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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1604.08890 | Elizaveta Guseva | Elizaveta A Guseva, Ronald N Zuckermann, Ken A Dill | How did prebiotic polymers become informational foldamers? | 12 pages, 10 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A mystery about the origins of life is which molecular structures $-$ and
what spontaneous processes $-$ drove the autocatalytic transition from simple
chemistry to biology? Using the HP lattice model of polymer sequence spaces
leads to the prediction that random sequences of hydrophobic ($H$) and polar
($P$) monomers can collapse into relatively compact structures, exposing
hydrophobic surfaces, acting as primitive versions of today's protein
catalysts, elongating other such HP polymers, as ribosomes would now do. Such
foldamer-catalysts form an autocatalytic set, growing short chains into longer
chains that have particular sequences. The system has capacity for the
multimodality: ability to settle at multiple distinct quasi-stable states
characterized by different groups of dominating polymers. This is a testable
mechanism that we believe is relevant to the early origins of life.
| [
{
"created": "Thu, 28 Apr 2016 19:07:45 GMT",
"version": "v1"
}
] | 2016-05-02 | [
[
"Guseva",
"Elizaveta A",
""
],
[
"Zuckermann",
"Ronald N",
""
],
[
"Dill",
"Ken A",
""
]
] | A mystery about the origins of life is which molecular structures $-$ and what spontaneous processes $-$ drove the autocatalytic transition from simple chemistry to biology? Using the HP lattice model of polymer sequence spaces leads to the prediction that random sequences of hydrophobic ($H$) and polar ($P$) monomers can collapse into relatively compact structures, exposing hydrophobic surfaces, acting as primitive versions of today's protein catalysts, elongating other such HP polymers, as ribosomes would now do. Such foldamer-catalysts form an autocatalytic set, growing short chains into longer chains that have particular sequences. The system has capacity for the multimodality: ability to settle at multiple distinct quasi-stable states characterized by different groups of dominating polymers. This is a testable mechanism that we believe is relevant to the early origins of life. |
1910.05271 | Elena Kalinina | Elena Kalinina, Fabian Pedregosa, Vittorio Iacovella, Emanuele
Olivetti, Paolo Avesani | A Test for Shared Patterns in Cross-modal Brain Activation Analysis | 5 figures, tables after References (as required by SciRep template) | null | null | null | q-bio.NC cs.LG stat.ML stat.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Determining the extent to which different cognitive modalities (understood
here as the set of cognitive processes underlying the elaboration of a stimulus
by the brain) rely on overlapping neural representations is a fundamental issue
in cognitive neuroscience. In the last decade, the identification of shared
activity patterns has been mostly framed as a supervised learning problem. For
instance, a classifier is trained to discriminate categories (e.g. faces vs.
houses) in modality I (e.g. perception) and tested on the same categories in
modality II (e.g. imagery). This type of analysis is often referred to as
cross-modal decoding. In this paper we take a different approach and instead
formulate the problem of assessing shared patterns across modalities within the
framework of statistical hypothesis testing. We propose both an appropriate
test statistic and a scheme based on permutation testing to compute the
significance of this test while making only minimal distributional assumption.
We denote this test cross-modal permutation test (CMPT). We also provide
empirical evidence on synthetic datasets that our approach has greater
statistical power than the cross-modal decoding method while maintaining low
Type I errors (rejecting a true null hypothesis). We compare both approaches on
an fMRI dataset with three different cognitive modalities (perception, imagery,
visual search). Finally, we show how CMPT can be combined with Searchlight
analysis to explore spatial distribution of shared activity patterns.
| [
{
"created": "Tue, 8 Oct 2019 19:33:49 GMT",
"version": "v1"
}
] | 2019-10-14 | [
[
"Kalinina",
"Elena",
""
],
[
"Pedregosa",
"Fabian",
""
],
[
"Iacovella",
"Vittorio",
""
],
[
"Olivetti",
"Emanuele",
""
],
[
"Avesani",
"Paolo",
""
]
] | Determining the extent to which different cognitive modalities (understood here as the set of cognitive processes underlying the elaboration of a stimulus by the brain) rely on overlapping neural representations is a fundamental issue in cognitive neuroscience. In the last decade, the identification of shared activity patterns has been mostly framed as a supervised learning problem. For instance, a classifier is trained to discriminate categories (e.g. faces vs. houses) in modality I (e.g. perception) and tested on the same categories in modality II (e.g. imagery). This type of analysis is often referred to as cross-modal decoding. In this paper we take a different approach and instead formulate the problem of assessing shared patterns across modalities within the framework of statistical hypothesis testing. We propose both an appropriate test statistic and a scheme based on permutation testing to compute the significance of this test while making only minimal distributional assumption. We denote this test cross-modal permutation test (CMPT). We also provide empirical evidence on synthetic datasets that our approach has greater statistical power than the cross-modal decoding method while maintaining low Type I errors (rejecting a true null hypothesis). We compare both approaches on an fMRI dataset with three different cognitive modalities (perception, imagery, visual search). Finally, we show how CMPT can be combined with Searchlight analysis to explore spatial distribution of shared activity patterns. |
1512.00745 | Jayanta Kumar Das | Jayanta Kumar Das, Atrayee Majumder, Pabitra Pal Choudhury | Understanding of Genetic Code Degeneracy and New Way of Classifying of
Protein Family: A Mathematical Approach | pages-5, Tables-6 | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The genetic code is the set of rules by which information encoded in genetic
material (DNA or RNA sequences) is translated into proteins (amino acid
sequences) by living cells. The code defines a mapping between tri-nucleotide
sequences, called codons, and amino acids. Since there are 20 amino acids and
64 possible tri-nucleotide sequences, more than one among these 64 triplets can
code for a single amino acid which incorporates the problem of degeneracy. This
manuscript explains the underlying logic of degeneracy of genetic code based on
a mathematical point of view using a parameter named Impression. Classification
of protein family is also a long standing problem in the field of Bio-chemistry
and Genomics. Proteins belonging to a particular class have some similar
bio-chemical properties which are of utmost importance for new drug design.
Using the same parameter Impression and using graph theoretic properties we
have also devised a new way of classifying a protein family.
| [
{
"created": "Mon, 30 Nov 2015 11:01:49 GMT",
"version": "v1"
}
] | 2015-12-03 | [
[
"Das",
"Jayanta Kumar",
""
],
[
"Majumder",
"Atrayee",
""
],
[
"Choudhury",
"Pabitra Pal",
""
]
] | The genetic code is the set of rules by which information encoded in genetic material (DNA or RNA sequences) is translated into proteins (amino acid sequences) by living cells. The code defines a mapping between tri-nucleotide sequences, called codons, and amino acids. Since there are 20 amino acids and 64 possible tri-nucleotide sequences, more than one among these 64 triplets can code for a single amino acid which incorporates the problem of degeneracy. This manuscript explains the underlying logic of degeneracy of genetic code based on a mathematical point of view using a parameter named Impression. Classification of protein family is also a long standing problem in the field of Bio-chemistry and Genomics. Proteins belonging to a particular class have some similar bio-chemical properties which are of utmost importance for new drug design. Using the same parameter Impression and using graph theoretic properties we have also devised a new way of classifying a protein family. |
1205.0321 | Ramon Ferrer i Cancho | Ramon Ferrer-i-Cancho and Brenda McCowan | The span of correlations in dolphin whistle sequences | New Tables 3 and 4 | Journal of Statistical Mechanics, P06002 (2012) | 10.1088/1742-5468/2012/06/P06002 | null | q-bio.NC physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Long-range correlations are found in symbolic sequences from human language,
music and DNA. Determining the span of correlations in dolphin whistle
sequences is crucial for shedding light on their communicative complexity.
Dolphin whistles share various statistical properties with human words, i.e.
Zipf's law for word frequencies (namely that the probability of the $i$th most
frequent word of a text is about $i^{-\alpha}$) and a parallel of the tendency
of more frequent words to have more meanings. The finding of Zipf's law for
word frequencies in dolphin whistles has been the topic of an intense debate on
its implications. One of the major arguments against the relevance of Zipf's
law in dolphin whistles is that is not possible to distinguish the outcome of a
die rolling experiment from that of a linguistic or communicative source
producing Zipf's law for word frequencies. Here we show that statistically
significant whistle-whistle correlations extend back to the 2nd previous
whistle in the sequence using a global randomization test and to the 4th
previous whistle using a local randomization test. None of these correlations
are expected by a die rolling experiment and other simple explanation of Zipf's
law for word frequencies such as Simon's model that produce sequences of
unpredictable elements.
| [
{
"created": "Wed, 2 May 2012 04:49:19 GMT",
"version": "v1"
},
{
"created": "Wed, 9 May 2012 12:29:23 GMT",
"version": "v2"
}
] | 2014-12-03 | [
[
"Ferrer-i-Cancho",
"Ramon",
""
],
[
"McCowan",
"Brenda",
""
]
] | Long-range correlations are found in symbolic sequences from human language, music and DNA. Determining the span of correlations in dolphin whistle sequences is crucial for shedding light on their communicative complexity. Dolphin whistles share various statistical properties with human words, i.e. Zipf's law for word frequencies (namely that the probability of the $i$th most frequent word of a text is about $i^{-\alpha}$) and a parallel of the tendency of more frequent words to have more meanings. The finding of Zipf's law for word frequencies in dolphin whistles has been the topic of an intense debate on its implications. One of the major arguments against the relevance of Zipf's law in dolphin whistles is that is not possible to distinguish the outcome of a die rolling experiment from that of a linguistic or communicative source producing Zipf's law for word frequencies. Here we show that statistically significant whistle-whistle correlations extend back to the 2nd previous whistle in the sequence using a global randomization test and to the 4th previous whistle using a local randomization test. None of these correlations are expected by a die rolling experiment and other simple explanation of Zipf's law for word frequencies such as Simon's model that produce sequences of unpredictable elements. |
2303.02015 | Birgitta Dresp-Langley | Birgitta Dresp-Langley | The Grossberg Code: Universal Neural Network Signatures of Perceptual
Experience | null | Information 2023; 14(2):82 | 10.3390/info14020082 | null | q-bio.NC cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two universal functional principles of Adaptive Resonance Theory simulate the
brain code of all biological learning and adaptive intelligence. Low level
representations of multisensory stimuli in their immediate environmental
context are formed on the basis of bottom up activation and under the control
of top down matching rules that integrate high level long term traces of
contextual configuration. These universal coding principles lead to the
establishment of lasting brain signatures of perceptual experience in all
living species, from aplysiae to primates. They are revisited in this paper
here on the basis of examples drawn from the original code and from some of the
most recent related empirical findings on contextual modulation in the brain,
highlighting the potential of Grossberg's pioneering insights and
groundbreaking theoretical work for intelligent solutions in the domain of
developmental and cognitive robotics.
| [
{
"created": "Fri, 3 Mar 2023 15:31:14 GMT",
"version": "v1"
}
] | 2023-03-06 | [
[
"Dresp-Langley",
"Birgitta",
""
]
] | Two universal functional principles of Adaptive Resonance Theory simulate the brain code of all biological learning and adaptive intelligence. Low level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom up activation and under the control of top down matching rules that integrate high level long term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are revisited in this paper here on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg's pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics. |
2001.07284 | Jose A Capitan | Jose A. Capitan, Sara Cuenda, and David Alonso | Competitive dominance in plant communities: Modeling approaches and
theoretical predictions | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantitative predictions about the processes that promote species coexistence
are a subject of active research in ecology. In particular, competitive
interactions are known to shape and maintain ecological communities, and
situations where some species out-compete or dominate over some others are key
to describe natural ecosystems. Here we develop ecological theory using a
stochastic, synthetic framework for plant community assembly that leads to
predictions amenable to empirical testing. We propose two stochastic
continuous-time Markov models that incorporate competitive dominance through a
hierarchy of species heights. The first model, which is spatially implicit,
predicts both the expected number of species that survive and the conditions
under which heights are clustered in realized model communities. The second one
allows spatially-explicit interactions of individuals and alternative
mechanisms that can help shorter plants overcome height-driven competition, and
it demonstrates that clustering patterns remain not only locally but also
across increasing spatial scales. Moreover, although plants are actually
height-clustered in the spatially-explicit model, it allows for plant species
abundances not necessarily skewed to taller plants.
| [
{
"created": "Mon, 20 Jan 2020 23:33:08 GMT",
"version": "v1"
}
] | 2020-01-22 | [
[
"Capitan",
"Jose A.",
""
],
[
"Cuenda",
"Sara",
""
],
[
"Alonso",
"David",
""
]
] | Quantitative predictions about the processes that promote species coexistence are a subject of active research in ecology. In particular, competitive interactions are known to shape and maintain ecological communities, and situations where some species out-compete or dominate over some others are key to describe natural ecosystems. Here we develop ecological theory using a stochastic, synthetic framework for plant community assembly that leads to predictions amenable to empirical testing. We propose two stochastic continuous-time Markov models that incorporate competitive dominance through a hierarchy of species heights. The first model, which is spatially implicit, predicts both the expected number of species that survive and the conditions under which heights are clustered in realized model communities. The second one allows spatially-explicit interactions of individuals and alternative mechanisms that can help shorter plants overcome height-driven competition, and it demonstrates that clustering patterns remain not only locally but also across increasing spatial scales. Moreover, although plants are actually height-clustered in the spatially-explicit model, it allows for plant species abundances not necessarily skewed to taller plants. |
q-bio/0508009 | Trinh Xuan Hoang | Jayanth R. Banavar, Trinh Xuan Hoang, Amos Maritan | Proteins and polymers | 7 pages, 6 figures | J. Chem. Phys. 122, 234910 (2005) | 10.1063/1.1940059 | null | q-bio.BM cond-mat.soft | null | Proteins, chain molecules of amino acids, behave in ways which are similar to
each other yet quite distinct from standard compact polymers. We demonstrate
that the Flory theorem, derived for polymer melts, holds for compact protein
native state structures and is not incompatible with the existence of
structured building blocks such as $\alpha$-helices and $\beta$-strands. We
present a discussion on how the notion of the thickness of a polymer chain,
besides being useful in describing a chain molecule in the continuum limit,
plays a vital role in interpolating between conventional polymer physics and
the phase of matter associated with protein structures.
| [
{
"created": "Mon, 8 Aug 2005 11:48:51 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Banavar",
"Jayanth R.",
""
],
[
"Hoang",
"Trinh Xuan",
""
],
[
"Maritan",
"Amos",
""
]
] | Proteins, chain molecules of amino acids, behave in ways which are similar to each other yet quite distinct from standard compact polymers. We demonstrate that the Flory theorem, derived for polymer melts, holds for compact protein native state structures and is not incompatible with the existence of structured building blocks such as $\alpha$-helices and $\beta$-strands. We present a discussion on how the notion of the thickness of a polymer chain, besides being useful in describing a chain molecule in the continuum limit, plays a vital role in interpolating between conventional polymer physics and the phase of matter associated with protein structures. |
1608.06314 | Stephen Montgomery-Smith | Stephen Montgomery-Smith and Hesam Oveys | Age-dependent Branching Processes and Applications to the
Luria-Delbr\"uck Experiment | null | Electron. J. Differential Equations, Vol. 2021 (2021), No. 56, pp.
1-22 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Microbial populations adapt to their environment by acquiring advantageous
mutations, but in the early twentieth century, questions about how these
organisms acquire mutations arose. The experiment of Salvador Luria and Max
Delbr\"uck that won them a Nobel Prize in 1969 confirmed that mutations don't
occur out of necessity, but instead can occur many generations before there is
a selective advantage, and thus organisms follow Darwinian evolution instead of
Lamarckian. Since then, new areas of research involving microbial evolution
have spawned as a result of their experiment. Determining the mutation rate of
a cell is one such area. Probability distributions that determine the number of
mutants in a large population have been derived by D. E. Lea, C. A. Coulson,
and J. B. S. Haldane. However, not much work has been done when time of cell
division is dependent on the cell age, and even less so when cell division is
asymmetric, which is the case in most microbial populations. Using probability
generating function methods, we rigorously construct a probability distribution
for the cell population size given a life-span distribution for both mother and
daughter cells, and then determine its asymptotic growth rate. We use this to
construct a probability distribution for the number of mutants in a large cell
population, which can be used with likelihood methods to estimate the cell
mutation rate.
| [
{
"created": "Mon, 22 Aug 2016 21:05:41 GMT",
"version": "v1"
}
] | 2021-06-24 | [
[
"Montgomery-Smith",
"Stephen",
""
],
[
"Oveys",
"Hesam",
""
]
] | Microbial populations adapt to their environment by acquiring advantageous mutations, but in the early twentieth century, questions about how these organisms acquire mutations arose. The experiment of Salvador Luria and Max Delbr\"uck that won them a Nobel Prize in 1969 confirmed that mutations don't occur out of necessity, but instead can occur many generations before there is a selective advantage, and thus organisms follow Darwinian evolution instead of Lamarckian. Since then, new areas of research involving microbial evolution have spawned as a result of their experiment. Determining the mutation rate of a cell is one such area. Probability distributions that determine the number of mutants in a large population have been derived by D. E. Lea, C. A. Coulson, and J. B. S. Haldane. However, not much work has been done when time of cell division is dependent on the cell age, and even less so when cell division is asymmetric, which is the case in most microbial populations. Using probability generating function methods, we rigorously construct a probability distribution for the cell population size given a life-span distribution for both mother and daughter cells, and then determine its asymptotic growth rate. We use this to construct a probability distribution for the number of mutants in a large cell population, which can be used with likelihood methods to estimate the cell mutation rate. |
1307.8252 | Simone Pigolotti | Simone Pigolotti and Roberto Benzi | Selective advantage of diffusing faster | 8 pages, 5 figures (Main Text + Supplementary Information). Accepted
version | Phys. Rev. Lett. 112, 188102 (2014) | 10.1103/PhysRevLett.112.188102 | null | q-bio.PE cond-mat.stat-mech nlin.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a stochastic spatial model of biological competition in which two
species have the same birth and death rates, but different diffusion constants.
In the absence of this difference, the model can be considered as an
off-lattice version of the Voter model and presents similar coarsening
properties. We show that even a relative difference in diffusivity on the order
of a few percent may lead to a strong bias in the coarsening process favoring
the more agile species. We theoretically quantify this selective advantage and
present analytical formulas for the average growth of the fastest species and
its fixation probability.
| [
{
"created": "Wed, 31 Jul 2013 08:50:12 GMT",
"version": "v1"
},
{
"created": "Fri, 16 May 2014 09:45:02 GMT",
"version": "v2"
}
] | 2015-06-16 | [
[
"Pigolotti",
"Simone",
""
],
[
"Benzi",
"Roberto",
""
]
] | We study a stochastic spatial model of biological competition in which two species have the same birth and death rates, but different diffusion constants. In the absence of this difference, the model can be considered as an off-lattice version of the Voter model and presents similar coarsening properties. We show that even a relative difference in diffusivity on the order of a few percent may lead to a strong bias in the coarsening process favoring the more agile species. We theoretically quantify this selective advantage and present analytical formulas for the average growth of the fastest species and its fixation probability. |
1407.7566 | Eric Strobl | Eric V. Strobl, Shyam Visweswaran | Dependence versus Conditional Dependence in Local Causal Discovery from
Gene Expression Data | 11 pages, 2 algorithms, 4 figures, 5 tables | null | null | null | q-bio.QM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Algorithms that discover variables which are causally related to
a target may inform the design of experiments. With observational gene
expression data, many methods discover causal variables by measuring each
variable's degree of statistical dependence with the target using dependence
measures (DMs). However, other methods measure each variable's ability to
explain the statistical dependence between the target and the remaining
variables in the data using conditional dependence measures (CDMs), since this
strategy is guaranteed to find the target's direct causes, direct effects, and
direct causes of the direct effects in the infinite sample limit. In this
paper, we design a new algorithm in order to systematically compare the
relative abilities of DMs and CDMs in discovering causal variables from gene
expression data.
Results: The proposed algorithm using a CDM is sample efficient, since it
consistently outperforms other state-of-the-art local causal discovery
algorithms when samples sizes are small. However, the proposed algorithm using
a CDM outperforms the proposed algorithm using a DM only when sample sizes are
above several hundred. These results suggest that accurate causal discovery
from gene expression data using current CDM-based algorithms requires datasets
with at least several hundred samples.
Availability: The proposed algorithm is freely available at
https://github.com/ericstrobl/DvCD.
| [
{
"created": "Mon, 28 Jul 2014 20:52:18 GMT",
"version": "v1"
}
] | 2014-07-30 | [
[
"Strobl",
"Eric V.",
""
],
[
"Visweswaran",
"Shyam",
""
]
] | Motivation: Algorithms that discover variables which are causally related to a target may inform the design of experiments. With observational gene expression data, many methods discover causal variables by measuring each variable's degree of statistical dependence with the target using dependence measures (DMs). However, other methods measure each variable's ability to explain the statistical dependence between the target and the remaining variables in the data using conditional dependence measures (CDMs), since this strategy is guaranteed to find the target's direct causes, direct effects, and direct causes of the direct effects in the infinite sample limit. In this paper, we design a new algorithm in order to systematically compare the relative abilities of DMs and CDMs in discovering causal variables from gene expression data. Results: The proposed algorithm using a CDM is sample efficient, since it consistently outperforms other state-of-the-art local causal discovery algorithms when samples sizes are small. However, the proposed algorithm using a CDM outperforms the proposed algorithm using a DM only when sample sizes are above several hundred. These results suggest that accurate causal discovery from gene expression data using current CDM-based algorithms requires datasets with at least several hundred samples. Availability: The proposed algorithm is freely available at https://github.com/ericstrobl/DvCD. |
1403.1043 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Coupling-Induced Population Synchronization in An Excitatory Population
of Subthreshold Izhikevich Neurons | null | Cognitive Neurodynamics, 7, 495-503 (2013) | 10.1007/s11571-013-9256-y | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider an excitatory population of subthreshold Izhikevich neurons which
exhibit noise-induced firings. By varying the coupling strength $J$, we
investigate population synchronization between the noise-induced firings which
may be used for efficient cognitive processing such as sensory perception,
multisensory binding, selective attention, and memory formation. As $J$ is
increased, rich types of population synchronization (e.g., spike, burst, and
fast spike synchronization) are found to occur. Transitions between population
synchronization and incoherence are well described in terms of an order
parameter $\cal{O}$. As a final step, the coupling induces oscillator death
(quenching of noise-induced spikings) because each neuron is attracted to a
noisy equilibrium state. The oscillator death leads to a transition from firing
to non-firing states at the population level, which may be well described in
terms of the time-averaged population spike rate $\overline{R}$. In addition to
the statistical-mechanical analysis using $\cal{O}$ and $\overline{R}$, each
population and individual state are also characterized by using the techniques
of nonlinear dynamics such as the raster plot of neural spikes, the time series
of the membrane potential, and the phase portrait. We note that population
synchronization of noise-induced firings may lead to emergence of synchronous
brain rhythms in a noisy environment, associated with diverse cognitive
functions.
| [
{
"created": "Wed, 5 Mar 2014 08:40:33 GMT",
"version": "v1"
}
] | 2014-03-06 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | We consider an excitatory population of subthreshold Izhikevich neurons which exhibit noise-induced firings. By varying the coupling strength $J$, we investigate population synchronization between the noise-induced firings which may be used for efficient cognitive processing such as sensory perception, multisensory binding, selective attention, and memory formation. As $J$ is increased, rich types of population synchronization (e.g., spike, burst, and fast spike synchronization) are found to occur. Transitions between population synchronization and incoherence are well described in terms of an order parameter $\cal{O}$. As a final step, the coupling induces oscillator death (quenching of noise-induced spikings) because each neuron is attracted to a noisy equilibrium state. The oscillator death leads to a transition from firing to non-firing states at the population level, which may be well described in terms of the time-averaged population spike rate $\overline{R}$. In addition to the statistical-mechanical analysis using $\cal{O}$ and $\overline{R}$, each population and individual state are also characterized by using the techniques of nonlinear dynamics such as the raster plot of neural spikes, the time series of the membrane potential, and the phase portrait. We note that population synchronization of noise-induced firings may lead to emergence of synchronous brain rhythms in a noisy environment, associated with diverse cognitive functions. |
2106.06537 | Johan Broekaert M. | Johan M. Broekaert | The Auditory Tuning of a Keyboard | 6 pages, 5 figures, 5 tables, submitted to MTO, Nature of replacement
: Addition of an ACKNOWLEDGEMENT, citations, and a list of WORKS CITED. Note:
the initial submission was seen as a kind of "announcement" only, and did
therefore not meet requirements that are normal for articles. The goal was to
have a very readable text only, without elements enforcing the content of the
text | null | null | null | q-bio.NC physics.hist-ph | http://creativecommons.org/publicdomain/zero/1.0/ | An optimal auditory tunable well (circular) temperament is determined. A
temperament that is applicable in practice is derived from this optimum. No
other historical temperament fits as well, with this optimum. A brief
comparison of temperaments is worked out.
| [
{
"created": "Fri, 11 Jun 2021 19:25:13 GMT",
"version": "v1"
}
] | 2021-06-15 | [
[
"Broekaert",
"Johan M.",
""
]
] | An optimal auditory tunable well (circular) temperament is determined. A temperament that is applicable in practice is derived from this optimum. No other historical temperament fits as well, with this optimum. A brief comparison of temperaments is worked out. |
1703.04869 | May Anne Mata | May Anne E. Mata, Priscilla E. Greenwood, and Rebecca C. Tyson | The roles of direct and environmental transmission in stochastic avian
flu epidemic recurrence | 36 pages, 11 figures | null | null | null | q-bio.PE math.DS math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an analysis of an avian flu model that yields insight into the
role of different transmission routes in the recurrence of avian influenza
epidemics. Recent modelling work on avian influenza in wild bird populations
takes into account demographic stochasticity and highlights the importance of
environmental transmission in determining the outbreak periodicity, but only
for a weak between-host transmission rate. We determine the relative
contribution of environmental and direct transmission routes to the intensity
of outbreaks. We use an approximation method to simulate noise sustained
oscillations in a stochastic avian flu model with environmental and direct
transmission routes. We see that the oscillations are governed by the product
of a rotation and a slowly varying standard Ornstein-Uhlenbeck process (i.e.,
mean-reverting process). The intrinsic frequency of the damped deterministic
version of the system predicts the dominant period of outbreaks. We show, using
analytic computation of the intrinsic frequency and theoretical power spectral
density, that the outbreak periodicity can be explained in terms of either or
both types of transmission.The amplitude of outbreaks tends to be high when
both types of transmission are strong.
| [
{
"created": "Wed, 15 Mar 2017 01:26:33 GMT",
"version": "v1"
}
] | 2017-03-16 | [
[
"Mata",
"May Anne E.",
""
],
[
"Greenwood",
"Priscilla E.",
""
],
[
"Tyson",
"Rebecca C.",
""
]
] | We present an analysis of an avian flu model that yields insight into the role of different transmission routes in the recurrence of avian influenza epidemics. Recent modelling work on avian influenza in wild bird populations takes into account demographic stochasticity and highlights the importance of environmental transmission in determining the outbreak periodicity, but only for a weak between-host transmission rate. We determine the relative contribution of environmental and direct transmission routes to the intensity of outbreaks. We use an approximation method to simulate noise sustained oscillations in a stochastic avian flu model with environmental and direct transmission routes. We see that the oscillations are governed by the product of a rotation and a slowly varying standard Ornstein-Uhlenbeck process (i.e., mean-reverting process). The intrinsic frequency of the damped deterministic version of the system predicts the dominant period of outbreaks. We show, using analytic computation of the intrinsic frequency and theoretical power spectral density, that the outbreak periodicity can be explained in terms of either or both types of transmission.The amplitude of outbreaks tends to be high when both types of transmission are strong. |
2006.08702 | David Castineira | Courtney Cochrane, David Castineira, Nisreen Shiban and Pavlos
Protopapas | Application of Machine Learning to Predict the Risk of Alzheimer's
Disease: An Accurate and Practical Solution for Early Diagnostics | null | null | null | null | q-bio.QM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million
Americans and creates an enormous strain on the health care system. This paper
proposes a machine learning predictive model for AD development without medical
imaging and with fewer clinical visits and tests, in hopes of earlier and
cheaper diagnoses. That earlier diagnoses could be critical in the
effectiveness of any drug or medical treatment to cure this disease. Our model
is trained and validated using demographic, biomarker and cognitive test data
from two prominent research studies: Alzheimer's Disease Neuroimaging
Initiative (ADNI) and Australian Imaging, Biomarker Lifestyle Flagship Study of
Aging (AIBL). We systematically explore different machine learning models,
pre-processing methods and feature selection techniques. The most performant
model demonstrates greater than 90% accuracy and recall in predicting AD, and
the results generalize across sub-studies of ADNI and to the independent AIBL
study. We also demonstrate that these results are robust to reducing the number
of clinical visits or tests per visit. Using a metaclassification algorithm and
longitudinal data analysis we are able to produce a "lean" diagnostic protocol
with only 3 tests and 4 clinical visits that can predict Alzheimer's
development with 87% accuracy and 79% recall. This novel work can be adapted
into a practical early diagnostic tool for predicting the development of
Alzheimer's that maximizes accuracy while minimizing the number of necessary
diagnostic tests and clinical visits.
| [
{
"created": "Tue, 2 Jun 2020 14:52:51 GMT",
"version": "v1"
}
] | 2020-06-17 | [
[
"Cochrane",
"Courtney",
""
],
[
"Castineira",
"David",
""
],
[
"Shiban",
"Nisreen",
""
],
[
"Protopapas",
"Pavlos",
""
]
] | Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests, in hopes of earlier and cheaper diagnoses. That earlier diagnoses could be critical in the effectiveness of any drug or medical treatment to cure this disease. Our model is trained and validated using demographic, biomarker and cognitive test data from two prominent research studies: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker Lifestyle Flagship Study of Aging (AIBL). We systematically explore different machine learning models, pre-processing methods and feature selection techniques. The most performant model demonstrates greater than 90% accuracy and recall in predicting AD, and the results generalize across sub-studies of ADNI and to the independent AIBL study. We also demonstrate that these results are robust to reducing the number of clinical visits or tests per visit. Using a metaclassification algorithm and longitudinal data analysis we are able to produce a "lean" diagnostic protocol with only 3 tests and 4 clinical visits that can predict Alzheimer's development with 87% accuracy and 79% recall. This novel work can be adapted into a practical early diagnostic tool for predicting the development of Alzheimer's that maximizes accuracy while minimizing the number of necessary diagnostic tests and clinical visits. |
1809.03587 | Fang Ou | Fang Ou, Cushla McGoverin, Simon Swift, Fr\'ed\'erique Vanholsbeeck | Near real-time enumeration of live and dead bacteria using a fibre-based
spectroscopic device | 13 pages, 5 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A rapid, cost-effective and easy method that allows on-site determination of
the concentration of live and dead bacterial cells using a fibre-based
spectroscopic device (the optrode system) is proposed and demonstrated.
Identification of live and dead bacteria was achieved by using the commercially
available dyes SYTO 9 and propidium iodide, and fluorescence spectra were
measured by the optrode. Three spectral processing methods were evaluated for
their effectiveness in predicting the original bacterial concentration in the
samples: principal components regression (PCR), partial least squares
regression (PLSR) and support vector regression (SVR). Without any sample
pre-concentration, PCR achieved the most reliable results. It was able to
quantify live bacteria from $10^{8}$ down to $10^{6.2}$ bacteria/mL and showed
the potential to detect as low as $10^{5.7}$ bacteria/mL. Meanwhile,
enumeration of dead bacteria using PCR was achieved between $10^{8}$ and
$10^{7}$ bacteria/mL. The general procedures described in this article can be
applied or modified for the enumeration of bacteria within populations stained
with fluorescent dyes. The optrode is a promising device for the enumeration of
live and dead bacterial populations particularly where rapid, on-site
measurement and analysis is required.
| [
{
"created": "Mon, 10 Sep 2018 20:38:28 GMT",
"version": "v1"
}
] | 2018-09-12 | [
[
"Ou",
"Fang",
""
],
[
"McGoverin",
"Cushla",
""
],
[
"Swift",
"Simon",
""
],
[
"Vanholsbeeck",
"Frédérique",
""
]
] | A rapid, cost-effective and easy method that allows on-site determination of the concentration of live and dead bacterial cells using a fibre-based spectroscopic device (the optrode system) is proposed and demonstrated. Identification of live and dead bacteria was achieved by using the commercially available dyes SYTO 9 and propidium iodide, and fluorescence spectra were measured by the optrode. Three spectral processing methods were evaluated for their effectiveness in predicting the original bacterial concentration in the samples: principal components regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR). Without any sample pre-concentration, PCR achieved the most reliable results. It was able to quantify live bacteria from $10^{8}$ down to $10^{6.2}$ bacteria/mL and showed the potential to detect as low as $10^{5.7}$ bacteria/mL. Meanwhile, enumeration of dead bacteria using PCR was achieved between $10^{8}$ and $10^{7}$ bacteria/mL. The general procedures described in this article can be applied or modified for the enumeration of bacteria within populations stained with fluorescent dyes. The optrode is a promising device for the enumeration of live and dead bacterial populations particularly where rapid, on-site measurement and analysis is required. |
2109.12281 | Mareike Fischer | Mareike Fischer and Lina Herbst and Sophie Kersting and Luise K\"uhn
and Kristina Wicke | Tree balance indices: a comprehensive survey | main manuscript: 23 pages, fact sheets (one per balance index): 41
pages, appendix with detailed proofs: 80 pages. ATTENTION: This manuscript
has been superseded by the SpringerNature book "Tree balance indices -- A
comprehensive survey", ISBN 978-3-031-39799-8 and 978-3-031-39800-1 | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tree balance plays an important role in phylogenetics and other research
areas, which is why several indices to measure tree balance have been
introduced over the years. Nevertheless, a formal definition of what a balance
index actually is and what makes it a useful measure of balance (or, in other
cases, imbalance), has so far not been introduced in the literature. While the
established indices all summarize the (im)balance of a tree in a single number,
they vary in their definitions and underlying principles. It is the aim of the
present manuscript to introduce formal definitions of balance and imbalance
indices that classify desirable properties of such indices and to analyze and
categorize established indices accordingly. In this regard, we review 19
established (im)balance indices from the literature, summarize their general,
statistical and combinatorial properties (where known), prove numerous
additional results and indicate directions for future research by making
explicit open questions and gaps in the literature. We also prove that a few
tree shape statistics that have been used to measure tree balance in the
literature do not fulfill our definition of an (im)balance index, which might
indicate that their properties are not as useful for practical purposes.
Moreover, we show that five additional tree shape statistics from other
contexts actually are tree (im)balance indices according to our definition. The
manuscript is accompanied by the website \url{treebalance.wordpress.com}
containing fact sheets of the discussed indices. Moreover, we introduce the
software package \verb|treebalance| implemented in $\mathsf{R}$ that can be
used to calculate all indices discussed.
| [
{
"created": "Sat, 25 Sep 2021 05:51:24 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Nov 2023 10:00:38 GMT",
"version": "v2"
}
] | 2023-11-10 | [
[
"Fischer",
"Mareike",
""
],
[
"Herbst",
"Lina",
""
],
[
"Kersting",
"Sophie",
""
],
[
"Kühn",
"Luise",
""
],
[
"Wicke",
"Kristina",
""
]
] | Tree balance plays an important role in phylogenetics and other research areas, which is why several indices to measure tree balance have been introduced over the years. Nevertheless, a formal definition of what a balance index actually is and what makes it a useful measure of balance (or, in other cases, imbalance), has so far not been introduced in the literature. While the established indices all summarize the (im)balance of a tree in a single number, they vary in their definitions and underlying principles. It is the aim of the present manuscript to introduce formal definitions of balance and imbalance indices that classify desirable properties of such indices and to analyze and categorize established indices accordingly. In this regard, we review 19 established (im)balance indices from the literature, summarize their general, statistical and combinatorial properties (where known), prove numerous additional results and indicate directions for future research by making explicit open questions and gaps in the literature. We also prove that a few tree shape statistics that have been used to measure tree balance in the literature do not fulfill our definition of an (im)balance index, which might indicate that their properties are not as useful for practical purposes. Moreover, we show that five additional tree shape statistics from other contexts actually are tree (im)balance indices according to our definition. The manuscript is accompanied by the website \url{treebalance.wordpress.com} containing fact sheets of the discussed indices. Moreover, we introduce the software package \verb|treebalance| implemented in $\mathsf{R}$ that can be used to calculate all indices discussed. |
1811.03335 | Dr. Alexander Paraskevov | A.V. Paraskevov, D.K. Zendrikov | A spatially resolved network spike in model neuronal cultures reveals
nucleation centers, circular traveling waves and drifting spiral waves | 14 pages, 7 figures | Phys. Biol. 14, 026003 (2017) | 10.1088/1478-3975/aa5fc3 | null | q-bio.NC cond-mat.dis-nn nlin.AO nlin.PS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that in model neuronal cultures, where the probability of
interneuronal connection formation decreases exponentially with increasing
distance between the neurons, there exists a small number of spatial nucleation
centers of a network spike, from where the synchronous spiking activity starts
propagating in the network typically in the form of circular traveling waves.
The number of nucleation centers and their spatial locations are unique and
unchanged for a given realization of neuronal network but are different for
different networks. In contrast, if the probability of interneuronal connection
formation is independent of the distance between neurons, then the nucleation
centers do not arise and the synchronization of spiking activity during a
network spike occurs spatially uniform throughout the network. Therefore one
can conclude that spatial proximity of connections between neurons is important
for the formation of nucleation centers. It is also shown that fluctuations of
the spatial density of neurons at their random homogeneous distribution typical
for the experiments $\textit{in vitro}$ do not determine the locations of the
nucleation centers. The simulation results are qualitatively consistent with
the experimental observations.
| [
{
"created": "Thu, 8 Nov 2018 09:56:49 GMT",
"version": "v1"
}
] | 2018-11-09 | [
[
"Paraskevov",
"A. V.",
""
],
[
"Zendrikov",
"D. K.",
""
]
] | We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments $\textit{in vitro}$ do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations. |
2201.00195 | Jayden Macklin-Cordes | Jayden L. Macklin-Cordes, Erich R. Round | Challenges of sampling and how phylogenetic comparative methods help:
With a case study of the Pama-Nyungan laminal contrast | Accepted for publication in Linguistic Typology. Supplementary data
at https://doi.org/10.5281/zenodo.5602216. 96 total pages (Main text: 41
pages, 6 figures, 3 tables. Supplementary S1: 34 pages, 1 figure.
Supplementary S2: 21 pages) | null | null | null | q-bio.PE cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic comparative methods are new in our field and are shrouded, for
most linguists, in at least a little mystery. Yet the path that led to their
discovery in comparative biology is so similar to the methodological history of
balanced sampling, that it is only an accident of history that they were not
discovered by a typologist. Here we clarify the essential logic behind
phylogenetic comparative methods and their fundamental relatedness to a deep
intellectual tradition focussed on sampling. Then we introduce concepts,
methods and tools which will enable typologists to use these methods in
everyday typological research. The key commonality of phylogenetic comparative
methods and balanced sampling is that they attempt to deal with statistical
non-independence due to genealogy. Whereas sampling can never achieve
independence and requires most comparative data to be discarded, phylogenetic
comparative methods achieve independence while retaining and using all data. We
discuss the essential notions of phylogenetic signal; uncertainty about trees;
typological averages and proportions that are sensitive to genealogy;
comparison across language families; and the effects of areality. Extensive
supplementary materials illustrate computational tools for practical analysis
and we illustrate the methods discussed with a typological case study of the
laminal contrast in Pama-Nyungan.
| [
{
"created": "Sat, 1 Jan 2022 14:33:20 GMT",
"version": "v1"
}
] | 2022-01-04 | [
[
"Macklin-Cordes",
"Jayden L.",
""
],
[
"Round",
"Erich R.",
""
]
] | Phylogenetic comparative methods are new in our field and are shrouded, for most linguists, in at least a little mystery. Yet the path that led to their discovery in comparative biology is so similar to the methodological history of balanced sampling, that it is only an accident of history that they were not discovered by a typologist. Here we clarify the essential logic behind phylogenetic comparative methods and their fundamental relatedness to a deep intellectual tradition focussed on sampling. Then we introduce concepts, methods and tools which will enable typologists to use these methods in everyday typological research. The key commonality of phylogenetic comparative methods and balanced sampling is that they attempt to deal with statistical non-independence due to genealogy. Whereas sampling can never achieve independence and requires most comparative data to be discarded, phylogenetic comparative methods achieve independence while retaining and using all data. We discuss the essential notions of phylogenetic signal; uncertainty about trees; typological averages and proportions that are sensitive to genealogy; comparison across language families; and the effects of areality. Extensive supplementary materials illustrate computational tools for practical analysis and we illustrate the methods discussed with a typological case study of the laminal contrast in Pama-Nyungan. |
2010.09568 | Vince Grolmusz | Laszlo Keresztes and Evelin Szogi and Balint Varga and Vince Grolmusz | Introducing and Applying Newtonian Blurring: An Augmented Dataset of
126,000 Human Connectomes at braingraph.org | null | null | null | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gaussian blurring is a well-established method for image data augmentation:
it may generate a large set of images from a small set of pictures for training
and testing purposes for Artificial Intelligence (AI) applications. When we
apply AI for non-imagelike biological data, hardly any related method exists.
Here we introduce the "Newtonian blurring" in human braingraph (or connectome)
augmentation: Started from a dataset of 1053 subjects, we first repeat a
probabilistic weighted braingraph construction algorithm 10 times for
describing the connections of distinct cerebral areas, then take 7 repetitions
in every possible way, delete the lower and upper extremes, and average the
remaining 7-2=5 edge-weights for the data of each subject. This way we augment
the 1053 graph-set to 120 x 1053 = 126,360 graphs. In augmentation techniques,
it is an important requirement that no artificial additions should be
introduced into the dataset. Gaussian blurring and also this Newtonian blurring
satisfy this goal. The resulting dataset of 126,360 graphs, each in 5
resolutions (i.e., 631,800 graphs in total), is freely available at the site
https://braingraph.org/cms/download-pit-group-connectomes/. Augmenting with
Newtonian blurring may also be applicable in other non-image related fields,
where probabilistic processing and data averaging are implemented.
| [
{
"created": "Mon, 19 Oct 2020 14:51:59 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Oct 2020 07:36:01 GMT",
"version": "v2"
},
{
"created": "Wed, 21 Oct 2020 16:31:26 GMT",
"version": "v3"
}
] | 2020-10-22 | [
[
"Keresztes",
"Laszlo",
""
],
[
"Szogi",
"Evelin",
""
],
[
"Varga",
"Balint",
""
],
[
"Grolmusz",
"Vince",
""
]
] | Gaussian blurring is a well-established method for image data augmentation: it may generate a large set of images from a small set of pictures for training and testing purposes for Artificial Intelligence (AI) applications. When we apply AI for non-imagelike biological data, hardly any related method exists. Here we introduce the "Newtonian blurring" in human braingraph (or connectome) augmentation: Started from a dataset of 1053 subjects, we first repeat a probabilistic weighted braingraph construction algorithm 10 times for describing the connections of distinct cerebral areas, then take 7 repetitions in every possible way, delete the lower and upper extremes, and average the remaining 7-2=5 edge-weights for the data of each subject. This way we augment the 1053 graph-set to 120 x 1053 = 126,360 graphs. In augmentation techniques, it is an important requirement that no artificial additions should be introduced into the dataset. Gaussian blurring and also this Newtonian blurring satisfy this goal. The resulting dataset of 126,360 graphs, each in 5 resolutions (i.e., 631,800 graphs in total), is freely available at the site https://braingraph.org/cms/download-pit-group-connectomes/. Augmenting with Newtonian blurring may also be applicable in other non-image related fields, where probabilistic processing and data averaging are implemented. |
1810.12244 | James Faeder | Jose-Juan Tapia, Ali Sinan Saglam, Jacob Czech, Robert Kuczewski,
Thomas M. Bartol, Terrence J. Sejnowski, and James R. Faeder | MCell-R: A particle-resolution network-free spatial modeling framework | null | null | null | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatial heterogeneity can have dramatic effects on the biochemical networks
that drive cell regulation and decision-making. For this reason, a number of
methods have been developed to model spatial heterogeneity and incorporated
into widely used modeling platforms. Unfortunately, the standard approaches for
specifying and simulating chemical reaction networks become untenable when
dealing with multi-state, multi-component systems that are characterized by
combinatorial complexity. To address this issue, we developed MCell-R, a
framework that extends the particle-based spatial Monte Carlo simulator, MCell,
with the rule-based model specification and simulation capabilities provided by
BioNetGen and NFsim. The BioNetGen syntax enables the specification of
biomolecules as structured objects whose components can have different internal
states that represent such features as covalent modification and conformation
and which can bind components of other molecules to form molecular complexes.
The network-free simulation algorithm used by NFsim enables efficient
simulation of rule-based models even when the size of the network implied by
the biochemical rules is too large to enumerate explicitly, which frequently
occurs in detailed models of biochemical signaling. The result is a framework
that can efficiently simulate systems characterized by combinatorial complexity
at the level of spatially-resolved individual molecules over biologically
relevant time and length scales.
| [
{
"created": "Mon, 29 Oct 2018 16:44:58 GMT",
"version": "v1"
}
] | 2018-10-30 | [
[
"Tapia",
"Jose-Juan",
""
],
[
"Saglam",
"Ali Sinan",
""
],
[
"Czech",
"Jacob",
""
],
[
"Kuczewski",
"Robert",
""
],
[
"Bartol",
"Thomas M.",
""
],
[
"Sejnowski",
"Terrence J.",
""
],
[
"Faeder",
"James R.",
... | Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed to model spatial heterogeneity and incorporated into widely used modeling platforms. Unfortunately, the standard approaches for specifying and simulating chemical reaction networks become untenable when dealing with multi-state, multi-component systems that are characterized by combinatorial complexity. To address this issue, we developed MCell-R, a framework that extends the particle-based spatial Monte Carlo simulator, MCell, with the rule-based model specification and simulation capabilities provided by BioNetGen and NFsim. The BioNetGen syntax enables the specification of biomolecules as structured objects whose components can have different internal states that represent such features as covalent modification and conformation and which can bind components of other molecules to form molecular complexes. The network-free simulation algorithm used by NFsim enables efficient simulation of rule-based models even when the size of the network implied by the biochemical rules is too large to enumerate explicitly, which frequently occurs in detailed models of biochemical signaling. The result is a framework that can efficiently simulate systems characterized by combinatorial complexity at the level of spatially-resolved individual molecules over biologically relevant time and length scales. |
1006.0459 | Oren Elrad | Oren M. Elrad and Michael F. Hagan | Encapsulation of a polymer by an icosahedral virus | 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. The definitive publisher authenticated version
is expected to be published online in November 2010 | null | 10.1088/1478-3975/7/4/045003 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The coat proteins of many viruses spontaneously form icosahedral capsids
around nucleic acids or other polymers. Elucidating the role of the packaged
polymer in capsid formation could promote biomedical efforts to block viral
replication and enable use of capsids in nanomaterials applications. To this
end, we perform Brownian dynamics on a coarse-grained model that describes the
dynamics of icosahedral capsid assembly around a flexible polymer. We identify
several mechanisms by which the polymer plays an active role in its
encapsulation, including cooperative polymer-protein motions. These mechanisms
are related to experimentally controllable parameters such as polymer length,
protein concentration, and solution conditions. Furthermore, the simulations
demonstrate that assembly mechanisms are correlated to encapsulation
efficiency, and we present a phase diagram that predicts assembly outcomes as a
function of experimental parameters. We anticipate that our simulation results
will provide a framework for designing in vitro assembly experiments on
single-stranded RNA virus capsids.
| [
{
"created": "Wed, 2 Jun 2010 18:06:10 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Sep 2010 19:11:06 GMT",
"version": "v2"
}
] | 2015-05-19 | [
[
"Elrad",
"Oren M.",
""
],
[
"Hagan",
"Michael F.",
""
]
] | The coat proteins of many viruses spontaneously form icosahedral capsids around nucleic acids or other polymers. Elucidating the role of the packaged polymer in capsid formation could promote biomedical efforts to block viral replication and enable use of capsids in nanomaterials applications. To this end, we perform Brownian dynamics on a coarse-grained model that describes the dynamics of icosahedral capsid assembly around a flexible polymer. We identify several mechanisms by which the polymer plays an active role in its encapsulation, including cooperative polymer-protein motions. These mechanisms are related to experimentally controllable parameters such as polymer length, protein concentration, and solution conditions. Furthermore, the simulations demonstrate that assembly mechanisms are correlated to encapsulation efficiency, and we present a phase diagram that predicts assembly outcomes as a function of experimental parameters. We anticipate that our simulation results will provide a framework for designing in vitro assembly experiments on single-stranded RNA virus capsids. |
1505.07513 | Jitendra Jonnagaddala | Jitendra Jonnagaddala, Damian Sue | A Report on the Workshop on Biobanking Informatics | 5 Pages, Workshop | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Workshop on Biobanking Informatics in NSW 2013 (WBIN13) was held on
Friday, 10 May 2013 at The Wallace Wurth Building in the University of New
South Wales. This report summarises the keynotes, presentations and discussions
in WBIN13 which discusses current research in the field of Biobanking
Informatics in Australia and internationally.
| [
{
"created": "Wed, 27 May 2015 23:33:34 GMT",
"version": "v1"
}
] | 2015-05-29 | [
[
"Jonnagaddala",
"Jitendra",
""
],
[
"Sue",
"Damian",
""
]
] | The Workshop on Biobanking Informatics in NSW 2013 (WBIN13) was held on Friday, 10 May 2013 at The Wallace Wurth Building in the University of New South Wales. This report summarises the keynotes, presentations and discussions in WBIN13 which discusses current research in the field of Biobanking Informatics in Australia and internationally. |
2207.10080 | Ningyu Zhang | Siyuan Cheng, Xiaozhuan Liang, Zhen Bi, Huajun Chen, Ningyu Zhang | Multi-modal Protein Knowledge Graph Construction and Applications | Accepted by AAAI 2023 (Student Abstract). Dataset available in
https://zjunlp.github.io/project/ProteinKG65/ | null | null | null | q-bio.QM cs.AI cs.CL cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing data-centric methods for protein science generally cannot
sufficiently capture and leverage biology knowledge, which may be crucial for
many protein tasks. To facilitate research in this field, we create
ProteinKG65, a knowledge graph for protein science. Using gene ontology and
Uniprot knowledge base as a basis, we transform and integrate various kinds of
knowledge with aligned descriptions and protein sequences, respectively, to GO
terms and protein entities. ProteinKG65 is mainly dedicated to providing a
specialized protein knowledge graph, bringing the knowledge of Gene Ontology to
protein function and structure prediction. We also illustrate the potential
applications of ProteinKG65 with a prototype. Our dataset can be downloaded at
https://w3id.org/proteinkg65.
| [
{
"created": "Fri, 27 May 2022 08:18:56 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Sep 2022 09:35:07 GMT",
"version": "v2"
},
{
"created": "Mon, 14 Nov 2022 16:26:52 GMT",
"version": "v3"
}
] | 2022-11-15 | [
[
"Cheng",
"Siyuan",
""
],
[
"Liang",
"Xiaozhuan",
""
],
[
"Bi",
"Zhen",
""
],
[
"Chen",
"Huajun",
""
],
[
"Zhang",
"Ningyu",
""
]
] | Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this field, we create ProteinKG65, a knowledge graph for protein science. Using gene ontology and Uniprot knowledge base as a basis, we transform and integrate various kinds of knowledge with aligned descriptions and protein sequences, respectively, to GO terms and protein entities. ProteinKG65 is mainly dedicated to providing a specialized protein knowledge graph, bringing the knowledge of Gene Ontology to protein function and structure prediction. We also illustrate the potential applications of ProteinKG65 with a prototype. Our dataset can be downloaded at https://w3id.org/proteinkg65. |
2101.00650 | Songting Shi | Songting Shi | A Tutorial on the Mathematical Model of Single Cell Variational
Inference | null | null | null | null | q-bio.OT cs.LG q-bio.GN stat.ML | http://creativecommons.org/licenses/by/4.0/ | As the large amount of sequencing data accumulated in past decades and it is
still accumulating, we need to handle the more and more sequencing data. As the
fast development of the computing technologies, we now can handle a large
amount of data by a reasonable of time using the neural network based model.
This tutorial will introduce the the mathematical model of the single cell
variational inference (scVI), which use the variational auto-encoder (building
on the neural networks) to learn the distribution of the data to gain insights.
It was written for beginners in the simple and intuitive way with many
deduction details to encourage more researchers into this field.
| [
{
"created": "Sun, 3 Jan 2021 16:02:36 GMT",
"version": "v1"
}
] | 2021-01-05 | [
[
"Shi",
"Songting",
""
]
] | As the large amount of sequencing data accumulated in past decades and it is still accumulating, we need to handle the more and more sequencing data. As the fast development of the computing technologies, we now can handle a large amount of data by a reasonable of time using the neural network based model. This tutorial will introduce the the mathematical model of the single cell variational inference (scVI), which use the variational auto-encoder (building on the neural networks) to learn the distribution of the data to gain insights. It was written for beginners in the simple and intuitive way with many deduction details to encourage more researchers into this field. |
1302.2234 | Nen Saito | Nen Saito, Shuji Ishihara and Kunihiko Kaneko | Evolution of Genetic Redundancy : The Relevance of Complexity in
Genotype-Phenotype Mapping | 5 pages, 2 figures | null | 10.1088/1367-2630/16/6/063013 | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genetic redundancy is ubiquitous and can be found in any organism. However,
it has been argued that genetic redundancy reduces total population fitness,
and therefore, redundancy is unlikely to evolve. In this letter, we study an
evolutionary model with high-dimensional genotype-phenotype mapping (GPM) to
investigate the relevance of complexity in GPM to the evolution of genetic
redundancy. By applying the replica method to deal with quenched randomness,
the redundancy dependence of the fitness is analytically obtained, which
demonstrates that genetic redundancy can indeed evolve, provided that the GPM
is complex. Our result provides a novel insight into how genetic redundancy
evolves.
| [
{
"created": "Sat, 9 Feb 2013 14:09:21 GMT",
"version": "v1"
}
] | 2015-06-15 | [
[
"Saito",
"Nen",
""
],
[
"Ishihara",
"Shuji",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | Genetic redundancy is ubiquitous and can be found in any organism. However, it has been argued that genetic redundancy reduces total population fitness, and therefore, redundancy is unlikely to evolve. In this letter, we study an evolutionary model with high-dimensional genotype-phenotype mapping (GPM) to investigate the relevance of complexity in GPM to the evolution of genetic redundancy. By applying the replica method to deal with quenched randomness, the redundancy dependence of the fitness is analytically obtained, which demonstrates that genetic redundancy can indeed evolve, provided that the GPM is complex. Our result provides a novel insight into how genetic redundancy evolves. |
2311.16308 | Alexis B\'enichou | Alexis B\'enichou, Jean-Baptiste Masson, Christian L. Vestergaard | Compression-based inference of network motif sets | null | null | null | null | q-bio.QM cond-mat.stat-mech cs.SI physics.data-an q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Physical and functional constraints on biological networks lead to complex
topological patterns across multiple scales in their organization. A particular
type of higher-order network feature that has received considerable interest is
network motifs, defined as statistically regular subgraphs. These may implement
fundamental logical and computational circuits and are referred as ``building
blocks of complex networks''. Their well-defined structures and small sizes
also enables the testing of their functions in synthetic and natural biological
experiments. The statistical inference of network motifs is however fraught
with difficulties, from defining and sampling the right null model to
accounting for the large number of possible motifs and their potential
correlations in statistical testing. Here we develop a framework for motif
mining based on lossless network compression using subgraph contractions. The
minimum description length principle allows us to select the most significant
set of motifs as well as other prominent network features in terms of their
combined compression of the network. The approach inherently accounts for
multiple testing and correlations between subgraphs and does not rely on a
priori specification of an appropriate null model. This provides an alternative
definition of motif significance which guarantees more robust statistical
inference. Our approach overcomes the common problems in classic testing-based
motif analysis. We apply our methodology to perform comparative connectomics by
evaluating the compressibility and the circuit motifs of a range of
synaptic-resolution neural connectomes.
| [
{
"created": "Mon, 27 Nov 2023 20:49:11 GMT",
"version": "v1"
}
] | 2023-11-29 | [
[
"Bénichou",
"Alexis",
""
],
[
"Masson",
"Jean-Baptiste",
""
],
[
"Vestergaard",
"Christian L.",
""
]
] | Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred as ``building blocks of complex networks''. Their well-defined structures and small sizes also enables the testing of their functions in synthetic and natural biological experiments. The statistical inference of network motifs is however fraught with difficulties, from defining and sampling the right null model to accounting for the large number of possible motifs and their potential correlations in statistical testing. Here we develop a framework for motif mining based on lossless network compression using subgraph contractions. The minimum description length principle allows us to select the most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. The approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. This provides an alternative definition of motif significance which guarantees more robust statistical inference. Our approach overcomes the common problems in classic testing-based motif analysis. We apply our methodology to perform comparative connectomics by evaluating the compressibility and the circuit motifs of a range of synaptic-resolution neural connectomes. |
q-bio/0610006 | Liu Quanxing | Quan-Xing Liu and Zhen Jin | Formation of spatial patterns in epidemic model with constant removal
rate of the infectives | 7 figures, 7 pages; The modification according to the referees'
remark | J. Stat. Mech. (2007) P05002 | 10.1088/1742-5468/2007/05/P05002 | null | q-bio.PE | null | This paper addresses the question of how population diffusion affects the
formation of the spatial patterns in the spatial epidemic model by Turing
mechanisms. In particular, we present theoretical analysis to results of the
numerical simulations in two dimensions. Moreover, there is a critical value
for the system within the linear regime. Below the critical value the spatial
patterns are impermanent, whereas above it stationary spot and stripe patterns
can coexist over time. We have observed the striking formation of spatial
patterns during the evolution, but the isolated ordered spot patterns don't
emerge in the space.
| [
{
"created": "Tue, 3 Oct 2006 02:03:48 GMT",
"version": "v1"
},
{
"created": "Tue, 10 Oct 2006 07:16:31 GMT",
"version": "v2"
},
{
"created": "Mon, 20 Nov 2006 01:51:44 GMT",
"version": "v3"
},
{
"created": "Tue, 6 Feb 2007 07:35:32 GMT",
"version": "v4"
}
] | 2009-09-29 | [
[
"Liu",
"Quan-Xing",
""
],
[
"Jin",
"Zhen",
""
]
] | This paper addresses the question of how population diffusion affects the formation of the spatial patterns in the spatial epidemic model by Turing mechanisms. In particular, we present theoretical analysis to results of the numerical simulations in two dimensions. Moreover, there is a critical value for the system within the linear regime. Below the critical value the spatial patterns are impermanent, whereas above it stationary spot and stripe patterns can coexist over time. We have observed the striking formation of spatial patterns during the evolution, but the isolated ordered spot patterns don't emerge in the space. |
2112.12147 | Mahta Ramezanian Panahi | Mahta Ramezanian Panahi, Germ\'an Abrevaya, Jean-Christophe
Gagnon-Audet, Vikram Voleti, Irina Rish and Guillaume Dumas | Generative Models of Brain Dynamics -- A review | Updated to two-column format with 15 pages (excluding refs), 3 figs,
submitted to Frontiers | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The principled design and discovery of biologically- and physically-informed
models of neuronal dynamics has been advancing since the mid-twentieth century.
Recent developments in artificial intelligence (AI) have accelerated this
progress. This review article gives a high-level overview of the approaches
across different scales of organization and levels of abstraction. The studies
covered in this paper include fundamental models in computational neuroscience,
nonlinear dynamics, data-driven methods, as well as emergent practices. While
not all of these models span the intersection of neuroscience, AI, and system
dynamics, all of them do or can work in tandem as generative models, which, as
we argue, provide superior properties for the analysis of neuroscientific data.
We discuss the limitations and unique dynamical traits of brain data and the
complementary need for hypothesis- and data-driven modeling. By way of
conclusion, we present several hybrid generative models from recent literature
in scientific machine learning, which can be efficiently deployed to yield
interpretable models of neural dynamics.
| [
{
"created": "Wed, 22 Dec 2021 18:59:21 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Dec 2021 18:53:38 GMT",
"version": "v2"
}
] | 2021-12-24 | [
[
"Panahi",
"Mahta Ramezanian",
""
],
[
"Abrevaya",
"Germán",
""
],
[
"Gagnon-Audet",
"Jean-Christophe",
""
],
[
"Voleti",
"Vikram",
""
],
[
"Rish",
"Irina",
""
],
[
"Dumas",
"Guillaume",
""
]
] | The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics. |
1404.1017 | Harold P. de Vladar | Harold P. de Vladar and Nick Barton | Stability and response of polygenic traits to stabilizing selection and
mutation | Accepted in Genetics | null | null | null | q-bio.PE | http://creativecommons.org/licenses/publicdomain/ | When polygenic traits are under stabilizing selection, many different
combinations of alleles allow close adaptation to the optimum. If alleles have
equal effects, all combinations that result in the same deviation from the
optimum are equivalent. Furthermore, the genetic variance that is maintained by
mutation-selection balance is $2 \mu/S$ per locus, where $\mu$ is the mutation
rate and $S$ the strength of stabilizing selection. In reality, alleles vary in
their effects, making the fitness landscape asymmetric, and complicating
analysis of the equilibria. We show that that the resulting genetic variance
depends on the fraction of alleles near fixation, which contribute by $2
\mu/S$, and on the total mutational effects of alleles that are at intermediate
frequency. The interplay between stabilizing selection and mutation leads to a
sharp transition: alleles with effects smaller than a threshold value of
$2\sqrt{\mu / S}$ remain polymorphic, whereas those with larger effects are
fixed. The genetic load in equilibrium is less than for traits of equal
effects, and the fitness equilibria are more similar. We find that if the
optimum is displaced, alleles with effects close to the threshold value sweep
first, and their rate of increase is bounded by $\sqrt{\mu S}$. Long term
response leads in general to well-adapted traits, unlike the case of equal
effects that often end up at a sub-optimal fitness peak. However, the
particular peaks to which the populations converge are extremely sensitive to
the initial states, and to the speed of the shift of the optimum trait value.
| [
{
"created": "Thu, 3 Apr 2014 17:13:29 GMT",
"version": "v1"
}
] | 2014-04-04 | [
[
"de Vladar",
"Harold P.",
""
],
[
"Barton",
"Nick",
""
]
] | When polygenic traits are under stabilizing selection, many different combinations of alleles allow close adaptation to the optimum. If alleles have equal effects, all combinations that result in the same deviation from the optimum are equivalent. Furthermore, the genetic variance that is maintained by mutation-selection balance is $2 \mu/S$ per locus, where $\mu$ is the mutation rate and $S$ the strength of stabilizing selection. In reality, alleles vary in their effects, making the fitness landscape asymmetric, and complicating analysis of the equilibria. We show that that the resulting genetic variance depends on the fraction of alleles near fixation, which contribute by $2 \mu/S$, and on the total mutational effects of alleles that are at intermediate frequency. The interplay between stabilizing selection and mutation leads to a sharp transition: alleles with effects smaller than a threshold value of $2\sqrt{\mu / S}$ remain polymorphic, whereas those with larger effects are fixed. The genetic load in equilibrium is less than for traits of equal effects, and the fitness equilibria are more similar. We find that if the optimum is displaced, alleles with effects close to the threshold value sweep first, and their rate of increase is bounded by $\sqrt{\mu S}$. Long term response leads in general to well-adapted traits, unlike the case of equal effects that often end up at a sub-optimal fitness peak. However, the particular peaks to which the populations converge are extremely sensitive to the initial states, and to the speed of the shift of the optimum trait value. |
2308.07818 | Willem Diepeveen | Willem Diepeveen, Carlos Esteve-Yag\"ue, Jan Lellmann, Ozan \"Oktem,
Carola-Bibiane Sch\"onlieb | Riemannian geometry for efficient analysis of protein dynamics data | null | null | null | null | q-bio.BM cs.NA math.DG math.NA | http://creativecommons.org/licenses/by-nc-nd/4.0/ | An increasingly common viewpoint is that protein dynamics data sets reside in
a non-linear subspace of low conformational energy. Ideal data analysis tools
for such data sets should therefore account for such non-linear geometry. The
Riemannian geometry setting can be suitable for a variety of reasons. First, it
comes with a rich structure to account for a wide range of geometries that can
be modelled after an energy landscape. Second, many standard data analysis
tools initially developed for data in Euclidean space can also be generalised
to data on a Riemannian manifold. In the context of protein dynamics, a
conceptual challenge comes from the lack of a suitable smooth manifold and the
lack of guidelines for constructing a smooth Riemannian structure based on an
energy landscape. In addition, computational feasibility in computing geodesics
and related mappings poses a major challenge. This work considers these
challenges. The first part of the paper develops a novel local approximation
technique for computing geodesics and related mappings on Riemannian manifolds
in a computationally feasible manner. The second part constructs a smooth
manifold of point clouds modulo rigid body group actions and a Riemannian
structure that is based on an energy landscape for protein conformations. The
resulting Riemannian geometry is tested on several data analysis tasks relevant
for protein dynamics data. It performs exceptionally well on coarse-grained
molecular dynamics simulated data. In particular, the geodesics with given
start- and end-points approximately recover corresponding molecular dynamics
trajectories for proteins that undergo relatively ordered transitions with
medium sized deformations. The Riemannian protein geometry also gives
physically realistic summary statistics and retrieves the underlying dimension
even for large-sized deformations within seconds on a laptop.
| [
{
"created": "Tue, 15 Aug 2023 14:52:09 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Oct 2023 10:47:23 GMT",
"version": "v2"
}
] | 2023-10-27 | [
[
"Diepeveen",
"Willem",
""
],
[
"Esteve-Yagüe",
"Carlos",
""
],
[
"Lellmann",
"Jan",
""
],
[
"Öktem",
"Ozan",
""
],
[
"Schönlieb",
"Carola-Bibiane",
""
]
] | An increasingly common viewpoint is that protein dynamics data sets reside in a non-linear subspace of low conformational energy. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich structure to account for a wide range of geometries that can be modelled after an energy landscape. Second, many standard data analysis tools initially developed for data in Euclidean space can also be generalised to data on a Riemannian manifold. In the context of protein dynamics, a conceptual challenge comes from the lack of a suitable smooth manifold and the lack of guidelines for constructing a smooth Riemannian structure based on an energy landscape. In addition, computational feasibility in computing geodesics and related mappings poses a major challenge. This work considers these challenges. The first part of the paper develops a novel local approximation technique for computing geodesics and related mappings on Riemannian manifolds in a computationally feasible manner. The second part constructs a smooth manifold of point clouds modulo rigid body group actions and a Riemannian structure that is based on an energy landscape for protein conformations. The resulting Riemannian geometry is tested on several data analysis tasks relevant for protein dynamics data. It performs exceptionally well on coarse-grained molecular dynamics simulated data. In particular, the geodesics with given start- and end-points approximately recover corresponding molecular dynamics trajectories for proteins that undergo relatively ordered transitions with medium sized deformations. The Riemannian protein geometry also gives physically realistic summary statistics and retrieves the underlying dimension even for large-sized deformations within seconds on a laptop. |
2402.11472 | Yingying Wang | Yingying Wang, Yun Xiong, Xixi Wu, Xiangguo Sun, Jiawei Zhang | Advanced Drug Interaction Event Prediction | null | null | null | null | q-bio.BM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting drug-drug interaction adverse events, so-called DDI events, is
increasingly valuable as it facilitates the study of mechanisms underlying drug
use or adverse reactions. Existing models often neglect the distinctive
characteristics of individual event classes when integrating multi-source
features, which contributes to systematic unfairness when dealing with highly
imbalanced event samples. Moreover, the limited capacity of these models to
abstract the unique attributes of each event subclass considerably hampers
their application in predicting rare drug-drug interaction events with a
limited sample size. Reducing dataset bias and abstracting event subclass
characteristics are two unresolved challenges. Recently, prompt tuning with
frozen pre-trained graph models, namely "pre-train, prompt, fine-tune"
strategy, has demonstrated impressive performance in few-shot tasks. Motivated
by this, we propose an advanced method as a solution to address these
aforementioned challenges. Specifically, our proposed approach entails a
hierarchical pre-training task that aims to capture crucial aspects of drug
molecular structure and intermolecular interactions while effectively
mitigating implicit dataset bias within the node embeddings. Furthermore, we
construct a prototypical graph by strategically sampling data from distinct
event types and design subgraph prompts utilizing pre-trained node features.
Through comprehensive benchmark experiments, we validate the efficacy of our
subgraph prompts in accurately representing event classes and achieve exemplary
results in both overall and subclass prediction tasks.
| [
{
"created": "Sun, 18 Feb 2024 06:22:01 GMT",
"version": "v1"
},
{
"created": "Thu, 9 May 2024 08:26:51 GMT",
"version": "v2"
},
{
"created": "Tue, 21 May 2024 12:47:40 GMT",
"version": "v3"
},
{
"created": "Wed, 22 May 2024 19:39:52 GMT",
"version": "v4"
}
] | 2024-05-24 | [
[
"Wang",
"Yingying",
""
],
[
"Xiong",
"Yun",
""
],
[
"Wu",
"Xixi",
""
],
[
"Sun",
"Xiangguo",
""
],
[
"Zhang",
"Jiawei",
""
]
] | Predicting drug-drug interaction adverse events, so-called DDI events, is increasingly valuable as it facilitates the study of mechanisms underlying drug use or adverse reactions. Existing models often neglect the distinctive characteristics of individual event classes when integrating multi-source features, which contributes to systematic unfairness when dealing with highly imbalanced event samples. Moreover, the limited capacity of these models to abstract the unique attributes of each event subclass considerably hampers their application in predicting rare drug-drug interaction events with a limited sample size. Reducing dataset bias and abstracting event subclass characteristics are two unresolved challenges. Recently, prompt tuning with frozen pre-trained graph models, namely "pre-train, prompt, fine-tune" strategy, has demonstrated impressive performance in few-shot tasks. Motivated by this, we propose an advanced method as a solution to address these aforementioned challenges. Specifically, our proposed approach entails a hierarchical pre-training task that aims to capture crucial aspects of drug molecular structure and intermolecular interactions while effectively mitigating implicit dataset bias within the node embeddings. Furthermore, we construct a prototypical graph by strategically sampling data from distinct event types and design subgraph prompts utilizing pre-trained node features. Through comprehensive benchmark experiments, we validate the efficacy of our subgraph prompts in accurately representing event classes and achieve exemplary results in both overall and subclass prediction tasks. |
1910.01559 | Jesus Malo | Jesus Malo | Spatio-Chromatic Information available from different Neural Layers via
Gaussianization | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How much visual information about the retinal images can be extracted from
the different layers of the visual pathway?. Separate subsystems (e.g. opponent
channels, spatial filters, nonlinearities of the texture sensors) have been
suggested to be organized for optimal information transmission. However, the
efficiency of these different layers has not been measured when they operate
together on colorimetrically calibrated natural images and using multivariate
information-theoretic units over the joint spatio-chromatic array of responses.
In this work we present a statistical tool to address this question in an
appropriate (multivariate) way. Specifically, we propose an empirical estimate
of the information transmitted by the system based on a recent Gaussianization
technique that reduces the challenging multivariate PDF estimation problem to a
set of simpler univariate estimations. Total correlation measured using the
proposed estimator is consistent with predictions based on the analytical
Jacobian of a standard spatio-chromatic model of the retina-cortex pathway. If
the noise at certain representation is proportional to the dynamic range of the
response, and one assumes sensors of equivalent noise level, transmitted
information shows the following trends: (1) progressively deeper
representations are better in terms of the amount of information about the
input, (2) the transmitted information up to the cortical representation
follows the PDF of natural scenes over the chromatic and achromatic dimensions
of the stimulus space, (3) the contribution of spatial transforms to capture
visual information is substantially bigger than the contribution of chromatic
transforms, and (4) nonlinearities of the responses contribute substantially to
the transmitted information but less than the linear transforms.
| [
{
"created": "Thu, 3 Oct 2019 15:51:43 GMT",
"version": "v1"
},
{
"created": "Thu, 31 Oct 2019 17:36:31 GMT",
"version": "v2"
},
{
"created": "Mon, 25 May 2020 12:44:57 GMT",
"version": "v3"
}
] | 2020-05-26 | [
[
"Malo",
"Jesus",
""
]
] | How much visual information about the retinal images can be extracted from the different layers of the visual pathway?. Separate subsystems (e.g. opponent channels, spatial filters, nonlinearities of the texture sensors) have been suggested to be organized for optimal information transmission. However, the efficiency of these different layers has not been measured when they operate together on colorimetrically calibrated natural images and using multivariate information-theoretic units over the joint spatio-chromatic array of responses. In this work we present a statistical tool to address this question in an appropriate (multivariate) way. Specifically, we propose an empirical estimate of the information transmitted by the system based on a recent Gaussianization technique that reduces the challenging multivariate PDF estimation problem to a set of simpler univariate estimations. Total correlation measured using the proposed estimator is consistent with predictions based on the analytical Jacobian of a standard spatio-chromatic model of the retina-cortex pathway. If the noise at certain representation is proportional to the dynamic range of the response, and one assumes sensors of equivalent noise level, transmitted information shows the following trends: (1) progressively deeper representations are better in terms of the amount of information about the input, (2) the transmitted information up to the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space, (3) the contribution of spatial transforms to capture visual information is substantially bigger than the contribution of chromatic transforms, and (4) nonlinearities of the responses contribute substantially to the transmitted information but less than the linear transforms. |
2110.03842 | Michael Fuchs | Michael Fuchs, Hexuan Liu, Guan-Ru Yu | A Short Note on the Exact Counting of Tree-Child Networks | 6 pages | null | null | null | q-bio.PE math.CO | http://creativecommons.org/licenses/by/4.0/ | Tree-child networks are an important network class which are used in
phylogenetics to model reticulate evolution. In a recent paper, Pons and Batle
(2021) conjectured a relation between tree-child networks and certain words. In
this short note, we prove their conjecture for the (important) class of
one-component tree-child networks.
| [
{
"created": "Fri, 8 Oct 2021 00:59:48 GMT",
"version": "v1"
}
] | 2021-10-11 | [
[
"Fuchs",
"Michael",
""
],
[
"Liu",
"Hexuan",
""
],
[
"Yu",
"Guan-Ru",
""
]
] | Tree-child networks are an important network class which are used in phylogenetics to model reticulate evolution. In a recent paper, Pons and Batle (2021) conjectured a relation between tree-child networks and certain words. In this short note, we prove their conjecture for the (important) class of one-component tree-child networks. |
0804.4375 | Erez Ben-Yaacov | Erez Ben-Yaacov, Yonina Eldar | A Fast and Flexible Method for the Segmentation of aCGH Data | 7 pages, 5 figures, preprint, accepted for publication in
Bioinformatics (Proceedings of ECCB08) | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Array Comparative Genomic Hybridization (aCGH) is used to scan
the entire genome for variations in DNA copy number. A central task in the
analysis of aCGH data is the segmentation into groups of probes sharing the
same DNA copy number. Some well known segmentation methods suffer from very
long running times, preventing interactive data analysis. Results: We suggest a
new segmentation method based on wavelet decomposition and thresholding, which
detects significant breakpoints in the data. Our algorithm is over 1,000 times
faster than leading approaches, with similar performance. Another key advantage
of the proposed method is its simplicity and flexibility. Due to its intuitive
structure it can be easily generalized to incorporate several types of side
information. Here we consider two extensions which include side information
indicating the reliability of each measurement, and compensating for a changing
variability in the measurement noise. The resulting algorithm outperforms
existing methods, both in terms of speed and performance, when applied on real
high density CGH data. Availability: Implementation is available under software
tab at: http://www.ee.technion.ac.il/Sites/People/YoninaEldar/ Contact:
yonina@ee.technion.ac.il
| [
{
"created": "Mon, 28 Apr 2008 11:10:22 GMT",
"version": "v1"
}
] | 2008-04-29 | [
[
"Ben-Yaacov",
"Erez",
""
],
[
"Eldar",
"Yonina",
""
]
] | Motivation: Array Comparative Genomic Hybridization (aCGH) is used to scan the entire genome for variations in DNA copy number. A central task in the analysis of aCGH data is the segmentation into groups of probes sharing the same DNA copy number. Some well known segmentation methods suffer from very long running times, preventing interactive data analysis. Results: We suggest a new segmentation method based on wavelet decomposition and thresholding, which detects significant breakpoints in the data. Our algorithm is over 1,000 times faster than leading approaches, with similar performance. Another key advantage of the proposed method is its simplicity and flexibility. Due to its intuitive structure it can be easily generalized to incorporate several types of side information. Here we consider two extensions which include side information indicating the reliability of each measurement, and compensating for a changing variability in the measurement noise. The resulting algorithm outperforms existing methods, both in terms of speed and performance, when applied on real high density CGH data. Availability: Implementation is available under software tab at: http://www.ee.technion.ac.il/Sites/People/YoninaEldar/ Contact: yonina@ee.technion.ac.il |
1805.09133 | Supreeth Prajwal Shashikumar | Supreeth P. Shashikumar, Amit J. Shah, Gari D. Clifford, and Shamim
Nemati | Detection of Paroxysmal Atrial Fibrillation using Attention-based
Bidirectional Recurrent Neural Networks | Accepted to the 24th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining (KDD 2018), London, UK, 2018 | null | null | null | q-bio.NC cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is
difficult since many cases of AF are usually clinically silent and undiagnosed.
In particular paroxysmal AF is a form of AF that occurs occasionally, and has a
higher probability of being undetected. In this work, we present an attention
based deep learning framework for detection of paroxysmal AF episodes from a
sequence of windows. Time-frequency representation of 30 seconds recording
windows, over a 10 minute data segment, are fed sequentially into a deep
convolutional neural network for image-based feature extraction, which are then
presented to a bidirectional recurrent neural network with an attention layer
for AF detection. To demonstrate the effectiveness of the proposed framework
for transient AF detection, we use a database of 24 hour Holter
Electrocardiogram (ECG) recordings acquired from 2850 patients at the
University of Virginia heart station. The algorithm achieves an AUC of 0.94 on
the testing set, which exceeds the performance of baseline models. We also
demonstrate the cross-domain generalizablity of the approach by adapting the
learned model parameters from one recording modality (ECG) to another
(photoplethysmogram) with improved AF detection performance. The proposed high
accuracy, low false alarm algorithm for detecting paroxysmal AF has potential
applications in long-term monitoring using wearable sensors.
| [
{
"created": "Mon, 7 May 2018 20:34:17 GMT",
"version": "v1"
}
] | 2018-05-24 | [
[
"Shashikumar",
"Supreeth P.",
""
],
[
"Shah",
"Amit J.",
""
],
[
"Clifford",
"Gari D.",
""
],
[
"Nemati",
"Shamim",
""
]
] | Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability of being undetected. In this work, we present an attention based deep learning framework for detection of paroxysmal AF episodes from a sequence of windows. Time-frequency representation of 30 seconds recording windows, over a 10 minute data segment, are fed sequentially into a deep convolutional neural network for image-based feature extraction, which are then presented to a bidirectional recurrent neural network with an attention layer for AF detection. To demonstrate the effectiveness of the proposed framework for transient AF detection, we use a database of 24 hour Holter Electrocardiogram (ECG) recordings acquired from 2850 patients at the University of Virginia heart station. The algorithm achieves an AUC of 0.94 on the testing set, which exceeds the performance of baseline models. We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance. The proposed high accuracy, low false alarm algorithm for detecting paroxysmal AF has potential applications in long-term monitoring using wearable sensors. |
1006.1327 | Robert Burger PhD | John Robert Burger | The Electron Capture Hypothesis - A Challenge to Neuroscientists | Editing for clarity; Figs 5 & 6 changed to Figs 4 & 5 | null | null | null | q-bio.NC physics.bio-ph physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lower speed impinging ions (with hydration shells) cannot transverse ion
channels once internal charge goes positive. Yet neural pulse waveforms fail to
show the expected risetime distortion beginning at zero voltage. Observed
waveforms cannot be explained unless electron capture is considered.
| [
{
"created": "Mon, 7 Jun 2010 18:43:28 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Sep 2010 17:57:48 GMT",
"version": "v2"
},
{
"created": "Fri, 17 Sep 2010 16:52:49 GMT",
"version": "v3"
}
] | 2010-09-20 | [
[
"Burger",
"John Robert",
""
]
] | Lower speed impinging ions (with hydration shells) cannot transverse ion channels once internal charge goes positive. Yet neural pulse waveforms fail to show the expected risetime distortion beginning at zero voltage. Observed waveforms cannot be explained unless electron capture is considered. |
0904.3124 | Kyung Hyuk Kim | Kyung Hyuk Kim, Herbert M. Sauro | Stochastic Control Analysis for Biochemical Reaction Systems | 34 pages, 11 figures | null | null | null | q-bio.QM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate how stochastic reaction processes are affected
by external perturbations. We describe an extension of the deterministic
metabolic control analysis (MCA) to the stochastic regime. We introduce
stochastic sensitivities for mean and covariance values of reactant
concentrations and reaction fluxes and show that there exist MCA-like summation
theorems among these sensitivities. The summation theorems for flux variances
are shown to depend on the size of the measurement time window ($\epsilon$),
within which reaction events are counted for measuring a single flux. The
degree of the $\epsilon$-dependency can become significant for processes
involving multi-time-scale dynamics and is estimated by introducing a new
measure of time scale separation. This $\epsilon$-dependency is shown to be
closely related to the power-law scaling observed in flux fluctuations in
various complex networks. We propose a systematic way to control fluctuations
of reactant concentrations while minimizing changes in mean concentration
levels. Such orthogonal control is obtained by introducing a control vector
indicating the strength and direction of parameter perturbations leading to a
sensitive control. We also propose a possible implication in the control of
flux fluctuation: The control distribution for flux fluctuations changes with
the measurement time window size, $\epsilon$. When a control engineer applies a
specific control operation on a reaction system, the system can respond
contrary to what is expected, depending on the time window size $\epsilon$.
| [
{
"created": "Mon, 20 Apr 2009 21:43:18 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Apr 2009 20:12:55 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Aug 2009 20:05:43 GMT",
"version": "v3"
}
] | 2009-08-21 | [
[
"Kim",
"Kyung Hyuk",
""
],
[
"Sauro",
"Herbert M.",
""
]
] | In this paper, we investigate how stochastic reaction processes are affected by external perturbations. We describe an extension of the deterministic metabolic control analysis (MCA) to the stochastic regime. We introduce stochastic sensitivities for mean and covariance values of reactant concentrations and reaction fluxes and show that there exist MCA-like summation theorems among these sensitivities. The summation theorems for flux variances are shown to depend on the size of the measurement time window ($\epsilon$), within which reaction events are counted for measuring a single flux. The degree of the $\epsilon$-dependency can become significant for processes involving multi-time-scale dynamics and is estimated by introducing a new measure of time scale separation. This $\epsilon$-dependency is shown to be closely related to the power-law scaling observed in flux fluctuations in various complex networks. We propose a systematic way to control fluctuations of reactant concentrations while minimizing changes in mean concentration levels. Such orthogonal control is obtained by introducing a control vector indicating the strength and direction of parameter perturbations leading to a sensitive control. We also propose a possible implication in the control of flux fluctuation: The control distribution for flux fluctuations changes with the measurement time window size, $\epsilon$. When a control engineer applies a specific control operation on a reaction system, the system can respond contrary to what is expected, depending on the time window size $\epsilon$. |
0909.3129 | Wentian Li | Wentian Li, Annette Lee, Peter K Gregersen | Copy-number-variation and copy-number-alteration region detection by
cumulative plots | null | BMC Bioinformatics, 10(suppl 1):S67 (2009) | 10.1186/1471-2105-10-S1-S67 | null | q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Regions with copy number variations (in germline cells) or copy
number alteration (in somatic cells) are of great interest for human disease
gene mapping and cancer studies. They represent a new type of mutation and are
larger-scaled than the single nucleotide polymorphisms. Using genotyping
microarray for copy number variation detection has become standard, and there
is a need for improving analysis methods. Results: We apply the cumulative plot
to the detection of regions with copy number variation/alteration, on samples
taken from a chronic lymphocytic leukemia patient. Two sets of whole-genome
genotyping of 317k single nucleotide polymorphisms, one from the normal cell
and another from the cancer cell, are analyzed. We demonstrate the utility of
cumulative plot in detecting a 9Mb (9 x 10^6 bases) hemizygous deletion and 1Mb
homozygous deletion on chromosome 13. We also show the possibility to detect
smaller copy number variation/alteration regions below the 100kb range.
Conclusions: As a graphic tool, the cumulative plot is an intuitive and a
scale-free (window-less) way for detecting copy number variation/alteration
regions, especially when such regions are small.
| [
{
"created": "Wed, 16 Sep 2009 23:57:31 GMT",
"version": "v1"
}
] | 2012-05-07 | [
[
"Li",
"Wentian",
""
],
[
"Lee",
"Annette",
""
],
[
"Gregersen",
"Peter K",
""
]
] | Background: Regions with copy number variations (in germline cells) or copy number alteration (in somatic cells) are of great interest for human disease gene mapping and cancer studies. They represent a new type of mutation and are larger-scaled than the single nucleotide polymorphisms. Using genotyping microarray for copy number variation detection has become standard, and there is a need for improving analysis methods. Results: We apply the cumulative plot to the detection of regions with copy number variation/alteration, on samples taken from a chronic lymphocytic leukemia patient. Two sets of whole-genome genotyping of 317k single nucleotide polymorphisms, one from the normal cell and another from the cancer cell, are analyzed. We demonstrate the utility of cumulative plot in detecting a 9Mb (9 x 10^6 bases) hemizygous deletion and 1Mb homozygous deletion on chromosome 13. We also show the possibility to detect smaller copy number variation/alteration regions below the 100kb range. Conclusions: As a graphic tool, the cumulative plot is an intuitive and a scale-free (window-less) way for detecting copy number variation/alteration regions, especially when such regions are small. |
0705.2504 | Yuichi Togashi | Yuichi Togashi, Alexander S. Mikhailov | Nonlinear Relaxation Dynamics in Elastic Networks and Design Principles
of Molecular Machines | 12 pages, 9 figures | Proc. Natl. Acad. Sci. (USA) 104, 8697 (2007) | 10.1073/pnas.0702950104 | null | q-bio.BM cond-mat.soft physics.chem-ph | null | Analyzing nonlinear conformational relaxation dynamics in elastic networks
corresponding to two classical motor proteins, we find that they respond by
well-defined internal mechanical motions to various initial deformations and
that these motions are robust against external perturbations. We show that this
behavior is not characteristic for random elastic networks. However, special
network architectures with such properties can be designed by evolutionary
optimization methods. Using them, an example of an artificial elastic network,
operating as a cyclic machine powered by ligand binding, is constructed.
| [
{
"created": "Thu, 17 May 2007 10:21:26 GMT",
"version": "v1"
}
] | 2007-06-13 | [
[
"Togashi",
"Yuichi",
""
],
[
"Mikhailov",
"Alexander S.",
""
]
] | Analyzing nonlinear conformational relaxation dynamics in elastic networks corresponding to two classical motor proteins, we find that they respond by well-defined internal mechanical motions to various initial deformations and that these motions are robust against external perturbations. We show that this behavior is not characteristic for random elastic networks. However, special network architectures with such properties can be designed by evolutionary optimization methods. Using them, an example of an artificial elastic network, operating as a cyclic machine powered by ligand binding, is constructed. |
1810.12777 | Daniel Cooney | Daniel B. Cooney | The Replicator Dynamics for Multilevel Selection in Evolutionary Games | 44 pages, 7 figures, Version 2: Revised Discussion | Journal of Mathematical Biology (2009), 1-54 | 10.1007/s00285-019-01352-5 | null | q-bio.PE math.AP math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a stochastic model for evolution of group-structured populations
in which interactions between group members correspond to the Prisoner's
Dilemma or the Hawk-Dove game. Selection operates at two organization levels:
individuals compete with peer group members based on individual payoff, while
groups also compete with other groups based on average payoff of group members.
In the Prisoner's Dilemma, this creates a tension between the two levels of
selection, as defectors are favored at the individual level, whereas groups
with at least some cooperators outperform groups of defectors at the
between-group level. In the limit of infinite group size and infinite number of
groups, we derive a non-local PDE that describes the probability distribution
of group compositions in the population. For special families of payoff
matrices, we characterize the long-time behavior of solutions of our equation,
finding a threshold level of between-group selection required to sustain
density steady states and the survival of cooperation. When all-cooperator
groups are most fit, the average and most abundant group composition at steady
state range from featuring all-defector groups when individual-level selection
dominates to featuring all-cooperator groups when group-level selection
dominates. When the most fit groups have a mix of cooperators and defectors,
then the average and most abundant group compositions always feature a smaller
fraction of cooperators than required for the optimal mix, even in the limit
where group-level selection is infinitely stronger than individual-level
selection. In such cases, the conflict between the two levels of selection
cannot be decoupled, and cooperation cannot be sustained at all in the case
when between-group competition favors an even mix of cooperators and defectors.
| [
{
"created": "Tue, 30 Oct 2018 14:43:06 GMT",
"version": "v1"
},
{
"created": "Sun, 16 Dec 2018 20:02:47 GMT",
"version": "v2"
}
] | 2019-04-12 | [
[
"Cooney",
"Daniel B.",
""
]
] | We consider a stochastic model for evolution of group-structured populations in which interactions between group members correspond to the Prisoner's Dilemma or the Hawk-Dove game. Selection operates at two organization levels: individuals compete with peer group members based on individual payoff, while groups also compete with other groups based on average payoff of group members. In the Prisoner's Dilemma, this creates a tension between the two levels of selection, as defectors are favored at the individual level, whereas groups with at least some cooperators outperform groups of defectors at the between-group level. In the limit of infinite group size and infinite number of groups, we derive a non-local PDE that describes the probability distribution of group compositions in the population. For special families of payoff matrices, we characterize the long-time behavior of solutions of our equation, finding a threshold level of between-group selection required to sustain density steady states and the survival of cooperation. When all-cooperator groups are most fit, the average and most abundant group composition at steady state range from featuring all-defector groups when individual-level selection dominates to featuring all-cooperator groups when group-level selection dominates. When the most fit groups have a mix of cooperators and defectors, then the average and most abundant group compositions always feature a smaller fraction of cooperators than required for the optimal mix, even in the limit where group-level selection is infinitely stronger than individual-level selection. In such cases, the conflict between the two levels of selection cannot be decoupled, and cooperation cannot be sustained at all in the case when between-group competition favors an even mix of cooperators and defectors. |
2306.01634 | Aaron Ge | Aaron Ge, Tongwu Zhang, Clara Bodelon, Montserrat Garcia-Closas, Jonas
Almeida, Jeya Balasubramanian | A FAIR platform for reproducing mutational signature detection on tumor
sequencing data | Our proposed in-browser platform is publicly available under the MIT
license at https://aaronge-2020.github.io/Sig3-Detection/. No data leaves
this privacy-preserving environment, which can be cloned or forked and served
from other domains with no restrictions. All the code and relevant data used
to create this platform can be found at
https://github.com/aaronge-2020/Sig3-Detection | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | This paper presents a portable, privacy-preserving, in-browser platform for
the reproducible assessment of mutational signature detection methods from
sparse sequencing data generated by targeted gene panels. The platform aims to
address the reproducibility challenges in mutational signature research by
adhering to the FAIR principles, making it findable, accessible, interoperable,
and reusable. Our approach focuses on the detection of specific mutational
signatures, such as SBS3, which have been linked to specific mutagenic
processes. The platform relies on publicly available data, simulation,
downsampling techniques, and machine learning algorithms to generate training
data and labels and to train and evaluate models. The key achievement of our
platform is its transparency, reusability, and privacy preservation, enabling
researchers and clinicians to analyze mutational signatures with the guarantee
that no data circulates outside the client machine.
| [
{
"created": "Fri, 2 Jun 2023 15:53:29 GMT",
"version": "v1"
}
] | 2023-06-05 | [
[
"Ge",
"Aaron",
""
],
[
"Zhang",
"Tongwu",
""
],
[
"Bodelon",
"Clara",
""
],
[
"Garcia-Closas",
"Montserrat",
""
],
[
"Almeida",
"Jonas",
""
],
[
"Balasubramanian",
"Jeya",
""
]
] | This paper presents a portable, privacy-preserving, in-browser platform for the reproducible assessment of mutational signature detection methods from sparse sequencing data generated by targeted gene panels. The platform aims to address the reproducibility challenges in mutational signature research by adhering to the FAIR principles, making it findable, accessible, interoperable, and reusable. Our approach focuses on the detection of specific mutational signatures, such as SBS3, which have been linked to specific mutagenic processes. The platform relies on publicly available data, simulation, downsampling techniques, and machine learning algorithms to generate training data and labels and to train and evaluate models. The key achievement of our platform is its transparency, reusability, and privacy preservation, enabling researchers and clinicians to analyze mutational signatures with the guarantee that no data circulates outside the client machine. |
1108.0209 | David Murrugarra | Reinhard Laubenbacher, David Murrugarra, and Alan Veliz-Cuba | Structure and Dynamics of Polynomial Dynamical Systems | 10 pages, 3 figures. NSF CMMI Research and Innovation Conference 2011 | null | null | null | q-bio.MN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discrete models have a long tradition in engineering, including finite state
machines, Boolean networks, Petri nets, and agent-based models. Of particular
importance is the question of how the model structure constrains its dynamics.
This paper discusses an algebraic framework to study such questions. The
systems discussed here are given by mappings on an affine space over a finite
field, whose coordinate functions are polynomials. They form a general class of
models which can represent many discrete model types. Assigning to such a
system its dependency graph, that is, the directed graph that indicates the
variable dependencies, provides a mapping from systems to graphs. A basic
property of this mapping is derived and used to prove that dynamical systems
with an acyclic dependency graph can only have a unique fixed point in their
phase space and no periodic orbits. This result is then applied to a published
model of in vitro virus competition.
| [
{
"created": "Sun, 31 Jul 2011 22:21:14 GMT",
"version": "v1"
}
] | 2011-08-02 | [
[
"Laubenbacher",
"Reinhard",
""
],
[
"Murrugarra",
"David",
""
],
[
"Veliz-Cuba",
"Alan",
""
]
] | Discrete models have a long tradition in engineering, including finite state machines, Boolean networks, Petri nets, and agent-based models. Of particular importance is the question of how the model structure constrains its dynamics. This paper discusses an algebraic framework to study such questions. The systems discussed here are given by mappings on an affine space over a finite field, whose coordinate functions are polynomials. They form a general class of models which can represent many discrete model types. Assigning to such a system its dependency graph, that is, the directed graph that indicates the variable dependencies, provides a mapping from systems to graphs. A basic property of this mapping is derived and used to prove that dynamical systems with an acyclic dependency graph can only have a unique fixed point in their phase space and no periodic orbits. This result is then applied to a published model of in vitro virus competition. |
q-bio/0609014 | Gabriele Scheler | Gabriele Scheler | Dynamic re-wiring of protein interaction: The case of transactivation | 4 pages; presented at NIPS 2004 workshop | null | null | null | q-bio.MN | null | We are looking at local protein interaction networks from the perspective of
directed, labeled graphs with quantitative values for monotonic changes in
concentrations. These systems can be used to perform stability analysis for a
stable attractor, given initial values. They can also show re-configuration of
whole system states by dynamic insertion of links, given specific patterns of
input. The latter issue seems particularly relevant for the concept of
multistability in cellular memory. We attempt to show that this level of
analysis is well-suited for a number of relevant biological subsystems, such as
transactivation in cardiac myocytes or G-protein coupling to adrenergic
receptors. In particular, we analyse the 'motif' of an "overflow gate" as a
concentration-dependent system reconfiguration.
| [
{
"created": "Sun, 10 Sep 2006 03:03:53 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Scheler",
"Gabriele",
""
]
] | We are looking at local protein interaction networks from the perspective of directed, labeled graphs with quantitative values for monotonic changes in concentrations. These systems can be used to perform stability analysis for a stable attractor, given initial values. They can also show re-configuration of whole system states by dynamic insertion of links, given specific patterns of input. The latter issue seems particularly relevant for the concept of multistability in cellular memory. We attempt to show that this level of analysis is well-suited for a number of relevant biological subsystems, such as transactivation in cardiac myocytes or G-protein coupling to adrenergic receptors. In particular, we analyse the 'motif' of an "overflow gate" as a concentration-dependent system reconfiguration. |
1505.05096 | Guo-Wei Wei | Kristopher Opron, Kelin Xia and Guo-Wei Wei | Capturing protein multiscale thermal fluctuations | 16 pages, 8 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing elastic network models are typically parametrized at a given cutoff
distance and often fail to properly predict the thermal fluctuation of many
macromolecules that involve multiple characteristic length scales. We introduce
a multiscale flexibility-rigidity index (mFRI) method to resolve this problem.
The proposed mFRI utilizes two or three correlation kernels parametrized at
different length scales to capture protein interactions at corresponding
scales. It is about 20% more accurate than the Gaussian network model (GNM) in
the B-factor prediction of a set of 364 proteins. Additionally, the present
method is able to delivery accurate predictions for multiscale macromolecules
that fail GNM. Finally, or a protein of $N$ residues, mFRI is of linear scaling
(O(N)) in computational complexity, in contrast to the order of O(N^3) for GNM.
| [
{
"created": "Tue, 19 May 2015 17:32:13 GMT",
"version": "v1"
},
{
"created": "Wed, 20 May 2015 01:43:43 GMT",
"version": "v2"
}
] | 2015-05-21 | [
[
"Opron",
"Kristopher",
""
],
[
"Xia",
"Kelin",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Existing elastic network models are typically parametrized at a given cutoff distance and often fail to properly predict the thermal fluctuation of many macromolecules that involve multiple characteristic length scales. We introduce a multiscale flexibility-rigidity index (mFRI) method to resolve this problem. The proposed mFRI utilizes two or three correlation kernels parametrized at different length scales to capture protein interactions at corresponding scales. It is about 20% more accurate than the Gaussian network model (GNM) in the B-factor prediction of a set of 364 proteins. Additionally, the present method is able to delivery accurate predictions for multiscale macromolecules that fail GNM. Finally, or a protein of $N$ residues, mFRI is of linear scaling (O(N)) in computational complexity, in contrast to the order of O(N^3) for GNM. |
1408.1869 | Nicolae Radu Zabet | Nicolae Radu Zabet | Negative Feedback and Physical Limits of Genes | 17 pages, 7 figures, 1 table | Journal of Theoretical Biology 248:1 (2011) 82-91 | 10.1016/j.jtbi.2011.06.021 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper compares the auto-repressed gene to a simple one (a gene without
auto-regulation) in terms of response time and output noise under the
assumption of fixed metabolic cost. The analysis shows that, in the case of
non-vanishing leak expression rate, the negative feedback reduces both the
switching on and switching off times of a gene. The noise of the auto-repressed
gene will be lower than the one of the simple gene only for low leak expression
rates. Summing up, for low, but non-vanishing leak expression rates, the
auto-repressed gene is both faster and less noisier compared to the simple one.
| [
{
"created": "Fri, 8 Aug 2014 14:36:06 GMT",
"version": "v1"
}
] | 2014-08-11 | [
[
"Zabet",
"Nicolae Radu",
""
]
] | This paper compares the auto-repressed gene to a simple one (a gene without auto-regulation) in terms of response time and output noise under the assumption of fixed metabolic cost. The analysis shows that, in the case of non-vanishing leak expression rate, the negative feedback reduces both the switching on and switching off times of a gene. The noise of the auto-repressed gene will be lower than the one of the simple gene only for low leak expression rates. Summing up, for low, but non-vanishing leak expression rates, the auto-repressed gene is both faster and less noisier compared to the simple one. |
1502.01409 | David Budden | David M Budden, Daniel G Hurley and Edmund J Crampin | TREEOME: A framework for epigenetic and transcriptomic data integration
to explore regulatory interactions controlling transcription | 14 pages, 6 figures | Epigenetics & Chromatin (2015) 8:21 | 10.1186/s13072-015-0013-9 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Predictive modelling of gene expression is a powerful framework
for the in silico exploration of transcriptional regulatory interactions
through the integration of high-throughput -omics data. A major limitation of
previous approaches is their inability to handle conditional and synergistic
interactions that emerge when collectively analysing genes subject to different
regulatory mechanisms. This limitation reduces overall predictive power and
thus the reliability of downstream biological inference.
Results: We introduce an analytical modelling framework (TREEOME: tree of
models of expression) that integrates epigenetic and transcriptomic data by
separating genes into putative regulatory classes. Current predictive modelling
approaches have found both DNA methylation and histone modification epigenetic
data to provide little or no improvement in accuracy of prediction of
transcript abundance despite, for example, distinct anti-correlation between
mRNA levels and promoter-localised DNA methylation. To improve on this, in
TREEOME we evaluate four possible methods of formulating gene-level DNA
methylation metrics, which provide a foundation for identifying gene-level
methylation events and subsequent differential analysis, whereas most previous
techniques operate at the level of individual CpG dinucleotides. We demonstrate
TREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histone
modification (ChIP-seq) data to accurately predict genome-wide mRNA transcript
abundance (RNA-seq) for H1-hESC and GM12878 cell lines.
Availability: TREEOME is implemented using open-source software and made
available as a pre-configured bootable reference environment. All scripts and
data presented in this study are available online at
http://sourceforge.net/projects/budden2015treeome/.
| [
{
"created": "Thu, 5 Feb 2015 02:06:14 GMT",
"version": "v1"
}
] | 2018-08-14 | [
[
"Budden",
"David M",
""
],
[
"Hurley",
"Daniel G",
""
],
[
"Crampin",
"Edmund J",
""
]
] | Motivation: Predictive modelling of gene expression is a powerful framework for the in silico exploration of transcriptional regulatory interactions through the integration of high-throughput -omics data. A major limitation of previous approaches is their inability to handle conditional and synergistic interactions that emerge when collectively analysing genes subject to different regulatory mechanisms. This limitation reduces overall predictive power and thus the reliability of downstream biological inference. Results: We introduce an analytical modelling framework (TREEOME: tree of models of expression) that integrates epigenetic and transcriptomic data by separating genes into putative regulatory classes. Current predictive modelling approaches have found both DNA methylation and histone modification epigenetic data to provide little or no improvement in accuracy of prediction of transcript abundance despite, for example, distinct anti-correlation between mRNA levels and promoter-localised DNA methylation. To improve on this, in TREEOME we evaluate four possible methods of formulating gene-level DNA methylation metrics, which provide a foundation for identifying gene-level methylation events and subsequent differential analysis, whereas most previous techniques operate at the level of individual CpG dinucleotides. We demonstrate TREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histone modification (ChIP-seq) data to accurately predict genome-wide mRNA transcript abundance (RNA-seq) for H1-hESC and GM12878 cell lines. Availability: TREEOME is implemented using open-source software and made available as a pre-configured bootable reference environment. All scripts and data presented in this study are available online at http://sourceforge.net/projects/budden2015treeome/. |
2004.03384 | Kerstin Ritter | Matthias Ritter, Derek V.M. Ott, Friedemann Paul, John-Dylan Haynes,
Kerstin Ritter | Covid-19 -- A simple statistical model for predicting ICU load in early
phases of the disease | null | null | null | null | q-bio.PE stat.AP | http://creativecommons.org/licenses/by/4.0/ | One major bottleneck in the ongoing COVID-19 pandemic is the limited number
of critical care beds. Due to the dynamic development of infections and the
time lag between when patients are infected and when a proportion of them
enters an intensive care unit (ICU), the need for future intensive care can
easily be underestimated. To infer future ICU load from reported infections, we
suggest a simple statistical model that (1) accounts for time lags and (2)
allows for making predictions depending on different future growth of
infections. We have evaluated our model for three regions, namely Berlin
(Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment
measures made an impact, we first estimate the region-specific model
parameters. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and an
average stay of 12 days in ICU provide the best fit of the data, for Lombardy
and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3
days) and the average stay (4 and 8 days) in ICU shorter. The region-specific
models are then used to predict future ICU load assuming either a continued
exponential phase with varying growth rates (0-15%) or linear growth. Thus, the
model can help to predict a potential exceedance of ICU capacity. Although our
predictions are based on small data sets and disregard non-stationary dynamics,
our model is simple, robust, and can be used in early phases of the disease
when data are scarce.
| [
{
"created": "Mon, 6 Apr 2020 17:54:18 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Jul 2020 14:50:28 GMT",
"version": "v2"
}
] | 2020-07-28 | [
[
"Ritter",
"Matthias",
""
],
[
"Ott",
"Derek V. M.",
""
],
[
"Paul",
"Friedemann",
""
],
[
"Haynes",
"John-Dylan",
""
],
[
"Ritter",
"Kerstin",
""
]
] | One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three regions, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and an average stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the average stay (4 and 8 days) in ICU shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0-15%) or linear growth. Thus, the model can help to predict a potential exceedance of ICU capacity. Although our predictions are based on small data sets and disregard non-stationary dynamics, our model is simple, robust, and can be used in early phases of the disease when data are scarce. |
2005.11255 | Jenny Poulton | Jenny Marie Poulton, Thomas Edward Ouldridge | Edge-effects dominate copying thermodynamics for finite-length molecular
oligomers | null | null | 10.1088/1367-2630/ac0389 | null | q-bio.SC cond-mat.stat-mech q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Living systems produce copies of information-carrying molecules such as DNA
by assembling monomer units into finite-length oligomer (short polymer) copies.
We explore the role of initiation and termination of the copy process in the
thermodynamics of copying. By splitting the free-energy change of copy
formation into informational and chemical terms, we show that copy accuracy
plays no direct role in the overall thermodynamics. Instead, it is
thermodynamically costly to produce outputs that are more similar to the
oligomers in the environment than sequences obtained by randomly sampling
monomers. Copy accuracy can be thermodynamically neutral, or even favoured,
depending on the surroundings. Oligomer copying mechanisms can thus function as
information engines that interconvert chemical and information-based free
energy. Hard thermodynamic constraints on accuracy derived for infinite-length
polymers instead manifest as kinetic barriers experienced while the copy is
template-attached. These barriers are easily surmounted by shorter oligomers.
| [
{
"created": "Fri, 22 May 2020 16:05:11 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Mar 2021 16:32:11 GMT",
"version": "v2"
}
] | 2021-08-11 | [
[
"Poulton",
"Jenny Marie",
""
],
[
"Ouldridge",
"Thomas Edward",
""
]
] | Living systems produce copies of information-carrying molecules such as DNA by assembling monomer units into finite-length oligomer (short polymer) copies. We explore the role of initiation and termination of the copy process in the thermodynamics of copying. By splitting the free-energy change of copy formation into informational and chemical terms, we show that copy accuracy plays no direct role in the overall thermodynamics. Instead, it is thermodynamically costly to produce outputs that are more similar to the oligomers in the environment than sequences obtained by randomly sampling monomers. Copy accuracy can be thermodynamically neutral, or even favoured, depending on the surroundings. Oligomer copying mechanisms can thus function as information engines that interconvert chemical and information-based free energy. Hard thermodynamic constraints on accuracy derived for infinite-length polymers instead manifest as kinetic barriers experienced while the copy is template-attached. These barriers are easily surmounted by shorter oligomers. |
1605.03090 | Yogesh Virkar | Yogesh S. Virkar and Woodrow L. Shew and Juan G. Restrepo and Edward
Ott | Metabolite transport through glial networks stabilizes the dynamics of
learning | 8 pages, 5 figures | Phys. Rev. E 94, 042310 (2016) | 10.1103/PhysRevE.94.042310 | null | q-bio.NC cond-mat.dis-nn nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning and memory are acquired through long-lasting changes in synapses. In
the simplest models, such synaptic potentiation typically leads to runaway
excitation, but in reality there must exist processes that robustly preserve
overall stability of the neural system dynamics. How is this accomplished?
Various approaches to this basic question have been considered. Here we propose
a particularly compelling and natural mechanism for preserving stability of
learning neural systems. This mechanism is based on the global processes by
which metabolic resources are distributed to the neurons by glial cells.
Specifically, we introduce and study a model comprised of two interacting
networks: a model neural network interconnected by synapses which undergo
spike-timing dependent plasticity (STDP); and a model glial network
interconnected by gap junctions which diffusively transport metabolic resources
among the glia and, ultimately, to neural synapses where they are consumed. Our
main result is that the biophysical constraints imposed by diffusive transport
of metabolic resources through the glial network can prevent runaway growth of
synaptic strength, both during ongoing activity and during learning. Our
findings suggest a previously unappreciated role for glial transport of
metabolites in the feedback control stabilization of neural network dynamics
during learning.
| [
{
"created": "Tue, 10 May 2016 16:37:30 GMT",
"version": "v1"
}
] | 2016-10-26 | [
[
"Virkar",
"Yogesh S.",
""
],
[
"Shew",
"Woodrow L.",
""
],
[
"Restrepo",
"Juan G.",
""
],
[
"Ott",
"Edward",
""
]
] | Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model comprised of two interacting networks: a model neural network interconnected by synapses which undergo spike-timing dependent plasticity (STDP); and a model glial network interconnected by gap junctions which diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning. |
2206.14874 | Zachary Fox | Zachary R Fox | Extracting Information from Stochastic Trajectories of Gene Expression | 6 pages, 4 figures | null | null | null | q-bio.QM stat.AP | http://creativecommons.org/licenses/by/4.0/ | Gene expression is a stochastic process in which cells produce biomolecules
essential to the function of life. Modern experimental methods allow for the
measurement of biomolecules at single-cell and single-molecule resolution over
time. Mathematical models are used to make sense of these experiments. The
codesign of experiments and models allows one to use models to design optimal
experiments, and to find experiments which provide as much information as
possible about relevant model parameters. Here, we provide a formulation of
Fisher information for trajectories sampled from the continuous time Markov
processes often used to model biological systems, and apply the result to
potentially correlated measurements of stochastic gene expression. We validate
the result on two commonly used models of gene expression and show it can be
used to optimize measurement periods for simulated single-cell fluorescence
microscopy experiments. Finally, we use a connection between Fisher information
and mutual information to derive channel capacities of nonlinearly regulated
gene expression.
| [
{
"created": "Wed, 29 Jun 2022 19:38:04 GMT",
"version": "v1"
}
] | 2022-07-01 | [
[
"Fox",
"Zachary R",
""
]
] | Gene expression is a stochastic process in which cells produce biomolecules essential to the function of life. Modern experimental methods allow for the measurement of biomolecules at single-cell and single-molecule resolution over time. Mathematical models are used to make sense of these experiments. The codesign of experiments and models allows one to use models to design optimal experiments, and to find experiments which provide as much information as possible about relevant model parameters. Here, we provide a formulation of Fisher information for trajectories sampled from the continuous time Markov processes often used to model biological systems, and apply the result to potentially correlated measurements of stochastic gene expression. We validate the result on two commonly used models of gene expression and show it can be used to optimize measurement periods for simulated single-cell fluorescence microscopy experiments. Finally, we use a connection between Fisher information and mutual information to derive channel capacities of nonlinearly regulated gene expression. |
1603.05261 | Richard Betzel | Richard F. Betzel, Shi Gu, John D. Medaglia, Fabio Pasqualetti,
Danielle S. Bassett | Optimally controlling the human connectome: the role of network topology | 23 pages, 6 figures, 9 supplementary figures | null | 10.1038/srep30770 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To meet ongoing cognitive demands, the human brain must seamlessly transition
from one brain state to another, in the process drawing on different cognitive
systems. How does the brain's network of anatomical connections help facilitate
such transitions? Which features of this network contribute to making one
transition easy and another transition difficult? Here, we address these
questions using network control theory. We calculate the optimal input signals
to drive the brain to and from states dominated by different cognitive systems.
The input signals allow us to assess the contributions made by different brain
regions. We show that such contributions, which we measure as energy, are
correlated with regions' weighted degrees. We also show that the network
communicability, a measure of direct and indirect connectedness between brain
regions, predicts the extent to which brain regions compensate when input to
another region is suppressed. Finally, we identify optimal states in which the
brain should start (and finish) in order to minimize transition energy. We show
that the optimal target states display high activity in hub regions,
implicating the brain's rich club. Furthermore, when rich club organization is
destroyed, the energy cost associated with state transitions increases
significantly, demonstrating that it is the richness of brain regions that
makes them ideal targets.
| [
{
"created": "Wed, 16 Mar 2016 20:13:56 GMT",
"version": "v1"
}
] | 2016-09-08 | [
[
"Betzel",
"Richard F.",
""
],
[
"Gu",
"Shi",
""
],
[
"Medaglia",
"John D.",
""
],
[
"Pasqualetti",
"Fabio",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | To meet ongoing cognitive demands, the human brain must seamlessly transition from one brain state to another, in the process drawing on different cognitive systems. How does the brain's network of anatomical connections help facilitate such transitions? Which features of this network contribute to making one transition easy and another transition difficult? Here, we address these questions using network control theory. We calculate the optimal input signals to drive the brain to and from states dominated by different cognitive systems. The input signals allow us to assess the contributions made by different brain regions. We show that such contributions, which we measure as energy, are correlated with regions' weighted degrees. We also show that the network communicability, a measure of direct and indirect connectedness between brain regions, predicts the extent to which brain regions compensate when input to another region is suppressed. Finally, we identify optimal states in which the brain should start (and finish) in order to minimize transition energy. We show that the optimal target states display high activity in hub regions, implicating the brain's rich club. Furthermore, when rich club organization is destroyed, the energy cost associated with state transitions increases significantly, demonstrating that it is the richness of brain regions that makes them ideal targets. |
2205.02665 | Adrien Peyrache | Adrien Peyrache | Querying hippocampal replay with subcortical inputs | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | During sleep, the hippocampus recapitulates neuronal patterns corresponding
to behavioral trajectories during previous experiences. This hippocampal replay
supports the formation of long-term memories. Yet, whether replay originates
within the hippocampal circuitry or is initiated by extrahippocampal inputs is
unknown. Here, I review recent findings regarding the organization of neuronal
activity upstream to the hippocampus, in the head-direction (HD) and grid cell
networks. I argue that hippocampal activity is under the influence of primary
spatial signals, which originate from subcortical structures and set the stage
for memory replay. In turn, hippocampal replay resets the HD network activity
to select a new direction for the next replay event. This reciprocal
interaction between the HD network and the hippocampus may be essential in
providing meaning to hippocampal activity, specifically by training decoders of
hippocampal sequences. Neuronal dynamics in thalamo-hippocampal loops may thus
be instrumental for memory processes during sleep.
| [
{
"created": "Thu, 5 May 2022 14:16:05 GMT",
"version": "v1"
}
] | 2022-05-06 | [
[
"Peyrache",
"Adrien",
""
]
] | During sleep, the hippocampus recapitulates neuronal patterns corresponding to behavioral trajectories during previous experiences. This hippocampal replay supports the formation of long-term memories. Yet, whether replay originates within the hippocampal circuitry or is initiated by extrahippocampal inputs is unknown. Here, I review recent findings regarding the organization of neuronal activity upstream to the hippocampus, in the head-direction (HD) and grid cell networks. I argue that hippocampal activity is under the influence of primary spatial signals, which originate from subcortical structures and set the stage for memory replay. In turn, hippocampal replay resets the HD network activity to select a new direction for the next replay event. This reciprocal interaction between the HD network and the hippocampus may be essential in providing meaning to hippocampal activity, specifically by training decoders of hippocampal sequences. Neuronal dynamics in thalamo-hippocampal loops may thus be instrumental for memory processes during sleep. |
1802.04087 | Min Xu | Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg,
Eric Xing, Min Xu | Deep learning based supervised semantic segmentation of Electron
Cryo-Subtomograms | 9 pages | IEEE International Conference on Image Processing (ICIP) 2018 | null | null | q-bio.QM cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for
the 3D visualization of cellular structure and organization at submolecular
resolution. It enables analyzing the native structures of macromolecular
complexes and their spatial organization inside single cells. However, due to
the high degree of structural complexity and practical imaging limitations,
systematic macromolecular structural recovery inside CECT images remains
challenging. Particularly, the recovery of a macromolecule is likely to be
biased by its neighbor structures due to the high molecular crowding. To reduce
the bias, here we introduce a novel 3D convolutional neural network inspired by
Fully Convolutional Network and Encoder-Decoder Architecture for the supervised
segmentation of macromolecules of interest in subtomograms. The tests of our
models on realistically simulated CECT data demonstrate that our new approach
has significantly improved segmentation performance compared to our baseline
approach. Also, we demonstrate that the proposed model has generalization
ability to segment new structures that do not exist in training data.
| [
{
"created": "Mon, 12 Feb 2018 14:54:49 GMT",
"version": "v1"
}
] | 2018-05-16 | [
[
"Liu",
"Chang",
""
],
[
"Zeng",
"Xiangrui",
""
],
[
"Lin",
"Ruogu",
""
],
[
"Liang",
"Xiaodan",
""
],
[
"Freyberg",
"Zachary",
""
],
[
"Xing",
"Eric",
""
],
[
"Xu",
"Min",
""
]
] | Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree of structural complexity and practical imaging limitations, systematic macromolecular structural recovery inside CECT images remains challenging. Particularly, the recovery of a macromolecule is likely to be biased by its neighbor structures due to the high molecular crowding. To reduce the bias, here we introduce a novel 3D convolutional neural network inspired by Fully Convolutional Network and Encoder-Decoder Architecture for the supervised segmentation of macromolecules of interest in subtomograms. The tests of our models on realistically simulated CECT data demonstrate that our new approach has significantly improved segmentation performance compared to our baseline approach. Also, we demonstrate that the proposed model has generalization ability to segment new structures that do not exist in training data. |
1412.4875 | Petter Holme | Petter Holme, Taro Takaguchi | Time evolution of predictability of epidemics on networks | null | Phys. Rev. E 91, 042811 (2015) | 10.1103/PhysRevE.91.042811 | null | q-bio.PE cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epidemic outbreaks of new pathogens, or known pathogens in new populations,
cause a great deal of fear because they are hard to predict. For theoretical
models of disease spreading, on the other hand, quantities characterizing the
outbreak converge to deterministic functions of time. Our goal in this paper is
to shed some light on this apparent discrepancy. We measure the diversity of
(and, thus, the predictability of) outbreak sizes and extinction times as
functions of time given different scenarios of the amount of information
available. Under the assumption of perfect information -- i.e., knowing the
state of each individual with respect to the disease -- the predictability
decreases exponentially, or faster, with time. The decay is slowest for
intermediate values of the per-contact transmission probability. With a weaker
assumption on the information available, assuming that we know only the
fraction of currently infectious, recovered, or susceptible individuals, the
predictability also decreases exponentially most of the time. There are,
however, some peculiar regions in this scenario where the predictability
decreases. In other words, to predict its final size with a given accuracy, we
would need increasingly more information about the outbreak.
| [
{
"created": "Tue, 16 Dec 2014 04:55:20 GMT",
"version": "v1"
},
{
"created": "Tue, 5 May 2015 12:34:23 GMT",
"version": "v2"
}
] | 2015-05-20 | [
[
"Holme",
"Petter",
""
],
[
"Takaguchi",
"Taro",
""
]
] | Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information -- i.e., knowing the state of each individual with respect to the disease -- the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak. |
1508.01737 | David Schnoerr | David Schnoerr, Guido Sanguinetti and Ramon Grima | Comparison of different moment-closure approximations for stochastic
chemical kinetics | 36 pages, 14 figures | J. Chem. Phys. 143, 185101 (2015) | 10.1063/1.4934990 | null | q-bio.QM physics.chem-ph q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years moment-closure approximations (MA) of the chemical master
equation have become a popular method for the study of stochastic effects in
chemical reaction systems. Several different MA methods have been proposed and
applied in the literature, but it remains unclear how they perform with respect
to each other. In this paper we study the normal, Poisson, log-normal and
central-moment-neglect MAs by applying them to understand the stochastic
properties of chemical systems whose deterministic rate equations show the
properties of bistability, ultrasensitivity and oscillatory behaviour. Our
results suggest that the normal MA is favourable over the other studied MAs. In
particular we found that (i) the size of the region of parameter space where a
closure gives physically meaningful results, e.g. positive mean and variance,
is considerably larger for the normal closure than for the other three
closures; (ii) the accuracy of the predictions of the four closures (relative
to simulations using the stochastic simulation algorithm) is comparable in
those regions of parameter space where all closures give physically meaningful
results; (iii) the Poisson and log-normal MAs are not uniquely defined for
systems involving conservation laws in molecule numbers. We also describe the
new software package MOCA which enables the automated numerical analysis of
various MA methods in a graphical user interface and which was used to perform
the comparative analysis presented in this paper. MOCA allows the user to
develop novel closure methods and can treat polynomial, non-polynomial, as well
as time-dependent propensity functions, thus being applicable to virtually any
chemical reaction system.
| [
{
"created": "Fri, 7 Aug 2015 16:00:34 GMT",
"version": "v1"
},
{
"created": "Sat, 7 Nov 2015 11:18:55 GMT",
"version": "v2"
}
] | 2015-11-17 | [
[
"Schnoerr",
"David",
""
],
[
"Sanguinetti",
"Guido",
""
],
[
"Grima",
"Ramon",
""
]
] | In recent years moment-closure approximations (MA) of the chemical master equation have become a popular method for the study of stochastic effects in chemical reaction systems. Several different MA methods have been proposed and applied in the literature, but it remains unclear how they perform with respect to each other. In this paper we study the normal, Poisson, log-normal and central-moment-neglect MAs by applying them to understand the stochastic properties of chemical systems whose deterministic rate equations show the properties of bistability, ultrasensitivity and oscillatory behaviour. Our results suggest that the normal MA is favourable over the other studied MAs. In particular we found that (i) the size of the region of parameter space where a closure gives physically meaningful results, e.g. positive mean and variance, is considerably larger for the normal closure than for the other three closures; (ii) the accuracy of the predictions of the four closures (relative to simulations using the stochastic simulation algorithm) is comparable in those regions of parameter space where all closures give physically meaningful results; (iii) the Poisson and log-normal MAs are not uniquely defined for systems involving conservation laws in molecule numbers. We also describe the new software package MOCA which enables the automated numerical analysis of various MA methods in a graphical user interface and which was used to perform the comparative analysis presented in this paper. MOCA allows the user to develop novel closure methods and can treat polynomial, non-polynomial, as well as time-dependent propensity functions, thus being applicable to virtually any chemical reaction system. |
2210.07345 | Megan Chambers | Megan Chambers, Natalie Johnston, Ian Livengood, Miya Spinelli,
Radmila Sazdanovic, Mette S Olufsen | A Topological Data Analysis Study on Murine Pulmonary Arterial Trees
with Pulmonary Hypertension | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Pulmonary hypertension (PH), defined by a mean pulmonary arterial blood
pressure above 20 mmHg, is a cardiovascular disease impacting the pulmonary
vasculature. PH is accompanied by vascular remodeling, wherein vessels become
stiffer, large vessels dilate, and smaller vessels constrict. Some types of PH,
including hypoxia-induced PH (HPH), lead to microvascular rarefaction. The goal
of this study is to analyze the change in pulmonary arterial network
morphometry in the presence of HPH. To do so, we use novel methods from
topological data analysis (TDA), employing persistent homology to quantify
arterial network morphometry for control and hypertensive mice. These methods
are used to characterize arterial trees extracted from micro-computed
tomography (micro-CT) images. To compare results between control and
hypertensive animals, we normalize generated networks using three pruning
algorithms. This proof-of-concept study shows that the pruning methods effects
the spatial tree statistics and complexities of the trees. Results show that
HPH trees have higher depth and that the directional complexities correlate
with branch number, except for trees pruned by vessel radius, where the left
and anterior complexity are lower compared to control trees. While more data is
required to make a conclusion about the overall effect of HPH on network
topology, this study provides a framework for analyzing the topology of
biological networks and is a step towards the extraction of relevant
information for diagnosing and detecting HPH.
| [
{
"created": "Thu, 13 Oct 2022 20:35:37 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Feb 2023 23:49:41 GMT",
"version": "v2"
}
] | 2023-02-03 | [
[
"Chambers",
"Megan",
""
],
[
"Johnston",
"Natalie",
""
],
[
"Livengood",
"Ian",
""
],
[
"Spinelli",
"Miya",
""
],
[
"Sazdanovic",
"Radmila",
""
],
[
"Olufsen",
"Mette S",
""
]
] | Pulmonary hypertension (PH), defined by a mean pulmonary arterial blood pressure above 20 mmHg, is a cardiovascular disease impacting the pulmonary vasculature. PH is accompanied by vascular remodeling, wherein vessels become stiffer, large vessels dilate, and smaller vessels constrict. Some types of PH, including hypoxia-induced PH (HPH), lead to microvascular rarefaction. The goal of this study is to analyze the change in pulmonary arterial network morphometry in the presence of HPH. To do so, we use novel methods from topological data analysis (TDA), employing persistent homology to quantify arterial network morphometry for control and hypertensive mice. These methods are used to characterize arterial trees extracted from micro-computed tomography (micro-CT) images. To compare results between control and hypertensive animals, we normalize generated networks using three pruning algorithms. This proof-of-concept study shows that the pruning methods effects the spatial tree statistics and complexities of the trees. Results show that HPH trees have higher depth and that the directional complexities correlate with branch number, except for trees pruned by vessel radius, where the left and anterior complexity are lower compared to control trees. While more data is required to make a conclusion about the overall effect of HPH on network topology, this study provides a framework for analyzing the topology of biological networks and is a step towards the extraction of relevant information for diagnosing and detecting HPH. |
q-bio/0509013 | Atul Narang | Jason T. Noel, Brenton Cox, Atul Narang | Identification of the growth-limiting step in continuous cultures from
initial rates measured in response to substrate-excess conditions | 12 pages | null | null | null | q-bio.MN | null | When steady state chemostat cultures are abruptly exposed to substrate-excess
conditions, they exhibit long lags before adjusting to the new environment. The
identity of the rate-limiting step for this slow response can be inferred from
the initial yields and specific growth rates measured by exposing steady state
cultures at various dilution rates to substrate-excess conditions. We measured
these parameters for glucose-limited cultures of E. coli ML308 growing at
various dilution rates between 0.03 and 0.6 1/hr. In all the cases, the initial
yields were 20-30% less than the steady state yields. The decline of the yield
implies that overflow metabolism is triggered in response to excess glucose. It
is therefore unlikely that the initial response of the cells is limited by
substrate uptake. The initial specific growth rates of cultures growing at low
dilution rates (D = 0.03, 0.05, 0.075, 0.1, 0.3 1/hr) were significantly higher
than the steady state specific growth rates. However, the increment in the
specific growth rate decreased with the dilution rate, and at D=0.6 1/hr, there
was no improvement in the specific growth rate. The initial specific growth
rates varied hyperbolically with the dilution, decreasing sharply at dilution
rates below 0.1 1/hr and saturating at D=0.6 1/hr. This is consistent with a
picture in which the initial response is limited by the activity of glutamate
dehydrogenase.
| [
{
"created": "Mon, 12 Sep 2005 20:22:46 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Noel",
"Jason T.",
""
],
[
"Cox",
"Brenton",
""
],
[
"Narang",
"Atul",
""
]
] | When steady state chemostat cultures are abruptly exposed to substrate-excess conditions, they exhibit long lags before adjusting to the new environment. The identity of the rate-limiting step for this slow response can be inferred from the initial yields and specific growth rates measured by exposing steady state cultures at various dilution rates to substrate-excess conditions. We measured these parameters for glucose-limited cultures of E. coli ML308 growing at various dilution rates between 0.03 and 0.6 1/hr. In all the cases, the initial yields were 20-30% less than the steady state yields. The decline of the yield implies that overflow metabolism is triggered in response to excess glucose. It is therefore unlikely that the initial response of the cells is limited by substrate uptake. The initial specific growth rates of cultures growing at low dilution rates (D = 0.03, 0.05, 0.075, 0.1, 0.3 1/hr) were significantly higher than the steady state specific growth rates. However, the increment in the specific growth rate decreased with the dilution rate, and at D=0.6 1/hr, there was no improvement in the specific growth rate. The initial specific growth rates varied hyperbolically with the dilution, decreasing sharply at dilution rates below 0.1 1/hr and saturating at D=0.6 1/hr. This is consistent with a picture in which the initial response is limited by the activity of glutamate dehydrogenase. |
2309.14841 | Florian Ahrens | Florian Ahrens, Mihai Pomarlan, Daniel Be{\ss}ler, Thorsten Fehr,
Michael Beetz, Manfred Herrmann | Towards a Neuronally Consistent Ontology for Robotic Agents | Preprint of paper accepted for the European Conference on Artificial
Intelligence (ECAI) 2023 (minor typo corrections) | null | null | null | q-bio.NC cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Collaborative Research Center for Everyday Activity Science & Engineering
(CRC EASE) aims to enable robots to perform environmental interaction tasks
with close to human capacity. It therefore employs a shared ontology to model
the activity of both kinds of agents, empowering robots to learn from human
experiences. To properly describe these human experiences, the ontology will
strongly benefit from incorporating characteristics of neuronal information
processing which are not accessible from a behavioral perspective alone. We,
therefore, propose the analysis of human neuroimaging data for evaluation and
validation of concepts and events defined in the ontology model underlying most
of the CRC projects. In an exploratory analysis, we employed an Independent
Component Analysis (ICA) on functional Magnetic Resonance Imaging (fMRI) data
from participants who were presented with the same complex video stimuli of
activities as robotic and human agents in different environments and contexts.
We then correlated the activity patterns of brain networks represented by
derived components with timings of annotated event categories as defined by the
ontology model. The present results demonstrate a subset of common networks
with stable correlations and specificity towards particular event classes and
groups, associated with environmental and contextual factors. These neuronal
characteristics will open up avenues for adapting the ontology model to be more
consistent with human information processing.
| [
{
"created": "Tue, 26 Sep 2023 11:13:02 GMT",
"version": "v1"
}
] | 2023-09-27 | [
[
"Ahrens",
"Florian",
""
],
[
"Pomarlan",
"Mihai",
""
],
[
"Beßler",
"Daniel",
""
],
[
"Fehr",
"Thorsten",
""
],
[
"Beetz",
"Michael",
""
],
[
"Herrmann",
"Manfred",
""
]
] | The Collaborative Research Center for Everyday Activity Science & Engineering (CRC EASE) aims to enable robots to perform environmental interaction tasks with close to human capacity. It therefore employs a shared ontology to model the activity of both kinds of agents, empowering robots to learn from human experiences. To properly describe these human experiences, the ontology will strongly benefit from incorporating characteristics of neuronal information processing which are not accessible from a behavioral perspective alone. We, therefore, propose the analysis of human neuroimaging data for evaluation and validation of concepts and events defined in the ontology model underlying most of the CRC projects. In an exploratory analysis, we employed an Independent Component Analysis (ICA) on functional Magnetic Resonance Imaging (fMRI) data from participants who were presented with the same complex video stimuli of activities as robotic and human agents in different environments and contexts. We then correlated the activity patterns of brain networks represented by derived components with timings of annotated event categories as defined by the ontology model. The present results demonstrate a subset of common networks with stable correlations and specificity towards particular event classes and groups, associated with environmental and contextual factors. These neuronal characteristics will open up avenues for adapting the ontology model to be more consistent with human information processing. |
0806.3489 | Juan G. Restrepo | Juan G. Restrepo and Alain Karma | Line-Defect Patterns of Unstable Spiral Waves in Cardiac Tissue | 4 pages, 5 figures | null | 10.1103/PhysRevE.79.030906 | null | q-bio.TO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spiral wave propagation in period-2 excitable media is often accompanied by
line-defects, the locus of points with period-1 oscillations. Here we
investigate spiral line-defects in cardiac tissue where period-2 behavior has a
known arrhythmogenic role. We find that the number of line defects, which is
constrained to be an odd integer, is three for a freely rotating spiral, with
and without meander, but one for a spiral anchored around a fixed
heterogeneity. We interpret analytically this finding using a simple theory
where spiral wave unstable modes with different numbers of line-defects
correspond to quantized solutions of a Helmholtz equation. Furthermore, the
slow inward rotation of spiral line-defects is described in different regimes.
| [
{
"created": "Fri, 20 Jun 2008 23:28:46 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Restrepo",
"Juan G.",
""
],
[
"Karma",
"Alain",
""
]
] | Spiral wave propagation in period-2 excitable media is often accompanied by line-defects, the locus of points with period-1 oscillations. Here we investigate spiral line-defects in cardiac tissue where period-2 behavior has a known arrhythmogenic role. We find that the number of line defects, which is constrained to be an odd integer, is three for a freely rotating spiral, with and without meander, but one for a spiral anchored around a fixed heterogeneity. We interpret analytically this finding using a simple theory where spiral wave unstable modes with different numbers of line-defects correspond to quantized solutions of a Helmholtz equation. Furthermore, the slow inward rotation of spiral line-defects is described in different regimes. |
2108.01973 | Farzad Fatehi | Farzad Fatehi, Richard J. Bingham, Pierre-Philippe Dechant, Peter G.
Stockley, and Reidun Twarock | Therapeutic Interfering Particles Exploiting Viral Replication and
Assembly Mechanisms Show Promising Performance: A Modelling Study | Accepted version for publication in Scientific Reports after a minor
revision | Scientific Reports, 11, 23847 (2021) | 10.1038/s41598-021-03168-0 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Defective interfering particles arise spontaneously during a viral infection
as mutants lacking essential parts of the viral genome. Their ability to
replicate in the presence of the wild-type (WT) virus (at the expense of viable
viral particles) is mimicked and exploited by therapeutic interfering
particles. We propose a strategy for the design of therapeutic interfering RNAs
(tiRNAs) against positive-sense single-stranded RNA viruses that assemble via
packaging signal-mediated assembly. These tiRNAs contain both an optimised
version of the virus assembly manual that is encoded by multiple dispersed RNA
packaging signals and a replication signal for viral polymerase, but lack any
protein coding information. We use an intracellular model for hepatitis C viral
(HCV) infection that captures key aspects of the competition dynamics between
tiRNAs and viral genomes for virally produced capsid protein and polymerase. We
show that only a small increase in the assembly and replication efficiency of
the tiRNAs compared with WT virus is required in order to achieve a treatment
efficacy greater than 99%. This demonstrates that the proposed tiRNA design
could be a promising treatment option for RNA viral infections.
| [
{
"created": "Wed, 4 Aug 2021 11:30:34 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Dec 2021 15:48:42 GMT",
"version": "v2"
}
] | 2021-12-16 | [
[
"Fatehi",
"Farzad",
""
],
[
"Bingham",
"Richard J.",
""
],
[
"Dechant",
"Pierre-Philippe",
""
],
[
"Stockley",
"Peter G.",
""
],
[
"Twarock",
"Reidun",
""
]
] | Defective interfering particles arise spontaneously during a viral infection as mutants lacking essential parts of the viral genome. Their ability to replicate in the presence of the wild-type (WT) virus (at the expense of viable viral particles) is mimicked and exploited by therapeutic interfering particles. We propose a strategy for the design of therapeutic interfering RNAs (tiRNAs) against positive-sense single-stranded RNA viruses that assemble via packaging signal-mediated assembly. These tiRNAs contain both an optimised version of the virus assembly manual that is encoded by multiple dispersed RNA packaging signals and a replication signal for viral polymerase, but lack any protein coding information. We use an intracellular model for hepatitis C viral (HCV) infection that captures key aspects of the competition dynamics between tiRNAs and viral genomes for virally produced capsid protein and polymerase. We show that only a small increase in the assembly and replication efficiency of the tiRNAs compared with WT virus is required in order to achieve a treatment efficacy greater than 99%. This demonstrates that the proposed tiRNA design could be a promising treatment option for RNA viral infections. |
0904.1815 | Josh Mitteldorf PhD | Josh Mitteldorf | Female Fertility and Longevity | null | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Does bearing children shorten a woman's life expectancy? Several demographic
studies, historic and current, have found no such effect. But the Caerphilly
cohort study is far the most prominent and frequently-cited, and it answers in
the affirmative. Why has this study found an effect that others fail to see?
Their analysis is based on Poisson regression, a statistical technique that is
accurate only if the underlying data are Poisson distributed. But the
distribution of the number of children born to women in the Caerphilly database
departs strongly from Poisson at the high end. This makes the result overly
sensitive to a handful of women with 15 children or more who lived before 1700.
When these 5 women are removed from a database of more than 2,900, the Poisson
regression no longer shows a significant result. Bi-linear regression relating
life span to fertility and date of birth results in a positive coefficient for
fertility.
| [
{
"created": "Sat, 11 Apr 2009 17:16:37 GMT",
"version": "v1"
}
] | 2009-04-14 | [
[
"Mitteldorf",
"Josh",
""
]
] | Does bearing children shorten a woman's life expectancy? Several demographic studies, historic and current, have found no such effect. But the Caerphilly cohort study is far the most prominent and frequently-cited, and it answers in the affirmative. Why has this study found an effect that others fail to see? Their analysis is based on Poisson regression, a statistical technique that is accurate only if the underlying data are Poisson distributed. But the distribution of the number of children born to women in the Caerphilly database departs strongly from Poisson at the high end. This makes the result overly sensitive to a handful of women with 15 children or more who lived before 1700. When these 5 women are removed from a database of more than 2,900, the Poisson regression no longer shows a significant result. Bi-linear regression relating life span to fertility and date of birth results in a positive coefficient for fertility. |
2004.05639 | Efr\'en M. Benavides | Efren M.Benavides | Robust predictive model for Carriers, Infections and Recoveries (CIR):
first update for CoVid-19 in Spain | 9 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article reports a first update on the assesment of the model previously
presented in arXiv:2003.13890v1. New available data have been used to feed the
model and the comparison with real data still shows good agreement. The main
novelty of the model is that it keeps track of the date of infection of a
single individual and uses stochastic distributions to aggregate individuals
who share the same date of infection. In addition, it uses two types of
infections, mild and serious, with a different recovery time. These features
are implemented in a set of differential equations which determine the number
of Carriers, Infections, Recoveries, Hospitalized and Deaths.
| [
{
"created": "Sun, 12 Apr 2020 15:55:43 GMT",
"version": "v1"
}
] | 2020-04-14 | [
[
"Benavides",
"Efren M.",
""
]
] | This article reports a first update on the assesment of the model previously presented in arXiv:2003.13890v1. New available data have been used to feed the model and the comparison with real data still shows good agreement. The main novelty of the model is that it keeps track of the date of infection of a single individual and uses stochastic distributions to aggregate individuals who share the same date of infection. In addition, it uses two types of infections, mild and serious, with a different recovery time. These features are implemented in a set of differential equations which determine the number of Carriers, Infections, Recoveries, Hospitalized and Deaths. |
1510.08813 | Daniel Okamoto | Daniel K. Okamoto | Competition among eggs shifts to cooperation along a sperm supply
gradient in an external fertilizer | In Press in The American Naturalist. 22 pages, 4 figures, 3 tables, 4
appendices | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Competition among gametes for fertilization imposes strong selection. For
external fertilizers, this selective pressure extends to eggs for which
spawning conditions can range from sperm limitation (competition among eggs) to
sexual conflict (overabundance of competing sperm toxic to eggs). Yet existing
fertilization models ignore dynamics that can alter the functional nature of
gamete interactions. These factors include attraction of sperm to eggs, egg
crowding effects or other nonlinearities in per capita rates of sperm-egg
interaction. Such processes potentially allow egg concentrations to drastically
affect viable fertilization probabilities. I experimentally tested whether such
egg effects occur using the urchin $\textit{Strongylocentrotus purpuratus}$ and
parameterized a newly derived model of fertilization dynamics and existing
models modified to include such interactions. The experiments revealed that at
low sperm concentrations, eggs compete for sperm while at high sperm
concentrations eggs cooperatively reduce abnormal fertilization (a proxy for
polyspermy). I show that these observations are consistent with declines in the
per capita rate at which sperm and eggs interact as eggs increase in density.
The results suggest a fitness trade-off of egg release during spawning: as
sperm range from scarce to superabundant, interactions among eggs transition
from highly competitive to cooperative in terms of viable fertilization
probabilities.
| [
{
"created": "Thu, 29 Oct 2015 18:32:52 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Nov 2015 16:57:49 GMT",
"version": "v2"
},
{
"created": "Fri, 11 Dec 2015 14:59:20 GMT",
"version": "v3"
}
] | 2015-12-14 | [
[
"Okamoto",
"Daniel K.",
""
]
] | Competition among gametes for fertilization imposes strong selection. For external fertilizers, this selective pressure extends to eggs for which spawning conditions can range from sperm limitation (competition among eggs) to sexual conflict (overabundance of competing sperm toxic to eggs). Yet existing fertilization models ignore dynamics that can alter the functional nature of gamete interactions. These factors include attraction of sperm to eggs, egg crowding effects or other nonlinearities in per capita rates of sperm-egg interaction. Such processes potentially allow egg concentrations to drastically affect viable fertilization probabilities. I experimentally tested whether such egg effects occur using the urchin $\textit{Strongylocentrotus purpuratus}$ and parameterized a newly derived model of fertilization dynamics and existing models modified to include such interactions. The experiments revealed that at low sperm concentrations, eggs compete for sperm while at high sperm concentrations eggs cooperatively reduce abnormal fertilization (a proxy for polyspermy). I show that these observations are consistent with declines in the per capita rate at which sperm and eggs interact as eggs increase in density. The results suggest a fitness trade-off of egg release during spawning: as sperm range from scarce to superabundant, interactions among eggs transition from highly competitive to cooperative in terms of viable fertilization probabilities. |
1502.07829 | Saloni Agrawal | Asif Javed, Saloni Agrawal, Pauline C. Ng | Phen-Gen: combining phenotype and genotype to analyze rare disorders | null | Nat Methods. 2014 Sep;11(9):935-7 | 10.1038/nmeth.3046 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce Phen-Gen, a method which combines patient disease symptoms and
sequencing data with prior domain knowledge to identify the causative gene(s)
for rare disorders.
| [
{
"created": "Fri, 27 Feb 2015 08:54:49 GMT",
"version": "v1"
}
] | 2015-03-02 | [
[
"Javed",
"Asif",
""
],
[
"Agrawal",
"Saloni",
""
],
[
"Ng",
"Pauline C.",
""
]
] | We introduce Phen-Gen, a method which combines patient disease symptoms and sequencing data with prior domain knowledge to identify the causative gene(s) for rare disorders. |
2002.07064 | Michele Gentili | Michele Gentili, Leonardo Martini, Manuela Petti, Lorenzo Farina and
Luca Becchetti | Biological Random Walks: integrating heterogeneous data in disease gene
prioritization | null | 2019 IEEE Conference on Computational Intelligence in
Bioinformatics and Computational Biology (CIBCB), 2019, 1-8 | 10.1109/CIBCB.2019.8791472 | null | q-bio.MN cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work proposes a unified framework to leverage biological information in
network propagation-based gene prioritization algorithms. Preliminary results
on breast cancer data show significant improvements over state-of-the-art
baselines, such as the prioritization of genes that are not identified as
potential candidates by interactome-based algorithms, but that appear to be
involved in/or potentially related to breast cancer, according to a functional
analysis based on recent literature.
| [
{
"created": "Fri, 14 Feb 2020 17:46:35 GMT",
"version": "v1"
}
] | 2020-02-18 | [
[
"Gentili",
"Michele",
""
],
[
"Martini",
"Leonardo",
""
],
[
"Petti",
"Manuela",
""
],
[
"Farina",
"Lorenzo",
""
],
[
"Becchetti",
"Luca",
""
]
] | This work proposes a unified framework to leverage biological information in network propagation-based gene prioritization algorithms. Preliminary results on breast cancer data show significant improvements over state-of-the-art baselines, such as the prioritization of genes that are not identified as potential candidates by interactome-based algorithms, but that appear to be involved in/or potentially related to breast cancer, according to a functional analysis based on recent literature. |
2407.03977 | Lars Lammers | Lars Lammers, Tom M. W. Nye, Stephan F. Huckemann | Statistics for Phylogenetic Trees in the Presence of Stickiness | 37 pages, 16 figures | null | null | null | q-bio.PE math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Samples of phylogenetic trees arise in a variety of evolutionary and
biomedical applications, and the Fr\'echet mean in Billera-Holmes-Vogtmann tree
space is a summary tree shown to have advantages over other mean or consensus
trees. However, use of the Fr\'echet mean raises computational and statistical
issues which we explore in this paper. The Fr\'echet sample mean is known often
to contain fewer internal edges than the trees in the sample, and in this
circumstance calculating the mean by iterative schemes can be problematic due
to slow convergence. We present new methods for identifying edges which must
lie in the Fr\'echet sample mean and apply these to a data set of gene trees
relating organisms from the apicomplexa which cause a variety of parasitic
infections. When a sample of trees contains a significant level of
heterogeneity in the branching patterns, or topologies, displayed by the trees
then the Fr\'echet mean is often a star tree, lacking any internal edges. Not
only in this situation, the population Fr\'echet mean is affected by a
non-Euclidean phenomenon called stickness which impacts upon asymptotics, and
we examine two data sets for which the mean tree is a star tree. The first
consists of trees representing the physical shape of artery structures in a
sample of medical images of human brains in which the branching patterns are
very diverse. The second consists of gene trees from a population of baboons in
which there is evidence of substantial hybridization. We develop hypothesis
tests which work in the presence of stickiness. The first is a test for the
presence of a given edge in the Fr\'echet population mean; the second is a
two-sample test for differences in two distributions which share the same
sticky population mean.
| [
{
"created": "Thu, 4 Jul 2024 14:50:42 GMT",
"version": "v1"
}
] | 2024-07-08 | [
[
"Lammers",
"Lars",
""
],
[
"Nye",
"Tom M. W.",
""
],
[
"Huckemann",
"Stephan F.",
""
]
] | Samples of phylogenetic trees arise in a variety of evolutionary and biomedical applications, and the Fr\'echet mean in Billera-Holmes-Vogtmann tree space is a summary tree shown to have advantages over other mean or consensus trees. However, use of the Fr\'echet mean raises computational and statistical issues which we explore in this paper. The Fr\'echet sample mean is known often to contain fewer internal edges than the trees in the sample, and in this circumstance calculating the mean by iterative schemes can be problematic due to slow convergence. We present new methods for identifying edges which must lie in the Fr\'echet sample mean and apply these to a data set of gene trees relating organisms from the apicomplexa which cause a variety of parasitic infections. When a sample of trees contains a significant level of heterogeneity in the branching patterns, or topologies, displayed by the trees then the Fr\'echet mean is often a star tree, lacking any internal edges. Not only in this situation, the population Fr\'echet mean is affected by a non-Euclidean phenomenon called stickness which impacts upon asymptotics, and we examine two data sets for which the mean tree is a star tree. The first consists of trees representing the physical shape of artery structures in a sample of medical images of human brains in which the branching patterns are very diverse. The second consists of gene trees from a population of baboons in which there is evidence of substantial hybridization. We develop hypothesis tests which work in the presence of stickiness. The first is a test for the presence of a given edge in the Fr\'echet population mean; the second is a two-sample test for differences in two distributions which share the same sticky population mean. |
2311.10563 | Andreas Grigorjew | Andreas Grigorjew, Fernando H. C. Dias, Andrea Cracco, Romeo Rizzi,
Alexandru I. Tomescu | Accelerating ILP solvers for Minimum Flow Decompositions through search
space and dimensionality reductions | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Given a flow network, the Minimum Flow Decomposition (MFD) problem is finding
the smallest possible set of weighted paths whose superposition equals the
flow. It is a classical, strongly NP-hard problem that is proven to be useful
in RNA transcript assembly and applications outside of Bioinformatics. We
improve an existing ILP (Integer Linear Programming) model by Dias et al.
[RECOMB 2022] for DAGs by decreasing the solver's search space using solution
safety and several other optimizations. This results in a significant speedup
compared to the original ILP, of up to 55-90x on average on the hardest
instances. Moreover, we show that our optimizations apply also to MFD problem
variants, resulting in similar speedups, going up to 123x on the hardest
instances. We also developed an ILP model of reduced dimensionality for an MFD
variant in which the solution path weights are restricted to a given set. This
model can find an optimal MFD solution for most instances, and overall, its
accuracy significantly outperforms that of previous greedy algorithms while
being up to an order of magnitude faster than our optimized ILP.
| [
{
"created": "Fri, 17 Nov 2023 14:55:56 GMT",
"version": "v1"
}
] | 2023-11-20 | [
[
"Grigorjew",
"Andreas",
""
],
[
"Dias",
"Fernando H. C.",
""
],
[
"Cracco",
"Andrea",
""
],
[
"Rizzi",
"Romeo",
""
],
[
"Tomescu",
"Alexandru I.",
""
]
] | Given a flow network, the Minimum Flow Decomposition (MFD) problem is finding the smallest possible set of weighted paths whose superposition equals the flow. It is a classical, strongly NP-hard problem that is proven to be useful in RNA transcript assembly and applications outside of Bioinformatics. We improve an existing ILP (Integer Linear Programming) model by Dias et al. [RECOMB 2022] for DAGs by decreasing the solver's search space using solution safety and several other optimizations. This results in a significant speedup compared to the original ILP, of up to 55-90x on average on the hardest instances. Moreover, we show that our optimizations apply also to MFD problem variants, resulting in similar speedups, going up to 123x on the hardest instances. We also developed an ILP model of reduced dimensionality for an MFD variant in which the solution path weights are restricted to a given set. This model can find an optimal MFD solution for most instances, and overall, its accuracy significantly outperforms that of previous greedy algorithms while being up to an order of magnitude faster than our optimized ILP. |
1403.6358 | Ariful Azad | Ariful Azad, Bartek Rajwa, Alex Pothen | Immunophenotypes of Acute Myeloid Leukemia From Flow Cytometry Data
Using Templates | 9 pages, 5 figures | null | null | null | q-bio.QM cs.CE | http://creativecommons.org/licenses/publicdomain/ | Motivation: We investigate whether a template-based classification pipeline
could be used to identify immunophenotypes in (and thereby classify) a
heterogeneous disease with many subtypes. The disease we consider here is Acute
Myeloid Leukemia, which is heterogeneous at the morphologic, cytogenetic and
molecular levels, with several known subtypes. The prognosis and treatment for
AML depends on the subtype.
Results: We apply flowMatch, an algorithmic pipeline for flow cytometry data
created in earlier work, to compute templates succinctly summarizing classes of
AML and healthy samples. We develop a scoring function that accounts for
features of the AML data such as heterogeneity to identify immunophenotypes
corresponding to various AML subtypes, including APL. All of the AML samples in
the test set are classified correctly with high confidence.
Availability: flowMatch is available at
www.bioconductor.org/packages/devel/bioc/html/flowMatch.html; programs specific
to immunophenotyping AML are at www.cs.purdue.edu/homes/aazad/software.html.
| [
{
"created": "Sat, 22 Mar 2014 02:23:28 GMT",
"version": "v1"
}
] | 2014-03-26 | [
[
"Azad",
"Ariful",
""
],
[
"Rajwa",
"Bartek",
""
],
[
"Pothen",
"Alex",
""
]
] | Motivation: We investigate whether a template-based classification pipeline could be used to identify immunophenotypes in (and thereby classify) a heterogeneous disease with many subtypes. The disease we consider here is Acute Myeloid Leukemia, which is heterogeneous at the morphologic, cytogenetic and molecular levels, with several known subtypes. The prognosis and treatment for AML depends on the subtype. Results: We apply flowMatch, an algorithmic pipeline for flow cytometry data created in earlier work, to compute templates succinctly summarizing classes of AML and healthy samples. We develop a scoring function that accounts for features of the AML data such as heterogeneity to identify immunophenotypes corresponding to various AML subtypes, including APL. All of the AML samples in the test set are classified correctly with high confidence. Availability: flowMatch is available at www.bioconductor.org/packages/devel/bioc/html/flowMatch.html; programs specific to immunophenotyping AML are at www.cs.purdue.edu/homes/aazad/software.html. |
1301.5357 | Steven Frank | Steven A. Frank | Natural selection. VI. Partitioning the information in fitness and
characters by path analysis | null | Journal of Evolutionary Biology 26:457-471 (2013) | 10.1111/jeb.12066 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Three steps aid in the analysis of selection. First, describe phenotypes by
their component causes. Components include genes, maternal effects, symbionts,
and any other predictors of phenotype that are of interest. Second, describe
fitness by its component causes, such as an individual's phenotype, its
neighbors' phenotypes, resource availability, and so on. Third, put the
predictors of phenotype and fitness into an exact equation for evolutionary
change, providing a complete expression of selection and other evolutionary
processes. The complete expression separates the distinct causal roles of the
various hypothesized components of phenotypes and fitness. Traditionally, those
components are given by the covariance, variance, and regression terms of
evolutionary models. I show how to interpret those statistical expressions with
respect to information theory. The resulting interpretation allows one to read
the fundamental equations of selection and evolution as sentences that express
how various causes lead to the accumulation of information by selection and the
decay of information by other evolutionary processes. The interpretation in
terms of information leads to a deeper understanding of selection and
heritability, and a clearer sense of how to formulate causal hypotheses about
evolutionary process. Kin selection appears as a particular type of causal
analysis that partitions social effects into meaningful components.
| [
{
"created": "Tue, 22 Jan 2013 22:31:47 GMT",
"version": "v1"
}
] | 2013-02-14 | [
[
"Frank",
"Steven A.",
""
]
] | Three steps aid in the analysis of selection. First, describe phenotypes by their component causes. Components include genes, maternal effects, symbionts, and any other predictors of phenotype that are of interest. Second, describe fitness by its component causes, such as an individual's phenotype, its neighbors' phenotypes, resource availability, and so on. Third, put the predictors of phenotype and fitness into an exact equation for evolutionary change, providing a complete expression of selection and other evolutionary processes. The complete expression separates the distinct causal roles of the various hypothesized components of phenotypes and fitness. Traditionally, those components are given by the covariance, variance, and regression terms of evolutionary models. I show how to interpret those statistical expressions with respect to information theory. The resulting interpretation allows one to read the fundamental equations of selection and evolution as sentences that express how various causes lead to the accumulation of information by selection and the decay of information by other evolutionary processes. The interpretation in terms of information leads to a deeper understanding of selection and heritability, and a clearer sense of how to formulate causal hypotheses about evolutionary process. Kin selection appears as a particular type of causal analysis that partitions social effects into meaningful components. |
1804.10828 | Hiroshi Ashikaga | Hiroshi Ashikaga, Konstantinos N. Aronis, Susumu Tao, Ryan G. James | Causal Scale Shift Associated with Phase Transition to Human Atrial
Fibrillation | 9 pages, 7 figures | null | null | null | q-bio.TO nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An example of phase transition in natural complex systems is the qualitative
and sudden change in the heart rhythm between sinus rhythm and atrial
fibrillation (AF), the most common irregular heart rhythm in humans. While the
system behavior is centrally controlled by the behavior of the sinoatrial node
in sinus rhythm, the macro-scale collective behavior of the heart causes the
micro-scale behavior in AF. To quantitatively analyze this causation shift
associated with phase transition in human heart, we evaluated the causal
architecture of the human cardiac system using the time series of multi-lead
intracardiac unipolar electrograms in a series of spatiotemporal scales by
generating a stochastic renormalization group. We found that the phase
transition between sinus rhythm and AF is associated with a significant shift
of the peak causation from macroscopic to microscopic scales. Causal
architecture analysis may improve our understanding of causality in phase
transitions in other natural and social complex systems.
| [
{
"created": "Sat, 28 Apr 2018 16:27:34 GMT",
"version": "v1"
},
{
"created": "Tue, 1 May 2018 00:44:11 GMT",
"version": "v2"
}
] | 2018-05-02 | [
[
"Ashikaga",
"Hiroshi",
""
],
[
"Aronis",
"Konstantinos N.",
""
],
[
"Tao",
"Susumu",
""
],
[
"James",
"Ryan G.",
""
]
] | An example of phase transition in natural complex systems is the qualitative and sudden change in the heart rhythm between sinus rhythm and atrial fibrillation (AF), the most common irregular heart rhythm in humans. While the system behavior is centrally controlled by the behavior of the sinoatrial node in sinus rhythm, the macro-scale collective behavior of the heart causes the micro-scale behavior in AF. To quantitatively analyze this causation shift associated with phase transition in human heart, we evaluated the causal architecture of the human cardiac system using the time series of multi-lead intracardiac unipolar electrograms in a series of spatiotemporal scales by generating a stochastic renormalization group. We found that the phase transition between sinus rhythm and AF is associated with a significant shift of the peak causation from macroscopic to microscopic scales. Causal architecture analysis may improve our understanding of causality in phase transitions in other natural and social complex systems. |
1701.08995 | Leo van Iersel | Leo van Iersel, Vincent Moulton, Eveline de Swart and Taoyang Wu | Binets: fundamental building blocks for phylogenetic networks | null | null | null | null | q-bio.PE cs.DS math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic networks are a generalization of evolutionary trees that are
used by biologists to represent the evolution of organisms which have undergone
reticulate evolution. Essentially, a phylogenetic network is a directed acyclic
graph having a unique root in which the leaves are labelled by a given set of
species. Recently, some approaches have been developed to construct
phylogenetic networks from collections of networks on 2- and 3-leaved networks,
which are known as binets and trinets, respectively. Here we study in more
depth properties of collections of binets, one of the simplest possible types
of networks into which a phylogenetic network can be decomposed. More
specifically, we show that if a collection of level-1 binets is compatible with
some binary network, then it is also compatible with a binary level-1 network.
Our proofs are based on useful structural results concerning lowest stable
ancestors in networks. In addition, we show that, although the binets do not
determine the topology of the network, they do determine the number of
reticulations in the network, which is one of its most important parameters. We
also consider algorithmic questions concerning binets. We show that deciding
whether an arbitrary set of binets is compatible with some network is at least
as hard as the well-known Graph Isomorphism problem. However, if we restrict to
level-1 binets, it is possible to decide in polynomial time whether there
exists a binary network that displays all the binets. We also show that to find
a network that displays a maximum number of the binets is NP-hard, but that
there exists a simple polynomial-time 1/3-approximation algorithm for this
problem. It is hoped that these results will eventually assist in the
development of new methods for constructing phylogenetic networks from
collections of smaller networks.
| [
{
"created": "Tue, 31 Jan 2017 11:18:42 GMT",
"version": "v1"
}
] | 2017-02-01 | [
[
"van Iersel",
"Leo",
""
],
[
"Moulton",
"Vincent",
""
],
[
"de Swart",
"Eveline",
""
],
[
"Wu",
"Taoyang",
""
]
] | Phylogenetic networks are a generalization of evolutionary trees that are used by biologists to represent the evolution of organisms which have undergone reticulate evolution. Essentially, a phylogenetic network is a directed acyclic graph having a unique root in which the leaves are labelled by a given set of species. Recently, some approaches have been developed to construct phylogenetic networks from collections of networks on 2- and 3-leaved networks, which are known as binets and trinets, respectively. Here we study in more depth properties of collections of binets, one of the simplest possible types of networks into which a phylogenetic network can be decomposed. More specifically, we show that if a collection of level-1 binets is compatible with some binary network, then it is also compatible with a binary level-1 network. Our proofs are based on useful structural results concerning lowest stable ancestors in networks. In addition, we show that, although the binets do not determine the topology of the network, they do determine the number of reticulations in the network, which is one of its most important parameters. We also consider algorithmic questions concerning binets. We show that deciding whether an arbitrary set of binets is compatible with some network is at least as hard as the well-known Graph Isomorphism problem. However, if we restrict to level-1 binets, it is possible to decide in polynomial time whether there exists a binary network that displays all the binets. We also show that to find a network that displays a maximum number of the binets is NP-hard, but that there exists a simple polynomial-time 1/3-approximation algorithm for this problem. It is hoped that these results will eventually assist in the development of new methods for constructing phylogenetic networks from collections of smaller networks. |
1402.5289 | Paolo Moretti | Pablo Villegas, Paolo Moretti, Miguel A. Mu\~noz | Frustrated hierarchical synchronization and emergent complexity in the
human connectome network | 4 Figures | Scientific reports 4 (2014) 5990 | 10.1038/srep05990 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The spontaneous emergence of coherent behavior through synchronization plays
a key role in neural function, and its anomalies often lie at the basis of
pathologies. Here we employ a parsimonious (mesoscopic) approach to study
analytically and computationally the synchronization (Kuramoto) dynamics on the
actual human-brain connectome network. We elucidate the existence of a
so-far-uncovered intermediate phase, placed between the standard synchronous
and asynchronous phases, i.e. between order and disorder. This novel phase
stems from the hierarchical modular organization of the connectome. Where one
would expect a hierarchical synchronization process, we show that the interplay
between structural bottlenecks and quenched intrinsic frequency heterogeneities
at many different scales, gives rise to frustrated synchronization,
metastability, and chimera-like states, resulting in a very rich and complex
phenomenology. We uncover the origin of the dynamic freezing behind these
features by using spectral graph theory and discuss how the emerging complex
synchronization patterns relate to the need for the brain to access --in a
robust though flexible way-- a large variety of functional attractors and
dynamical repertoires without ad hoc fine-tuning to a critical point.
| [
{
"created": "Fri, 21 Feb 2014 13:17:15 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Jul 2014 15:07:31 GMT",
"version": "v2"
}
] | 2014-09-30 | [
[
"Villegas",
"Pablo",
""
],
[
"Moretti",
"Paolo",
""
],
[
"Muñoz",
"Miguel A.",
""
]
] | The spontaneous emergence of coherent behavior through synchronization plays a key role in neural function, and its anomalies often lie at the basis of pathologies. Here we employ a parsimonious (mesoscopic) approach to study analytically and computationally the synchronization (Kuramoto) dynamics on the actual human-brain connectome network. We elucidate the existence of a so-far-uncovered intermediate phase, placed between the standard synchronous and asynchronous phases, i.e. between order and disorder. This novel phase stems from the hierarchical modular organization of the connectome. Where one would expect a hierarchical synchronization process, we show that the interplay between structural bottlenecks and quenched intrinsic frequency heterogeneities at many different scales, gives rise to frustrated synchronization, metastability, and chimera-like states, resulting in a very rich and complex phenomenology. We uncover the origin of the dynamic freezing behind these features by using spectral graph theory and discuss how the emerging complex synchronization patterns relate to the need for the brain to access --in a robust though flexible way-- a large variety of functional attractors and dynamical repertoires without ad hoc fine-tuning to a critical point. |
1909.10344 | Laura Wadkin MMath | L E Wadkin, S Orozco-Fuentes, I Neganova, M Lako, A Shukurov and N G
Parker | The recent advances in the mathematical modelling of human pluripotent
stem cells | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human pluripotent stem cells hold great promise for developments in
regenerative medicine and drug design. The mathematical modelling of stem cells
and their properties is necessary to understand and quantify key behaviours and
develop non-invasive prognostic modelling tools to assist in the optimisation
of laboratory experiments. Here, the recent advances in the mathematical
modelling of hPSCs are discussed, including cell kinematics, cell proliferation
and colony formation, and pluripotency and differentiation.
| [
{
"created": "Mon, 23 Sep 2019 12:58:29 GMT",
"version": "v1"
}
] | 2019-09-24 | [
[
"Wadkin",
"L E",
""
],
[
"Orozco-Fuentes",
"S",
""
],
[
"Neganova",
"I",
""
],
[
"Lako",
"M",
""
],
[
"Shukurov",
"A",
""
],
[
"Parker",
"N G",
""
]
] | Human pluripotent stem cells hold great promise for developments in regenerative medicine and drug design. The mathematical modelling of stem cells and their properties is necessary to understand and quantify key behaviours and develop non-invasive prognostic modelling tools to assist in the optimisation of laboratory experiments. Here, the recent advances in the mathematical modelling of hPSCs are discussed, including cell kinematics, cell proliferation and colony formation, and pluripotency and differentiation. |
0906.3912 | Vladimir Privman | Vladimir Privman, Valber Pedrosa, Dmitriy Melnikov, Marcos Pita,
Aleksandr Simonian, Evgeny Katz | Enzymatic AND-Gate Based on Electrode-Immobilized Glucose-6-Phosphate
Dehydrogenase: Towards Digital Biosensors and Biochemical Logic Systems with
Low Noise | null | Biosens. Bioelectron. 25, 695-701 (2009) | 10.1016/j.bios.2009.08.014 | null | q-bio.MN cond-mat.soft q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electrode-immobilized glucose-6-phosphate dehydrogenase is used to catalyze
an enzymatic reaction which carries out the AND logic gate. This logic function
is considered here in the context of biocatalytic processes utilized for the
biocomputing applications for "digital" (threshold) sensing/actuation. We
outline the response functions desirable for such applications and report the
first experimental realization of a sigmoid-shape response in one of the
inputs. A kinetic model is developed and utilized to evaluate the extent to
which the experimentally realized gate is close to optimal.
| [
{
"created": "Mon, 22 Jun 2009 03:26:34 GMT",
"version": "v1"
}
] | 2010-10-12 | [
[
"Privman",
"Vladimir",
""
],
[
"Pedrosa",
"Valber",
""
],
[
"Melnikov",
"Dmitriy",
""
],
[
"Pita",
"Marcos",
""
],
[
"Simonian",
"Aleksandr",
""
],
[
"Katz",
"Evgeny",
""
]
] | Electrode-immobilized glucose-6-phosphate dehydrogenase is used to catalyze an enzymatic reaction which carries out the AND logic gate. This logic function is considered here in the context of biocatalytic processes utilized for the biocomputing applications for "digital" (threshold) sensing/actuation. We outline the response functions desirable for such applications and report the first experimental realization of a sigmoid-shape response in one of the inputs. A kinetic model is developed and utilized to evaluate the extent to which the experimentally realized gate is close to optimal. |
1706.01188 | Diederik Aerts | Diederik Aerts, Jonito Aerts Argu\"elles, Lester Beltran, Suzette
Geriente, Massimiliano Sassoli de Bianchi, Sandro Sozzo and Tomas Veloz | Spin and Wind Directions II: A Bell State Quantum Model | This a the second half of a two-part article, the first half being
entitled 'Spin and Wind Directions I: Identifying Entanglement in Nature and
Cognition' and to be found at arXiv:1508.00434 | Foundations of Science, 23, pp. 337-365 (2018) | 10.1007/s10699-017-9530-2 | null | q-bio.NC quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the first half of this two-part article, we analyzed a cognitive
psychology experiment where participants were asked to select pairs of
directions that they considered to be the best example of 'Two Different Wind
Directions', and showed that the data violate the CHSH version of Bell's
inequality, with same magnitude as in typical Bell-test experiments in physics.
In this second part, we complete our analysis by presenting a symmetrized
version of the experiment, still violating the CHSH inequality but now also
obeying the marginal law, for which we provide a full quantum modeling in
Hilbert space, using a singlet state and suitably chosen product measurements.
We also address some of the criticisms that have been recently directed at
experiments of this kind, according to which they would not highlight the
presence of genuine forms of entanglement. We explain that these criticisms are
based on a view of entanglement that is too restrictive, thus unable to capture
all possible ways physical and conceptual entities can connect and form systems
behaving as a whole. We also provide an example of a mechanical model showing
that the violations of the marginal law and Bell inequalities are generally to
be associated with different mechanisms.
| [
{
"created": "Mon, 5 Jun 2017 04:24:31 GMT",
"version": "v1"
}
] | 2019-02-12 | [
[
"Aerts",
"Diederik",
""
],
[
"Arguëlles",
"Jonito Aerts",
""
],
[
"Beltran",
"Lester",
""
],
[
"Geriente",
"Suzette",
""
],
[
"de Bianchi",
"Massimiliano Sassoli",
""
],
[
"Sozzo",
"Sandro",
""
],
[
"Veloz",
"T... | In the first half of this two-part article, we analyzed a cognitive psychology experiment where participants were asked to select pairs of directions that they considered to be the best example of 'Two Different Wind Directions', and showed that the data violate the CHSH version of Bell's inequality, with same magnitude as in typical Bell-test experiments in physics. In this second part, we complete our analysis by presenting a symmetrized version of the experiment, still violating the CHSH inequality but now also obeying the marginal law, for which we provide a full quantum modeling in Hilbert space, using a singlet state and suitably chosen product measurements. We also address some of the criticisms that have been recently directed at experiments of this kind, according to which they would not highlight the presence of genuine forms of entanglement. We explain that these criticisms are based on a view of entanglement that is too restrictive, thus unable to capture all possible ways physical and conceptual entities can connect and form systems behaving as a whole. We also provide an example of a mechanical model showing that the violations of the marginal law and Bell inequalities are generally to be associated with different mechanisms. |
1410.8497 | Jaan Aru | Kristjan Korjus, Andero Uusberg, Helen Uibo, Nele Kuldkepp, Kairi
Kreegipuu, J\"uri Allik, Raul Vicente, Jaan Aru | Personality cannot be predicted from the power of resting state EEG | 14 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the present study we asked whether it is possible to decode personality
traits from resting state EEG data. EEG was recorded from a large sample of
subjects (N = 309) who had answered questionnaires measuring personality trait
scores of the 5 dimensions as well as the 10 subordinate aspects of the Big
Five. Machine learning algorithms were used to build a classifier to predict
each personality trait from power spectra of the resting state EEG data. The
results indicate that the five dimensions as well as their subordinate aspects
could not be predicted from the resting state EEG data. Finally, to demonstrate
that this result is not due to systematic algorithmic or implementation
mistakes the same methods were used to successfully classify whether the
subject had eyes open or eyes closed and whether the subject was male or
female. These results indicate that the extraction of personality traits from
the power spectra of resting state EEG is extremely noisy, if possible at all.
| [
{
"created": "Thu, 30 Oct 2014 18:59:13 GMT",
"version": "v1"
}
] | 2014-10-31 | [
[
"Korjus",
"Kristjan",
""
],
[
"Uusberg",
"Andero",
""
],
[
"Uibo",
"Helen",
""
],
[
"Kuldkepp",
"Nele",
""
],
[
"Kreegipuu",
"Kairi",
""
],
[
"Allik",
"Jüri",
""
],
[
"Vicente",
"Raul",
""
],
[
"Aru... | In the present study we asked whether it is possible to decode personality traits from resting state EEG data. EEG was recorded from a large sample of subjects (N = 309) who had answered questionnaires measuring personality trait scores of the 5 dimensions as well as the 10 subordinate aspects of the Big Five. Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data. The results indicate that the five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data. Finally, to demonstrate that this result is not due to systematic algorithmic or implementation mistakes the same methods were used to successfully classify whether the subject had eyes open or eyes closed and whether the subject was male or female. These results indicate that the extraction of personality traits from the power spectra of resting state EEG is extremely noisy, if possible at all. |
2105.08342 | Anne Modat | J\'er\^ome Prunier (SETE), Keoni Saint-P\'e (SETE), Simon Blanchet
(SETE, EDB), G\'eraldine Loot (SETE, EDB), Olivier Rey (IHPE) | Molecular approaches reveal weak sibship aggregation and a high
dispersal propensity in a non-native fish parasite | null | Ecology and Evolution, Wiley Open Access, 2021 | 10.1002/ece3.7415 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inferring parameters related to the aggregation pattern of parasites and to
their dispersal propensity is important for predicting their ecological
consequences and evolutionary potential. Nonetheless, it is notoriously
difficult to infer these parameters from wildlife parasites given the
difficulty in tracking these organisms. Molecular-based inferences constitute a
promising approach that has yet rarely been applied in the wild.Here, we
combinedseveral population genetic analyses including sibship reconstruction to
documentthe genetic structure, patterns of sibship aggregation and the
dispersal dynamics of a non-native parasite of fish, the freshwater copepod
ectoparasiteTracheliastespolycolpus. We collected parasites according to a
hierarchical sampling design,withthe sampling of all parasites from all host
individualscapturedineight sites spread along an upstream-downstream river
gradient. Individual multilocus genotypes were obtained from 14 microsatellite
markers, and used to assign parasites to full-sib families and to investigate
the genetic structure of T.polycolpus among both hosts and sampling sites. The
distribution of full-sibs obtainedamong the sampling sites was used to estimate
individual dispersal distances within families. Our results showed that T.
polycolpus sibs tend to be aggregated within sites but not withinhost
individuals. We detected important upstream-to-downstream dispersal events of
T.polycolpusbetween sites (modal distance: 25.4 km; 95% CI [22.9, 27.7]),
becoming scarcer as the geographic distance from their family core location
increases. Such a dispersal pattern likely contributes to the strong
isolation-by-distance observed at the river scale. We also detected some
downstream-to-upstream dispersal events (modal distance: 2.6 km; 95% CI
[2.2-23.3]) that likely result from movementsof infected hosts.Within each
site, the dispersal of free-living infective larvae among hosts likely
contributes to increasing genetic diversity on hosts, possibly fostering the
evolutionary potential of T. polycolpus.
| [
{
"created": "Tue, 18 May 2021 08:10:40 GMT",
"version": "v1"
}
] | 2021-05-19 | [
[
"Prunier",
"Jérôme",
"",
"SETE"
],
[
"Saint-Pé",
"Keoni",
"",
"SETE"
],
[
"Blanchet",
"Simon",
"",
"SETE, EDB"
],
[
"Loot",
"Géraldine",
"",
"SETE, EDB"
],
[
"Rey",
"Olivier",
"",
"IHPE"
]
] | Inferring parameters related to the aggregation pattern of parasites and to their dispersal propensity is important for predicting their ecological consequences and evolutionary potential. Nonetheless, it is notoriously difficult to infer these parameters from wildlife parasites given the difficulty in tracking these organisms. Molecular-based inferences constitute a promising approach that has yet rarely been applied in the wild.Here, we combinedseveral population genetic analyses including sibship reconstruction to documentthe genetic structure, patterns of sibship aggregation and the dispersal dynamics of a non-native parasite of fish, the freshwater copepod ectoparasiteTracheliastespolycolpus. We collected parasites according to a hierarchical sampling design,withthe sampling of all parasites from all host individualscapturedineight sites spread along an upstream-downstream river gradient. Individual multilocus genotypes were obtained from 14 microsatellite markers, and used to assign parasites to full-sib families and to investigate the genetic structure of T.polycolpus among both hosts and sampling sites. The distribution of full-sibs obtainedamong the sampling sites was used to estimate individual dispersal distances within families. Our results showed that T. polycolpus sibs tend to be aggregated within sites but not withinhost individuals. We detected important upstream-to-downstream dispersal events of T.polycolpusbetween sites (modal distance: 25.4 km; 95% CI [22.9, 27.7]), becoming scarcer as the geographic distance from their family core location increases. Such a dispersal pattern likely contributes to the strong isolation-by-distance observed at the river scale. We also detected some downstream-to-upstream dispersal events (modal distance: 2.6 km; 95% CI [2.2-23.3]) that likely result from movementsof infected hosts.Within each site, the dispersal of free-living infective larvae among hosts likely contributes to increasing genetic diversity on hosts, possibly fostering the evolutionary potential of T. polycolpus. |
2112.10362 | Arti Dua | Subham Pal, Manmath Panigrahy and Arti Dua | Non-classical transient regime and violation of detailed balance in
mesoscopic Michaelis-Menten kinetics | 10 pages, 6 figures | null | null | null | q-bio.MN physics.bio-ph physics.chem-ph q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Classical (deterministic) and single-enzyme (stochastic) description of the
Michaelis-Menten (MM) kinetics, assume fast equilibration between enzyme and
complex, and identify detailed balance as a sufficient condition for the
hyperbolic substrate dependence of the MM equation (MME). Stochastic MM
kinetics based on the chemical master equation (CME), with no a priori
assumption of fast equilibration, however, unravels an observably long
non-classical transient regime at mesoscopic enzyme concentrations. The
enzymatic velocity in the transient regime is non-hyperbolic for product
turnovers below a critical time, but asymptotically recovers the hyperbolic MME
at long times. Here, we use this description to introduce a new kinetic
measure, the turnover number dependent fractional enzyme velocity. This measure
quantifies the degree of non-hyperbolicity in the non-classical transient
regime with respect to the hyperbolic MME. From this, we obtain a generalized
rate parameter condition for detailed balance in mesoscopic MM kinetics. This
condition, while subsuming the fast equilibrium approximation of the classical
MM kinetics, provides a strict lower bound on the magnitude of the catalytic
rate parameter. Further, from the condition of stationarity of the generating
function solution of the CME, we quantify the duration of the non-classical
regime. Our results show that the violation of detailed balance condition in
the transient regime is inextricably linked to the non-hyperbolic substrate
dependence of the enzymatic velocity. In the steady-state, when an effective
equilibrium between enzyme and complex is asymptotically established, the
condition of detailed balance emerges as a sufficient condition for the
hyperbolic substrate dependence of the MME.
| [
{
"created": "Mon, 20 Dec 2021 07:00:49 GMT",
"version": "v1"
}
] | 2021-12-21 | [
[
"Pal",
"Subham",
""
],
[
"Panigrahy",
"Manmath",
""
],
[
"Dua",
"Arti",
""
]
] | Classical (deterministic) and single-enzyme (stochastic) description of the Michaelis-Menten (MM) kinetics, assume fast equilibration between enzyme and complex, and identify detailed balance as a sufficient condition for the hyperbolic substrate dependence of the MM equation (MME). Stochastic MM kinetics based on the chemical master equation (CME), with no a priori assumption of fast equilibration, however, unravels an observably long non-classical transient regime at mesoscopic enzyme concentrations. The enzymatic velocity in the transient regime is non-hyperbolic for product turnovers below a critical time, but asymptotically recovers the hyperbolic MME at long times. Here, we use this description to introduce a new kinetic measure, the turnover number dependent fractional enzyme velocity. This measure quantifies the degree of non-hyperbolicity in the non-classical transient regime with respect to the hyperbolic MME. From this, we obtain a generalized rate parameter condition for detailed balance in mesoscopic MM kinetics. This condition, while subsuming the fast equilibrium approximation of the classical MM kinetics, provides a strict lower bound on the magnitude of the catalytic rate parameter. Further, from the condition of stationarity of the generating function solution of the CME, we quantify the duration of the non-classical regime. Our results show that the violation of detailed balance condition in the transient regime is inextricably linked to the non-hyperbolic substrate dependence of the enzymatic velocity. In the steady-state, when an effective equilibrium between enzyme and complex is asymptotically established, the condition of detailed balance emerges as a sufficient condition for the hyperbolic substrate dependence of the MME. |
1205.3417 | Leo van Iersel | Leo van Iersel, Steven Kelk, Nela Leki\'c and Celine Scornavacca | A practical approximation algorithm for solving massive instances of
hybridization number for binary and nonbinary trees | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reticulate events play an important role in determining evolutionary
relationships. The problem of computing the minimum number of such events to
explain discordance between two phylogenetic trees is a hard computational
problem. Even for binary trees, exact solvers struggle to solve instances with
reticulation number larger than 40-50. Here we present CycleKiller and
NonbinaryCycleKiller, the first methods to produce solutions verifiably close
to optimality for instances with hundreds or even thousands of reticulations.
Using simulations, we demonstrate that these algorithms run quickly for large
and difficult instances, producing solutions that are very close to optimality.
As a spin-off from our simulations we also present TerminusEst, which is the
fastest exact method currently available that can handle nonbinary trees: this
is used to measure the accuracy of the NonbinaryCycleKiller algorithm. All
three methods are based on extensions of previous theoretical work and are
publicly available. We also apply our methods to real data.
| [
{
"created": "Tue, 15 May 2012 15:33:13 GMT",
"version": "v1"
},
{
"created": "Mon, 21 May 2012 07:42:02 GMT",
"version": "v2"
},
{
"created": "Thu, 1 May 2014 12:13:04 GMT",
"version": "v3"
}
] | 2014-05-02 | [
[
"van Iersel",
"Leo",
""
],
[
"Kelk",
"Steven",
""
],
[
"Lekić",
"Nela",
""
],
[
"Scornavacca",
"Celine",
""
]
] | Reticulate events play an important role in determining evolutionary relationships. The problem of computing the minimum number of such events to explain discordance between two phylogenetic trees is a hard computational problem. Even for binary trees, exact solvers struggle to solve instances with reticulation number larger than 40-50. Here we present CycleKiller and NonbinaryCycleKiller, the first methods to produce solutions verifiably close to optimality for instances with hundreds or even thousands of reticulations. Using simulations, we demonstrate that these algorithms run quickly for large and difficult instances, producing solutions that are very close to optimality. As a spin-off from our simulations we also present TerminusEst, which is the fastest exact method currently available that can handle nonbinary trees: this is used to measure the accuracy of the NonbinaryCycleKiller algorithm. All three methods are based on extensions of previous theoretical work and are publicly available. We also apply our methods to real data. |
2402.11589 | Michael Habeck | Felix Lambrecht, Andreas Kr\"opelin, Mario L\"uttich, Michael Habeck,
David Haselbach, Holger Stark | CowScape: Quantitative reconstruction of the conformational landscape of
biological macromolecules from cryo-EM data | 15 pages + 4 figures (main text) | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Cryo-EM data processing typically focuses on the structure of the main
conformational state under investigation and discards images that belong to
other states. This approach can reach atomic resolution, but ignores vast
amounts of valuable information about the underlying conformational ensemble
and its dynamics. CowScape analyzes an entire cryo-EM dataset and thereby
obtains a quantitative description of structural variability of macromolecular
complexes that represents the biochemically relevant conformational space. By
combining extensive image classification with principal component analysis
(PCA) of the classified 3D volumes and kernel density estimation, CowScape can
be used as a quantitative tool to analyze this variability. PCA projects all 3D
structures along the major modes spanning a low-dimensional space that captures
a large portion of structural variability. The number of particle images in a
given state can be used to calculate an energy landscape based on kernel
density estimation and Boltzmann inversion. By revealing allosteric
interactions in macromolecular complexes, CowScape allows us to distinguish and
interpret dynamic changes in macromolecular complexes during function and
regulation.
| [
{
"created": "Sun, 18 Feb 2024 13:47:26 GMT",
"version": "v1"
}
] | 2024-02-20 | [
[
"Lambrecht",
"Felix",
""
],
[
"Kröpelin",
"Andreas",
""
],
[
"Lüttich",
"Mario",
""
],
[
"Habeck",
"Michael",
""
],
[
"Haselbach",
"David",
""
],
[
"Stark",
"Holger",
""
]
] | Cryo-EM data processing typically focuses on the structure of the main conformational state under investigation and discards images that belong to other states. This approach can reach atomic resolution, but ignores vast amounts of valuable information about the underlying conformational ensemble and its dynamics. CowScape analyzes an entire cryo-EM dataset and thereby obtains a quantitative description of structural variability of macromolecular complexes that represents the biochemically relevant conformational space. By combining extensive image classification with principal component analysis (PCA) of the classified 3D volumes and kernel density estimation, CowScape can be used as a quantitative tool to analyze this variability. PCA projects all 3D structures along the major modes spanning a low-dimensional space that captures a large portion of structural variability. The number of particle images in a given state can be used to calculate an energy landscape based on kernel density estimation and Boltzmann inversion. By revealing allosteric interactions in macromolecular complexes, CowScape allows us to distinguish and interpret dynamic changes in macromolecular complexes during function and regulation. |
2307.15471 | Joao Pedro De Magalhaes | Kasit Chatsirisupachai, Jo\~ao Pedro de Magalh\~aes | Somatic mutations in human ageing: New insights from DNA sequencing and
inherited mutations | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | The accumulation of somatic mutations is a driver of cancer and has long been
associated with ageing. Due to limitations in quantifying mutation burden with
age in non-cancerous tissues, the impact of somatic mutations in other ageing
phenotypes is unclear. Recent advances in DNA sequencing technologies have
allowed the large-scale quantification of somatic mutations in ageing. These
studies have revealed a gradual accumulation of mutations in most normal
tissues with age as well as a substantial clonal expansion driven mostly by
cancer-related mutations. Nevertheless, because of the relatively modest burden
of age-related somatic mutations identified so far and their stochastic nature,
it is difficult to envision how somatic mutation accumulation alone can explain
most ageing phenotypes that develop gradually. Studies across species have also
found that longer-lived species have lower somatic mutation rates, though these
could be explained by selective pressures to reduce or postpone cancer as
longevity increases. Overall, with a few exceptions like cancer, results from
recent DNA sequencing studies do not add weight to the idea that somatic
mutations with age drive ageing phenotypes and the phenotypic role, if any, of
somatic mutations in ageing remains unclear. Recent studies in patients with
somatic mutation burden and no signs of accelerated ageing further question the
role of somatic mutations in ageing.
| [
{
"created": "Fri, 28 Jul 2023 10:43:36 GMT",
"version": "v1"
}
] | 2023-07-31 | [
[
"Chatsirisupachai",
"Kasit",
""
],
[
"de Magalhães",
"João Pedro",
""
]
] | The accumulation of somatic mutations is a driver of cancer and has long been associated with ageing. Due to limitations in quantifying mutation burden with age in non-cancerous tissues, the impact of somatic mutations in other ageing phenotypes is unclear. Recent advances in DNA sequencing technologies have allowed the large-scale quantification of somatic mutations in ageing. These studies have revealed a gradual accumulation of mutations in most normal tissues with age as well as a substantial clonal expansion driven mostly by cancer-related mutations. Nevertheless, because of the relatively modest burden of age-related somatic mutations identified so far and their stochastic nature, it is difficult to envision how somatic mutation accumulation alone can explain most ageing phenotypes that develop gradually. Studies across species have also found that longer-lived species have lower somatic mutation rates, though these could be explained by selective pressures to reduce or postpone cancer as longevity increases. Overall, with a few exceptions like cancer, results from recent DNA sequencing studies do not add weight to the idea that somatic mutations with age drive ageing phenotypes and the phenotypic role, if any, of somatic mutations in ageing remains unclear. Recent studies in patients with somatic mutation burden and no signs of accelerated ageing further question the role of somatic mutations in ageing. |
1208.0636 | Mike Steel Prof. | Sha Zhu, Mike Steel | Is the Random Tree Puzzle process the same as the Yule-Harding process? | 8 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has been suggested that a Random Tree Puzzle (RTP) process leads to a
Yule-Harding (YH) distribution, when the number of taxa becomes large. In this
study, we formalize this conjecture, and we prove that the two tree
distributions converge for two particular properties, which suggests that the
conjecture may be true. However, we present evidence that, while the two
distributions are close, the RTP appears to converge on a different
distribution than does the YH.
| [
{
"created": "Fri, 3 Aug 2012 00:53:47 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Aug 2012 04:08:00 GMT",
"version": "v2"
}
] | 2012-08-10 | [
[
"Zhu",
"Sha",
""
],
[
"Steel",
"Mike",
""
]
] | It has been suggested that a Random Tree Puzzle (RTP) process leads to a Yule-Harding (YH) distribution, when the number of taxa becomes large. In this study, we formalize this conjecture, and we prove that the two tree distributions converge for two particular properties, which suggests that the conjecture may be true. However, we present evidence that, while the two distributions are close, the RTP appears to converge on a different distribution than does the YH. |
2311.10913 | William Howard-Snyder | William Howard-Snyder, Will Dumm, Mary Barker, Ognian Milanov, Claris
Winston, David H. Rich, Frederick A Matsen IV | Densely sampled phylogenies frequently deviate from maximum parsimony in
simple and local ways | 18 pages, 7 figures, submitted to RECOMB 2024 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Why do phylogenetic algorithms fail when they return incorrect answers? This
simple question has not been answered in detail, even for maximum parsimony
(MP), the simplest phylogenetic criterion. Understanding MP has recently gained
relevance in the regime of extremely dense sampling, where each virus sample
commonly differs by zero or one mutation from another previously sampled virus.
Although recent research shows that evolutionary histories in this regime are
close to being maximally parsimonious, the structure of their deviations from
MP is not yet understood. In this paper, we develop algorithms to understand
how the correct tree deviates from being MP in the densely sampled case. By
applying these algorithms to simulations that realistically mimic the evolution
of SARS-CoV-2, we find that simulated trees frequently only deviate from
maximally parsimonious trees locally, through simple structures consisting of
the same mutation appearing independently on sister branches.
| [
{
"created": "Fri, 17 Nov 2023 23:46:19 GMT",
"version": "v1"
}
] | 2023-11-21 | [
[
"Howard-Snyder",
"William",
""
],
[
"Dumm",
"Will",
""
],
[
"Barker",
"Mary",
""
],
[
"Milanov",
"Ognian",
""
],
[
"Winston",
"Claris",
""
],
[
"Rich",
"David H.",
""
],
[
"Matsen",
"Frederick A",
"IV"
]
... | Why do phylogenetic algorithms fail when they return incorrect answers? This simple question has not been answered in detail, even for maximum parsimony (MP), the simplest phylogenetic criterion. Understanding MP has recently gained relevance in the regime of extremely dense sampling, where each virus sample commonly differs by zero or one mutation from another previously sampled virus. Although recent research shows that evolutionary histories in this regime are close to being maximally parsimonious, the structure of their deviations from MP is not yet understood. In this paper, we develop algorithms to understand how the correct tree deviates from being MP in the densely sampled case. By applying these algorithms to simulations that realistically mimic the evolution of SARS-CoV-2, we find that simulated trees frequently only deviate from maximally parsimonious trees locally, through simple structures consisting of the same mutation appearing independently on sister branches. |
1705.10922 | Sahil Shah | Sahil D. Shah and Rosemary Braun | Network-based identification of disease genes in expression data: the
GeneSurrounder method | We have extended the application and evaluation of our GeneSurrounder
method to a second disease (gene expression data from bladder cancer) and
added additional analyses of GeneSurrounder's ability to identify known
cancer-associated genes | null | null | null | q-bio.QM q-bio.GN q-bio.MN stat.AP stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of high--throughput transcription profiling technologies has
enabled identification of genes and pathways associated with disease, providing
new avenues for precision medicine. A key challenge is to analyze this data in
the context of the regulatory networks and pathways that control cellular
processes, while still obtaining insights that can be used to design new
diagnostic and therapeutic interventions. While classical differential
expression analysis provides specific and hence targetable gene-level insights,
it does not include any systems-level information. On the other hand, pathway
analyses integrate systems-level information with expression data, but are
often limited in their ability to indicate specific molecular targets. We
introduce GeneSurrounder, an analysis method that takes into account the
complex structure of interaction networks to identify specific genes that
disrupt pathway activity in a disease-specific manner. GeneSurrounder
integrates transcriptomic data and pathway network information in a novel
two-step procedure to detect genes that (i) appear to influence the expression
of other genes local to it in the network and (ii) are part of a subnetwork of
differentially expressed genes. Combined, this evidence can be used to pinpoint
specific genes that have a mechanistic role in the phenotype of interest.
Applying GeneSurrounder to three distinct ovarian cancer studies using a global
KEGG network, we show that our method is able to identify biologically relevant
genes and genes missed by single-gene association tests, integrate pathway and
expression data, and yield more consistent results across multiple studies of
the same phenotype than competing methods.
| [
{
"created": "Wed, 31 May 2017 02:40:18 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Jan 2019 23:08:36 GMT",
"version": "v2"
}
] | 2019-01-11 | [
[
"Shah",
"Sahil D.",
""
],
[
"Braun",
"Rosemary",
""
]
] | The advent of high--throughput transcription profiling technologies has enabled identification of genes and pathways associated with disease, providing new avenues for precision medicine. A key challenge is to analyze this data in the context of the regulatory networks and pathways that control cellular processes, while still obtaining insights that can be used to design new diagnostic and therapeutic interventions. While classical differential expression analysis provides specific and hence targetable gene-level insights, it does not include any systems-level information. On the other hand, pathway analyses integrate systems-level information with expression data, but are often limited in their ability to indicate specific molecular targets. We introduce GeneSurrounder, an analysis method that takes into account the complex structure of interaction networks to identify specific genes that disrupt pathway activity in a disease-specific manner. GeneSurrounder integrates transcriptomic data and pathway network information in a novel two-step procedure to detect genes that (i) appear to influence the expression of other genes local to it in the network and (ii) are part of a subnetwork of differentially expressed genes. Combined, this evidence can be used to pinpoint specific genes that have a mechanistic role in the phenotype of interest. Applying GeneSurrounder to three distinct ovarian cancer studies using a global KEGG network, we show that our method is able to identify biologically relevant genes and genes missed by single-gene association tests, integrate pathway and expression data, and yield more consistent results across multiple studies of the same phenotype than competing methods. |
2401.14928 | Sean Edwards | Sean M. Edwards, Amy L. Harding, Joseph A. Leedale, Steve D. Webb,
Helen E. Colley, Craig Murdoch, Rachel N. Bearon | An innovative in silico model of the oral mucosa reveals the impact of
extracellular spaces on chemical permeation through epithelium | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | In pharmaceutical therapeutic design or toxicology, accurately predicting the
permeation of chemicals through human epithelial tissues is crucial, where
permeation is significantly influenced by the tissue's cellular architecture.
Current mathematical models for multi-layered epithelium such as the oral
mucosa only use simplistic 'bricks and mortar' geometries and therefore do not
account for the complex cellular architecture of these tissues at the
microscale level, such as the extensive plasma membrane convolutions that
define the extracellular spaces between cells. Chemicals often permeate tissues
via this paracellular route, meaning that permeation is underestimated. To
address this, measurements of human buccal mucosal tissue were conducted to
ascertain the width and tortuosity of extracellular spaces across the
epithelium. Using mechanistic mathematical modelling, we show that the
convoluted geometry of extracellular spaces significantly impacts chemical
permeation and that this can be approximated, provided that extracellular
tortuosity is accounted for. We next developed an advanced physically-relevant
in silico model of oral mucosal chemical permeation using partial differential
equations, fitted to chemical permeation in vitro assays on tissue-engineered
human oral mucosa. Tissue geometries were measured and captured in silico, and
permeation examined and predicted for chemicals with different physicochemical
properties. The effect of altering the extracellular space to mimic permeation
enhancers was also assessed by perturbing the in silico model. This novel in
vitro-in silico approach has the potential to expedite pharmaceutical
innovation for testing oromucosal chemical permeation, providing a more
accurate, physiologically-relevant model which can reduce animal testing with
early screening based on chemical properties.
| [
{
"created": "Fri, 26 Jan 2024 15:06:31 GMT",
"version": "v1"
}
] | 2024-01-29 | [
[
"Edwards",
"Sean M.",
""
],
[
"Harding",
"Amy L.",
""
],
[
"Leedale",
"Joseph A.",
""
],
[
"Webb",
"Steve D.",
""
],
[
"Colley",
"Helen E.",
""
],
[
"Murdoch",
"Craig",
""
],
[
"Bearon",
"Rachel N.",
""
]... | In pharmaceutical therapeutic design or toxicology, accurately predicting the permeation of chemicals through human epithelial tissues is crucial, where permeation is significantly influenced by the tissue's cellular architecture. Current mathematical models for multi-layered epithelium such as the oral mucosa only use simplistic 'bricks and mortar' geometries and therefore do not account for the complex cellular architecture of these tissues at the microscale level, such as the extensive plasma membrane convolutions that define the extracellular spaces between cells. Chemicals often permeate tissues via this paracellular route, meaning that permeation is underestimated. To address this, measurements of human buccal mucosal tissue were conducted to ascertain the width and tortuosity of extracellular spaces across the epithelium. Using mechanistic mathematical modelling, we show that the convoluted geometry of extracellular spaces significantly impacts chemical permeation and that this can be approximated, provided that extracellular tortuosity is accounted for. We next developed an advanced physically-relevant in silico model of oral mucosal chemical permeation using partial differential equations, fitted to chemical permeation in vitro assays on tissue-engineered human oral mucosa. Tissue geometries were measured and captured in silico, and permeation examined and predicted for chemicals with different physicochemical properties. The effect of altering the extracellular space to mimic permeation enhancers was also assessed by perturbing the in silico model. This novel in vitro-in silico approach has the potential to expedite pharmaceutical innovation for testing oromucosal chemical permeation, providing a more accurate, physiologically-relevant model which can reduce animal testing with early screening based on chemical properties. |
2004.14291 | Victor M. Perez-Garcia | V\'ictor M. P\'erez-Garc\'ia, O. Le\'on-Triana, M. Rosa, A.
P\'erez-Mart\'inez | CAR T cells for T-cell leukemias: Insights from mathematical models | null | null | 10.1016/j.cnsns.2020.105684 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Immunotherapy has the potential to change the way all cancer types are
treated and cured. Cancer immunotherapies use elements of the patient immune
system to attack tumor cells. One of the most successful types of immunotherapy
is CAR-T cells. This treatment works by extracting patient's T-cells and adding
to them an antigen receptor allowing tumor cells to be recognized and targeted.
These new cells are called CAR-T cells and are re-infused back into the patient
after expansion in-vitro. This approach has been successfully used to treat
B-cell malignancies (B-cell leukemias and lymphomas). However, its application
to the treatment of T-cell leukemias faces several problems. One of these is
fratricide, since the CAR-T cells target both tumor and other CAR-T cells. This
leads to nonlinear dynamical phenomena amenable to mathematical modeling. In
this paper we construct a mathematical model describing the competition of
CAR-T, tumor and normal T-cells and studied some basic properties of the model
and its practical implications. Specifically, we found that the model
reproduced the observed difficulties for in-vitro expansion of the therapeutic
cells found in the laboratory. The mathematical model predicted that CAR-T cell
expansion in the patient would be possible due to the initial presence of a
large number of targets. We also show that, in the context of our mathematical
approach, CAR-T cells could control tumor growth but not eradicate the disease.
| [
{
"created": "Sun, 26 Apr 2020 15:57:49 GMT",
"version": "v1"
}
] | 2021-02-03 | [
[
"Pérez-García",
"Víctor M.",
""
],
[
"León-Triana",
"O.",
""
],
[
"Rosa",
"M.",
""
],
[
"Pérez-Martínez",
"A.",
""
]
] | Immunotherapy has the potential to change the way all cancer types are treated and cured. Cancer immunotherapies use elements of the patient immune system to attack tumor cells. One of the most successful types of immunotherapy is CAR-T cells. This treatment works by extracting patient's T-cells and adding to them an antigen receptor allowing tumor cells to be recognized and targeted. These new cells are called CAR-T cells and are re-infused back into the patient after expansion in-vitro. This approach has been successfully used to treat B-cell malignancies (B-cell leukemias and lymphomas). However, its application to the treatment of T-cell leukemias faces several problems. One of these is fratricide, since the CAR-T cells target both tumor and other CAR-T cells. This leads to nonlinear dynamical phenomena amenable to mathematical modeling. In this paper we construct a mathematical model describing the competition of CAR-T, tumor and normal T-cells and studied some basic properties of the model and its practical implications. Specifically, we found that the model reproduced the observed difficulties for in-vitro expansion of the therapeutic cells found in the laboratory. The mathematical model predicted that CAR-T cell expansion in the patient would be possible due to the initial presence of a large number of targets. We also show that, in the context of our mathematical approach, CAR-T cells could control tumor growth but not eradicate the disease. |
1605.08612 | Sonja Schmid | Sonja Schmid, Markus G\"otz, Thorsten Hugel | Experiment-friendly kinetic analysis of single molecule data in and out
of equilibrium | 11 pages, 8 figures | Biophysical Journal 111,1375-1384, October 4, 2016 | 10.1016/j.bpj.2016.08.023 | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a simple and robust technique to extract kinetic rate models and
thermodynamic quantities from single molecule time traces. SMACKS (Single
Molecule Analysis of Complex Kinetic Sequences) is a maximum likelihood
approach that works equally well for long trajectories as for a set of short
ones. It resolves all statistically relevant rates and also their
uncertainties. This is achieved by optimizing one global kinetic model based on
the complete dataset, while allowing for experimental variations between
individual trajectories. In particular, neither a priori models nor equilibrium
have to be assumed. The power of SMACKS is demonstrated on the kinetics of the
multi-domain protein Hsp90 measured by smFRET (single molecule F\"orster
resonance energy transfer). Experiments in and out of equilibrium are analyzed
and compared to simulations, shedding new light on the role of Hsp90's ATPase
function. SMACKS pushes the boundaries of single molecule kinetics far beyond
current methods.
| [
{
"created": "Fri, 27 May 2016 12:44:24 GMT",
"version": "v1"
}
] | 2022-03-10 | [
[
"Schmid",
"Sonja",
""
],
[
"Götz",
"Markus",
""
],
[
"Hugel",
"Thorsten",
""
]
] | We present a simple and robust technique to extract kinetic rate models and thermodynamic quantities from single molecule time traces. SMACKS (Single Molecule Analysis of Complex Kinetic Sequences) is a maximum likelihood approach that works equally well for long trajectories as for a set of short ones. It resolves all statistically relevant rates and also their uncertainties. This is achieved by optimizing one global kinetic model based on the complete dataset, while allowing for experimental variations between individual trajectories. In particular, neither a priori models nor equilibrium have to be assumed. The power of SMACKS is demonstrated on the kinetics of the multi-domain protein Hsp90 measured by smFRET (single molecule F\"orster resonance energy transfer). Experiments in and out of equilibrium are analyzed and compared to simulations, shedding new light on the role of Hsp90's ATPase function. SMACKS pushes the boundaries of single molecule kinetics far beyond current methods. |
1210.1472 | Manish Gupta | Naman Turakhia, Nilay Chheda, Manish K. Gupta, Ruchin Shah and Jigar
Raisinghani | Biospectrogram: a tool for spectral analysis of biological sequences | 2 pages, 1 figure, submitted to Bioinformatics Journal,
Biospectrogram is available at http://www.guptalab.org/biospectrogram | null | null | null | q-bio.QM cs.CE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Summary: Biospectrogam is an open-source software for the spectral analysis
of DNA and protein sequences. The software can fetch (from NCBI server), import
and manage biological data. One can analyze the data using Digital Signal
Processing (DSP) techniques since the software allows the user to convert the
symbolic data into numerical data using 23 popular encodings and then apply
popular transformations such as Fast Fourier Transform (FFT) etc. and export
it. The ability of exporting (both encoding files and transform files) as a
MATLAB .m file gives the user an option to apply variety of techniques of DSP.
User can also do window analysis (both sliding in forward and backward
directions and stagnant) with different size windows and search for meaningful
spectral pattern with the help of exported MATLAB file in a dynamic manner by
choosing time delay in the plot using Biospectrogram. Random encodings and user
choice encoding allows software to search for many possibilities in spectral
space.
Availability: Biospectrogam is written in Java and is available to download
freely from http://www.guptalab.org/biospectrogram. Software has been optimized
to run on Windows, Mac OSX and Linux. User manual and you-tube (product demo)
tutorial is also available on the website. We are in the process of acquiring
open source license for it.
| [
{
"created": "Thu, 4 Oct 2012 14:42:50 GMT",
"version": "v1"
}
] | 2012-10-05 | [
[
"Turakhia",
"Naman",
""
],
[
"Chheda",
"Nilay",
""
],
[
"Gupta",
"Manish K.",
""
],
[
"Shah",
"Ruchin",
""
],
[
"Raisinghani",
"Jigar",
""
]
] | Summary: Biospectrogam is an open-source software for the spectral analysis of DNA and protein sequences. The software can fetch (from NCBI server), import and manage biological data. One can analyze the data using Digital Signal Processing (DSP) techniques since the software allows the user to convert the symbolic data into numerical data using 23 popular encodings and then apply popular transformations such as Fast Fourier Transform (FFT) etc. and export it. The ability of exporting (both encoding files and transform files) as a MATLAB .m file gives the user an option to apply variety of techniques of DSP. User can also do window analysis (both sliding in forward and backward directions and stagnant) with different size windows and search for meaningful spectral pattern with the help of exported MATLAB file in a dynamic manner by choosing time delay in the plot using Biospectrogram. Random encodings and user choice encoding allows software to search for many possibilities in spectral space. Availability: Biospectrogam is written in Java and is available to download freely from http://www.guptalab.org/biospectrogram. Software has been optimized to run on Windows, Mac OSX and Linux. User manual and you-tube (product demo) tutorial is also available on the website. We are in the process of acquiring open source license for it. |
q-bio/0703061 | Roderick C. Dewar | Roderick C. Dewar, Annabel Porte | Statistical mechanics unifies different ecological patterns | 38 pages, 4 figures, final revision, major rewrite with many
clarifications and simplifications, amended title. Accepted by Journal of
Theoretical Biology, 12 December 2007 | null | null | null | q-bio.PE | null | Recently there has been growing interest in the use of Maximum Relative
Entropy (MaxREnt) as a tool for statistical inference in ecology. In contrast,
here we propose MaxREnt as a tool for applying statistical mechanics to
ecology. We use MaxREnt to explain and predict species abundance patterns in
ecological communities in terms of the most probable behaviour under given
environmental constraints, in the same way that statistical mechanics explains
and predicts the behaviour of thermodynamic systems. We show that MaxREnt
unifies a number of different ecological patterns: (i) at relatively local
scales a unimodal biodiversity-productivity relationship is predicted in good
agreement with published data on grassland communities, (ii) the predicted
relative frequency of rare vs. abundant species is very similar to the
empirical lognormal distribution, (iii) both neutral and non-neutral species
abundance patterns are explained, (iv) on larger scales a monotonic
biodiversity-productivity relationship is predicted in agreement with the
species-energy law, (v) energetic equivalence and power-law self-thinning
behaviour are predicted in resource-rich communities. We identify mathematical
similarities between these ecological patterns and the behaviour of
thermodynamic systems, and conclude that the explanation of ecological patterns
is not unique to ecology but rather reflects the generic statistical behaviour
of complex systems with many degrees of freedom under very general types of
environmental constraints.
| [
{
"created": "Wed, 28 Mar 2007 08:33:24 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Sep 2007 15:02:04 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Dec 2007 10:18:51 GMT",
"version": "v3"
}
] | 2007-12-13 | [
[
"Dewar",
"Roderick C.",
""
],
[
"Porte",
"Annabel",
""
]
] | Recently there has been growing interest in the use of Maximum Relative Entropy (MaxREnt) as a tool for statistical inference in ecology. In contrast, here we propose MaxREnt as a tool for applying statistical mechanics to ecology. We use MaxREnt to explain and predict species abundance patterns in ecological communities in terms of the most probable behaviour under given environmental constraints, in the same way that statistical mechanics explains and predicts the behaviour of thermodynamic systems. We show that MaxREnt unifies a number of different ecological patterns: (i) at relatively local scales a unimodal biodiversity-productivity relationship is predicted in good agreement with published data on grassland communities, (ii) the predicted relative frequency of rare vs. abundant species is very similar to the empirical lognormal distribution, (iii) both neutral and non-neutral species abundance patterns are explained, (iv) on larger scales a monotonic biodiversity-productivity relationship is predicted in agreement with the species-energy law, (v) energetic equivalence and power-law self-thinning behaviour are predicted in resource-rich communities. We identify mathematical similarities between these ecological patterns and the behaviour of thermodynamic systems, and conclude that the explanation of ecological patterns is not unique to ecology but rather reflects the generic statistical behaviour of complex systems with many degrees of freedom under very general types of environmental constraints. |
2407.01248 | Rodrigo Dorantes-Gilardi | Rodrigo Dorantes-Gilardi, Kerry Ivey, Lauren Costa, Rachael Matty,
Kelly Cho, John Michael Gaziano and Albert-L\'aszl\'o Barab\'asi | Quantifying the Impact of Biobanks and Cohort Studies | 14 pages, 5 figures | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Biobanks advance biomedical and clinical research by collecting and offering
data and biological samples for numerous studies. However, the impact of these
repositories varies greatly due to differences in their purpose, scope,
governance, and data collected. Here, we computationally identified 2,663
biobanks and their textual mentions in 228,761 scientific articles, 16,210
grants, 15,469 patents, 1,769 clinical trials, and 9,468 public policy
documents, helping characterize the academic communities that utilize and
support them. We found a strong concentration of biobank-related research on a
few diseases, where 20\% of publications focus on obesity, Alzheimer's disease,
breast cancer, and diabetes. Moreover, collaboration, rather than citation
count, shapes the community's recognition of a biobank. We show that, on
average, 41.1\% of articles miss to reference any of the biobank's reference
papers and 59.6\% include a biobank member as a co-author. Using a generalized
linear model, we identified the key factors that contribute to the impact of a
biobank, finding that an impactful biobank tends to be more open to external
researchers, and that quality data -- especially linked medical records -- as
opposed to large data, correlates with a higher impact in science, innovation,
and disease. The collected data and findings are accessible through an
open-access web application intended to inform strategies to expand access and
maximize the value of these valuable resources.
| [
{
"created": "Mon, 1 Jul 2024 12:51:39 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Dorantes-Gilardi",
"Rodrigo",
""
],
[
"Ivey",
"Kerry",
""
],
[
"Costa",
"Lauren",
""
],
[
"Matty",
"Rachael",
""
],
[
"Cho",
"Kelly",
""
],
[
"Gaziano",
"John Michael",
""
],
[
"Barabási",
"Albert-László",
... | Biobanks advance biomedical and clinical research by collecting and offering data and biological samples for numerous studies. However, the impact of these repositories varies greatly due to differences in their purpose, scope, governance, and data collected. Here, we computationally identified 2,663 biobanks and their textual mentions in 228,761 scientific articles, 16,210 grants, 15,469 patents, 1,769 clinical trials, and 9,468 public policy documents, helping characterize the academic communities that utilize and support them. We found a strong concentration of biobank-related research on a few diseases, where 20\% of publications focus on obesity, Alzheimer's disease, breast cancer, and diabetes. Moreover, collaboration, rather than citation count, shapes the community's recognition of a biobank. We show that, on average, 41.1\% of articles miss to reference any of the biobank's reference papers and 59.6\% include a biobank member as a co-author. Using a generalized linear model, we identified the key factors that contribute to the impact of a biobank, finding that an impactful biobank tends to be more open to external researchers, and that quality data -- especially linked medical records -- as opposed to large data, correlates with a higher impact in science, innovation, and disease. The collected data and findings are accessible through an open-access web application intended to inform strategies to expand access and maximize the value of these valuable resources. |
2101.05956 | Sean Lawley | Gregory Handy and Sean D Lawley | Revising Berg-Purcell for finite receptor kinetics | 26 pages, 5 figures | null | 10.1016/j.bpj.2021.03.021 | null | q-bio.QM math.PR q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From nutrient uptake, to chemoreception, to synaptic transmission, many
systems in cell biology depend on molecules diffusing and binding to membrane
receptors. Mathematical analysis of such systems often neglects the fact that
receptors process molecules at finite kinetic rates. A key example is the
celebrated formula of Berg and Purcell for the rate that cell surface receptors
capture extracellular molecules. Indeed, this influential result is only valid
if receptors transport molecules through the cell wall at a rate much faster
than molecules arrive at receptors. From a mathematical perspective, ignoring
receptor kinetics is convenient because it makes the diffusing molecules
independent. In contrast, including receptor kinetics introduces correlations
between the diffusing molecules since, for example, bound receptors may be
temporarily blocked from binding additional molecules. In this work, we present
a modeling framework for coupling bulk diffusion to surface receptors with
finite kinetic rates. The framework uses boundary homogenization to couple the
diffusion equation to nonlinear ordinary differential equations on the
boundary. We use this framework to derive an explicit formula for the cellular
uptake rate and show that the analysis of Berg and Purcell significantly
overestimates uptake in some typical biophysical scenarios. We confirm our
analysis by numerical simulations of a many particle stochastic system.
| [
{
"created": "Fri, 15 Jan 2021 03:33:21 GMT",
"version": "v1"
}
] | 2021-06-16 | [
[
"Handy",
"Gregory",
""
],
[
"Lawley",
"Sean D",
""
]
] | From nutrient uptake, to chemoreception, to synaptic transmission, many systems in cell biology depend on molecules diffusing and binding to membrane receptors. Mathematical analysis of such systems often neglects the fact that receptors process molecules at finite kinetic rates. A key example is the celebrated formula of Berg and Purcell for the rate that cell surface receptors capture extracellular molecules. Indeed, this influential result is only valid if receptors transport molecules through the cell wall at a rate much faster than molecules arrive at receptors. From a mathematical perspective, ignoring receptor kinetics is convenient because it makes the diffusing molecules independent. In contrast, including receptor kinetics introduces correlations between the diffusing molecules since, for example, bound receptors may be temporarily blocked from binding additional molecules. In this work, we present a modeling framework for coupling bulk diffusion to surface receptors with finite kinetic rates. The framework uses boundary homogenization to couple the diffusion equation to nonlinear ordinary differential equations on the boundary. We use this framework to derive an explicit formula for the cellular uptake rate and show that the analysis of Berg and Purcell significantly overestimates uptake in some typical biophysical scenarios. We confirm our analysis by numerical simulations of a many particle stochastic system. |
1706.04946 | Stefano Fusi | Stefano Fusi | Computational models of long term plasticity and memory | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Memory is often defined as the mental capacity of retaining information about
facts, events, procedures and more generally about any type of previous
experience. Memories are remembered as long as they influence our thoughts,
feelings, and behavior at the present time. Memory is also one of the
fundamental components of learning, our ability to acquire any type of
knowledge or skills. In the brain it is not easy to identify the physical
substrate of memory. Basically, any long-lasting alteration of a biochemical
process can be considered a form of memory, although some of these alterations
last only a few milliseconds, and most of them, if taken individually, cannot
influence our behavior. However, if we want to understand memory, we need to
keep in mind that memory is not a unitary phenomenon, and it certainly involves
several distinct mechanisms that operate at different spatial and temporal
levels. One of the goals of theoretical neuroscience is to try to understand
how these processes are orchestrated to store memories rapidly and preserve
them over a lifetime. Theorists have mostly focused on synaptic plasticity, as
it is one of the most studied memory mechanisms in experimental neuroscience
and it is known to be highly effective in training artificial neural networks
to perform real world tasks. Some of the synaptic plasticity models are purely
phenomenological, some others have been designed to solve computational
problems. In this article I will review some of these models and I will try to
identify computational principles that underlie memory storage and
preservation.
| [
{
"created": "Thu, 15 Jun 2017 16:13:24 GMT",
"version": "v1"
}
] | 2017-06-16 | [
[
"Fusi",
"Stefano",
""
]
] | Memory is often defined as the mental capacity of retaining information about facts, events, procedures and more generally about any type of previous experience. Memories are remembered as long as they influence our thoughts, feelings, and behavior at the present time. Memory is also one of the fundamental components of learning, our ability to acquire any type of knowledge or skills. In the brain it is not easy to identify the physical substrate of memory. Basically, any long-lasting alteration of a biochemical process can be considered a form of memory, although some of these alterations last only a few milliseconds, and most of them, if taken individually, cannot influence our behavior. However, if we want to understand memory, we need to keep in mind that memory is not a unitary phenomenon, and it certainly involves several distinct mechanisms that operate at different spatial and temporal levels. One of the goals of theoretical neuroscience is to try to understand how these processes are orchestrated to store memories rapidly and preserve them over a lifetime. Theorists have mostly focused on synaptic plasticity, as it is one of the most studied memory mechanisms in experimental neuroscience and it is known to be highly effective in training artificial neural networks to perform real world tasks. Some of the synaptic plasticity models are purely phenomenological, some others have been designed to solve computational problems. In this article I will review some of these models and I will try to identify computational principles that underlie memory storage and preservation. |
1104.5674 | Stanley Lazic | Stanley E. Lazic | Using causal models to distinguish between neurogenesis-dependent and
-independent effects on behaviour | To be published in the Journal of the Royal Society Interface | null | 10.1098/?rsif.2011.0510 | null | q-bio.NC stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There has been a substantial amount of research on the relationship between
hippocampal neurogenesis and behaviour over the past fifteen years, but the
causal role that new neurons have on cognitive and affective behavioural tasks
is still far from clear. This is partly due to the difficulty of manipulating
levels of neurogenesis without inducing off-target effects, which might also
influence behaviour. In addition, the analytical methods typically used do not
directly test whether neurogenesis mediates the effect of an intervention on
behaviour. Previous studies may have incorrectly attributed changes in
behavioural performance to neurogenesis because the role of known (or unknown)
neurogenesis-independent mechanisms were not formally taken into consideration
during the analysis. Causal models can tease apart complex causal relationships
and were used to demonstrate that the effect of exercise on pattern separation
is via neurogenesis-independent mechanisms. Many studies in the neurogenesis
literature would benefit from the use of statistical methods that can separate
neurogenesis-dependent from neurogenesis-independent effects on behaviour.
| [
{
"created": "Fri, 29 Apr 2011 16:32:00 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Sep 2011 20:35:55 GMT",
"version": "v2"
}
] | 2014-11-11 | [
[
"Lazic",
"Stanley E.",
""
]
] | There has been a substantial amount of research on the relationship between hippocampal neurogenesis and behaviour over the past fifteen years, but the causal role that new neurons have on cognitive and affective behavioural tasks is still far from clear. This is partly due to the difficulty of manipulating levels of neurogenesis without inducing off-target effects, which might also influence behaviour. In addition, the analytical methods typically used do not directly test whether neurogenesis mediates the effect of an intervention on behaviour. Previous studies may have incorrectly attributed changes in behavioural performance to neurogenesis because the role of known (or unknown) neurogenesis-independent mechanisms were not formally taken into consideration during the analysis. Causal models can tease apart complex causal relationships and were used to demonstrate that the effect of exercise on pattern separation is via neurogenesis-independent mechanisms. Many studies in the neurogenesis literature would benefit from the use of statistical methods that can separate neurogenesis-dependent from neurogenesis-independent effects on behaviour. |
1311.1241 | Ngan Nguyen | Ngan Nguyen, Glenn Hickey, Brian J. Raney, Joel Armstrong, Hiram
Clawson, Ann Zweig, Jim Kent, David Haussler, Benedict Paten | Comparative Assembly Hubs: Web Accessible Browsers for Comparative
Genomics | 10 pages, 3 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a pipeline to easily generate collections of web accessible UCSC
genome browsers interrelated by an alignment. Using the alignment, all
annotations and the alignment itself can be efficiently viewed with reference
to any genome in the collection, symmetrically. A new, intelligently scaled
alignment display makes it simple to view all changes between the genomes at
all levels of resolution, from substitutions to complex structural
rearrangements, including duplications.
| [
{
"created": "Tue, 5 Nov 2013 22:28:16 GMT",
"version": "v1"
}
] | 2013-11-07 | [
[
"Nguyen",
"Ngan",
""
],
[
"Hickey",
"Glenn",
""
],
[
"Raney",
"Brian J.",
""
],
[
"Armstrong",
"Joel",
""
],
[
"Clawson",
"Hiram",
""
],
[
"Zweig",
"Ann",
""
],
[
"Kent",
"Jim",
""
],
[
"Haussler",
... | We introduce a pipeline to easily generate collections of web accessible UCSC genome browsers interrelated by an alignment. Using the alignment, all annotations and the alignment itself can be efficiently viewed with reference to any genome in the collection, symmetrically. A new, intelligently scaled alignment display makes it simple to view all changes between the genomes at all levels of resolution, from substitutions to complex structural rearrangements, including duplications. |
1806.00897 | Griffin Chure | Griffin Chure, Heun Jin Lee, Rob Phillips | Connecting the dots between mechanosensitive channel abundance, osmotic
shock, and survival at single-cell resolution | null | null | null | null | q-bio.CB q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Rapid changes in extracellular osmolarity are one of many insults microbial
cells face on a daily basis. To protect against such shocks, Escherichia coli
and other microbes express several types of transmembrane channels which open
and close in response to changes in membrane tension. In E. coli, one of the
most abundant channels is the mechanosensitive channel of large conductance
(MscL). While this channel has been heavily characterized through structural
methods, electrophysiology, and theoretical modeling, our understanding of its
physiological role in preventing cell death by alleviating high membrane
tension remains tenuous. In this work, we examine the contribution of MscL
alone to cell survival after osmotic shock at single cell resolution using
quantitative fluorescence microscopy. We conduct these experiments in an E.
coli strain which is lacking all mechanosensitive channel genes save for MscL
whose expression is tuned across three orders of magnitude through
modifications of the Shine-Dalgarno sequence. While theoretical models suggest
that only a few MscL channels would be needed to alleviate even large changes
in osmotic pressure, we find that between 500 and 700 channels per cell are
needed to convey upwards of 80% survival. This number agrees with the average
MscL copy number measured in wild-type E. coli cells through proteomic studies
and quantitative Western blotting. Furthermore, we observe zero survival events
in cells with less than 100 channels per cell. This work opens new questions
concerning the contribution of other mechanosensitive channels to survival as
well as regulation of their activity.
| [
{
"created": "Sun, 3 Jun 2018 23:40:52 GMT",
"version": "v1"
}
] | 2018-06-05 | [
[
"Chure",
"Griffin",
""
],
[
"Lee",
"Heun Jin",
""
],
[
"Phillips",
"Rob",
""
]
] | Rapid changes in extracellular osmolarity are one of many insults microbial cells face on a daily basis. To protect against such shocks, Escherichia coli and other microbes express several types of transmembrane channels which open and close in response to changes in membrane tension. In E. coli, one of the most abundant channels is the mechanosensitive channel of large conductance (MscL). While this channel has been heavily characterized through structural methods, electrophysiology, and theoretical modeling, our understanding of its physiological role in preventing cell death by alleviating high membrane tension remains tenuous. In this work, we examine the contribution of MscL alone to cell survival after osmotic shock at single cell resolution using quantitative fluorescence microscopy. We conduct these experiments in an E. coli strain which is lacking all mechanosensitive channel genes save for MscL whose expression is tuned across three orders of magnitude through modifications of the Shine-Dalgarno sequence. While theoretical models suggest that only a few MscL channels would be needed to alleviate even large changes in osmotic pressure, we find that between 500 and 700 channels per cell are needed to convey upwards of 80% survival. This number agrees with the average MscL copy number measured in wild-type E. coli cells through proteomic studies and quantitative Western blotting. Furthermore, we observe zero survival events in cells with less than 100 channels per cell. This work opens new questions concerning the contribution of other mechanosensitive channels to survival as well as regulation of their activity. |
2205.04235 | Raul Fernandez Rojas | Niraj Hirachan, Anita Mathews, Julio Romero, Raul Fernandez Rojas | Measuring Cognitive Workload Using Multimodal Sensors | null | null | null | null | q-bio.NC cs.AI cs.HC | http://creativecommons.org/licenses/by/4.0/ | This study aims to identify a set of indicators to estimate cognitive
workload using a multimodal sensing approach and machine learning. A set of
three cognitive tests were conducted to induce cognitive workload in twelve
participants at two levels of task difficulty (Easy and Hard). Four sensors
were used to measure the participants' physiological change, including,
Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and
blood oxygen saturation (SpO2). To understand the perceived cognitive workload,
NASA-TLX was used after each test and analysed using Chi-Square test. Three
well-know classifiers (LDA, SVM, and DT) were trained and tested independently
using the physiological data. The statistical analysis showed that
participants' perceived cognitive workload was significantly different
(p<0.001) between the tests, which demonstrated the validity of the
experimental conditions to induce different cognitive levels. Classification
results showed that a fusion of ECG and EDA presented good discriminating power
(acc=0.74) for cognitive workload detection. This study provides preliminary
results in the identification of a possible set of indicators of cognitive
workload. Future work needs to be carried out to validate the indicators using
more realistic scenarios and with a larger population.
| [
{
"created": "Thu, 5 May 2022 23:18:00 GMT",
"version": "v1"
}
] | 2022-05-10 | [
[
"Hirachan",
"Niraj",
""
],
[
"Mathews",
"Anita",
""
],
[
"Romero",
"Julio",
""
],
[
"Rojas",
"Raul Fernandez",
""
]
] | This study aims to identify a set of indicators to estimate cognitive workload using a multimodal sensing approach and machine learning. A set of three cognitive tests were conducted to induce cognitive workload in twelve participants at two levels of task difficulty (Easy and Hard). Four sensors were used to measure the participants' physiological change, including, Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and blood oxygen saturation (SpO2). To understand the perceived cognitive workload, NASA-TLX was used after each test and analysed using Chi-Square test. Three well-know classifiers (LDA, SVM, and DT) were trained and tested independently using the physiological data. The statistical analysis showed that participants' perceived cognitive workload was significantly different (p<0.001) between the tests, which demonstrated the validity of the experimental conditions to induce different cognitive levels. Classification results showed that a fusion of ECG and EDA presented good discriminating power (acc=0.74) for cognitive workload detection. This study provides preliminary results in the identification of a possible set of indicators of cognitive workload. Future work needs to be carried out to validate the indicators using more realistic scenarios and with a larger population. |
2209.15611 | Kevin Wu | Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X.
Lu, Ava P. Amini | Protein structure generation via folding diffusion | null | null | null | null | q-bio.BM cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | The ability to computationally generate novel yet physically foldable protein
structures could lead to new biological discoveries and new treatments
targeting yet incurable diseases. Despite recent advances in protein structure
prediction, directly generating diverse, novel protein structures from neural
networks remains difficult. In this work, we present a new diffusion-based
generative model that designs protein backbone structures via a procedure that
mirrors the native folding process. We describe protein backbone structure as a
series of consecutive angles capturing the relative orientation of the
constituent amino acid residues, and generate new structures by denoising from
a random, unfolded state towards a stable folded structure. Not only does this
mirror how proteins biologically twist into energetically favorable
conformations, the inherent shift and rotational invariance of this
representation crucially alleviates the need for complex equivariant networks.
We train a denoising diffusion probabilistic model with a simple transformer
backbone and demonstrate that our resulting model unconditionally generates
highly realistic protein structures with complexity and structural patterns
akin to those of naturally-occurring proteins. As a useful resource, we release
the first open-source codebase and trained models for protein structure
diffusion.
| [
{
"created": "Fri, 30 Sep 2022 17:35:53 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Nov 2022 04:05:41 GMT",
"version": "v2"
}
] | 2022-11-28 | [
[
"Wu",
"Kevin E.",
""
],
[
"Yang",
"Kevin K.",
""
],
[
"Berg",
"Rianne van den",
""
],
[
"Zou",
"James Y.",
""
],
[
"Lu",
"Alex X.",
""
],
[
"Amini",
"Ava P.",
""
]
] | The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a new diffusion-based generative model that designs protein backbone structures via a procedure that mirrors the native folding process. We describe protein backbone structure as a series of consecutive angles capturing the relative orientation of the constituent amino acid residues, and generate new structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this mirror how proteins biologically twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for complex equivariant networks. We train a denoising diffusion probabilistic model with a simple transformer backbone and demonstrate that our resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins. As a useful resource, we release the first open-source codebase and trained models for protein structure diffusion. |
1807.00527 | Henrik Jeldtoft Jensen | Katharina Brinck and Henrik Jeldtoft Jensen | Bottom-up versus top-down control and the transfer of information in
complex model ecosystems | 14 pages, 3 figures and 3 tables. Submitted to J Theo. Bio | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ecological systems are emergent features of ecological and adaptive dynamics
of a community of interacting species. By natural selection through the abiotic
environment and by co-adaptation within the community, species evolve, thereby
giving rise to the ecological networks we regard as ecosystems. This
reductionist perspective can be contrasted with the view that as species have
to fit in the surrounding system, the system itself exerts selection pressure
on the evolutionary pathways of the species. This interplay of bottom-up and
top-down control in the development and growth of ecological systems has long
been discussed, however empirical ecosystem data is scarce and a comprehensive
mathematical framework is lacking. We present a way of quantifying the relative
weight of natural selection and coadaptation grounded in information theory, to
assess the relative role of bottom-up and top-down control in the evolution of
ecological systems, and analyse the information transfer in an individual based
stochastic complex systems model, the Tangled Nature Model of evolutionary
ecology. We show that ecological communities evolve from mainly bottom-up
controlled early-successional systems to more strongly top-down controlled
late-successional systems, as coadaptation progresses. Species which have a
high influence on selection are also generally more abundant. Hence our
findings imply that ecological communities are shaped by a dialogue of
bottom-up and top-down control, where the role of the systemic selection and
integrity becomes more pronounced the further the ecosystem is developed.
| [
{
"created": "Mon, 2 Jul 2018 08:30:11 GMT",
"version": "v1"
}
] | 2018-07-03 | [
[
"Brinck",
"Katharina",
""
],
[
"Jensen",
"Henrik Jeldtoft",
""
]
] | Ecological systems are emergent features of ecological and adaptive dynamics of a community of interacting species. By natural selection through the abiotic environment and by co-adaptation within the community, species evolve, thereby giving rise to the ecological networks we regard as ecosystems. This reductionist perspective can be contrasted with the view that as species have to fit in the surrounding system, the system itself exerts selection pressure on the evolutionary pathways of the species. This interplay of bottom-up and top-down control in the development and growth of ecological systems has long been discussed, however empirical ecosystem data is scarce and a comprehensive mathematical framework is lacking. We present a way of quantifying the relative weight of natural selection and coadaptation grounded in information theory, to assess the relative role of bottom-up and top-down control in the evolution of ecological systems, and analyse the information transfer in an individual based stochastic complex systems model, the Tangled Nature Model of evolutionary ecology. We show that ecological communities evolve from mainly bottom-up controlled early-successional systems to more strongly top-down controlled late-successional systems, as coadaptation progresses. Species which have a high influence on selection are also generally more abundant. Hence our findings imply that ecological communities are shaped by a dialogue of bottom-up and top-down control, where the role of the systemic selection and integrity becomes more pronounced the further the ecosystem is developed. |
1211.1990 | Choongseok Park | Choongseok Park and Leonid Rubchinsky | Potential mechanisms for imperfect synchronization in parkinsonian basal
ganglia | 27 pages, 9 figures | PLoS One. 2012; 7(12): e51530 | 10.1371/journal.pone.0051530 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural activity in the brain of parkinsonian patients is characterized by the
intermittently synchronized oscillatory dynamics. This imperfect
synchronization, observed in the beta frequency band, is believed to be related
to the hypokinetic motor symptoms of the disorder. Our study explores potential
mechanisms behind this intermittent synchrony. We study the response of a
bursting pallidal neuron to different patterns of synaptic input from
subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe)
neuron is sensitive to the phase of the input from the STN cell and can exhibit
intermittent phase-locking with the input in the beta band. The temporal
properties of this intermittent phase-locking show similarities to the
intermittent synchronization observed in experiments. We also study the
synchronization of GPe cells to synaptic input from the STN cell with
dependence on the dopamine-modulated parameters. Dopamine also affects the
cellular properties of neurons. We show how the changes in firing patterns of
STN neuron due to the lack of dopamine may lead to transition from a lower to a
higher coherent state, roughly matching the synchrony levels observed in basal
ganglia in normal and parkinsonian states. The intermittent nature of the
neural beta band synchrony in Parkinson's disease is achieved in the model due
to the interplay of the timing of STN input to pallidum and pallidal neuronal
dynamics, resulting in sensitivity of pallidal output to the phase of the
arriving STN input. Thus the mechanism considered here (the change in firing
pattern of subthalamic neurons through the dopamine-induced change of membrane
properties) may be one of the potential mechanisms responsible for the
generation of the intermittent synchronization observed in Parkinson's disease.
| [
{
"created": "Thu, 8 Nov 2012 21:19:23 GMT",
"version": "v1"
}
] | 2013-02-11 | [
[
"Park",
"Choongseok",
""
],
[
"Rubchinsky",
"Leonid",
""
]
] | Neural activity in the brain of parkinsonian patients is characterized by the intermittently synchronized oscillatory dynamics. This imperfect synchronization, observed in the beta frequency band, is believed to be related to the hypokinetic motor symptoms of the disorder. Our study explores potential mechanisms behind this intermittent synchrony. We study the response of a bursting pallidal neuron to different patterns of synaptic input from subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe) neuron is sensitive to the phase of the input from the STN cell and can exhibit intermittent phase-locking with the input in the beta band. The temporal properties of this intermittent phase-locking show similarities to the intermittent synchronization observed in experiments. We also study the synchronization of GPe cells to synaptic input from the STN cell with dependence on the dopamine-modulated parameters. Dopamine also affects the cellular properties of neurons. We show how the changes in firing patterns of STN neuron due to the lack of dopamine may lead to transition from a lower to a higher coherent state, roughly matching the synchrony levels observed in basal ganglia in normal and parkinsonian states. The intermittent nature of the neural beta band synchrony in Parkinson's disease is achieved in the model due to the interplay of the timing of STN input to pallidum and pallidal neuronal dynamics, resulting in sensitivity of pallidal output to the phase of the arriving STN input. Thus the mechanism considered here (the change in firing pattern of subthalamic neurons through the dopamine-induced change of membrane properties) may be one of the potential mechanisms responsible for the generation of the intermittent synchronization observed in Parkinson's disease. |
1309.3329 | Alberto d'Onofrio | Alberto d'Onofrio | Fractal growth of tumors and other cellular populations: Linking the
mechanistic to the phenomenological modeling and vice versa | 8 pages | Chaos Solitons and Fractals 41: 875-880 (2009) | 10.1016/j.chaos.2008.04.014 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we study and extend the mechanistic mean field theory of growth
of cellular populations proposed by Mombach et al in (Mombach J. C. M. et al.,
Europhysics Letter, 59 (2002) 923) (MLBI model), and we demonstrate that the
original model and our generalizations lead to inferences of biological
interest. In the first part of this paper, we show that the model in study is
widely general since it admits, as particular cases, the main phenomenological
models of cellular growth. In the second part of this work, we generalize the
\emph{MLBI} model to a wider family of models by allowing the cells to have a
generic unspecified biologically plausible interaction. Then, we derive a
relationship between this generic microscopic interaction function and the
growth rate of the corresponding macroscopic model. Finally, we propose to use
this relationship in order to help the investigation of the biological
plausibility of phenomenological models of cancer growth.
| [
{
"created": "Thu, 12 Sep 2013 23:05:17 GMT",
"version": "v1"
}
] | 2013-09-16 | [
[
"d'Onofrio",
"Alberto",
""
]
] | In this paper we study and extend the mechanistic mean field theory of growth of cellular populations proposed by Mombach et al in (Mombach J. C. M. et al., Europhysics Letter, 59 (2002) 923) (MLBI model), and we demonstrate that the original model and our generalizations lead to inferences of biological interest. In the first part of this paper, we show that the model in study is widely general since it admits, as particular cases, the main phenomenological models of cellular growth. In the second part of this work, we generalize the \emph{MLBI} model to a wider family of models by allowing the cells to have a generic unspecified biologically plausible interaction. Then, we derive a relationship between this generic microscopic interaction function and the growth rate of the corresponding macroscopic model. Finally, we propose to use this relationship in order to help the investigation of the biological plausibility of phenomenological models of cancer growth. |
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