id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1203.6465 | Viktor Wixler | Sergey V. Chesnokov, Lina G. Chesnokov and Viktor Wixler | Phenomenon of irreducible genetic markers for TATAAA motifs in human
chromosome 1 | 17 pages including 1 Figure and 6 Tables | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is well known that the general transcription factors (GTF) specifically
recognize correct TATA boxes, distinguishing them from many others. Employing
the principles of determinacy analysis (mathematical theory of rules) we
analyzed a fragment of human chromosome 1 DNA sequence and identified specific
genetic markers (IG-markers = Irreducible Genetic markers) in the nearest
proximity to TATAAA motifs. The IG-markers enable determining the exact
location of any TATAAA motif within the investigated DNA fragment. Based on our
data we hypothesize that the GTF recognize the
{\guillemotleft}true{\guillemotright} transcriptional start TATA box by means
of IG-markers. The math method described here is universal and can be used to
find IG-markers that will provide, like a global navigation satellite system,
for the specific location of any distinct sequence motif within larger DNA
sequence content.
| [
{
"created": "Thu, 29 Mar 2012 08:21:44 GMT",
"version": "v1"
}
] | 2012-03-30 | [
[
"Chesnokov",
"Sergey V.",
""
],
[
"Chesnokov",
"Lina G.",
""
],
[
"Wixler",
"Viktor",
""
]
] | It is well known that the general transcription factors (GTF) specifically recognize correct TATA boxes, distinguishing them from many others. Employing the principles of determinacy analysis (mathematical theory of rules) we analyzed a fragment of human chromosome 1 DNA sequence and identified specific genetic markers (IG-markers = Irreducible Genetic markers) in the nearest proximity to TATAAA motifs. The IG-markers enable determining the exact location of any TATAAA motif within the investigated DNA fragment. Based on our data we hypothesize that the GTF recognize the {\guillemotleft}true{\guillemotright} transcriptional start TATA box by means of IG-markers. The math method described here is universal and can be used to find IG-markers that will provide, like a global navigation satellite system, for the specific location of any distinct sequence motif within larger DNA sequence content. |
1609.04649 | Joana Grah | Joana Sarah Grah, Jennifer Alison Harrington, Siang Boon Koh, Jeremy
Andrew Pike, Alexander Schreiner, Martin Burger, Carola-Bibiane Sch\"onlieb,
Stefanie Reichelt | Mathematical Imaging Methods for Mitosis Analysis in Live-Cell Phase
Contrast Microscopy | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose a workflow to detect and track mitotic cells in
time-lapse microscopy image sequences. In order to avoid the requirement for
cell lines expressing fluorescent markers and the associated phototoxicity,
phase contrast microscopy is often preferred over fluorescence microscopy in
live-cell imaging. However, common specific image characteristics complicate
image processing and impede use of standard methods. Nevertheless, automated
analysis is desirable due to manual analysis being subjective, biased and
extremely time-consuming for large data sets. Here, we present the following
workflow based on mathematical imaging methods. In the first step, mitosis
detection is performed by means of the circular Hough transform. The obtained
circular contour subsequently serves as an initialisation for the tracking
algorithm based on variational methods. It is sub-divided into two parts: in
order to determine the beginning of the whole mitosis cycle, a backwards
tracking procedure is performed. After that, the cell is tracked forwards in
time until the end of mitosis. As a result, the average of mitosis duration and
ratios of different cell fates (cell death, no division, division into two or
more daughter cells) can be measured and statistics on cell morphologies can be
obtained. All of the tools are featured in the user-friendly
MATLAB$^{\circledR}$ Graphical User Interface MitosisAnalyser.
| [
{
"created": "Wed, 14 Sep 2016 16:38:04 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Sep 2016 10:10:55 GMT",
"version": "v2"
},
{
"created": "Fri, 10 Feb 2017 11:50:01 GMT",
"version": "v3"
}
] | 2017-02-13 | [
[
"Grah",
"Joana Sarah",
""
],
[
"Harrington",
"Jennifer Alison",
""
],
[
"Koh",
"Siang Boon",
""
],
[
"Pike",
"Jeremy Andrew",
""
],
[
"Schreiner",
"Alexander",
""
],
[
"Burger",
"Martin",
""
],
[
"Schönlieb",
"Carola-Bibiane",
""
],
[
"Reichelt",
"Stefanie",
""
]
] | In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB$^{\circledR}$ Graphical User Interface MitosisAnalyser. |
1801.09589 | Chendi Wang | Chendi Wang, Rafeef Abugharbieh | Coactivated Clique Based Multisource Overlapping Brain Subnetwork
Extraction | 18 pages, 5 figures | null | null | null | q-bio.NC cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Subnetwork extraction using community detection methods is commonly used to
study the brain's modular structure. Recent studies indicated that certain
brain regions are known to interact with multiple subnetworks. However, most
existing methods are mainly for non-overlapping subnetwork extraction. In this
paper, we present an approach for overlapping brain subnetwork extraction using
cliques, which we defined as co-activated node groups performing multiple
tasks. We proposed a multisource subnetwork extraction approach based on the
co-activated clique, which (1) uses task co-activation and task connectivity
strength information for clique identification, (2) automatically detects
cliques of different sizes having more neuroscientific justifications, and (3)
shares the subnetwork membership, derived from a fusion of rest and task data,
among the nodes within a clique for overlapping subnetwork extraction. On real
data, compared to the commonly used overlapping community detection techniques,
we showed that our approach improved subnetwork extraction in terms of
group-level and subject-wise reproducibility. We also showed that our
multisource approach identified subnetwork overlaps within brain regions that
matched well with hubs defined using functional and anatomical information,
which enables us to study the interactions between the subnetworks and how hubs
play their role in information flow across different subnetworks. We further
demonstrated that the assignments of interacting/individual nodes using our
approach correspond with the posterior probability derived independently from
our multimodal random walker based approach.
| [
{
"created": "Fri, 26 Jan 2018 18:56:23 GMT",
"version": "v1"
}
] | 2018-01-30 | [
[
"Wang",
"Chendi",
""
],
[
"Abugharbieh",
"Rafeef",
""
]
] | Subnetwork extraction using community detection methods is commonly used to study the brain's modular structure. Recent studies indicated that certain brain regions are known to interact with multiple subnetworks. However, most existing methods are mainly for non-overlapping subnetwork extraction. In this paper, we present an approach for overlapping brain subnetwork extraction using cliques, which we defined as co-activated node groups performing multiple tasks. We proposed a multisource subnetwork extraction approach based on the co-activated clique, which (1) uses task co-activation and task connectivity strength information for clique identification, (2) automatically detects cliques of different sizes having more neuroscientific justifications, and (3) shares the subnetwork membership, derived from a fusion of rest and task data, among the nodes within a clique for overlapping subnetwork extraction. On real data, compared to the commonly used overlapping community detection techniques, we showed that our approach improved subnetwork extraction in terms of group-level and subject-wise reproducibility. We also showed that our multisource approach identified subnetwork overlaps within brain regions that matched well with hubs defined using functional and anatomical information, which enables us to study the interactions between the subnetworks and how hubs play their role in information flow across different subnetworks. We further demonstrated that the assignments of interacting/individual nodes using our approach correspond with the posterior probability derived independently from our multimodal random walker based approach. |
2009.08378 | Timo C. Wunderlich | Timo C. Wunderlich, Christian Pehle | Event-Based Backpropagation can compute Exact Gradients for Spiking
Neural Networks | null | null | 10.1038/s41598-021-91786-z | null | q-bio.NC cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spiking neural networks combine analog computation with event-based
communication using discrete spikes. While the impressive advances of deep
learning are enabled by training non-spiking artificial neural networks using
the backpropagation algorithm, applying this algorithm to spiking networks was
previously hindered by the existence of discrete spike events and
discontinuities. For the first time, this work derives the backpropagation
algorithm for a continuous-time spiking neural network and a general loss
function by applying the adjoint method together with the proper partial
derivative jumps, allowing for backpropagation through discrete spike events
without approximations. This algorithm, EventProp, backpropagates errors at
spike times in order to compute the exact gradient in an event-based,
temporally and spatially sparse fashion. We use gradients computed via
EventProp to train networks on the Yin-Yang and MNIST datasets using either a
spike time or voltage based loss function and report competitive performance.
Our work supports the rigorous study of gradient-based learning algorithms in
spiking neural networks and provides insights toward their implementation in
novel brain-inspired hardware.
| [
{
"created": "Thu, 17 Sep 2020 15:45:00 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Sep 2020 15:59:39 GMT",
"version": "v2"
},
{
"created": "Mon, 31 May 2021 18:00:07 GMT",
"version": "v3"
}
] | 2021-06-22 | [
[
"Wunderlich",
"Timo C.",
""
],
[
"Pehle",
"Christian",
""
]
] | Spiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm, applying this algorithm to spiking networks was previously hindered by the existence of discrete spike events and discontinuities. For the first time, this work derives the backpropagation algorithm for a continuous-time spiking neural network and a general loss function by applying the adjoint method together with the proper partial derivative jumps, allowing for backpropagation through discrete spike events without approximations. This algorithm, EventProp, backpropagates errors at spike times in order to compute the exact gradient in an event-based, temporally and spatially sparse fashion. We use gradients computed via EventProp to train networks on the Yin-Yang and MNIST datasets using either a spike time or voltage based loss function and report competitive performance. Our work supports the rigorous study of gradient-based learning algorithms in spiking neural networks and provides insights toward their implementation in novel brain-inspired hardware. |
2304.02198 | Bowen Jing | Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie
Berger, Tommi Jaakkola | EigenFold: Generative Protein Structure Prediction with Diffusion Models | ICLR MLDD workshop 2023 | null | null | null | q-bio.BM cs.LG physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein structure prediction has reached revolutionary levels of accuracy on
single structures, yet distributional modeling paradigms are needed to capture
the conformational ensembles and flexibility that underlie biological function.
Towards this goal, we develop EigenFold, a diffusion generative modeling
framework for sampling a distribution of structures from a given protein
sequence. We define a diffusion process that models the structure as a system
of harmonic oscillators and which naturally induces a cascading-resolution
generative process along the eigenmodes of the system. On recent CAMEO targets,
EigenFold achieves a median TMScore of 0.84, while providing a more
comprehensive picture of model uncertainty via the ensemble of sampled
structures relative to existing methods. We then assess EigenFold's ability to
model and predict conformational heterogeneity for fold-switching proteins and
ligand-induced conformational change. Code is available at
https://github.com/bjing2016/EigenFold.
| [
{
"created": "Wed, 5 Apr 2023 02:46:13 GMT",
"version": "v1"
}
] | 2023-04-06 | [
[
"Jing",
"Bowen",
""
],
[
"Erives",
"Ezra",
""
],
[
"Pao-Huang",
"Peter",
""
],
[
"Corso",
"Gabriele",
""
],
[
"Berger",
"Bonnie",
""
],
[
"Jaakkola",
"Tommi",
""
]
] | Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function. Towards this goal, we develop EigenFold, a diffusion generative modeling framework for sampling a distribution of structures from a given protein sequence. We define a diffusion process that models the structure as a system of harmonic oscillators and which naturally induces a cascading-resolution generative process along the eigenmodes of the system. On recent CAMEO targets, EigenFold achieves a median TMScore of 0.84, while providing a more comprehensive picture of model uncertainty via the ensemble of sampled structures relative to existing methods. We then assess EigenFold's ability to model and predict conformational heterogeneity for fold-switching proteins and ligand-induced conformational change. Code is available at https://github.com/bjing2016/EigenFold. |
2208.04275 | Maxwell J. D. Ramstead | Maxwell J. D Ramstead | One person's modus ponens...: Comment on "The Markov blanket trick: On
the scope of the free energy principle and active inference" by Raja and
colleagues (2021) | null | null | 10.1016/j.plrev.2022.11.001 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this comment on "The Markov blanket trick: On the scope of the free energy
principle and active inference" by Raja and colleagues (2021) in Physics of
Life Reviews, I argue that the argument presented by the authors is valid;
however, I claim that the argument contains a flawed premise, which undermines
their conclusions. In addition, I argue that work on the FEP that has appeared
since the target paper was published underwrites a cogent response to the
issues that are raised by Raja and colleagues.
| [
{
"created": "Mon, 8 Aug 2022 17:14:47 GMT",
"version": "v1"
}
] | 2022-11-30 | [
[
"Ramstead",
"Maxwell J. D",
""
]
] | In this comment on "The Markov blanket trick: On the scope of the free energy principle and active inference" by Raja and colleagues (2021) in Physics of Life Reviews, I argue that the argument presented by the authors is valid; however, I claim that the argument contains a flawed premise, which undermines their conclusions. In addition, I argue that work on the FEP that has appeared since the target paper was published underwrites a cogent response to the issues that are raised by Raja and colleagues. |
1902.02463 | Mike Steel Prof. | Kristina Wicke and Mike Steel | Combinatorial properties of phylogenetic diversity indices | 31 pages, 7 figures | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic diversity indices provide a formal way to apportion
'evolutionary heritage' across species. Two natural diversity indices are Fair
Proportion (FP) and Equal Splits (ES). FP is also called 'evolutionary
distinctiveness' and, for rooted trees, is identical to the Shapley Value (SV),
which arises from cooperative game theory. In this paper, we investigate the
extent to which FP and ES can differ, characterise tree shapes on which the
indices are identical, and study the equivalence of FP and SV and its
implications in more detail. We also define and investigate analogues of these
indices on unrooted trees (where SV was originally defined), including an index
that is closely related to the Pauplin representation of phylogenetic
diversity.
| [
{
"created": "Thu, 7 Feb 2019 03:45:34 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Jul 2019 08:56:21 GMT",
"version": "v2"
},
{
"created": "Wed, 2 Oct 2019 20:39:58 GMT",
"version": "v3"
}
] | 2019-10-04 | [
[
"Wicke",
"Kristina",
""
],
[
"Steel",
"Mike",
""
]
] | Phylogenetic diversity indices provide a formal way to apportion 'evolutionary heritage' across species. Two natural diversity indices are Fair Proportion (FP) and Equal Splits (ES). FP is also called 'evolutionary distinctiveness' and, for rooted trees, is identical to the Shapley Value (SV), which arises from cooperative game theory. In this paper, we investigate the extent to which FP and ES can differ, characterise tree shapes on which the indices are identical, and study the equivalence of FP and SV and its implications in more detail. We also define and investigate analogues of these indices on unrooted trees (where SV was originally defined), including an index that is closely related to the Pauplin representation of phylogenetic diversity. |
1106.3035 | Chuan Xue | Chuan Xue and Elena O. Budrene and Hans G. Othmer | Radial and spiral stream formation in Proteus mirabilis | null | null | 10.1371/journal.pcbi.1002332 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The enteric bacterium Proteus mirabilis, which is a pathogen that forms
biofilms in vivo, can swarm over hard surfaces and form concentric ring
patterns in colonies. Colony formation involves two distinct cell types:
swarmer cells that dominate near the surface and the leading edge, and swimmer
cells that prefer a less viscous medium, but the mechanisms underlying pattern
formation are not understood. New experimental investigations reported here
show that swimmer cells in the center of the colony stream inward toward the
inoculation site and in the process form many complex patterns, including
radial and spiral streams, in addition to concentric rings. These new
observations suggest that swimmers are motile and that indirect interactions
between them are essential in the pattern formation. To explain these
observations we develop a hybrid cell-based model that incorporates a
chemotactic response of swimmers to a chemical they produce. The model predicts
that formation of radial streams can be explained as the modulation of the
local attractant concentration by the cells, and that the chirality of the
spiral streams can be predicted by incorporating a swimming bias of the cells
near the surface of the substrate. The spatial patterns generated from the
model are in qualitative agreement with the experimental observations.
| [
{
"created": "Wed, 15 Jun 2011 17:41:32 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Jun 2011 21:45:55 GMT",
"version": "v2"
}
] | 2015-05-28 | [
[
"Xue",
"Chuan",
""
],
[
"Budrene",
"Elena O.",
""
],
[
"Othmer",
"Hans G.",
""
]
] | The enteric bacterium Proteus mirabilis, which is a pathogen that forms biofilms in vivo, can swarm over hard surfaces and form concentric ring patterns in colonies. Colony formation involves two distinct cell types: swarmer cells that dominate near the surface and the leading edge, and swimmer cells that prefer a less viscous medium, but the mechanisms underlying pattern formation are not understood. New experimental investigations reported here show that swimmer cells in the center of the colony stream inward toward the inoculation site and in the process form many complex patterns, including radial and spiral streams, in addition to concentric rings. These new observations suggest that swimmers are motile and that indirect interactions between them are essential in the pattern formation. To explain these observations we develop a hybrid cell-based model that incorporates a chemotactic response of swimmers to a chemical they produce. The model predicts that formation of radial streams can be explained as the modulation of the local attractant concentration by the cells, and that the chirality of the spiral streams can be predicted by incorporating a swimming bias of the cells near the surface of the substrate. The spatial patterns generated from the model are in qualitative agreement with the experimental observations. |
1510.08729 | Gabriel Silva | Gabriel A. Silva | The prevalence of small world networks explained by modeling the
competing dynamics of local signaling events in geometric networks | Updated version of the paper | null | null | null | q-bio.MN math.DS physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Networks are ubiquitous throughout science and engineering. A number of
methods, including some from our own group, have explored how one goes about
computing or predicting the dynamics of networks given information about
internal models of individual nodes and network connectivity, possibly with
additional information provided by statistical or descriptive metrics that
characterize the network. But what can be inferred about network dynamics when
there is no knowledge or information about the internal model or dynamics of
participating nodes? Here, we explore how connected subsets of nodes
competitively interact in order to activate a common downstream node they
connect into. We achieve this by assuming a simple set of rules borrowed from
neurophysiology. The model we develop reflects a local process from which
global network dynamics emerges. We call this model a competitive refractory
dynanics model. It is derived from a consideration of spatial and temporal
summation in biological neurons, whereby summating post synaptic potentials
(PSPs) along the dendritic tree contribute towards the membrane potential at
the initial segment reaching a threshold potential. We first show how the
'winning node' or set of 'winning' nodes that achieve activation of a
downstream node is computable by the model. We then derive a formal definition
of optimized network signaling within our framework. We define a ratio between
the signaling latencies on the edges of the network and the internal time it
takes individual nodes to process incoming signals. We show that an optimal
ratio is one where the speed of information propagation between connected nodes
does not exceed the internal dynamic time scale of the nodes. We then show how
we can use these results to arrive at a unique interpretation for the
prevalence of the small world network topology in natural and engineered
systems.
| [
{
"created": "Fri, 9 Oct 2015 06:50:08 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Mar 2016 18:11:27 GMT",
"version": "v2"
},
{
"created": "Thu, 12 May 2016 23:20:21 GMT",
"version": "v3"
},
{
"created": "Fri, 12 May 2017 03:22:27 GMT",
"version": "v4"
},
{
"created": "Fri, 3 Nov 2017 03:09:12 GMT",
"version": "v5"
}
] | 2017-11-06 | [
[
"Silva",
"Gabriel A.",
""
]
] | Networks are ubiquitous throughout science and engineering. A number of methods, including some from our own group, have explored how one goes about computing or predicting the dynamics of networks given information about internal models of individual nodes and network connectivity, possibly with additional information provided by statistical or descriptive metrics that characterize the network. But what can be inferred about network dynamics when there is no knowledge or information about the internal model or dynamics of participating nodes? Here, we explore how connected subsets of nodes competitively interact in order to activate a common downstream node they connect into. We achieve this by assuming a simple set of rules borrowed from neurophysiology. The model we develop reflects a local process from which global network dynamics emerges. We call this model a competitive refractory dynanics model. It is derived from a consideration of spatial and temporal summation in biological neurons, whereby summating post synaptic potentials (PSPs) along the dendritic tree contribute towards the membrane potential at the initial segment reaching a threshold potential. We first show how the 'winning node' or set of 'winning' nodes that achieve activation of a downstream node is computable by the model. We then derive a formal definition of optimized network signaling within our framework. We define a ratio between the signaling latencies on the edges of the network and the internal time it takes individual nodes to process incoming signals. We show that an optimal ratio is one where the speed of information propagation between connected nodes does not exceed the internal dynamic time scale of the nodes. We then show how we can use these results to arrive at a unique interpretation for the prevalence of the small world network topology in natural and engineered systems. |
0708.3599 | Siebe van Albada | Siebe B. van Albada and Pieter Rein ten Wolde | Enzyme localization can drastically affect signal amplification in
signal transduction pathways | PLoS Comp Biol, in press. 32 pages including 6 figures and supporting
information | null | 10.1371/journal.pcbi.0030195.eor | null | q-bio.MN | null | Push-pull networks are ubiquitous in signal transduction pathways in both
prokaryotic and eukaryotic cells. They allow cells to strongly amplify signals
via the mechanism of zero-order ultrasensitivity. In a push-pull network, two
antagonistic enzymes control the activity of a protein by covalent
modification. These enzymes are often uniformly distributed in the cytoplasm.
They can, however, also be colocalized in space, for instance, near the pole of
the cell. Moreover, it is increasingly recognized that these enzymes can also
be spatially separated, leading to gradients of the active form of the
messenger protein. Here, we investigate the consequences of the spatial
distributions of the enzymes for the amplification properties of push-pull
networks. Our calculations reveal that enzyme localization by itself can have a
dramatic effect on the gain. The gain is maximized when the two enzymes are
either uniformly distributed or colocalized in one region in the cell.
Depending on the diffusion constants, however, the sharpness of the response
can be strongly reduced when the enzymes are spatially separated. We discuss
how our predictions could be tested experimentally.
| [
{
"created": "Mon, 27 Aug 2007 14:40:17 GMT",
"version": "v1"
}
] | 2007-08-28 | [
[
"van Albada",
"Siebe B.",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | Push-pull networks are ubiquitous in signal transduction pathways in both prokaryotic and eukaryotic cells. They allow cells to strongly amplify signals via the mechanism of zero-order ultrasensitivity. In a push-pull network, two antagonistic enzymes control the activity of a protein by covalent modification. These enzymes are often uniformly distributed in the cytoplasm. They can, however, also be colocalized in space, for instance, near the pole of the cell. Moreover, it is increasingly recognized that these enzymes can also be spatially separated, leading to gradients of the active form of the messenger protein. Here, we investigate the consequences of the spatial distributions of the enzymes for the amplification properties of push-pull networks. Our calculations reveal that enzyme localization by itself can have a dramatic effect on the gain. The gain is maximized when the two enzymes are either uniformly distributed or colocalized in one region in the cell. Depending on the diffusion constants, however, the sharpness of the response can be strongly reduced when the enzymes are spatially separated. We discuss how our predictions could be tested experimentally. |
1507.00368 | Piotr S{\l}owi\'nski | Piotr S{\l}owi\'nski, Chao Zhai, Francesco Alderisio, Robin Salesse,
Mathieu Gueugnon, Ludovic Marin, Benoit G. Bardy, Mario di Bernardo, and
Krasimira Tsaneva-Atanasova | Dynamic similarity promotes interpersonal coordination in joint-action | null | J. R. Soc. Interface 2016, 13, 20151093 | 10.1098/rsif.2015.1093 | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human movement has been studied for decades and dynamic laws of motion that
are common to all humans have been derived. Yet, every individual moves
differently from everyone else (faster/slower, harder/smoother etc). We propose
here an index of such variability, namely an individual motor signature (IMS)
able to capture the subtle differences in the way each of us moves. We show
that the IMS of a person is time-invariant and that it significantly differs
from those of other individuals. This allows us to quantify the dynamic
similarity, a measure of rapport between dynamics of different individuals'
movements, and demonstrate that it facilitates coordination during interaction.
We use our measure to confirm a key prediction of the theory of similarity that
coordination between two individuals performing a joint-action task is higher
if their motions share similar dynamic features. Furthermore, we use a virtual
avatar driven by an interactive cognitive architecture based on feedback
control theory to explore the effects of different kinematic features of the
avatar motion on the coordination with human players.
| [
{
"created": "Wed, 1 Jul 2015 20:41:07 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Dec 2015 08:16:07 GMT",
"version": "v2"
}
] | 2016-03-24 | [
[
"Słowiński",
"Piotr",
""
],
[
"Zhai",
"Chao",
""
],
[
"Alderisio",
"Francesco",
""
],
[
"Salesse",
"Robin",
""
],
[
"Gueugnon",
"Mathieu",
""
],
[
"Marin",
"Ludovic",
""
],
[
"Bardy",
"Benoit G.",
""
],
[
"di Bernardo",
"Mario",
""
],
[
"Tsaneva-Atanasova",
"Krasimira",
""
]
] | Human movement has been studied for decades and dynamic laws of motion that are common to all humans have been derived. Yet, every individual moves differently from everyone else (faster/slower, harder/smoother etc). We propose here an index of such variability, namely an individual motor signature (IMS) able to capture the subtle differences in the way each of us moves. We show that the IMS of a person is time-invariant and that it significantly differs from those of other individuals. This allows us to quantify the dynamic similarity, a measure of rapport between dynamics of different individuals' movements, and demonstrate that it facilitates coordination during interaction. We use our measure to confirm a key prediction of the theory of similarity that coordination between two individuals performing a joint-action task is higher if their motions share similar dynamic features. Furthermore, we use a virtual avatar driven by an interactive cognitive architecture based on feedback control theory to explore the effects of different kinematic features of the avatar motion on the coordination with human players. |
2303.12651 | Joshua Kaste | Joshua A.M. Kaste and Yair Shachar-Hill | Model Validation and Selection in Metabolic Flux Analysis and Flux
Balance Analysis | 23 pages, 2 figures, 1 table | null | null | null | q-bio.MN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | 13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are
widely used to investigate the operation of biochemical networks in both
biological and biotechnological research. Both of these methods use metabolic
reaction network models of metabolism operating at steady state, so that
reaction rates (fluxes) and the levels of metabolic intermediates are
constrained to be invariant. They provide estimated (MFA) or predicted (FBA)
values of the fluxes through the network in vivo, which cannot be measured
directly. A number of approaches have been taken to test the reliability of
estimates and predictions from constraint-based methods and to decide on and/or
discriminate between alternative model architectures. Despite advances in other
areas of the statistical evaluation of metabolic models, validation and model
selection methods have been underappreciated and underexplored. We review the
history and state-of-the-art in constraint-based metabolic model validation and
model selection. Applications and limitations of the X2-test of
goodness-of-fit, the most widely used quantitative validation and selection
approach in 13C-MFA, are discussed, and complementary and alternative forms of
validation and selection are proposed. A combined model validation and
selection framework for 13C-MFA incorporating metabolite pool size information
that leverages new developments in the field is presented and advocated for.
Finally, we discuss how the adoption of robust validation and selection
procedures can enhance confidence in constraint-based modeling as a whole and
ultimately facilitate more widespread use of FBA in biotechnology in
particular.
| [
{
"created": "Wed, 22 Mar 2023 15:32:01 GMT",
"version": "v1"
}
] | 2023-03-23 | [
[
"Kaste",
"Joshua A. M.",
""
],
[
"Shachar-Hill",
"Yair",
""
]
] | 13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both of these methods use metabolic reaction network models of metabolism operating at steady state, so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. A number of approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to decide on and/or discriminate between alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the X2-test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how the adoption of robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology in particular. |
1503.08527 | Alexey Shipunov | Brandon Chrisman, Allison Rabe, Ranelle Ivens, Sarah Lopez, and Alexey
Shipunov | The ecological impact of flooding: a study of tree damage | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/publicdomain/ | The objective of this research was to identify factors affecting tree damage
in the historical Minot flood of 2011. We hypothesized that tree height,
identity, origin, and maximum water height affect in the severity of damage
sustained by a tree in a flood event. All these factors were significant but
highly interactive. The results from this research can influence planting
practices in valleys and other flood prone areas to mitigate future damage.
| [
{
"created": "Mon, 30 Mar 2015 03:21:01 GMT",
"version": "v1"
}
] | 2015-03-31 | [
[
"Chrisman",
"Brandon",
""
],
[
"Rabe",
"Allison",
""
],
[
"Ivens",
"Ranelle",
""
],
[
"Lopez",
"Sarah",
""
],
[
"Shipunov",
"Alexey",
""
]
] | The objective of this research was to identify factors affecting tree damage in the historical Minot flood of 2011. We hypothesized that tree height, identity, origin, and maximum water height affect in the severity of damage sustained by a tree in a flood event. All these factors were significant but highly interactive. The results from this research can influence planting practices in valleys and other flood prone areas to mitigate future damage. |
1702.00360 | Thomas Ouldridge | Thomas E. Ouldridge | The importance of thermodynamics for molecular systems, and the
importance of molecular systems for thermodynamics | To appear in Nat. Comput. Special issue for DNA22 | null | null | null | q-bio.MN cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Improved understanding of molecular systems has only emphasised the
sophistication of networks within the cell. Simultaneously, the advance of
nucleic acid nanotechnology, a platform within which reactions can be
exquisitely controlled, has made the development of artificial architectures
and devices possible. Vital to this progress has been a solid foundation in the
thermodynamics of molecular systems. In this pedagogical review and
perspective, I will discuss how thermodynamics determines both the overall
potential of molecular networks, and the minute details of design. I will then
argue that, in turn, the need to understand molecular systems is helping to
drive the development of theories of thermodynamics at the microscopic scale.
| [
{
"created": "Wed, 1 Feb 2017 17:14:50 GMT",
"version": "v1"
},
{
"created": "Mon, 2 Oct 2017 17:02:05 GMT",
"version": "v2"
}
] | 2017-10-03 | [
[
"Ouldridge",
"Thomas E.",
""
]
] | Improved understanding of molecular systems has only emphasised the sophistication of networks within the cell. Simultaneously, the advance of nucleic acid nanotechnology, a platform within which reactions can be exquisitely controlled, has made the development of artificial architectures and devices possible. Vital to this progress has been a solid foundation in the thermodynamics of molecular systems. In this pedagogical review and perspective, I will discuss how thermodynamics determines both the overall potential of molecular networks, and the minute details of design. I will then argue that, in turn, the need to understand molecular systems is helping to drive the development of theories of thermodynamics at the microscopic scale. |
2402.00207 | Lucas Machado Moschen | Lucas Machado Moschen, Mar\'ia Soledad Aronna | Optimal vaccination strategies on networks and in metropolitan areas | 29 pages, 23 figures | null | null | null | q-bio.PE math.OC q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This study presents a mathematical model for optimal vaccination strategies
in interconnected metropolitan areas, considering commuting patterns. It is a
compartmental model with a vaccination rate for each city, acting as a control
function. The commuting patterns are incorporated through a weighted adjacency
matrix and a parameter that selects day and night periods. The optimal control
problem is formulated to minimize a functional cost that balances the number of
hospitalizations and vaccines, including restrictions of a weekly availability
cap and an application capacity of vaccines per unit of time. The key findings
of this work are bounds for the basic reproduction number, particularly in the
case of a metropolitan area, and the study of the optimal control problem.
Theoretical analysis and numerical simulations provide insights into disease
dynamics and the effectiveness of control measures. The research highlights the
importance of prioritizing vaccination in the capital to better control the
disease spread, as we depicted in our numerical simulations. This model serves
as a tool to improve resource allocation in epidemic control across
metropolitan regions.
| [
{
"created": "Wed, 31 Jan 2024 22:09:22 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Apr 2024 15:03:50 GMT",
"version": "v2"
}
] | 2024-04-29 | [
[
"Moschen",
"Lucas Machado",
""
],
[
"Aronna",
"María Soledad",
""
]
] | This study presents a mathematical model for optimal vaccination strategies in interconnected metropolitan areas, considering commuting patterns. It is a compartmental model with a vaccination rate for each city, acting as a control function. The commuting patterns are incorporated through a weighted adjacency matrix and a parameter that selects day and night periods. The optimal control problem is formulated to minimize a functional cost that balances the number of hospitalizations and vaccines, including restrictions of a weekly availability cap and an application capacity of vaccines per unit of time. The key findings of this work are bounds for the basic reproduction number, particularly in the case of a metropolitan area, and the study of the optimal control problem. Theoretical analysis and numerical simulations provide insights into disease dynamics and the effectiveness of control measures. The research highlights the importance of prioritizing vaccination in the capital to better control the disease spread, as we depicted in our numerical simulations. This model serves as a tool to improve resource allocation in epidemic control across metropolitan regions. |
1002.1023 | Michael B\"orsch | Michael Boersch | Targeting cytochrome C oxidase in mitochondria with Pt(II)-porphyrins
for Photodynamic Therapy | 11 pages, 5 figures | null | 10.1117/12.841284 | null | q-bio.BM physics.bio-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mitochondria are the power house of living cells, where the synthesis of the
chemical "energy currency" adenosine triphosphate (ATP) occurs. Oxidative
phosphorylation by a series of membrane protein complexes I to IV, that is, the
electron transport chain, is the source of the electrochemical potential
difference or proton motive force (PMF) of protons across the inner
mitochondrial membrane. The PMF is required for ATP production by complex V of
the electron transport chain, i.e. by FoF1-ATP synthase. Destroying cytochrome
C oxidase (COX; complex IV) in Photodynamic Therapy (PDT) is achieved by the
cationic photosensitizer Pt(II)-TMPyP. Electron microscopy revealed the
disruption of the mitochondrial christae as a primary step of PDT. Time
resolved phosphorescence measurements identified COX as the binding site for
Pt(II)-TMPyP in living HeLa cells. As this photosensitizer competed with
cytochrome C in binding to COX, destruction of COX might not only disturb ATP
synthesis but could expedite the release of cytochrome C to the cytosol
inducing apoptosis.
| [
{
"created": "Thu, 4 Feb 2010 15:43:01 GMT",
"version": "v1"
}
] | 2015-05-18 | [
[
"Boersch",
"Michael",
""
]
] | Mitochondria are the power house of living cells, where the synthesis of the chemical "energy currency" adenosine triphosphate (ATP) occurs. Oxidative phosphorylation by a series of membrane protein complexes I to IV, that is, the electron transport chain, is the source of the electrochemical potential difference or proton motive force (PMF) of protons across the inner mitochondrial membrane. The PMF is required for ATP production by complex V of the electron transport chain, i.e. by FoF1-ATP synthase. Destroying cytochrome C oxidase (COX; complex IV) in Photodynamic Therapy (PDT) is achieved by the cationic photosensitizer Pt(II)-TMPyP. Electron microscopy revealed the disruption of the mitochondrial christae as a primary step of PDT. Time resolved phosphorescence measurements identified COX as the binding site for Pt(II)-TMPyP in living HeLa cells. As this photosensitizer competed with cytochrome C in binding to COX, destruction of COX might not only disturb ATP synthesis but could expedite the release of cytochrome C to the cytosol inducing apoptosis. |
1801.01823 | Ulisse Ferrari | Ulisse Ferrari, Stephane Deny, Matthew Chalk, Gasper Tkacik, Olivier
Marre, Thierry Mora | Separating intrinsic interactions from extrinsic correlations in a
network of sensory neurons | null | Phys. Rev. E 98, 042410 (2018) | 10.1103/PhysRevE.98.042410 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Correlations in sensory neural networks have both extrinsic and intrinsic
origins. Extrinsic or stimulus correlations arise from shared inputs to the
network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise
correlations reflect biophysical mechanisms of interactions between neurons,
which are expected to be robust to changes of the stimulus ensemble. Despite
the importance of this distinction for understanding how sensory networks
encode information collectively, no method exists to reliably separate
intrinsic interactions from extrinsic correlations in neural activity data,
limiting our ability to build predictive models of the network response. In
this paper we introduce a general strategy to infer {population models of
interacting neurons that collectively encode stimulus information}. The key to
disentangling intrinsic from extrinsic correlations is to infer the {couplings
between neurons} separately from the encoding model, and to combine the two
using corrections calculated in a mean-field approximation. We demonstrate the
effectiveness of this approach on retinal recordings. The same coupling network
is inferred from responses to radically different stimulus ensembles, showing
that these couplings indeed reflect stimulus-independent interactions between
neurons. The inferred model predicts accurately the collective response of
retinal ganglion cell populations as a function of the stimulus.
| [
{
"created": "Fri, 5 Jan 2018 16:36:56 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Feb 2018 15:54:44 GMT",
"version": "v2"
}
] | 2018-11-05 | [
[
"Ferrari",
"Ulisse",
""
],
[
"Deny",
"Stephane",
""
],
[
"Chalk",
"Matthew",
""
],
[
"Tkacik",
"Gasper",
""
],
[
"Marre",
"Olivier",
""
],
[
"Mora",
"Thierry",
""
]
] | Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer {population models of interacting neurons that collectively encode stimulus information}. The key to disentangling intrinsic from extrinsic correlations is to infer the {couplings between neurons} separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus. |
2211.05658 | Mo Wang | Mo Wang, Kexin Lou, Zeming Liu, Pengfei Wei, Quanying Liu | Multi-objective optimization via evolutionary algorithm (MOVEA) for
high-definition transcranial electrical stimulation of the human brain | null | NeuroImage, Volume 280, 2020 | 10.1016/j.neuroimage.2023.120348 | null | q-bio.QM cs.NE q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Designing a transcranial electrical stimulation (TES) strategy requires
considering multiple objectives, such as intensity in the target area,
focality, stimulation depth, and avoidance zone, which are often mutually
exclusive. A computational framework for optimizing different strategies and
comparing trade-offs between these objectives is currently lacking. In this
paper, we propose a general framework called multi-objective optimization via
evolutionary algorithms (MOVEA) to address the non-convex optimization problem
in designing TES strategies without predefined direction. MOVEA enables
simultaneous optimization of multiple targets through Pareto optimization,
generating a Pareto front after a single run without manual weight adjustment
and allowing easy expansion to more targets. This Pareto front consists of
optimal solutions that meet various requirements while respecting trade-off
relationships between conflicting objectives such as intensity and focality.
MOVEA is versatile and suitable for both transcranial alternating current
stimulation (tACS) and transcranial temporal interference stimulation (tTIS)
based on high definition (HD) and two-pair systems. We performed a
comprehensive comparison between tACS and tTIS in terms of intensity, focality,
and steerability for targets at different depths.MOVEA facilitates the
optimization of TES based on specific objectives and constraints, advancing
tTIS and tACS-based neuromodulation in understanding the causal relationship
between brain regions and cognitive functions and in treating diseases. The
code for MOVEA is available at https://github.com/ncclabsustech/MOVEA.
| [
{
"created": "Thu, 10 Nov 2022 15:42:06 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Apr 2023 19:55:07 GMT",
"version": "v2"
}
] | 2023-09-13 | [
[
"Wang",
"Mo",
""
],
[
"Lou",
"Kexin",
""
],
[
"Liu",
"Zeming",
""
],
[
"Wei",
"Pengfei",
""
],
[
"Liu",
"Quanying",
""
]
] | Designing a transcranial electrical stimulation (TES) strategy requires considering multiple objectives, such as intensity in the target area, focality, stimulation depth, and avoidance zone, which are often mutually exclusive. A computational framework for optimizing different strategies and comparing trade-offs between these objectives is currently lacking. In this paper, we propose a general framework called multi-objective optimization via evolutionary algorithms (MOVEA) to address the non-convex optimization problem in designing TES strategies without predefined direction. MOVEA enables simultaneous optimization of multiple targets through Pareto optimization, generating a Pareto front after a single run without manual weight adjustment and allowing easy expansion to more targets. This Pareto front consists of optimal solutions that meet various requirements while respecting trade-off relationships between conflicting objectives such as intensity and focality. MOVEA is versatile and suitable for both transcranial alternating current stimulation (tACS) and transcranial temporal interference stimulation (tTIS) based on high definition (HD) and two-pair systems. We performed a comprehensive comparison between tACS and tTIS in terms of intensity, focality, and steerability for targets at different depths.MOVEA facilitates the optimization of TES based on specific objectives and constraints, advancing tTIS and tACS-based neuromodulation in understanding the causal relationship between brain regions and cognitive functions and in treating diseases. The code for MOVEA is available at https://github.com/ncclabsustech/MOVEA. |
q-bio/0703048 | Azi Lipshtat | Azi Lipshtat | An "All Possible Steps" Approach to the Accelerated Use of Gillespie's
Algorithm | Accepted for publication at the Journal of Chemical Physics. 19
pages, including 2 Tables and 4 Figures | null | 10.1063/1.2730507 | null | q-bio.QM physics.comp-ph | null | Many physical and biological processes are stochastic in nature.
Computational models and simulations of such processes are a mathematical and
computational challenge. The basic stochastic simulation algorithm was
published by D. Gillespie about three decades ago [D.T. Gillespie, J. Phys.
Chem. {\bf 81}, 2340, (1977)]. Since then, intensive work has been done to make
the algorithm more efficient in terms of running time. All accelerated versions
of the algorithm are aimed at minimizing the running time required to produce a
stochastic trajectory in state space. In these simulations, a necessary
condition for reliable statistics is averaging over a large number of
simulations. In this study I present a new accelerating approach which does not
alter the stochastic algorithm, but reduces the number of required runs. By
analysis of collected data I demonstrate high precision levels with fewer
simulations. Moreover, the suggested approach provides a good estimation of
statistical error, which may serve as a tool for determining the number of
required runs.
| [
{
"created": "Thu, 22 Mar 2007 12:57:11 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Lipshtat",
"Azi",
""
]
] | Many physical and biological processes are stochastic in nature. Computational models and simulations of such processes are a mathematical and computational challenge. The basic stochastic simulation algorithm was published by D. Gillespie about three decades ago [D.T. Gillespie, J. Phys. Chem. {\bf 81}, 2340, (1977)]. Since then, intensive work has been done to make the algorithm more efficient in terms of running time. All accelerated versions of the algorithm are aimed at minimizing the running time required to produce a stochastic trajectory in state space. In these simulations, a necessary condition for reliable statistics is averaging over a large number of simulations. In this study I present a new accelerating approach which does not alter the stochastic algorithm, but reduces the number of required runs. By analysis of collected data I demonstrate high precision levels with fewer simulations. Moreover, the suggested approach provides a good estimation of statistical error, which may serve as a tool for determining the number of required runs. |
2307.04052 | Tinglin Huang | Tinglin Huang, Ziniu Hu, Rex Ying | Learning to Group Auxiliary Datasets for Molecule | Accepted at NeurIPS 2023, Camera Ready Version | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The limited availability of annotations in small molecule datasets presents a
challenge to machine learning models. To address this, one common strategy is
to collaborate with additional auxiliary datasets. However, having more data
does not always guarantee improvements. Negative transfer can occur when the
knowledge in the target dataset differs or contradicts that of the auxiliary
molecule datasets. In light of this, identifying the auxiliary molecule
datasets that can benefit the target dataset when jointly trained remains a
critical and unresolved problem. Through an empirical analysis, we observe that
combining graph structure similarity and task similarity can serve as a more
reliable indicator for identifying high-affinity auxiliary datasets. Motivated
by this insight, we propose MolGroup, which separates the dataset affinity into
task and structure affinity to predict the potential benefits of each auxiliary
molecule dataset. MolGroup achieves this by utilizing a routing mechanism
optimized through a bi-level optimization framework. Empowered by the meta
gradient, the routing mechanism is optimized toward maximizing the target
dataset's performance and quantifies the affinity as the gating score. As a
result, MolGroup is capable of predicting the optimal combination of auxiliary
datasets for each target dataset. Our extensive experiments demonstrate the
efficiency and effectiveness of MolGroup, showing an average improvement of
4.41%/3.47% for GIN/Graphormer trained with the group of molecule datasets
selected by MolGroup on 11 target molecule datasets.
| [
{
"created": "Sat, 8 Jul 2023 22:02:22 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Nov 2023 23:03:35 GMT",
"version": "v2"
}
] | 2023-11-10 | [
[
"Huang",
"Tinglin",
""
],
[
"Hu",
"Ziniu",
""
],
[
"Ying",
"Rex",
""
]
] | The limited availability of annotations in small molecule datasets presents a challenge to machine learning models. To address this, one common strategy is to collaborate with additional auxiliary datasets. However, having more data does not always guarantee improvements. Negative transfer can occur when the knowledge in the target dataset differs or contradicts that of the auxiliary molecule datasets. In light of this, identifying the auxiliary molecule datasets that can benefit the target dataset when jointly trained remains a critical and unresolved problem. Through an empirical analysis, we observe that combining graph structure similarity and task similarity can serve as a more reliable indicator for identifying high-affinity auxiliary datasets. Motivated by this insight, we propose MolGroup, which separates the dataset affinity into task and structure affinity to predict the potential benefits of each auxiliary molecule dataset. MolGroup achieves this by utilizing a routing mechanism optimized through a bi-level optimization framework. Empowered by the meta gradient, the routing mechanism is optimized toward maximizing the target dataset's performance and quantifies the affinity as the gating score. As a result, MolGroup is capable of predicting the optimal combination of auxiliary datasets for each target dataset. Our extensive experiments demonstrate the efficiency and effectiveness of MolGroup, showing an average improvement of 4.41%/3.47% for GIN/Graphormer trained with the group of molecule datasets selected by MolGroup on 11 target molecule datasets. |
1309.0936 | Namiko Mitarai | Namiko Mitarai and Steen Pedersen | Control of ribosome traffic by position-dependent choice of synonymous
codons | 12 pages, 6 Figures. This is an author-created, un-copyedited version
of an article accepted for publication in Physical Biology. IOP Publishing
Ltd is not responsible for any errors or omissions in this version of the
manuscript or any version derived from it | Phys. Biol. 10 (2013) 056011 | 10.1088/1478-3975/10/5/056011 | null | q-bio.SC q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Messenger RNA encodes a sequence of amino acids by using codons. For most
amino acids there are multiple synonymous codons that can encode the amino
acid. The translation speed can vary from one codon to another, thus there is
room for changing the ribosome speed while keeping the amino acid sequence and
hence the resulting protein. Recently, it has been noticed that the choice of
the synonymous codon, via the resulting distribution of slow- and
fast-translated codons, affects not only on the average speed of one ribosome
translating the messenger RNA (mRNA) but also might have an effect on nearby
ribosomes by affecting the appearance of "traffic jams" where multiple
ribosomes collide and form queues. To test this "context effect" further, we
here investigate the effect of the sequence of synonymous codons on the
ribosome traffic by using a ribosome traffic model with codon-dependent rates,
estimated from experiments. We compare the ribosome traffic on wild type
sequences and sequences where the synonymous codons were swapped randomly. By
simulating translation of 87 genes, we demonstrate that the wild type
sequences, especially those with a high bias in codon usage, tend to have the
ability to reduce ribosome collisions, hence optimizing the cellular investment
in the translation apparatus. The magnitude of such reduction of the
translation time might have a significant impact on the cellular growth rate
and thereby have importance for the survival of the species.
| [
{
"created": "Wed, 4 Sep 2013 08:05:06 GMT",
"version": "v1"
}
] | 2013-10-10 | [
[
"Mitarai",
"Namiko",
""
],
[
"Pedersen",
"Steen",
""
]
] | Messenger RNA encodes a sequence of amino acids by using codons. For most amino acids there are multiple synonymous codons that can encode the amino acid. The translation speed can vary from one codon to another, thus there is room for changing the ribosome speed while keeping the amino acid sequence and hence the resulting protein. Recently, it has been noticed that the choice of the synonymous codon, via the resulting distribution of slow- and fast-translated codons, affects not only on the average speed of one ribosome translating the messenger RNA (mRNA) but also might have an effect on nearby ribosomes by affecting the appearance of "traffic jams" where multiple ribosomes collide and form queues. To test this "context effect" further, we here investigate the effect of the sequence of synonymous codons on the ribosome traffic by using a ribosome traffic model with codon-dependent rates, estimated from experiments. We compare the ribosome traffic on wild type sequences and sequences where the synonymous codons were swapped randomly. By simulating translation of 87 genes, we demonstrate that the wild type sequences, especially those with a high bias in codon usage, tend to have the ability to reduce ribosome collisions, hence optimizing the cellular investment in the translation apparatus. The magnitude of such reduction of the translation time might have a significant impact on the cellular growth rate and thereby have importance for the survival of the species. |
2206.12997 | Corey Keller | Juha Gogulski, Jessica M. Ross, Austin Talbot, Christopher Cline,
Francesco L Donati, Saachi Munot, Naryeong Kim, Ciara Gibbs, Nikita Bastin,
Jessica Yang, Christopher B. Minasi, Manjima Sarkar, Jade Truong, Corey J
Keller | Personalized rTMS for Depression: A Review | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Personalized treatments are gaining momentum across all fields of medicine.
Precision medicine can be applied to neuromodulatory techniques, where focused
brain stimulation treatments such as repetitive transcranial magnetic
stimulation (rTMS) are used to modulate brain circuits and alleviate clinical
symptoms. rTMS is well-tolerated and clinically effective for
treatment-resistant depression (TRD) and other neuropsychiatric disorders.
However, despite its wide stimulation parameter space (location, angle,
pattern, frequency, and intensity can be adjusted), rTMS is currently applied
in a one-size-fits-all manner, potentially contributing to its suboptimal
clinical response (~50%). In this review, we examine components of rTMS that
can be optimized to account for inter-individual variability in neural function
and anatomy. We discuss current treatment options for TRD, the neural
mechanisms thought to underlie treatment, differences in FDA-cleared devices,
targeting strategies, stimulation parameter selection, and adaptive closed-loop
rTMS to improve treatment outcomes. We suggest that better understanding of the
wide and modifiable parameter space of rTMS will greatly improve clinical
outcome.
| [
{
"created": "Mon, 27 Jun 2022 00:04:07 GMT",
"version": "v1"
}
] | 2022-07-20 | [
[
"Gogulski",
"Juha",
""
],
[
"Ross",
"Jessica M.",
""
],
[
"Talbot",
"Austin",
""
],
[
"Cline",
"Christopher",
""
],
[
"Donati",
"Francesco L",
""
],
[
"Munot",
"Saachi",
""
],
[
"Kim",
"Naryeong",
""
],
[
"Gibbs",
"Ciara",
""
],
[
"Bastin",
"Nikita",
""
],
[
"Yang",
"Jessica",
""
],
[
"Minasi",
"Christopher B.",
""
],
[
"Sarkar",
"Manjima",
""
],
[
"Truong",
"Jade",
""
],
[
"Keller",
"Corey J",
""
]
] | Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, where focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) are used to modulate brain circuits and alleviate clinical symptoms. rTMS is well-tolerated and clinically effective for treatment-resistant depression (TRD) and other neuropsychiatric disorders. However, despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (~50%). In this review, we examine components of rTMS that can be optimized to account for inter-individual variability in neural function and anatomy. We discuss current treatment options for TRD, the neural mechanisms thought to underlie treatment, differences in FDA-cleared devices, targeting strategies, stimulation parameter selection, and adaptive closed-loop rTMS to improve treatment outcomes. We suggest that better understanding of the wide and modifiable parameter space of rTMS will greatly improve clinical outcome. |
2105.06036 | Americo Cunha Jr | Eber Dantas, Michel Tosin, Americo Cunha Jr | Calibration of a SEIR-SEI epidemic model to describe the Zika virus
outbreak in Brazil | null | Applied Mathematics and Computation, vol. 338, pp. 249-259, 2018 | 10.1016/j.amc.2018.06.024 | null | q-bio.PE cs.NA math.DS math.NA math.OC stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multiple instances of Zika virus epidemic have been reported around the world
in the last two decades, turning the related illness into an international
concern. In this context the use of mathematical models for epidemics is of
great importance, since they are useful tools to study the underlying outbreak
numbers and allow one to test the effectiveness of different strategies used to
combat the associated diseases. This work deals with the development and
calibration of an epidemic model to describe the 2016 outbreak of Zika virus in
Brazil. A system of 8 differential equations with 8 parameters is employed to
model the evolution of the infection through two populations. Nominal values
for the model parameters are estimated from the literature. An inverse problem
is formulated and solved by comparing the system response to real data from the
outbreak. The calibrated results presents realistic parameters and returns
reasonable descriptions, with the curve shape similar to the outbreak evolution
and peak value close to the highest number of infected people during 2016.
Considerations about the lack of data for some initial conditions are also made
through an analysis over the response behavior according to their change in
value.
| [
{
"created": "Thu, 13 May 2021 01:51:20 GMT",
"version": "v1"
}
] | 2021-05-14 | [
[
"Dantas",
"Eber",
""
],
[
"Tosin",
"Michel",
""
],
[
"Cunha",
"Americo",
"Jr"
]
] | Multiple instances of Zika virus epidemic have been reported around the world in the last two decades, turning the related illness into an international concern. In this context the use of mathematical models for epidemics is of great importance, since they are useful tools to study the underlying outbreak numbers and allow one to test the effectiveness of different strategies used to combat the associated diseases. This work deals with the development and calibration of an epidemic model to describe the 2016 outbreak of Zika virus in Brazil. A system of 8 differential equations with 8 parameters is employed to model the evolution of the infection through two populations. Nominal values for the model parameters are estimated from the literature. An inverse problem is formulated and solved by comparing the system response to real data from the outbreak. The calibrated results presents realistic parameters and returns reasonable descriptions, with the curve shape similar to the outbreak evolution and peak value close to the highest number of infected people during 2016. Considerations about the lack of data for some initial conditions are also made through an analysis over the response behavior according to their change in value. |
q-bio/0501018 | Peter F. Arndt | Peter F. Arndt and Terence Hwa | Identification and Measurement of Neighbor Dependent Nucleotide
Substitution Processes | 15 pages, 3 figures | null | null | null | q-bio.GN | null | The presence of neighbor dependencies generated a specific pattern of
dinucleotide frequencies in all organisms. Especially, the
CpG-methylation-deamination process is the predominant substitution process in
vertebrates and needs to be incorporated into a more realistic model for
nucleotide substitutions. Based on a general framework of nucleotide
substitutions we develop a method that is able to identify the most relevant
neighbor dependent substitution processes, measure their strength, and judge
their importance to be included into the modeling. Starting from a model for
neighbor independent nucleotide substitution we successively add neighbor
dependent substitution processes in the order of their ability to increase the
likelihood of the model describing given data. The analysis of neighbor
dependent nucleotide substitutions in human, zebrafish and fruit fly is
presented. A web server to perform the presented analysis is publicly
available.
| [
{
"created": "Thu, 13 Jan 2005 12:22:47 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Arndt",
"Peter F.",
""
],
[
"Hwa",
"Terence",
""
]
] | The presence of neighbor dependencies generated a specific pattern of dinucleotide frequencies in all organisms. Especially, the CpG-methylation-deamination process is the predominant substitution process in vertebrates and needs to be incorporated into a more realistic model for nucleotide substitutions. Based on a general framework of nucleotide substitutions we develop a method that is able to identify the most relevant neighbor dependent substitution processes, measure their strength, and judge their importance to be included into the modeling. Starting from a model for neighbor independent nucleotide substitution we successively add neighbor dependent substitution processes in the order of their ability to increase the likelihood of the model describing given data. The analysis of neighbor dependent nucleotide substitutions in human, zebrafish and fruit fly is presented. A web server to perform the presented analysis is publicly available. |
1902.10168 | Leonardo Pellegrina | Leonardo Pellegrina, Cinzia Pizzi, Fabio Vandin | Fast Approximation of Frequent $k$-mers and Applications to Metagenomics | Accepted for RECOMB 2019 | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Estimating the abundances of all $k$-mers in a set of biological sequences is
a fundamental and challenging problem with many applications in biological
analysis. While several methods have been designed for the exact or approximate
solution of this problem, they all require to process the entire dataset, that
can be extremely expensive for high-throughput sequencing datasets. While in
some applications it is crucial to estimate all $k$-mers and their abundances,
in other situations reporting only frequent $k$-mers, that appear with
relatively high frequency in a dataset, may suffice. This is the case, for
example, in the computation of $k$-mers' abundance-based distances among
datasets of reads, commonly used in metagenomic analyses. In this work, we
develop, analyze, and test, a sampling-based approach, called SAKEIMA, to
approximate the frequent $k$-mers and their frequencies in a high-throughput
sequencing dataset while providing rigorous guarantees on the quality of the
approximation. SAKEIMA employs an advanced sampling scheme and we show how the
characterization of the VC dimension, a core concept from statistical learning
theory, of a properly defined set of functions leads to practical bounds on the
sample size required for a rigorous approximation. Our experimental evaluation
shows that SAKEIMA allows to rigorously approximate frequent $k$-mers by
processing only a fraction of a dataset and that the frequencies estimated by
SAKEIMA lead to accurate estimates of $k$-mer based distances between
high-throughput sequencing datasets. Overall, SAKEIMA is an efficient and
rigorous tool to estimate $k$-mers abundances providing significant speed-ups
in the analysis of large sequencing datasets.
| [
{
"created": "Tue, 26 Feb 2019 19:09:05 GMT",
"version": "v1"
}
] | 2019-02-28 | [
[
"Pellegrina",
"Leonardo",
""
],
[
"Pizzi",
"Cinzia",
""
],
[
"Vandin",
"Fabio",
""
]
] | Estimating the abundances of all $k$-mers in a set of biological sequences is a fundamental and challenging problem with many applications in biological analysis. While several methods have been designed for the exact or approximate solution of this problem, they all require to process the entire dataset, that can be extremely expensive for high-throughput sequencing datasets. While in some applications it is crucial to estimate all $k$-mers and their abundances, in other situations reporting only frequent $k$-mers, that appear with relatively high frequency in a dataset, may suffice. This is the case, for example, in the computation of $k$-mers' abundance-based distances among datasets of reads, commonly used in metagenomic analyses. In this work, we develop, analyze, and test, a sampling-based approach, called SAKEIMA, to approximate the frequent $k$-mers and their frequencies in a high-throughput sequencing dataset while providing rigorous guarantees on the quality of the approximation. SAKEIMA employs an advanced sampling scheme and we show how the characterization of the VC dimension, a core concept from statistical learning theory, of a properly defined set of functions leads to practical bounds on the sample size required for a rigorous approximation. Our experimental evaluation shows that SAKEIMA allows to rigorously approximate frequent $k$-mers by processing only a fraction of a dataset and that the frequencies estimated by SAKEIMA lead to accurate estimates of $k$-mer based distances between high-throughput sequencing datasets. Overall, SAKEIMA is an efficient and rigorous tool to estimate $k$-mers abundances providing significant speed-ups in the analysis of large sequencing datasets. |
2309.13565 | Joydeb Gomasta Mr. | Hasina Sultana, Sharmila Rani Mallick, Jahidul Hassan, Joydeb Gomasta,
Md. Humayun Kabir, Md. Sakibul Alam Sakib, Mahmuda Hossen, Muhammad Mustakim
Billah, Emrul Kayesh | Nutritional composition and bioactive compounds of mini watermelon
genotypes in Bangladesh | 22 pages, 6 tables, 3 figures | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | Given the present rising trends in changing lifestyle and consumption
patterns, watermelon production has shifted from big to small-sized fruits
having desirable quality attributes. Hence, analyses of fruit quality traits of
mini watermelon are crucial to develop improved cultivars with enhanced
nutritional compositions, consumer-preferred traits and extended storage life.
In this context, fruit morphological and nutritional attributes of five mini
watermelon genotypes namely BARI watermelon 1 (W1), BARI watermelon 2 (W2),
L-32468 (W3), L-32236 (W4) and L-32394 (W5) were evaluated to appraise
promising genotypes with better fruit quality. The evaluated genotypes
expressed different levels of diversity for fruit physical qualitative traits
including differences in shape, rind and flesh color and texture. The study
also revealed significant variability among the genotypes regarding all
observed fruit morphological and nutritional aspects as well as bioactive
compounds. Among the studied genotypes, W1 stood out with the highest TSS as
well as rind vitamin C and total phenolic content accompanied by higher fruit
weight and thick rind. On the other hand, W3 genotype was featured with higher
amount of \b{eta} carotene, total phenolic and flavonoid content in its flesh
along with rind enriched with \b{eta} carotene and minerals. However,
comparatively higher amount of sugar and total flavonoid content was recorded
in the rind of W5 genotype. Therefore, W1 and W3 could be exploited for table
purpose and using in breeding program to develop mini watermelon cultivars with
more attractive fruits in terms of quality acceptance and nutritional value in
Bangladesh. Furthermore, rind of BARI watermelon 1 and L-32394 could be
considered as the potential cheap source of bioactive compounds to be used for
dietary and industrial purpose which would decrease the solid waste in the
environment.
| [
{
"created": "Sun, 24 Sep 2023 06:43:00 GMT",
"version": "v1"
}
] | 2023-09-26 | [
[
"Sultana",
"Hasina",
""
],
[
"Mallick",
"Sharmila Rani",
""
],
[
"Hassan",
"Jahidul",
""
],
[
"Gomasta",
"Joydeb",
""
],
[
"Kabir",
"Md. Humayun",
""
],
[
"Sakib",
"Md. Sakibul Alam",
""
],
[
"Hossen",
"Mahmuda",
""
],
[
"Billah",
"Muhammad Mustakim",
""
],
[
"Kayesh",
"Emrul",
""
]
] | Given the present rising trends in changing lifestyle and consumption patterns, watermelon production has shifted from big to small-sized fruits having desirable quality attributes. Hence, analyses of fruit quality traits of mini watermelon are crucial to develop improved cultivars with enhanced nutritional compositions, consumer-preferred traits and extended storage life. In this context, fruit morphological and nutritional attributes of five mini watermelon genotypes namely BARI watermelon 1 (W1), BARI watermelon 2 (W2), L-32468 (W3), L-32236 (W4) and L-32394 (W5) were evaluated to appraise promising genotypes with better fruit quality. The evaluated genotypes expressed different levels of diversity for fruit physical qualitative traits including differences in shape, rind and flesh color and texture. The study also revealed significant variability among the genotypes regarding all observed fruit morphological and nutritional aspects as well as bioactive compounds. Among the studied genotypes, W1 stood out with the highest TSS as well as rind vitamin C and total phenolic content accompanied by higher fruit weight and thick rind. On the other hand, W3 genotype was featured with higher amount of \b{eta} carotene, total phenolic and flavonoid content in its flesh along with rind enriched with \b{eta} carotene and minerals. However, comparatively higher amount of sugar and total flavonoid content was recorded in the rind of W5 genotype. Therefore, W1 and W3 could be exploited for table purpose and using in breeding program to develop mini watermelon cultivars with more attractive fruits in terms of quality acceptance and nutritional value in Bangladesh. Furthermore, rind of BARI watermelon 1 and L-32394 could be considered as the potential cheap source of bioactive compounds to be used for dietary and industrial purpose which would decrease the solid waste in the environment. |
1403.7104 | Brian Williams Dr | Brian Williams | Elimination of HIV in South Africa through expanded access to
antiretroviral therapy: Cautions, caveats and the importance of parsimony | Two pages. One figure embedded in text | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a recent article Hontelez and colleagues investigate the prospects for
elimination of HIV in South Africa through expanded access to antiretroviral
therapy (ART) using STDSIM, a micro-simulation model. One of the first
published models to suggest that expanded access to ART could lead to the
elimination of HIV, referred to by the authors as the Granich Model, was
developed and implemented by the present author. The notion that expanded
access to ART could lead to the end of the AIDS epidemic gave rise to
considerable interest and debate and remains contentious. In considering this
notion Hontelez et al. start by stripping down STDSIM to a simple model that is
equivalent to the model developed by the present author3 but is a stochastic
event driven model. Hontelez and colleagues then reintroduce levels of
complexity to explore ways in which the model structure affects the results. In
contrast to our earlier conclusions Hontelez and colleagues conclude that
universal voluntary counselling and testing with immediate ART at 90% coverage
should result in the elimination of HIV but would take three times longer than
predicted by the model developed by the present author. Hontelez et al. suggest
that the current scale-up of ART at CD4 cell counts less than 350 cells/microL
will lead to elimination of HIV in 30 years. I disagree with both claims and
believe that their more complex models rely on unwarranted and unsubstantiated
assumptions.
| [
{
"created": "Wed, 26 Mar 2014 04:45:57 GMT",
"version": "v1"
}
] | 2014-03-28 | [
[
"Williams",
"Brian",
""
]
] | In a recent article Hontelez and colleagues investigate the prospects for elimination of HIV in South Africa through expanded access to antiretroviral therapy (ART) using STDSIM, a micro-simulation model. One of the first published models to suggest that expanded access to ART could lead to the elimination of HIV, referred to by the authors as the Granich Model, was developed and implemented by the present author. The notion that expanded access to ART could lead to the end of the AIDS epidemic gave rise to considerable interest and debate and remains contentious. In considering this notion Hontelez et al. start by stripping down STDSIM to a simple model that is equivalent to the model developed by the present author3 but is a stochastic event driven model. Hontelez and colleagues then reintroduce levels of complexity to explore ways in which the model structure affects the results. In contrast to our earlier conclusions Hontelez and colleagues conclude that universal voluntary counselling and testing with immediate ART at 90% coverage should result in the elimination of HIV but would take three times longer than predicted by the model developed by the present author. Hontelez et al. suggest that the current scale-up of ART at CD4 cell counts less than 350 cells/microL will lead to elimination of HIV in 30 years. I disagree with both claims and believe that their more complex models rely on unwarranted and unsubstantiated assumptions. |
1812.00105 | Ehtibar Dzhafarov | Victor H. Cervantes and Ehtibar N. Dzhafarov | True Contextuality in a Psychophysical Experiment | version 2 is a minor revision | null | null | null | q-bio.NC math.PR quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent crowdsourcing experiments have shown that true contextuality of the
kind found in quantum mechanics can also be present in human behavior. In these
experiments simple human choices were aggregated over large numbers of
respondents, with each respondent dealing with a single context (set of
questions asked). In this paper we present experimental evidence of
contextuality in individual human behavior, in a psychophysical experiment with
repeated presentations of visual stimuli in randomly varying contexts
(arrangements of stimuli). The analysis is based on the
Contextuality-by-Default (CbD) theory whose relevant aspects are reviewed in
the paper. CbD allows one to detect contextuality in the presence of direct
influences, i.e., when responses to the same stimuli have different
distributions in different contexts. The experiment presented is also the first
one in which contextuality is demonstrated for responses that are not
dichotomous, with five options to choose among. CbD requires that random
variables representing such responses be dichotomized before they are subjected
to contextuality analysis. A theorem says that a system consisting of all
possible dichotomizations of responses has to be contextual if these responses
violate a certain condition, called nominal dominance. In our experiment
nominal dominance was violated in all data sets, with very high statistical
reliability established by bootstrapping.
| [
{
"created": "Sat, 1 Dec 2018 00:28:10 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Feb 2019 22:15:31 GMT",
"version": "v2"
}
] | 2019-02-26 | [
[
"Cervantes",
"Victor H.",
""
],
[
"Dzhafarov",
"Ehtibar N.",
""
]
] | Recent crowdsourcing experiments have shown that true contextuality of the kind found in quantum mechanics can also be present in human behavior. In these experiments simple human choices were aggregated over large numbers of respondents, with each respondent dealing with a single context (set of questions asked). In this paper we present experimental evidence of contextuality in individual human behavior, in a psychophysical experiment with repeated presentations of visual stimuli in randomly varying contexts (arrangements of stimuli). The analysis is based on the Contextuality-by-Default (CbD) theory whose relevant aspects are reviewed in the paper. CbD allows one to detect contextuality in the presence of direct influences, i.e., when responses to the same stimuli have different distributions in different contexts. The experiment presented is also the first one in which contextuality is demonstrated for responses that are not dichotomous, with five options to choose among. CbD requires that random variables representing such responses be dichotomized before they are subjected to contextuality analysis. A theorem says that a system consisting of all possible dichotomizations of responses has to be contextual if these responses violate a certain condition, called nominal dominance. In our experiment nominal dominance was violated in all data sets, with very high statistical reliability established by bootstrapping. |
1206.5904 | Dipjyoti Das | Dipjyoti Das and Dibyendu Das and Ashok Prasad | Giant number fluctuations in microbial ecologies | 18 pages, 5 figures | Journal Theoretical Biology, Vol. 308, pp. 96-104 (2012) | 10.1016/j.jtbi.2012.05.030 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Statistical fluctuations in population sizes of microbes may be quite large
depending on the nature of their underlying stochastic dynamics. For example,
the variance of the population size of a microbe undergoing a pure birth
process with unlimited resources is proportional to the square of its mean. We
refer to such large fluctuations, with the variance growing as square of the
mean, as Giant Number Fluctuations (GNF). Luria and Delbruck showed that
spontaneous mutation processes in microbial populations exhibit GNF. We explore
whether GNF can arise in other microbial ecologies. We study certain simple
ecological models evolving via stochastic processes: (i) bi-directional
mutation, (ii) lysis-lysogeny of bacteria by bacteriophage, and (iii)
horizontal gene transfer (HGT). For the case of bi-directional mutation
process, we show analytically exactly that the GNF relationship holds at large
times. For the ecological model of bacteria undergoing lysis or lysogeny under
viral infection, we show that if the viral population can be experimentally
manipulated to stay quasi-stationary, the process of lysogeny maps essentially
to one-way mutation process and hence the GNF property of the lysogens follows.
Finally, we show that even the process of HGT may map to the mutation process
at large times, and thereby exhibits GNF.
| [
{
"created": "Tue, 26 Jun 2012 07:46:12 GMT",
"version": "v1"
}
] | 2012-06-27 | [
[
"Das",
"Dipjyoti",
""
],
[
"Das",
"Dibyendu",
""
],
[
"Prasad",
"Ashok",
""
]
] | Statistical fluctuations in population sizes of microbes may be quite large depending on the nature of their underlying stochastic dynamics. For example, the variance of the population size of a microbe undergoing a pure birth process with unlimited resources is proportional to the square of its mean. We refer to such large fluctuations, with the variance growing as square of the mean, as Giant Number Fluctuations (GNF). Luria and Delbruck showed that spontaneous mutation processes in microbial populations exhibit GNF. We explore whether GNF can arise in other microbial ecologies. We study certain simple ecological models evolving via stochastic processes: (i) bi-directional mutation, (ii) lysis-lysogeny of bacteria by bacteriophage, and (iii) horizontal gene transfer (HGT). For the case of bi-directional mutation process, we show analytically exactly that the GNF relationship holds at large times. For the ecological model of bacteria undergoing lysis or lysogeny under viral infection, we show that if the viral population can be experimentally manipulated to stay quasi-stationary, the process of lysogeny maps essentially to one-way mutation process and hence the GNF property of the lysogens follows. Finally, we show that even the process of HGT may map to the mutation process at large times, and thereby exhibits GNF. |
1404.5010 | Benedict Paten | Benedict Paten, Adam Novak and David Haussler | Mapping to a Reference Genome Structure | 25 pages | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To support comparative genomics, population genetics, and medical genetics,
we propose that a reference genome should come with a scheme for mapping each
base in any DNA string to a position in that reference genome. We refer to a
collection of one or more reference genomes and a scheme for mapping to their
positions as a reference structure. Here we describe the desirable properties
of reference structures and give examples. To account for natural genetic
variation, we consider the more general case in which a reference genome is
represented by a graph rather than a set of phased chromosomes; the latter is
treated as a special case.
| [
{
"created": "Sun, 20 Apr 2014 04:48:24 GMT",
"version": "v1"
}
] | 2014-04-22 | [
[
"Paten",
"Benedict",
""
],
[
"Novak",
"Adam",
""
],
[
"Haussler",
"David",
""
]
] | To support comparative genomics, population genetics, and medical genetics, we propose that a reference genome should come with a scheme for mapping each base in any DNA string to a position in that reference genome. We refer to a collection of one or more reference genomes and a scheme for mapping to their positions as a reference structure. Here we describe the desirable properties of reference structures and give examples. To account for natural genetic variation, we consider the more general case in which a reference genome is represented by a graph rather than a set of phased chromosomes; the latter is treated as a special case. |
2009.04519 | Laura Schaposnik | Vishaal Ram, Laura P. Schaposnik, Nikos Konstantinou, Eliz Volkan,
Marietta Papadatou-Pastou, Banu Manav, Domicele Jonauskaite, Christine Mohr | Extrapolating continuous color emotions through deep learning | To appear in Physical Review R. (8 pages, 13 figures) | Physical Review RESEARCH 2, 033350 (2020) | 10.1103/PhysRevResearch.2.033350 | null | q-bio.QM cs.LG physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | By means of an experimental dataset, we use deep learning to implement an RGB
extrapolation of emotions associated to color, and do a mathematical study of
the results obtained through this neural network. In particular, we see that
males typically associate a given emotion with darker colors while females with
brighter colors. A similar trend was observed with older people and
associations to lighter colors. Moreover, through our classification matrix, we
identify which colors have weak associations to emotions and which colors are
typically confused with other colors.
| [
{
"created": "Wed, 5 Aug 2020 02:08:29 GMT",
"version": "v1"
}
] | 2022-10-18 | [
[
"Ram",
"Vishaal",
""
],
[
"Schaposnik",
"Laura P.",
""
],
[
"Konstantinou",
"Nikos",
""
],
[
"Volkan",
"Eliz",
""
],
[
"Papadatou-Pastou",
"Marietta",
""
],
[
"Manav",
"Banu",
""
],
[
"Jonauskaite",
"Domicele",
""
],
[
"Mohr",
"Christine",
""
]
] | By means of an experimental dataset, we use deep learning to implement an RGB extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males typically associate a given emotion with darker colors while females with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors. |
1808.08662 | Luay Nakhleh | R.A.L. Elworth, H.A. Ogilvie, J. Zhu, L. Nakhleh | Advances in Computational Methods for Phylogenetic Networks in the
Presence of Hybridization | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Phylogenetic networks extend phylogenetic trees to allow for modeling
reticulate evolutionary processes such as hybridization. They take the shape of
a rooted, directed, acyclic graph, and when parameterized with evolutionary
parameters, such as divergence times and population sizes, they form a
generative process of molecular sequence evolution. Early work on computational
methods for phylogenetic network inference focused exclusively on reticulations
and sought networks with the fewest number of reticulations to fit the data. As
processes such as incomplete lineage sorting (ILS) could be at play
concurrently with hybridization, work in the last decade has shifted to
computational approaches for phylogenetic network inference in the presence of
ILS. In such a short period, significant advances have been made on developing
and implementing such computational approaches. In particular, parsimony,
likelihood, and Bayesian methods have been devised for estimating phylogenetic
networks and associated parameters using estimated gene trees as data. Use of
those inference methods has been augmented with statistical tests for specific
hypotheses of hybridization, like the D-statistic. Most recently, Bayesian
approaches for inferring phylogenetic networks directly from sequence data were
developed and implemented. In this chapter, we survey such advances and discuss
model assumptions as well as methods' strengths and limitations. We also
discuss parallel efforts in the population genetics community aimed at
inferring similar structures. Finally, we highlight major directions for future
research in this area.
| [
{
"created": "Mon, 27 Aug 2018 01:46:06 GMT",
"version": "v1"
}
] | 2018-08-28 | [
[
"Elworth",
"R. A. L.",
""
],
[
"Ogilvie",
"H. A.",
""
],
[
"Zhu",
"J.",
""
],
[
"Nakhleh",
"L.",
""
]
] | Phylogenetic networks extend phylogenetic trees to allow for modeling reticulate evolutionary processes such as hybridization. They take the shape of a rooted, directed, acyclic graph, and when parameterized with evolutionary parameters, such as divergence times and population sizes, they form a generative process of molecular sequence evolution. Early work on computational methods for phylogenetic network inference focused exclusively on reticulations and sought networks with the fewest number of reticulations to fit the data. As processes such as incomplete lineage sorting (ILS) could be at play concurrently with hybridization, work in the last decade has shifted to computational approaches for phylogenetic network inference in the presence of ILS. In such a short period, significant advances have been made on developing and implementing such computational approaches. In particular, parsimony, likelihood, and Bayesian methods have been devised for estimating phylogenetic networks and associated parameters using estimated gene trees as data. Use of those inference methods has been augmented with statistical tests for specific hypotheses of hybridization, like the D-statistic. Most recently, Bayesian approaches for inferring phylogenetic networks directly from sequence data were developed and implemented. In this chapter, we survey such advances and discuss model assumptions as well as methods' strengths and limitations. We also discuss parallel efforts in the population genetics community aimed at inferring similar structures. Finally, we highlight major directions for future research in this area. |
1401.2897 | Andrei Khrennikov Yu | Masanari Asano, Takahisa Hashimoto, Andrei Khrennikov, Masanori Ohya,
Yoshiharu Tanaka | Violation of contextual generalization of the Leggett-Garg inequality
for recognition of ambiguous figures | Presented at the conference Quantum Interactions 14, University of
Leicester, July 2014; submitted to Physica Scripta, IOP; new version contains
discussions on Bell and contextuality, marginal selectivity,
Kolmogorovization of contextual data | Phys. Scr. T 163 (2014) 014006 | 10.1088/0031-8949/2014/T163/014006 | null | q-bio.NC math.PR math.ST quant-ph stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We interpret the Leggett-Garg (LG) inequality as a kind of contextual
probabilistic inequality in which one combines data collected in experiments
performed for three different contexts. In the original version of the
inequality these contexts have the temporal nature and they are given by three
pairs of instances of time, $(t_1, t_2), (t_2, t_3), (t_3, t_4),$ where $t_1 <
t_2 < t_3.$ We generalize LG conditions of macroscopic realism and noninvasive
measurability in the general contextual framework. Our formulation is done in
the purely probabilistic terms: existence of the context independent joint
probability distribution $P$ and the possibility to reconstruct the
experimentally found marginal (two dimensional) probability distributions from
the $P.$ We derive an analog of the LG inequality, "contextual LG inequality",
and use it as a test of "quantum-likeness" of statistical data collected in a
series of experiments on recognition of ambiguous figures. In our experimental
study the figure under recognition is the Schroeder stair which is shown with
rotations for different angles. Contexts are encoded by dynamics of rotations:
clockwise, anticlockwise, and random. Our data demonstrated violation of the
contextual LG inequality for some combinations of aforementioned contexts.
Since in quantum theory and experiments with quantum physical systems this
inequality is violated, e.g., in the form of the original LG-inequality, our
result can be interpreted as a sign that the quantum(-like) models can provide
a more adequate description of the data generated in the process of recognition
of ambiguous figures.
| [
{
"created": "Fri, 10 Jan 2014 09:33:13 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Jan 2014 18:16:15 GMT",
"version": "v2"
},
{
"created": "Fri, 2 May 2014 14:01:00 GMT",
"version": "v3"
}
] | 2015-06-18 | [
[
"Asano",
"Masanari",
""
],
[
"Hashimoto",
"Takahisa",
""
],
[
"Khrennikov",
"Andrei",
""
],
[
"Ohya",
"Masanori",
""
],
[
"Tanaka",
"Yoshiharu",
""
]
] | We interpret the Leggett-Garg (LG) inequality as a kind of contextual probabilistic inequality in which one combines data collected in experiments performed for three different contexts. In the original version of the inequality these contexts have the temporal nature and they are given by three pairs of instances of time, $(t_1, t_2), (t_2, t_3), (t_3, t_4),$ where $t_1 < t_2 < t_3.$ We generalize LG conditions of macroscopic realism and noninvasive measurability in the general contextual framework. Our formulation is done in the purely probabilistic terms: existence of the context independent joint probability distribution $P$ and the possibility to reconstruct the experimentally found marginal (two dimensional) probability distributions from the $P.$ We derive an analog of the LG inequality, "contextual LG inequality", and use it as a test of "quantum-likeness" of statistical data collected in a series of experiments on recognition of ambiguous figures. In our experimental study the figure under recognition is the Schroeder stair which is shown with rotations for different angles. Contexts are encoded by dynamics of rotations: clockwise, anticlockwise, and random. Our data demonstrated violation of the contextual LG inequality for some combinations of aforementioned contexts. Since in quantum theory and experiments with quantum physical systems this inequality is violated, e.g., in the form of the original LG-inequality, our result can be interpreted as a sign that the quantum(-like) models can provide a more adequate description of the data generated in the process of recognition of ambiguous figures. |
1204.2822 | Elena Shchekinova Y | E. Shchekinova, M. G. J. L\"oder, M. Boersma, K. H. Wiltshire | The Effect of Differentiation of Prey Community on Stable Coexistence in
a Three-Species Food--Web Model | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Food webs with intraguild predation (IGP) are widespread in natural habitats.
Their adaptation and resilience behaviour is principal for understanding
restructuring of ecological communities. In spite of the importance of IGP food
webs their behaviour even for the simplest 3-species systems has not been fully
explored. One fundamental question is how an increase of diversity of the
lowest trophic level impacts the persistence of higher trophic levels in IGP
relationships. We analyze a 3-species food web model with a heterogeneous
resources and IGP. The model consists of two predators directly coupled via IGP
relation and indirectly via competition for resource. The resource is
subdivided into distinct subpopulations. Individuals in the subpopulations are
grazed at different rates by the predators. We consider two models: an IGP
module with immobilization by the top predator and an IGP module with species
turnover. We examine the effect of increasing enrichment and varying
immobilization (resource transfer) rate on a stable coexistence of predators
and resources. We explore how the predictions from the basic 3-species model
are altered when the IGP module is extended to multiple resource
subpopulations. We investigate which parameters support a robust coexistence in
the IGP system. For the case of multiple subpopulations of the resource we
present a numerical comparison of the percentage of food webs with stable
coexistence for different dimensionalities of the resource community. At low
immobilization (transfer) rates our model predicts a stable 3-species
coexistence only for intermediate enrichment meanwhile at high rates a large
set of stable equilibrium configurations is found for high enrichment as well.
| [
{
"created": "Thu, 12 Apr 2012 08:24:21 GMT",
"version": "v1"
}
] | 2012-04-16 | [
[
"Shchekinova",
"E.",
""
],
[
"Löder",
"M. G. J.",
""
],
[
"Boersma",
"M.",
""
],
[
"Wiltshire",
"K. H.",
""
]
] | Food webs with intraguild predation (IGP) are widespread in natural habitats. Their adaptation and resilience behaviour is principal for understanding restructuring of ecological communities. In spite of the importance of IGP food webs their behaviour even for the simplest 3-species systems has not been fully explored. One fundamental question is how an increase of diversity of the lowest trophic level impacts the persistence of higher trophic levels in IGP relationships. We analyze a 3-species food web model with a heterogeneous resources and IGP. The model consists of two predators directly coupled via IGP relation and indirectly via competition for resource. The resource is subdivided into distinct subpopulations. Individuals in the subpopulations are grazed at different rates by the predators. We consider two models: an IGP module with immobilization by the top predator and an IGP module with species turnover. We examine the effect of increasing enrichment and varying immobilization (resource transfer) rate on a stable coexistence of predators and resources. We explore how the predictions from the basic 3-species model are altered when the IGP module is extended to multiple resource subpopulations. We investigate which parameters support a robust coexistence in the IGP system. For the case of multiple subpopulations of the resource we present a numerical comparison of the percentage of food webs with stable coexistence for different dimensionalities of the resource community. At low immobilization (transfer) rates our model predicts a stable 3-species coexistence only for intermediate enrichment meanwhile at high rates a large set of stable equilibrium configurations is found for high enrichment as well. |
1011.0322 | Mauro Mobilia | Michael Assaf, Mauro Mobilia | Fixation of a Deleterious Allele under Mutation Pressure and Finite
Selection Intensity | 26 pages, 5 figures. Accepted by the Journal of Theoretical Biology | J. Theor. Biol. 275, 93-103 (2011) | 10.1016/j.jtbi.2011.01.025 | null | q-bio.PE cond-mat.stat-mech nlin.AO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The mean fixation time of a deleterious mutant allele is studied beyond the
diffusion approximation. As in Kimura's classical work [M. Kimura, Proc. Natl.
Acad. Sci. U.S.A. Vol.77, 522 (1980)], that was motivated by the problem of
fixation in the presence of amorphic or hypermorphic mutations, we consider a
diallelic model at a single locus comprising a wild-type A and a mutant allele
A' produced irreversibly from A at small uniform rate v. The relative fitnesses
of the mutant homozygotes A'A', mutant heterozygotes A'A and wild-type
homozygotes AA are 1-s, 1-h and 1, respectively, where it is assumed that v<<
s. Here, we adopt an approach based on the direct treatment of the underlying
Markov chain (birth-death process) obeyed by the allele frequency (whose
dynamics is prescribed by the Moran model), which allows to accurately account
for the effects of large fluctuations. After a general description of the
theory, we focus on the case of a deleterious mutant allele (i.e. s>0) and
discuss three situations: when the mutant is (i) completely dominant (s=h);
(ii) completely recessive (h=0), and (iii) semi-dominant (h=s/2). Our
theoretical predictions for the mean fixation time and the quasi-stationary
distribution of the mutant population in the coexistence state, are shown to be
in excellent agreement with numerical simulations. Furthermore, when s is
finite, we demonstrate that our results are superior to those of the diffusion
theory that is shown to be an accurate approximation only when N_e s^2 << 1,
where N_e is the effective population size.
| [
{
"created": "Mon, 1 Nov 2010 14:02:54 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Jan 2011 11:52:53 GMT",
"version": "v2"
}
] | 2011-02-22 | [
[
"Assaf",
"Michael",
""
],
[
"Mobilia",
"Mauro",
""
]
] | The mean fixation time of a deleterious mutant allele is studied beyond the diffusion approximation. As in Kimura's classical work [M. Kimura, Proc. Natl. Acad. Sci. U.S.A. Vol.77, 522 (1980)], that was motivated by the problem of fixation in the presence of amorphic or hypermorphic mutations, we consider a diallelic model at a single locus comprising a wild-type A and a mutant allele A' produced irreversibly from A at small uniform rate v. The relative fitnesses of the mutant homozygotes A'A', mutant heterozygotes A'A and wild-type homozygotes AA are 1-s, 1-h and 1, respectively, where it is assumed that v<< s. Here, we adopt an approach based on the direct treatment of the underlying Markov chain (birth-death process) obeyed by the allele frequency (whose dynamics is prescribed by the Moran model), which allows to accurately account for the effects of large fluctuations. After a general description of the theory, we focus on the case of a deleterious mutant allele (i.e. s>0) and discuss three situations: when the mutant is (i) completely dominant (s=h); (ii) completely recessive (h=0), and (iii) semi-dominant (h=s/2). Our theoretical predictions for the mean fixation time and the quasi-stationary distribution of the mutant population in the coexistence state, are shown to be in excellent agreement with numerical simulations. Furthermore, when s is finite, we demonstrate that our results are superior to those of the diffusion theory that is shown to be an accurate approximation only when N_e s^2 << 1, where N_e is the effective population size. |
1408.6694 | Erik Volz | Erik M. Volz and Simon DW Frost | Sampling through time and phylodynamic inference with coalescent and
birth-death models | Submitted | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many population genetic models have been developed for the purpose of
inferring population size and growth rates from random samples of genetic data.
We examine two popular approaches to this problem, the coalescent and the
birth-death-sampling model, in the context of estimating population size and
birth rates in a population growing exponentially according to the birth-death
branching process. For sequences sampled at a single time, we found the
coalescent and the birth-death-sampling model gave virtually indistinguishable
results in terms of the growth rates and fraction of the population sampled,
even when sampling from a small population. For sequences sampled at multiple
time points, we find that the birth-death model estimators are subject to large
bias if the sampling process is misspecified. Since birth-death-sampling models
incorporate a model of the sampling process, we show how much of the
statistical power of birth-death-sampling models arises from the sequence of
sample times and not from the genealogical tree. This motivates the development
of a new coalescent estimator, which is augmented with a model of the known
sampling process and is potentially more precise than the coalescent that does
not use sample time information.
| [
{
"created": "Thu, 28 Aug 2014 12:13:37 GMT",
"version": "v1"
}
] | 2014-08-29 | [
[
"Volz",
"Erik M.",
""
],
[
"Frost",
"Simon DW",
""
]
] | Many population genetic models have been developed for the purpose of inferring population size and growth rates from random samples of genetic data. We examine two popular approaches to this problem, the coalescent and the birth-death-sampling model, in the context of estimating population size and birth rates in a population growing exponentially according to the birth-death branching process. For sequences sampled at a single time, we found the coalescent and the birth-death-sampling model gave virtually indistinguishable results in terms of the growth rates and fraction of the population sampled, even when sampling from a small population. For sequences sampled at multiple time points, we find that the birth-death model estimators are subject to large bias if the sampling process is misspecified. Since birth-death-sampling models incorporate a model of the sampling process, we show how much of the statistical power of birth-death-sampling models arises from the sequence of sample times and not from the genealogical tree. This motivates the development of a new coalescent estimator, which is augmented with a model of the known sampling process and is potentially more precise than the coalescent that does not use sample time information. |
2006.04684 | Ali Salari | Ali Salari, Gregory Kiar, Lindsay Lewis, Alan C. Evans, Tristan
Glatard | File-based localization of numerical perturbations in data analysis
pipelines | 10 pages, 6 figures, 2 tables | null | null | null | q-bio.QM eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data analysis pipelines are known to be impacted by computational conditions,
presumably due to the creation and propagation of numerical errors. While this
process could play a major role in the current reproducibility crisis, the
precise causes of such instabilities and the path along which they propagate in
pipelines are unclear. We present Spot, a tool to identify which processes in a
pipeline create numerical differences when executed in different computational
conditions. Spot leverages system-call interception through ReproZip to
reconstruct and compare provenance graphs without pipeline instrumentation. By
applying Spot to the structural pre-processing pipelines of the Human
Connectome Project, we found that linear and non-linear registration are the
cause of most numerical instabilities in these pipelines, which confirms
previous findings.
| [
{
"created": "Wed, 3 Jun 2020 19:11:40 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Sep 2020 01:00:09 GMT",
"version": "v2"
}
] | 2020-09-30 | [
[
"Salari",
"Ali",
""
],
[
"Kiar",
"Gregory",
""
],
[
"Lewis",
"Lindsay",
""
],
[
"Evans",
"Alan C.",
""
],
[
"Glatard",
"Tristan",
""
]
] | Data analysis pipelines are known to be impacted by computational conditions, presumably due to the creation and propagation of numerical errors. While this process could play a major role in the current reproducibility crisis, the precise causes of such instabilities and the path along which they propagate in pipelines are unclear. We present Spot, a tool to identify which processes in a pipeline create numerical differences when executed in different computational conditions. Spot leverages system-call interception through ReproZip to reconstruct and compare provenance graphs without pipeline instrumentation. By applying Spot to the structural pre-processing pipelines of the Human Connectome Project, we found that linear and non-linear registration are the cause of most numerical instabilities in these pipelines, which confirms previous findings. |
q-bio/0505054 | Sagi Snir | Benny Chor, Michael D. Hendy and Sagi Snir | Maximum Likelihood Jukes-Cantor Triplets: Analytic Solutions | null | null | null | null | q-bio.PE | null | Complex systems of polynomial equations have to be set up and solved
algebraically in order to obtain analytic solutions for maximum likelihood on
phylogenetic trees. This has restricted the types of systems previously
resolved to the simplest models - three and four taxa under a molecular clock,
with just two state characters. In this work we give, for the first time,
analytic solutions for a family of trees with four state characters, like
normal DNA or RNA. The model of substitution we use is the Jukes-Cantor model,
and the trees are on three taxa under molecular clock, namely rooted triplets.
We employ a number of approaches and tools to solve this system: Spectral
methods (Hadamard conjugation), a new representation of variables (the path-set
spectrum), and algebraic geometry tools (the resultant of two polynomials). All
these, combined with heavy application of computer algebra packages (Maple),
let us derive the desired solution.
| [
{
"created": "Fri, 27 May 2005 18:09:56 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Chor",
"Benny",
""
],
[
"Hendy",
"Michael D.",
""
],
[
"Snir",
"Sagi",
""
]
] | Complex systems of polynomial equations have to be set up and solved algebraically in order to obtain analytic solutions for maximum likelihood on phylogenetic trees. This has restricted the types of systems previously resolved to the simplest models - three and four taxa under a molecular clock, with just two state characters. In this work we give, for the first time, analytic solutions for a family of trees with four state characters, like normal DNA or RNA. The model of substitution we use is the Jukes-Cantor model, and the trees are on three taxa under molecular clock, namely rooted triplets. We employ a number of approaches and tools to solve this system: Spectral methods (Hadamard conjugation), a new representation of variables (the path-set spectrum), and algebraic geometry tools (the resultant of two polynomials). All these, combined with heavy application of computer algebra packages (Maple), let us derive the desired solution. |
1604.08921 | Artur Fassoni | Artur C. Fassoni and Hyun M. Yang | An ecological resilience perspective on cancer: insights from a toy
model | null | null | null | null | q-bio.PE math.DS q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose an ecological resilience point of view on cancer.
This view is based on the analysis of a simple ODE model for the interactions
between cancer and normal cells. The model presents two regimes for tumor
growth. In the first, cancer arises due to three reasons: a partial corruption
of the functions that avoid the growth of mutated cells, an aggressive
phenotype of tumor cells and exposure to external carcinogenic factors. In this
case, treatments may be effective if they drive the system to the basin of
attraction of the cancer cure state. In the second regime, cancer arises
because the repair system is intrinsically corrupted. In this case, the
complete cure is not possible since the cancer cure state is no more stable,
but tumor recurrence may be delayed if treatment is prolongued. We review three
indicators of the resilience of a stable equilibrium, related with size and
shape of its basin of attraction: latitude, precariousness and resistance. A
novel method to calculate these indicators is proposed. This method is simpler
and more efficient than those currently used, and may be easily applied to
other population dynamics models. We apply this method to the model and
investigate how these indicators behave with parameters changes. Finally, we
present some simulations to illustrate how the resilience analysis can be
applied to validated models in order to obtain indicators for personalized
cancer treatments.
Keywords: Tumor growth; Chemotherapy; Basins of Attraction; Regime shifts;
Critical transitions
| [
{
"created": "Fri, 29 Apr 2016 17:43:41 GMT",
"version": "v1"
},
{
"created": "Wed, 31 Aug 2016 19:37:47 GMT",
"version": "v2"
}
] | 2016-09-01 | [
[
"Fassoni",
"Artur C.",
""
],
[
"Yang",
"Hyun M.",
""
]
] | In this paper we propose an ecological resilience point of view on cancer. This view is based on the analysis of a simple ODE model for the interactions between cancer and normal cells. The model presents two regimes for tumor growth. In the first, cancer arises due to three reasons: a partial corruption of the functions that avoid the growth of mutated cells, an aggressive phenotype of tumor cells and exposure to external carcinogenic factors. In this case, treatments may be effective if they drive the system to the basin of attraction of the cancer cure state. In the second regime, cancer arises because the repair system is intrinsically corrupted. In this case, the complete cure is not possible since the cancer cure state is no more stable, but tumor recurrence may be delayed if treatment is prolongued. We review three indicators of the resilience of a stable equilibrium, related with size and shape of its basin of attraction: latitude, precariousness and resistance. A novel method to calculate these indicators is proposed. This method is simpler and more efficient than those currently used, and may be easily applied to other population dynamics models. We apply this method to the model and investigate how these indicators behave with parameters changes. Finally, we present some simulations to illustrate how the resilience analysis can be applied to validated models in order to obtain indicators for personalized cancer treatments. Keywords: Tumor growth; Chemotherapy; Basins of Attraction; Regime shifts; Critical transitions |
q-bio/0501025 | Fabio De Blasio | Birgitte Freiesleben De Blasio, Fabio Vittorio De Blasio | Dynamics of competing species in a model of adaptive radiations and
macroevolution | null | null | null | null | q-bio.PE | null | We present a simple model of adaptive radiations in evolution based on
species competition. Competition is found to promote species divergence and
branching, and to dampen the net species production. In the model simulations,
high taxonomic diversification and branching take place during the beginning of
the radiation. The results show striking similarities with empirical data and
highlight the mechanism of competition as an important driving factor for
accelerated evolutionary transformation.
| [
{
"created": "Tue, 18 Jan 2005 19:15:19 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"De Blasio",
"Birgitte Freiesleben",
""
],
[
"De Blasio",
"Fabio Vittorio",
""
]
] | We present a simple model of adaptive radiations in evolution based on species competition. Competition is found to promote species divergence and branching, and to dampen the net species production. In the model simulations, high taxonomic diversification and branching take place during the beginning of the radiation. The results show striking similarities with empirical data and highlight the mechanism of competition as an important driving factor for accelerated evolutionary transformation. |
2206.09398 | Alexis Thual | Alexis Thual, Huy Tran, Tatiana Zemskova, Nicolas Courty, R\'emi
Flamary, Stanislas Dehaene, Bertrand Thirion | Aligning individual brains with Fused Unbalanced Gromov-Wasserstein | null | Advances in Neural Information Processing Systems, 35 (2022)
21792-21804 | null | null | q-bio.NC stat.ML | http://creativecommons.org/licenses/by/4.0/ | Individual brains vary in both anatomy and functional organization, even
within a given species. Inter-individual variability is a major impediment when
trying to draw generalizable conclusions from neuroimaging data collected on
groups of subjects. Current co-registration procedures rely on limited data,
and thus lead to very coarse inter-subject alignments. In this work, we present
a novel method for inter-subject alignment based on Optimal Transport, denoted
as Fused Unbalanced Gromov Wasserstein (FUGW). The method aligns cortical
surfaces based on the similarity of their functional signatures in response to
a variety of stimulation settings, while penalizing large deformations of
individual topographic organization. We demonstrate that FUGW is well-suited
for whole-brain landmark-free alignment. The unbalanced feature allows to deal
with the fact that functional areas vary in size across subjects. Our results
show that FUGW alignment significantly increases between-subject correlation of
activity for independent functional data, and leads to more precise mapping at
the group level.
| [
{
"created": "Sun, 19 Jun 2022 13:06:11 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Nov 2022 16:38:23 GMT",
"version": "v2"
},
{
"created": "Tue, 22 Aug 2023 23:02:57 GMT",
"version": "v3"
}
] | 2023-09-28 | [
[
"Thual",
"Alexis",
""
],
[
"Tran",
"Huy",
""
],
[
"Zemskova",
"Tatiana",
""
],
[
"Courty",
"Nicolas",
""
],
[
"Flamary",
"Rémi",
""
],
[
"Dehaene",
"Stanislas",
""
],
[
"Thirion",
"Bertrand",
""
]
] | Individual brains vary in both anatomy and functional organization, even within a given species. Inter-individual variability is a major impediment when trying to draw generalizable conclusions from neuroimaging data collected on groups of subjects. Current co-registration procedures rely on limited data, and thus lead to very coarse inter-subject alignments. In this work, we present a novel method for inter-subject alignment based on Optimal Transport, denoted as Fused Unbalanced Gromov Wasserstein (FUGW). The method aligns cortical surfaces based on the similarity of their functional signatures in response to a variety of stimulation settings, while penalizing large deformations of individual topographic organization. We demonstrate that FUGW is well-suited for whole-brain landmark-free alignment. The unbalanced feature allows to deal with the fact that functional areas vary in size across subjects. Our results show that FUGW alignment significantly increases between-subject correlation of activity for independent functional data, and leads to more precise mapping at the group level. |
1509.02450 | Olivier Rivoire | S\'ebastien Boyer, Dipanwita Biswas, Ananda Kumar Soshee, Natale
Scaramozzino, Cl\'ement Nizak, Olivier Rivoire | Hierarchy and extremes in selections from pools of randomized proteins | null | null | 10.1073/pnas.1517813113 | null | q-bio.PE q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Variation and selection are the core principles of Darwinian evolution, yet
quantitatively relating the diversity of a population to its capacity to
respond to selection is challenging. Here, we examine this problem at a
molecular level in the context of populations of partially randomized proteins
selected for binding to well-defined targets. We built several minimal protein
libraries, screened them in vitro by phage display and analyzed their response
to selection by high-throughput sequencing. A statistical analysis of the
results reveals two main findings: first, libraries with same sequence
diversity but built around different "frameworks" typically have vastly
different responses, second, the distribution of responses within a library
follows a simple scaling law. We show how an elementary probabilistic model
based on extreme value theory rationalizes these findings. Our results have
implications for designing synthetic protein libraries, for estimating the
density of functional biomolecules in sequence space, for characterizing
diversity in natural populations and for experimentally investigating the
concept of evolvability, or potential for future evolution.
| [
{
"created": "Tue, 8 Sep 2015 17:14:46 GMT",
"version": "v1"
}
] | 2016-04-27 | [
[
"Boyer",
"Sébastien",
""
],
[
"Biswas",
"Dipanwita",
""
],
[
"Soshee",
"Ananda Kumar",
""
],
[
"Scaramozzino",
"Natale",
""
],
[
"Nizak",
"Clément",
""
],
[
"Rivoire",
"Olivier",
""
]
] | Variation and selection are the core principles of Darwinian evolution, yet quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings: first, libraries with same sequence diversity but built around different "frameworks" typically have vastly different responses, second, the distribution of responses within a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes these findings. Our results have implications for designing synthetic protein libraries, for estimating the density of functional biomolecules in sequence space, for characterizing diversity in natural populations and for experimentally investigating the concept of evolvability, or potential for future evolution. |
2008.04172 | Sara Hamis | Sara J Hamis, Fiona R Macfarlane | A single-cell mathematical model of SARS-CoV-2 induced pyroptosis and
the effects of anti-inflammatory intervention | 35 pages including the appendix, 9 figures in the main manuscript,
Supporting Information (PDF) included | null | null | null | q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | Pyroptosis is an inflammatory mode of cell death that can contribute to the
cytokine storm associated with severe cases of coronavirus disease 2019
(COVID-19). The formation of the NLRP3 inflammasome is central to pyroptosis,
which may be induced by severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2). Inflammasome formation, and by extension pyroptosis, may be
inhibited by certain anti-inflammatory drugs. In this study, we present a
single-cell mathematical model that captures the formation of the NLRP3
inflammasome, pyroptotic cell death and responses to anti-inflammatory
intervention that hinder the formation of the NLRP3 inflammasome. The model is
formulated in terms of a system of ordinary differential equations (ODEs) that
describe the dynamics of the biological components involved in pyroptosis. Our
results demonstrate that an anti-inflammatory drug can delay the formation of
the NLRP3 inflammasome, and thus may alter the mode of cell death from
inflammatory (pyroptosis) to non-inflammatory e.g., apoptosis). The single-cell
model is being implemented in a SARS-CoV-2 Tissue Simulator, in collaboration
with a multidisciplinary coalition investigating within host-dynamics of
COVID-19. In this paper, we provide an overview of the SARS-CoV-2 Tissue
Simulator and highlight the effects of pyroptosis on a cellular level.
| [
{
"created": "Mon, 10 Aug 2020 14:51:34 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Dec 2020 22:13:25 GMT",
"version": "v2"
},
{
"created": "Tue, 23 Mar 2021 15:37:33 GMT",
"version": "v3"
},
{
"created": "Mon, 29 Mar 2021 14:26:51 GMT",
"version": "v4"
}
] | 2021-03-30 | [
[
"Hamis",
"Sara J",
""
],
[
"Macfarlane",
"Fiona R",
""
]
] | Pyroptosis is an inflammatory mode of cell death that can contribute to the cytokine storm associated with severe cases of coronavirus disease 2019 (COVID-19). The formation of the NLRP3 inflammasome is central to pyroptosis, which may be induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Inflammasome formation, and by extension pyroptosis, may be inhibited by certain anti-inflammatory drugs. In this study, we present a single-cell mathematical model that captures the formation of the NLRP3 inflammasome, pyroptotic cell death and responses to anti-inflammatory intervention that hinder the formation of the NLRP3 inflammasome. The model is formulated in terms of a system of ordinary differential equations (ODEs) that describe the dynamics of the biological components involved in pyroptosis. Our results demonstrate that an anti-inflammatory drug can delay the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory e.g., apoptosis). The single-cell model is being implemented in a SARS-CoV-2 Tissue Simulator, in collaboration with a multidisciplinary coalition investigating within host-dynamics of COVID-19. In this paper, we provide an overview of the SARS-CoV-2 Tissue Simulator and highlight the effects of pyroptosis on a cellular level. |
1305.5369 | Daniel Remondini | G. Menichetti, G. Bianconi, E. Giampieri, G. Castellani, D. Remondini | Network Entropy measures applied to different systemic perturbations of
cell basal state | NOTE: includes supplementary material | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We characterize different cell states, related to cancer and ageing
phenotypes, by a measure of entropy of network ensembles, integrating gene
expression values and protein interaction networks. The entropy measure
estimates the parameter space available to the network ensemble, that can be
interpreted as the level of plasticity of the system for high entropy values
(the ability to change its internal parameters, e.g. in response to
environmental stimuli), or as a fine tuning of the parameters (that restricts
the range of possible parameter values) in the opposite case. This approach can
be applied at different scales, from whole cell to single biological functions,
by defining appropriate subnetworks based on a priori biological knowledge,
thus allowing a deeper understanding of the cell processes involved. In our
analysis we used specific network features (degree sequence, subnetwork
structure and distance between gene profiles) to obtain informations at
different biological scales, providing a novel point of view for the
integration of experimental transcriptomic data and a priori biological
knowledge, but the entropy measure can also highlight other aspects of the
biological systems studied depending on the constraints introduced in the model
(e.g. community structures).
| [
{
"created": "Thu, 23 May 2013 10:29:30 GMT",
"version": "v1"
}
] | 2013-05-24 | [
[
"Menichetti",
"G.",
""
],
[
"Bianconi",
"G.",
""
],
[
"Giampieri",
"E.",
""
],
[
"Castellani",
"G.",
""
],
[
"Remondini",
"D.",
""
]
] | We characterize different cell states, related to cancer and ageing phenotypes, by a measure of entropy of network ensembles, integrating gene expression values and protein interaction networks. The entropy measure estimates the parameter space available to the network ensemble, that can be interpreted as the level of plasticity of the system for high entropy values (the ability to change its internal parameters, e.g. in response to environmental stimuli), or as a fine tuning of the parameters (that restricts the range of possible parameter values) in the opposite case. This approach can be applied at different scales, from whole cell to single biological functions, by defining appropriate subnetworks based on a priori biological knowledge, thus allowing a deeper understanding of the cell processes involved. In our analysis we used specific network features (degree sequence, subnetwork structure and distance between gene profiles) to obtain informations at different biological scales, providing a novel point of view for the integration of experimental transcriptomic data and a priori biological knowledge, but the entropy measure can also highlight other aspects of the biological systems studied depending on the constraints introduced in the model (e.g. community structures). |
1109.1108 | Marco Chierici | Marco Chierici and Giuseppe Jurman and Marco Roncador and Cesare
Furlanello | Single-base mismatch profiles for NGS samples | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Within the preprocessing pipeline of a Next Generation Sequencing sample, its
set of Single-Base Mismatches is one of the first outcomes, together with the
number of correctly aligned reads. The union of these two sets provides a 4x4
matrix (called Single Base Indicator, SBI in what follows) representing a
blueprint of the sample and its preprocessing ingredients such as the
sequencer, the alignment software, the pipeline parameters. In this note we
show that, under the same technological conditions, there is a strong relation
between the SBI and the biological nature of the sample. To reach this goal we
need to introduce a similarity measure between SBIs: we also show how two
measures commonly used in machine learning can be of help in this context.
| [
{
"created": "Tue, 6 Sep 2011 08:25:14 GMT",
"version": "v1"
}
] | 2011-09-07 | [
[
"Chierici",
"Marco",
""
],
[
"Jurman",
"Giuseppe",
""
],
[
"Roncador",
"Marco",
""
],
[
"Furlanello",
"Cesare",
""
]
] | Within the preprocessing pipeline of a Next Generation Sequencing sample, its set of Single-Base Mismatches is one of the first outcomes, together with the number of correctly aligned reads. The union of these two sets provides a 4x4 matrix (called Single Base Indicator, SBI in what follows) representing a blueprint of the sample and its preprocessing ingredients such as the sequencer, the alignment software, the pipeline parameters. In this note we show that, under the same technological conditions, there is a strong relation between the SBI and the biological nature of the sample. To reach this goal we need to introduce a similarity measure between SBIs: we also show how two measures commonly used in machine learning can be of help in this context. |
1611.06834 | Laurent Perrinet | Cesar Ravello (CINV), Maria-Jose Escobar, Adrian Palacios (CINV),
Laurent Perrinet (INT) | Differential response of the retinal neural code with respect to the
sparseness of natural images | arXiv admin note: substantial text overlap with arXiv:1702.02485 | null | 10.5281/zenodo.5823016 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Natural images follow statistics inherited by the structure of our physical
(visual) environment. In particular, a prominent facet of this structure is
that images can be described by a relatively sparse number of features. To
investigate the role of this sparseness in the efficiency of the neural code,
we designed a new class of random textured stimuli with a controlled sparseness
value inspired by measurements of natural images. Then, we tested the impact of
this sparseness parameter on the firing pattern observed in a population of
retinal ganglion cells recorded ex vivo in the retina of a rodent, the Octodon
degus. These recordings showed in particular that the reliability of spike
timings varies with respect to the sparseness with globally a similar trend
than the distribution of sparseness statistics observed in natural images.
These results suggest that the code represented in the spike pattern of
ganglion cells may adapt to this aspect of the statistics of natural images.
| [
{
"created": "Mon, 21 Nov 2016 15:28:16 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Jan 2022 19:53:44 GMT",
"version": "v2"
}
] | 2022-01-07 | [
[
"Ravello",
"Cesar",
"",
"CINV"
],
[
"Escobar",
"Maria-Jose",
"",
"CINV"
],
[
"Palacios",
"Adrian",
"",
"CINV"
],
[
"Perrinet",
"Laurent",
"",
"INT"
]
] | Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. To investigate the role of this sparseness in the efficiency of the neural code, we designed a new class of random textured stimuli with a controlled sparseness value inspired by measurements of natural images. Then, we tested the impact of this sparseness parameter on the firing pattern observed in a population of retinal ganglion cells recorded ex vivo in the retina of a rodent, the Octodon degus. These recordings showed in particular that the reliability of spike timings varies with respect to the sparseness with globally a similar trend than the distribution of sparseness statistics observed in natural images. These results suggest that the code represented in the spike pattern of ganglion cells may adapt to this aspect of the statistics of natural images. |
q-bio/0401041 | Thorsten Poeschel | Thorsten Poeschel, Cornelius Froemmel, Christoph Gille | Online tool for the discrimination of equi-distributions | 12 pages, 8 figures | BMC Bioinformatics, Vol. 4, 580 (2003) | null | null | q-bio.GN cond-mat.stat-mech | null | For many applications one wishes to decide whether a certain set of numbers
originates from an equiprobability distribution or whether they are unequally
distributed. Distributions of relative frequencies may deviate significantly
from the corresponding probability distributions due to finite sample effects.
Hence, it is not trivial to discriminate between an equiprobability
distribution and non-equally distributed probabilities when knowing only
frequencies. Based on analytical results we provide a software tool which
allows to decide whether data correspond to an equiprobability distribution.
The tool is available at http://bioinf.charite.de/equifreq/. Its application is
demonstrated for the distribution of point mutations in coding genes.
| [
{
"created": "Wed, 28 Jan 2004 14:06:57 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Poeschel",
"Thorsten",
""
],
[
"Froemmel",
"Cornelius",
""
],
[
"Gille",
"Christoph",
""
]
] | For many applications one wishes to decide whether a certain set of numbers originates from an equiprobability distribution or whether they are unequally distributed. Distributions of relative frequencies may deviate significantly from the corresponding probability distributions due to finite sample effects. Hence, it is not trivial to discriminate between an equiprobability distribution and non-equally distributed probabilities when knowing only frequencies. Based on analytical results we provide a software tool which allows to decide whether data correspond to an equiprobability distribution. The tool is available at http://bioinf.charite.de/equifreq/. Its application is demonstrated for the distribution of point mutations in coding genes. |
2004.03806 | Dweipayan Goswami Dr. | Priyashi Rao, Arpit Shukla, Paritosh Parmar, Dweipayan Goswami | Proposing a fungal metabolite-Flaviolin as a potential inhibitor of
3CLpro of novel coronavirus SARS-CoV2 using docking and molecular dynamics | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here after performing docking and molecular dynamics of various small
molecules derived as a secondary metabolite from fungi, we propose Flaviolin to
act as potent inhibitor of 3-chymotrypsin (3C) like protease (3CLpro) of noval
corona virus SARS-CoV2 responsible for pandemic condition caused by coronavirus
disease 2019 (COVID-19).
| [
{
"created": "Wed, 8 Apr 2020 04:37:03 GMT",
"version": "v1"
}
] | 2020-04-09 | [
[
"Rao",
"Priyashi",
""
],
[
"Shukla",
"Arpit",
""
],
[
"Parmar",
"Paritosh",
""
],
[
"Goswami",
"Dweipayan",
""
]
] | Here after performing docking and molecular dynamics of various small molecules derived as a secondary metabolite from fungi, we propose Flaviolin to act as potent inhibitor of 3-chymotrypsin (3C) like protease (3CLpro) of noval corona virus SARS-CoV2 responsible for pandemic condition caused by coronavirus disease 2019 (COVID-19). |
0908.0339 | Robert Hilborn | Robert C. Hilborn and Jessie D. Erwin | Stochastic Coherence in an Oscillatory Gene Circuit Model | null | J. Theor. Biology 253 (2008) 349 | 10.1016/j.jtbi.2008.03.012 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that noise-induced oscillations in a gene circuit model display
stochastic coherence, that is, a maximum in the regularity of the oscillations
as a function of noise amplitude. The effect is manifest as a system-size
effect in a purely stochastic molecular reaction description of the circuit
dynamics. We compare the molecular reaction model behavior with that predicted
by a rate equation version of the same system. In addition, we show that
commonly used reduced models that ignore fast operator reactions do not capture
the full stochastic behavior of the gene circuit. Stochastic coherence occurs
under conditions that may be physiologically relevant.
| [
{
"created": "Mon, 3 Aug 2009 20:18:43 GMT",
"version": "v1"
}
] | 2009-08-05 | [
[
"Hilborn",
"Robert C.",
""
],
[
"Erwin",
"Jessie D.",
""
]
] | We show that noise-induced oscillations in a gene circuit model display stochastic coherence, that is, a maximum in the regularity of the oscillations as a function of noise amplitude. The effect is manifest as a system-size effect in a purely stochastic molecular reaction description of the circuit dynamics. We compare the molecular reaction model behavior with that predicted by a rate equation version of the same system. In addition, we show that commonly used reduced models that ignore fast operator reactions do not capture the full stochastic behavior of the gene circuit. Stochastic coherence occurs under conditions that may be physiologically relevant. |
2309.02343 | Stefan Schuster | Stefan Schuster and Tatjana Malycheva | Enumeration of saturated and unsaturated substituted N-heterocycles | 11 pages, 4 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Mathematical and computational approaches in chemistry and biochemistry fill
a gap in respect to the analysis of the physicochemical features of compounds
and their functionality and provide an overview of known as well as yet
unknown, but hypothetically possible structures. Nitrogen containing
heterocycles such as aziridine, azetidine and pyrrolidine bear a high potential
in pharmacology, biotechnology and synthetic biology. Here, we present a
mathematical enumeration procedure for all possible azaheterocycles with at
least one substituent depending on the number of atoms in the ring, in the
sense of saturated and unsaturated congeners. One subgroup belonging to that
substance class is constituted by ring-shaped amino acids with a secondary
amino group such as proline. A recursion formula is derived, which results in a
modified Lucas number series. Moreover, an explicit formula for determining the
number of such substances based on the Golden Ratio is given and a second one,
based on binomial coefficients, is newly derived. This enumeration is a helpful
tool for construction or complementation of virtual compound databases and for
computer-assisted chemical synthesis route planning.
| [
{
"created": "Tue, 5 Sep 2023 15:59:41 GMT",
"version": "v1"
}
] | 2023-09-06 | [
[
"Schuster",
"Stefan",
""
],
[
"Malycheva",
"Tatjana",
""
]
] | Mathematical and computational approaches in chemistry and biochemistry fill a gap in respect to the analysis of the physicochemical features of compounds and their functionality and provide an overview of known as well as yet unknown, but hypothetically possible structures. Nitrogen containing heterocycles such as aziridine, azetidine and pyrrolidine bear a high potential in pharmacology, biotechnology and synthetic biology. Here, we present a mathematical enumeration procedure for all possible azaheterocycles with at least one substituent depending on the number of atoms in the ring, in the sense of saturated and unsaturated congeners. One subgroup belonging to that substance class is constituted by ring-shaped amino acids with a secondary amino group such as proline. A recursion formula is derived, which results in a modified Lucas number series. Moreover, an explicit formula for determining the number of such substances based on the Golden Ratio is given and a second one, based on binomial coefficients, is newly derived. This enumeration is a helpful tool for construction or complementation of virtual compound databases and for computer-assisted chemical synthesis route planning. |
1706.03839 | Aya Kabbara | Aya Kabbara, Mahmoud Hassan, Mohamad Khalil, Wassim El Falou, Hassan
Eid | A scalp-EEG network-based analysis of Alzheimer's disease patients at
rest | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most brain disorders including Alzheimer's disease (AD) are related to
alterations in the normal brain network organization and function. Exploring
these network alterations using non-invasive and easy to use technique is a
topic of great interest. In this paper, we collected EEG resting-state data
from AD patients and healthy control subjects. Functional connectivity between
scalp EEG signals was quantified using the phase locking value (PLV) for 6
frequency bands. To assess the differences in network properties,
graph-theoretical analysis was performed. AD patients showed decrease of mean
connectivity, average clustering and global efficiency in the lower alpha band.
Positive correlation between the cognitive score and the extracted graph
measures was obtained, suggesting that EEG could be a promising technique to
derive new biomarkers of AD diagnosis.
| [
{
"created": "Mon, 12 Jun 2017 20:27:48 GMT",
"version": "v1"
}
] | 2017-06-14 | [
[
"Kabbara",
"Aya",
""
],
[
"Hassan",
"Mahmoud",
""
],
[
"Khalil",
"Mohamad",
""
],
[
"Falou",
"Wassim El",
""
],
[
"Eid",
"Hassan",
""
]
] | Most brain disorders including Alzheimer's disease (AD) are related to alterations in the normal brain network organization and function. Exploring these network alterations using non-invasive and easy to use technique is a topic of great interest. In this paper, we collected EEG resting-state data from AD patients and healthy control subjects. Functional connectivity between scalp EEG signals was quantified using the phase locking value (PLV) for 6 frequency bands. To assess the differences in network properties, graph-theoretical analysis was performed. AD patients showed decrease of mean connectivity, average clustering and global efficiency in the lower alpha band. Positive correlation between the cognitive score and the extracted graph measures was obtained, suggesting that EEG could be a promising technique to derive new biomarkers of AD diagnosis. |
2306.11965 | Nima Dehghani | Nima Dehghani | Symmetry's Edge in Cortical Dynamics: Multiscale Dynamics of Ensemble
Excitation and Inhibition | null | null | null | null | q-bio.NC cond-mat.dis-nn nlin.AO physics.bio-ph | http://creativecommons.org/licenses/by-sa/4.0/ | Creating a quantitative theory for the cortex poses several challenges and
raises numerous questions. For instance, what are the significant scales of the
system? Are they micro, meso or macroscopic? What are the relevant
interactions? Are they pairwise, higher order or mean-field? And what are the
control parameters? Are they noisy, dissipative or emergent?
To tackle these issues, we suggest using an approach akin to what has
transformed our understanding of the state of matter. This includes identifying
invariances in the ensemble dynamics of various neuron functional classes,
searching for order parameters that connect important degrees of freedom and
distinguish macroscopic system states, and identifying broken symmetries in the
order parameter space to comprehend the emerging laws when many neurons
interact and coordinate their activation.
By utilizing multielectrode and multiscale neural recordings, we measure the
scale-invariant balance between excitatory and inhibitory neurons at a
population level, referred to as ensemble E/I balance. This differs from the
input E/I balance typically studied at the single-neuron level, focusing
instead on the collective behavior of large neural populations. We investigate
a set of parameters that can assist us in differentiating between various
functional system states (such as the wake/sleep cycle) and pinpointing broken
symmetries that serve different information processing and memory functions.
Furthermore, we identify broken symmetries that result in pathological states
like seizures.
This study provides new insights into the multiscale dynamics of excitation
and inhibition in cortical networks, advancing our understanding of the
underlying principles governing neural computation and dysfunction.
| [
{
"created": "Wed, 21 Jun 2023 01:38:42 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Jan 2024 23:32:12 GMT",
"version": "v2"
},
{
"created": "Thu, 4 Jul 2024 17:33:21 GMT",
"version": "v3"
}
] | 2024-07-08 | [
[
"Dehghani",
"Nima",
""
]
] | Creating a quantitative theory for the cortex poses several challenges and raises numerous questions. For instance, what are the significant scales of the system? Are they micro, meso or macroscopic? What are the relevant interactions? Are they pairwise, higher order or mean-field? And what are the control parameters? Are they noisy, dissipative or emergent? To tackle these issues, we suggest using an approach akin to what has transformed our understanding of the state of matter. This includes identifying invariances in the ensemble dynamics of various neuron functional classes, searching for order parameters that connect important degrees of freedom and distinguish macroscopic system states, and identifying broken symmetries in the order parameter space to comprehend the emerging laws when many neurons interact and coordinate their activation. By utilizing multielectrode and multiscale neural recordings, we measure the scale-invariant balance between excitatory and inhibitory neurons at a population level, referred to as ensemble E/I balance. This differs from the input E/I balance typically studied at the single-neuron level, focusing instead on the collective behavior of large neural populations. We investigate a set of parameters that can assist us in differentiating between various functional system states (such as the wake/sleep cycle) and pinpointing broken symmetries that serve different information processing and memory functions. Furthermore, we identify broken symmetries that result in pathological states like seizures. This study provides new insights into the multiscale dynamics of excitation and inhibition in cortical networks, advancing our understanding of the underlying principles governing neural computation and dysfunction. |
q-bio/0604004 | Giuseppe Gaeta | G. Gaeta | Solitons in Yakushevich-like models of DNA dynamics with improved
intrapair potential | null | J. Nonlin. Math. Phys. 14 (2007), 57-81 | 10.2991/jnmp.2007.14.1.6 | null | q-bio.BM | null | The Yakushevich (Y) model provides a very simple pictures of DNA torsion
dynamics, yet yields remarkably correct predictions on certain physical
characteristics of the dynamics. In the standard Y model, the interaction
between bases of a pair is modelled by a harmonic potential, which becomes
anharmonic when described in terms of the rotation angles; here we substitute
to this different types of improved potentials, providing a more physical
description of the H-bond mediated interactions between the bases. We focus in
particular on soliton solutions; the Y model predicts the correct size of the
nonlinear excitations supposed to model the ``transcription bubbles'', and this
is essentially unchanged with the improved potential. Other features of soliton
dynamics, in particular curvature of soliton field configurations and the
Peierls-Nabarro barrier, are instead significantly changed.
| [
{
"created": "Tue, 4 Apr 2006 13:33:08 GMT",
"version": "v1"
}
] | 2015-06-26 | [
[
"Gaeta",
"G.",
""
]
] | The Yakushevich (Y) model provides a very simple pictures of DNA torsion dynamics, yet yields remarkably correct predictions on certain physical characteristics of the dynamics. In the standard Y model, the interaction between bases of a pair is modelled by a harmonic potential, which becomes anharmonic when described in terms of the rotation angles; here we substitute to this different types of improved potentials, providing a more physical description of the H-bond mediated interactions between the bases. We focus in particular on soliton solutions; the Y model predicts the correct size of the nonlinear excitations supposed to model the ``transcription bubbles'', and this is essentially unchanged with the improved potential. Other features of soliton dynamics, in particular curvature of soliton field configurations and the Peierls-Nabarro barrier, are instead significantly changed. |
2210.13564 | Alfonso Nieto-Castanon | Alfonso Nieto-Castanon | Preparing fMRI Data for Statistical Analysis | null | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This chapter describes several procedures used to prepare fMRI data for
statistical analyses. It includes the description of common preprocessing
steps, such as spatial realignment, coregistration, and spatial normalization,
aimed at the spatial alignment of all fMRI data within- and between- subjects,
as well as several denoising procedures aimed at minimizing the impact of
common noise sources, including physiological and residual subject motion
effects, on the BOLD signal time series. The chapter ends with a description of
quality control procedures recommended for detecting potential problems in the
fMRI data and evaluating its suitability for subsequent statistical analyses.
| [
{
"created": "Mon, 24 Oct 2022 19:38:45 GMT",
"version": "v1"
}
] | 2022-10-26 | [
[
"Nieto-Castanon",
"Alfonso",
""
]
] | This chapter describes several procedures used to prepare fMRI data for statistical analyses. It includes the description of common preprocessing steps, such as spatial realignment, coregistration, and spatial normalization, aimed at the spatial alignment of all fMRI data within- and between- subjects, as well as several denoising procedures aimed at minimizing the impact of common noise sources, including physiological and residual subject motion effects, on the BOLD signal time series. The chapter ends with a description of quality control procedures recommended for detecting potential problems in the fMRI data and evaluating its suitability for subsequent statistical analyses. |
1802.05539 | Irina Kareva | Irina Kareva | Using mathematical modeling to ask meaningful biological questions
through combination of bifurcation analysis and population heterogeneity | 26 pages, 9 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Classical approaches to analyzing dynamical systems, including bifurcation
analysis, can provide invaluable insights into underlying structure of a
mathematical model, and the spectrum of all possible dynamical behaviors.
However, these models frequently fail to take into account population
heterogeneity, which, while critically important to understanding and
predicting the behavior of any evolving system, is a common simplification that
is made in analysis of many mathematical models of ecological systems. Attempts
to include population heterogeneity frequently result in expanding system
dimensionality, effectively preventing qualitative analysis. Reduction Theorem,
or Hidden keystone variable (HKV) method, allows incorporating population
heterogeneity while still permitting the use of previously existing classical
bifurcation analysis. A combination of these methods allows visualization of
evolutionary trajectories and making meaningful predictions about dynamics over
time of evolving populations. Here, we discuss three examples of combination of
these methods to augment understanding of evolving ecological systems. We
demonstrate what new meaningful questions can be asked through this approach,
and propose that the large existing literature of fully analyzed models can
reveal new and meaningful dynamical behaviors with the application of the
HKV-method, if the right questions are asked.
| [
{
"created": "Thu, 15 Feb 2018 14:15:30 GMT",
"version": "v1"
}
] | 2018-02-16 | [
[
"Kareva",
"Irina",
""
]
] | Classical approaches to analyzing dynamical systems, including bifurcation analysis, can provide invaluable insights into underlying structure of a mathematical model, and the spectrum of all possible dynamical behaviors. However, these models frequently fail to take into account population heterogeneity, which, while critically important to understanding and predicting the behavior of any evolving system, is a common simplification that is made in analysis of many mathematical models of ecological systems. Attempts to include population heterogeneity frequently result in expanding system dimensionality, effectively preventing qualitative analysis. Reduction Theorem, or Hidden keystone variable (HKV) method, allows incorporating population heterogeneity while still permitting the use of previously existing classical bifurcation analysis. A combination of these methods allows visualization of evolutionary trajectories and making meaningful predictions about dynamics over time of evolving populations. Here, we discuss three examples of combination of these methods to augment understanding of evolving ecological systems. We demonstrate what new meaningful questions can be asked through this approach, and propose that the large existing literature of fully analyzed models can reveal new and meaningful dynamical behaviors with the application of the HKV-method, if the right questions are asked. |
1209.0312 | Spyros Papageorgiou Dr | Spyros Papageorgiou | An explanation of unexpected Hoxd expressions in mutant mice | 11 pages, 2 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Hox gene collinearity enigma has often been approached using models based
on biomolecular mechanisms. The biophysical model, is an alternative approach,
speculating that collinearity is caused by physical forces pulling the Hox
clusters from a territory where they are inactive to a distinct spatial domain
where they are activated in a step by step manner.
Hox gene translocations have recently been observed in support of the
biophysical model. Furthermore, genetic engineering experiments, performed in
embryonic mice, gave rise to some unexpected mutant expressions that
biomolecular models could not predict. In several cases when anterior Hoxd
genes are deleted, the expression of the genes whose expression is probed in
the mutants are impossible to anticipate. On the contrary, the biophysical
model offers convincing explanation.
All these experimental results support the idea of physical forces being
responsible for Hox gene collinearity. In order to test the validity of the
various models further, certain experiment involving gene deletions are
proposed. The biophysical and biomolecular models predict different results for
these experiments, hence the expected outcome will confirm or question the
validity of these models.
| [
{
"created": "Mon, 3 Sep 2012 11:41:06 GMT",
"version": "v1"
}
] | 2012-09-04 | [
[
"Papageorgiou",
"Spyros",
""
]
] | The Hox gene collinearity enigma has often been approached using models based on biomolecular mechanisms. The biophysical model, is an alternative approach, speculating that collinearity is caused by physical forces pulling the Hox clusters from a territory where they are inactive to a distinct spatial domain where they are activated in a step by step manner. Hox gene translocations have recently been observed in support of the biophysical model. Furthermore, genetic engineering experiments, performed in embryonic mice, gave rise to some unexpected mutant expressions that biomolecular models could not predict. In several cases when anterior Hoxd genes are deleted, the expression of the genes whose expression is probed in the mutants are impossible to anticipate. On the contrary, the biophysical model offers convincing explanation. All these experimental results support the idea of physical forces being responsible for Hox gene collinearity. In order to test the validity of the various models further, certain experiment involving gene deletions are proposed. The biophysical and biomolecular models predict different results for these experiments, hence the expected outcome will confirm or question the validity of these models. |
2110.04892 | Eitan Asher Mr. | Eitan E. Asher, Maya Slovik, Rae Mitelman, Hagai Bergman, Shlomo
Havlin and Shay Moshel | Local Field Potential Journey into the Basal Ganglia | null | null | null | null | q-bio.NC physics.data-an | http://creativecommons.org/licenses/by/4.0/ | Local Field potential (LFP) in the basal ganglia (BG) nuclei in the brain
have attracted much research and clinical interest. However, the origin of this
signal is still under debate throughout the last decades. The question is
whether it is a local subthreshold phenomenon, synaptic input to neurons or it
is a flow of electrical signals merged as volume conduction which are generated
from simultaneous firing neurons in the cerebral cortex and obeys the Maxwell
equations. In this study, we recorded in a monkey brain simultaneously LFP's
from the cerebral cortex, in the frontal lobe and primary motor cortex (M1) and
in sites in all BG nuclei: the striatum, globus pallidus, and subthalamic
nucleus. All the records were taken from human primate model (vervet monkey),
during spontaneous activity. Developing and applying a novel method to identify
significant cross-correlations (potential links) while removing "spurious"
correlations, we found a tool that may discriminate between the two major
phenomena of synaptic inputs (as we define as information flow) and volume
conduction. We find mainly two major paths flows of field potential, that
propagates with two different time delays, from the primary motor cortex, and
from the frontal cortex.
Our results indicate that the two path flows may represent the two mechanisms
of volume conduction and information flow.
| [
{
"created": "Sun, 10 Oct 2021 20:17:53 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Dec 2021 14:29:34 GMT",
"version": "v2"
},
{
"created": "Wed, 16 Feb 2022 11:17:18 GMT",
"version": "v3"
},
{
"created": "Wed, 19 Oct 2022 05:59:11 GMT",
"version": "v4"
}
] | 2022-10-20 | [
[
"Asher",
"Eitan E.",
""
],
[
"Slovik",
"Maya",
""
],
[
"Mitelman",
"Rae",
""
],
[
"Bergman",
"Hagai",
""
],
[
"Havlin",
"Shlomo",
""
],
[
"Moshel",
"Shay",
""
]
] | Local Field potential (LFP) in the basal ganglia (BG) nuclei in the brain have attracted much research and clinical interest. However, the origin of this signal is still under debate throughout the last decades. The question is whether it is a local subthreshold phenomenon, synaptic input to neurons or it is a flow of electrical signals merged as volume conduction which are generated from simultaneous firing neurons in the cerebral cortex and obeys the Maxwell equations. In this study, we recorded in a monkey brain simultaneously LFP's from the cerebral cortex, in the frontal lobe and primary motor cortex (M1) and in sites in all BG nuclei: the striatum, globus pallidus, and subthalamic nucleus. All the records were taken from human primate model (vervet monkey), during spontaneous activity. Developing and applying a novel method to identify significant cross-correlations (potential links) while removing "spurious" correlations, we found a tool that may discriminate between the two major phenomena of synaptic inputs (as we define as information flow) and volume conduction. We find mainly two major paths flows of field potential, that propagates with two different time delays, from the primary motor cortex, and from the frontal cortex. Our results indicate that the two path flows may represent the two mechanisms of volume conduction and information flow. |
1905.12570 | James Yearsley | James M Yearsley and Jonathan J Halliwell | Contextuality in Human Decision Making in the Presence of Direct
Influences: A Comment on Basieva et al. (2019) | 5 pages. V2: Substantial Revisions | null | null | null | q-bio.NC quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a recent paper Basieva, Cervantes, Dzhafarov, and Khrennikov (2019)
presented a series of experiments which they claimed show evidence for
contextuality in human judgments. This was based on a set of modified Bell-like
inequalities designed to rule out effects caused by signalling. In this comment
we show that it is, however, possible to construct a non-contextual model which
explains the experimental data via direct influences, which we take to mean
that a measurement outcome has a (model-specific) causal dependence on other
measurements. We trace the apparent inconsistency to a definition of signalling
which does not account for all possible forms of direct influence. Further, we
cast doubt on the idea that any experimental data in psychology could provide
conclusive evidence for contextuality beyond that explainable by direct
influence.
| [
{
"created": "Tue, 7 May 2019 14:19:59 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Oct 2019 12:16:36 GMT",
"version": "v2"
}
] | 2019-10-14 | [
[
"Yearsley",
"James M",
""
],
[
"Halliwell",
"Jonathan J",
""
]
] | In a recent paper Basieva, Cervantes, Dzhafarov, and Khrennikov (2019) presented a series of experiments which they claimed show evidence for contextuality in human judgments. This was based on a set of modified Bell-like inequalities designed to rule out effects caused by signalling. In this comment we show that it is, however, possible to construct a non-contextual model which explains the experimental data via direct influences, which we take to mean that a measurement outcome has a (model-specific) causal dependence on other measurements. We trace the apparent inconsistency to a definition of signalling which does not account for all possible forms of direct influence. Further, we cast doubt on the idea that any experimental data in psychology could provide conclusive evidence for contextuality beyond that explainable by direct influence. |
1611.05137 | Takahiro Ezaki | Takahiro Ezaki, Takamitsu Watanabe, Masayuki Ohzeki, Naoki Masuda | Energy landscape analysis of neuroimaging data | 22 pages, 4 figures, 1 table | Phil. Trans. R. Soc. A 375, 20160287 (2017) | 10.1098/rsta.2016.0287 | null | q-bio.NC cond-mat.stat-mech physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational neuroscience models have been used for understanding neural
dynamics in the brain and how they may be altered when physiological or other
conditions change. We review and develop a data-driven approach to neuroimaging
data called the energy landscape analysis. The methods are rooted in
statistical physics theory, in particular the Ising model, also known as the
(pairwise) maximum entropy model and Boltzmann machine. The methods have been
applied to fitting electrophysiological data in neuroscience for a decade, but
their use in neuroimaging data is still in its infancy. We first review the
methods and discuss some algorithms and technical aspects. Then, we apply the
methods to functional magnetic resonance imaging data recorded from healthy
individuals to inspect the relationship between the accuracy of fitting, the
size of the brain system to be analyzed, and the data length.
| [
{
"created": "Wed, 16 Nov 2016 04:17:12 GMT",
"version": "v1"
},
{
"created": "Thu, 25 May 2017 14:01:27 GMT",
"version": "v2"
}
] | 2017-05-26 | [
[
"Ezaki",
"Takahiro",
""
],
[
"Watanabe",
"Takamitsu",
""
],
[
"Ohzeki",
"Masayuki",
""
],
[
"Masuda",
"Naoki",
""
]
] | Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analyzed, and the data length. |
1305.3544 | Florian Hartig | Florian Hartig and Carsten F. Dormann | Does "model-free" forecasting really outperform the "true" model? A
reply to Perretti et al | Letter submitted to PNAS, with additional supplementary information.
R code included in the latex source | Proceedings of the National Academy of Sciences, 110, E3975, 2013 | 10.1073/pnas.1308603110 | null | q-bio.PE nlin.CD stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Estimating population models from uncertain observations is an important
problem in ecology. Perretti et al. observed that standard Bayesian state-space
solutions to this problem may provide biased parameter estimates when the
underlying dynamics are chaotic. Consequently, forecasts based on these
estimates showed poor predictive accuracy compared to simple "model-free"
methods, which lead Perretti et al. to conclude that "Model-free forecasting
outperforms the correct mechanistic model for simulated and experimental data".
However, a simple modification of the statistical methods also suffices to
remove the bias and reverse their results.
| [
{
"created": "Wed, 15 May 2013 17:01:13 GMT",
"version": "v1"
}
] | 2013-10-28 | [
[
"Hartig",
"Florian",
""
],
[
"Dormann",
"Carsten F.",
""
]
] | Estimating population models from uncertain observations is an important problem in ecology. Perretti et al. observed that standard Bayesian state-space solutions to this problem may provide biased parameter estimates when the underlying dynamics are chaotic. Consequently, forecasts based on these estimates showed poor predictive accuracy compared to simple "model-free" methods, which lead Perretti et al. to conclude that "Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data". However, a simple modification of the statistical methods also suffices to remove the bias and reverse their results. |
2104.01589 | Guy Katriel | Guy Katriel | Dispersal-induced growth in a time-periodic environment | null | J. Math. Biol. 85, 24 (2022) | 10.1007/s00285-022-01791-7 | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dispersal-induced growth (DIG) occurs when two populations with time-varying
growth rates, each of which, when isolated, would become extinct, are able to
persist and grow exponentially when dispersal among the two populations is
present. This work provides a mathematical exploration of this surprising
phenomenon, in the context of a deterministic model with periodic variation of
growth rates, and characterizes the factors which are important in generating
the DIG effect and the corresponding conditions on the parameters involved.
| [
{
"created": "Sun, 4 Apr 2021 11:29:53 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Apr 2021 14:52:42 GMT",
"version": "v2"
},
{
"created": "Sat, 10 Jul 2021 05:02:07 GMT",
"version": "v3"
},
{
"created": "Thu, 28 Apr 2022 05:14:08 GMT",
"version": "v4"
}
] | 2022-08-31 | [
[
"Katriel",
"Guy",
""
]
] | Dispersal-induced growth (DIG) occurs when two populations with time-varying growth rates, each of which, when isolated, would become extinct, are able to persist and grow exponentially when dispersal among the two populations is present. This work provides a mathematical exploration of this surprising phenomenon, in the context of a deterministic model with periodic variation of growth rates, and characterizes the factors which are important in generating the DIG effect and the corresponding conditions on the parameters involved. |
q-bio/0703001 | Sidney Redner | T. Antal, P. L. Krapivsky, S. Redner, M. Mailman, B. Chakraborty | Dynamics of Microtubule Growth and Catastrophe | 12 pages, 6 figures, 2-column revtex4; version 2: published version
for PRE; contains various small changes in response to referee comments | Phys. Rev. E 76, 041907 (2007) | 10.1103/PhysRevE.76.041907 | null | q-bio.QM cond-mat.stat-mech physics.bio-ph q-bio.BM | null | We investigate a simple model of microtubule dynamics in which a microtubule
evolves by: (i) attachment of guanosine triphosphate (GTP) to its end at rate
lambda, (ii) GTP converting irreversibly to guanosine diphosphate (GDP) at rate
1, and (iii) detachment of GDP from the end of a microtubule at rate mu. As a
function of these elemental rates, the microtubule can grow steadily or its
length can fluctuate wildly. A master equation approach is developed to
characterize these intriguing features. For mu=0, we find exact expressions for
tubule and GTP cap length distributions, as well as a power-law length
distributions of GTP and GDP islands. For mu=oo, we find the average time
between catastrophes, where the microtubule shrinks to zero length, and extend
this approach to also determine the size distribution of avalanches (sequence
of consecutive GDP detachment events). We obtain the phase diagram for general
rates and verify our predictions by numerical simulations.
| [
{
"created": "Thu, 1 Mar 2007 19:41:08 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Apr 2008 23:55:13 GMT",
"version": "v2"
}
] | 2008-04-23 | [
[
"Antal",
"T.",
""
],
[
"Krapivsky",
"P. L.",
""
],
[
"Redner",
"S.",
""
],
[
"Mailman",
"M.",
""
],
[
"Chakraborty",
"B.",
""
]
] | We investigate a simple model of microtubule dynamics in which a microtubule evolves by: (i) attachment of guanosine triphosphate (GTP) to its end at rate lambda, (ii) GTP converting irreversibly to guanosine diphosphate (GDP) at rate 1, and (iii) detachment of GDP from the end of a microtubule at rate mu. As a function of these elemental rates, the microtubule can grow steadily or its length can fluctuate wildly. A master equation approach is developed to characterize these intriguing features. For mu=0, we find exact expressions for tubule and GTP cap length distributions, as well as a power-law length distributions of GTP and GDP islands. For mu=oo, we find the average time between catastrophes, where the microtubule shrinks to zero length, and extend this approach to also determine the size distribution of avalanches (sequence of consecutive GDP detachment events). We obtain the phase diagram for general rates and verify our predictions by numerical simulations. |
1502.02442 | Angelika Manhart | Angelika Manhart, Christian Schmeiser, Nikolaos Sfakianakis, Dietmar
Oelz | An Extended Filament Based Lamellipodium Model Produces Various Moving
Cell Shapes in the Presence of Chemotactic Signals | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Filament Based Lamellipodium Model (FBLM) is a two-phase two-dimensional
continuum model, describing the dynamcis of two interacting families of locally
parallel actin filaments (C.Schmeiser and D.Oelz, How do cells move?
Mathematical modeling of cytoskeleton dynamics and cell migration. Cell
mechanics: from single scale-based models to multiscale modeling. Chapman and
Hall, 2010). It contains accounts of the filaments' bending stiffness, of
adhesion to the substrate, and of cross-links connecting the two families.
An extension of the model is presented with contributions from nucleation of
filaments by branching, from capping, from contraction by actin-myosin
interaction, and from a pressure-like repulsion between parallel filaments due
to Coulomb interaction. The effect of a chemoattractant is described by a
simple signal transduction model influencing the polymerization speed.
Simulations with the extended model show its potential for describing various
moving cell shapes, depending on the signal transduction procedure, and for
predicting transients between nonmoving and moving states as well as changes of
direction.
| [
{
"created": "Mon, 9 Feb 2015 11:26:13 GMT",
"version": "v1"
}
] | 2015-02-10 | [
[
"Manhart",
"Angelika",
""
],
[
"Schmeiser",
"Christian",
""
],
[
"Sfakianakis",
"Nikolaos",
""
],
[
"Oelz",
"Dietmar",
""
]
] | The Filament Based Lamellipodium Model (FBLM) is a two-phase two-dimensional continuum model, describing the dynamcis of two interacting families of locally parallel actin filaments (C.Schmeiser and D.Oelz, How do cells move? Mathematical modeling of cytoskeleton dynamics and cell migration. Cell mechanics: from single scale-based models to multiscale modeling. Chapman and Hall, 2010). It contains accounts of the filaments' bending stiffness, of adhesion to the substrate, and of cross-links connecting the two families. An extension of the model is presented with contributions from nucleation of filaments by branching, from capping, from contraction by actin-myosin interaction, and from a pressure-like repulsion between parallel filaments due to Coulomb interaction. The effect of a chemoattractant is described by a simple signal transduction model influencing the polymerization speed. Simulations with the extended model show its potential for describing various moving cell shapes, depending on the signal transduction procedure, and for predicting transients between nonmoving and moving states as well as changes of direction. |
1405.7963 | Fabien Campillo | Coralie Fritsch (INRIA Sophia Antipolis, MISTEA, I3M), J\'er\^ome
Harmand (INRIA Sophia Antipolis, LBE), Fabien Campillo (INRIA Sophia
Antipolis, MISTEA) | A modeling approach of the chemostat | arXiv admin note: substantial text overlap with arXiv:1308.2411 | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Population dynamics and in particular microbial population dynamics, though
they are complex but also intrinsically discrete and random, are conventionally
represented as deterministic differential equations systems. We propose to
revisit this approach by complementing these classic formalisms by stochastic
formalisms and to explain the links between these representations in terms of
mathematical analysis but also in terms of modeling and numerical simulations.
We illustrate this approach on the model of chemostat.
| [
{
"created": "Fri, 30 May 2014 19:30:15 GMT",
"version": "v1"
}
] | 2014-06-02 | [
[
"Fritsch",
"Coralie",
"",
"INRIA Sophia Antipolis, MISTEA, I3M"
],
[
"Harmand",
"Jérôme",
"",
"INRIA Sophia Antipolis, LBE"
],
[
"Campillo",
"Fabien",
"",
"INRIA Sophia\n Antipolis, MISTEA"
]
] | Population dynamics and in particular microbial population dynamics, though they are complex but also intrinsically discrete and random, are conventionally represented as deterministic differential equations systems. We propose to revisit this approach by complementing these classic formalisms by stochastic formalisms and to explain the links between these representations in terms of mathematical analysis but also in terms of modeling and numerical simulations. We illustrate this approach on the model of chemostat. |
1608.00108 | Roland Kr\"amer | Ulrich Warttinger, Christina Giese, Job Harenberg, Roland Kr\"amer | Direct quantification of brown algae-derived fucoidans in human plasma
by a fluorescent probe assay | article, 15 pages, 2 schemes, 3 figures, 3 tables | null | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fucoidan is a generic term for a class of fucose rich, structurally diverse
sulfated polysaccharides that are found in brown algae and other marine
organisms. Depending on the species from which the fucoidan is extracted, a
wide variety of biological activities including antitumor, antiinflammatory,
immune-modulating, antiviral, antibacterial and pro- and anticoagulant
activities has been described. Fucoidans have the advantage of low toxicity and
oral bioavailibiity and are viable drug candidates, preclinical and pilot
clinical trials show promising results. The availability of robust assays, in
particular for analysing the blood levels of fucoidan, is a fundamental
requirement for pharmacokinetic analysis in drug development projects. This
contribution describes the application of a commercially availbale,
protein-free fluorescent probe assay (Heparin Red) for the direct
quantification of several fucoidans (from Fucus vesiculosus, Macrocystis
pyrifera, and Undaria pinnatifida) in human plasma. By only minor adapation of
the established protocol for heparin detection, a concentration range 0,5 to 20
microgram per mL fucoidan can be addressed. A preliminary analysis of matrix
effects suggests acceptable interindividual variability and no interference by
endogeneous chondroitin sulfate. This study identifies the Heparin Red assay as
a simple, time-saving mix-and-read method for the quantification of fucoidans
in human plasma.
| [
{
"created": "Sat, 30 Jul 2016 12:06:19 GMT",
"version": "v1"
}
] | 2016-08-02 | [
[
"Warttinger",
"Ulrich",
""
],
[
"Giese",
"Christina",
""
],
[
"Harenberg",
"Job",
""
],
[
"Krämer",
"Roland",
""
]
] | Fucoidan is a generic term for a class of fucose rich, structurally diverse sulfated polysaccharides that are found in brown algae and other marine organisms. Depending on the species from which the fucoidan is extracted, a wide variety of biological activities including antitumor, antiinflammatory, immune-modulating, antiviral, antibacterial and pro- and anticoagulant activities has been described. Fucoidans have the advantage of low toxicity and oral bioavailibiity and are viable drug candidates, preclinical and pilot clinical trials show promising results. The availability of robust assays, in particular for analysing the blood levels of fucoidan, is a fundamental requirement for pharmacokinetic analysis in drug development projects. This contribution describes the application of a commercially availbale, protein-free fluorescent probe assay (Heparin Red) for the direct quantification of several fucoidans (from Fucus vesiculosus, Macrocystis pyrifera, and Undaria pinnatifida) in human plasma. By only minor adapation of the established protocol for heparin detection, a concentration range 0,5 to 20 microgram per mL fucoidan can be addressed. A preliminary analysis of matrix effects suggests acceptable interindividual variability and no interference by endogeneous chondroitin sulfate. This study identifies the Heparin Red assay as a simple, time-saving mix-and-read method for the quantification of fucoidans in human plasma. |
2201.08714 | Shuwen Yang | Shuwen Yang, Tianyu Wen, Ziyao Li and Guojie Song | Equivalent Distance Geometry Error for Molecular Conformation Comparison | null | null | null | null | q-bio.BM cs.LG physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | Straight-forward conformation generation models, which generate 3-D
structures directly from input molecular graphs, play an important role in
various molecular tasks with machine learning, such as 3D-QSAR and virtual
screening in drug design. However, existing loss functions in these models
either cost overmuch time or fail to guarantee the equivalence during
optimization, which means treating different items unfairly, resulting in poor
local geometry in generated conformation. So, we propose Equivalent Distance
Geometry Error (EDGE) to calculate the differential discrepancy between
conformations where the essential factors of three kinds in conformation
geometry (i.e. bond lengths, bond angles and dihedral angles) are equivalently
optimized with certain weights. And in the improved version of our method, the
optimization features minimizing linear transformations of atom-pair distances
within 3-hop. Extensive experiments show that, compared with existing loss
functions, EDGE performs effectively and efficiently in two tasks under the
same backbones.
| [
{
"created": "Sat, 13 Nov 2021 09:04:55 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Mar 2022 04:39:32 GMT",
"version": "v2"
}
] | 2022-03-16 | [
[
"Yang",
"Shuwen",
""
],
[
"Wen",
"Tianyu",
""
],
[
"Li",
"Ziyao",
""
],
[
"Song",
"Guojie",
""
]
] | Straight-forward conformation generation models, which generate 3-D structures directly from input molecular graphs, play an important role in various molecular tasks with machine learning, such as 3D-QSAR and virtual screening in drug design. However, existing loss functions in these models either cost overmuch time or fail to guarantee the equivalence during optimization, which means treating different items unfairly, resulting in poor local geometry in generated conformation. So, we propose Equivalent Distance Geometry Error (EDGE) to calculate the differential discrepancy between conformations where the essential factors of three kinds in conformation geometry (i.e. bond lengths, bond angles and dihedral angles) are equivalently optimized with certain weights. And in the improved version of our method, the optimization features minimizing linear transformations of atom-pair distances within 3-hop. Extensive experiments show that, compared with existing loss functions, EDGE performs effectively and efficiently in two tasks under the same backbones. |
2204.11843 | Yuanxiang Gao | Yuanxiang Gao | A Computational Theory of Learning Flexible Reward-Seeking Behavior with
Place Cells | 14 pages, 23 figures | null | null | null | q-bio.NC cs.AI cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An important open question in computational neuroscience is how various
spatially tuned neurons, such as place cells, are used to support the learning
of reward-seeking behavior of an animal. Existing computational models either
lack biological plausibility or fall short of behavioral flexibility when
environments change. In this paper, we propose a computational theory that
achieves behavioral flexibility with better biological plausibility. We first
train a mixture of Gaussian distributions to model the ensemble of firing
fields of place cells. Then we propose a Hebbian-like rule to learn the
synaptic strength matrix among place cells. This matrix is interpreted as the
transition rate matrix of a continuous time Markov chain to generate the
sequential replay of place cells. During replay, the synaptic strengths from
place cells to medium spiny neurons (MSN) are learned by a temporal-difference
like rule to store place-reward associations. After replay, the activation of
MSN will ramp up when an animal approaches the rewarding place, so the animal
can move along the direction where the MSN activation is increasing to find the
rewarding place. We implement our theory into a high-fidelity virtual rat in
the MuJoCo physics simulator. In a complex maze, the rat shows significantly
better learning efficiency and behavioral flexibility than a rat that
implements a neuroscience-inspired reinforcement learning algorithm, deep
Q-network.
| [
{
"created": "Fri, 22 Apr 2022 16:06:44 GMT",
"version": "v1"
},
{
"created": "Tue, 17 May 2022 05:39:54 GMT",
"version": "v2"
}
] | 2022-05-18 | [
[
"Gao",
"Yuanxiang",
""
]
] | An important open question in computational neuroscience is how various spatially tuned neurons, such as place cells, are used to support the learning of reward-seeking behavior of an animal. Existing computational models either lack biological plausibility or fall short of behavioral flexibility when environments change. In this paper, we propose a computational theory that achieves behavioral flexibility with better biological plausibility. We first train a mixture of Gaussian distributions to model the ensemble of firing fields of place cells. Then we propose a Hebbian-like rule to learn the synaptic strength matrix among place cells. This matrix is interpreted as the transition rate matrix of a continuous time Markov chain to generate the sequential replay of place cells. During replay, the synaptic strengths from place cells to medium spiny neurons (MSN) are learned by a temporal-difference like rule to store place-reward associations. After replay, the activation of MSN will ramp up when an animal approaches the rewarding place, so the animal can move along the direction where the MSN activation is increasing to find the rewarding place. We implement our theory into a high-fidelity virtual rat in the MuJoCo physics simulator. In a complex maze, the rat shows significantly better learning efficiency and behavioral flexibility than a rat that implements a neuroscience-inspired reinforcement learning algorithm, deep Q-network. |
2310.17226 | Francois Boue | Maja Napieraj (MMB), Annie Br\^ulet (MMB), Javier Perez, Fran\c{c}ois
Bou\'e (MMB), Evelyne Lutton | In situ digestion of canola protein gel observed by synchrotron X-Ray
Scattering | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the issue of structure changes of a canola protein gel (as a solid
food model) during gastrointestinal digestion. We present a method for
synchrotron Small-Angle X-ray Scattering analysis of the digestion of a gel in
a capillary. Scanning the capillary allows tracking the digestion under
diffusion of enzymatic juices. The fitting parameters characterizing the sizes,
scattering intensities and structures allow to distinguish the compact,
unfolded or aggregated states of proteins. The evolutions of these parameters
enable to detail the complex changes of proteins during gel digestion,
involving back-and-forth evolutions with proteins unfolding (1 st and 3 rd
steps), re-compaction (2 nd step) due to gastrointestinal pH and enzyme
actions, before final protein scissions (4 th step) resulting in small
peptides. This complexity is related to the wide ranges of successive pH and
enzyme activity acting on large and charged protein assemblies. Digestion is
therefore impacted by the conditions of food preparation.
| [
{
"created": "Thu, 26 Oct 2023 08:24:15 GMT",
"version": "v1"
}
] | 2023-10-27 | [
[
"Napieraj",
"Maja",
"",
"MMB"
],
[
"Brûlet",
"Annie",
"",
"MMB"
],
[
"Perez",
"Javier",
"",
"MMB"
],
[
"Boué",
"François",
"",
"MMB"
],
[
"Lutton",
"Evelyne",
""
]
] | We address the issue of structure changes of a canola protein gel (as a solid food model) during gastrointestinal digestion. We present a method for synchrotron Small-Angle X-ray Scattering analysis of the digestion of a gel in a capillary. Scanning the capillary allows tracking the digestion under diffusion of enzymatic juices. The fitting parameters characterizing the sizes, scattering intensities and structures allow to distinguish the compact, unfolded or aggregated states of proteins. The evolutions of these parameters enable to detail the complex changes of proteins during gel digestion, involving back-and-forth evolutions with proteins unfolding (1 st and 3 rd steps), re-compaction (2 nd step) due to gastrointestinal pH and enzyme actions, before final protein scissions (4 th step) resulting in small peptides. This complexity is related to the wide ranges of successive pH and enzyme activity acting on large and charged protein assemblies. Digestion is therefore impacted by the conditions of food preparation. |
1309.2055 | Tomasz Rutkowski | Tomasz M. Rutkowski | Beyond visual P300 based brain-computer interfacing paradigms | 7 pages, 5 figures, Proceedings of the Third Postgraduate Consortium
International Workshop on Innovations in Information and Communication
Science and Technology, (E. Cooper, G. A. Kobzev, A. F. Uvarov, and V. V.
Kryssanov, eds.), (Tomsk, Russia), pp. 277-283, TUSUR and Ritsumeikan,
September 2-5, 2013. ISBN 978-5-86889-7 | null | null | null | q-bio.NC cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper reviews and summarizes recent developments in spatial auditory and
tactile brain-computer interfacing neurotechology applications. It serves as
the latest developments summary in "non-visual" brain-computer interfacing
solutions presented in a tutorial delivered by the author at the IICST 2013
workshop. The novel concepts of unimodal auditory or tactile, as well as a
bimodal combined paradigms are described and supported with recent research
results from our BCI-lab research group at Life Science Center, University of
Tsukuba, Japan. The newly developed experimental paradigms fit perfectly to
needs of paralyzed or hearing impaired users, in case of tactile stimulus, as
well as for able users who cannot utilize vision in computer or machine
interaction (driving or operation of machinery required not disturbed
eyesight). We present and review the EEG event related potential responses
useful for brain computer interfacing applications beyond state-of-the-art
visual paradigms. In conclusion the recent results are discussed and
suggestions for further applications are drawn.
| [
{
"created": "Mon, 9 Sep 2013 07:18:17 GMT",
"version": "v1"
}
] | 2013-09-10 | [
[
"Rutkowski",
"Tomasz M.",
""
]
] | The paper reviews and summarizes recent developments in spatial auditory and tactile brain-computer interfacing neurotechology applications. It serves as the latest developments summary in "non-visual" brain-computer interfacing solutions presented in a tutorial delivered by the author at the IICST 2013 workshop. The novel concepts of unimodal auditory or tactile, as well as a bimodal combined paradigms are described and supported with recent research results from our BCI-lab research group at Life Science Center, University of Tsukuba, Japan. The newly developed experimental paradigms fit perfectly to needs of paralyzed or hearing impaired users, in case of tactile stimulus, as well as for able users who cannot utilize vision in computer or machine interaction (driving or operation of machinery required not disturbed eyesight). We present and review the EEG event related potential responses useful for brain computer interfacing applications beyond state-of-the-art visual paradigms. In conclusion the recent results are discussed and suggestions for further applications are drawn. |
1208.1604 | Sanzo Miyazawa | Sanzo Miyazawa | Inference of Co-Evolving Site Pairs: an Excellent Predictor of Contact
Residue Pairs in Protein 3D structures | 17 pages, 4 figures, and 4 tables with supplementary information of 5
figures | PLoS ONE 8(1): e54252, 2013 | 10.1371/journal.pone.0054252 | null | q-bio.BM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Residue-residue interactions that fold a protein into a unique
three-dimensional structure and make it play a specific function impose
structural and functional constraints on each residue site. Selective
constraints on residue sites are recorded in amino acid orders in homologous
sequences and also in the evolutionary trace of amino acid substitutions. A
challenge is to extract direct dependences between residue sites by removing
indirect dependences through other residues within a protein or even through
other molecules. Recent attempts of disentangling direct from indirect
dependences of amino acid types between residue positions in multiple sequence
alignments have revealed that the strength of inferred residue pair couplings
is an excellent predictor of residue-residue proximity in folded structures.
Here, we report an alternative attempt of inferring co-evolving site pairs from
concurrent and compensatory substitutions between sites in each branch of a
phylogenetic tree. First, branch lengths of a phylogenetic tree inferred by the
neighbor-joining method are optimized as well as other parameters by maximizing
a likelihood of the tree in a mechanistic codon substitution model. Mean
changes of quantities, which are characteristic of concurrent and compensatory
substitutions, accompanied by substitutions at each site in each branch of the
tree are estimated with the likelihood of each substitution. Partial
correlation coefficients of the characteristic changes along branches between
sites are calculated and used to rank co-evolving site pairs. Accuracy of
contact prediction based on the present co-evolution score is comparable to
that achieved by a maximum entropy model of protein sequences for 15 protein
families taken from the Pfam release 26.0. Besides, this excellent accuracy
indicates that compensatory substitutions are significant in protein evolution.
| [
{
"created": "Wed, 8 Aug 2012 07:50:05 GMT",
"version": "v1"
}
] | 2013-01-18 | [
[
"Miyazawa",
"Sanzo",
""
]
] | Residue-residue interactions that fold a protein into a unique three-dimensional structure and make it play a specific function impose structural and functional constraints on each residue site. Selective constraints on residue sites are recorded in amino acid orders in homologous sequences and also in the evolutionary trace of amino acid substitutions. A challenge is to extract direct dependences between residue sites by removing indirect dependences through other residues within a protein or even through other molecules. Recent attempts of disentangling direct from indirect dependences of amino acid types between residue positions in multiple sequence alignments have revealed that the strength of inferred residue pair couplings is an excellent predictor of residue-residue proximity in folded structures. Here, we report an alternative attempt of inferring co-evolving site pairs from concurrent and compensatory substitutions between sites in each branch of a phylogenetic tree. First, branch lengths of a phylogenetic tree inferred by the neighbor-joining method are optimized as well as other parameters by maximizing a likelihood of the tree in a mechanistic codon substitution model. Mean changes of quantities, which are characteristic of concurrent and compensatory substitutions, accompanied by substitutions at each site in each branch of the tree are estimated with the likelihood of each substitution. Partial correlation coefficients of the characteristic changes along branches between sites are calculated and used to rank co-evolving site pairs. Accuracy of contact prediction based on the present co-evolution score is comparable to that achieved by a maximum entropy model of protein sequences for 15 protein families taken from the Pfam release 26.0. Besides, this excellent accuracy indicates that compensatory substitutions are significant in protein evolution. |
1110.6194 | Joseph Rusinko | Joe Rusinko, Brian Hipp | Invariant Based Quartet Puzzling | 9 pages 1 figure | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional Quartet Puzzling algorithms use maximum likelihood methods to
reconstruct quartet trees, and a puzzling algorithm to combine these quartets
into a tree for the full collection of $n$ taxa. We propose a variation of
Quartet Puzzling in which the quartet trees are reconstructed using
biologically symmetric invariants. We find that under certain conditions,
invariant based quartet puzzling outperforms Quartet Puzzling using maximum
likelihood.
| [
{
"created": "Thu, 27 Oct 2011 20:27:58 GMT",
"version": "v1"
}
] | 2011-10-31 | [
[
"Rusinko",
"Joe",
""
],
[
"Hipp",
"Brian",
""
]
] | Traditional Quartet Puzzling algorithms use maximum likelihood methods to reconstruct quartet trees, and a puzzling algorithm to combine these quartets into a tree for the full collection of $n$ taxa. We propose a variation of Quartet Puzzling in which the quartet trees are reconstructed using biologically symmetric invariants. We find that under certain conditions, invariant based quartet puzzling outperforms Quartet Puzzling using maximum likelihood. |
1309.3640 | Alain Barrat | Philippe Vanhems, Alain Barrat, Ciro Cattuto, Jean-Fran\c{c}ois
Pinton, Nagham Khanafer, Corinne R\'egis, Byeul-a Kim, Brigitte Comte,
Nicolas Voirin | Estimating Potential Infection Transmission Routes in Hospital Wards
Using Wearable Proximity Sensors | null | PLoS ONE 8(9): e73970 (2013) | 10.1371/journal.pone.0073970 | null | q-bio.QM physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Contacts between patients, patients and health care workers (HCWs) and among
HCWs represent one of the important routes of transmission of hospital-acquired
infections (HAI). A detailed description and quantification of contacts in
hospitals provides key information for HAIs epidemiology and for the design and
validation of control measures. We used wearable sensors to detect close-range
interactions ("contacts") between individuals in the geriatric unit of a
university hospital. Contact events were measured with a spatial resolution of
about 1.5 meters and a temporal resolution of 20 seconds. The study included 46
HCWs and 29 patients and lasted for 4 days and 4 nights. 14037 contacts were
recorded. The number and duration of contacts varied between mornings,
afternoons and nights, and contact matrices describing the mixing patterns
between HCW and patients were built for each time period. Contact patterns were
qualitatively similar from one day to the next. 38% of the contacts occurred
between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts
including at least one patient, suggesting a population of individuals who
could potentially act as super-spreaders. Wearable sensors represent a novel
tool for the measurement of contact patterns in hospitals. The collected data
provides information on important aspects that impact the spreading patterns of
infectious diseases, such as the strong heterogeneity of contact numbers and
durations across individuals, the variability in the number of contacts during
a day, and the fraction of repeated contacts across days. This variability is
associated with a marked statistical stability of contact and mixing patterns
across days. Our results highlight the need for such measurement efforts in
order to correctly inform mathematical models of HAIs and use them to inform
the design and evaluation of prevention strategies.
| [
{
"created": "Sat, 14 Sep 2013 09:46:52 GMT",
"version": "v1"
}
] | 2013-09-17 | [
[
"Vanhems",
"Philippe",
""
],
[
"Barrat",
"Alain",
""
],
[
"Cattuto",
"Ciro",
""
],
[
"Pinton",
"Jean-François",
""
],
[
"Khanafer",
"Nagham",
""
],
[
"Régis",
"Corinne",
""
],
[
"Kim",
"Byeul-a",
""
],
[
"Comte",
"Brigitte",
""
],
[
"Voirin",
"Nicolas",
""
]
] | Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. We used wearable sensors to detect close-range interactions ("contacts") between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14037 contacts were recorded. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data provides information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies. |
2101.05546 | Sylvia N\"urnberg | Alexander Denker, Anastasia Steshina, Theresa Grooss, Frank Ueckert,
Sylvia N\"urnberg | Feature reduction for machine learning on molecular features: The
GeneScore | 11 pages, 9 figures, 4 tables | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We present the GeneScore, a concept of feature reduction for Machine Learning
analysis of biomedical data. Using expert knowledge, the GeneScore integrates
different molecular data types into a single score. We show that the GeneScore
is superior to a binary matrix in the classification of cancer entities from
SNV, Indel, CNV, gene fusion and gene expression data. The GeneScore is a
straightforward way to facilitate state-of-the-art analysis, while making use
of the available scientific knowledge on the nature of molecular data features
used.
| [
{
"created": "Thu, 14 Jan 2021 10:58:39 GMT",
"version": "v1"
}
] | 2021-01-15 | [
[
"Denker",
"Alexander",
""
],
[
"Steshina",
"Anastasia",
""
],
[
"Grooss",
"Theresa",
""
],
[
"Ueckert",
"Frank",
""
],
[
"Nürnberg",
"Sylvia",
""
]
] | We present the GeneScore, a concept of feature reduction for Machine Learning analysis of biomedical data. Using expert knowledge, the GeneScore integrates different molecular data types into a single score. We show that the GeneScore is superior to a binary matrix in the classification of cancer entities from SNV, Indel, CNV, gene fusion and gene expression data. The GeneScore is a straightforward way to facilitate state-of-the-art analysis, while making use of the available scientific knowledge on the nature of molecular data features used. |
0806.2181 | Lee Altenberg | Lee Altenberg | The Evolutionary Reduction Principle for Linear Variation in Genetic
Transmission | 22 pages, 1 figure | Bulletin of Mathematical Biology, Volume 71, Number 5, 1264-1284
(2009) | 10.1007/s11538-009-9401-2 | null | q-bio.PE math.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution of genetic systems has been analyzed through the use of
modifier gene models, in which a neutral gene is posited to control the
transmission of other genes under selection. Analysis of modifier gene models
has found the manifestations of an "evolutionary reduction principle": in a
population near equilibrium, a new modifier allele that scales equally all
transition probabilities between different genotypes under selection can invade
if and only if it reduces the transition probabilities. Analytical results on
the reduction principle have always required some set of constraints for
tractability: limitations to one or two selected loci, two alleles per locus,
specific selection regimes or weak selection, specific genetic processes being
modified, extreme or infinitesimal effects of the modifier allele, or tight
linkage between modifier and selected loci. Here, I prove the reduction
principle in the absence of any of these constraints, confirming a twenty-year
old conjecture. The proof is obtained by a wider application of Karlin's
Theorem 5.2 (1982) and its extension to ML-matrices, substochastic matrices,
and reducible matrices.
| [
{
"created": "Fri, 13 Jun 2008 03:56:42 GMT",
"version": "v1"
}
] | 2013-02-04 | [
[
"Altenberg",
"Lee",
""
]
] | The evolution of genetic systems has been analyzed through the use of modifier gene models, in which a neutral gene is posited to control the transmission of other genes under selection. Analysis of modifier gene models has found the manifestations of an "evolutionary reduction principle": in a population near equilibrium, a new modifier allele that scales equally all transition probabilities between different genotypes under selection can invade if and only if it reduces the transition probabilities. Analytical results on the reduction principle have always required some set of constraints for tractability: limitations to one or two selected loci, two alleles per locus, specific selection regimes or weak selection, specific genetic processes being modified, extreme or infinitesimal effects of the modifier allele, or tight linkage between modifier and selected loci. Here, I prove the reduction principle in the absence of any of these constraints, confirming a twenty-year old conjecture. The proof is obtained by a wider application of Karlin's Theorem 5.2 (1982) and its extension to ML-matrices, substochastic matrices, and reducible matrices. |
1812.06234 | Yogatheesan Varatharajah | Yogatheesan Varatharajah, Brent Berry, Jan Cimbalnik, Vaclav Kremen,
Jamie Van Gompel, Matt Stead, Benjamin Brinkmann, Ravishankar Iyer, and
Gregory Worrell | Integrating Artificial Intelligence with Real-time Intracranial EEG
Monitoring to Automate Interictal Identification of Seizure Onset Zones in
Focal Epilepsy | 25 pages, Journal of neural engineering (2018) | null | 10.1088/1741-2552/aac960 | null | q-bio.NC cs.AI q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An ability to map seizure-generating brain tissue, i.e., the seizure onset
zone (SOZ), without recording actual seizures could reduce the duration of
invasive EEG monitoring for patients with drug-resistant epilepsy. A
widely-adopted practice in the literature is to compare the incidence
(events/time) of putative pathological electrophysiological biomarkers
associated with epileptic brain tissue with the SOZ determined from spontaneous
seizures recorded with intracranial EEG, primarily using a single biomarker.
Clinical translation of the previous efforts suffers from their inability to
generalize across multiple patients because of (a) the inter-patient
variability and (b) the temporal variability in the epileptogenic activity.
Here, we report an artificial intelligence-based approach for combining
multiple interictal electrophysiological biomarkers and their temporal
characteristics as a way of accounting for the above barriers and show that it
can reliably identify seizure onset zones in a study cohort of 82 patients who
underwent evaluation for drug-resistant epilepsy. Our investigation provides
evidence that utilizing the complementary information provided by multiple
electrophysiological biomarkers and their temporal characteristics can
significantly improve the localization potential compared to previously
published single-biomarker incidence-based approaches, resulting in an average
area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our
results also suggest that recording durations between ninety minutes and two
hours are sufficient to localize SOZs with accuracies that may prove clinically
relevant. The successful validation of our approach on a large cohort of 82
patients warrants future investigation on the feasibility of utilizing
intra-operative EEG monitoring and artificial intelligence to localize
epileptogenic brain tissue.
| [
{
"created": "Sat, 15 Dec 2018 05:15:40 GMT",
"version": "v1"
}
] | 2018-12-18 | [
[
"Varatharajah",
"Yogatheesan",
""
],
[
"Berry",
"Brent",
""
],
[
"Cimbalnik",
"Jan",
""
],
[
"Kremen",
"Vaclav",
""
],
[
"Van Gompel",
"Jamie",
""
],
[
"Stead",
"Matt",
""
],
[
"Brinkmann",
"Benjamin",
""
],
[
"Iyer",
"Ravishankar",
""
],
[
"Worrell",
"Gregory",
""
]
] | An ability to map seizure-generating brain tissue, i.e., the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between ninety minutes and two hours are sufficient to localize SOZs with accuracies that may prove clinically relevant. The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. |
2010.12600 | William Lemaire | William Lemaire (1), Maher Benhouria (1), Konin Koua (1), Wei Tong
(2), Gabriel Martin-Hardy (1), Melanie Stamp (3), Kumaravelu Ganesan (3),
Louis-Philippe Gauthier (1), Marwan Besrour (1), Arman Ahnood (4), David John
Garrett (4), S\'ebastien Roy (1), Michael Ibbotson (2,5), Steven Prawer (3),
R\'ejean Fontaine (1) ((1) Interdisciplinary Institute for Technological
Innovation (3IT), Universit\'e de Sherbrooke, Sherbrooke, Quebec, Canada, (2)
National Vision Research Institute, Australian College of Optometry, Carlton,
Victoria, Australia, (3) School of Physics, The University of Melbourne,
Parkville, Victoria, Australia, (4) School of Engineering, RMIT University,
Melbourne, Victoria, Australia, (5) Department of Optometry and Vision
Sciences, The University of Melbourne, Parkville, Victoria, Australia) | Feasibility Assessment of an Optically Powered Digital Retinal
Prosthesis Architecture for Retinal Ganglion Cell Stimulation | 11 pages, 13 figures | null | null | null | q-bio.NC cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clinical trials previously demonstrated the notable capacity to elicit visual
percepts in blind patients affected with retinal diseases by electrically
stimulating the remaining neurons on the retina. However, these implants
restored very limited visual acuity and required transcutaneous cables
traversing the eyeball, leading to reduced reliability and complex surgery with
high postoperative infection risks. To overcome the limitations imposed by
cables, a retinal implant architecture in which near-infrared illumination
carries both power and data through the pupil to a digital stimulation
controller is presented. A high efficiency multi-junction photovoltaic cell
transduces the optical power to a CMOS stimulator capable of delivering
flexible interleaved sequential stimulation through a diamond microelectrode
array. To demonstrate the capacity to elicit a neural response with this
approach while complying with the optical irradiance limit at the pupil,
fluorescence imaging with a calcium indicator is used on a degenerate rat
retina. The power delivered by the laser at the permissible irradiance of 4
mW/mm2 at 850 nm is shown to be sufficient to both power the stimulator ASIC
and elicit a response in retinal ganglion cells (RGCs), with the ability to
generate of up to 35 000 pulses per second at the average stimulation
threshold. This confirms the feasibility of generating a response in RGCs with
an infrared-powered digital architecture capable of delivering complex
sequential stimulation patterns at high repetition rates, albeit with some
limitations.
| [
{
"created": "Fri, 23 Oct 2020 18:11:56 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Oct 2023 14:23:52 GMT",
"version": "v2"
}
] | 2023-10-16 | [
[
"Lemaire",
"William",
""
],
[
"Benhouria",
"Maher",
""
],
[
"Koua",
"Konin",
""
],
[
"Tong",
"Wei",
""
],
[
"Martin-Hardy",
"Gabriel",
""
],
[
"Stamp",
"Melanie",
""
],
[
"Ganesan",
"Kumaravelu",
""
],
[
"Gauthier",
"Louis-Philippe",
""
],
[
"Besrour",
"Marwan",
""
],
[
"Ahnood",
"Arman",
""
],
[
"Garrett",
"David John",
""
],
[
"Roy",
"Sébastien",
""
],
[
"Ibbotson",
"Michael",
""
],
[
"Prawer",
"Steven",
""
],
[
"Fontaine",
"Réjean",
""
]
] | Clinical trials previously demonstrated the notable capacity to elicit visual percepts in blind patients affected with retinal diseases by electrically stimulating the remaining neurons on the retina. However, these implants restored very limited visual acuity and required transcutaneous cables traversing the eyeball, leading to reduced reliability and complex surgery with high postoperative infection risks. To overcome the limitations imposed by cables, a retinal implant architecture in which near-infrared illumination carries both power and data through the pupil to a digital stimulation controller is presented. A high efficiency multi-junction photovoltaic cell transduces the optical power to a CMOS stimulator capable of delivering flexible interleaved sequential stimulation through a diamond microelectrode array. To demonstrate the capacity to elicit a neural response with this approach while complying with the optical irradiance limit at the pupil, fluorescence imaging with a calcium indicator is used on a degenerate rat retina. The power delivered by the laser at the permissible irradiance of 4 mW/mm2 at 850 nm is shown to be sufficient to both power the stimulator ASIC and elicit a response in retinal ganglion cells (RGCs), with the ability to generate of up to 35 000 pulses per second at the average stimulation threshold. This confirms the feasibility of generating a response in RGCs with an infrared-powered digital architecture capable of delivering complex sequential stimulation patterns at high repetition rates, albeit with some limitations. |
1902.11236 | Melisa B Maidana Capitan | Melisa Maidana Capit\'an, Nuria C\'ampora, Claudio Sebasti\'an,
Sigvard Silvia Kochen, In\'es Samengo | Time- and frequency-resolved covariance analysis for detection and
characterization of seizures from intracraneal EEG recordings | 21 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The amount of power in different frequency bands of the electroencephalogram
(EEG) carries information about the behavioral state of a subject. Hence,
neurologists treating epileptic patients monitor the temporal evolution of the
different bands. We propose a covariance-based method to detect and
characterize epileptic seizures operating on the band-filtered EEG signal. The
algorithm is unsupervised, and performs a principal component analysis of
intra-cranial EEG recordings, detecting transient fluctuations of the power in
each frequency band. Its simplicity makes it suitable for online
implementation. Good sampling of the non-ictal periods is required, while no
demands are imposed on the amount of data during ictal activity. We tested the
method with 32 seizures registered in 5 patients. The area below the resulting
receiver-operating characteristic curves was 87\% for the detection of seizures
and 91\% for the detection of recruited electrodes. To identify the
behaviorally relevant correlates of the physiological signal, we identified
transient changes in the variance of each band that were correlated with the
degree of loss of consciousness, the latter assessed by the so-called
Consciousness Seizure Scale, summarizing the performance of the subject in a
number of behavioral tests requested during seizures. We concluded that those
crisis with maximal impairment of consciousness tended to exhibit an increase
of variance approximately 40 seconds after seizure onset, with predominant
power in the theta and alpha bands, and reduced delta and beta activity.
| [
{
"created": "Thu, 28 Feb 2019 17:35:43 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jun 2020 18:43:45 GMT",
"version": "v2"
}
] | 2020-06-17 | [
[
"Capitán",
"Melisa Maidana",
""
],
[
"Cámpora",
"Nuria",
""
],
[
"Sebastián",
"Claudio",
""
],
[
"Kochen",
"Sigvard Silvia",
""
],
[
"Samengo",
"Inés",
""
]
] | The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. Hence, neurologists treating epileptic patients monitor the temporal evolution of the different bands. We propose a covariance-based method to detect and characterize epileptic seizures operating on the band-filtered EEG signal. The algorithm is unsupervised, and performs a principal component analysis of intra-cranial EEG recordings, detecting transient fluctuations of the power in each frequency band. Its simplicity makes it suitable for online implementation. Good sampling of the non-ictal periods is required, while no demands are imposed on the amount of data during ictal activity. We tested the method with 32 seizures registered in 5 patients. The area below the resulting receiver-operating characteristic curves was 87\% for the detection of seizures and 91\% for the detection of recruited electrodes. To identify the behaviorally relevant correlates of the physiological signal, we identified transient changes in the variance of each band that were correlated with the degree of loss of consciousness, the latter assessed by the so-called Consciousness Seizure Scale, summarizing the performance of the subject in a number of behavioral tests requested during seizures. We concluded that those crisis with maximal impairment of consciousness tended to exhibit an increase of variance approximately 40 seconds after seizure onset, with predominant power in the theta and alpha bands, and reduced delta and beta activity. |
1712.05035 | Zvi Rosen | Zvi Rosen, Anand Bhaskar, Sebastien Roch, Yun S. Song | Geometry of the sample frequency spectrum and the perils of demographic
inference | 21 pages, 5 figures | null | null | null | q-bio.PE math.AG math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The sample frequency spectrum (SFS), which describes the distribution of
mutant alleles in a sample of DNA sequences, is a widely used summary statistic
in population genetics. The expected SFS has a strong dependence on the
historical population demography and this property is exploited by popular
statistical methods to infer complex demographic histories from DNA sequence
data. Most, if not all, of these inference methods exhibit pathological
behavior, however. Specifically, they often display runaway behavior in
optimization, where the inferred population sizes and epoch durations can
degenerate to 0 or diverge to infinity, and show undesirable sensitivity of the
inferred demography to perturbations in the data. The goal of this paper is to
provide theoretical insights into why such problems arise. To this end, we
characterize the geometry of the expected SFS for piecewise-constant
demographic histories and use our results to show that the aforementioned
pathological behavior of popular inference methods is intrinsic to the geometry
of the expected SFS. We provide explicit descriptions and visualizations for a
toy model with sample size 4, and generalize our intuition to arbitrary sample
sizes n using tools from convex and algebraic geometry. We also develop a
universal characterization result which shows that the expected SFS of a sample
of size n under an arbitrary population history can be recapitulated by a
piecewise-constant demography with only k(n) epochs, where k(n) is between n/2
and 2n-1. The set of expected SFS for piecewise-constant demographies with
fewer than k(n) epochs is open and non-convex, which causes the above phenomena
for inference from data.
| [
{
"created": "Wed, 13 Dec 2017 22:52:21 GMT",
"version": "v1"
}
] | 2017-12-19 | [
[
"Rosen",
"Zvi",
""
],
[
"Bhaskar",
"Anand",
""
],
[
"Roch",
"Sebastien",
""
],
[
"Song",
"Yun S.",
""
]
] | The sample frequency spectrum (SFS), which describes the distribution of mutant alleles in a sample of DNA sequences, is a widely used summary statistic in population genetics. The expected SFS has a strong dependence on the historical population demography and this property is exploited by popular statistical methods to infer complex demographic histories from DNA sequence data. Most, if not all, of these inference methods exhibit pathological behavior, however. Specifically, they often display runaway behavior in optimization, where the inferred population sizes and epoch durations can degenerate to 0 or diverge to infinity, and show undesirable sensitivity of the inferred demography to perturbations in the data. The goal of this paper is to provide theoretical insights into why such problems arise. To this end, we characterize the geometry of the expected SFS for piecewise-constant demographic histories and use our results to show that the aforementioned pathological behavior of popular inference methods is intrinsic to the geometry of the expected SFS. We provide explicit descriptions and visualizations for a toy model with sample size 4, and generalize our intuition to arbitrary sample sizes n using tools from convex and algebraic geometry. We also develop a universal characterization result which shows that the expected SFS of a sample of size n under an arbitrary population history can be recapitulated by a piecewise-constant demography with only k(n) epochs, where k(n) is between n/2 and 2n-1. The set of expected SFS for piecewise-constant demographies with fewer than k(n) epochs is open and non-convex, which causes the above phenomena for inference from data. |
0911.0814 | Wojciech Waga | Wojciech Waga, Marta Zawierta, Stanislaw Cebrat | Modelling the Evolution of Spatially Distributed Populations in the
Uniformly Changing Environment - Sympatric Speciation | 15 pages, 7 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have simulated the evolution of age structured populations whose
individuals represented by their diploid genomes were distributed on a square
lattice. The environmental conditions on the whole territory changed
simultaneously in the same way by switching on or off some requirements.
Mutations accumulated in the genes dispensable during a given period of time
were neutral, but they could cause a genetic death of individuals if the
environment required their functions again. Populations survived due to
retaining some surplus of genetic information in the individual genomes. The
changes of the environment caused the fluctuations of the population size.
Since the simulations were performed with individuals spatially distributed on
the lattice and the maximal distance between mating partners was set as a
parameter of the model, the inbreeding coefficient in populations changed
unevenly, following the fluctuation of population size and enhancing the
speciation phenomena.
| [
{
"created": "Wed, 4 Nov 2009 13:13:21 GMT",
"version": "v1"
}
] | 2009-11-05 | [
[
"Waga",
"Wojciech",
""
],
[
"Zawierta",
"Marta",
""
],
[
"Cebrat",
"Stanislaw",
""
]
] | We have simulated the evolution of age structured populations whose individuals represented by their diploid genomes were distributed on a square lattice. The environmental conditions on the whole territory changed simultaneously in the same way by switching on or off some requirements. Mutations accumulated in the genes dispensable during a given period of time were neutral, but they could cause a genetic death of individuals if the environment required their functions again. Populations survived due to retaining some surplus of genetic information in the individual genomes. The changes of the environment caused the fluctuations of the population size. Since the simulations were performed with individuals spatially distributed on the lattice and the maximal distance between mating partners was set as a parameter of the model, the inbreeding coefficient in populations changed unevenly, following the fluctuation of population size and enhancing the speciation phenomena. |
1404.7529 | B. Roy Frieden | B.R. Frieden and R.A. Gatenby | Cell development obeys maximum Fisher information | 24 pages, 2 figures | null | null | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Eukaryotic cell development has been optimized by natural selection to obey
maximal intracellular flux of messenger proteins. This, in turn, implies
maximum Fisher information on angular position about a target nuclear pore
complex (NPR). The cell is simply modeled as spherical, with cell membrane (CM)
diameter 10 micron and concentric nuclear membrane (NM) diameter 6 micron. The
NM contains about 3000 nuclear pore complexes (NPCs). Development requires
messenger ligands to travel from the CM-NPC-DNA target binding sites. Ligands
acquire negative charge by phosphorylation, passing through the cytoplasm over
Newtonian trajectories toward positively charged NPCs (utilizing positive
nuclear localization sequences). The CM-NPC channel obeys maximized mean
protein flux F and Fisher information I at the NPC, with first-order delta I =
0 and approximate 2nd-order delta I = 0 stability to environmental
perturbations. Many of its predictions are confirmed, including the dominance
of protein pathways of from 1-4 proteins, a 4nm size for the EGFR protein and
the approximate flux value F =10^16 proteins/m2-s. After entering the nucleus,
each protein ultimately delivers its ligand information to a DNA target site
with maximum probability, i.e. maximum Kullback-Liebler entropy HKL. In a
smoothness limit HKL approaches IDNA/2, so that the total CM-NPC-DNA channel
obeys maximum Fisher I. Thus maximum information approaches non-equilibrium,
one condition for life.
| [
{
"created": "Tue, 29 Apr 2014 20:55:56 GMT",
"version": "v1"
}
] | 2014-05-01 | [
[
"Frieden",
"B. R.",
""
],
[
"Gatenby",
"R. A.",
""
]
] | Eukaryotic cell development has been optimized by natural selection to obey maximal intracellular flux of messenger proteins. This, in turn, implies maximum Fisher information on angular position about a target nuclear pore complex (NPR). The cell is simply modeled as spherical, with cell membrane (CM) diameter 10 micron and concentric nuclear membrane (NM) diameter 6 micron. The NM contains about 3000 nuclear pore complexes (NPCs). Development requires messenger ligands to travel from the CM-NPC-DNA target binding sites. Ligands acquire negative charge by phosphorylation, passing through the cytoplasm over Newtonian trajectories toward positively charged NPCs (utilizing positive nuclear localization sequences). The CM-NPC channel obeys maximized mean protein flux F and Fisher information I at the NPC, with first-order delta I = 0 and approximate 2nd-order delta I = 0 stability to environmental perturbations. Many of its predictions are confirmed, including the dominance of protein pathways of from 1-4 proteins, a 4nm size for the EGFR protein and the approximate flux value F =10^16 proteins/m2-s. After entering the nucleus, each protein ultimately delivers its ligand information to a DNA target site with maximum probability, i.e. maximum Kullback-Liebler entropy HKL. In a smoothness limit HKL approaches IDNA/2, so that the total CM-NPC-DNA channel obeys maximum Fisher I. Thus maximum information approaches non-equilibrium, one condition for life. |
2301.02659 | Pouya Baniasadi | Pouya Baniasadi | Bayesian modelling of visual discrimination learning in mice | Unpublished Masters Project Report for research conducted at the
University of Cambridge (2020) | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The brain constantly turns large flows of sensory information into selective
representations of the environment. It, therefore, needs to learn to process
those sensory inputs that are most relevant for behaviour. It is not well
understood how learning changes neural circuits in visual and decision-making
brain areas to adjust and improve its visually guided decision-making. To
address this question, head-fixed mice were trained to move through virtual
reality environments and learn visual discrimination while neural activity was
recorded with two-photon calcium imaging. Previously, descriptive models of
neuronal activity were fitted to the data, which was used to compare the
activity of excitatory and different inhibitory cell types. However, the
previous models did not take the internal representations and learning dynamics
into account. Here, I present a framework to infer a model of internal
representations that are used to generate the behaviour during the task. We
model the learning process from untrained mice to trained mice within the
normative framework of the ideal Bayesian observer and provide a Markov model
for generating the movement and licking. The framework provides a space of
models where a range of hypotheses about the internal representations could be
compared for a given data set.
| [
{
"created": "Tue, 15 Nov 2022 16:59:20 GMT",
"version": "v1"
}
] | 2023-01-10 | [
[
"Baniasadi",
"Pouya",
""
]
] | The brain constantly turns large flows of sensory information into selective representations of the environment. It, therefore, needs to learn to process those sensory inputs that are most relevant for behaviour. It is not well understood how learning changes neural circuits in visual and decision-making brain areas to adjust and improve its visually guided decision-making. To address this question, head-fixed mice were trained to move through virtual reality environments and learn visual discrimination while neural activity was recorded with two-photon calcium imaging. Previously, descriptive models of neuronal activity were fitted to the data, which was used to compare the activity of excitatory and different inhibitory cell types. However, the previous models did not take the internal representations and learning dynamics into account. Here, I present a framework to infer a model of internal representations that are used to generate the behaviour during the task. We model the learning process from untrained mice to trained mice within the normative framework of the ideal Bayesian observer and provide a Markov model for generating the movement and licking. The framework provides a space of models where a range of hypotheses about the internal representations could be compared for a given data set. |
1812.04435 | Rosalind J Allen | Rosalind J Allen and Bartlomiej Waclaw | Bacterial growth: a statistical physicist's guide | null | null | 10.1088/1361-6633/aae546 | null | q-bio.CB q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bacterial growth presents many beautiful phenomena that pose new theoretical
challenges to statistical physicists, and are also amenable to laboratory
experimentation. This review provides some of the essential biological
background, discusses recent applications of statistical physics in this field,
and highlights the potential for future research.
| [
{
"created": "Tue, 11 Dec 2018 14:38:51 GMT",
"version": "v1"
}
] | 2018-12-19 | [
[
"Allen",
"Rosalind J",
""
],
[
"Waclaw",
"Bartlomiej",
""
]
] | Bacterial growth presents many beautiful phenomena that pose new theoretical challenges to statistical physicists, and are also amenable to laboratory experimentation. This review provides some of the essential biological background, discusses recent applications of statistical physics in this field, and highlights the potential for future research. |
1003.3391 | Adilson Enio Motter | Adilson E. Motter | Improved Network Performance via Antagonism: From Synthetic Rescues to
Multi-drug Combinations | Online Open "Problems and Paradigms" article | A.E. Motter, BioEssays 32, 236 (2010) | 10.1002/bies.200900128 | null | q-bio.MN nlin.AO physics.soc-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research shows that a faulty or sub-optimally operating metabolic
network can often be rescued by the targeted removal of enzyme-coding
genes--the exact opposite of what traditional gene therapy would suggest.
Predictions go as far as to assert that certain gene knockouts can restore the
growth of otherwise nonviable gene-deficient cells. Many questions follow from
this discovery: What are the underlying mechanisms? How generalizable is this
effect? What are the potential applications? Here, I will approach these
questions from the perspective of compensatory perturbations on networks.
Relations will be drawn between such synthetic rescues and naturally occurring
cascades of reaction inactivation, as well as their analogues in physical and
other biological networks. I will specially discuss how rescue interactions can
lead to the rational design of antagonistic drug combinations that select
against resistance and how they can illuminate medical research on cancer,
antibiotics, and metabolic diseases.
| [
{
"created": "Wed, 17 Mar 2010 15:13:45 GMT",
"version": "v1"
}
] | 2010-03-18 | [
[
"Motter",
"Adilson E.",
""
]
] | Recent research shows that a faulty or sub-optimally operating metabolic network can often be rescued by the targeted removal of enzyme-coding genes--the exact opposite of what traditional gene therapy would suggest. Predictions go as far as to assert that certain gene knockouts can restore the growth of otherwise nonviable gene-deficient cells. Many questions follow from this discovery: What are the underlying mechanisms? How generalizable is this effect? What are the potential applications? Here, I will approach these questions from the perspective of compensatory perturbations on networks. Relations will be drawn between such synthetic rescues and naturally occurring cascades of reaction inactivation, as well as their analogues in physical and other biological networks. I will specially discuss how rescue interactions can lead to the rational design of antagonistic drug combinations that select against resistance and how they can illuminate medical research on cancer, antibiotics, and metabolic diseases. |
1208.4660 | Yohei Kondo | Yohei Kondo, Kunihiko Kaneko, and Shuji Ishihara | Identifying dynamical systems with bifurcations from noisy partial
observation | 16 pages, 6 figures | null | 10.1103/PhysRevE.87.042716 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamical systems are used to model a variety of phenomena in which the
bifurcation structure is a fundamental characteristic. Here we propose a
statistical machine-learning approach to derive lowdimensional models that
automatically integrate information in noisy time-series data from partial
observations. The method is tested using artificial data generated from two
cell-cycle control system models that exhibit different bifurcations, and the
learned systems are shown to robustly inherit the bifurcation structure.
| [
{
"created": "Thu, 23 Aug 2012 02:41:36 GMT",
"version": "v1"
}
] | 2015-06-11 | [
[
"Kondo",
"Yohei",
""
],
[
"Kaneko",
"Kunihiko",
""
],
[
"Ishihara",
"Shuji",
""
]
] | Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically integrate information in noisy time-series data from partial observations. The method is tested using artificial data generated from two cell-cycle control system models that exhibit different bifurcations, and the learned systems are shown to robustly inherit the bifurcation structure. |
2006.10651 | Chris Antonopoulos Dr | Ian Cooper, Argha Mondal, Chris G. Antonopoulos | A SIR model assumption for the spread of COVID-19 in different
communities | 18 pages, 17 figures | null | 10.1016/j.chaos.2020.110057 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the effectiveness of the modelling approach on the
pandemic due to the spreading of the novel COVID-19 disease and develop a
susceptible-infected-removed (SIR) model that provides a theoretical framework
to investigate its spread within a community. Here, the model is based upon the
well-known susceptible-infected-removed (SIR) model with the difference that a
total population is not defined or kept constant per se and the number of
susceptible individuals does not decline monotonically. To the contrary, as we
show herein, it can be increased in surge periods! In particular, we
investigate the time evolution of different populations and monitor diverse
significant parameters for the spread of the disease in various communities,
represented by countries and the state of Texas in the USA. The SIR model can
provide us with insights and predictions of the spread of the virus in
communities that the recorded data alone cannot. Our work shows the importance
of modelling the spread of COVID-19 by the SIR model that we propose here, as
it can help to assess the impact of the disease by offering valuable
predictions. Our analysis takes into account data from January to June, 2020,
the period that contains the data before and during the implementation of
strict and control measures. We propose predictions on various parameters
related to the spread of COVID-19 and on the number of susceptible, infected
and removed populations until September 2020. By comparing the recorded data
with the data from our modelling approaches, we deduce that the spread of
COVID-19 can be under control in all communities considered, if proper
restrictions and strong policies are implemented to control the infection rates
early from the spread of the disease.
| [
{
"created": "Thu, 18 Jun 2020 16:22:23 GMT",
"version": "v1"
}
] | 2020-08-26 | [
[
"Cooper",
"Ian",
""
],
[
"Mondal",
"Argha",
""
],
[
"Antonopoulos",
"Chris G.",
""
]
] | In this paper, we study the effectiveness of the modelling approach on the pandemic due to the spreading of the novel COVID-19 disease and develop a susceptible-infected-removed (SIR) model that provides a theoretical framework to investigate its spread within a community. Here, the model is based upon the well-known susceptible-infected-removed (SIR) model with the difference that a total population is not defined or kept constant per se and the number of susceptible individuals does not decline monotonically. To the contrary, as we show herein, it can be increased in surge periods! In particular, we investigate the time evolution of different populations and monitor diverse significant parameters for the spread of the disease in various communities, represented by countries and the state of Texas in the USA. The SIR model can provide us with insights and predictions of the spread of the virus in communities that the recorded data alone cannot. Our work shows the importance of modelling the spread of COVID-19 by the SIR model that we propose here, as it can help to assess the impact of the disease by offering valuable predictions. Our analysis takes into account data from January to June, 2020, the period that contains the data before and during the implementation of strict and control measures. We propose predictions on various parameters related to the spread of COVID-19 and on the number of susceptible, infected and removed populations until September 2020. By comparing the recorded data with the data from our modelling approaches, we deduce that the spread of COVID-19 can be under control in all communities considered, if proper restrictions and strong policies are implemented to control the infection rates early from the spread of the disease. |
2209.02063 | Daniel Cooney | Daniel B. Cooney, Simon A. Levin, Yoichiro Mori, Joshua B. Plotkin | Evolutionary Dynamics Within and Among Competing Groups | 48 pages, 8 figures | null | 10.1073/pnas.2216186120 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological and social systems are structured at multiple scales, and the
incentives of individuals who interact in a group may diverge from the
collective incentive of the group as a whole. Mechanisms to resolve this
tension are responsible for profound transitions in evolutionary history,
including the origin of cellular life, multi-cellular life, and even societies.
Here we synthesize a growing literature that extends evolutionary game theory
to describe multilevel evolutionary dynamics, using nested birth-death
processes and partial differential equations to model natural selection acting
on competition within and among groups of individuals. We apply this theory to
analyze how mechanisms known to promote cooperation within a single group --
including assortment, reciprocity, and population structure -- alter
evolutionary outcomes in the presence of competition among groups. We find that
population structures most conducive to cooperation in multi-scale systems may
differ from those most conducive within a single group. Likewise, for
competitive interactions with a continuous range of strategies we find that
among-group selection may fail to produce socially optimal outcomes, but it can
nonetheless produce second-best solutions that balance individual incentives to
defect with the collective incentives for cooperation. We conclude by
describing the broad applicability of multi-scale evolutionary models to
problems ranging from the production of diffusible metabolites in microbes to
the management of common-pool resources in human societies.
| [
{
"created": "Mon, 5 Sep 2022 17:16:55 GMT",
"version": "v1"
}
] | 2023-05-31 | [
[
"Cooney",
"Daniel B.",
""
],
[
"Levin",
"Simon A.",
""
],
[
"Mori",
"Yoichiro",
""
],
[
"Plotkin",
"Joshua B.",
""
]
] | Biological and social systems are structured at multiple scales, and the incentives of individuals who interact in a group may diverge from the collective incentive of the group as a whole. Mechanisms to resolve this tension are responsible for profound transitions in evolutionary history, including the origin of cellular life, multi-cellular life, and even societies. Here we synthesize a growing literature that extends evolutionary game theory to describe multilevel evolutionary dynamics, using nested birth-death processes and partial differential equations to model natural selection acting on competition within and among groups of individuals. We apply this theory to analyze how mechanisms known to promote cooperation within a single group -- including assortment, reciprocity, and population structure -- alter evolutionary outcomes in the presence of competition among groups. We find that population structures most conducive to cooperation in multi-scale systems may differ from those most conducive within a single group. Likewise, for competitive interactions with a continuous range of strategies we find that among-group selection may fail to produce socially optimal outcomes, but it can nonetheless produce second-best solutions that balance individual incentives to defect with the collective incentives for cooperation. We conclude by describing the broad applicability of multi-scale evolutionary models to problems ranging from the production of diffusible metabolites in microbes to the management of common-pool resources in human societies. |
2402.16409 | Francesco Sannino | Stefan Hohenegger and Francesco Sannino | Renormalisation Group Methods for Effective Epidemiological Models | 36 pages, 19 figures | null | null | null | q-bio.PE hep-th stat.AP | http://creativecommons.org/licenses/by/4.0/ | Epidemiological models describe the spread of an infectious disease within a
population. They capture microscopic details on how the disease is passed on
among individuals in various different ways, while making predictions about the
state of the entirety of the population. However, the type and structure of the
specific model considered typically depend on the size of the population under
consideration. To analyse this effect, we study a family of effective
epidemiological models in space and time that are related to each other through
scaling transformations. Inspired by a similar treatment of diffusion
processes, we interpret the latter as renormalisation group transformations,
both at the level of the underlying differential equations and their solutions.
We show that in the large scale limit, the microscopic details of the infection
process become irrelevant, safe for a simple real number, which plays the role
of the infection rate in a basic compartmental model.
| [
{
"created": "Mon, 26 Feb 2024 09:05:13 GMT",
"version": "v1"
}
] | 2024-02-27 | [
[
"Hohenegger",
"Stefan",
""
],
[
"Sannino",
"Francesco",
""
]
] | Epidemiological models describe the spread of an infectious disease within a population. They capture microscopic details on how the disease is passed on among individuals in various different ways, while making predictions about the state of the entirety of the population. However, the type and structure of the specific model considered typically depend on the size of the population under consideration. To analyse this effect, we study a family of effective epidemiological models in space and time that are related to each other through scaling transformations. Inspired by a similar treatment of diffusion processes, we interpret the latter as renormalisation group transformations, both at the level of the underlying differential equations and their solutions. We show that in the large scale limit, the microscopic details of the infection process become irrelevant, safe for a simple real number, which plays the role of the infection rate in a basic compartmental model. |
1805.07061 | Yu Terada | Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima | Objective and efficient inference for couplings in neuronal networks | null | null | 10.1088/1742-5468/ab3219 | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inferring directional couplings from the spike data of networks is desired in
various scientific fields such as neuroscience. Here, we apply a recently
proposed objective procedure to the spike data obtained from the
Hodgkin--Huxley type models and in vitro neuronal networks cultured in a
circular structure. As a result, we succeed in reconstructing synaptic
connections accurately from the evoked activity as well as the spontaneous one.
To obtain the results, we invent an analytic formula approximately implementing
a method of screening relevant couplings. This significantly reduces the
computational cost of the screening method employed in the proposed objective
procedure, making it possible to treat large-size systems as in this study.
| [
{
"created": "Fri, 18 May 2018 06:19:37 GMT",
"version": "v1"
}
] | 2020-01-29 | [
[
"Terada",
"Yu",
""
],
[
"Obuchi",
"Tomoyuki",
""
],
[
"Isomura",
"Takuya",
""
],
[
"Kabashima",
"Yoshiyuki",
""
]
] | Inferring directional couplings from the spike data of networks is desired in various scientific fields such as neuroscience. Here, we apply a recently proposed objective procedure to the spike data obtained from the Hodgkin--Huxley type models and in vitro neuronal networks cultured in a circular structure. As a result, we succeed in reconstructing synaptic connections accurately from the evoked activity as well as the spontaneous one. To obtain the results, we invent an analytic formula approximately implementing a method of screening relevant couplings. This significantly reduces the computational cost of the screening method employed in the proposed objective procedure, making it possible to treat large-size systems as in this study. |
2306.14329 | Somya Mehra | Somya Mehra, James M. McCaw, Peter G. Taylor | Superinfection and the hypnozoite reservoir for Plasmodium vivax: a
general framework | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Malaria is a parasitic disease, transmitted by mosquito vectors. Plasmodium
vivax presents particular challenges for disease control, in light of an
undetectable reservoir of latent parasites (hypnozoites) within the host liver.
Superinfection, which is driven by temporally proximate mosquito inoculation
and/or hypnozoite activation events, is an important feature of P. vivax. Here,
we present a model of hypnozoite accrual and superinfection for P. vivax. To
couple host and vector dynamics, we construct a density-dependent Markov
population process with countably many types, for which disease extinction is
shown to occur almost surely. We also establish a functional law of large
numbers, taking the form of an infinite-dimensional system of ordinary
differential equations that can also be recovered under the hybrid
approximation or a standard compartment modelling approach. Recognising that
the subset of these equations that models the infection status of human hosts
has precisely the same form as the Kolmogorov forward equations for a Markovian
network of infinite server queues with an inhomogeneous batch arrival process,
we use physical insight into the evolution of the latter to write down a
time-dependent multivariate generating function for the solution. We use this
characterisation to collapse the infinite-compartment model into a single
integrodifferential equation (IDE) governing the intensity of mosquito-to-human
transmission. Through a steady state analysis, we recover a threshold
phenomenon for this IDE in terms of a bifurcation parameter $R_0$, with the
disease-free equilibrium shown to be uniformly asymptotically stable if $R_0<1$
and an endemic equilibrium solution emerging if $R_0>1$. Our work provides a
theoretical basis to explore the epidemiology of P. vivax, and introduces a
general strategy for constructing tractable population-level models of malarial
superinfection.
| [
{
"created": "Sun, 25 Jun 2023 19:59:09 GMT",
"version": "v1"
}
] | 2023-06-27 | [
[
"Mehra",
"Somya",
""
],
[
"McCaw",
"James M.",
""
],
[
"Taylor",
"Peter G.",
""
]
] | Malaria is a parasitic disease, transmitted by mosquito vectors. Plasmodium vivax presents particular challenges for disease control, in light of an undetectable reservoir of latent parasites (hypnozoites) within the host liver. Superinfection, which is driven by temporally proximate mosquito inoculation and/or hypnozoite activation events, is an important feature of P. vivax. Here, we present a model of hypnozoite accrual and superinfection for P. vivax. To couple host and vector dynamics, we construct a density-dependent Markov population process with countably many types, for which disease extinction is shown to occur almost surely. We also establish a functional law of large numbers, taking the form of an infinite-dimensional system of ordinary differential equations that can also be recovered under the hybrid approximation or a standard compartment modelling approach. Recognising that the subset of these equations that models the infection status of human hosts has precisely the same form as the Kolmogorov forward equations for a Markovian network of infinite server queues with an inhomogeneous batch arrival process, we use physical insight into the evolution of the latter to write down a time-dependent multivariate generating function for the solution. We use this characterisation to collapse the infinite-compartment model into a single integrodifferential equation (IDE) governing the intensity of mosquito-to-human transmission. Through a steady state analysis, we recover a threshold phenomenon for this IDE in terms of a bifurcation parameter $R_0$, with the disease-free equilibrium shown to be uniformly asymptotically stable if $R_0<1$ and an endemic equilibrium solution emerging if $R_0>1$. Our work provides a theoretical basis to explore the epidemiology of P. vivax, and introduces a general strategy for constructing tractable population-level models of malarial superinfection. |
2011.14241 | David Winkler | Sakshi Piplani, Puneet Singh, David A. Winkler, Nikolai Petrovsky | Computationally repurposed drugs and natural products against RNA
dependent RNA polymerase as potential COVID-19 therapies | 38 pages plus supplementary, 11 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | For fast development of COVID-19, it is only feasible to use drugs (off label
use) or approved natural products that are already registered or been assessed
for safety in previous human trials. These agents can be quickly assessed in
COVID-19 patients, as their safety and pharmacokinetics should already be well
understood. Computational methods offer promise for rapidly screening such
products for potential SARS-CoV-2 activity by predicting and ranking the
affinities of these compounds for specific virus protein targets. The
RNA-dependent RNA polymerase (RdRP) is a promising target for SARS-CoV-2 drug
development given it has no human homologs making RdRP inhibitors potentially
safer, with fewer off-target effects that drugs targeting other viral proteins.
We combined robust Vina docking on RdRP with molecular dynamic (MD) simulation
of the top 80 identified drug candidates to yield a list of the most promising
RdRP inhibitors. Literature reviews revealed that many of the predicted
inhibitors had been shown to have activity in in vitro assays or had been
predicted by other groups to have activity. The novel hits revealed by our
screen can now be conveniently tested for activity in RdRP inhibition assays
and if conformed testing for antiviral activity invitro before being tested in
human trials
| [
{
"created": "Sun, 29 Nov 2020 00:17:26 GMT",
"version": "v1"
}
] | 2020-12-01 | [
[
"Piplani",
"Sakshi",
""
],
[
"Singh",
"Puneet",
""
],
[
"Winkler",
"David A.",
""
],
[
"Petrovsky",
"Nikolai",
""
]
] | For fast development of COVID-19, it is only feasible to use drugs (off label use) or approved natural products that are already registered or been assessed for safety in previous human trials. These agents can be quickly assessed in COVID-19 patients, as their safety and pharmacokinetics should already be well understood. Computational methods offer promise for rapidly screening such products for potential SARS-CoV-2 activity by predicting and ranking the affinities of these compounds for specific virus protein targets. The RNA-dependent RNA polymerase (RdRP) is a promising target for SARS-CoV-2 drug development given it has no human homologs making RdRP inhibitors potentially safer, with fewer off-target effects that drugs targeting other viral proteins. We combined robust Vina docking on RdRP with molecular dynamic (MD) simulation of the top 80 identified drug candidates to yield a list of the most promising RdRP inhibitors. Literature reviews revealed that many of the predicted inhibitors had been shown to have activity in in vitro assays or had been predicted by other groups to have activity. The novel hits revealed by our screen can now be conveniently tested for activity in RdRP inhibition assays and if conformed testing for antiviral activity invitro before being tested in human trials |
1711.04828 | Gregory Way | Gregory P. Way and Casey S. Greene | Evaluating deep variational autoencoders trained on pan-cancer gene
expression | 4 pages, 3 figures, 2 tables, NIPS 2017 | null | null | null | q-bio.GN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Cancer is a heterogeneous disease with diverse molecular etiologies and
outcomes. The Cancer Genome Atlas (TCGA) has released a large compendium of
over 10,000 tumors with RNA-seq gene expression measurements. Gene expression
captures the diverse molecular profiles of tumors and can be interrogated to
reveal differential pathway activations. Deep unsupervised models, including
Variational Autoencoders (VAE) can be used to reveal these underlying patterns.
We compare a one-hidden layer VAE to two alternative VAE architectures with
increased depth. We determine the additional capacity marginally improves
performance. We train and compare the three VAE architectures to other
dimensionality reduction techniques including principal components analysis
(PCA), independent components analysis (ICA), non-negative matrix factorization
(NMF), and analysis of gene expression by denoising autoencoders (ADAGE). We
compare performance in a supervised learning task predicting gene inactivation
pan-cancer and in a latent space analysis of high grade serous ovarian cancer
(HGSC) subtypes. We do not observe substantial differences across algorithms in
the classification task. VAE latent spaces offer biological insights into HGSC
subtype biology.
| [
{
"created": "Mon, 13 Nov 2017 20:11:26 GMT",
"version": "v1"
}
] | 2017-11-15 | [
[
"Way",
"Gregory P.",
""
],
[
"Greene",
"Casey S.",
""
]
] | Cancer is a heterogeneous disease with diverse molecular etiologies and outcomes. The Cancer Genome Atlas (TCGA) has released a large compendium of over 10,000 tumors with RNA-seq gene expression measurements. Gene expression captures the diverse molecular profiles of tumors and can be interrogated to reveal differential pathway activations. Deep unsupervised models, including Variational Autoencoders (VAE) can be used to reveal these underlying patterns. We compare a one-hidden layer VAE to two alternative VAE architectures with increased depth. We determine the additional capacity marginally improves performance. We train and compare the three VAE architectures to other dimensionality reduction techniques including principal components analysis (PCA), independent components analysis (ICA), non-negative matrix factorization (NMF), and analysis of gene expression by denoising autoencoders (ADAGE). We compare performance in a supervised learning task predicting gene inactivation pan-cancer and in a latent space analysis of high grade serous ovarian cancer (HGSC) subtypes. We do not observe substantial differences across algorithms in the classification task. VAE latent spaces offer biological insights into HGSC subtype biology. |
2208.01569 | Lyle Poley | Lyle Poley, Joseph W. Baron, Tobias Galla | Generalised Lotka-Volterra model with hierarchical interactions | 10 pages, 6 Figures | null | 10.1103/PhysRevE.107.024313 | null | q-bio.PE cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the analysis of complex ecosystems it is common to use random interaction
coefficients, often assumed to be such that all species are statistically
equivalent. In this work we relax this assumption by choosing interactions
according to the cascade model, which we incorporate into a generalised
Lotka-Volterra dynamical system. These interactions impose a hierarchy in the
community. Species benefit more, on average, from interactions with species
further below them in the hierarchy than from interactions with those above.
Using dynamic mean-field theory, we demonstrate that a strong hierarchical
structure is stabilising, but that it reduces the number of species in the
surviving community, as well as their abundances. Additionally, we show that
increased heterogeneity in the variances of the interaction coefficients across
positions in the hierarchy is destabilising. We also comment on the structure
of the surviving community and demonstrate that the abundance and probability
of survival of a species is dependent on its position in the hierarchy.
| [
{
"created": "Tue, 2 Aug 2022 16:16:50 GMT",
"version": "v1"
}
] | 2023-03-08 | [
[
"Poley",
"Lyle",
""
],
[
"Baron",
"Joseph W.",
""
],
[
"Galla",
"Tobias",
""
]
] | In the analysis of complex ecosystems it is common to use random interaction coefficients, often assumed to be such that all species are statistically equivalent. In this work we relax this assumption by choosing interactions according to the cascade model, which we incorporate into a generalised Lotka-Volterra dynamical system. These interactions impose a hierarchy in the community. Species benefit more, on average, from interactions with species further below them in the hierarchy than from interactions with those above. Using dynamic mean-field theory, we demonstrate that a strong hierarchical structure is stabilising, but that it reduces the number of species in the surviving community, as well as their abundances. Additionally, we show that increased heterogeneity in the variances of the interaction coefficients across positions in the hierarchy is destabilising. We also comment on the structure of the surviving community and demonstrate that the abundance and probability of survival of a species is dependent on its position in the hierarchy. |
1612.04532 | Yong Chen | Yong Chen | Influence of cell-cell interactions on the population growth rate in a
tumor | 5 pages, 2 figures | Commun. Theor. Phys. 68, 798 (2017) | 10.1088/0253-6102/68/6/798 | null | q-bio.PE physics.bio-ph q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The understanding of the macroscopic phenomenological models of the
population growth at a microscopic level is important to predict the population
behaviors emerged from the interactions between the individuals. In this work
we consider the influence of the cell-cell interaction on the population growth
rate $R$ in a tumor system, and show that, in most cases especially small
proliferative probabilities, the regulative role of the interaction will be
strengthened with the decline of the intrinsic proliferative probabilities. For
the high replication rates of an individual and the cooperative interactions,
the proliferative probability almost has no effect. We compute the dependences
of $R$ on the interactions between the cells under the approximation of the
nearest neighbor in the rim of an avascular tumor. Our results are helpful to
qualitatively understand the influence of the interactions between the
individuals on the growth rate in population systems.
| [
{
"created": "Wed, 14 Dec 2016 08:38:49 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Nov 2017 02:43:42 GMT",
"version": "v2"
}
] | 2017-11-23 | [
[
"Chen",
"Yong",
""
]
] | The understanding of the macroscopic phenomenological models of the population growth at a microscopic level is important to predict the population behaviors emerged from the interactions between the individuals. In this work we consider the influence of the cell-cell interaction on the population growth rate $R$ in a tumor system, and show that, in most cases especially small proliferative probabilities, the regulative role of the interaction will be strengthened with the decline of the intrinsic proliferative probabilities. For the high replication rates of an individual and the cooperative interactions, the proliferative probability almost has no effect. We compute the dependences of $R$ on the interactions between the cells under the approximation of the nearest neighbor in the rim of an avascular tumor. Our results are helpful to qualitatively understand the influence of the interactions between the individuals on the growth rate in population systems. |
2402.10990 | Francesco Galimberti | Francesco Galimberti (1), Stephanie Bopp (1), Alessandro Carletti (1),
Rui Catarino (1), Martin Claverie (1), Pietro Florio (1), Alessio Ippolito
(2), Arwyn Jones (1), Flavio Marchetto (3), Michael Olvedy (1), Alberto
Pistocchi (1), Astrid Verhegghen (1), Marijn Van Der Velde (1), Diana Vieira
(1), Raphael d'Andrimont (4) ((1) European Commission, Joint Research Centre
(JRC), Ispra, Italy (2) European Food Safety Authority (EFSA), Parma, Italy
(3) European Chemicals Agency (ECHA), Helsinki, Finland (4) European
Commission, Joint Research Centre (JRC), Brussels, Belgium) | From parcels to people: development of a spatially explicit risk
indicator to monitor residential pesticide exposure in agricultural areas | 40 pages, 4 tables, 22 figures | null | null | null | q-bio.QM q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The increase in global pesticide use has mirrored the rising demand for food
over the last decades, resulting in a boost in crop yields. However, concerns
about the impact of pesticides on biodiversity, ecosystems, and human health,
especially for populations residing close to cultivated areas, are growing.
This study investigates how exposure and possible risks to residents can be
estimated at high spatial granularity based on plant protection product data.
The complexities of such analysis were explored in France, where relevant data
with good granularity are publicly available. Integrating sets of spatial
datasets and exposure assessment methodologies, we have developed an indicator
to monitor the levels of pesticide risk faced by residents. By spatialising
pesticide sales data according to their authorization on specific crops, we
developed a detailed map depicting potential pesticide loads at parcel level
across France. This spatial distribution served as the basis for an exposure
and risk assessment, modelled following the European Food Safety Authority's
guidelines. Combining the risk map with population distribution data, we have
developed an indicator that allows to monitor patterns in non-dietary exposure
to pesticides. Our results show that in France, on average, 13% of people might
be exposed to pesticides due to living in the proximity to treated crops. This
exposure is in the lower range for 34%, moderate range for 40% and higher range
for 25% of the exposed population. The risk evaluation is based on worst case
assumptions and values should not be taken as a regulatory risk assessment but
as indicator to use, for example, for monitoring time trends. The purpose of
this indicator is to demonstrate that more granular pesticide data can improve
risk reduction strategies. Harmonized and high-resolution data can help in
identifying regions where to focus on sustainable farming.
| [
{
"created": "Fri, 16 Feb 2024 12:05:21 GMT",
"version": "v1"
}
] | 2024-02-20 | [
[
"Galimberti",
"Francesco",
""
],
[
"Bopp",
"Stephanie",
""
],
[
"Carletti",
"Alessandro",
""
],
[
"Catarino",
"Rui",
""
],
[
"Claverie",
"Martin",
""
],
[
"Florio",
"Pietro",
""
],
[
"Ippolito",
"Alessio",
""
],
[
"Jones",
"Arwyn",
""
],
[
"Marchetto",
"Flavio",
""
],
[
"Olvedy",
"Michael",
""
],
[
"Pistocchi",
"Alberto",
""
],
[
"Verhegghen",
"Astrid",
""
],
[
"Van Der Velde",
"Marijn",
""
],
[
"Vieira",
"Diana",
""
],
[
"d'Andrimont",
"Raphael",
""
]
] | The increase in global pesticide use has mirrored the rising demand for food over the last decades, resulting in a boost in crop yields. However, concerns about the impact of pesticides on biodiversity, ecosystems, and human health, especially for populations residing close to cultivated areas, are growing. This study investigates how exposure and possible risks to residents can be estimated at high spatial granularity based on plant protection product data. The complexities of such analysis were explored in France, where relevant data with good granularity are publicly available. Integrating sets of spatial datasets and exposure assessment methodologies, we have developed an indicator to monitor the levels of pesticide risk faced by residents. By spatialising pesticide sales data according to their authorization on specific crops, we developed a detailed map depicting potential pesticide loads at parcel level across France. This spatial distribution served as the basis for an exposure and risk assessment, modelled following the European Food Safety Authority's guidelines. Combining the risk map with population distribution data, we have developed an indicator that allows to monitor patterns in non-dietary exposure to pesticides. Our results show that in France, on average, 13% of people might be exposed to pesticides due to living in the proximity to treated crops. This exposure is in the lower range for 34%, moderate range for 40% and higher range for 25% of the exposed population. The risk evaluation is based on worst case assumptions and values should not be taken as a regulatory risk assessment but as indicator to use, for example, for monitoring time trends. The purpose of this indicator is to demonstrate that more granular pesticide data can improve risk reduction strategies. Harmonized and high-resolution data can help in identifying regions where to focus on sustainable farming. |
1803.01236 | Marcos Trevisan Dr. | Alan Taitz, Diego E Shalom, Marcos A Trevisan | Vocal effort modulates the motor planning of short speech structures | 17 pages, 3 figures | Phys. Rev. E 97, 052406 (2018) | 10.1103/PhysRevE.97.052406 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Speech requires programming the sequence of vocal gestures that produce the
sounds of words. Here we explored the timing of this program by asking our
participants to pronounce, as quickly as possible, a sequence of
consonant-consonant-vowel (CCV) structures appearing on screen. We measured the
delay between visual presentation and voice onset. In the case of plosive
consonants, produced by sharp and well defined movements of the vocal tract, we
found that delays are positively correlated with the duration of the transition
between consonants. We then used a battery of statistical tests and
mathematical vocal models to show that delays reflect the motor planning of
CCVs and transitions are proxy indicators of the vocal effort needed to produce
them. These results support that the effort required to produce the sequence of
movements of a vocal gesture modulates the onset of the motor plan.
| [
{
"created": "Sat, 3 Mar 2018 20:45:33 GMT",
"version": "v1"
}
] | 2018-05-23 | [
[
"Taitz",
"Alan",
""
],
[
"Shalom",
"Diego E",
""
],
[
"Trevisan",
"Marcos A",
""
]
] | Speech requires programming the sequence of vocal gestures that produce the sounds of words. Here we explored the timing of this program by asking our participants to pronounce, as quickly as possible, a sequence of consonant-consonant-vowel (CCV) structures appearing on screen. We measured the delay between visual presentation and voice onset. In the case of plosive consonants, produced by sharp and well defined movements of the vocal tract, we found that delays are positively correlated with the duration of the transition between consonants. We then used a battery of statistical tests and mathematical vocal models to show that delays reflect the motor planning of CCVs and transitions are proxy indicators of the vocal effort needed to produce them. These results support that the effort required to produce the sequence of movements of a vocal gesture modulates the onset of the motor plan. |
1005.4342 | Iain Johnston | Sam F. Greenbury and Iain G. Johnston and Matthew A. Smith and
Jonathan P. K. Doye and Ard A. Louis | The effect of scale-free topology on the robustness and evolvability of
genetic regulatory networks | 16 pages, 15 figures | J. Theor. Biol. 267, 48-61 (2010) | 10.1016/j.jtbi.2010.08.006 | null | q-bio.PE q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect the
interplay between evolvability and robustness of model gene regulatory networks
with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006)
we find that networks with SFin topologies, that is SF topology for incoming
nodes and ER topology for outgoing nodes, are significantly more evolvable
towards specific oscillatory targets than networks with ER topology for both
incoming and outgoing nodes. Similar results are found for networks with SFboth
and SFout topologies. The functionality of the SFout topology, which most
closely resembles the structure of biological gene networks (Babu et al.,
2004), is compared to the ER topology in further detail through an extension to
multiple target outputs, with either an oscillatory or a non-oscillatory
nature. For multiple oscillatory targets of the same length, the differences
between SFout and ER networks are enhanced, but for non-oscillatory targets
both types of networks show fairly similar evolvability. We find that SF
networks generate oscillations much more easily than ER networks do, and this
may explain why SF networks are more evolvable than ER networks are for
oscillatory phenotypes. In spite of their greater evolvability, we find that
networks with SFout topologies are also more robust to mutations than ER
networks. Furthermore, the SFout topologies are more robust to changes in
initial conditions (environmental robustness). For both topologies, we find
that once a population of networks has reached the target state, further
neutral evolution can lead to an increase in both the mutational robustness and
the environmental robustness to changes in initial conditions.
| [
{
"created": "Mon, 24 May 2010 14:49:37 GMT",
"version": "v1"
}
] | 2013-09-03 | [
[
"Greenbury",
"Sam F.",
""
],
[
"Johnston",
"Iain G.",
""
],
[
"Smith",
"Matthew A.",
""
],
[
"Doye",
"Jonathan P. K.",
""
],
[
"Louis",
"Ard A.",
""
]
] | We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect the interplay between evolvability and robustness of model gene regulatory networks with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006) we find that networks with SFin topologies, that is SF topology for incoming nodes and ER topology for outgoing nodes, are significantly more evolvable towards specific oscillatory targets than networks with ER topology for both incoming and outgoing nodes. Similar results are found for networks with SFboth and SFout topologies. The functionality of the SFout topology, which most closely resembles the structure of biological gene networks (Babu et al., 2004), is compared to the ER topology in further detail through an extension to multiple target outputs, with either an oscillatory or a non-oscillatory nature. For multiple oscillatory targets of the same length, the differences between SFout and ER networks are enhanced, but for non-oscillatory targets both types of networks show fairly similar evolvability. We find that SF networks generate oscillations much more easily than ER networks do, and this may explain why SF networks are more evolvable than ER networks are for oscillatory phenotypes. In spite of their greater evolvability, we find that networks with SFout topologies are also more robust to mutations than ER networks. Furthermore, the SFout topologies are more robust to changes in initial conditions (environmental robustness). For both topologies, we find that once a population of networks has reached the target state, further neutral evolution can lead to an increase in both the mutational robustness and the environmental robustness to changes in initial conditions. |
1309.5458 | Tom McLeish FRS | Tom C B McLeish, Thomas L Rogers, Mark R Wilson | Allostery without conformation change: modelling protein dynamics at
multiple scales | 20 Pages, 8 figures | T. C. B. McLeish, T. L. Rodgers and M. R. Wilson, Phys. Biol., 10,
056004 (2013) | 10.1088/1478-3975/10/5/056004 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The original ideas of Cooper and Dryden, that allosteric signalling can be
induced between distant binding sites on proteins without any change in mean
structural conformation, has proved to be a remarkably prescient insight into
the rich structure of protein dynamics. It represents an alternative to the
celebrated Monod-Wyman-Changeux mechanism and proposes that modulation of the
amplitude of thermal fluctuations around a mean structure, rather than shifts
in the structure itself, give rise to allostery in ligand binding. In a
complementary approach to experiments on real proteins, here we take a
theoretical route to identify the necessary structural components of this
mechanism. By reviewing and extending an approach that moves from very
coarse-grained to more detailed models, we show that, a fundamental requirement
for a body supporting fluctuation-induced allostery is a strongly inhomogeneous
elastic modulus. This requirement is reflected in many real proteins, where a
good approximation of the elastic structure maps strongly coherent domains onto
rigid blocks connected by more flexible interface regions.
| [
{
"created": "Sat, 21 Sep 2013 10:13:36 GMT",
"version": "v1"
}
] | 2013-09-24 | [
[
"McLeish",
"Tom C B",
""
],
[
"Rogers",
"Thomas L",
""
],
[
"Wilson",
"Mark R",
""
]
] | The original ideas of Cooper and Dryden, that allosteric signalling can be induced between distant binding sites on proteins without any change in mean structural conformation, has proved to be a remarkably prescient insight into the rich structure of protein dynamics. It represents an alternative to the celebrated Monod-Wyman-Changeux mechanism and proposes that modulation of the amplitude of thermal fluctuations around a mean structure, rather than shifts in the structure itself, give rise to allostery in ligand binding. In a complementary approach to experiments on real proteins, here we take a theoretical route to identify the necessary structural components of this mechanism. By reviewing and extending an approach that moves from very coarse-grained to more detailed models, we show that, a fundamental requirement for a body supporting fluctuation-induced allostery is a strongly inhomogeneous elastic modulus. This requirement is reflected in many real proteins, where a good approximation of the elastic structure maps strongly coherent domains onto rigid blocks connected by more flexible interface regions. |
1409.2182 | Garrett Evans | Garrett N. Evans | Convolution Metric for Neuron Membrane Potential Recordings | 31 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I provide a convolution metric which takes neural membrane potential
recordings as arguments and compares their subthreshold features along with the
timing and number of spikes within them--summarizing differences in these with
a single "distance" between the recordings. Based on van Rossum's 2001 metric
for spike trains, the metric relies on a convolution operation that it performs
on the input data. The kernel used for the convolution is carefully chosen such
that it produces a desirable frequency space response and, unlike van Rossum's
kernel, causes the metric to be first order both in differences between nearby
spike times and in differences between same-time membrane potential values: an
important trait. 31 pages, 4 figures.
| [
{
"created": "Mon, 8 Sep 2014 01:27:18 GMT",
"version": "v1"
}
] | 2014-09-09 | [
[
"Evans",
"Garrett N.",
""
]
] | I provide a convolution metric which takes neural membrane potential recordings as arguments and compares their subthreshold features along with the timing and number of spikes within them--summarizing differences in these with a single "distance" between the recordings. Based on van Rossum's 2001 metric for spike trains, the metric relies on a convolution operation that it performs on the input data. The kernel used for the convolution is carefully chosen such that it produces a desirable frequency space response and, unlike van Rossum's kernel, causes the metric to be first order both in differences between nearby spike times and in differences between same-time membrane potential values: an important trait. 31 pages, 4 figures. |
0704.3948 | Alexey Mazur K | Alexey K. Mazur | The Worm-Like Chain Theory And Bending Of Short DNA | 4 pages, 3 figures, to appear in PRL | Phys. Rev. Lett. 98, 218102, 2007. | 10.1103/PhysRevLett.98.218102 | null | q-bio.BM cond-mat.soft physics.bio-ph | null | The probability distributions for bending angles in double helical DNA
obtained in all-atom molecular dynamics simulations are compared with
theoretical predictions. The computed distributions remarkably agree with the
worm-like chain theory for double helices of one helical turn and longer, and
qualitatively differ from predictions of the semi-elastic chain model. The
computed data exhibit only small anomalies in the apparent flexibility of short
DNA and cannot account for the recently reported AFM data (Wiggins et al,
Nature nanotechnology 1, 137 (2006)). It is possible that the current atomistic
DNA models miss some essential mechanisms of DNA bending on intermediate length
scales. Analysis of bent DNA structures reveals, however, that the bending
motion is structurally heterogeneous and directionally anisotropic on the
intermediate length scales where the experimental anomalies were detected.
These effects are essential for interpretation of the experimental data and
they also can be responsible for the apparent discrepancy.
| [
{
"created": "Mon, 30 Apr 2007 14:24:59 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Mazur",
"Alexey K.",
""
]
] | The probability distributions for bending angles in double helical DNA obtained in all-atom molecular dynamics simulations are compared with theoretical predictions. The computed distributions remarkably agree with the worm-like chain theory for double helices of one helical turn and longer, and qualitatively differ from predictions of the semi-elastic chain model. The computed data exhibit only small anomalies in the apparent flexibility of short DNA and cannot account for the recently reported AFM data (Wiggins et al, Nature nanotechnology 1, 137 (2006)). It is possible that the current atomistic DNA models miss some essential mechanisms of DNA bending on intermediate length scales. Analysis of bent DNA structures reveals, however, that the bending motion is structurally heterogeneous and directionally anisotropic on the intermediate length scales where the experimental anomalies were detected. These effects are essential for interpretation of the experimental data and they also can be responsible for the apparent discrepancy. |
1308.1564 | Tibor Antal | Tibor Antal, P. L. Krapivsky, M. A. Nowak | Spatial evolution of tumors with successive driver mutations | 16 pages | null | null | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the spatial evolutionary dynamics of solid tumors as they obtain
additional driver mutations. We start with a cancer clone that expands
uniformly in three dimensions giving rise to a spherical shape. We assume that
cell division occurs on the surface of the growing tumor. Each cell division
has a chance to give rise to a mutation that activates an additional driver
gene. The resulting clone has an enhanced growth rate, which generates a local
ensemble of faster growing cells, thereby distorting the spherical shape of the
tumor. We derive analytic formulas for the geometric boundary that separates
the original cancer clone from the new mutant as well as the expanding frontier
of the new mutant. The total number of original cancer cells converges to a
constant as time goes to infinity, because this clone becomes enveloped by
mutants. We derive formulas for the abundance and diversity of additional
driver mutations as function of time. Our model is semi-deterministic: the
spatial growth of the various cancer clones follows deterministic equations,
but the arrival of a new mutant is a stochastic event.
| [
{
"created": "Wed, 7 Aug 2013 13:19:26 GMT",
"version": "v1"
}
] | 2013-08-08 | [
[
"Antal",
"Tibor",
""
],
[
"Krapivsky",
"P. L.",
""
],
[
"Nowak",
"M. A.",
""
]
] | We study the spatial evolutionary dynamics of solid tumors as they obtain additional driver mutations. We start with a cancer clone that expands uniformly in three dimensions giving rise to a spherical shape. We assume that cell division occurs on the surface of the growing tumor. Each cell division has a chance to give rise to a mutation that activates an additional driver gene. The resulting clone has an enhanced growth rate, which generates a local ensemble of faster growing cells, thereby distorting the spherical shape of the tumor. We derive analytic formulas for the geometric boundary that separates the original cancer clone from the new mutant as well as the expanding frontier of the new mutant. The total number of original cancer cells converges to a constant as time goes to infinity, because this clone becomes enveloped by mutants. We derive formulas for the abundance and diversity of additional driver mutations as function of time. Our model is semi-deterministic: the spatial growth of the various cancer clones follows deterministic equations, but the arrival of a new mutant is a stochastic event. |
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