the first section, we describe several open research
questions related to the combinatorics of phylogenetic networks. In the second,
we describe problems related to understanding phylogenetic statistical models
as algebraic varieties.
| [
{
"created": "Tue, 4 Jun 2019 17:00:12 GMT",
"version": "v1"
}
] | 2019-06-05 | [
[
"Gross",
"Elizabeth",
""
],
[
"Long",
"Colby",
""
],
[
"Rusinko",
"Joseph",
""
]
] | Phylogenetics is the study of the evolutionary relationships between organisms. One of the main challenges in the field is to take biological data for a group of organisms and to infer an evolutionary tree, a graph that represents these relationships. Developing practical and efficient methods for inferring phylogenetic trees has lead to a number of interesting mathematical questions across a variety of fields. However, due to hybridization and gene flow, a phylogenetic network may be a better representation of the evolutionary history of some groups of organisms. In this chapter, we introduce some of the basic concepts in phylogenetics, and present related undergraduate research projects on phylogenetic networks that touch on areas of graph theory and abstract algebra. In the first section, we describe several open research questions related to the combinatorics of phylogenetic networks. In the second, we describe problems related to understanding phylogenetic statistical models as algebraic varieties. |
1504.07761 | Olivier Rivoire | Olivier Rivoire | Informations in models of evolutionary dynamics | contribution for a special issue of the Journal of Statistical
Physics on "Information Processing in Living Systems" | null | 10.1007/s10955-015-1381-z | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological organisms adapt to changes by processing informations from
different sources, most notably from their ancestors and from their
environment. We review an approach to quantify these informations by analyzing
mathematical models of evolutionary dynamics, and show how explicit results are
obtained for a solvable subclass of these models. In several limits, the
results coincide with those obtained in studies of information processing for
communication, gambling or thermodynamics. In the most general case, however,
information processing by biological populations shows unique features that
motivate the analysis of specific models.
| [
{
"created": "Wed, 29 Apr 2015 08:28:45 GMT",
"version": "v1"
}
] | 2016-03-23 | [
[
"Rivoire",
"Olivier",
""
]
] | Biological organisms adapt to changes by processing informations from different sources, most notably from their ancestors and from their environment. We review an approach to quantify these informations by analyzing mathematical models of evolutionary dynamics, and show how explicit results are obtained for a solvable subclass of these models. In several limits, the results coincide with those obtained in studies of information processing for communication, gambling or thermodynamics. In the most general case, however, information processing by biological populations shows unique features that motivate the analysis of specific models. |
2310.19830 | Galib Muhammad Shahriar Himel | Galib Muhammad Shahriar Himel, Md Masudul Islam | GalliformeSpectra: A Hen Breed Dataset | null | null | null | null | q-bio.QM cs.AI | http://creativecommons.org/licenses/by/4.0/ | This article presents a comprehensive dataset featuring ten distinct hen
breeds, sourced from various regions, capturing the unique characteristics and
traits of each breed. The dataset encompasses Bielefeld, Blackorpington,
Brahma, Buckeye, Fayoumi, Leghorn, Newhampshire, Plymouthrock, Sussex, and
Turken breeds, offering a diverse representation of poultry commonly bred
worldwide. A total of 1010 original JPG images were meticulously collected,
showcasing the physical attributes, feather patterns, and distinctive features
of each hen breed. These images were subsequently standardized, resized, and
converted to PNG format for consistency within the dataset. The compilation,
although unevenly distributed across the breeds, provides a rich resource,
serving as a foundation for research and applications in poultry science,
genetics, and agricultural studies. This dataset holds significant potential to
contribute to various fields by enabling the exploration and analysis of unique
characteristics and genetic traits across different hen breeds, thereby
supporting advancements in poultry breeding, farming, and genetic research.
| [
{
"created": "Sat, 28 Oct 2023 11:03:06 GMT",
"version": "v1"
}
] | 2023-11-01 | [
[
"Himel",
"Galib Muhammad Shahriar",
""
],
[
"Islam",
"Md Masudul",
""
]
] | This article presents a comprehensive dataset featuring ten distinct hen breeds, sourced from various regions, capturing the unique characteristics and traits of each breed. The dataset encompasses Bielefeld, Blackorpington, Brahma, Buckeye, Fayoumi, Leghorn, Newhampshire, Plymouthrock, Sussex, and Turken breeds, offering a diverse representation of poultry commonly bred worldwide. A total of 1010 original JPG images were meticulously collected, showcasing the physical attributes, feather patterns, and distinctive features of each hen breed. These images were subsequently standardized, resized, and converted to PNG format for consistency within the dataset. The compilation, although unevenly distributed across the breeds, provides a rich resource, serving as a foundation for research and applications in poultry science, genetics, and agricultural studies. This dataset holds significant potential to contribute to various fields by enabling the exploration and analysis of unique characteristics and genetic traits across different hen breeds, thereby supporting advancements in poultry breeding, farming, and genetic research. |
1702.02879 | Christophe Guyeux | Jacques M. Bahi and Christophe Guyeux and Antoine Perasso | Predicting the Evolution of Gene $ura3$ in the Yeast Saccharomyces
Cerevisiae | Published in Procedia Computer Science. arXiv admin note: substantial
text overlap with arXiv:1608.06107 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since the late `60s, various genome evolutionary models have been proposed to
predict the evolution of a DNA sequence as the generations pass. Most of these
models are based on nucleotides evolution, so they use a mutation matrix of
size 4x4. They encompass for instance the well-known models of Jukes and
Cantor, Kimura, and Tamura. By essence, all of these models relate the
evolution of DNA sequences to the computation of the successive powers of a
mutation matrix. To make this computation possible, particular forms for the
mutation matrix are assumed, which are not compatible with mutation rates that
have been recently obtained experimentally on gene ura3 of the Yeast
Saccharomyces cerevisiae. Using this experimental study, authors of this paper
have deduced a simple mutation matrice, compute the future evolution of the
rate purine/pyrimidine for ura3, investigate the particular behavior of
cytosines and thymines compared to purines, and simulate the evolution of each
nucleotide.
| [
{
"created": "Wed, 8 Feb 2017 16:28:51 GMT",
"version": "v1"
}
] | 2017-02-10 | [
[
"Bahi",
"Jacques M.",
""
],
[
"Guyeux",
"Christophe",
""
],
[
"Perasso",
"Antoine",
""
]
] | Since the late `60s, various genome evolutionary models have been proposed to predict the evolution of a DNA sequence as the generations pass. Most of these models are based on nucleotides evolution, so they use a mutation matrix of size 4x4. They encompass for instance the well-known models of Jukes and Cantor, Kimura, and Tamura. By essence, all of these models relate the evolution of DNA sequences to the computation of the successive powers of a mutation matrix. To make this computation possible, particular forms for the mutation matrix are assumed, which are not compatible with mutation rates that have been recently obtained experimentally on gene ura3 of the Yeast Saccharomyces cerevisiae. Using this experimental study, authors of this paper have deduced a simple mutation matrice, compute the future evolution of the rate purine/pyrimidine for ura3, investigate the particular behavior of cytosines and thymines compared to purines, and simulate the evolution of each nucleotide. |
1306.0025 | Martin Kreitman Dr. | Soo-Young Park, Michael Z. Ludwig, Natalia A. Tamarina, Bin Z. He,
Sarah H. Carl, Desiree A. Dickerson, Levi Barse, Bharath Arun, Calvin
Williams, Cecelia M. Miles, Louis H. Philipson, Donald F. Steiner, Graeme I.
Bell, Martin Kreitman | Genetic Complexity in a Drosophila Model of Diabetes-Associated
Misfolded Human Proinsulin | 60 pages; 6 figures; 8 supporting figures; 11 supporting tables | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here we use Drosophila melanogaster to create a genetic model of human
permanent neonatal diabetes mellitus and present experimental results
describing dimensions of this complexity. The approach involves the transgenic
expression of a misfolded mutant of human preproinsulin, hINSC96Y, which is a
cause of the disease. When expressed in fly imaginal discs, hINSC96Y causes a
reduction of adult structures, including the eye, wing and notum. Eye imaginal
discs exhibit defects in both the structure and arrangement of ommatidia. In
the wing, expression of hINSC96Y leads to ectopic expression of veins and
mechano-sensory organs, indicating disruption of wild type signaling processes
regulating cell fates. These readily measurable disease phenotypes are
sensitive to temperature, gene dose and sex. Mutant (but not wild type)
proinsulin expression in the eye imaginal disc induces IRE1-mediated Xbp1
alternative splicing, a signal for endoplasmic reticulum stress response
activation, and produces global change in gene expression. Mutant hINS
transgene tester strains, when crossed to stocks from the Drosophila Genetic
Reference Panel produces F1 adults with a continuous range of disease
phenotypes and large broad-sense heritability. Surprisingly, the severity of
mutant hINS-induced disease in the eye is not correlated with that in the notum
in these crosses, nor with eye reduction phenotypes caused by the expression of
two dominant eye mutants acting in two different eye development pathways, Drop
(Dr) or Lobe (L) when crossed into the same genetic backgrounds. The tissue
specificity of genetic variability for mutant hINS-induced disease thus has its
own distinct signature. The genetic dominance of disease-specific phenotypic
variability makes this approach amenable to genome-wide association study
(GWAS) in a simple F1 screen of natural variation.
| [
{
"created": "Fri, 31 May 2013 20:55:54 GMT",
"version": "v1"
}
] | 2013-06-04 | [
[
"Park",
"Soo-Young",
""
],
[
"Ludwig",
"Michael Z.",
""
],
[
"Tamarina",
"Natalia A.",
""
],
[
"He",
"Bin Z.",
""
],
[
"Carl",
"Sarah H.",
""
],
[
"Dickerson",
"Desiree A.",
""
],
[
"Barse",
"Levi",
""
],... | Here we use Drosophila melanogaster to create a genetic model of human permanent neonatal diabetes mellitus and present experimental results describing dimensions of this complexity. The approach involves the transgenic expression of a misfolded mutant of human preproinsulin, hINSC96Y, which is a cause of the disease. When expressed in fly imaginal discs, hINSC96Y causes a reduction of adult structures, including the eye, wing and notum. Eye imaginal discs exhibit defects in both the structure and arrangement of ommatidia. In the wing, expression of hINSC96Y leads to ectopic expression of veins and mechano-sensory organs, indicating disruption of wild type signaling processes regulating cell fates. These readily measurable disease phenotypes are sensitive to temperature, gene dose and sex. Mutant (but not wild type) proinsulin expression in the eye imaginal disc induces IRE1-mediated Xbp1 alternative splicing, a signal for endoplasmic reticulum stress response activation, and produces global change in gene expression. Mutant hINS transgene tester strains, when crossed to stocks from the Drosophila Genetic Reference Panel produces F1 adults with a continuous range of disease phenotypes and large broad-sense heritability. Surprisingly, the severity of mutant hINS-induced disease in the eye is not correlated with that in the notum in these crosses, nor with eye reduction phenotypes caused by the expression of two dominant eye mutants acting in two different eye development pathways, Drop (Dr) or Lobe (L) when crossed into the same genetic backgrounds. The tissue specificity of genetic variability for mutant hINS-induced disease thus has its own distinct signature. The genetic dominance of disease-specific phenotypic variability makes this approach amenable to genome-wide association study (GWAS) in a simple F1 screen of natural variation. |
1902.01185 | Seung Ki Baek | Minjae Kim, Hyeong-Chai Jeong, and Seung Ki Baek | Sex-ratio bias induced by mutation | 18 pages, 11 figures | Phys. Rev. E 99, 022403 (2019) | 10.1103/PhysRevE.99.022403 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A question in evolutionary biology is why the number of males is
approximately equal to that of females in many species, and Fisher's theory of
equal investment answers that it is the evolutionarily stable state. The
Fisherian mechanism can be given a concrete form by a genetic model based on
the following assumptions: (1) Males and females mate at random. (2) An allele
acts on the father to determine the expected progeny sex ratio. (3) The
offspring inherits the allele from either side of the parents with equal
probability. The model is known to achieve the 1:1 sex ratio due to the
invasion of mutant alleles with different progeny sex ratios. In this study,
however, we argue that mutation plays a more subtle role in that fluctuations
caused by mutation renormalize the sex ratio and thereby keep it away from 1:1
in general. This finding shows how the sex ratio is affected by mutation in a
systematic way, whereby the effective mutation rate can be estimated from an
observed sex ratio.
| [
{
"created": "Mon, 4 Feb 2019 13:58:32 GMT",
"version": "v1"
}
] | 2019-02-05 | [
[
"Kim",
"Minjae",
""
],
[
"Jeong",
"Hyeong-Chai",
""
],
[
"Baek",
"Seung Ki",
""
]
] | A question in evolutionary biology is why the number of males is approximately equal to that of females in many species, and Fisher's theory of equal investment answers that it is the evolutionarily stable state. The Fisherian mechanism can be given a concrete form by a genetic model based on the following assumptions: (1) Males and females mate at random. (2) An allele acts on the father to determine the expected progeny sex ratio. (3) The offspring inherits the allele from either side of the parents with equal probability. The model is known to achieve the 1:1 sex ratio due to the invasion of mutant alleles with different progeny sex ratios. In this study, however, we argue that mutation plays a more subtle role in that fluctuations caused by mutation renormalize the sex ratio and thereby keep it away from 1:1 in general. This finding shows how the sex ratio is affected by mutation in a systematic way, whereby the effective mutation rate can be estimated from an observed sex ratio. |
1908.08135 | Qi Li | Qi Li and Tijana Milenkovi\'c | Supervised prediction of aging-related genes from a context-specific
protein interaction subnetwork | This is a Journal extension of "10.1109/BIBM47256.2019.8983063". So
we use the same title as our conference paper | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background. Human aging is linked to many prevalent diseases. The aging
process is highly influenced by genetic factors. Hence, it is important to
identify human aging-related genes. We focus on supervised prediction of such
genes. Gene expression-based methods for this purpose study genes in isolation
from each other. While protein-protein interaction (PPI) network-based methods
for this purpose account for interactions between genes' protein products,
current PPI network data are context-unspecific, spanning different biological
conditions. Instead, here, we focus on an aging-specific subnetwork of the
entire PPI network, obtained by integrating aging-specific gene expression data
and PPI network data. The potential of such data integration has been
recognized but mostly in the context of cancer. So, we are the first to propose
a supervised learning framework for predicting aging-related genes from an
aging-specific PPI subnetwork.
Results. In a systematic and comprehensive evaluation, we find that in many
of the evaluation tests: (i) using an aging-specific subnetwork indeed yields
more accurate aging-related gene predictions than using the entire network, and
(ii) predictive methods from our framework that have not previously been used
for supervised prediction of aging-related genes outperform existing prominent
methods for the same purpose.
Conclusion. These results justify the need for our framework.
| [
{
"created": "Wed, 21 Aug 2019 22:51:29 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Aug 2019 15:01:56 GMT",
"version": "v2"
},
{
"created": "Sat, 26 Oct 2019 01:35:40 GMT",
"version": "v3"
},
{
"created": "Sun, 26 Apr 2020 00:34:58 GMT",
"version": "v4"
}
] | 2020-04-28 | [
[
"Li",
"Qi",
""
],
[
"Milenković",
"Tijana",
""
]
] | Background. Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes' protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. Results. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. Conclusion. These results justify the need for our framework. |
2311.12879 | Pablo Torrijos Arenas | Pablo Torrijos, Jos\'e A. G\'amez, Jos\'e M. Puerta | MiniAnDE: a reduced AnDE ensemble to deal with microarray data | null | Engineering Applications of Neural Networks. EANN 2023.
Communications in Computer and Information Science, vol 1826. Springer, Cham | 10.1007/978-3-031-34204-2_12 | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article focuses on the supervised classification of datasets with a
large number of variables and a small number of instances. This is the case,
for example, for microarray data sets commonly used in bioinformatics. Complex
classifiers that require estimating statistics over many variables are not
suitable for this type of data. Probabilistic classifiers with low-order
probability tables, e.g. NB and AODE, are good alternatives for dealing with
this type of data. AODE usually improves NB in accuracy, but suffers from high
spatial complexity since $k$ models, each with $n+1$ variables, are included in
the AODE ensemble. In this paper, we propose MiniAnDE, an algorithm that
includes only a small number of heterogeneous base classifiers in the ensemble,
i.e., each model only includes a different subset of the $k$ predictive
variables. Experimental evaluation shows that using MiniAnDE classifiers on
microarray data is feasible and outperforms NB and other ensembles such as
bagging and random forest.
| [
{
"created": "Mon, 20 Nov 2023 18:12:55 GMT",
"version": "v1"
}
] | 2023-11-23 | [
[
"Torrijos",
"Pablo",
""
],
[
"Gámez",
"José A.",
""
],
[
"Puerta",
"José M.",
""
]
] | This article focuses on the supervised classification of datasets with a large number of variables and a small number of instances. This is the case, for example, for microarray data sets commonly used in bioinformatics. Complex classifiers that require estimating statistics over many variables are not suitable for this type of data. Probabilistic classifiers with low-order probability tables, e.g. NB and AODE, are good alternatives for dealing with this type of data. AODE usually improves NB in accuracy, but suffers from high spatial complexity since $k$ models, each with $n+1$ variables, are included in the AODE ensemble. In this paper, we propose MiniAnDE, an algorithm that includes only a small number of heterogeneous base classifiers in the ensemble, i.e., each model only includes a different subset of the $k$ predictive variables. Experimental evaluation shows that using MiniAnDE classifiers on microarray data is feasible and outperforms NB and other ensembles such as bagging and random forest. |
2305.08724 | Valerie Carabetta | Olaitan Akintunde, Trichina Tucker, and Valerie J. Carabetta | The evolution of next-generation sequencing technologies | 36 pages, 4 figures, 2 tables | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | The genetic information that dictates the structure and function of all life
forms is encoded in the DNA. In 1953, Watson and Crick first presented the
double helical structure of a DNA molecule. Their findings unearthed the desire
to elucidate the exact composition and sequence of DNA molecules. Discoveries
and the subsequent development and optimization of techniques that allowed for
deciphering the DNA sequence has opened new doors in research, biotech, and
healthcare. The application of high-throughput sequencing technologies in these
industries has positively impacted and will continue to contribute to the
betterment of humanity and the global economy. Improvements, such as the use of
radioactive molecules for DNA sequencing to the use of florescent dyes and the
implementation of polymerase chain reaction (PCR) for amplification, led to
sequencing a few hundred base pairs in days, to automation, where sequencing of
thousands of base pairs in hours became possible. Significant advances have
been made, but there is still room for improvement. Here, we look at the
history and the technology of the currently available next-generation
sequencing platforms and the possible applications of such technologies to
biomedical research and beyond.
| [
{
"created": "Mon, 15 May 2023 15:36:01 GMT",
"version": "v1"
}
] | 2023-05-16 | [
[
"Akintunde",
"Olaitan",
""
],
[
"Tucker",
"Trichina",
""
],
[
"Carabetta",
"Valerie J.",
""
]
] | The genetic information that dictates the structure and function of all life forms is encoded in the DNA. In 1953, Watson and Crick first presented the double helical structure of a DNA molecule. Their findings unearthed the desire to elucidate the exact composition and sequence of DNA molecules. Discoveries and the subsequent development and optimization of techniques that allowed for deciphering the DNA sequence has opened new doors in research, biotech, and healthcare. The application of high-throughput sequencing technologies in these industries has positively impacted and will continue to contribute to the betterment of humanity and the global economy. Improvements, such as the use of radioactive molecules for DNA sequencing to the use of florescent dyes and the implementation of polymerase chain reaction (PCR) for amplification, led to sequencing a few hundred base pairs in days, to automation, where sequencing of thousands of base pairs in hours became possible. Significant advances have been made, but there is still room for improvement. Here, we look at the history and the technology of the currently available next-generation sequencing platforms and the possible applications of such technologies to biomedical research and beyond. |
2303.14193 | Xuexin An | Hui Zhang, Xuexin An, Qiang He, Yudong Yao, Yudong Zhang, Feng-Lei
Fan, Yueyang Teng | Quadratic Graph Attention Network (Q-GAT) for Robust Construction of
Gene Regulatory Networks | null | null | null | null | q-bio.MN cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene regulatory relationships can be abstracted as a gene regulatory network
(GRN), which plays a key role in characterizing complex cellular processes and
pathways. Recently, graph neural networks (GNNs), as a class of deep learning
models, have emerged as a useful tool to infer gene regulatory relationships
from gene expression data. However, deep learning models have been found to be
vulnerable to noise, which greatly hinders the adoption of deep learning in
constructing GRNs, because high noise is often unavoidable in the process of
gene expression measurement. Can we preferably prototype a robust GNN for
constructing GRNs? In this paper, we give a positive answer by proposing a
Quadratic Graph Attention Network (Q-GAT) with a dual attention mechanism. We
study the changes in the predictive accuracy of Q-GAT and 9 state-of-the-art
baselines by introducing different levels of adversarial perturbations.
Experiments in the E. coli and S. cerevisiae datasets suggest that Q-GAT
outperforms the state-of-the-art models in robustness. Lastly, we dissect why
Q-GAT is robust through the signal-to-noise ratio (SNR) and interpretability
analyses. The former informs that nonlinear aggregation of quadratic neurons
can amplify useful signals and suppress unwanted noise, thereby facilitating
robustness, while the latter reveals that Q-GAT can leverage more features in
prediction thanks to the dual attention mechanism, which endows Q-GAT with the
ability to confront adversarial perturbation. We have shared our code in
https://github.com/Minorway/Q-GAT_for_Robust_Construction_of_GRN for readers'
evaluation.
| [
{
"created": "Fri, 24 Mar 2023 01:09:35 GMT",
"version": "v1"
},
{
"created": "Sat, 4 Nov 2023 13:51:58 GMT",
"version": "v2"
}
] | 2023-11-07 | [
[
"Zhang",
"Hui",
""
],
[
"An",
"Xuexin",
""
],
[
"He",
"Qiang",
""
],
[
"Yao",
"Yudong",
""
],
[
"Zhang",
"Yudong",
""
],
[
"Fan",
"Feng-Lei",
""
],
[
"Teng",
"Yueyang",
""
]
] | Gene regulatory relationships can be abstracted as a gene regulatory network (GRN), which plays a key role in characterizing complex cellular processes and pathways. Recently, graph neural networks (GNNs), as a class of deep learning models, have emerged as a useful tool to infer gene regulatory relationships from gene expression data. However, deep learning models have been found to be vulnerable to noise, which greatly hinders the adoption of deep learning in constructing GRNs, because high noise is often unavoidable in the process of gene expression measurement. Can we preferably prototype a robust GNN for constructing GRNs? In this paper, we give a positive answer by proposing a Quadratic Graph Attention Network (Q-GAT) with a dual attention mechanism. We study the changes in the predictive accuracy of Q-GAT and 9 state-of-the-art baselines by introducing different levels of adversarial perturbations. Experiments in the E. coli and S. cerevisiae datasets suggest that Q-GAT outperforms the state-of-the-art models in robustness. Lastly, we dissect why Q-GAT is robust through the signal-to-noise ratio (SNR) and interpretability analyses. The former informs that nonlinear aggregation of quadratic neurons can amplify useful signals and suppress unwanted noise, thereby facilitating robustness, while the latter reveals that Q-GAT can leverage more features in prediction thanks to the dual attention mechanism, which endows Q-GAT with the ability to confront adversarial perturbation. We have shared our code in https://github.com/Minorway/Q-GAT_for_Robust_Construction_of_GRN for readers' evaluation. |
2112.04915 | J. C. Phillips | J. C. Phillips | What Omicron Does, and How It Does It | 10 pages, 5 figures | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | Improvement of protein function by evolution (natural selection) is expected
on general grounds, but even with the modern database positive proof has
remained a difficult problem for theory. Here we extend our recent analysis of
the evolution of CoV-1 to much more contagious CoV-2, to Omicron, which appears
to be qualitatively different from other recent strains like Delta. Overall the
synchronized dynamics of Omicron is more elaborate than CoV-2 or its variants
like Delta. The surprising result is that while Omicron could be more
contagious than even Delta, it is probably much less dangerous
| [
{
"created": "Wed, 8 Dec 2021 16:42:07 GMT",
"version": "v1"
}
] | 2021-12-10 | [
[
"Phillips",
"J. C.",
""
]
] | Improvement of protein function by evolution (natural selection) is expected on general grounds, but even with the modern database positive proof has remained a difficult problem for theory. Here we extend our recent analysis of the evolution of CoV-1 to much more contagious CoV-2, to Omicron, which appears to be qualitatively different from other recent strains like Delta. Overall the synchronized dynamics of Omicron is more elaborate than CoV-2 or its variants like Delta. The surprising result is that while Omicron could be more contagious than even Delta, it is probably much less dangerous |
2311.05442 | Marina Vegu\'e Llorente | Marina Vegu\'e, Antoine Allard, Patrick Desrosiers | Firing rate distributions in plastic networks of spiking neurons | 29 pages, 7 figures | null | null | null | q-bio.NC math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recurrent networks of leaky integrate-and-fire (LIF) neurons, mean-field
theory has proven successful in describing various statistical properties of
neuronal activity at equilibrium, such as firing rate distributions. Mean-field
theory has been applied to networks in which either the synaptic weights are
homogeneous across synapses and the number of incoming connections of
individual neurons is heterogeneous, or vice versa. Here we extend the previous
mean-field formalisms to treat networks in which these two sources of
structural heterogeneity occur simultaneously, including networks whose
synapses are subject to plastic, activity-dependent modulation. The plasticity
in our model is mediated by the introduction of one spike trace per neuron: a
chemical signal that is released every time the neuron emits a spike and which
is degraded over time. The temporal evolution of the trace is controlled by its
degradation rate $r_p$ and by the neuron's underlying firing rate $\nu$. When
the ratio $\alpha=\nu / r_p$ tends to infinity, the trace can be rescaled to be
a reliable estimation of the neuron's firing rate. In this regime, the value of
any synaptic weight at equilibrium is a function of the pre- and post-synaptic
firing rates, and this relation can be used in the mean-field formalism. The
solution to the mean-field equations specifies the firing rate and synaptic
weight distributions at equilibrium. These equations are exact in the limit of
reliable traces but they already provide accurate results when the degradation
rate lies within a reasonable range, as we show by comparison with simulations
of the full neuronal dynamics in networks composed of excitatory and inhibitory
LIF neurons. Overall, this work offers a way to explore and better understand
the way in which plasticity shapes both activity and structure in neuronal
networks.
| [
{
"created": "Thu, 9 Nov 2023 15:29:40 GMT",
"version": "v1"
}
] | 2023-11-10 | [
[
"Vegué",
"Marina",
""
],
[
"Allard",
"Antoine",
""
],
[
"Desrosiers",
"Patrick",
""
]
] | In recurrent networks of leaky integrate-and-fire (LIF) neurons, mean-field theory has proven successful in describing various statistical properties of neuronal activity at equilibrium, such as firing rate distributions. Mean-field theory has been applied to networks in which either the synaptic weights are homogeneous across synapses and the number of incoming connections of individual neurons is heterogeneous, or vice versa. Here we extend the previous mean-field formalisms to treat networks in which these two sources of structural heterogeneity occur simultaneously, including networks whose synapses are subject to plastic, activity-dependent modulation. The plasticity in our model is mediated by the introduction of one spike trace per neuron: a chemical signal that is released every time the neuron emits a spike and which is degraded over time. The temporal evolution of the trace is controlled by its degradation rate $r_p$ and by the neuron's underlying firing rate $\nu$. When the ratio $\alpha=\nu / r_p$ tends to infinity, the trace can be rescaled to be a reliable estimation of the neuron's firing rate. In this regime, the value of any synaptic weight at equilibrium is a function of the pre- and post-synaptic firing rates, and this relation can be used in the mean-field formalism. The solution to the mean-field equations specifies the firing rate and synaptic weight distributions at equilibrium. These equations are exact in the limit of reliable traces but they already provide accurate results when the degradation rate lies within a reasonable range, as we show by comparison with simulations of the full neuronal dynamics in networks composed of excitatory and inhibitory LIF neurons. Overall, this work offers a way to explore and better understand the way in which plasticity shapes both activity and structure in neuronal networks. |
1912.12371 | Ye Ye | Y. Ye, R. D. Boyce, M.K. Davis, K. Elliston, C. Davatzikos, A.
Fedorov, J. C. Fillion-Robin, I. Foster, J. Gilbertson, M. Heiskanen, J.
Klemm, A. Lasso, J. V. Miller, M. Morgan, S. Pieper, B. Raumann, B. Sarachan,
G. Savova, J. C. Silverstein, D. Taylor, J. Zelnis, G. Q. Zhang, M. J. Becich | Open Source Software Sustainability Models: Initial White Paper from the
Informatics Technology for Cancer Research Sustainability and Industry
Partnership Work Group | 21-page main manuscript, 43-page supplemental file | null | null | null | q-bio.OT cs.SE | http://creativecommons.org/licenses/by/4.0/ | The Sustainability and Industry Partnership Work Group (SIP-WG) is a part of
the National Cancer Institute Informatics Technology for Cancer Research (ITCR)
program. The charter of the SIP-WG is to investigate options of long-term
sustainability of open source software (OSS) developed by the ITCR, in part by
developing a collection of business model archetypes that can serve as
sustainability plans for ITCR OSS development initiatives. The workgroup
assembled models from the ITCR program, from other studies, and via engagement
of its extensive network of relationships with other organizations (e.g., Chan
Zuckerberg Initiative, Open Source Initiative and Software Sustainability
Institute). This article reviews existing sustainability models and describes
ten OSS use cases disseminated by the SIP-WG and others, and highlights five
essential attributes (alignment with unmet scientific needs, dedicated
development team, vibrant user community, feasible licensing model, and
sustainable financial model) to assist academic software developers in
achieving best practice in software sustainability.
| [
{
"created": "Fri, 27 Dec 2019 23:57:01 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Jan 2020 03:01:37 GMT",
"version": "v2"
}
] | 2020-01-03 | [
[
"Ye",
"Y.",
""
],
[
"Boyce",
"R. D.",
""
],
[
"Davis",
"M. K.",
""
],
[
"Elliston",
"K.",
""
],
[
"Davatzikos",
"C.",
""
],
[
"Fedorov",
"A.",
""
],
[
"Fillion-Robin",
"J. C.",
""
],
[
"Foster",
... | The Sustainability and Industry Partnership Work Group (SIP-WG) is a part of the National Cancer Institute Informatics Technology for Cancer Research (ITCR) program. The charter of the SIP-WG is to investigate options of long-term sustainability of open source software (OSS) developed by the ITCR, in part by developing a collection of business model archetypes that can serve as sustainability plans for ITCR OSS development initiatives. The workgroup assembled models from the ITCR program, from other studies, and via engagement of its extensive network of relationships with other organizations (e.g., Chan Zuckerberg Initiative, Open Source Initiative and Software Sustainability Institute). This article reviews existing sustainability models and describes ten OSS use cases disseminated by the SIP-WG and others, and highlights five essential attributes (alignment with unmet scientific needs, dedicated development team, vibrant user community, feasible licensing model, and sustainable financial model) to assist academic software developers in achieving best practice in software sustainability. |
2006.05502 | Brenda Crowe | Mary Nilsson (1), Brenda Crowe (1), Greg Anglin (1), Greg Ball (2),
Melvin Munsaka (3), Seta Shahin (4), Wei Wang (5) ((1) Eli Lilly and Company,
Indianapolis, IN USA, (2) Merck & Co., Inc., Rahway, NJ, USA, (3) AbbVie
Inc., North Chicago, IL, USA, (4) Amgen Inc., Thousand Oaks, CA, USA, (5) Eli
Lilly Canada Inc., Toronto, Ontario, Canada) | Clinical Trial Drug Safety Assessment for Studies and Submissions
Impacted by COVID-19 | 21 pages, 0 figures, submitted to Statistics in Biopharmaceutical
Research | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we provide guidance on how standard safety analyses and
reporting of clinical trial safety data may need to be modified, given the
potential impact of the COVID-19 pandemic. The impact could include missed
visits, alternative methods for assessments (such as virtual visits),
alternative locations for assessments (such as local labs), and study drug
interruptions. We focus on safety planning for Phase 2-4 clinical trials and
integrated summaries for submissions. Starting from the recommended safety
analyses proposed in white papers and a workshop, created as part of an
FDA/PHUSE collaboration (PHUSE 2013, 2015, 2017, 2019), we assess what
modifications might be needed. Impact from COVID-19 will likely affect
treatment arms equally, so analyses of adverse events from controlled data can,
to a large extent, remain unchanged. However, interpretation of summaries from
uncontrolled data (summaries that include open-label extension data) will
require even more caution than usual. Special consideration will be needed for
safety topics of interest, especially events expected to have a higher
incidence due to a COVID-19 infection or due to quarantine or travel
restrictions (e.g., depression). Analyses of laboratory measurements may need
to be modified to account for the combination of measurements from local and
central laboratories.
| [
{
"created": "Fri, 5 Jun 2020 17:35:55 GMT",
"version": "v1"
}
] | 2020-06-11 | [
[
"Nilsson",
"Mary",
""
],
[
"Crowe",
"Brenda",
""
],
[
"Anglin",
"Greg",
""
],
[
"Ball",
"Greg",
""
],
[
"Munsaka",
"Melvin",
""
],
[
"Shahin",
"Seta",
""
],
[
"Wang",
"Wei",
""
]
] | In this paper, we provide guidance on how standard safety analyses and reporting of clinical trial safety data may need to be modified, given the potential impact of the COVID-19 pandemic. The impact could include missed visits, alternative methods for assessments (such as virtual visits), alternative locations for assessments (such as local labs), and study drug interruptions. We focus on safety planning for Phase 2-4 clinical trials and integrated summaries for submissions. Starting from the recommended safety analyses proposed in white papers and a workshop, created as part of an FDA/PHUSE collaboration (PHUSE 2013, 2015, 2017, 2019), we assess what modifications might be needed. Impact from COVID-19 will likely affect treatment arms equally, so analyses of adverse events from controlled data can, to a large extent, remain unchanged. However, interpretation of summaries from uncontrolled data (summaries that include open-label extension data) will require even more caution than usual. Special consideration will be needed for safety topics of interest, especially events expected to have a higher incidence due to a COVID-19 infection or due to quarantine or travel restrictions (e.g., depression). Analyses of laboratory measurements may need to be modified to account for the combination of measurements from local and central laboratories. |
2010.09682 | Magdalena Djordjevic | Igor Salom, Andjela Rodic, Ognjen Milicevic, Dusan Zigic, Magdalena
Djordjevic, Marko Djordjevic | Effects of demographic and weather parameters on COVID-19 basic
reproduction number | 28 pages, 6 figures, 7 tables | Frontiers in Ecology and Evolution 8 (2021) 524 | 10.3389/fevo.2020.617841 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Timely prediction of the COVID-19 progression is not possible without a
comprehensive understanding of environmental factors that may affect the
infection transmissibility. Studies addressing parameters that may influence
COVID-19 progression relied on either the total numbers of detected cases and
similar proxies and/or a small number of analyzed factors, including analysis
of regions that display a narrow range of these parameters. We here apply a
novel approach, exploiting widespread growth regimes in COVID-19 detected case
counts. By applying nonlinear dynamics methods to the exponential regime, we
extract basic reproductive number R0 (i.e., the measure of COVID-19 inherent
biological transmissibility), applying to the completely naive population in
the absence of social distancing, for 118 different countries. We then use
bioinformatics methods to systematically collect data on a large number of
demographics and weather parameters from these countries, and seek their
correlations with the rate of COVID-19 spread. In addition to some of the
already reported tendencies, we show a number of both novel results and those
that help settle existing disputes: the absence of dependence on wind speed and
air pressure, negative correlation with precipitation; significant positive
correlation with society development level (human development index)
irrespective of testing policies, and percent of the urban population, but an
absence of correlation with population density per se. We find a strong
positive correlation of transmissibility on alcohol consumption, and the
absence of correlation on refugee numbers, contrary to some widespread beliefs.
Significant tendencies with health-related factors are reported, including a
detailed analysis of the blood type group showing consistent tendencies on Rh
factor, and a strong positive correlation of transmissibility with cholesterol
levels.
| [
{
"created": "Mon, 19 Oct 2020 17:19:35 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Mar 2021 11:39:47 GMT",
"version": "v2"
}
] | 2021-03-30 | [
[
"Salom",
"Igor",
""
],
[
"Rodic",
"Andjela",
""
],
[
"Milicevic",
"Ognjen",
""
],
[
"Zigic",
"Dusan",
""
],
[
"Djordjevic",
"Magdalena",
""
],
[
"Djordjevic",
"Marko",
""
]
] | Timely prediction of the COVID-19 progression is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. Studies addressing parameters that may influence COVID-19 progression relied on either the total numbers of detected cases and similar proxies and/or a small number of analyzed factors, including analysis of regions that display a narrow range of these parameters. We here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive number R0 (i.e., the measure of COVID-19 inherent biological transmissibility), applying to the completely naive population in the absence of social distancing, for 118 different countries. We then use bioinformatics methods to systematically collect data on a large number of demographics and weather parameters from these countries, and seek their correlations with the rate of COVID-19 spread. In addition to some of the already reported tendencies, we show a number of both novel results and those that help settle existing disputes: the absence of dependence on wind speed and air pressure, negative correlation with precipitation; significant positive correlation with society development level (human development index) irrespective of testing policies, and percent of the urban population, but an absence of correlation with population density per se. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. Significant tendencies with health-related factors are reported, including a detailed analysis of the blood type group showing consistent tendencies on Rh factor, and a strong positive correlation of transmissibility with cholesterol levels. |
1811.09512 | Fangting Li | Yao Zhao (1,2), Dedi Wang (1,2), Zhiwen Zhang (1,2), Ying Lu (3),
Xiaojing Yang (2), Qi Ouyang (1,2), Chao Tang (1,2), and Fangting Li (1,2)
((1) School of Physics, Peking University, Beijing, China, (2) Center for
Quantitative Biology, Peking University, Beijing, China, (3) Department of
Systems Biology, Harvard Medical School, Boston, MA) | Critical slowing down and attractive manifold: a mechanism for dynamic
robustness in yeast cell-cycle process | 27 pages, 12 figures | Phys. Rev. E 101, 042405 (2020) | 10.1103/PhysRevE.101.042405 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The biological processes that execute complex multiple functions, such as
cell cycle, must ensure the order of sequential events and keep the dynamic
robustness against various fluctuations. Here, we examine the dynamic mechanism
and the fundamental structure to achieve these properties in the cell-cycle
process of budding yeast Saccharomyces cerevisiae. We show that the budding
yeast cell-cycle process behaves like an excitable system containing three
well-coupled saddle-node bifurcations to execute DNA replication and mitosis
events. The yeast cell-cycle regulatory network can be separated into G1/S
phase module, early M module and late M phase module, where the positive
feedbacks in each module and the interactions among the modules play important
role. If the cell-cycle process operates near the critical points of the
saddle-node bifurcations, there is a critical slowing down or ghost effect.
This can provide the cell-cycle process with a sufficient duration for each
event and an attractive manifold for the state checking of the completion of
DNA replication and mitosis; moreover, the fluctuation in the early
module/event is forbidden to transmit to the latter module/event. Our results
suggest both a fundamental structure of cell-cycle regulatory network and a
hint for the evolution of eukaryotic cell-cycle processes, from the dynamic
checking mechanism to the molecule checkpoint pathway.
| [
{
"created": "Fri, 23 Nov 2018 15:14:50 GMT",
"version": "v1"
}
] | 2020-04-22 | [
[
"Zhao",
"Yao",
""
],
[
"Wang",
"Dedi",
""
],
[
"Zhang",
"Zhiwen",
""
],
[
"Lu",
"Ying",
""
],
[
"Yang",
"Xiaojing",
""
],
[
"Ouyang",
"Qi",
""
],
[
"Tang",
"Chao",
""
],
[
"Li",
"Fangting",
... | The biological processes that execute complex multiple functions, such as cell cycle, must ensure the order of sequential events and keep the dynamic robustness against various fluctuations. Here, we examine the dynamic mechanism and the fundamental structure to achieve these properties in the cell-cycle process of budding yeast Saccharomyces cerevisiae. We show that the budding yeast cell-cycle process behaves like an excitable system containing three well-coupled saddle-node bifurcations to execute DNA replication and mitosis events. The yeast cell-cycle regulatory network can be separated into G1/S phase module, early M module and late M phase module, where the positive feedbacks in each module and the interactions among the modules play important role. If the cell-cycle process operates near the critical points of the saddle-node bifurcations, there is a critical slowing down or ghost effect. This can provide the cell-cycle process with a sufficient duration for each event and an attractive manifold for the state checking of the completion of DNA replication and mitosis; moreover, the fluctuation in the early module/event is forbidden to transmit to the latter module/event. Our results suggest both a fundamental structure of cell-cycle regulatory network and a hint for the evolution of eukaryotic cell-cycle processes, from the dynamic checking mechanism to the molecule checkpoint pathway. |
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