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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2401.13495 | Alice Longhena | Alice Longhena, Martin Guillemaud, Mario Chavez | Detecting local perturbations of networks in a latent hyperbolic
embedding space | null | Chaos 1 June 2024; 34 (6): 063117 | 10.1063/5.0199546 | null | q-bio.QM physics.data-an | http://creativecommons.org/licenses/by/4.0/ | Graph theoretical approaches have been proven to be effective in the
characterization of connected systems, as well as in quantifying their
dysfunction due to perturbation. In this paper, we show the advantage of a
non-Euclidean (hyperbolic) representation of networks to identify local
connectivity perturbations and to characterize the induced effects on a large
scale. We propose two perturbation scores based on representations of the
networks in a latent geometric space, obtained through an embedding onto the
hyperbolic Poincar\'e disk. We numerically demonstrate that these methods are
able to localize perturbations in networks with homogeneous or heterogeneous
degree connectivity. We apply this framework to identify the most perturbed
brain areas in epileptic patients following surgery. This study is conceived in
the effort of developing more powerful tools to represent and analyze brain
networks, and it is the first to apply geometric network embedding techniques
to the case of epilepsy.
| [
{
"created": "Wed, 24 Jan 2024 14:42:19 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Apr 2024 15:17:30 GMT",
"version": "v2"
},
{
"created": "Fri, 7 Jun 2024 14:52:45 GMT",
"version": "v3"
}
] | 2024-06-10 | [
[
"Longhena",
"Alice",
""
],
[
"Guillemaud",
"Martin",
""
],
[
"Chavez",
"Mario",
""
]
] | Graph theoretical approaches have been proven to be effective in the characterization of connected systems, as well as in quantifying their dysfunction due to perturbation. In this paper, we show the advantage of a non-Euclidean (hyperbolic) representation of networks to identify local connectivity perturbations and to characterize the induced effects on a large scale. We propose two perturbation scores based on representations of the networks in a latent geometric space, obtained through an embedding onto the hyperbolic Poincar\'e disk. We numerically demonstrate that these methods are able to localize perturbations in networks with homogeneous or heterogeneous degree connectivity. We apply this framework to identify the most perturbed brain areas in epileptic patients following surgery. This study is conceived in the effort of developing more powerful tools to represent and analyze brain networks, and it is the first to apply geometric network embedding techniques to the case of epilepsy. |
1704.07906 | Chandre Dharma-wardana | M.W.C. Dharma-wardana (NRC-Canada) | Chronic Kidney Disease of Unknown aetiology (CKDu) and multiple-ion
interactions in drinking water | 14 pages, one figure | Environmental Geochemistry and Health 1st September (2017) | 10.1007/s10653-017-0017-4 | null | q-bio.TO cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent experimental work on the nephrotoxicity of contaminants in drinking
water using laboratory mice, motivated by the need to understand the origin of
chronic kidney disease of unknown aetiology is examined within our
understanding of the hydration of ions and proteins. Qualitative considerations
based on Hofmeister-type action of these ions, as well as quantitative
electrochemical models for the Gibbs free-energy change for ion-pair formation
are used to explain why Cd$^{2+}$ in the presence of F$^-$ and water hardness
due to Mg$^{2+}$ ions (but not Ca$^{2+}$) can be expected to be more
nephrotoxic, while AsO$_3^{3-}$ in the presence of F$^-$ and hardness may be
expected to be less nephrotoxic. The analysis is applied to a variety of ionic
species typically found in water to predict their likely combined
electro-chemical action. These results clarify the origins of chronic kidney
disease in the north-central province of Sri Lanka. The conclusion is further
strengthened by a study of the dietary load of Cd and As, where the dietary
loads are found to be safe, especially when the mitigating effects of
micronutrient ionic forms of Zn and Se, as well as corrections for
bio-availability are taken in to account. The resulting aetiological picture
supports the views that F$^-$, Cd$^{2+}$ (to a lesser extent), and Mg$^{2+}$
ions found in stagnant household well water act together with enhanced
toxicity, becoming the most likely causative factor of the disease. Similar
incidence of CKDu found in other tropical climates may have similar geological
origins.
| [
{
"created": "Fri, 7 Apr 2017 02:34:04 GMT",
"version": "v1"
}
] | 2017-12-15 | [
[
"Dharma-wardana",
"M. W. C.",
"",
"NRC-Canada"
]
] | Recent experimental work on the nephrotoxicity of contaminants in drinking water using laboratory mice, motivated by the need to understand the origin of chronic kidney disease of unknown aetiology is examined within our understanding of the hydration of ions and proteins. Qualitative considerations based on Hofmeister-type action of these ions, as well as quantitative electrochemical models for the Gibbs free-energy change for ion-pair formation are used to explain why Cd$^{2+}$ in the presence of F$^-$ and water hardness due to Mg$^{2+}$ ions (but not Ca$^{2+}$) can be expected to be more nephrotoxic, while AsO$_3^{3-}$ in the presence of F$^-$ and hardness may be expected to be less nephrotoxic. The analysis is applied to a variety of ionic species typically found in water to predict their likely combined electro-chemical action. These results clarify the origins of chronic kidney disease in the north-central province of Sri Lanka. The conclusion is further strengthened by a study of the dietary load of Cd and As, where the dietary loads are found to be safe, especially when the mitigating effects of micronutrient ionic forms of Zn and Se, as well as corrections for bio-availability are taken in to account. The resulting aetiological picture supports the views that F$^-$, Cd$^{2+}$ (to a lesser extent), and Mg$^{2+}$ ions found in stagnant household well water act together with enhanced toxicity, becoming the most likely causative factor of the disease. Similar incidence of CKDu found in other tropical climates may have similar geological origins. |
2004.08320 | Vignayanandam Ravindernath Muddapu | Vignayanandam R. Muddapu, Karthik Vijayakumar, Keerthiga Ramakrishnan,
V Srinivasa Chakravarthy | A Computational Model of Levodopa-Induced Toxicity in Substantia Nigra
Pars Compacta in Parkinson's Disease | null | null | null | null | q-bio.NC q-bio.TO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Parkinson's disease (PD) is caused by the progressive loss of dopaminergic
cells in substantia nigra pars compacta (SNc). The root cause of this cell loss
in PD is still not decisively elucidated. A recent line of thinking traces the
cause of PD neurodegeneration to metabolic deficiency. Due to exceptionally
high energy demand, SNc neurons exhibit a higher basal metabolic rate and
higher oxygen consumption rate, which results in oxidative stress. Recently, we
have suggested that the excitotoxic loss of SNc cells might be due to energy
deficiency occurring at different levels of neural hierarchy. Levodopa (LDOPA),
a precursor of dopamine, which is used as a symptom-relieving treatment for PD,
leads to outcomes that are both positive and negative. Several researchers
suggested that LDOPA might be harmful to SNc cells due to oxidative stress. The
role of LDOPA in the course of PD pathogenesis is still debatable. We
hypothesize that energy deficiency can lead to LDOPA-induced toxicity (LIT) in
two ways: by promoting dopamine-induced oxidative stress and by exacerbating
excitotoxicity in SNc. We present a multiscale computational model of
SNc-striatum system, which will help us in understanding the mechanism behind
neurodegeneration postulated above and provides insights for developing
disease-modifying therapeutics. It was observed that SNc terminals are more
vulnerable to energy deficiency than SNc somas. During LDOPA therapy, it was
observed that higher LDOPA dosage results in increased loss of somas and
terminals in SNc. It was also observed that co-administration of LDOPA and
glutathione (antioxidant) evades LDOPA-induced toxicity in SNc neurons. We show
that our proposed model was able to capture LDOPA-induced toxicity in SNc,
caused by energy deficiency.
| [
{
"created": "Wed, 1 Apr 2020 11:04:52 GMT",
"version": "v1"
}
] | 2020-04-20 | [
[
"Muddapu",
"Vignayanandam R.",
""
],
[
"Vijayakumar",
"Karthik",
""
],
[
"Ramakrishnan",
"Keerthiga",
""
],
[
"Chakravarthy",
"V Srinivasa",
""
]
] | Parkinson's disease (PD) is caused by the progressive loss of dopaminergic cells in substantia nigra pars compacta (SNc). The root cause of this cell loss in PD is still not decisively elucidated. A recent line of thinking traces the cause of PD neurodegeneration to metabolic deficiency. Due to exceptionally high energy demand, SNc neurons exhibit a higher basal metabolic rate and higher oxygen consumption rate, which results in oxidative stress. Recently, we have suggested that the excitotoxic loss of SNc cells might be due to energy deficiency occurring at different levels of neural hierarchy. Levodopa (LDOPA), a precursor of dopamine, which is used as a symptom-relieving treatment for PD, leads to outcomes that are both positive and negative. Several researchers suggested that LDOPA might be harmful to SNc cells due to oxidative stress. The role of LDOPA in the course of PD pathogenesis is still debatable. We hypothesize that energy deficiency can lead to LDOPA-induced toxicity (LIT) in two ways: by promoting dopamine-induced oxidative stress and by exacerbating excitotoxicity in SNc. We present a multiscale computational model of SNc-striatum system, which will help us in understanding the mechanism behind neurodegeneration postulated above and provides insights for developing disease-modifying therapeutics. It was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During LDOPA therapy, it was observed that higher LDOPA dosage results in increased loss of somas and terminals in SNc. It was also observed that co-administration of LDOPA and glutathione (antioxidant) evades LDOPA-induced toxicity in SNc neurons. We show that our proposed model was able to capture LDOPA-induced toxicity in SNc, caused by energy deficiency. |
2308.07954 | Hyun Park | Hyun Park, Parth Patel, Roland Haas, E. A. Huerta | APACE: AlphaFold2 and advanced computing as a service for accelerated
discovery in biophysics | 7 pages, 4 figures, 2 tables | Proceedings of the National Academy of Sciences, 121, 27, (2024) | 10.1073/pnas.2311888121 | null | q-bio.BM cs.AI cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The prediction of protein 3D structure from amino acid sequence is a
computational grand challenge in biophysics, and plays a key role in robust
protein structure prediction algorithms, from drug discovery to genome
interpretation. The advent of AI models, such as AlphaFold, is revolutionizing
applications that depend on robust protein structure prediction algorithms. To
maximize the impact, and ease the usability, of these novel AI tools we
introduce APACE, AlphaFold2 and advanced computing as a service, a novel
computational framework that effectively handles this AI model and its TB-size
database to conduct accelerated protein structure prediction analyses in modern
supercomputing environments. We deployed APACE in the Delta and Polaris
supercomputers, and quantified its performance for accurate protein structure
predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up
to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE
is up to two orders of magnitude faster than off-the-self AlphaFold2
implementations, reducing time-to-solution from weeks to minutes. This
computational approach may be readily linked with robotics laboratories to
automate and accelerate scientific discovery.
| [
{
"created": "Tue, 15 Aug 2023 18:00:01 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Jul 2024 20:25:05 GMT",
"version": "v2"
}
] | 2024-07-03 | [
[
"Park",
"Hyun",
""
],
[
"Patel",
"Parth",
""
],
[
"Haas",
"Roland",
""
],
[
"Huerta",
"E. A.",
""
]
] | The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these novel AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a novel computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers, and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. |
1205.3435 | Raphael Cerf | Rapha\"el Cerf | Critical population and error threshold on the sharp peak landscape for
a Moran model | In the first version, there was a wrong use of correlation
inequalities (application of Harris theorem to a discrete time process). This
is fixed here with the help of an exponential estimate | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of this work is to propose a finite population counterpart to
Eigen's model, which incorporates stochastic effects. We consider a Moran model
describing the evolution of a population of size $m$ of chromosomes of length
$\ell$ over an alphabet of cardinality $\kappa$. The mutation probability per
locus is $q$. We deal only with the sharp peak landscape: the replication rate
is $\sigma>1$ for the master sequence and 1 for the other sequences. We study
the equilibrium distribution of the process in the regime where $\ell, m\to
+\infty$, $q\to 0$, $\ell q \to a$, $m/\ell\to\alpha$. We obtain an equation
$\alpha\phi(a)=\ln\kappa$ in the parameter space $(a,\alpha)$ separating the
regime where the equilibrium population is totally random from the regime where
a quasispecies is formed. We observe the existence of a critical population
size necessary for a quasispecies to emerge and we recover the finite
population counterpart of the error threshold. These results are supported by
computer simulations.
| [
{
"created": "Tue, 15 May 2012 16:31:35 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Oct 2012 21:11:11 GMT",
"version": "v2"
}
] | 2012-10-25 | [
[
"Cerf",
"Raphaël",
""
]
] | The goal of this work is to propose a finite population counterpart to Eigen's model, which incorporates stochastic effects. We consider a Moran model describing the evolution of a population of size $m$ of chromosomes of length $\ell$ over an alphabet of cardinality $\kappa$. The mutation probability per locus is $q$. We deal only with the sharp peak landscape: the replication rate is $\sigma>1$ for the master sequence and 1 for the other sequences. We study the equilibrium distribution of the process in the regime where $\ell, m\to +\infty$, $q\to 0$, $\ell q \to a$, $m/\ell\to\alpha$. We obtain an equation $\alpha\phi(a)=\ln\kappa$ in the parameter space $(a,\alpha)$ separating the regime where the equilibrium population is totally random from the regime where a quasispecies is formed. We observe the existence of a critical population size necessary for a quasispecies to emerge and we recover the finite population counterpart of the error threshold. These results are supported by computer simulations. |
2311.00667 | Aniruddha Acharya | Aniruddha Acharya | Development and application of SEM/EDS in biological, biomedical &
nanotechnological research | 32 pages, 5 figures, 1 table, unpublished work | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | This comprehensive review discusses the development of scanning electron
microscopy and the application of this technology in different fields such as
biology, nanobiotechnology and biomedical science. Besides being a tool for
high resolution imaging of surface or topography, the technology is coupled
with analytical techniques such as energy dispersive spectroscopy for elemental
mapping. Since the commercialization of the technology, it has developed
manifold and currently very high-resolution nano scale imaging is possible by
this technology. The development of FIB-SEM has allowed three-dimensional
imaging of materials while the development of cryostage allows imaging of
hydrated biological samples. Though variable pressure or environmental SEM can
be used for imaging hydrated samples, they cannot capture a high-resolution
image. SBEM and ATUM-SEM has automated the sampling process while improved and
more powerful software along with user-friendly computer interface has made
image analysis faster and more reliable. This review presents one of the most
widely used analytical techniques used across the globe for scientific
investigation. The power and potential of SEM is expanding with the development
of accessory technology.
| [
{
"created": "Wed, 1 Nov 2023 17:14:52 GMT",
"version": "v1"
}
] | 2023-11-02 | [
[
"Acharya",
"Aniruddha",
""
]
] | This comprehensive review discusses the development of scanning electron microscopy and the application of this technology in different fields such as biology, nanobiotechnology and biomedical science. Besides being a tool for high resolution imaging of surface or topography, the technology is coupled with analytical techniques such as energy dispersive spectroscopy for elemental mapping. Since the commercialization of the technology, it has developed manifold and currently very high-resolution nano scale imaging is possible by this technology. The development of FIB-SEM has allowed three-dimensional imaging of materials while the development of cryostage allows imaging of hydrated biological samples. Though variable pressure or environmental SEM can be used for imaging hydrated samples, they cannot capture a high-resolution image. SBEM and ATUM-SEM has automated the sampling process while improved and more powerful software along with user-friendly computer interface has made image analysis faster and more reliable. This review presents one of the most widely used analytical techniques used across the globe for scientific investigation. The power and potential of SEM is expanding with the development of accessory technology. |
1206.3031 | Mike Steel Prof. | Iain Martyn and Mike Steel | The impact and interplay of long and short branches on phylogenetic
information content | 20 pages, 2 figures | null | null | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In molecular systematics, evolutionary trees are reconstructed from sequences
at the tips under simple models of site substitution. A central question is how
much sequence data is required to reconstruct a tree accurately? The answer
depends on the lengths of the branches (edges) of the tree, with very short and
very long edges requiring long sequences for accurate tree inference,
particularly when these branch lengths are arranged in certain ways. For
four-taxon trees, the sequence length question was settled for the case of a
rapid speciation event in the distant past. Here, we generalize this result and
show that the same sequence length requirement holds even when the speciation
event is recent, provided that at least one of the four taxa is distantly
related to the others. However, this equivalence disappears if a molecular
clock applies, since the length of the long outgroup edge becomes largely
irrelevant in the estimation of the tree topology for a recent (but not a deep)
divergence. We also show how our results can be extended to models in which
substitution rates vary across sites, and to settings where more than four taxa
are involved.
| [
{
"created": "Thu, 14 Jun 2012 08:17:15 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Jul 2012 20:16:19 GMT",
"version": "v2"
}
] | 2012-07-18 | [
[
"Martyn",
"Iain",
""
],
[
"Steel",
"Mike",
""
]
] | In molecular systematics, evolutionary trees are reconstructed from sequences at the tips under simple models of site substitution. A central question is how much sequence data is required to reconstruct a tree accurately? The answer depends on the lengths of the branches (edges) of the tree, with very short and very long edges requiring long sequences for accurate tree inference, particularly when these branch lengths are arranged in certain ways. For four-taxon trees, the sequence length question was settled for the case of a rapid speciation event in the distant past. Here, we generalize this result and show that the same sequence length requirement holds even when the speciation event is recent, provided that at least one of the four taxa is distantly related to the others. However, this equivalence disappears if a molecular clock applies, since the length of the long outgroup edge becomes largely irrelevant in the estimation of the tree topology for a recent (but not a deep) divergence. We also show how our results can be extended to models in which substitution rates vary across sites, and to settings where more than four taxa are involved. |
2307.04603 | Jaemyung Lee | Jaemyung Lee, Kyeongtak Han, Jaehoon Kim, Hasun Yu, Youhan Lee | Solvent: A Framework for Protein Folding | preprint, 9pages | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Consistency and reliability are crucial for conducting AI research. Many
famous research fields, such as object detection, have been compared and
validated with solid benchmark frameworks. After AlphaFold2, the protein
folding task has entered a new phase, and many methods are proposed based on
the component of AlphaFold2. The importance of a unified research framework in
protein folding contains implementations and benchmarks to consistently and
fairly compare various approaches. To achieve this, we present Solvent, a
protein folding framework that supports significant components of
state-of-the-art models in the manner of an off-the-shelf interface Solvent
contains different models implemented in a unified codebase and supports
training and evaluation for defined models on the same dataset. We benchmark
well-known algorithms and their components and provide experiments that give
helpful insights into the protein structure modeling field. We hope that
Solvent will increase the reliability and consistency of proposed models and
give efficiency in both speed and costs, resulting in acceleration on protein
folding modeling research. The code is available at
https://github.com/kakaobrain/solvent, and the project will continue to be
developed.
| [
{
"created": "Fri, 7 Jul 2023 09:01:42 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jul 2023 05:18:51 GMT",
"version": "v2"
},
{
"created": "Wed, 19 Jul 2023 05:43:44 GMT",
"version": "v3"
},
{
"created": "Thu, 20 Jul 2023 00:49:13 GMT",
"version": "v4"
},
{
"created": "Mon, 31 Jul 2023 05:29:16 GMT",
"version": "v5"
}
] | 2023-08-01 | [
[
"Lee",
"Jaemyung",
""
],
[
"Han",
"Kyeongtak",
""
],
[
"Kim",
"Jaehoon",
""
],
[
"Yu",
"Hasun",
""
],
[
"Lee",
"Youhan",
""
]
] | Consistency and reliability are crucial for conducting AI research. Many famous research fields, such as object detection, have been compared and validated with solid benchmark frameworks. After AlphaFold2, the protein folding task has entered a new phase, and many methods are proposed based on the component of AlphaFold2. The importance of a unified research framework in protein folding contains implementations and benchmarks to consistently and fairly compare various approaches. To achieve this, we present Solvent, a protein folding framework that supports significant components of state-of-the-art models in the manner of an off-the-shelf interface Solvent contains different models implemented in a unified codebase and supports training and evaluation for defined models on the same dataset. We benchmark well-known algorithms and their components and provide experiments that give helpful insights into the protein structure modeling field. We hope that Solvent will increase the reliability and consistency of proposed models and give efficiency in both speed and costs, resulting in acceleration on protein folding modeling research. The code is available at https://github.com/kakaobrain/solvent, and the project will continue to be developed. |
1706.00125 | Alyssa Morrow | Alyssa Morrow, Vaishaal Shankar, Devin Petersohn, Anthony Joseph,
Benjamin Recht, Nir Yosef | Convolutional Kitchen Sinks for Transcription Factor Binding Site
Prediction | 5 pages, 2 tables, NIPS MLCB Workshop 2016 | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a simple and efficient method for prediction of transcription
factor binding sites from DNA sequence. Our method computes a random
approximation of a convolutional kernel feature map from DNA sequence and then
learns a linear model from the approximated feature map. Our method outperforms
state-of-the-art deep learning methods on five out of six test datasets from
the ENCODE consortium, while training in less than one eighth the time.
| [
{
"created": "Wed, 31 May 2017 23:39:11 GMT",
"version": "v1"
}
] | 2017-06-02 | [
[
"Morrow",
"Alyssa",
""
],
[
"Shankar",
"Vaishaal",
""
],
[
"Petersohn",
"Devin",
""
],
[
"Joseph",
"Anthony",
""
],
[
"Recht",
"Benjamin",
""
],
[
"Yosef",
"Nir",
""
]
] | We present a simple and efficient method for prediction of transcription factor binding sites from DNA sequence. Our method computes a random approximation of a convolutional kernel feature map from DNA sequence and then learns a linear model from the approximated feature map. Our method outperforms state-of-the-art deep learning methods on five out of six test datasets from the ENCODE consortium, while training in less than one eighth the time. |
1103.2397 | Peter Ralph | Alistair N. Boettiger, Peter L. Ralph, Steven N. Evans | Transcriptional regulation: Effects of promoter proximal pausing on
speed, synchrony and reliability | 21 pages, 6 figures; to be published in PLoS Computational Biology | PLoS Comput Biol 7(5): e1001136 (2011) | 10.1371/journal.pcbi.1001136 | null | q-bio.MN math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent whole genome polymerase binding assays have shown that a large
proportion of unexpressed genes have pre-assembled RNA pol II transcription
initiation complex stably bound to their promoters. Some such promoter
proximally paused genes are regulated at transcription elongation rather than
at initiation; it has been proposed that this difference allows these genes to
both express faster and achieve more synchronous expression across populations
of cells, thus overcoming molecular "noise" arising from low copy number
factors. It has been established experimentally that genes which are regulated
at elongation tend to express faster and more synchronously; however, it has
not been shown directly whether or not it is the change in the regulated step
{\em per se} that causes this increase in speed and synchrony. We investigate
this question by proposing and analyzing a continuous-time Markov chain model
of polymerase complex assembly regulated at one of two steps: initial
polymerase association with DNA, or release from a paused, transcribing state.
Our analysis demonstrates that, over a wide range of physical parameters,
increased speed and synchrony are functional consequences of elongation
control. Further, we make new predictions about the effect of elongation
regulation on the consistent control of total transcript number between cells,
and identify which elements in the transcription induction pathway are most
sensitive to molecular noise and thus may be most evolutionarily constrained.
Our methods produce symbolic expressions for quantities of interest with
reasonable computational effort and can be used to explore the interplay
between interaction topology and molecular noise in a broader class of
biochemical networks. We provide general-purpose code implementing these
methods.
| [
{
"created": "Fri, 11 Mar 2011 23:43:31 GMT",
"version": "v1"
}
] | 2015-03-19 | [
[
"Boettiger",
"Alistair N.",
""
],
[
"Ralph",
"Peter L.",
""
],
[
"Evans",
"Steven N.",
""
]
] | Recent whole genome polymerase binding assays have shown that a large proportion of unexpressed genes have pre-assembled RNA pol II transcription initiation complex stably bound to their promoters. Some such promoter proximally paused genes are regulated at transcription elongation rather than at initiation; it has been proposed that this difference allows these genes to both express faster and achieve more synchronous expression across populations of cells, thus overcoming molecular "noise" arising from low copy number factors. It has been established experimentally that genes which are regulated at elongation tend to express faster and more synchronously; however, it has not been shown directly whether or not it is the change in the regulated step {\em per se} that causes this increase in speed and synchrony. We investigate this question by proposing and analyzing a continuous-time Markov chain model of polymerase complex assembly regulated at one of two steps: initial polymerase association with DNA, or release from a paused, transcribing state. Our analysis demonstrates that, over a wide range of physical parameters, increased speed and synchrony are functional consequences of elongation control. Further, we make new predictions about the effect of elongation regulation on the consistent control of total transcript number between cells, and identify which elements in the transcription induction pathway are most sensitive to molecular noise and thus may be most evolutionarily constrained. Our methods produce symbolic expressions for quantities of interest with reasonable computational effort and can be used to explore the interplay between interaction topology and molecular noise in a broader class of biochemical networks. We provide general-purpose code implementing these methods. |
1808.01951 | Islem Rekik | Mayssa Soussia and Islem Rekik | A Review on Image- and Network-based Brain Data Analysis Techniques for
Alzheimer's Disease Diagnosis Reveals a Gap in Developing Predictive Methods
for Prognosis | MICCAI Connectomics in NeuroImaging Workshop (2018) | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unveiling pathological brain changes associated with Alzheimer's disease (AD)
is a challenging task especially that people do not show symptoms of dementia
until it is late. Over the past years, neuroimaging techniques paved the way
for computer-based diagnosis and prognosis to facilitate the automation of
medical decision support and help clinicians identify cognitively intact
subjects that are at high-risk of developing AD. As a progressive
neurodegenerative disorder, researchers investigated how AD affects the brain
using different approaches: 1) image-based methods where mainly neuroimaging
modalities are used to provide early AD biomarkers, and 2) network-based
methods which focus on functional and structural brain connectivities to give
insights into how AD alters brain wiring. In this study, we reviewed
neuroimaging-based technical methods developed for AD and mild-cognitive
impairment (MCI) classification and prediction tasks, selected by screening all
MICCAI proceedings published between 2010 and 2016. We included papers that fit
into image-based or network-based categories. The majority of papers focused on
classifying MCI vs. AD brain states, which has enabled the discovery of
discriminative or altered brain regions and connections. However, very few
works aimed to predict MCI progression based on early neuroimaging-based
observations. Despite the high importance of reliably identifying which early
MCI patient will convert to AD, remain stable or reverse to normal over
months/years, predictive models are still lagging behind.
| [
{
"created": "Mon, 6 Aug 2018 15:00:17 GMT",
"version": "v1"
}
] | 2018-08-07 | [
[
"Soussia",
"Mayssa",
""
],
[
"Rekik",
"Islem",
""
]
] | Unveiling pathological brain changes associated with Alzheimer's disease (AD) is a challenging task especially that people do not show symptoms of dementia until it is late. Over the past years, neuroimaging techniques paved the way for computer-based diagnosis and prognosis to facilitate the automation of medical decision support and help clinicians identify cognitively intact subjects that are at high-risk of developing AD. As a progressive neurodegenerative disorder, researchers investigated how AD affects the brain using different approaches: 1) image-based methods where mainly neuroimaging modalities are used to provide early AD biomarkers, and 2) network-based methods which focus on functional and structural brain connectivities to give insights into how AD alters brain wiring. In this study, we reviewed neuroimaging-based technical methods developed for AD and mild-cognitive impairment (MCI) classification and prediction tasks, selected by screening all MICCAI proceedings published between 2010 and 2016. We included papers that fit into image-based or network-based categories. The majority of papers focused on classifying MCI vs. AD brain states, which has enabled the discovery of discriminative or altered brain regions and connections. However, very few works aimed to predict MCI progression based on early neuroimaging-based observations. Despite the high importance of reliably identifying which early MCI patient will convert to AD, remain stable or reverse to normal over months/years, predictive models are still lagging behind. |
2308.12735 | Casper Asbj{\o}rn Eriksen | Casper Asbj{\o}rn Eriksen, Jakob Lykke Andersen, Rolf Fagerberg,
Daniel Merkle | Reconciling Inconsistent Molecular Structures from Biochemical Databases | 14 pages, 4 figures, accepted at ISBRA 2023 | null | null | null | q-bio.BM cs.DB q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Information on the structure of molecules, retrieved via biochemical
databases, plays a pivotal role in various disciplines, such as metabolomics,
systems biology, and drug discovery. However, no such database can be complete,
and the chemical structure for a given compound is not necessarily consistent
between databases. This paper presents StructRecon, a novel tool for resolving
unique and correct molecular structures from database identifiers. StructRecon
traverses the cross-links between database entries in different databases to
construct what we call an identifier graph, which offers a more complete view
of the total information available on a particular compound across all the
databases. In order to reconcile discrepancies between databases, we first
present an extensible model for chemical structure which supports multiple
independent levels of detail, allowing standardisation of the structure to be
applied iteratively. In some cases, our standardisation approach results in
multiple structures for a given compound, in which case a random walk-based
algorithm is used to select the most likely structure among incompatible
alternates. We applied StructRecon to the EColiCore2 model, resolving a unique
chemical structure for 85.11 % of identifiers. StructRecon is open-source and
modular, which enables the potential support for more databases in the future.
| [
{
"created": "Thu, 24 Aug 2023 12:26:20 GMT",
"version": "v1"
}
] | 2023-08-25 | [
[
"Eriksen",
"Casper Asbjørn",
""
],
[
"Andersen",
"Jakob Lykke",
""
],
[
"Fagerberg",
"Rolf",
""
],
[
"Merkle",
"Daniel",
""
]
] | Information on the structure of molecules, retrieved via biochemical databases, plays a pivotal role in various disciplines, such as metabolomics, systems biology, and drug discovery. However, no such database can be complete, and the chemical structure for a given compound is not necessarily consistent between databases. This paper presents StructRecon, a novel tool for resolving unique and correct molecular structures from database identifiers. StructRecon traverses the cross-links between database entries in different databases to construct what we call an identifier graph, which offers a more complete view of the total information available on a particular compound across all the databases. In order to reconcile discrepancies between databases, we first present an extensible model for chemical structure which supports multiple independent levels of detail, allowing standardisation of the structure to be applied iteratively. In some cases, our standardisation approach results in multiple structures for a given compound, in which case a random walk-based algorithm is used to select the most likely structure among incompatible alternates. We applied StructRecon to the EColiCore2 model, resolving a unique chemical structure for 85.11 % of identifiers. StructRecon is open-source and modular, which enables the potential support for more databases in the future. |
0712.1365 | Alexei Vazquez | Alexei Vazquez | Population stratification using a statistical model on hypergraphs | 7 pages, 6 figures | Phys. Rev. E 77, 066106 (2008) | 10.1103/PhysRevE.77.066106 | null | q-bio.PE cs.AI physics.data-an | null | Population stratification is a problem encountered in several areas of
biology and public health. We tackle this problem by mapping a population and
its elements attributes into a hypergraph, a natural extension of the concept
of graph or network to encode associations among any number of elements. On
this hypergraph, we construct a statistical model reflecting our intuition
about how the elements attributes can emerge from a postulated population
structure. Finally, we introduce the concept of stratification
representativeness as a mean to identify the simplest stratification already
containing most of the information about the population structure. We
demonstrate the power of this framework stratifying an animal and a human
population based on phenotypic and genotypic properties, respectively.
| [
{
"created": "Sun, 9 Dec 2007 20:53:45 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Vazquez",
"Alexei",
""
]
] | Population stratification is a problem encountered in several areas of biology and public health. We tackle this problem by mapping a population and its elements attributes into a hypergraph, a natural extension of the concept of graph or network to encode associations among any number of elements. On this hypergraph, we construct a statistical model reflecting our intuition about how the elements attributes can emerge from a postulated population structure. Finally, we introduce the concept of stratification representativeness as a mean to identify the simplest stratification already containing most of the information about the population structure. We demonstrate the power of this framework stratifying an animal and a human population based on phenotypic and genotypic properties, respectively. |
2311.07117 | Kevin Sean Chen | Kevin S. Chen, Anuj K. Sharma, Jonathan W. Pillow, Andrew M. Leifer | Olfactory learning alters navigation strategies and behavioral
variability in C. elegans | null | null | null | null | q-bio.NC physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Animals adjust their behavioral response to sensory input adaptively
depending on past experiences. The flexible brain computation is crucial for
survival and is of great interest in neuroscience. The nematode C. elegans
modulates its navigation behavior depending on the association of odor butanone
with food (appetitive training) or starvation (aversive training), and will
then climb up the butanone gradient or ignore it, respectively. However, the
exact change in navigation strategy in response to learning is still unknown.
Here we study the learned odor navigation in worms by combining precise
experimental measurement and a novel descriptive model of navigation. Our model
consists of two known navigation strategies in worms: biased random walk and
weathervaning. We infer weights on these strategies by applying the model to
worm navigation trajectories and the exact odor concentration it experiences.
Compared to naive worms, appetitive trained worms up-regulate the biased random
walk strategy, and aversive trained worms down-regulate the weathervaning
strategy. The statistical model provides prediction with $>90 \%$ accuracy of
the past training condition given navigation data, which outperforms the
classical chemotaxis metric. We find that the behavioral variability is altered
by learning, such that worms are less variable after training compared to naive
ones. The model further predicts the learning-dependent response and
variability under optogenetic perturbation of the olfactory neuron
AWC$^\mathrm{ON}$. Lastly, we investigate neural circuits downstream from
AWC$^\mathrm{ON}$ that are differentially recruited for learned odor-guided
navigation. Together, we provide a new paradigm to quantify flexible navigation
algorithms and pinpoint the underlying neural substrates.
| [
{
"created": "Mon, 13 Nov 2023 07:21:22 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Feb 2024 05:42:33 GMT",
"version": "v2"
}
] | 2024-02-26 | [
[
"Chen",
"Kevin S.",
""
],
[
"Sharma",
"Anuj K.",
""
],
[
"Pillow",
"Jonathan W.",
""
],
[
"Leifer",
"Andrew M.",
""
]
] | Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its navigation behavior depending on the association of odor butanone with food (appetitive training) or starvation (aversive training), and will then climb up the butanone gradient or ignore it, respectively. However, the exact change in navigation strategy in response to learning is still unknown. Here we study the learned odor navigation in worms by combining precise experimental measurement and a novel descriptive model of navigation. Our model consists of two known navigation strategies in worms: biased random walk and weathervaning. We infer weights on these strategies by applying the model to worm navigation trajectories and the exact odor concentration it experiences. Compared to naive worms, appetitive trained worms up-regulate the biased random walk strategy, and aversive trained worms down-regulate the weathervaning strategy. The statistical model provides prediction with $>90 \%$ accuracy of the past training condition given navigation data, which outperforms the classical chemotaxis metric. We find that the behavioral variability is altered by learning, such that worms are less variable after training compared to naive ones. The model further predicts the learning-dependent response and variability under optogenetic perturbation of the olfactory neuron AWC$^\mathrm{ON}$. Lastly, we investigate neural circuits downstream from AWC$^\mathrm{ON}$ that are differentially recruited for learned odor-guided navigation. Together, we provide a new paradigm to quantify flexible navigation algorithms and pinpoint the underlying neural substrates. |
2407.14798 | Harvey Wang | Harvey Wang, Selena Singh, Thomas Trappenberg, Abraham Nunes | An Information-Geometric Formulation of Pattern Separation and
Evaluation of Existing Indices | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Pattern separation is a computational process by which dissimilar neural
patterns are generated from similar input patterns. We present an
information-geometric formulation of pattern separation, where a pattern
separator is modelled as a family of statistical distributions on a manifold.
Such a manifold maps an input (i.e. coordinates) to a probability distribution
that generates firing patterns. Pattern separation occurs when small coordinate
changes result in large distances between samples from the corresponding
distributions. Under this formulation, we implement a two-neuron system whose
probability law forms a 3-dimensional manifold with mutually orthogonal
coordinates representing the neurons' marginal and correlational firing rates.
We use this highly controlled system to examine the behaviour of spike train
similarity indices commonly used in pattern separation research. We found that
all indices (except scaling factor) were sensitive to relative differences in
marginal firing rates, but no index adequately captured differences in spike
trains that resulted from altering the correlation in activity between the two
neurons. That is, existing pattern separation metrics appear (A) sensitive to
patterns that are encoded by different neurons, but (B) insensitive to patterns
that differ only in relative spike timing (e.g. synchrony between neurons in
the ensemble).
| [
{
"created": "Sat, 20 Jul 2024 07:58:23 GMT",
"version": "v1"
}
] | 2024-07-23 | [
[
"Wang",
"Harvey",
""
],
[
"Singh",
"Selena",
""
],
[
"Trappenberg",
"Thomas",
""
],
[
"Nunes",
"Abraham",
""
]
] | Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modelled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e. coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a 3-dimensional manifold with mutually orthogonal coordinates representing the neurons' marginal and correlational firing rates. We use this highly controlled system to examine the behaviour of spike train similarity indices commonly used in pattern separation research. We found that all indices (except scaling factor) were sensitive to relative differences in marginal firing rates, but no index adequately captured differences in spike trains that resulted from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons, but (B) insensitive to patterns that differ only in relative spike timing (e.g. synchrony between neurons in the ensemble). |
1804.11195 | Samir Farooq | Samir Farooq, Samuel J. Weisenthal, Melissa Trayhan, Robert J. White,
Kristen Bush, Peter R. Mariuz, Martin S. Zand | Revealing patterns in HIV viral load data and classifying patients via a
novel machine learning cluster summarization method | 17 page paper with additional 10 pages of references and
supplementary material. 7 figures and 9 supplementary figures | null | null | null | q-bio.QM cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | HIV RNA viral load (VL) is an important outcome variable in studies of HIV
infected persons. There exists only a handful of methods which classify
patients by viral load patterns. Most methods place limits on the use of viral
load measurements, are often specific to a particular study design, and do not
account for complex, temporal variation. To address this issue, we propose a
set of four unambiguous computable characteristics (features) of time-varying
HIV viral load patterns, along with a novel centroid-based classification
algorithm, which we use to classify a population of 1,576 HIV positive clinic
patients into one of five different viral load patterns (clusters) often found
in the literature: durably suppressed viral load (DSVL), sustained low viral
load (SLVL), sustained high viral load (SHVL), high viral load suppression
(HVLS), and rebounding viral load (RVL). The centroid algorithm summarizes
these clusters in terms of their centroids and radii. We show that this allows
new viral load patterns to be assigned pattern membership based on the distance
from the centroid relative to its radius, which we term radial normalization
classification. This method has the benefit of providing an objective and
quantitative method to assign viral load pattern membership with a concise and
interpretable model that aids clinical decision making. This method also
facilitates meta-analyses by providing computably distinct HIV categories.
Finally we propose that this novel centroid algorithm could also be useful in
the areas of cluster comparison for outcomes research and data reduction in
machine learning.
| [
{
"created": "Wed, 25 Apr 2018 22:40:03 GMT",
"version": "v1"
}
] | 2018-05-01 | [
[
"Farooq",
"Samir",
""
],
[
"Weisenthal",
"Samuel J.",
""
],
[
"Trayhan",
"Melissa",
""
],
[
"White",
"Robert J.",
""
],
[
"Bush",
"Kristen",
""
],
[
"Mariuz",
"Peter R.",
""
],
[
"Zand",
"Martin S.",
""
]
] | HIV RNA viral load (VL) is an important outcome variable in studies of HIV infected persons. There exists only a handful of methods which classify patients by viral load patterns. Most methods place limits on the use of viral load measurements, are often specific to a particular study design, and do not account for complex, temporal variation. To address this issue, we propose a set of four unambiguous computable characteristics (features) of time-varying HIV viral load patterns, along with a novel centroid-based classification algorithm, which we use to classify a population of 1,576 HIV positive clinic patients into one of five different viral load patterns (clusters) often found in the literature: durably suppressed viral load (DSVL), sustained low viral load (SLVL), sustained high viral load (SHVL), high viral load suppression (HVLS), and rebounding viral load (RVL). The centroid algorithm summarizes these clusters in terms of their centroids and radii. We show that this allows new viral load patterns to be assigned pattern membership based on the distance from the centroid relative to its radius, which we term radial normalization classification. This method has the benefit of providing an objective and quantitative method to assign viral load pattern membership with a concise and interpretable model that aids clinical decision making. This method also facilitates meta-analyses by providing computably distinct HIV categories. Finally we propose that this novel centroid algorithm could also be useful in the areas of cluster comparison for outcomes research and data reduction in machine learning. |
q-bio/0407037 | Taguchi Y.-H. | Y.-h. Taguchi and Y. Oono | Relational patterns of gene expression via nonmetric multidimensional
scaling analysis | 16 pages, 7 figures, to appear in Bioinformatics | null | 10.1093/bioinformatics/bti067 | null | q-bio.GN q-bio.CB | null | Motivation:Microarray experiments result in large scale data sets that
require extensive mining and refining to extract useful information. We
demonstrate the usefulness of (nonmetric) multidimensional scaling (MDS) method
in analyzing a large number of genes. Applying MDS to the microarray data is
certainly not new, but the existing works are all on small numbers
(< 100) of points to be analyzed. We have been developing an efficient novel
algorithm for nonmetric multidimensional scaling (nMDS) analysis for very large
data sets as a maximally unsupervised data mining device. We wish to
demonstrate its usefulness in the context of bioinformatics (unraveling
relational patterns among genes from time series data in this paper).
Results: The Pearson correlation coefficient with its sign flipped is used to
measure the dissimilarity of the gene activities in transcriptional response of
cell-cycle-synchronized human fibroblasts to serum [Iyer {\it et al}., Science
{\bf 283}, 83 (1999)]. These dissimilarity data have been analyzed with our
nMDS algorithm to produce an almost circular relational pattern of the genes.
The obtained pattern expresses a temporal order in the data in this example;
the temporal expression pattern of the genes rotates along this circular
arrangement and is related to the cell cycle. For the data we analyze in this
paper we observe the following. If an appropriate preparation procedure is
applied to the original data set, linear methods such as the principal
component analysis (PCA) could achieve reasonable results, but without data
preprocessing linear methods such as PCA cannot achieve a useful picture.
Furthermore, even with an appropriate data preprocessing, the outcomes of
linear procedures are not as clearcut as those by nMDS without preprocessing.
| [
{
"created": "Thu, 29 Jul 2004 06:48:09 GMT",
"version": "v1"
},
{
"created": "Sat, 18 Sep 2004 20:46:21 GMT",
"version": "v2"
}
] | 2009-09-29 | [
[
"Taguchi",
"Y. -h.",
""
],
[
"Oono",
"Y.",
""
]
] | Motivation:Microarray experiments result in large scale data sets that require extensive mining and refining to extract useful information. We demonstrate the usefulness of (nonmetric) multidimensional scaling (MDS) method in analyzing a large number of genes. Applying MDS to the microarray data is certainly not new, but the existing works are all on small numbers (< 100) of points to be analyzed. We have been developing an efficient novel algorithm for nonmetric multidimensional scaling (nMDS) analysis for very large data sets as a maximally unsupervised data mining device. We wish to demonstrate its usefulness in the context of bioinformatics (unraveling relational patterns among genes from time series data in this paper). Results: The Pearson correlation coefficient with its sign flipped is used to measure the dissimilarity of the gene activities in transcriptional response of cell-cycle-synchronized human fibroblasts to serum [Iyer {\it et al}., Science {\bf 283}, 83 (1999)]. These dissimilarity data have been analyzed with our nMDS algorithm to produce an almost circular relational pattern of the genes. The obtained pattern expresses a temporal order in the data in this example; the temporal expression pattern of the genes rotates along this circular arrangement and is related to the cell cycle. For the data we analyze in this paper we observe the following. If an appropriate preparation procedure is applied to the original data set, linear methods such as the principal component analysis (PCA) could achieve reasonable results, but without data preprocessing linear methods such as PCA cannot achieve a useful picture. Furthermore, even with an appropriate data preprocessing, the outcomes of linear procedures are not as clearcut as those by nMDS without preprocessing. |
1201.0912 | Hung-Chung Huang | Rongqing Xie, Gaochao Lin, and Hung-Chung Huang | Experimental Evidence Supporting a New "Osmosis Law & Theory" Derived
New Formula that Improves van't Hoff Osmotic Pressure Equation | This is a revised and improved version providing proof of the
experimental data and evidence on the validity of the new osmotic pressure
formula described in this article. (which was missing in previous version) | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experimental data were used to support a new concept of osmotic force and a
new osmotic law that can explain the osmotic process without the difficulties
encountered with van't Hoff osmotic pressure theory. Derived new osmotic
formula with curvilinear equation (via new osmotic law) overcomes the
limitations and incompleteness of van't Hoff (linear) osmotic pressure
equation, $\pi=(n/v)RT$, (for ideal dilute solution only). The application of
this classical theory often resulted in contradiction regardless of
miscellaneous explaining efforts. This is due to the lack of a scientific
concept like "osmotic force" that we believe can elaborate the osmotic process.
Via this new concept, the proposed new osmotic law and derived new osmotic
pressure equation will greatly complete and improve the theoretical consistency
within the scientific framework of osmosis.
| [
{
"created": "Mon, 2 Jan 2012 04:21:59 GMT",
"version": "v1"
},
{
"created": "Sat, 31 Dec 2022 04:23:59 GMT",
"version": "v2"
}
] | 2023-01-03 | [
[
"Xie",
"Rongqing",
""
],
[
"Lin",
"Gaochao",
""
],
[
"Huang",
"Hung-Chung",
""
]
] | Experimental data were used to support a new concept of osmotic force and a new osmotic law that can explain the osmotic process without the difficulties encountered with van't Hoff osmotic pressure theory. Derived new osmotic formula with curvilinear equation (via new osmotic law) overcomes the limitations and incompleteness of van't Hoff (linear) osmotic pressure equation, $\pi=(n/v)RT$, (for ideal dilute solution only). The application of this classical theory often resulted in contradiction regardless of miscellaneous explaining efforts. This is due to the lack of a scientific concept like "osmotic force" that we believe can elaborate the osmotic process. Via this new concept, the proposed new osmotic law and derived new osmotic pressure equation will greatly complete and improve the theoretical consistency within the scientific framework of osmosis. |
1807.08570 | Jorge P. Rodr\'iguez | Jorge P. Rodr\'iguez, Juan Fern\'andez-Gracia, Michele Thums, Mark A.
Hindell, Ana M. M. Sequeira, Mark G. Meekan, Daniel P. Costa, Christophe
Guinet, Robert G. Harcourt, Clive R. McMahon, Monica Muelbert, Carlos M.
Duarte, V\'ictor M. Egu\'iluz | Big data analyses reveal patterns and drivers of the movements of
southern elephant seals | 18 pages, 5 figures, 6 supplementary figures | Sci. Rep. 7, 112 (2017) | 10.1038/s41598-017-00165-0 | null | q-bio.QM physics.bio-ph physics.data-an q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The growing number of large databases of animal tracking provides an
opportunity for analyses of movement patterns at the scales of populations and
even species. We used analytical approaches, developed to cope with big data,
that require no a priori assumptions about the behaviour of the target agents,
to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina)
in the Southern Ocean, that was comprised of >500,000 location estimates
collected over more than a decade. Our analyses showed that the displacements
of these seals were described by a truncated power law distribution across
several spatial and temporal scales, with a clear signature of directed
movement. This pattern was evident when analysing the aggregated tracks despite
a wide diversity of individual trajectories. We also identified marine
provinces that described the migratory and foraging habitats of these seals.
Our analysis provides evidence for the presence of intrinsic drivers of
movement, such as memory, that cannot be detected using common models of
movement behaviour. These results highlight the potential for big data
techniques to provide new insights into movement behaviour when applied to
large datasets of animal tracking.
| [
{
"created": "Mon, 23 Jul 2018 12:47:19 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Jul 2018 09:02:02 GMT",
"version": "v2"
}
] | 2018-07-25 | [
[
"Rodríguez",
"Jorge P.",
""
],
[
"Fernández-Gracia",
"Juan",
""
],
[
"Thums",
"Michele",
""
],
[
"Hindell",
"Mark A.",
""
],
[
"Sequeira",
"Ana M. M.",
""
],
[
"Meekan",
"Mark G.",
""
],
[
"Costa",
"Daniel P.",
""
],
[
"Guinet",
"Christophe",
""
],
[
"Harcourt",
"Robert G.",
""
],
[
"McMahon",
"Clive R.",
""
],
[
"Muelbert",
"Monica",
""
],
[
"Duarte",
"Carlos M.",
""
],
[
"Eguíluz",
"Víctor M.",
""
]
] | The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with big data, that require no a priori assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for big data techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking. |
1602.07207 | Christian R\"over | Steffen Unkel, Christian R\"over, Nigel Stallard, Norbert Benda,
Martin Posch, Sarah Zohar, Tim Friede | Systematic reviews in paediatric multiple sclerosis and
Creutzfeldt-Jakob disease exemplify shortcomings in methods used to evaluate
therapies in rare conditions | 11 pages, 2 figures, 3 tables | Orphanet Journal of Rare Diseases, 11:16, 2016 | 10.1186/s13023-016-0402-6 | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | BACKGROUND: Randomized controlled trials (RCTs) are the gold standard design
of clinical research to assess interventions. However, RCTs cannot always be
applied for practical or ethical reasons. To investigate the current practices
in rare diseases, we review evaluations of therapeutic interventions in
paediatric multiple sclerosis (MS) and Creutzfeldt-Jakob disease (CJD). In
particular, we shed light on the endpoints used, the study designs implemented
and the statistical methodologies applied.
METHODS: We conducted literature searches to identify relevant primary
studies. Data on study design, objectives, endpoints, patient characteristics,
randomization and masking, type of intervention, control, withdrawals and
statistical methodology were extracted from the selected studies. The risk of
bias and the quality of the studies were assessed.
RESULTS: Twelve (seven) primary studies on paediatric MS (CJD) were included
in the qualitative synthesis. No double-blind, randomized placebo-controlled
trial for evaluating interventions in paediatric MS has been published yet.
Evidence from one open-label RCT is available. The observational studies are
before-after studies or controlled studies. Three of the seven selected studies
on CJD are RCTs, of which two received the maximum mark on the Oxford Quality
Scale. Four trials are controlled observational studies.
CONCLUSIONS: Evidence from double-blind RCTs on the efficacy of treatments
appears to be variable between rare diseases. With regard to paediatric
conditions it remains to be seen what impact regulators will have through e.g.,
paediatric investigation plans. Overall, there is space for improvement by
using innovative trial designs and data analysis techniques.
| [
{
"created": "Sun, 21 Feb 2016 16:28:58 GMT",
"version": "v1"
}
] | 2016-02-25 | [
[
"Unkel",
"Steffen",
""
],
[
"Röver",
"Christian",
""
],
[
"Stallard",
"Nigel",
""
],
[
"Benda",
"Norbert",
""
],
[
"Posch",
"Martin",
""
],
[
"Zohar",
"Sarah",
""
],
[
"Friede",
"Tim",
""
]
] | BACKGROUND: Randomized controlled trials (RCTs) are the gold standard design of clinical research to assess interventions. However, RCTs cannot always be applied for practical or ethical reasons. To investigate the current practices in rare diseases, we review evaluations of therapeutic interventions in paediatric multiple sclerosis (MS) and Creutzfeldt-Jakob disease (CJD). In particular, we shed light on the endpoints used, the study designs implemented and the statistical methodologies applied. METHODS: We conducted literature searches to identify relevant primary studies. Data on study design, objectives, endpoints, patient characteristics, randomization and masking, type of intervention, control, withdrawals and statistical methodology were extracted from the selected studies. The risk of bias and the quality of the studies were assessed. RESULTS: Twelve (seven) primary studies on paediatric MS (CJD) were included in the qualitative synthesis. No double-blind, randomized placebo-controlled trial for evaluating interventions in paediatric MS has been published yet. Evidence from one open-label RCT is available. The observational studies are before-after studies or controlled studies. Three of the seven selected studies on CJD are RCTs, of which two received the maximum mark on the Oxford Quality Scale. Four trials are controlled observational studies. CONCLUSIONS: Evidence from double-blind RCTs on the efficacy of treatments appears to be variable between rare diseases. With regard to paediatric conditions it remains to be seen what impact regulators will have through e.g., paediatric investigation plans. Overall, there is space for improvement by using innovative trial designs and data analysis techniques. |
2208.01456 | Ben Lonnqvist | Ben Lonnqvist, Harshitha Machiraju, Michael H. Herzog | A comment on Guo et al. [arXiv:2206.11228] | null | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by/4.0/ | In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially
trained neural representations in deep networks may already be as robust as
corresponding primate IT neural representations. While we find the paper's
primary experiment illuminating, we have doubts about the interpretation and
phrasing of the results presented in the paper.
| [
{
"created": "Tue, 2 Aug 2022 13:47:40 GMT",
"version": "v1"
}
] | 2022-08-03 | [
[
"Lonnqvist",
"Ben",
""
],
[
"Machiraju",
"Harshitha",
""
],
[
"Herzog",
"Michael H.",
""
]
] | In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially trained neural representations in deep networks may already be as robust as corresponding primate IT neural representations. While we find the paper's primary experiment illuminating, we have doubts about the interpretation and phrasing of the results presented in the paper. |
1803.03146 | Jade Shi | Jade Shi (EteRNA players), Rhiju Das, and Vijay S. Pande | SentRNA: Improving computational RNA design by incorporating a prior of
human design strategies | 27 pages (not including Supplementary Information), 9 figures, 7
tables | null | null | null | q-bio.QM cs.AI stat.ML | http://creativecommons.org/licenses/by/4.0/ | Solving the RNA inverse folding problem is a critical prerequisite to RNA
design, an emerging field in bioengineering with a broad range of applications
from reaction catalysis to cancer therapy. Although significant progress has
been made in developing machine-based inverse RNA folding algorithms, current
approaches still have difficulty designing sequences for large or complex
targets. On the other hand, human players of the online RNA design game EteRNA
have consistently shown superior performance in this regard, being able to
readily design sequences for targets that are challenging for machine
algorithms. Here we present a novel approach to the RNA design problem,
SentRNA, a design agent consisting of a fully-connected neural network trained
end-to-end using human-designed RNA sequences. We show that through this
approach, SentRNA can solve complex targets previously unsolvable by any
machine-based approach and achieve state-of-the-art performance on two separate
challenging test sets. Our results demonstrate that incorporating human design
strategies into a design algorithm can significantly boost machine performance
and suggests a new paradigm for machine-based RNA design.
| [
{
"created": "Thu, 8 Mar 2018 15:12:16 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Mar 2019 01:01:53 GMT",
"version": "v2"
}
] | 2019-03-07 | [
[
"Shi",
"Jade",
"",
"EteRNA players"
],
[
"Das",
"Rhiju",
""
],
[
"Pande",
"Vijay S.",
""
]
] | Solving the RNA inverse folding problem is a critical prerequisite to RNA design, an emerging field in bioengineering with a broad range of applications from reaction catalysis to cancer therapy. Although significant progress has been made in developing machine-based inverse RNA folding algorithms, current approaches still have difficulty designing sequences for large or complex targets. On the other hand, human players of the online RNA design game EteRNA have consistently shown superior performance in this regard, being able to readily design sequences for targets that are challenging for machine algorithms. Here we present a novel approach to the RNA design problem, SentRNA, a design agent consisting of a fully-connected neural network trained end-to-end using human-designed RNA sequences. We show that through this approach, SentRNA can solve complex targets previously unsolvable by any machine-based approach and achieve state-of-the-art performance on two separate challenging test sets. Our results demonstrate that incorporating human design strategies into a design algorithm can significantly boost machine performance and suggests a new paradigm for machine-based RNA design. |
2110.01219 | Soojung Yang | Soojung Yang and Doyeong Hwang and Seul Lee and Seongok Ryu and Sung
Ju Hwang | Hit and Lead Discovery with Explorative RL and Fragment-based Molecule
Generation | To be published in NeurIPS 2021 | null | null | null | q-bio.QM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Recently, utilizing reinforcement learning (RL) to generate molecules with
desired properties has been highlighted as a promising strategy for drug
design. A molecular docking program - a physical simulation that estimates
protein-small molecule binding affinity - can be an ideal reward scoring
function for RL, as it is a straightforward proxy of the therapeutic potential.
Still, two imminent challenges exist for this task. First, the models often
fail to generate chemically realistic and pharmacochemically acceptable
molecules. Second, the docking score optimization is a difficult exploration
problem that involves many local optima and less smooth surfaces with respect
to molecular structure. To tackle these challenges, we propose a novel RL
framework that generates pharmacochemically acceptable molecules with large
docking scores. Our method - Fragment-based generative RL with Explorative
Experience replay for Drug design (FREED) - constrains the generated molecules
to a realistic and qualified chemical space and effectively explores the space
to find drugs by coupling our fragment-based generation method and a novel
error-prioritized experience replay (PER). We also show that our model performs
well on both de novo and scaffold-based schemes. Our model produces molecules
of higher quality compared to existing methods while achieving state-of-the-art
performance on two of three targets in terms of the docking scores of the
generated molecules. We further show with ablation studies that our method,
predictive error-PER (FREED(PE)), significantly improves the model performance.
| [
{
"created": "Mon, 4 Oct 2021 07:21:00 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Oct 2021 15:22:33 GMT",
"version": "v2"
},
{
"created": "Wed, 27 Oct 2021 03:44:26 GMT",
"version": "v3"
}
] | 2021-10-28 | [
[
"Yang",
"Soojung",
""
],
[
"Hwang",
"Doyeong",
""
],
[
"Lee",
"Seul",
""
],
[
"Ryu",
"Seongok",
""
],
[
"Hwang",
"Sung Ju",
""
]
] | Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties has been highlighted as a promising strategy for drug design. A molecular docking program - a physical simulation that estimates protein-small molecule binding affinity - can be an ideal reward scoring function for RL, as it is a straightforward proxy of the therapeutic potential. Still, two imminent challenges exist for this task. First, the models often fail to generate chemically realistic and pharmacochemically acceptable molecules. Second, the docking score optimization is a difficult exploration problem that involves many local optima and less smooth surfaces with respect to molecular structure. To tackle these challenges, we propose a novel RL framework that generates pharmacochemically acceptable molecules with large docking scores. Our method - Fragment-based generative RL with Explorative Experience replay for Drug design (FREED) - constrains the generated molecules to a realistic and qualified chemical space and effectively explores the space to find drugs by coupling our fragment-based generation method and a novel error-prioritized experience replay (PER). We also show that our model performs well on both de novo and scaffold-based schemes. Our model produces molecules of higher quality compared to existing methods while achieving state-of-the-art performance on two of three targets in terms of the docking scores of the generated molecules. We further show with ablation studies that our method, predictive error-PER (FREED(PE)), significantly improves the model performance. |
1906.09465 | David Murrugarra | David Murrugarra and Elena Dimitrova | Quantifying the Total Effect of Edge Interventions in Discrete
Multistate Networks | 10 pages, 8 figures | Automatica, 125, 109453, 2021 | 10.1016/j.automatica.2020.109453 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Developing efficient computational methods to assess the impact of external
interventions on the dynamics of a network model is an important problem in
systems biology. This paper focuses on quantifying the global changes that
result from the application of an intervention to produce a desired effect,
which we define as the total effect of the intervention. The type of
mathematical models that we will consider are discrete dynamical systems which
include the widely used Boolean networks and their generalizations. The
potential interventions can be represented by a set of nodes and edges that can
be manipulated to produce a desired effect on the system. We use a class of
regulatory rules called nested canalizing functions that frequently appear in
published models and were inspired by the concept of canalization in
evolutionary biology. In this paper, we provide a polynomial normal form based
on the canalizing properties of regulatory functions. Using this polynomial
normal form, we give a set of formulas for counting the maximum number of
transitions that will change in the state space upon an edge deletion in the
wiring diagram. These formulas rely on the canalizing structure of the target
function since the number of changed transitions depends on the canalizing
layer that includes the input to be deleted. We also present computations on
random networks to compare the exact number of changes with the upper bounds
provided by our formulas. Finally, we provide statistics on the sharpness of
these upper bounds in random networks.
| [
{
"created": "Sat, 22 Jun 2019 15:49:23 GMT",
"version": "v1"
},
{
"created": "Sun, 24 Nov 2019 18:03:13 GMT",
"version": "v2"
},
{
"created": "Tue, 21 Jul 2020 18:02:47 GMT",
"version": "v3"
},
{
"created": "Sun, 11 Oct 2020 20:18:30 GMT",
"version": "v4"
}
] | 2024-07-09 | [
[
"Murrugarra",
"David",
""
],
[
"Dimitrova",
"Elena",
""
]
] | Developing efficient computational methods to assess the impact of external interventions on the dynamics of a network model is an important problem in systems biology. This paper focuses on quantifying the global changes that result from the application of an intervention to produce a desired effect, which we define as the total effect of the intervention. The type of mathematical models that we will consider are discrete dynamical systems which include the widely used Boolean networks and their generalizations. The potential interventions can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. We use a class of regulatory rules called nested canalizing functions that frequently appear in published models and were inspired by the concept of canalization in evolutionary biology. In this paper, we provide a polynomial normal form based on the canalizing properties of regulatory functions. Using this polynomial normal form, we give a set of formulas for counting the maximum number of transitions that will change in the state space upon an edge deletion in the wiring diagram. These formulas rely on the canalizing structure of the target function since the number of changed transitions depends on the canalizing layer that includes the input to be deleted. We also present computations on random networks to compare the exact number of changes with the upper bounds provided by our formulas. Finally, we provide statistics on the sharpness of these upper bounds in random networks. |
2407.00175 | Paul Kirk | Leiv R{\o}nneberg, Vidhi Lalchand, Paul D. W. Kirk | Permutation invariant multi-output Gaussian Processes for drug
combination prediction in cancer | null | null | null | null | q-bio.QM cs.LG stat.AP stat.ML | http://creativecommons.org/licenses/by/4.0/ | Dose-response prediction in cancer is an active application field in machine
learning. Using large libraries of \textit{in-vitro} drug sensitivity screens,
the goal is to develop accurate predictive models that can be used to guide
experimental design or inform treatment decisions. Building on previous work
that makes use of permutation invariant multi-output Gaussian Processes in the
context of dose-response prediction for drug combinations, we develop a
variational approximation to these models. The variational approximation
enables a more scalable model that provides uncertainty quantification and
naturally handles missing data. Furthermore, we propose using a deep generative
model to encode the chemical space in a continuous manner, enabling prediction
for new drugs and new combinations. We demonstrate the performance of our model
in a simple setting using a high-throughput dataset and show that the model is
able to efficiently borrow information across outputs.
| [
{
"created": "Fri, 28 Jun 2024 18:28:38 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Rønneberg",
"Leiv",
""
],
[
"Lalchand",
"Vidhi",
""
],
[
"Kirk",
"Paul D. W.",
""
]
] | Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of \textit{in-vitro} drug sensitivity screens, the goal is to develop accurate predictive models that can be used to guide experimental design or inform treatment decisions. Building on previous work that makes use of permutation invariant multi-output Gaussian Processes in the context of dose-response prediction for drug combinations, we develop a variational approximation to these models. The variational approximation enables a more scalable model that provides uncertainty quantification and naturally handles missing data. Furthermore, we propose using a deep generative model to encode the chemical space in a continuous manner, enabling prediction for new drugs and new combinations. We demonstrate the performance of our model in a simple setting using a high-throughput dataset and show that the model is able to efficiently borrow information across outputs. |
1309.6015 | Heiko Enderling | Jan Poleszczuk, Heiko Enderling | A High-Performance Cellular Automaton Model of Tumor Growth with
Dynamically Growing Domains | 8 pages, 8 figures | null | null | null | q-bio.QM q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tumor growth from a single transformed cancer cell up to a clinically
apparent mass spans many spatial and temporal orders of magnitude.
Implementation of cellular automata simulations of such tumor growth can be
straightforward but computing performance often counterbalances simplicity.
Computationally convenient simulation times can be achieved by choosing
appropriate data structures, memory and cell handling as well as domain setup.
We propose a cellular automaton model of tumor growth with a domain that
expands dynamically as the tumor population increases. We discuss memory
access, data structures and implementation techniques that yield
high-performance multi-scale Monte Carlo simulations of tumor growth. We
present simulation results of the tumor growth model and discuss tumor
properties that favor the proposed high-performance design.
| [
{
"created": "Tue, 24 Sep 2013 00:42:47 GMT",
"version": "v1"
}
] | 2013-09-25 | [
[
"Poleszczuk",
"Jan",
""
],
[
"Enderling",
"Heiko",
""
]
] | Tumor growth from a single transformed cancer cell up to a clinically apparent mass spans many spatial and temporal orders of magnitude. Implementation of cellular automata simulations of such tumor growth can be straightforward but computing performance often counterbalances simplicity. Computationally convenient simulation times can be achieved by choosing appropriate data structures, memory and cell handling as well as domain setup. We propose a cellular automaton model of tumor growth with a domain that expands dynamically as the tumor population increases. We discuss memory access, data structures and implementation techniques that yield high-performance multi-scale Monte Carlo simulations of tumor growth. We present simulation results of the tumor growth model and discuss tumor properties that favor the proposed high-performance design. |
q-bio/0503038 | Hiroki Ueda M. D. . D. | Hiroki R. Ueda, John B. Hogenesch | Principles in the Evolution of Metabolic Networks | 37 pages(15 pages for main text, 18 pages for supplementary
information, 4 figures); 5 Supplementary Figures are omitted from this
submission because of file size limitation (<1MB). This work was presented on
March 15th 2004, at the closed meeting with Akutsu lab and Kanehisa lab in
Institute for Chemical Research, Kyoto University | null | null | null | q-bio.MN q-bio.GN | null | Understanding design principles of complex cellular organization is one of
the major challenges in biology. Recent analysis of the large-scale cellular
organization has revealed the scale-free nature and robustness of metabolic and
protein networks. However, the underlying evolutional process that creates such
a cellular organization is not fully elucidated. To approach this problem, we
analyzed the metabolic networks of 126 organisms, whose draft or complete
genome sequences have been published. This analysis has revealed that the
evolutional process of metabolic networks follows the same and surprisingly
simple principles in Archaea, Bacteria and Eukaryotes; where highly linked
metabolites change their chemical links more dynamically than less linked
metabolites. Here we demonstrate that this rich-travel-more mechanism rather
than the previously proposed rich-get-richer mechanism can generate the
observed scale-free organization of metabolic networks. These findings
illustrate universal principles in evolution of metabolic networks and suggest
marked flexibility of metabolic network throughout evolution.
| [
{
"created": "Mon, 28 Mar 2005 16:31:20 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Ueda",
"Hiroki R.",
""
],
[
"Hogenesch",
"John B.",
""
]
] | Understanding design principles of complex cellular organization is one of the major challenges in biology. Recent analysis of the large-scale cellular organization has revealed the scale-free nature and robustness of metabolic and protein networks. However, the underlying evolutional process that creates such a cellular organization is not fully elucidated. To approach this problem, we analyzed the metabolic networks of 126 organisms, whose draft or complete genome sequences have been published. This analysis has revealed that the evolutional process of metabolic networks follows the same and surprisingly simple principles in Archaea, Bacteria and Eukaryotes; where highly linked metabolites change their chemical links more dynamically than less linked metabolites. Here we demonstrate that this rich-travel-more mechanism rather than the previously proposed rich-get-richer mechanism can generate the observed scale-free organization of metabolic networks. These findings illustrate universal principles in evolution of metabolic networks and suggest marked flexibility of metabolic network throughout evolution. |
0912.5179 | Mauro Mobilia | Mauro Mobilia | Oscillatory Dynamics in Rock-Paper-Scissors Games with Mutations | 25 pages, 9 figures. To appear in the Journal of Theoretical Biology | J. Theor. Biol. 264, 1-10 (2010) | 10.1016/j.jtbi.2010.01.008 | null | q-bio.PE cond-mat.stat-mech nlin.AO physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the oscillatory dynamics in the generic three-species
rock-paper-scissors games with mutations. In the mean-field limit, different
behaviors are found: (a) for high mutation rate, there is a stable interior
fixed point with coexistence of all species; (b) for low mutation rates, there
is a region of the parameter space characterized by a limit cycle resulting
from a Hopf bifurcation; (c) in the absence of mutations, there is a region
where heteroclinic cycles yield oscillations of large amplitude (not robust
against noise). After a discussion on the main properties of the mean-field
dynamics, we investigate the stochastic version of the model within an
individual-based formulation. Demographic fluctuations are therefore naturally
accounted and their effects are studied using a diffusion theory complemented
by numerical simulations. It is thus shown that persistent erratic oscillations
(quasi-cycles) of large amplitude emerge from a noise-induced resonance
phenomenon. We also analytically and numerically compute the average escape
time necessary to reach a (quasi-)cycle on which the system oscillates at a
given amplitude.
| [
{
"created": "Mon, 28 Dec 2009 15:07:55 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Jan 2010 14:00:03 GMT",
"version": "v2"
}
] | 2010-03-17 | [
[
"Mobilia",
"Mauro",
""
]
] | We study the oscillatory dynamics in the generic three-species rock-paper-scissors games with mutations. In the mean-field limit, different behaviors are found: (a) for high mutation rate, there is a stable interior fixed point with coexistence of all species; (b) for low mutation rates, there is a region of the parameter space characterized by a limit cycle resulting from a Hopf bifurcation; (c) in the absence of mutations, there is a region where heteroclinic cycles yield oscillations of large amplitude (not robust against noise). After a discussion on the main properties of the mean-field dynamics, we investigate the stochastic version of the model within an individual-based formulation. Demographic fluctuations are therefore naturally accounted and their effects are studied using a diffusion theory complemented by numerical simulations. It is thus shown that persistent erratic oscillations (quasi-cycles) of large amplitude emerge from a noise-induced resonance phenomenon. We also analytically and numerically compute the average escape time necessary to reach a (quasi-)cycle on which the system oscillates at a given amplitude. |
2405.02853 | Jiabao Ren | Chen Zhenzhen (1,2), Ren Jiabao (1,2), Duan Tingyu (3), Chen Ke (4),
Hou Ruyi (5), Li Yimiao (5), Zeng Leixiao (5), Meng Xiaoxuan (6), Wu Yibo
(7), Liu Yu (2), ((1) College of Science, Minzu University of China, Beijing,
China, (2) School of Nursing, China Medical University, Shenyang, Liaoning
Province, China, (3) Hebei Institute of Communications, Hebei, China, (4)
Department of Social Science and Humanities, Harbin Medical University,
Harbin, Heilongjiang Province, China, (5) School of Journalism and
Communication, Renmin University of China, Beijing, China, (6) Tianjin
Medical University, Tianjin, China, (7) School of Public Health, Peking
University, Beijing, China) | Development and validation of a short form of the medication literacy
scale for Chinese College Students | 25 pages, 3 figures,3 tables | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-sa/4.0/ | Medication literacy is integral to health literacy, pivotal for medication
safety and adherence. It denotes an individual's capacity to discern,
comprehend, and convey medication-related information. Existing scales,
however, are time-consuming and predominantly cater to patients and community
dwellers, necessitating a more succinct instrument. This study presents the
development of a brief Medication Literacy Scale (MLS-14) utilizing classical
test theory (CTT) and item response theory (IRT), targeting a college student
demographic. The MLS-14's abbreviated version, a 6-item scale (MLS-SF), was
distilled through CTT and IRT methodologies, engaging 2431 Chinese college
students to scrutinize its psychometric properties. The MLS-SF demonstrated a
Cronbach's {\alpha} of 0.765, with three extracted factors via exploratory
factor analysis, accounting for 66% of the cumulative variance. All items
exhibited factor loadings above 0.5. The scale's three-factor structure was
substantiated through confirmatory factor analysis with satisfactory fit
indices (chi2/df=5.11, RMSEA=0.063, GFI=0.990, AGFI=0.966, NFI=0.984,
IFI=0.987, CFI=0.987). IRT modeling confirmed reasonable discrimination and
location parameters for all items, free of differential item functioning (DIF)
by gender. Except for items 4 and 10, the remaining items were informative at
medium theta levels, indicating their utility in assessing medication literacy
efficiently. The developed 6-item Medication Literacy Short Form (MLS-SF)
proves to be a reliable and valid instrument for the expedited evaluation of
college students' medication literacy, offering a valuable addition to the
arsenal of health literacy assessment tools.
| [
{
"created": "Sun, 5 May 2024 08:56:54 GMT",
"version": "v1"
}
] | 2024-05-07 | [
[
"Zhenzhen",
"Chen",
""
],
[
"Jiabao",
"Ren",
""
],
[
"Tingyu",
"Duan",
""
],
[
"Ke",
"Chen",
""
],
[
"Ruyi",
"Hou",
""
],
[
"Yimiao",
"Li",
""
],
[
"Leixiao",
"Zeng",
""
],
[
"Xiaoxuan",
"Meng",
""
],
[
"Yibo",
"Wu",
""
],
[
"Yu",
"Liu",
""
]
] | Medication literacy is integral to health literacy, pivotal for medication safety and adherence. It denotes an individual's capacity to discern, comprehend, and convey medication-related information. Existing scales, however, are time-consuming and predominantly cater to patients and community dwellers, necessitating a more succinct instrument. This study presents the development of a brief Medication Literacy Scale (MLS-14) utilizing classical test theory (CTT) and item response theory (IRT), targeting a college student demographic. The MLS-14's abbreviated version, a 6-item scale (MLS-SF), was distilled through CTT and IRT methodologies, engaging 2431 Chinese college students to scrutinize its psychometric properties. The MLS-SF demonstrated a Cronbach's {\alpha} of 0.765, with three extracted factors via exploratory factor analysis, accounting for 66% of the cumulative variance. All items exhibited factor loadings above 0.5. The scale's three-factor structure was substantiated through confirmatory factor analysis with satisfactory fit indices (chi2/df=5.11, RMSEA=0.063, GFI=0.990, AGFI=0.966, NFI=0.984, IFI=0.987, CFI=0.987). IRT modeling confirmed reasonable discrimination and location parameters for all items, free of differential item functioning (DIF) by gender. Except for items 4 and 10, the remaining items were informative at medium theta levels, indicating their utility in assessing medication literacy efficiently. The developed 6-item Medication Literacy Short Form (MLS-SF) proves to be a reliable and valid instrument for the expedited evaluation of college students' medication literacy, offering a valuable addition to the arsenal of health literacy assessment tools. |
1808.00204 | Heather Etchevers | Heather Etchevers (MMG, GMGF), Elisabeth Dupin, Nicole Le Douarin | The importance and impact of discoveries about neural crest fates | null | Development (Cambridge, England), Company of Biologists, In press | null | null | q-bio.CB q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We review here some of the historical highlights in exploratory studies of
the vertebrate embryonic structure known as the neural crest. The study of the
molecular properties of the cells that it produces, their migratory capacities
and plasticity, and the still-growing list of tissues that depend on their
presence for form and function, continue to enrich our understanding of
congenital malformations, pediatric cancers but also of evolutionary biology.
| [
{
"created": "Wed, 1 Aug 2018 07:29:25 GMT",
"version": "v1"
}
] | 2018-08-02 | [
[
"Etchevers",
"Heather",
"",
"MMG, GMGF"
],
[
"Dupin",
"Elisabeth",
""
],
[
"Douarin",
"Nicole Le",
""
]
] | We review here some of the historical highlights in exploratory studies of the vertebrate embryonic structure known as the neural crest. The study of the molecular properties of the cells that it produces, their migratory capacities and plasticity, and the still-growing list of tissues that depend on their presence for form and function, continue to enrich our understanding of congenital malformations, pediatric cancers but also of evolutionary biology. |
1902.01267 | Dafydd Gibbon | Dafydd Gibbon and Xuewei Lin | Rhythm Zone Theory: Speech Rhythms are Physical after all | 15 pages, 9 figures, submitted | null | null | null | q-bio.NC cs.CL cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Speech rhythms have been dealt with in three main ways: from the
introspective analyses of rhythm as a correlate of syllable and foot timing in
linguistics and applied linguistics, through analyses of durations of segments
of utterances associated with consonantal and vocalic properties, syllables,
feet and words, to models of rhythms in speech production and perception as
physical oscillations. The present study avoids introspection and
human-filtered annotation methods and extends the signal processing paradigm of
amplitude envelope spectrum analysis by adding an additional analytic step of
edge detection, and postulating the co-existence of multiple speech rhythms in
rhythm zones marked by identifiable edges (Rhythm Zone Theory, RZT). An
exploratory investigation of the utility of RZT is conducted, suggesting that
native and non-native readings of the same text are distinct sub-genres of read
speech: a reading by a US native speaker and non-native readings by relatively
low-performing Cantonese adult learners of English. The study concludes by
noting that with the methods used, RZT can distinguish between the speech
rhythms of well-defined sub-genres of native speaker reading vs. non-native
learner reading, but needs further refinement in order to be applied to the
paradoxically more complex speech of low-performing language learners, whose
speech rhythms are co-determined by non-fluency and disfluency factors in
addition to well-known linguistic factors of grammar, vocabulary and discourse
constraints.
| [
{
"created": "Thu, 31 Jan 2019 20:49:17 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Mar 2019 19:01:22 GMT",
"version": "v2"
}
] | 2019-03-14 | [
[
"Gibbon",
"Dafydd",
""
],
[
"Lin",
"Xuewei",
""
]
] | Speech rhythms have been dealt with in three main ways: from the introspective analyses of rhythm as a correlate of syllable and foot timing in linguistics and applied linguistics, through analyses of durations of segments of utterances associated with consonantal and vocalic properties, syllables, feet and words, to models of rhythms in speech production and perception as physical oscillations. The present study avoids introspection and human-filtered annotation methods and extends the signal processing paradigm of amplitude envelope spectrum analysis by adding an additional analytic step of edge detection, and postulating the co-existence of multiple speech rhythms in rhythm zones marked by identifiable edges (Rhythm Zone Theory, RZT). An exploratory investigation of the utility of RZT is conducted, suggesting that native and non-native readings of the same text are distinct sub-genres of read speech: a reading by a US native speaker and non-native readings by relatively low-performing Cantonese adult learners of English. The study concludes by noting that with the methods used, RZT can distinguish between the speech rhythms of well-defined sub-genres of native speaker reading vs. non-native learner reading, but needs further refinement in order to be applied to the paradoxically more complex speech of low-performing language learners, whose speech rhythms are co-determined by non-fluency and disfluency factors in addition to well-known linguistic factors of grammar, vocabulary and discourse constraints. |
1711.06865 | Yue Ren | Yue Ren and Johannes W. R. Martini and Jacinta Torres | Decoupled molecules with binding polynomials of bidegree (n,2) | 18 pages, 8 figures | Journal of Mathematical Biology (2019)
https://doi.org/10.1007/s00285-018-1295-x | null | null | q-bio.BM cs.SC math.AG physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a result on the number of decoupled molecules for systems binding
two different types of ligands. In the case of $n$ and $2$ binding sites
respectively, we show that, generically, there are $2(n!)^{2}$ decoupled
molecules with the same binding polynomial. For molecules with more binding
sites for the second ligand, we provide computational results.
| [
{
"created": "Sat, 18 Nov 2017 14:02:59 GMT",
"version": "v1"
}
] | 2020-02-12 | [
[
"Ren",
"Yue",
""
],
[
"Martini",
"Johannes W. R.",
""
],
[
"Torres",
"Jacinta",
""
]
] | We present a result on the number of decoupled molecules for systems binding two different types of ligands. In the case of $n$ and $2$ binding sites respectively, we show that, generically, there are $2(n!)^{2}$ decoupled molecules with the same binding polynomial. For molecules with more binding sites for the second ligand, we provide computational results. |
1510.03351 | Martin Weigt | Eleonora De Leonardis, Benjamin Lutz, Sebastian Ratz, Simona Cocco,
Remi Monasson, Alexander Schug, Martin Weigt | Direct-Coupling Analysis of nucleotide coevolution facilitates RNA
secondary and tertiary structure prediction | 22 pages, 8 figures, supplemental information available on the
publisher's webpage
(http://nar.oxfordjournals.org/content/early/2015/09/29/nar.gkv932.abstract) | Nucl. Acids Res. (2015) doi: 10.1093/nar/gkv932, First published
online: September 29, 2015 | 10.1093/nar/gkv932 | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Despite the biological importance of non-coding RNA, their structural
characterization remains challenging. Making use of the rapidly growing
sequence databases, we analyze nucleotide coevolution across homologous
sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide
contacts. For a representative set of riboswitches, we show that the results of
Direct-Coupling Analysis in combination with a generalized Nussinov algorithm
systematically improve the results of RNA secondary structure prediction beyond
traditional covariance approaches based on mutual information. Even more
importantly, we show that the results of Direct-Coupling Analysis are enriched
in tertiary structure contacts. By integrating these predictions into molecular
modeling tools, systematically improved tertiary structure predictions can be
obtained, as compared to using secondary structure information alone.
| [
{
"created": "Mon, 12 Oct 2015 16:17:04 GMT",
"version": "v1"
}
] | 2015-10-13 | [
[
"De Leonardis",
"Eleonora",
""
],
[
"Lutz",
"Benjamin",
""
],
[
"Ratz",
"Sebastian",
""
],
[
"Cocco",
"Simona",
""
],
[
"Monasson",
"Remi",
""
],
[
"Schug",
"Alexander",
""
],
[
"Weigt",
"Martin",
""
]
] | Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone. |
1006.0079 | Denis Boyer | Denis Boyer and Peter D. Walsh | Modeling the mobility of living organisms in heterogeneous landscapes:
Does memory improve foraging success? | 14 pages, 4 figures, improved discussion | null | 10.1098/rsta.2010.0275 | null | q-bio.PE cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Thanks to recent technological advances, it is now possible to track with an
unprecedented precision and for long periods of time the movement patterns of
many living organisms in their habitat. The increasing amount of data available
on single trajectories offers the possibility of understanding how animals move
and of testing basic movement models. Random walks have long represented the
main description for micro-organisms and have also been useful to understand
the foraging behaviour of large animals. Nevertheless, most vertebrates, in
particular humans and other primates, rely on sophisticated cognitive tools
such as spatial maps, episodic memory and travel cost discounting. These
properties call for other modeling approaches of mobility patterns. We propose
a foraging framework where a learning mobile agent uses a combination of
memory-based and random steps. We investigate how advantageous it is to use
memory for exploiting resources in heterogeneous and changing environments. An
adequate balance of determinism and random exploration is found to maximize the
foraging efficiency and to generate trajectories with an intricate
spatio-temporal order. Based on this approach, we propose some tools for
analysing the non-random nature of mobility patterns in general.
| [
{
"created": "Tue, 1 Jun 2010 08:24:26 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Oct 2010 18:21:38 GMT",
"version": "v2"
}
] | 2015-05-19 | [
[
"Boyer",
"Denis",
""
],
[
"Walsh",
"Peter D.",
""
]
] | Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophisticated cognitive tools such as spatial maps, episodic memory and travel cost discounting. These properties call for other modeling approaches of mobility patterns. We propose a foraging framework where a learning mobile agent uses a combination of memory-based and random steps. We investigate how advantageous it is to use memory for exploiting resources in heterogeneous and changing environments. An adequate balance of determinism and random exploration is found to maximize the foraging efficiency and to generate trajectories with an intricate spatio-temporal order. Based on this approach, we propose some tools for analysing the non-random nature of mobility patterns in general. |
1912.05625 | Seonwoo Min | Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Byunghan Lee,
Sungroh Yoon | Pre-Training of Deep Bidirectional Protein Sequence Representations with
Structural Information | Published in IEEE Access 2021
(https://ieeexplore.ieee.org/document/9529198) | null | null | null | q-bio.BM cs.LG q-bio.GN stat.ML | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Bridging the exponentially growing gap between the numbers of unlabeled and
labeled protein sequences, several studies adopted semi-supervised learning for
protein sequence modeling. In these studies, models were pre-trained with a
substantial amount of unlabeled data, and the representations were transferred
to various downstream tasks. Most pre-training methods solely rely on language
modeling and often exhibit limited performance. In this paper, we introduce a
novel pre-training scheme called PLUS, which stands for Protein sequence
representations Learned Using Structural information. PLUS consists of masked
language modeling and a complementary protein-specific pre-training task,
namely same-family prediction. PLUS can be used to pre-train various model
architectures. In this work, we use PLUS to pre-train a bidirectional recurrent
neural network and refer to the resulting model as PLUS-RNN. Our experiment
results demonstrate that PLUS-RNN outperforms other models of similar size
solely pre-trained with the language modeling in six out of seven widely used
protein biology tasks. Furthermore, we present the results from our qualitative
interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides
a novel way to exploit evolutionary relationships among unlabeled proteins and
is broadly applicable across a variety of protein biology tasks. We expect that
the gap between the numbers of unlabeled and labeled proteins will continue to
grow exponentially, and the proposed pre-training method will play a larger
role.
| [
{
"created": "Mon, 25 Nov 2019 10:12:10 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Feb 2020 09:06:30 GMT",
"version": "v2"
},
{
"created": "Sat, 25 Apr 2020 03:58:33 GMT",
"version": "v3"
},
{
"created": "Thu, 16 Sep 2021 23:13:47 GMT",
"version": "v4"
}
] | 2021-09-20 | [
[
"Min",
"Seonwoo",
""
],
[
"Park",
"Seunghyun",
""
],
[
"Kim",
"Siwon",
""
],
[
"Choi",
"Hyun-Soo",
""
],
[
"Lee",
"Byunghan",
""
],
[
"Yoon",
"Sungroh",
""
]
] | Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. Most pre-training methods solely rely on language modeling and often exhibit limited performance. In this paper, we introduce a novel pre-training scheme called PLUS, which stands for Protein sequence representations Learned Using Structural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction. PLUS can be used to pre-train various model architectures. In this work, we use PLUS to pre-train a bidirectional recurrent neural network and refer to the resulting model as PLUS-RNN. Our experiment results demonstrate that PLUS-RNN outperforms other models of similar size solely pre-trained with the language modeling in six out of seven widely used protein biology tasks. Furthermore, we present the results from our qualitative interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides a novel way to exploit evolutionary relationships among unlabeled proteins and is broadly applicable across a variety of protein biology tasks. We expect that the gap between the numbers of unlabeled and labeled proteins will continue to grow exponentially, and the proposed pre-training method will play a larger role. |
1904.11136 | Youshan Zhang | Youshan Zhang | Corticospinal Tract (CST) reconstruction based on fiber orientation
distributions(FODs) tractography | null | 2018 IEEE 18th International Conference on Bioinformatics and
Bioengineering (BIBE), Taichung, 2018, pp. 305-310 | 10.1109/BIBE.2018.00066 | null | q-bio.NC cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Corticospinal Tract (CST) is a part of pyramidal tract (PT), and it can
innervate the voluntary movement of skeletal muscle through spinal interneurons
(the 4th layer of the Rexed gray board layers), and anterior horn motorneurons
(which control trunk and proximal limb muscles). Spinal cord injury (SCI) is a
highly disabling disease often caused by traffic accidents. The recovery of CST
and the functional reconstruction of spinal anterior horn motor neurons play an
essential role in the treatment of SCI. However, the localization and
reconstruction of CST are still challenging issues; the accuracy of the
geometric reconstruction can directly affect the results of the surgery. The
main contribution of this paper is the reconstruction of the CST based on the
fiber orientation distributions (FODs) tractography. Differing from
tensor-based tractography in which the primary direction is a determined
orientation, the direction of FODs tractography is determined by the
probability. The spherical harmonics (SPHARM) can be used to approximate the
efficiency of FODs tractography. We manually delineate the three ROIs (the
posterior limb of the internal capsule, the cerebral peduncle, and the anterior
pontine area) by the ITK-SNAP software, and use the pipeline software to
reconstruct both the left and right sides of the CST fibers. Our results
demonstrate that FOD-based tractography can show more and correct anatomical
CST fiber bundles.
| [
{
"created": "Tue, 23 Apr 2019 16:19:06 GMT",
"version": "v1"
}
] | 2019-04-26 | [
[
"Zhang",
"Youshan",
""
]
] | The Corticospinal Tract (CST) is a part of pyramidal tract (PT), and it can innervate the voluntary movement of skeletal muscle through spinal interneurons (the 4th layer of the Rexed gray board layers), and anterior horn motorneurons (which control trunk and proximal limb muscles). Spinal cord injury (SCI) is a highly disabling disease often caused by traffic accidents. The recovery of CST and the functional reconstruction of spinal anterior horn motor neurons play an essential role in the treatment of SCI. However, the localization and reconstruction of CST are still challenging issues; the accuracy of the geometric reconstruction can directly affect the results of the surgery. The main contribution of this paper is the reconstruction of the CST based on the fiber orientation distributions (FODs) tractography. Differing from tensor-based tractography in which the primary direction is a determined orientation, the direction of FODs tractography is determined by the probability. The spherical harmonics (SPHARM) can be used to approximate the efficiency of FODs tractography. We manually delineate the three ROIs (the posterior limb of the internal capsule, the cerebral peduncle, and the anterior pontine area) by the ITK-SNAP software, and use the pipeline software to reconstruct both the left and right sides of the CST fibers. Our results demonstrate that FOD-based tractography can show more and correct anatomical CST fiber bundles. |
2301.00148 | Zixiang Luo | Zixiang Luo, Kaining Peng, Zhichao Liang, Shengyuan Cai, Chenyu Xu,
Dan Li, Yu Hu, Changsong Zhou, Quanying Liu | Mapping effective connectivity by virtually perturbing a surrogate brain | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Effective connectivity (EC), indicative of the causal interactions between
brain regions, is fundamental to understanding information processing in the
brain. Traditional approaches, which infer EC from neural responses to
stimulations, are not suited for mapping whole-brain EC in human due to being
invasive and limited spatial coverage of stimulations. To address this gap, we
present Neural Perturbational Inference (NPI), a data-driven framework designed
to map EC across the entire brain. NPI employs an artificial neural network
trained to learn large-scale neural dynamics as a computational surrogate of
the brain. NPI maps EC by perturbing each region of the surrogate brain and
observing the resulting responses in the rest of regions. NPI captures the
directionality, strength, and excitatory/inhibitory properties of EC on a
brain-wide scale. Our validation of NPI, using models with established EC,
shows its superiority over Granger Causality and Dynamic Causal Modeling.
Applying NPI to resting-state fMRI data from diverse datasets reveals
consistent and structurally supported EC. Applications on a disease-specific
dataset highlight the potential of using personalized EC as biomarkers for
neurological diseases. By transitioning from correlational to causal
understandings of brain functionality, NPI marks a stride in decoding the
brain's functional architecture and can facilitate neuroscience research and
clinical applications.
| [
{
"created": "Sat, 31 Dec 2022 08:09:13 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Mar 2023 08:38:51 GMT",
"version": "v2"
},
{
"created": "Thu, 14 Mar 2024 13:58:44 GMT",
"version": "v3"
}
] | 2024-03-15 | [
[
"Luo",
"Zixiang",
""
],
[
"Peng",
"Kaining",
""
],
[
"Liang",
"Zhichao",
""
],
[
"Cai",
"Shengyuan",
""
],
[
"Xu",
"Chenyu",
""
],
[
"Li",
"Dan",
""
],
[
"Hu",
"Yu",
""
],
[
"Zhou",
"Changsong",
""
],
[
"Liu",
"Quanying",
""
]
] | Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in human due to being invasive and limited spatial coverage of stimulations. To address this gap, we present Neural Perturbational Inference (NPI), a data-driven framework designed to map EC across the entire brain. NPI employs an artificial neural network trained to learn large-scale neural dynamics as a computational surrogate of the brain. NPI maps EC by perturbing each region of the surrogate brain and observing the resulting responses in the rest of regions. NPI captures the directionality, strength, and excitatory/inhibitory properties of EC on a brain-wide scale. Our validation of NPI, using models with established EC, shows its superiority over Granger Causality and Dynamic Causal Modeling. Applying NPI to resting-state fMRI data from diverse datasets reveals consistent and structurally supported EC. Applications on a disease-specific dataset highlight the potential of using personalized EC as biomarkers for neurological diseases. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain's functional architecture and can facilitate neuroscience research and clinical applications. |
q-bio/0605036 | Nigel Goldenfeld | Kalin Vetsigian, Carl Woese and Nigel Goldenfeld (University of
Illinois at Urbana-Champaign) | Collective evolution and the genetic code | null | null | 10.1073/pnas.0603780103 | null | q-bio.PE nlin.AO | null | A dynamical theory for the evolution of the genetic code is presented, which
accounts for its universality and optimality. The central concept is that a
variety of collective, but non-Darwinian, mechanisms likely to be present in
early communal life generically lead to refinement and selection of
innovation-sharing protocols, such as the genetic code. Our proposal is
illustrated using a simplified computer model, and placed within the context of
a sequence of transitions that early life may have made, prior to the emergence
of vertical descent.
| [
{
"created": "Mon, 22 May 2006 16:52:27 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Vetsigian",
"Kalin",
"",
"University of\n Illinois at Urbana-Champaign"
],
[
"Woese",
"Carl",
"",
"University of\n Illinois at Urbana-Champaign"
],
[
"Goldenfeld",
"Nigel",
"",
"University of\n Illinois at Urbana-Champaign"
]
] | A dynamical theory for the evolution of the genetic code is presented, which accounts for its universality and optimality. The central concept is that a variety of collective, but non-Darwinian, mechanisms likely to be present in early communal life generically lead to refinement and selection of innovation-sharing protocols, such as the genetic code. Our proposal is illustrated using a simplified computer model, and placed within the context of a sequence of transitions that early life may have made, prior to the emergence of vertical descent. |
1612.00644 | Peter Zeidman | Peter Zeidman, Edward Harry Silson, Dietrich Samuel Schwarzkopf, Chris
Ian Baker, Will Penny | Bayesian Population Receptive Field Modelling | 30 pages, 10 figures. Code available at
https://github.com/pzeidman/BayespRF | null | 10.1016/j.neuroimage.2017.09.008 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a probabilistic (Bayesian) framework and associated software
toolbox for mapping population receptive fields (pRFs) based on fMRI data. This
generic approach is intended to work with stimuli of any dimension and is
demonstrated and validated in the context of 2D retinotopic mapping. The
framework enables the experimenter to specify generative (encoding) models of
fMRI timeseries, in which experimental manipulations enter a pRF model of
neural activity, which in turns drives a nonlinear model of neurovascular
coupling and Blood Oxygenation Level Dependent (BOLD) response. The neuronal
and haemodynamic parameters are estimated together on a voxel-by-voxel or
region-of-interest basis using a Bayesian estimation algorithm (variational
Laplace). This offers several novel contributions to receptive field modelling.
The variance / covariance of parameters are estimated, enabling receptive
fields to be plotted while properly representing uncertainty about pRF size and
location. Variability in the haemodynamic response across the brain is
accounted for. Furthermore, the framework introduces formal hypothesis testing
to pRF analysis, enabling competing models to be evaluated based on their model
evidence (approximated by the variational free energy), which represents the
optimal tradeoff between accuracy and complexity. Using simulations and
empirical data, we found that parameters typically used to represent pRF size
and neuronal scaling are strongly correlated, which should be taken into
account when making inferences. We used the framework to compare the evidence
for six variants of pRF model using 7T functional MRI data and we found a
circular Difference of Gaussians (DoG) model to be the best explanation for our
data overall. We hope this framework will prove useful for mapping stimulus
spaces with any number of dimensions onto the anatomy of the brain.
| [
{
"created": "Fri, 2 Dec 2016 11:48:17 GMT",
"version": "v1"
}
] | 2018-05-21 | [
[
"Zeidman",
"Peter",
""
],
[
"Silson",
"Edward Harry",
""
],
[
"Schwarzkopf",
"Dietrich Samuel",
""
],
[
"Baker",
"Chris Ian",
""
],
[
"Penny",
"Will",
""
]
] | We introduce a probabilistic (Bayesian) framework and associated software toolbox for mapping population receptive fields (pRFs) based on fMRI data. This generic approach is intended to work with stimuli of any dimension and is demonstrated and validated in the context of 2D retinotopic mapping. The framework enables the experimenter to specify generative (encoding) models of fMRI timeseries, in which experimental manipulations enter a pRF model of neural activity, which in turns drives a nonlinear model of neurovascular coupling and Blood Oxygenation Level Dependent (BOLD) response. The neuronal and haemodynamic parameters are estimated together on a voxel-by-voxel or region-of-interest basis using a Bayesian estimation algorithm (variational Laplace). This offers several novel contributions to receptive field modelling. The variance / covariance of parameters are estimated, enabling receptive fields to be plotted while properly representing uncertainty about pRF size and location. Variability in the haemodynamic response across the brain is accounted for. Furthermore, the framework introduces formal hypothesis testing to pRF analysis, enabling competing models to be evaluated based on their model evidence (approximated by the variational free energy), which represents the optimal tradeoff between accuracy and complexity. Using simulations and empirical data, we found that parameters typically used to represent pRF size and neuronal scaling are strongly correlated, which should be taken into account when making inferences. We used the framework to compare the evidence for six variants of pRF model using 7T functional MRI data and we found a circular Difference of Gaussians (DoG) model to be the best explanation for our data overall. We hope this framework will prove useful for mapping stimulus spaces with any number of dimensions onto the anatomy of the brain. |
1803.11274 | Matteo Manica | Matteo Manica, Joris Cadow, Roland Mathis and Mar\'ia Rodr\'iguez
Mart\'inez | PIMKL: Pathway Induced Multiple Kernel Learning | null | npj Systems Biology and Applications (2019) | 10.1038/s41540-019-0086-3 | null | q-bio.MN stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reliable identification of molecular biomarkers is essential for accurate
patient stratification. While state-of-the-art machine learning approaches for
sample classification continue to push boundaries in terms of performance, most
of these methods are not able to integrate different data types and lack
generalization power, limiting their application in a clinical setting.
Furthermore, many methods behave as black boxes, and we have very little
understanding about the mechanisms that lead to the prediction. While
opaqueness concerning machine behaviour might not be a problem in deterministic
domains, in health care, providing explanations about the molecular factors and
phenotypes that are driving the classification is crucial to build trust in the
performance of the predictive system. We propose Pathway Induced Multiple
Kernel Learning (PIMKL), a novel methodology to reliably classify samples that
can also help gain insights into the molecular mechanisms that underlie the
classification. PIMKL exploits prior knowledge in the form of a molecular
interaction network and annotated gene sets, by optimizing a mixture of
pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an
approach that has demonstrated excellent performance in different machine
learning applications. After optimizing the combination of kernels for
prediction of a specific phenotype, the model provides a stable molecular
signature that can be interpreted in the light of the ingested prior knowledge
and that can be used in transfer learning tasks.
| [
{
"created": "Thu, 29 Mar 2018 22:28:51 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Apr 2018 13:20:51 GMT",
"version": "v2"
},
{
"created": "Thu, 5 Jul 2018 14:29:15 GMT",
"version": "v3"
}
] | 2019-11-07 | [
[
"Manica",
"Matteo",
""
],
[
"Cadow",
"Joris",
""
],
[
"Mathis",
"Roland",
""
],
[
"Martínez",
"María Rodríguez",
""
]
] | Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power, limiting their application in a clinical setting. Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction. While opaqueness concerning machine behaviour might not be a problem in deterministic domains, in health care, providing explanations about the molecular factors and phenotypes that are driving the classification is crucial to build trust in the performance of the predictive system. We propose Pathway Induced Multiple Kernel Learning (PIMKL), a novel methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an approach that has demonstrated excellent performance in different machine learning applications. After optimizing the combination of kernels for prediction of a specific phenotype, the model provides a stable molecular signature that can be interpreted in the light of the ingested prior knowledge and that can be used in transfer learning tasks. |
1306.2353 | Wai Lim Ku | Wai Lim Ku, Michelle Girvan, Guo-Cheng Yuan, Francesco Sorrentino,
Edward Ott | Modeling the dynamics of bivalent histone modifications | 23 pages, 10 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epigenetic modifications to histones may promote either activation or
repression of the transcription of nearby genes. Recent experimental studies
show that the promoters of many lineage-control genes in stem cells have
"bivalent domains" in which the nucleosomes contain both active (H3K4me3) and
repressive (H3K27me3) marks. It is generally agreed that bivalent domains play
an important role in stem cell differentiation, but the underlying mechanisms
remain unclear. Here we formulate a mathematical model to investigate the
dynamic properties of histone modification patterns. We then illustrate that
our modeling framework can be used to capture key features of experimentally
observed combinatorial chromatin states.
| [
{
"created": "Fri, 7 Jun 2013 15:52:11 GMT",
"version": "v1"
}
] | 2013-06-12 | [
[
"Ku",
"Wai Lim",
""
],
[
"Girvan",
"Michelle",
""
],
[
"Yuan",
"Guo-Cheng",
""
],
[
"Sorrentino",
"Francesco",
""
],
[
"Ott",
"Edward",
""
]
] | Epigenetic modifications to histones may promote either activation or repression of the transcription of nearby genes. Recent experimental studies show that the promoters of many lineage-control genes in stem cells have "bivalent domains" in which the nucleosomes contain both active (H3K4me3) and repressive (H3K27me3) marks. It is generally agreed that bivalent domains play an important role in stem cell differentiation, but the underlying mechanisms remain unclear. Here we formulate a mathematical model to investigate the dynamic properties of histone modification patterns. We then illustrate that our modeling framework can be used to capture key features of experimentally observed combinatorial chromatin states. |
1906.11398 | James Hope Mr | James Hope, Zaid Aqrawe, Marshall Lim, Frederique Vanholsbeeck, Andrew
McDaid | Increasing signal amplitude in electrical impedance tomography of neural
activity using a parallel resistor inductor capacitor (RLC) circuit | 18 pages, 14 figures, journal submission | null | 10.1088/1741-2552/ab462b | null | q-bio.NC physics.ins-det | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: To increase the impedance signal amplitude produced during neural
activity using a novel approach of implementing a parallel resistor inductor
capacitor (RLC) circuit across the current source used in electrical impedance
tomography (EIT) of peripheral nerve. Approach: Experiments were performed in
vitro on sciatic nerve of Sprague-Dawley rats. Design of the RLC circuit was
performed in electrical circuit modelling software, aided by in vitro impedance
measurements on nerve and nerve cuff in the range 5 Hz to 50 kHz. Main results:
The frequency range 17 +/- 1 kHz was selected for the RLC experiment. The RLC
experiment was performed on four subjects using an RLC circuit designed to
produce a resonant frequency of 17 kHz with a bandwidth of 3.6 kHz, and
containing a 22 mH inductive element and a 3.45 nF capacitive element. With the
RLC circuit connected, relative increases in the impedance signal (+/- 3sig
noise) of 44 % (+/-15 %), 33 % (+/-30 %), 37 % (+/-8.6 %), and 16 % (+/-19 %)
were produced. Significance: The increase in impedance signal amplitude at high
frequencies, generated by the novel implementation of a parallel RLC circuit
across the drive current, improves spatial resolution by increasing the number
of parallel drive currents which can be implemented in a frequency division
multiplexed (FDM) EIT system, and aids the long term goal of a real-time FDM
EIT system by reducing the need for ensemble averaging.
| [
{
"created": "Thu, 27 Jun 2019 00:19:25 GMT",
"version": "v1"
}
] | 2019-12-06 | [
[
"Hope",
"James",
""
],
[
"Aqrawe",
"Zaid",
""
],
[
"Lim",
"Marshall",
""
],
[
"Vanholsbeeck",
"Frederique",
""
],
[
"McDaid",
"Andrew",
""
]
] | Objective: To increase the impedance signal amplitude produced during neural activity using a novel approach of implementing a parallel resistor inductor capacitor (RLC) circuit across the current source used in electrical impedance tomography (EIT) of peripheral nerve. Approach: Experiments were performed in vitro on sciatic nerve of Sprague-Dawley rats. Design of the RLC circuit was performed in electrical circuit modelling software, aided by in vitro impedance measurements on nerve and nerve cuff in the range 5 Hz to 50 kHz. Main results: The frequency range 17 +/- 1 kHz was selected for the RLC experiment. The RLC experiment was performed on four subjects using an RLC circuit designed to produce a resonant frequency of 17 kHz with a bandwidth of 3.6 kHz, and containing a 22 mH inductive element and a 3.45 nF capacitive element. With the RLC circuit connected, relative increases in the impedance signal (+/- 3sig noise) of 44 % (+/-15 %), 33 % (+/-30 %), 37 % (+/-8.6 %), and 16 % (+/-19 %) were produced. Significance: The increase in impedance signal amplitude at high frequencies, generated by the novel implementation of a parallel RLC circuit across the drive current, improves spatial resolution by increasing the number of parallel drive currents which can be implemented in a frequency division multiplexed (FDM) EIT system, and aids the long term goal of a real-time FDM EIT system by reducing the need for ensemble averaging. |
1301.1730 | Pascal Grange | Pascal Grange, Michael Hawrylycz and Partha P. Mitra | Computational neuroanatomy and co-expression of genes in the adult mouse
brain, analysis tools for the Allen Brain Atlas | 25 pages, 8 figures, accepted in Quantitative Biology (2012) 0002 | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We review quantitative methods and software developed to analyze
genome-scale, brain-wide spatially-mapped gene-expression data. We expose new
methods based on the underlying high-dimensional geometry of voxel space and
gene space, and on simulations of the distribution of co-expression networks of
a given size. We apply them to the Allen Atlas of the adult mouse brain, and to
the co-expression network of a set of genes related to nicotine addiction
retrieved from the NicSNP database. The computational methods are implemented
in {\ttfamily{BrainGeneExpressionAnalysis}}, a Matlab toolbox available for
download.
| [
{
"created": "Wed, 9 Jan 2013 01:03:10 GMT",
"version": "v1"
}
] | 2013-01-10 | [
[
"Grange",
"Pascal",
""
],
[
"Hawrylycz",
"Michael",
""
],
[
"Mitra",
"Partha P.",
""
]
] | We review quantitative methods and software developed to analyze genome-scale, brain-wide spatially-mapped gene-expression data. We expose new methods based on the underlying high-dimensional geometry of voxel space and gene space, and on simulations of the distribution of co-expression networks of a given size. We apply them to the Allen Atlas of the adult mouse brain, and to the co-expression network of a set of genes related to nicotine addiction retrieved from the NicSNP database. The computational methods are implemented in {\ttfamily{BrainGeneExpressionAnalysis}}, a Matlab toolbox available for download. |
2310.03269 | Zeyuan Wang | Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong
Li, Huajun Chen | InstructProtein: Aligning Human and Protein Language via Knowledge
Instruction | null | null | null | null | q-bio.BM cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have revolutionized the field of natural
language processing, but they fall short in comprehending biological sequences
such as proteins. To address this challenge, we propose InstructProtein, an
innovative LLM that possesses bidirectional generation capabilities in both
human and protein languages: (i) taking a protein sequence as input to predict
its textual function description and (ii) using natural language to prompt
protein sequence generation. To achieve this, we first pre-train an LLM on both
protein and natural language corpora, enabling it to comprehend individual
languages. Then supervised instruction tuning is employed to facilitate the
alignment of these two distinct languages. Herein, we introduce a knowledge
graph-based instruction generation framework to construct a high-quality
instruction dataset, addressing annotation imbalance and instruction deficits
in existing protein-text corpus. In particular, the instructions inherit the
structural relations between proteins and function annotations in knowledge
graphs, which empowers our model to engage in the causal modeling of protein
functions, akin to the chain-of-thought processes in natural languages.
Extensive experiments on bidirectional protein-text generation tasks show that
InstructProtein outperforms state-of-the-art LLMs by large margins. Moreover,
InstructProtein serves as a pioneering step towards text-based protein function
prediction and sequence design, effectively bridging the gap between protein
and human language understanding.
| [
{
"created": "Thu, 5 Oct 2023 02:45:39 GMT",
"version": "v1"
}
] | 2023-10-06 | [
[
"Wang",
"Zeyuan",
""
],
[
"Zhang",
"Qiang",
""
],
[
"Ding",
"Keyan",
""
],
[
"Qin",
"Ming",
""
],
[
"Zhuang",
"Xiang",
""
],
[
"Li",
"Xiaotong",
""
],
[
"Chen",
"Huajun",
""
]
] | Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation. To achieve this, we first pre-train an LLM on both protein and natural language corpora, enabling it to comprehend individual languages. Then supervised instruction tuning is employed to facilitate the alignment of these two distinct languages. Herein, we introduce a knowledge graph-based instruction generation framework to construct a high-quality instruction dataset, addressing annotation imbalance and instruction deficits in existing protein-text corpus. In particular, the instructions inherit the structural relations between proteins and function annotations in knowledge graphs, which empowers our model to engage in the causal modeling of protein functions, akin to the chain-of-thought processes in natural languages. Extensive experiments on bidirectional protein-text generation tasks show that InstructProtein outperforms state-of-the-art LLMs by large margins. Moreover, InstructProtein serves as a pioneering step towards text-based protein function prediction and sequence design, effectively bridging the gap between protein and human language understanding. |
1601.07415 | Thomas House | Frank Ball and Thomas House | Heterogeneous network epidemics: real-time growth, variance and
extinction of infection | 30 pages, 4 figures, Journal of Mathematical Biology, 2017 | null | 10.1007/s00285-016-1092-3 | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent years have seen a large amount of interest in epidemics on networks as
a way of representing the complex structure of contacts capable of spreading
infections through the modern human population. The configuration model is a
popular choice in theoretical studies since it combines the ability to specify
the distribution of the number of contacts (degree) with analytical
tractability. Here we consider the early real-time behaviour of the Markovian
SIR epidemic model on a configuration model network using a multi-type
branching process. We find closed-form analytic expressions for the mean and
variance of the number of infectious individuals as a function of time and the
degree of the initially infected individual(s), and write down a system of
differential equations for the probability of extinction that are numerically
fast compared to Monte Carlo simulation. We show that these quantities are all
sensitive to the degree distribution - in particular we confirm that the mean
prevalence of infection depends on the first two moments of the degree
distribution and the variance in prevalence depends on the first three moments
of the degree distribution. In contrast to most existing analytic approaches,
the accuracy of these results does not depend on having a large number of
infectious individuals, meaning that in the large population limit they would
be asymptotically exact even for one initial infectious individual.
| [
{
"created": "Wed, 27 Jan 2016 15:41:23 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Jan 2017 15:54:12 GMT",
"version": "v2"
}
] | 2017-01-23 | [
[
"Ball",
"Frank",
""
],
[
"House",
"Thomas",
""
]
] | Recent years have seen a large amount of interest in epidemics on networks as a way of representing the complex structure of contacts capable of spreading infections through the modern human population. The configuration model is a popular choice in theoretical studies since it combines the ability to specify the distribution of the number of contacts (degree) with analytical tractability. Here we consider the early real-time behaviour of the Markovian SIR epidemic model on a configuration model network using a multi-type branching process. We find closed-form analytic expressions for the mean and variance of the number of infectious individuals as a function of time and the degree of the initially infected individual(s), and write down a system of differential equations for the probability of extinction that are numerically fast compared to Monte Carlo simulation. We show that these quantities are all sensitive to the degree distribution - in particular we confirm that the mean prevalence of infection depends on the first two moments of the degree distribution and the variance in prevalence depends on the first three moments of the degree distribution. In contrast to most existing analytic approaches, the accuracy of these results does not depend on having a large number of infectious individuals, meaning that in the large population limit they would be asymptotically exact even for one initial infectious individual. |
2102.02669 | Xiaoyu Zhang | Xiaoyu Zhang, Yuting Xing, Kai Sun, Yike Guo | OmiEmbed: a unified multi-task deep learning framework for multi-omics
data | 14 pages, 8 figures, 7 tables | Cancers 2021, 13(12), 3047 | 10.3390/cancers13123047 | null | q-bio.GN cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High-dimensional omics data contains intrinsic biomedical information that is
crucial for personalised medicine. Nevertheless, it is challenging to capture
them from the genome-wide data due to the large number of molecular features
and small number of available samples, which is also called 'the curse of
dimensionality' in machine learning. To tackle this problem and pave the way
for machine learning aided precision medicine, we proposed a unified multi-task
deep learning framework named OmiEmbed to capture biomedical information from
high-dimensional omics data with the deep embedding and downstream task
modules. The deep embedding module learnt an omics embedding that mapped
multiple omics data types into a latent space with lower dimensionality. Based
on the new representation of multi-omics data, different downstream task
modules were trained simultaneously and efficiently with the multi-task
strategy to predict the comprehensive phenotype profile of each sample.
OmiEmbed support multiple tasks for omics data including dimensionality
reduction, tumour type classification, multi-omics integration, demographic and
clinical feature reconstruction, and survival prediction. The framework
outperformed other methods on all three types of downstream tasks and achieved
better performance with the multi-task strategy comparing to training them
individually. OmiEmbed is a powerful and unified framework that can be widely
adapted to various application of high-dimensional omics data and has a great
potential to facilitate more accurate and personalised clinical decision
making.
| [
{
"created": "Wed, 3 Feb 2021 07:34:29 GMT",
"version": "v1"
},
{
"created": "Tue, 18 May 2021 15:45:00 GMT",
"version": "v2"
}
] | 2021-06-22 | [
[
"Zhang",
"Xiaoyu",
""
],
[
"Xing",
"Yuting",
""
],
[
"Sun",
"Kai",
""
],
[
"Guo",
"Yike",
""
]
] | High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed support multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy comparing to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various application of high-dimensional omics data and has a great potential to facilitate more accurate and personalised clinical decision making. |
1504.00283 | Joshua Weitz | Hayriye Gulbudak, Joshua S. Weitz | A Touch of Sleep: Biophysical Model of Contact-mediated Dormancy of
Archaea by Viruses | 8 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The canonical view of the interactions between viruses and their microbial
hosts presumes that changes in host and virus fate require the initiation of
infection of a host by a virus. That is, first virus particles diffuse randomly
outside of host cells, then the virus genome enters the target host cell, and
only then do intracellular dynamics and regulation of virus and host cell fate
unfold. Intracellular dynamics may lead to the death of the host cell and
release of viruses, to the elimination of the virus genome through cellular
defense mechanisms, or the integration of the virus genome with the host as a
chromosomal or extra-chromosomal element. Here we revisit this canonical view,
inspired by recent experimental findings of Bautista and colleagues (mBio,
2015) in which the majority of target host cells can be induced into a dormant
state when exposed to either active or de-activated viruses, even when viruses
are present at low relative titer. We propose that both the qualitative
phenomena and the quantitative time-scales of dormancy induction can be
reconciled given the hypothesis that cellular physiology can be altered by
contact on the surface of host cells rather than strictly by infection. We
develop a biophysical model of contact-mediated dynamics involving virus
particles and target cells. We show how in this model virus particles can
catalyze - extracellularly - cellular transformations amongst many cells, even
if they ultimately infect only one (or none). We discuss implications of the
present biophysical model relevant to the study of virus-microbe interactions
more generally.
| [
{
"created": "Wed, 1 Apr 2015 16:35:34 GMT",
"version": "v1"
}
] | 2015-04-02 | [
[
"Gulbudak",
"Hayriye",
""
],
[
"Weitz",
"Joshua S.",
""
]
] | The canonical view of the interactions between viruses and their microbial hosts presumes that changes in host and virus fate require the initiation of infection of a host by a virus. That is, first virus particles diffuse randomly outside of host cells, then the virus genome enters the target host cell, and only then do intracellular dynamics and regulation of virus and host cell fate unfold. Intracellular dynamics may lead to the death of the host cell and release of viruses, to the elimination of the virus genome through cellular defense mechanisms, or the integration of the virus genome with the host as a chromosomal or extra-chromosomal element. Here we revisit this canonical view, inspired by recent experimental findings of Bautista and colleagues (mBio, 2015) in which the majority of target host cells can be induced into a dormant state when exposed to either active or de-activated viruses, even when viruses are present at low relative titer. We propose that both the qualitative phenomena and the quantitative time-scales of dormancy induction can be reconciled given the hypothesis that cellular physiology can be altered by contact on the surface of host cells rather than strictly by infection. We develop a biophysical model of contact-mediated dynamics involving virus particles and target cells. We show how in this model virus particles can catalyze - extracellularly - cellular transformations amongst many cells, even if they ultimately infect only one (or none). We discuss implications of the present biophysical model relevant to the study of virus-microbe interactions more generally. |
2304.05823 | Nan Rosemary Ke | Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein, Silvia Chiappa,
Melanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane Wang, Theophane
Weber, David Barrett, Matthew Botvinick, Anirudh Goyal, Mike Mozer, Danilo
Rezende | DiscoGen: Learning to Discover Gene Regulatory Networks | null | null | null | null | q-bio.MN cs.LG q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Accurately inferring Gene Regulatory Networks (GRNs) is a critical and
challenging task in biology. GRNs model the activatory and inhibitory
interactions between genes and are inherently causal in nature. To accurately
identify GRNs, perturbational data is required. However, most GRN discovery
methods only operate on observational data. Recent advances in neural
network-based causal discovery methods have significantly improved causal
discovery, including handling interventional data, improvements in performance
and scalability. However, applying state-of-the-art (SOTA) causal discovery
methods in biology poses challenges, such as noisy data and a large number of
samples. Thus, adapting the causal discovery methods is necessary to handle
these challenges. In this paper, we introduce DiscoGen, a neural network-based
GRN discovery method that can denoise gene expression measurements and handle
interventional data. We demonstrate that our model outperforms SOTA neural
network-based causal discovery methods.
| [
{
"created": "Wed, 12 Apr 2023 13:02:49 GMT",
"version": "v1"
}
] | 2023-04-13 | [
[
"Ke",
"Nan Rosemary",
""
],
[
"Dunn",
"Sara-Jane",
""
],
[
"Bornschein",
"Jorg",
""
],
[
"Chiappa",
"Silvia",
""
],
[
"Rey",
"Melanie",
""
],
[
"Lespiau",
"Jean-Baptiste",
""
],
[
"Cassirer",
"Albin",
""
],
[
"Wang",
"Jane",
""
],
[
"Weber",
"Theophane",
""
],
[
"Barrett",
"David",
""
],
[
"Botvinick",
"Matthew",
""
],
[
"Goyal",
"Anirudh",
""
],
[
"Mozer",
"Mike",
""
],
[
"Rezende",
"Danilo",
""
]
] | Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inherently causal in nature. To accurately identify GRNs, perturbational data is required. However, most GRN discovery methods only operate on observational data. Recent advances in neural network-based causal discovery methods have significantly improved causal discovery, including handling interventional data, improvements in performance and scalability. However, applying state-of-the-art (SOTA) causal discovery methods in biology poses challenges, such as noisy data and a large number of samples. Thus, adapting the causal discovery methods is necessary to handle these challenges. In this paper, we introduce DiscoGen, a neural network-based GRN discovery method that can denoise gene expression measurements and handle interventional data. We demonstrate that our model outperforms SOTA neural network-based causal discovery methods. |
1906.07899 | Tomasz Rutkowski | Tomasz M. Rutkowski and Marcin Koculak and Masato S. Abe and Mihoko
Otake-Matsuura | Brain correlates of task-load and dementia elucidation with tensor
machine learning using oddball BCI paradigm | In ICASSP 2019 - 2019 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), pp. 8578-8582, May 2019 | null | 10.1109/ICASSP.2019.8682387 | null | q-bio.NC cs.LG eess.SP | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Dementia in the elderly has recently become the most usual cause of cognitive
decline. The proliferation of dementia cases in aging societies creates a
remarkable economic as well as medical problems in many communities worldwide.
A recently published report by The World Health Organization (WHO) estimates
that about 47 million people are suffering from dementia-related neurocognitive
declines worldwide. The number of dementia cases is predicted by 2050 to
triple, which requires the creation of an AI-based technology application to
support interventions with early screening for subsequent mental wellbeing
checking as well as preservation with digital-pharma (the so-called beyond a
pill) therapeutical approaches. We present an attempt and exploratory results
of brain signal (EEG) classification to establish digital biomarkers for
dementia stage elucidation. We discuss a comparison of various machine learning
approaches for automatic event-related potentials (ERPs) classification of a
high and low task-load sound stimulus recognition. These ERPs are similar to
those in dementia. The proposed winning method using tensor-based machine
learning in a deep fully connected neural network setting is a step forward to
develop AI-based approaches for a subsequent application for subjective- and
mild-cognitive impairment (SCI and MCI) diagnostics.
| [
{
"created": "Wed, 19 Jun 2019 03:43:39 GMT",
"version": "v1"
}
] | 2019-06-20 | [
[
"Rutkowski",
"Tomasz M.",
""
],
[
"Koculak",
"Marcin",
""
],
[
"Abe",
"Masato S.",
""
],
[
"Otake-Matsuura",
"Mihoko",
""
]
] | Dementia in the elderly has recently become the most usual cause of cognitive decline. The proliferation of dementia cases in aging societies creates a remarkable economic as well as medical problems in many communities worldwide. A recently published report by The World Health Organization (WHO) estimates that about 47 million people are suffering from dementia-related neurocognitive declines worldwide. The number of dementia cases is predicted by 2050 to triple, which requires the creation of an AI-based technology application to support interventions with early screening for subsequent mental wellbeing checking as well as preservation with digital-pharma (the so-called beyond a pill) therapeutical approaches. We present an attempt and exploratory results of brain signal (EEG) classification to establish digital biomarkers for dementia stage elucidation. We discuss a comparison of various machine learning approaches for automatic event-related potentials (ERPs) classification of a high and low task-load sound stimulus recognition. These ERPs are similar to those in dementia. The proposed winning method using tensor-based machine learning in a deep fully connected neural network setting is a step forward to develop AI-based approaches for a subsequent application for subjective- and mild-cognitive impairment (SCI and MCI) diagnostics. |
1606.07748 | Cameron Browne | Cameron J. Browne | Global Properties of Nested Network Model with Application to
Multi-Epitope HIV/CTL Dynamics | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematical modeling and analysis can provide insight on the dynamics of
ecosystems which maintain biodiversity in the face of competitive and
prey-predator interactions. Of primary interests are the underlying structure
and features which stabilize diverse ecological networks. Recently Korytowski
and Smith [17] proved that a perfectly nested infection network, along with
appropriate life history trade-offs, leads to coexistence and persistence of
bacteria-phage communities in a chemostat model. In this article, we generalize
their model in order to apply it to the within-host dynamics virus and immune
response, in particular HIV and CTL (Cytotoxic T Lymphocyte) cells. Our model
can produce a diverse hierarchy of viral and immune populations, built through
sequential viral escape from dominant immune responses and rise in subdominant
immune responses, consistent with observed patterns of HIV/CTL evolution. We
find a Lyapunov function for the system which leads to rigorous
characterization of persistent viral and immune variants, and informs upon
equilibria stability and global dynamics. Results are interpreted in the
context of within-host HIV/CTL evolution and numerical simulations are
provided.
| [
{
"created": "Fri, 24 Jun 2016 16:31:02 GMT",
"version": "v1"
},
{
"created": "Sun, 1 Jan 2017 20:13:54 GMT",
"version": "v2"
}
] | 2017-01-03 | [
[
"Browne",
"Cameron J.",
""
]
] | Mathematical modeling and analysis can provide insight on the dynamics of ecosystems which maintain biodiversity in the face of competitive and prey-predator interactions. Of primary interests are the underlying structure and features which stabilize diverse ecological networks. Recently Korytowski and Smith [17] proved that a perfectly nested infection network, along with appropriate life history trade-offs, leads to coexistence and persistence of bacteria-phage communities in a chemostat model. In this article, we generalize their model in order to apply it to the within-host dynamics virus and immune response, in particular HIV and CTL (Cytotoxic T Lymphocyte) cells. Our model can produce a diverse hierarchy of viral and immune populations, built through sequential viral escape from dominant immune responses and rise in subdominant immune responses, consistent with observed patterns of HIV/CTL evolution. We find a Lyapunov function for the system which leads to rigorous characterization of persistent viral and immune variants, and informs upon equilibria stability and global dynamics. Results are interpreted in the context of within-host HIV/CTL evolution and numerical simulations are provided. |
1407.2234 | Matthew Turner | Daniel J. G. Pearce and Matthew S. Turner | Density regulation in strictly metric-free swarms | null | null | 10.1088/1367-2630/16/8/082002 | null | q-bio.QM cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is now experimental evidence that nearest-neighbour interactions in
flocks of birds are metric free, i.e. they have no characteristic interaction
length scale. However, models that involve interactions between neighbours that
are assigned topologically are naturally invariant under spatial expansion,
supporting a continuous reduction in density towards zero, unless additional
cohesive interactions are introduced or the density is artificially controlled,
e.g. via a finite system size. We propose a solution that involves a
metric-free motional bias on those individuals that are topologically
identified to be on an edge of the swarm. This model has only two primary
control parameters, one controlling the relative strength of stochastic noise
to the degree of co-alignment and another controlling the degree of the
motional bias for those on the edge, relative to the tendency to co-align. We
find a novel power-law scaling of the real-space density with the number of
individuals N as well as a familiar order-to-disorder transition.
| [
{
"created": "Mon, 7 Jul 2014 15:40:00 GMT",
"version": "v1"
}
] | 2015-06-22 | [
[
"Pearce",
"Daniel J. G.",
""
],
[
"Turner",
"Matthew S.",
""
]
] | There is now experimental evidence that nearest-neighbour interactions in flocks of birds are metric free, i.e. they have no characteristic interaction length scale. However, models that involve interactions between neighbours that are assigned topologically are naturally invariant under spatial expansion, supporting a continuous reduction in density towards zero, unless additional cohesive interactions are introduced or the density is artificially controlled, e.g. via a finite system size. We propose a solution that involves a metric-free motional bias on those individuals that are topologically identified to be on an edge of the swarm. This model has only two primary control parameters, one controlling the relative strength of stochastic noise to the degree of co-alignment and another controlling the degree of the motional bias for those on the edge, relative to the tendency to co-align. We find a novel power-law scaling of the real-space density with the number of individuals N as well as a familiar order-to-disorder transition. |
2306.03696 | William Jacobs | Yaxin An, Michael A. Webb, William M. Jacobs | Active learning of the thermodynamics-dynamics tradeoff in protein
condensates | null | Science Advances 10, adj2448 2024 | 10.1126/sciadv.adj2448 | null | q-bio.BM cond-mat.soft physics.bio-ph physics.comp-ph | http://creativecommons.org/licenses/by/4.0/ | Phase-separated biomolecular condensates exhibit a wide range of dynamical
properties, which depend on the sequences of the constituent proteins and RNAs.
However, it is unclear to what extent condensate dynamics can be tuned without
also changing the thermodynamic properties that govern phase separation. Using
coarse-grained simulations of intrinsically disordered proteins, we show that
the dynamics and thermodynamics of homopolymer condensates are strongly
correlated, with increased condensate stability being coincident with low
mobilities and high viscosities. We then apply an "active learning" strategy to
identify heteropolymer sequences that break this correlation. This data-driven
approach and accompanying analysis reveal how heterogeneous amino-acid
compositions and non-uniform sequence patterning map to a range of
independently tunable dynamical and thermodynamic properties of biomolecular
condensates. Our results highlight key molecular determinants governing the
physical properties of biomolecular condensates and establish design rules for
the development of stimuli-responsive biomaterials.
| [
{
"created": "Tue, 6 Jun 2023 14:09:38 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Oct 2023 14:40:15 GMT",
"version": "v2"
},
{
"created": "Sat, 9 Dec 2023 16:15:44 GMT",
"version": "v3"
}
] | 2024-07-31 | [
[
"An",
"Yaxin",
""
],
[
"Webb",
"Michael A.",
""
],
[
"Jacobs",
"William M.",
""
]
] | Phase-separated biomolecular condensates exhibit a wide range of dynamical properties, which depend on the sequences of the constituent proteins and RNAs. However, it is unclear to what extent condensate dynamics can be tuned without also changing the thermodynamic properties that govern phase separation. Using coarse-grained simulations of intrinsically disordered proteins, we show that the dynamics and thermodynamics of homopolymer condensates are strongly correlated, with increased condensate stability being coincident with low mobilities and high viscosities. We then apply an "active learning" strategy to identify heteropolymer sequences that break this correlation. This data-driven approach and accompanying analysis reveal how heterogeneous amino-acid compositions and non-uniform sequence patterning map to a range of independently tunable dynamical and thermodynamic properties of biomolecular condensates. Our results highlight key molecular determinants governing the physical properties of biomolecular condensates and establish design rules for the development of stimuli-responsive biomaterials. |
1309.1898 | Hong Qian | Hong Qian | Fitness and entropy production in a cell population dynamics with
epigenetic phenotype switching | 16 pages | Quantitative Biology, vol. 2, 47-53 (2014) | 10.1007/s40484-014-0028-4 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by recent understandings in the stochastic natures of gene
expression, biochemical signaling, and spontaneous reversible epigenetic
switchings, we study a simple deterministic cell population dynamics in which
subpopulations grow with different rates and individual cells can
bi-directionally switch between a small number of different epigenetic
phenotypes. Two theories in the past, the population dynamics and
thermodynamics of master equations, separatedly defined two important concepts
in mathematical terms: the {\em fitness} in the former and the (non-adiabatic)
{\em entropy production} in the latter. Both play important roles in the
evolution of the cell population dynamics. The switching sustains the
variations among the subpopulation growth thus continuous natural selection. As
a form of Price's equation, the fitness increases with ($i$) natural selection
through variations and $(ii)$ a positive covariance between the per capita
growth and switching, which represents a Lamarchian-like behavior. A negative
covariance balances the natural selection in a fitness steady state | "the red
queen" scenario. At the same time the growth keeps the proportions of
subpopulations away from the "intrinsic" switching equilibrium of individual
cells, thus leads to a continous entropy production. A covariance, between the
per capita growth rate and the "chemical potential" of subpopulation,
counter-acts the entropy production. Analytical results are obtained for the
limiting cases of growth dominating switching and vice versa.
| [
{
"created": "Sat, 7 Sep 2013 19:46:41 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Aug 2014 04:53:00 GMT",
"version": "v2"
}
] | 2014-08-04 | [
[
"Qian",
"Hong",
""
]
] | Motivated by recent understandings in the stochastic natures of gene expression, biochemical signaling, and spontaneous reversible epigenetic switchings, we study a simple deterministic cell population dynamics in which subpopulations grow with different rates and individual cells can bi-directionally switch between a small number of different epigenetic phenotypes. Two theories in the past, the population dynamics and thermodynamics of master equations, separatedly defined two important concepts in mathematical terms: the {\em fitness} in the former and the (non-adiabatic) {\em entropy production} in the latter. Both play important roles in the evolution of the cell population dynamics. The switching sustains the variations among the subpopulation growth thus continuous natural selection. As a form of Price's equation, the fitness increases with ($i$) natural selection through variations and $(ii)$ a positive covariance between the per capita growth and switching, which represents a Lamarchian-like behavior. A negative covariance balances the natural selection in a fitness steady state | "the red queen" scenario. At the same time the growth keeps the proportions of subpopulations away from the "intrinsic" switching equilibrium of individual cells, thus leads to a continous entropy production. A covariance, between the per capita growth rate and the "chemical potential" of subpopulation, counter-acts the entropy production. Analytical results are obtained for the limiting cases of growth dominating switching and vice versa. |
1411.0395 | Robert Nowak M. | Robert M. Nowak | Assembly of repetitive regions using next-generation sequencing data | 11 pages, 5 figures, 6 tables. The C++ sources, the Python scripts
and the additional data are available at http://dnaasm.sourceforge.org | null | 10.1016/j.bbe.2014.12.001 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High read depth can be used to assemble short sequence repeats. The existing
genome assemblers fail in repetitive regions of longer than average read.
I propose a new algorithm for a DNA assembly which uses the relative
frequency of reads to properly reconstruct repetitive sequences. The
mathematical model shows the upper limits of accuracy of the results as a
function of read coverage. For high coverage, the estimation error depends
linearly on repetitive sequence length and inversely proportional to the
sequencing coverage. The algorithm requires high read depth, provided by the
next-generation sequencers and could use the existing data. The tests on
errorless reads, generated in silico from several model genomes, pointed the
properly reconstructed repetitive sequences, where existing assemblers fail.
| [
{
"created": "Mon, 3 Nov 2014 08:51:38 GMT",
"version": "v1"
}
] | 2015-01-08 | [
[
"Nowak",
"Robert M.",
""
]
] | High read depth can be used to assemble short sequence repeats. The existing genome assemblers fail in repetitive regions of longer than average read. I propose a new algorithm for a DNA assembly which uses the relative frequency of reads to properly reconstruct repetitive sequences. The mathematical model shows the upper limits of accuracy of the results as a function of read coverage. For high coverage, the estimation error depends linearly on repetitive sequence length and inversely proportional to the sequencing coverage. The algorithm requires high read depth, provided by the next-generation sequencers and could use the existing data. The tests on errorless reads, generated in silico from several model genomes, pointed the properly reconstructed repetitive sequences, where existing assemblers fail. |
1308.3843 | Jonathan Doye | Jonathan P.K. Doye, Thomas E. Ouldridge, Ard A. Louis, Flavio Romano,
Petr Sulc, Christian Matek, Benedict E.K. Snodin, Lorenzo Rovigatti, John S.
Schreck, Ryan M. Harrison and William P.J. Smith | Coarse-graining DNA for simulations of DNA nanotechnology | 20 pages, 9 figures | Phys. Chem. Chem. Phys. 15, 20395-20414 (2013) | 10.1039/C3CP53545B | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To simulate long time and length scale processes involving DNA it is
necessary to use a coarse-grained description. Here we provide an overview of
different approaches to such coarse graining, focussing on those at the
nucleotide level that allow the self-assembly processes associated with DNA
nanotechnology to be studied. OxDNA, our recently-developed coarse-grained DNA
model, is particularly suited to this task, and has opened up this field to
systematic study by simulations. We illustrate some of the range of DNA
nanotechnology systems to which the model is being applied, as well as the
insights it can provide into fundamental biophysical properties of DNA.
| [
{
"created": "Sun, 18 Aug 2013 08:41:39 GMT",
"version": "v1"
}
] | 2017-09-13 | [
[
"Doye",
"Jonathan P. K.",
""
],
[
"Ouldridge",
"Thomas E.",
""
],
[
"Louis",
"Ard A.",
""
],
[
"Romano",
"Flavio",
""
],
[
"Sulc",
"Petr",
""
],
[
"Matek",
"Christian",
""
],
[
"Snodin",
"Benedict E. K.",
""
],
[
"Rovigatti",
"Lorenzo",
""
],
[
"Schreck",
"John S.",
""
],
[
"Harrison",
"Ryan M.",
""
],
[
"Smith",
"William P. J.",
""
]
] | To simulate long time and length scale processes involving DNA it is necessary to use a coarse-grained description. Here we provide an overview of different approaches to such coarse graining, focussing on those at the nucleotide level that allow the self-assembly processes associated with DNA nanotechnology to be studied. OxDNA, our recently-developed coarse-grained DNA model, is particularly suited to this task, and has opened up this field to systematic study by simulations. We illustrate some of the range of DNA nanotechnology systems to which the model is being applied, as well as the insights it can provide into fundamental biophysical properties of DNA. |
1803.09107 | Moti Salti | Moti Salti, Asaf Harel, Sebastien Marti | Conscious Perception: Time for an Update? | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the neural mechanism underlying subjective representation has
become a central endeavor in cognitive-neuroscience. In theories of conscious
perception, stimulus gaining conscious access is usually considered as a
discrete neuronal event to be characterized in time or space, sometimes refer
to as a 'conscious episode'. Surprisingly, the alternative hypothesis according
to which conscious perception is a dynamic process has been rarely considered.
Here, we discuss this hypothesis and envisage its implications. We show how it
can reconcile inconsistent empirical findings on the timing of the neural
correlates of consciousness (NCCs), and make testable predictions. According to
this hypothesis, a stimulus is consciously perceived for as long as it is
recoded to fit an ongoing stream composed of all other perceived stimuli. We
suggest that this 'updating' process is governed by at least three factors (1)
context, (2) stimulus saliency and (3) observer's goal. Finally, this framework
forces us to reconsider the typical distinction between conscious and
unconscious information processing.
| [
{
"created": "Sat, 24 Mar 2018 13:25:26 GMT",
"version": "v1"
}
] | 2018-03-28 | [
[
"Salti",
"Moti",
""
],
[
"Harel",
"Asaf",
""
],
[
"Marti",
"Sebastien",
""
]
] | Understanding the neural mechanism underlying subjective representation has become a central endeavor in cognitive-neuroscience. In theories of conscious perception, stimulus gaining conscious access is usually considered as a discrete neuronal event to be characterized in time or space, sometimes refer to as a 'conscious episode'. Surprisingly, the alternative hypothesis according to which conscious perception is a dynamic process has been rarely considered. Here, we discuss this hypothesis and envisage its implications. We show how it can reconcile inconsistent empirical findings on the timing of the neural correlates of consciousness (NCCs), and make testable predictions. According to this hypothesis, a stimulus is consciously perceived for as long as it is recoded to fit an ongoing stream composed of all other perceived stimuli. We suggest that this 'updating' process is governed by at least three factors (1) context, (2) stimulus saliency and (3) observer's goal. Finally, this framework forces us to reconsider the typical distinction between conscious and unconscious information processing. |
1902.01210 | Liliana Camarillo Rodriguez | L. Camarillo-Rodriguez, Z.J. Waldman, I. Orosz, J. Stein, S. Das, R.
Gorniak, A.D. Sharan, R. Gross, B.C. Lega, K. Zaghloul, B.C. Jobst, K.A.
Davis, P.A. Wanda, G. Worrell, M.R. Sperling, S.A. Weiss | Epileptiform spikes in specific left temporal and mesial temporal
structures disrupt verbal episodic memory encoding | All of the co-authors of this article agree to withdraw it, because
it is not ready yet for its submission | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Patients diagnosed with epilepsy experience cognitive dysfunction that may be
due to a transient cognitive/memory impairment (TCI/TMI) caused by spontaneous
epileptiform spikes. We asked in a cohort of 166 adult patients with medically
refractory focal epilepsy if spikes in specific neuroanatomical regions during
verbal episodic memory encoding would significantly decrease the probability of
recall. We found using a na\"ive Bayesian machine learning model that the
probability of correct word recall decreased significantly by 11.9% when spikes
occurred in left Brodmann area 21 (BA)21, (p<0.001), 49.7% in left BA38
(p=0.01), and 32.2% in right BA38 (p<0.001), and 21.4% in left BA36 (p<0.01).
We also examined the influence of the seizure-onset zone and the language
dominant hemisphere on this effect. Our results demonstrate that spontaneous
epileptiform spikes produce a large effect TCI/TMI in brain regions known to be
important in semantic processing and episodic memory. Thus memory impairment in
patients with epilepsy may be attributable to cellular events associated with
abnormal inter-ictal electrical events.
| [
{
"created": "Thu, 31 Jan 2019 23:12:14 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Feb 2019 01:40:53 GMT",
"version": "v2"
}
] | 2019-02-19 | [
[
"Camarillo-Rodriguez",
"L.",
""
],
[
"Waldman",
"Z. J.",
""
],
[
"Orosz",
"I.",
""
],
[
"Stein",
"J.",
""
],
[
"Das",
"S.",
""
],
[
"Gorniak",
"R.",
""
],
[
"Sharan",
"A. D.",
""
],
[
"Gross",
"R.",
""
],
[
"Lega",
"B. C.",
""
],
[
"Zaghloul",
"K.",
""
],
[
"Jobst",
"B. C.",
""
],
[
"Davis",
"K. A.",
""
],
[
"Wanda",
"P. A.",
""
],
[
"Worrell",
"G.",
""
],
[
"Sperling",
"M. R.",
""
],
[
"Weiss",
"S. A.",
""
]
] | Patients diagnosed with epilepsy experience cognitive dysfunction that may be due to a transient cognitive/memory impairment (TCI/TMI) caused by spontaneous epileptiform spikes. We asked in a cohort of 166 adult patients with medically refractory focal epilepsy if spikes in specific neuroanatomical regions during verbal episodic memory encoding would significantly decrease the probability of recall. We found using a na\"ive Bayesian machine learning model that the probability of correct word recall decreased significantly by 11.9% when spikes occurred in left Brodmann area 21 (BA)21, (p<0.001), 49.7% in left BA38 (p=0.01), and 32.2% in right BA38 (p<0.001), and 21.4% in left BA36 (p<0.01). We also examined the influence of the seizure-onset zone and the language dominant hemisphere on this effect. Our results demonstrate that spontaneous epileptiform spikes produce a large effect TCI/TMI in brain regions known to be important in semantic processing and episodic memory. Thus memory impairment in patients with epilepsy may be attributable to cellular events associated with abnormal inter-ictal electrical events. |
q-bio/0409039 | Ken Kiyono | Ken Kiyono, Zbigniew R. Struzik, Naoko Aoyagi, Seiichiro Sakata,
Junichiro Hayano, Yoshiharu Yamamoto | Critical Scale-invariance in Healthy Human Heart Rate | 9 pages, 3 figures. Phys. Rev. Lett., to appear (2004) | null | 10.1103/PhysRevLett.93.178103 | null | q-bio.TO | null | We demonstrate the robust scale-invariance in the probability density
function (PDF) of detrended healthy human heart rate increments, which is
preserved not only in a quiescent condition, but also in a dynamic state where
the mean level of heart rate is dramatically changing. This scale-independent
and fractal structure is markedly different from the scale-dependent PDF
evolution observed in a turbulent-like, cascade heart rate model. These results
strongly support the view that healthy human heart rate is controlled to
converge continually to a critical state.
| [
{
"created": "Thu, 30 Sep 2004 23:46:04 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Kiyono",
"Ken",
""
],
[
"Struzik",
"Zbigniew R.",
""
],
[
"Aoyagi",
"Naoko",
""
],
[
"Sakata",
"Seiichiro",
""
],
[
"Hayano",
"Junichiro",
""
],
[
"Yamamoto",
"Yoshiharu",
""
]
] | We demonstrate the robust scale-invariance in the probability density function (PDF) of detrended healthy human heart rate increments, which is preserved not only in a quiescent condition, but also in a dynamic state where the mean level of heart rate is dramatically changing. This scale-independent and fractal structure is markedly different from the scale-dependent PDF evolution observed in a turbulent-like, cascade heart rate model. These results strongly support the view that healthy human heart rate is controlled to converge continually to a critical state. |
2004.05256 | Hao Tian | Hao Tian and Peng Tao | Deciphering the Protein Motion of S1 Subunit in SARS-CoV-2 Spike
Glycoprotein Through Integrated Computational Methods | null | null | null | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a
major worldwide public health emergency that has infected over $1.5$ million
people. The partially open state of S1 subunit in spike glycoprotein is
considered vital for its infection with host cell and is represented as a key
target for neutralizing antibodies. However, the mechanism elucidating the
transition from the closed state to the partially open state still remains
unclear. Here, we applied a combination of Markov state model, transition path
theory and random forest to analyze the S1 motion. Our results explored a
promising complete conformational movement of receptor-binding domain, from
buried, partially open, to detached states. We also numerically confirmed the
transition probability between those states. Based on the asymmetry in both the
dynamics behavior and backbone C$\alpha$ importance, we further suggested a
relation between chains in the trimer spike protein, which may help in the
vaccine design and antibody neutralization.
| [
{
"created": "Fri, 10 Apr 2020 23:27:55 GMT",
"version": "v1"
}
] | 2020-04-14 | [
[
"Tian",
"Hao",
""
],
[
"Tao",
"Peng",
""
]
] | The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major worldwide public health emergency that has infected over $1.5$ million people. The partially open state of S1 subunit in spike glycoprotein is considered vital for its infection with host cell and is represented as a key target for neutralizing antibodies. However, the mechanism elucidating the transition from the closed state to the partially open state still remains unclear. Here, we applied a combination of Markov state model, transition path theory and random forest to analyze the S1 motion. Our results explored a promising complete conformational movement of receptor-binding domain, from buried, partially open, to detached states. We also numerically confirmed the transition probability between those states. Based on the asymmetry in both the dynamics behavior and backbone C$\alpha$ importance, we further suggested a relation between chains in the trimer spike protein, which may help in the vaccine design and antibody neutralization. |
1001.3813 | David Morrison | David A. Morrison | How and where to look for tRNAs in Metazoan mitochondrial genomes, and
what you might find when you get there | 27 pages, including 1 Table and 9 Figures, plus 6 online Appendices | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to locate and annotate mitochondrial genes is an important
practical issue, given the rapidly increasing number of mitogenomes appearing
in the public databases. Unfortunately, tRNA genes in Metazoan mitochondria
have proved to be problematic because they often vary in number (genes missing
or duplicated) and also in the secondary structure of the transcribed tRNAs (T
or D arms missing). I have performed a series of comparative analyses of the
tRNA genes of a broad range of Metazoan mitogenomes in order to address this
issue. I conclude that no single computer program is necessarily capable of
finding all of the tRNA genes in any given mitogenome, and that use of both the
ARWEN and DOGMA programs is sometimes necessary because they produce
complementary false negatives. There are apparently a very large number of
erroneous annotations in the databased mitogenome sequences, including missed
genes, wrongly annotated locations, false complements, and inconsistent
criteria for assigning the 5' and 3' boundaries; and I have listed many of
these. The extent of overlap between genes is often greatly exaggerated due to
inconsistent annotations, although notable overlaps involving tRNAs are
apparently real. Finally, three novel hypotheses were examined and found to
have support from the comparative analyses: (1) some organisms have mitogenomic
locations that simultaneously code for multiple tRNAs; (2) some organisms have
mitogenomic locations that simultaneously code for tRNAs and proteins (but not
rRNAs); and (3) one group of nematodes has several genes that code for tRNAs
lacking both the D and T arms.
| [
{
"created": "Thu, 21 Jan 2010 14:33:36 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Feb 2012 13:46:19 GMT",
"version": "v2"
}
] | 2012-02-06 | [
[
"Morrison",
"David A.",
""
]
] | The ability to locate and annotate mitochondrial genes is an important practical issue, given the rapidly increasing number of mitogenomes appearing in the public databases. Unfortunately, tRNA genes in Metazoan mitochondria have proved to be problematic because they often vary in number (genes missing or duplicated) and also in the secondary structure of the transcribed tRNAs (T or D arms missing). I have performed a series of comparative analyses of the tRNA genes of a broad range of Metazoan mitogenomes in order to address this issue. I conclude that no single computer program is necessarily capable of finding all of the tRNA genes in any given mitogenome, and that use of both the ARWEN and DOGMA programs is sometimes necessary because they produce complementary false negatives. There are apparently a very large number of erroneous annotations in the databased mitogenome sequences, including missed genes, wrongly annotated locations, false complements, and inconsistent criteria for assigning the 5' and 3' boundaries; and I have listed many of these. The extent of overlap between genes is often greatly exaggerated due to inconsistent annotations, although notable overlaps involving tRNAs are apparently real. Finally, three novel hypotheses were examined and found to have support from the comparative analyses: (1) some organisms have mitogenomic locations that simultaneously code for multiple tRNAs; (2) some organisms have mitogenomic locations that simultaneously code for tRNAs and proteins (but not rRNAs); and (3) one group of nematodes has several genes that code for tRNAs lacking both the D and T arms. |
0808.2660 | Mike Steel Prof. | Mike Steel | A basic limitation on inferring phylogenies by pairwise sequence
comparisons | 13 pages, 2 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distance-based approaches in phylogenetics such as Neighbor-Joining are a
fast and popular approach for building trees. These methods take pairs of
sequences from them construct a value that, in expectation, is additive under a
stochastic model of site substitution. Most models assume a distribution of
rates across sites, often based on a gamma distribution. Provided the (shape)
parameter of this distribution is known, the method can correctly reconstruct
the tree. However, if the shape parameter is not known then we show that
topologically different trees, with different shape parameters and associated
positive branch lengths, can lead to exactly matching distributions on pairwise
site patterns between all pairs of taxa. Thus, one could not distinguish
between the two trees using pairs of sequences without some prior knowledge of
the shape parameter. More surprisingly, this can happen for {\em any} choice of
distinct shape parameters on the two trees, and thus the result is not peculiar
to a particular or contrived selection of the shape parameters. On a positive
note, we point out known conditions where identifiability can be restored
(namely, when the branch lengths are clocklike, or if methods such as maximum
likelihood are used).
| [
{
"created": "Tue, 19 Aug 2008 21:25:42 GMT",
"version": "v1"
}
] | 2008-08-21 | [
[
"Steel",
"Mike",
""
]
] | Distance-based approaches in phylogenetics such as Neighbor-Joining are a fast and popular approach for building trees. These methods take pairs of sequences from them construct a value that, in expectation, is additive under a stochastic model of site substitution. Most models assume a distribution of rates across sites, often based on a gamma distribution. Provided the (shape) parameter of this distribution is known, the method can correctly reconstruct the tree. However, if the shape parameter is not known then we show that topologically different trees, with different shape parameters and associated positive branch lengths, can lead to exactly matching distributions on pairwise site patterns between all pairs of taxa. Thus, one could not distinguish between the two trees using pairs of sequences without some prior knowledge of the shape parameter. More surprisingly, this can happen for {\em any} choice of distinct shape parameters on the two trees, and thus the result is not peculiar to a particular or contrived selection of the shape parameters. On a positive note, we point out known conditions where identifiability can be restored (namely, when the branch lengths are clocklike, or if methods such as maximum likelihood are used). |
1704.02533 | Kieran Fox | Jessica R. Andrews-Hanna, Zachary C. Irving, Kieran C.R. Fox, R.
Nathan Spreng, Kalina Christoff | The Neuroscience of Spontaneous Thought: An Evolving, Interdisciplinary
Field | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An often-overlooked characteristic of the human mind is its propensity to
wander. Despite growing interest in the science of mind-wandering, most studies
operationalize mind-wandering by its task-unrelated contents, which may be
orthogonal to the processes constraining how thoughts are evoked and unfold
over time. In this chapter, we emphasize the importance of incorporating such
processes into current definitions of mind-wandering, and proposing that
mind-wandering and other forms of spontaneous thought (such as dreaming and
creativity) are mental states that arise and transition relatively freely due
to an absence of constraints on cognition. We review existing psychological,
philosophical, and neuroscientific research on spontaneous thought through the
lens of this framework, and call for additional research into the dynamic
properties of the mind and brain.
| [
{
"created": "Sat, 8 Apr 2017 20:16:58 GMT",
"version": "v1"
}
] | 2017-04-11 | [
[
"Andrews-Hanna",
"Jessica R.",
""
],
[
"Irving",
"Zachary C.",
""
],
[
"Fox",
"Kieran C. R.",
""
],
[
"Spreng",
"R. Nathan",
""
],
[
"Christoff",
"Kalina",
""
]
] | An often-overlooked characteristic of the human mind is its propensity to wander. Despite growing interest in the science of mind-wandering, most studies operationalize mind-wandering by its task-unrelated contents, which may be orthogonal to the processes constraining how thoughts are evoked and unfold over time. In this chapter, we emphasize the importance of incorporating such processes into current definitions of mind-wandering, and proposing that mind-wandering and other forms of spontaneous thought (such as dreaming and creativity) are mental states that arise and transition relatively freely due to an absence of constraints on cognition. We review existing psychological, philosophical, and neuroscientific research on spontaneous thought through the lens of this framework, and call for additional research into the dynamic properties of the mind and brain. |
2006.09932 | Shashank Subramanian | Shashank Subramanian, Klaudius Scheufele, Naveen Himthani, George
Biros | Multiatlas Calibration of Biophysical Brain Tumor Growth Models with
Mass Effect | Provisionally accepted to MICCAI 2020 | null | null | null | q-bio.QM cs.CE physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a 3D fully-automatic method for the calibration of partial
differential equation (PDE) models of glioblastoma (GBM) growth with mass
effect, the deformation of brain tissue due to the tumor. We quantify the mass
effect, tumor proliferation, tumor migration, and the localized tumor initial
condition from a single multiparameteric Magnetic Resonance Imaging (mpMRI)
patient scan. The PDE is a reaction-advection-diffusion partial differential
equation coupled with linear elasticity equations to capture mass effect. The
single-scan calibration model is notoriously difficult because the precancerous
(healthy) brain anatomy is unknown. To solve this inherently ill-posed and
ill-conditioned optimization problem, we introduce a novel inversion scheme
that uses multiple brain atlases as proxies for the healthy precancer patient
brain resulting in robust and reliable parameter estimation. We apply our
method on both synthetic and clinical datasets representative of the
heterogeneous spatial landscape typically observed in glioblastomas to
demonstrate the validity and performance of our methods. In the synthetic data,
we report calibration errors (due to the ill-posedness and our solution scheme)
in the 10\%-20\% range. In the clinical data, we report good quantitative
agreement with the observed tumor and qualitative agreement with the mass
effect (for which we do not have a ground truth). Our method uses a minimal set
of parameters and provides both global and local quantitative measures of tumor
infiltration and mass effect.
| [
{
"created": "Wed, 17 Jun 2020 15:24:05 GMT",
"version": "v1"
}
] | 2020-06-18 | [
[
"Subramanian",
"Shashank",
""
],
[
"Scheufele",
"Klaudius",
""
],
[
"Himthani",
"Naveen",
""
],
[
"Biros",
"George",
""
]
] | We present a 3D fully-automatic method for the calibration of partial differential equation (PDE) models of glioblastoma (GBM) growth with mass effect, the deformation of brain tissue due to the tumor. We quantify the mass effect, tumor proliferation, tumor migration, and the localized tumor initial condition from a single multiparameteric Magnetic Resonance Imaging (mpMRI) patient scan. The PDE is a reaction-advection-diffusion partial differential equation coupled with linear elasticity equations to capture mass effect. The single-scan calibration model is notoriously difficult because the precancerous (healthy) brain anatomy is unknown. To solve this inherently ill-posed and ill-conditioned optimization problem, we introduce a novel inversion scheme that uses multiple brain atlases as proxies for the healthy precancer patient brain resulting in robust and reliable parameter estimation. We apply our method on both synthetic and clinical datasets representative of the heterogeneous spatial landscape typically observed in glioblastomas to demonstrate the validity and performance of our methods. In the synthetic data, we report calibration errors (due to the ill-posedness and our solution scheme) in the 10\%-20\% range. In the clinical data, we report good quantitative agreement with the observed tumor and qualitative agreement with the mass effect (for which we do not have a ground truth). Our method uses a minimal set of parameters and provides both global and local quantitative measures of tumor infiltration and mass effect. |
2303.06071 | Azra Bihorac | Esra Adiyeke, Yuanfang Ren, Ziyuan Guan, Matthew M. Ruppert, Parisa
Rashidi, Azra Bihorac, Tezcan Ozrazgat-Baslanti | Clinical Courses of Acute Kidney Injury in Hospitalized Patients: A
Multistate Analysis | null | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objectives: We aim to quantify longitudinal acute kidney injury (AKI)
trajectories and to describe transitions through progressing and recovery
states and outcomes among hospitalized patients using multistate models.
Methods: In this large, longitudinal cohort study, 138,449 adult patients
admitted to a quaternary care hospital between 2012 and 2019 were staged based
on Kidney Disease: Improving Global Outcomes serum creatinine criteria for the
first 14 days of their hospital stay. We fit multistate models to estimate
probability of being in a certain clinical state at a given time after entering
each one of the AKI stages. We investigated the effects of selected variables
on transition rates via Cox proportional hazards regression models. Results:
Twenty percent of hospitalized encounters (49,325/246,964) had AKI; among
patients with AKI, 66% had Stage 1 AKI, 18% had Stage 2 AKI, and 17% had AKI
Stage 3 with or without RRT. At seven days following Stage 1 AKI, 69% (95%
confidence interval [CI]: 68.8%-70.5%) were either resolved to No AKI or
discharged, while smaller proportions of recovery (26.8%, 95% CI: 26.1%-27.5%)
and discharge (17.4%, 95% CI: 16.8%-18.0%) were observed following AKI Stage 2.
At 14 days following Stage 1 AKI, patients with more frail conditions (Charlson
comorbidity index greater than or equal to 3 and had prolonged ICU stay) had
lower proportion of transitioning to No AKI or discharge states. Discussion:
Multistate analyses showed that the majority of Stage 2 and higher severity AKI
patients could not resolve within seven days; therefore, strategies preventing
the persistence or progression of AKI would contribute to the patients' life
quality. Conclusions: We demonstrate multistate modeling framework's utility as
a mechanism for a better understanding of the clinical course of AKI with the
potential to facilitate treatment and resource planning.
| [
{
"created": "Wed, 8 Mar 2023 19:06:39 GMT",
"version": "v1"
}
] | 2023-03-13 | [
[
"Adiyeke",
"Esra",
""
],
[
"Ren",
"Yuanfang",
""
],
[
"Guan",
"Ziyuan",
""
],
[
"Ruppert",
"Matthew M.",
""
],
[
"Rashidi",
"Parisa",
""
],
[
"Bihorac",
"Azra",
""
],
[
"Ozrazgat-Baslanti",
"Tezcan",
""
]
] | Objectives: We aim to quantify longitudinal acute kidney injury (AKI) trajectories and to describe transitions through progressing and recovery states and outcomes among hospitalized patients using multistate models. Methods: In this large, longitudinal cohort study, 138,449 adult patients admitted to a quaternary care hospital between 2012 and 2019 were staged based on Kidney Disease: Improving Global Outcomes serum creatinine criteria for the first 14 days of their hospital stay. We fit multistate models to estimate probability of being in a certain clinical state at a given time after entering each one of the AKI stages. We investigated the effects of selected variables on transition rates via Cox proportional hazards regression models. Results: Twenty percent of hospitalized encounters (49,325/246,964) had AKI; among patients with AKI, 66% had Stage 1 AKI, 18% had Stage 2 AKI, and 17% had AKI Stage 3 with or without RRT. At seven days following Stage 1 AKI, 69% (95% confidence interval [CI]: 68.8%-70.5%) were either resolved to No AKI or discharged, while smaller proportions of recovery (26.8%, 95% CI: 26.1%-27.5%) and discharge (17.4%, 95% CI: 16.8%-18.0%) were observed following AKI Stage 2. At 14 days following Stage 1 AKI, patients with more frail conditions (Charlson comorbidity index greater than or equal to 3 and had prolonged ICU stay) had lower proportion of transitioning to No AKI or discharge states. Discussion: Multistate analyses showed that the majority of Stage 2 and higher severity AKI patients could not resolve within seven days; therefore, strategies preventing the persistence or progression of AKI would contribute to the patients' life quality. Conclusions: We demonstrate multistate modeling framework's utility as a mechanism for a better understanding of the clinical course of AKI with the potential to facilitate treatment and resource planning. |
1412.1243 | Willy Rodr\'iguez | Olivier Mazet, Willy Rodr\'iguez, Loun\`es Chikhi | Demographic inference using genetic data from a single individual:
separating population size variation from population structure | 40 pages, 8 figures | null | null | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rapid development of sequencing technologies represents new opportunities
for population genetics research. It is expected that genomic data will
increase our ability to reconstruct the history of populations. While this
increase in genetic information will likely help biologists and anthropologists
to reconstruct the demographic history of populations, it also represents new
challenges. Recent work has shown that structured populations generate signals
of population size change. As a consequence it is often difficult to determine
whether demographic events such as expansions or contractions (bottlenecks)
inferred from genetic data are real or due to the fact that populations are
structured in nature. Given that few inferential methods allow us to account
for that structure, and that genomic data will necessarily increase the
precision of parameter estimates, it is important to develop new approaches. In
the present study we analyse two demographic models. The first is a model of
instantaneous population size change whereas the second is the classical
symmetric island model. We (i) re-derive the distribution of coalescence times
under the two models for a sample of size two, (ii) use a maximum likelihood
approach to estimate the parameters of these models (iii) validate this
estimation procedure under a wide array of parameter combinations, (iv)
implement and validate a model choice procedure by using a Kolmogorov-Smirnov
test. Altogether we show that it is possible to estimate parameters under
several models and perform efficient model choice using genetic data from a
single diploid individual.
| [
{
"created": "Wed, 3 Dec 2014 09:28:29 GMT",
"version": "v1"
}
] | 2014-12-04 | [
[
"Mazet",
"Olivier",
""
],
[
"Rodríguez",
"Willy",
""
],
[
"Chikhi",
"Lounès",
""
]
] | The rapid development of sequencing technologies represents new opportunities for population genetics research. It is expected that genomic data will increase our ability to reconstruct the history of populations. While this increase in genetic information will likely help biologists and anthropologists to reconstruct the demographic history of populations, it also represents new challenges. Recent work has shown that structured populations generate signals of population size change. As a consequence it is often difficult to determine whether demographic events such as expansions or contractions (bottlenecks) inferred from genetic data are real or due to the fact that populations are structured in nature. Given that few inferential methods allow us to account for that structure, and that genomic data will necessarily increase the precision of parameter estimates, it is important to develop new approaches. In the present study we analyse two demographic models. The first is a model of instantaneous population size change whereas the second is the classical symmetric island model. We (i) re-derive the distribution of coalescence times under the two models for a sample of size two, (ii) use a maximum likelihood approach to estimate the parameters of these models (iii) validate this estimation procedure under a wide array of parameter combinations, (iv) implement and validate a model choice procedure by using a Kolmogorov-Smirnov test. Altogether we show that it is possible to estimate parameters under several models and perform efficient model choice using genetic data from a single diploid individual. |
1802.08279 | Sarah Solomon | Sarah H. Solomon, John D. Medaglia, and Sharon L. Thompson-Schill | Implementing a Concept Network Model | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The same concept can mean different things or be instantiated in different
forms depending on context, suggesting a degree of flexibility within the
conceptual system. We propose that a compositional network model can be used to
capture and predict this flexibility. We modeled individual concepts (e.g.,
BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g.,
YELLOW, SWEET) were represented as nodes and their associations as edges. In
this framework, networks capture the within-concept statistics that reflect how
properties correlate with each other across instances of a concept. We ran a
classification analysis using graph eigendecomposition to validate these
models, and find that these models can successfully discriminate between object
concepts. We then computed formal measures from these concept networks and
explored their relationship to conceptual structure. We find that diversity
coefficients and core-periphery structure can be interpreted as network-based
measures of conceptual flexibility and stability, respectively. These results
support the feasibility of a concept network framework and highlight its
ability to formally capture important characteristics of the conceptual system.
| [
{
"created": "Thu, 22 Feb 2018 19:49:59 GMT",
"version": "v1"
},
{
"created": "Mon, 14 May 2018 16:29:43 GMT",
"version": "v2"
},
{
"created": "Tue, 22 May 2018 16:14:17 GMT",
"version": "v3"
},
{
"created": "Wed, 20 Mar 2019 16:04:03 GMT",
"version": "v4"
}
] | 2019-03-21 | [
[
"Solomon",
"Sarah H.",
""
],
[
"Medaglia",
"John D.",
""
],
[
"Thompson-Schill",
"Sharon L.",
""
]
] | The same concept can mean different things or be instantiated in different forms depending on context, suggesting a degree of flexibility within the conceptual system. We propose that a compositional network model can be used to capture and predict this flexibility. We modeled individual concepts (e.g., BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g., YELLOW, SWEET) were represented as nodes and their associations as edges. In this framework, networks capture the within-concept statistics that reflect how properties correlate with each other across instances of a concept. We ran a classification analysis using graph eigendecomposition to validate these models, and find that these models can successfully discriminate between object concepts. We then computed formal measures from these concept networks and explored their relationship to conceptual structure. We find that diversity coefficients and core-periphery structure can be interpreted as network-based measures of conceptual flexibility and stability, respectively. These results support the feasibility of a concept network framework and highlight its ability to formally capture important characteristics of the conceptual system. |
2108.00994 | Michael Assaf | Jason Hindes, Michael Assaf and Ira B. Schwartz | Extreme outbreak dynamics in epidemic models | 8 pages, 3 figures; to appear in Physical Review Letters (2022) | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by recent epidemic outbreaks, including those of COVID-19, we solve
the canonical problem of calculating the dynamics and likelihood of extensive
outbreaks in a population within a large class of stochastic epidemic models
with demographic noise, including the Susceptible-Infected-Recovered (SIR)
model and its general extensions. In the limit of large populations, we compute
the probability distribution for all extensive outbreaks, including those that
entail unusually large or small (extreme) proportions of the population
infected. Our approach reveals that, unlike other well-known examples of rare
events occurring in discrete-state stochastic systems, the statistics of
extreme outbreaks emanate from a full continuum of Hamiltonian paths, each
satisfying unique boundary conditions with a conserved probability flux.
| [
{
"created": "Mon, 2 Aug 2021 15:48:03 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Jan 2022 12:01:50 GMT",
"version": "v2"
}
] | 2022-01-31 | [
[
"Hindes",
"Jason",
""
],
[
"Assaf",
"Michael",
""
],
[
"Schwartz",
"Ira B.",
""
]
] | Motivated by recent epidemic outbreaks, including those of COVID-19, we solve the canonical problem of calculating the dynamics and likelihood of extensive outbreaks in a population within a large class of stochastic epidemic models with demographic noise, including the Susceptible-Infected-Recovered (SIR) model and its general extensions. In the limit of large populations, we compute the probability distribution for all extensive outbreaks, including those that entail unusually large or small (extreme) proportions of the population infected. Our approach reveals that, unlike other well-known examples of rare events occurring in discrete-state stochastic systems, the statistics of extreme outbreaks emanate from a full continuum of Hamiltonian paths, each satisfying unique boundary conditions with a conserved probability flux. |
1904.05238 | Paul Reiser | Paul A. Reiser | A Physical Model for Self-Similar Seashells | 34 pages, 5 figures | null | null | null | q-bio.QM | http://creativecommons.org/publicdomain/zero/1.0/ | This paper presents a simple physical model for self-similar (gnomonic, or
first-order) seashell growth which is expressed in coordinate-free terms. The
shell is expressed as the solution of a differential equation which expresses
the growth dynamics, and may be used to investigate shell growth from both the
local viewpoint of the organism building it and moving with the shell opening
(aperture), as well as that of a researcher making global measurements upon a
complete motionless shell. Coordinate systems needed to express the global and
local descriptions of the shell are chosen. The parameters of growth, or their
information equivalent, remain constant in the local system, and are used by
the organism to build the shell, and are likely mirrored in the DNA of the
organism building it. The transformations between local and global
representations are provided. The global model of Cortie, which is very similar
to the present model, is expressed in terms of the present model, and the
global parameters provided by Cortie for various species of mollusk may be used
to calculate the equivalent local parameters.Mathematica code is provided to
implement these transformations, as well as to plot the shells using both
global and local parameters.
| [
{
"created": "Fri, 5 Apr 2019 04:46:13 GMT",
"version": "v1"
}
] | 2019-04-11 | [
[
"Reiser",
"Paul A.",
""
]
] | This paper presents a simple physical model for self-similar (gnomonic, or first-order) seashell growth which is expressed in coordinate-free terms. The shell is expressed as the solution of a differential equation which expresses the growth dynamics, and may be used to investigate shell growth from both the local viewpoint of the organism building it and moving with the shell opening (aperture), as well as that of a researcher making global measurements upon a complete motionless shell. Coordinate systems needed to express the global and local descriptions of the shell are chosen. The parameters of growth, or their information equivalent, remain constant in the local system, and are used by the organism to build the shell, and are likely mirrored in the DNA of the organism building it. The transformations between local and global representations are provided. The global model of Cortie, which is very similar to the present model, is expressed in terms of the present model, and the global parameters provided by Cortie for various species of mollusk may be used to calculate the equivalent local parameters.Mathematica code is provided to implement these transformations, as well as to plot the shells using both global and local parameters. |
2005.12993 | Donghui Yan | Donghui Yan, Ying Xu, Pei Wang | Estimating the Number of Infected Cases in COVID-19 Pandemic | 20 pages, 10 figures | null | null | null | q-bio.PE physics.soc-ph stat.ME | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The COVID-19 pandemic has caused major disturbance to human life. An
important reason behind the widespread social anxiety is the huge uncertainty
about the pandemic. A fundamental uncertainty is how many or what percentage of
people have been infected. There are published and frequently updated data on
various statistics of the pandemic, at local, country or global level. However,
due to various reasons, many cases were not included in those reported numbers.
We propose a structured approach for the estimation of the number of unreported
cases, where we distinguish cases that arrive late in the reported numbers and
those who had mild or no symptoms and thus were not captured by any medical
system at all. We use post-report data for the estimation of the former and
population matching to the latter. We estimate that the reported number of
infected cases in the US should be corrected by multiplying a factor of 220.54%
as of Apr 20, 2020, while the infection ratio out of the US population is
estimated to be 0.53%, implying a case mortality rate at 2.85% which is close
to the 3.4% suggested by the WHO in Mar 2020. Towards the end of the summer of
2020, the overall infection ratio of the US rises to 2.49% while the case
mortality decreases to 2.09%, and the ratio of asymptomatic cases out of all
infected cases reduces from the pre-summer 35-40% to around 20-25%.
| [
{
"created": "Sun, 24 May 2020 22:19:43 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Mar 2021 13:53:05 GMT",
"version": "v2"
}
] | 2021-03-04 | [
[
"Yan",
"Donghui",
""
],
[
"Xu",
"Ying",
""
],
[
"Wang",
"Pei",
""
]
] | The COVID-19 pandemic has caused major disturbance to human life. An important reason behind the widespread social anxiety is the huge uncertainty about the pandemic. A fundamental uncertainty is how many or what percentage of people have been infected. There are published and frequently updated data on various statistics of the pandemic, at local, country or global level. However, due to various reasons, many cases were not included in those reported numbers. We propose a structured approach for the estimation of the number of unreported cases, where we distinguish cases that arrive late in the reported numbers and those who had mild or no symptoms and thus were not captured by any medical system at all. We use post-report data for the estimation of the former and population matching to the latter. We estimate that the reported number of infected cases in the US should be corrected by multiplying a factor of 220.54% as of Apr 20, 2020, while the infection ratio out of the US population is estimated to be 0.53%, implying a case mortality rate at 2.85% which is close to the 3.4% suggested by the WHO in Mar 2020. Towards the end of the summer of 2020, the overall infection ratio of the US rises to 2.49% while the case mortality decreases to 2.09%, and the ratio of asymptomatic cases out of all infected cases reduces from the pre-summer 35-40% to around 20-25%. |
q-bio/0702029 | P. Grassberger | Kim Baskerville, Peter Grassberger, and Maya Paczuski | Graph animals, subgraph sampling and motif search in large networks | 14 pages, uncludes 16 figures (color); version 2: several minor
changes; to be published in Phys. Rev. E | null | 10.1103/PhysRevE.76.036107 | null | q-bio.MN cond-mat.stat-mech physics.bio-ph | null | We generalize a sampling algorithm for lattice animals (connected clusters on
a regular lattice) to a Monte Carlo algorithm for `graph animals', i.e.
connected subgraphs in arbitrary networks. As with the algorithm in [N. Kashtan
et al., Bioinformatics 20, 1746 (2004)], it provides a weighted sample, but the
computation of the weights is much faster (linear in the size of subgraphs,
instead of super-exponential). This allows subgraphs with up to ten or more
nodes to be sampled with very high statistics, from arbitrarily large networks.
Using this together with a heuristic algorithm for rapidly classifying
isomorphic graphs, we present results for two protein interaction networks
obtained using the TAP high throughput method: one of Escherichia coli with 230
nodes and 695 links, and one for yeast (Saccharomyces cerevisiae) with roughly
ten times more nodes and links. We find in both cases that most connected
subgraphs are strong motifs (Z-scores >10) or anti-motifs (Z-scores <-10) when
the null model is the ensemble of networks with fixed degree sequence. Strong
differences appear between the two networks, with dominant motifs in E. coli
being (nearly) bipartite graphs and having many pairs of nodes which connect to
the same neighbors, while dominant motifs in yeast tend towards completeness or
contain large cliques. We also explore a number of methods that do not rely on
measurements of Z-scores or comparisons with null models. For instance, we
discuss the influence of specific complexes like the 26S proteasome in yeast,
where a small number of complexes dominate the $k$-cores with large k and have
a decisive effect on the strongest motifs with 6 to 8 nodes. We also present
Zipf plots of counts versus rank. They show broad distributions that are not
power laws, in contrast to the case when disconnected subgraphs are included.
| [
{
"created": "Tue, 13 Feb 2007 22:52:43 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Jun 2007 21:35:24 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Baskerville",
"Kim",
""
],
[
"Grassberger",
"Peter",
""
],
[
"Paczuski",
"Maya",
""
]
] | We generalize a sampling algorithm for lattice animals (connected clusters on a regular lattice) to a Monte Carlo algorithm for `graph animals', i.e. connected subgraphs in arbitrary networks. As with the algorithm in [N. Kashtan et al., Bioinformatics 20, 1746 (2004)], it provides a weighted sample, but the computation of the weights is much faster (linear in the size of subgraphs, instead of super-exponential). This allows subgraphs with up to ten or more nodes to be sampled with very high statistics, from arbitrarily large networks. Using this together with a heuristic algorithm for rapidly classifying isomorphic graphs, we present results for two protein interaction networks obtained using the TAP high throughput method: one of Escherichia coli with 230 nodes and 695 links, and one for yeast (Saccharomyces cerevisiae) with roughly ten times more nodes and links. We find in both cases that most connected subgraphs are strong motifs (Z-scores >10) or anti-motifs (Z-scores <-10) when the null model is the ensemble of networks with fixed degree sequence. Strong differences appear between the two networks, with dominant motifs in E. coli being (nearly) bipartite graphs and having many pairs of nodes which connect to the same neighbors, while dominant motifs in yeast tend towards completeness or contain large cliques. We also explore a number of methods that do not rely on measurements of Z-scores or comparisons with null models. For instance, we discuss the influence of specific complexes like the 26S proteasome in yeast, where a small number of complexes dominate the $k$-cores with large k and have a decisive effect on the strongest motifs with 6 to 8 nodes. We also present Zipf plots of counts versus rank. They show broad distributions that are not power laws, in contrast to the case when disconnected subgraphs are included. |
1510.04738 | Umut G\"u\c{c}l\"u | Umut G\"u\c{c}l\"u, Marcel A. J. van Gerven | Semantic vector space models predict neural responses to complex visual
stimuli | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Encoding models have as their objective to predict neural responses to
naturalistic stimuli with the aim of elucidating how sensory information is
represented in the brain. This prediction is achieved by representing the
stimulus in terms of a suitable feature space and using this feature space to
linearly predict observed neural responses. Here, we investigate to what extent
semantic vector space models can be used to predict neural responses to complex
visual stimuli. We show that these models provide good predictions of neural
responses in downstream visual areas, improving significantly over a low-level
control model based on Gabor wavelet pyramids. The outlined approach provides a
new way to model and map high-level semantic representations across cortex.
| [
{
"created": "Thu, 15 Oct 2015 22:52:42 GMT",
"version": "v1"
}
] | 2015-10-19 | [
[
"Güçlü",
"Umut",
""
],
[
"van Gerven",
"Marcel A. J.",
""
]
] | Encoding models have as their objective to predict neural responses to naturalistic stimuli with the aim of elucidating how sensory information is represented in the brain. This prediction is achieved by representing the stimulus in terms of a suitable feature space and using this feature space to linearly predict observed neural responses. Here, we investigate to what extent semantic vector space models can be used to predict neural responses to complex visual stimuli. We show that these models provide good predictions of neural responses in downstream visual areas, improving significantly over a low-level control model based on Gabor wavelet pyramids. The outlined approach provides a new way to model and map high-level semantic representations across cortex. |
2110.10105 | Giulia Laura Celora | Giulia L. Celora, Helen M. Byrne, P.G. Kevrekidis | Spatio-temporal modelling of phenotypic heterogeneity in tumour tissues
and its impact on radiotherapy treatment | null | null | 10.1016/j.jtbi.2022.111248 | null | q-bio.CB nlin.PS physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | We present a mathematical model that describes how tumour heterogeneity
evolves in a tissue slice that is oxygenated by a single blood vessel.
Phenotype is identified with the stemness level of a cell, $s$, that determines
its proliferative capacity, apoptosis propensity and response to treatment. Our
study is based on numerical bifurcation analysis and dynamical simulations of a
system of coupled non-local (in phenotypic space) partial differential
equations that links the phenotypic evolution of the tumour cells to local
oxygen levels in the tissue. In our formulation, we consider a 1D geometry
where oxygen is supplied by a blood vessel located on the domain boundary and
consumed by the tumour cells as it diffuses through the tissue. For
biologically relevant parameter values, the system exhibits multiple steady
states; in particular, depending on the initial conditions, the tumour is
either eliminated ("tumour-extinction") or it persists ("tumour-invasion"). We
conclude by using the model to investigate tumour responses to radiotherapy
(RT), and focus on establishing which RT strategies can eliminate the tumour.
Numerical simulations reveal how phenotypic heterogeneity evolves during
treatment and highlight the critical role of tissue oxygen levels on the
efficacy of radiation protocols that are commonly used clinically.
| [
{
"created": "Tue, 19 Oct 2021 16:59:30 GMT",
"version": "v1"
}
] | 2023-11-14 | [
[
"Celora",
"Giulia L.",
""
],
[
"Byrne",
"Helen M.",
""
],
[
"Kevrekidis",
"P. G.",
""
]
] | We present a mathematical model that describes how tumour heterogeneity evolves in a tissue slice that is oxygenated by a single blood vessel. Phenotype is identified with the stemness level of a cell, $s$, that determines its proliferative capacity, apoptosis propensity and response to treatment. Our study is based on numerical bifurcation analysis and dynamical simulations of a system of coupled non-local (in phenotypic space) partial differential equations that links the phenotypic evolution of the tumour cells to local oxygen levels in the tissue. In our formulation, we consider a 1D geometry where oxygen is supplied by a blood vessel located on the domain boundary and consumed by the tumour cells as it diffuses through the tissue. For biologically relevant parameter values, the system exhibits multiple steady states; in particular, depending on the initial conditions, the tumour is either eliminated ("tumour-extinction") or it persists ("tumour-invasion"). We conclude by using the model to investigate tumour responses to radiotherapy (RT), and focus on establishing which RT strategies can eliminate the tumour. Numerical simulations reveal how phenotypic heterogeneity evolves during treatment and highlight the critical role of tissue oxygen levels on the efficacy of radiation protocols that are commonly used clinically. |
2312.14249 | Yingzhou Lu | Yingzhou Lu, Minjie Shen, Yue Zhao, Chenhao Li, Fan Meng, Xiao Wang,
David Herrington, Yue Wang, Tim Fu, Capucine Van Rechem | GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for
High-Throughput Omics Data Analysis and Visualization | null | null | null | null | q-bio.GN cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The surge in high-throughput omics data has reshaped the landscape of
biological research, underlining the need for powerful, user-friendly data
analysis and interpretation tools. This paper presents GenoCraft, a web-based
comprehensive software solution designed to handle the entire pipeline of omics
data processing. GenoCraft offers a unified platform featuring advanced
bioinformatics tools, covering all aspects of omics data analysis. It
encompasses a range of functionalities, such as normalization, quality control,
differential analysis, network analysis, pathway analysis, and diverse
visualization techniques. This software makes state-of-the-art omics data
analysis more accessible to a wider range of users. With GenoCraft, researchers
and data scientists have access to an array of cutting-edge bioinformatics
tools under a user-friendly interface, making it a valuable resource for
managing and analyzing large-scale omics data. The API with an interactive web
interface is publicly available at https://genocraft.stanford. edu/. We also
release all the codes in https://github.com/futianfan/GenoCraft.
| [
{
"created": "Thu, 21 Dec 2023 19:06:34 GMT",
"version": "v1"
}
] | 2023-12-25 | [
[
"Lu",
"Yingzhou",
""
],
[
"Shen",
"Minjie",
""
],
[
"Zhao",
"Yue",
""
],
[
"Li",
"Chenhao",
""
],
[
"Meng",
"Fan",
""
],
[
"Wang",
"Xiao",
""
],
[
"Herrington",
"David",
""
],
[
"Wang",
"Yue",
""
],
[
"Fu",
"Tim",
""
],
[
"Van Rechem",
"Capucine",
""
]
] | The surge in high-throughput omics data has reshaped the landscape of biological research, underlining the need for powerful, user-friendly data analysis and interpretation tools. This paper presents GenoCraft, a web-based comprehensive software solution designed to handle the entire pipeline of omics data processing. GenoCraft offers a unified platform featuring advanced bioinformatics tools, covering all aspects of omics data analysis. It encompasses a range of functionalities, such as normalization, quality control, differential analysis, network analysis, pathway analysis, and diverse visualization techniques. This software makes state-of-the-art omics data analysis more accessible to a wider range of users. With GenoCraft, researchers and data scientists have access to an array of cutting-edge bioinformatics tools under a user-friendly interface, making it a valuable resource for managing and analyzing large-scale omics data. The API with an interactive web interface is publicly available at https://genocraft.stanford. edu/. We also release all the codes in https://github.com/futianfan/GenoCraft. |
2007.00159 | Niayesh Afshordi | Niayesh Afshordi (U-Waterloo/Perimeter), Benjamin Holder
(GVSU/U-Waterloo), Mohammad Bahrami, and Daniel Lichtblau (Wolfram Research) | Diverse local epidemics reveal the distinct effects of population
density, demographics, climate, depletion of susceptibles, and intervention
in the first wave of COVID-19 in the United States | 28 pages, 17 figures, COVID-19 cloud simulations and resources
available at https://nafshordi.com/covid/ and https://wolfr.am/COVID19Dash | null | null | null | q-bio.PE physics.soc-ph q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The SARS-CoV-2 pandemic has caused significant mortality and morbidity
worldwide, sparing almost no community. As the disease will likely remain a
threat for years to come, an understanding of the precise influences of human
demographics and settlement, as well as the dynamic factors of climate,
susceptible depletion, and intervention, on the spread of localized epidemics
will be vital for mounting an effective response. We consider the entire set of
local epidemics in the United States; a broad selection of demographic,
population density, and climate factors; and local mobility data, tracking
social distancing interventions, to determine the key factors driving the
spread and containment of the virus. Assuming first a linear model for the rate
of exponential growth (or decay) in cases/mortality, we find that
population-weighted density, humidity, and median age dominate the dynamics of
growth and decline, once interventions are accounted for. A focus on distinct
metropolitan areas suggests that some locales benefited from the timing of a
nearly simultaneous nationwide shutdown, and/or the regional climate conditions
in mid-March; while others suffered significant outbreaks prior to
intervention. Using a first-principles model of the infection spread, we then
develop predictions for the impact of the relaxation of social distancing and
local climate conditions. A few regions, where a significant fraction of the
population was infected, show evidence that the epidemic has partially resolved
via depletion of the susceptible population (i.e., "herd immunity"), while most
regions in the United States remain overwhelmingly susceptible. These results
will be important for optimal management of intervention strategies, which can
be facilitated using our online dashboard.
| [
{
"created": "Wed, 1 Jul 2020 00:19:39 GMT",
"version": "v1"
}
] | 2020-07-02 | [
[
"Afshordi",
"Niayesh",
"",
"U-Waterloo/Perimeter"
],
[
"Holder",
"Benjamin",
"",
"GVSU/U-Waterloo"
],
[
"Bahrami",
"Mohammad",
"",
"Wolfram Research"
],
[
"Lichtblau",
"Daniel",
"",
"Wolfram Research"
]
] | The SARS-CoV-2 pandemic has caused significant mortality and morbidity worldwide, sparing almost no community. As the disease will likely remain a threat for years to come, an understanding of the precise influences of human demographics and settlement, as well as the dynamic factors of climate, susceptible depletion, and intervention, on the spread of localized epidemics will be vital for mounting an effective response. We consider the entire set of local epidemics in the United States; a broad selection of demographic, population density, and climate factors; and local mobility data, tracking social distancing interventions, to determine the key factors driving the spread and containment of the virus. Assuming first a linear model for the rate of exponential growth (or decay) in cases/mortality, we find that population-weighted density, humidity, and median age dominate the dynamics of growth and decline, once interventions are accounted for. A focus on distinct metropolitan areas suggests that some locales benefited from the timing of a nearly simultaneous nationwide shutdown, and/or the regional climate conditions in mid-March; while others suffered significant outbreaks prior to intervention. Using a first-principles model of the infection spread, we then develop predictions for the impact of the relaxation of social distancing and local climate conditions. A few regions, where a significant fraction of the population was infected, show evidence that the epidemic has partially resolved via depletion of the susceptible population (i.e., "herd immunity"), while most regions in the United States remain overwhelmingly susceptible. These results will be important for optimal management of intervention strategies, which can be facilitated using our online dashboard. |
1708.08916 | Erfan Sayyari | Erfan Sayyari and Siavash Mirarab | Testing for polytomies in phylogenetic species trees using quartet
frequencies | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic species trees typically represent the speciation history as a
bifurcating tree. Speciation events that simultaneously create more than two
descendants, thereby creating polytomies in the phylogeny, are possible.
Moreover, the inability to resolve relationships is often shown as a (soft)
polytomy. Both types of polytomies have been traditionally studied in the
context of gene tree reconstruction from sequence data. However, polytomies in
the species tree cannot be detected or ruled out without considering gene tree
discordance. In this paper, we describe a statistical test based on properties
of the multi-species coalescent model to test the null hypothesis that a branch
in an estimated species tree should be replaced by a polytomy. On both
simulated and biological datasets, we show that the null hypothesis is rejected
for all but the shortest branches, and in most cases, it is retained for true
polytomies. The test, available as part of the ASTRAL package, can help
systematists decide whether their datasets are sufficient to resolve specific
relationships of interest.
| [
{
"created": "Tue, 29 Aug 2017 02:25:07 GMT",
"version": "v1"
},
{
"created": "Thu, 31 Aug 2017 04:08:47 GMT",
"version": "v2"
},
{
"created": "Sat, 2 Dec 2017 01:22:44 GMT",
"version": "v3"
},
{
"created": "Wed, 7 Feb 2018 03:52:24 GMT",
"version": "v4"
}
] | 2018-02-08 | [
[
"Sayyari",
"Erfan",
""
],
[
"Mirarab",
"Siavash",
""
]
] | Phylogenetic species trees typically represent the speciation history as a bifurcating tree. Speciation events that simultaneously create more than two descendants, thereby creating polytomies in the phylogeny, are possible. Moreover, the inability to resolve relationships is often shown as a (soft) polytomy. Both types of polytomies have been traditionally studied in the context of gene tree reconstruction from sequence data. However, polytomies in the species tree cannot be detected or ruled out without considering gene tree discordance. In this paper, we describe a statistical test based on properties of the multi-species coalescent model to test the null hypothesis that a branch in an estimated species tree should be replaced by a polytomy. On both simulated and biological datasets, we show that the null hypothesis is rejected for all but the shortest branches, and in most cases, it is retained for true polytomies. The test, available as part of the ASTRAL package, can help systematists decide whether their datasets are sufficient to resolve specific relationships of interest. |
1412.1399 | Lutz Brusch | Lionel Foret, Lutz Brusch and Frank J\"ulicher | Theory of cargo and membrane trafficking | review, 11 pages, 3 figures | null | null | null | q-bio.SC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Endocytosis underlies many cellular functions including signaling and
nutrient uptake. The endocytosed cargo gets redistributed across a dynamic
network of endosomes undergoing fusion and fission. Here, a theoretical
approach is reviewed which can explain how the microscopic properties of
endosome interactions cause the emergent macroscopic properties of cargo
trafficking in the endosomal network. Predictions by the theory have been
tested experimentally and include the inference of dependencies and parameter
values of the microscopic processes. This theory could also be used to infer
mechanisms of signal-trafficking crosstalk. It is applicable to in vivo systems
since fixed samples at few time points suffice as input data.
| [
{
"created": "Wed, 3 Dec 2014 16:53:11 GMT",
"version": "v1"
}
] | 2014-12-04 | [
[
"Foret",
"Lionel",
""
],
[
"Brusch",
"Lutz",
""
],
[
"Jülicher",
"Frank",
""
]
] | Endocytosis underlies many cellular functions including signaling and nutrient uptake. The endocytosed cargo gets redistributed across a dynamic network of endosomes undergoing fusion and fission. Here, a theoretical approach is reviewed which can explain how the microscopic properties of endosome interactions cause the emergent macroscopic properties of cargo trafficking in the endosomal network. Predictions by the theory have been tested experimentally and include the inference of dependencies and parameter values of the microscopic processes. This theory could also be used to infer mechanisms of signal-trafficking crosstalk. It is applicable to in vivo systems since fixed samples at few time points suffice as input data. |
2406.00735 | Jiahan Li | Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou
Ren, Jian Peng, Jianzhu Ma | Full-Atom Peptide Design based on Multi-modal Flow Matching | ICML 2024 | null | null | null | q-bio.BM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Peptides, short chains of amino acid residues, play a vital role in numerous
biological processes by interacting with other target molecules, offering
substantial potential in drug discovery. In this work, we present PepFlow, the
first multi-modal deep generative model grounded in the flow-matching framework
for the design of full-atom peptides that target specific protein receptors.
Drawing inspiration from the crucial roles of residue backbone orientations and
side-chain dynamics in protein-peptide interactions, we characterize the
peptide structure using rigid backbone frames within the $\mathrm{SE}(3)$
manifold and side-chain angles on high-dimensional tori. Furthermore, we
represent discrete residue types in the peptide sequence as categorical
distributions on the probability simplex. By learning the joint distributions
of each modality using derived flows and vector fields on corresponding
manifolds, our method excels in the fine-grained design of full-atom peptides.
Harnessing the multi-modal paradigm, our approach adeptly tackles various tasks
such as fix-backbone sequence design and side-chain packing through partial
sampling. Through meticulously crafted experiments, we demonstrate that PepFlow
exhibits superior performance in comprehensive benchmarks, highlighting its
significant potential in computational peptide design and analysis.
| [
{
"created": "Sun, 2 Jun 2024 12:59:54 GMT",
"version": "v1"
}
] | 2024-06-04 | [
[
"Li",
"Jiahan",
""
],
[
"Cheng",
"Chaoran",
""
],
[
"Wu",
"Zuofan",
""
],
[
"Guo",
"Ruihan",
""
],
[
"Luo",
"Shitong",
""
],
[
"Ren",
"Zhizhou",
""
],
[
"Peng",
"Jian",
""
],
[
"Ma",
"Jianzhu",
""
]
] | Peptides, short chains of amino acid residues, play a vital role in numerous biological processes by interacting with other target molecules, offering substantial potential in drug discovery. In this work, we present PepFlow, the first multi-modal deep generative model grounded in the flow-matching framework for the design of full-atom peptides that target specific protein receptors. Drawing inspiration from the crucial roles of residue backbone orientations and side-chain dynamics in protein-peptide interactions, we characterize the peptide structure using rigid backbone frames within the $\mathrm{SE}(3)$ manifold and side-chain angles on high-dimensional tori. Furthermore, we represent discrete residue types in the peptide sequence as categorical distributions on the probability simplex. By learning the joint distributions of each modality using derived flows and vector fields on corresponding manifolds, our method excels in the fine-grained design of full-atom peptides. Harnessing the multi-modal paradigm, our approach adeptly tackles various tasks such as fix-backbone sequence design and side-chain packing through partial sampling. Through meticulously crafted experiments, we demonstrate that PepFlow exhibits superior performance in comprehensive benchmarks, highlighting its significant potential in computational peptide design and analysis. |
2205.12629 | Erida Gjini | Ermanda Dekaj and Erida Gjini | Pneumococcus and the stress-gradient hypothesis: a trade-off links $R_0$
and susceptibility to co-colonization across countries | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Modern molecular technologies have revolutionized our understanding of
bacterial epidemiology, but reported data across different settings remain
under-integrated in common theoretical frameworks. Pneumococcus serotype
co-colonization, caused by the polymorphic bacteria Streptococcus pneumoniae,
has been increasingly investigated in recent years. While the global genomic
diversity and serotype distribution of S. pneumoniae are well-characterized,
there is limited information on how co-colonization patterns vary globally,
critical for understanding bacterial evolution and dynamics. Gathering a rich
dataset of cross-sectional pneumococcal colonization studies in the literature,
we quantified patterns of transmission intensity and co-colonization prevalence
in children populations across 17 geographic locations. Fitting these data to
an SIS model with co-colonization under the assumption of similarity among
interacting strains, our analysis reveals strong patterns of negative
co-variation between transmission intensity ($R_0$) and susceptibility to
co-colonization ($k$). In support of the stress-gradient hypothesis in ecology
(SGH), pneumococcus serotypes appear to compete more in high-transmission
settings and less in low-transmission settings, a trade-off which ultimately
leads to a conserved ratio of single to co-colonization $\mu=1/(R_0-1)k$.
Within our mathematical model, such conservation suggests preservation of
'stability-diversity-complexity' regimes in multi-strain coexistence. We find
no major study differences in serotype composition, pointing to underlying
adaptation of the same set of serotypes across environments. Our work
highlights that understanding pneumococcus transmission patterns from global
epidemiological data can benefit from simple analytical approaches that account
for quasi-neutrality among strains, co-colonization, as well as variable
environmental adaptation.
| [
{
"created": "Wed, 25 May 2022 10:08:47 GMT",
"version": "v1"
}
] | 2022-05-26 | [
[
"Dekaj",
"Ermanda",
""
],
[
"Gjini",
"Erida",
""
]
] | Modern molecular technologies have revolutionized our understanding of bacterial epidemiology, but reported data across different settings remain under-integrated in common theoretical frameworks. Pneumococcus serotype co-colonization, caused by the polymorphic bacteria Streptococcus pneumoniae, has been increasingly investigated in recent years. While the global genomic diversity and serotype distribution of S. pneumoniae are well-characterized, there is limited information on how co-colonization patterns vary globally, critical for understanding bacterial evolution and dynamics. Gathering a rich dataset of cross-sectional pneumococcal colonization studies in the literature, we quantified patterns of transmission intensity and co-colonization prevalence in children populations across 17 geographic locations. Fitting these data to an SIS model with co-colonization under the assumption of similarity among interacting strains, our analysis reveals strong patterns of negative co-variation between transmission intensity ($R_0$) and susceptibility to co-colonization ($k$). In support of the stress-gradient hypothesis in ecology (SGH), pneumococcus serotypes appear to compete more in high-transmission settings and less in low-transmission settings, a trade-off which ultimately leads to a conserved ratio of single to co-colonization $\mu=1/(R_0-1)k$. Within our mathematical model, such conservation suggests preservation of 'stability-diversity-complexity' regimes in multi-strain coexistence. We find no major study differences in serotype composition, pointing to underlying adaptation of the same set of serotypes across environments. Our work highlights that understanding pneumococcus transmission patterns from global epidemiological data can benefit from simple analytical approaches that account for quasi-neutrality among strains, co-colonization, as well as variable environmental adaptation. |
1703.04182 | Alexander Vasilyev | Alexander Yurievich Vasilyev | Optimal control of eye-movements during visual search | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of optimal oculomotor control during the execution of
visual search tasks. We introduce a computational model of human eye movements,
which takes into account various constraints of the human visual and oculomotor
systems. In the model, the choice of the subsequent fixation location is posed
as a problem of stochastic optimal control, which relies on reinforcement
learning methods. We show that if biological constraints are taken into
account, the trajectories simulated under learned policy share both basic
statistical properties and scaling behaviour with human eye movements. We
validated our model simulations with human psychophysical eye-tracking
experiments
| [
{
"created": "Sun, 12 Mar 2017 21:56:29 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Mar 2017 10:23:08 GMT",
"version": "v2"
},
{
"created": "Sun, 19 Mar 2017 16:59:39 GMT",
"version": "v3"
},
{
"created": "Thu, 13 Apr 2017 14:50:03 GMT",
"version": "v4"
},
{
"created": "Thu, 14 Sep 2017 21:39:50 GMT",
"version": "v5"
},
{
"created": "Wed, 4 Apr 2018 14:24:03 GMT",
"version": "v6"
},
{
"created": "Tue, 28 Aug 2018 20:42:53 GMT",
"version": "v7"
}
] | 2018-08-30 | [
[
"Vasilyev",
"Alexander Yurievich",
""
]
] | We study the problem of optimal oculomotor control during the execution of visual search tasks. We introduce a computational model of human eye movements, which takes into account various constraints of the human visual and oculomotor systems. In the model, the choice of the subsequent fixation location is posed as a problem of stochastic optimal control, which relies on reinforcement learning methods. We show that if biological constraints are taken into account, the trajectories simulated under learned policy share both basic statistical properties and scaling behaviour with human eye movements. We validated our model simulations with human psychophysical eye-tracking experiments |
1801.01853 | Trang-Anh Estelle Nghiem | Trang-Anh Nghiem, Bartosz Telenczuk, Olivier Marre, Alain Destexhe,
Ulisse Ferrari | Maximum entropy models reveal the excitatory and inhibitory correlation
structures in cortical neuronal activity | 17 pages, 11 figures (including 5 supplementary) | Phys. Rev. E 98, 012402 (2018) | 10.1103/PhysRevE.98.012402 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Maximum Entropy models can be inferred from large data-sets to uncover how
collective dynamics emerge from local interactions. Here, such models are
employed to investigate neurons recorded by multielectrode arrays in the human
and monkey cortex. Taking advantage of the separation of excitatory and
inhibitory neuron types, we construct a model including this distinction. This
approach allows to shed light upon differences between excitatory and
inhibitory activity across different brain states such as wakefulness and deep
sleep, in agreement with previous findings. Additionally, Maximum Entropy
models can also unveil novel features of neuronal interactions, which are found
to be dominated by pairwise interactions during wakefulness, but are
population-wide during deep sleep. In particular, inhibitory neurons are
observed to be strongly tuned to the inhibitory population. Overall, we
demonstrate Maximum Entropy models can be useful to analyze data-sets with
classified neuron types, and to reveal the respective roles of excitatory and
inhibitory neurons in organizing coherent dynamics in the cerebral cortex.
| [
{
"created": "Fri, 5 Jan 2018 17:49:05 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jan 2018 17:34:49 GMT",
"version": "v2"
},
{
"created": "Tue, 10 Jul 2018 13:25:59 GMT",
"version": "v3"
}
] | 2018-07-11 | [
[
"Nghiem",
"Trang-Anh",
""
],
[
"Telenczuk",
"Bartosz",
""
],
[
"Marre",
"Olivier",
""
],
[
"Destexhe",
"Alain",
""
],
[
"Ferrari",
"Ulisse",
""
]
] | Maximum Entropy models can be inferred from large data-sets to uncover how collective dynamics emerge from local interactions. Here, such models are employed to investigate neurons recorded by multielectrode arrays in the human and monkey cortex. Taking advantage of the separation of excitatory and inhibitory neuron types, we construct a model including this distinction. This approach allows to shed light upon differences between excitatory and inhibitory activity across different brain states such as wakefulness and deep sleep, in agreement with previous findings. Additionally, Maximum Entropy models can also unveil novel features of neuronal interactions, which are found to be dominated by pairwise interactions during wakefulness, but are population-wide during deep sleep. In particular, inhibitory neurons are observed to be strongly tuned to the inhibitory population. Overall, we demonstrate Maximum Entropy models can be useful to analyze data-sets with classified neuron types, and to reveal the respective roles of excitatory and inhibitory neurons in organizing coherent dynamics in the cerebral cortex. |
1904.10124 | Christos Skiadas H | Christos H Skiadas and Charilaos Skiadas | Relation of the Weibull Shape Parameter with the Healthy Life Years Lost
Estimates: Analytic Derivation and Estimation from an Extended Life Table | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Matsushita et al (1992) have done an interesting finding. They observed that
the shape parameter of the Weibull model presented systematic changes over time
and age when applied to mortality data for males and females in Japan. They
have also estimated that this parameter was smaller in the 1891-1898 data in
Japan compared to the 1980 mortality data and they presented an illustrative
figure for females where the values of the shape parameter are illustrated on
the diagram close to the corresponding survival curves. However, they have not
provided an analytical explanation of this behavior of the shape parameter of
the Weibull model. The cumulative hazard of this model can express the additive
process of applying a force in a material for enough time before cracking. To
pass to the human data, the Weibull model and the cumulative hazard can express
the additive process which disabilities and diseases cause the human organism
during the life span leading to healthy life years lost. In this paper we
further analytically derive a more general model of survival-mortality in which
we estimate a parameter related to the Healthy Life Years Lost (HLYL) and
leading to the Weibull model and the corresponding shape parameter as a
specific case. We have also demonstrated that the results found for the general
HLYL parameter we have proposed provides results similar to those provided by
the World Health Organization for the Healthy Life Expectancy (HALE) and the
corresponding HLYL estimates. An analytic derivation of the mathematical
formulas is presented along with an easy to apply Excel program. This program
is an extension of the classical life table including four more columns to
estimate the cumulative mortality, the average mortality, the person life years
lost and finally the HLYL parameter bx. The latest versions of this program
appear in the Demographics2019 website
| [
{
"created": "Tue, 23 Apr 2019 02:41:55 GMT",
"version": "v1"
}
] | 2019-04-24 | [
[
"Skiadas",
"Christos H",
""
],
[
"Skiadas",
"Charilaos",
""
]
] | Matsushita et al (1992) have done an interesting finding. They observed that the shape parameter of the Weibull model presented systematic changes over time and age when applied to mortality data for males and females in Japan. They have also estimated that this parameter was smaller in the 1891-1898 data in Japan compared to the 1980 mortality data and they presented an illustrative figure for females where the values of the shape parameter are illustrated on the diagram close to the corresponding survival curves. However, they have not provided an analytical explanation of this behavior of the shape parameter of the Weibull model. The cumulative hazard of this model can express the additive process of applying a force in a material for enough time before cracking. To pass to the human data, the Weibull model and the cumulative hazard can express the additive process which disabilities and diseases cause the human organism during the life span leading to healthy life years lost. In this paper we further analytically derive a more general model of survival-mortality in which we estimate a parameter related to the Healthy Life Years Lost (HLYL) and leading to the Weibull model and the corresponding shape parameter as a specific case. We have also demonstrated that the results found for the general HLYL parameter we have proposed provides results similar to those provided by the World Health Organization for the Healthy Life Expectancy (HALE) and the corresponding HLYL estimates. An analytic derivation of the mathematical formulas is presented along with an easy to apply Excel program. This program is an extension of the classical life table including four more columns to estimate the cumulative mortality, the average mortality, the person life years lost and finally the HLYL parameter bx. The latest versions of this program appear in the Demographics2019 website |
0809.0110 | Francesc Rossell\'o | Gabriel Cardona, Merce Llabres, Francesc Rossello, Gabriel Valiente | On Nakhleh's latest metric for phylogenetic networks | 15 pages | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/3.0/ | We prove that Nakhleh's latest dissimilarity measure for phylogenetic
networks is a metric on the classes of tree-child phylogenetic networks, of
semi-binary time consistent tree-sibling phylogenetic networks, and of
multi-labeled phylogenetic trees. We also prove that it distinguishes
phylogenetic networks with different reduced versions. In this way, it becomes
the dissimilarity measure for phylogenetic networks with the strongest
separation power available so far.
| [
{
"created": "Sun, 31 Aug 2008 10:27:04 GMT",
"version": "v1"
}
] | 2008-09-02 | [
[
"Cardona",
"Gabriel",
""
],
[
"Llabres",
"Merce",
""
],
[
"Rossello",
"Francesc",
""
],
[
"Valiente",
"Gabriel",
""
]
] | We prove that Nakhleh's latest dissimilarity measure for phylogenetic networks is a metric on the classes of tree-child phylogenetic networks, of semi-binary time consistent tree-sibling phylogenetic networks, and of multi-labeled phylogenetic trees. We also prove that it distinguishes phylogenetic networks with different reduced versions. In this way, it becomes the dissimilarity measure for phylogenetic networks with the strongest separation power available so far. |
1505.06726 | Sergey Denisov | Olena Tkachenko, Juzar Thingna, Sergey Denisov, Vasily Zaburdaev, and
Peter H\"anggi | Seasonal Floquet states in a game-driven evolutionary dynamics | 5 pages + 4 figures + supplementary material included | null | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mating preferences of many biological species are not constant but
season-dependent. Within the framework of evolutionary game theory this can be
modeled with two finite opposite-sex populations playing against each other
following the rules that are periodically changing. By combining Floquet theory
and the concept of quasi-stationary distributions, we reveal existence of
metastable time-periodic states in the evolution of finite game-driven
populations. The evolutionary Floquet states correspond to time-periodic
probability flows in the strategy space which cannot be resolved within the
mean-field framework. The lifetime of metastable Floquet states increases with
the size $N$ of populations so that they become attractors in the limit $N
\rightarrow \infty$.
| [
{
"created": "Mon, 25 May 2015 19:54:56 GMT",
"version": "v1"
},
{
"created": "Fri, 29 May 2015 19:37:57 GMT",
"version": "v2"
},
{
"created": "Wed, 12 Aug 2015 13:05:18 GMT",
"version": "v3"
}
] | 2015-08-13 | [
[
"Tkachenko",
"Olena",
""
],
[
"Thingna",
"Juzar",
""
],
[
"Denisov",
"Sergey",
""
],
[
"Zaburdaev",
"Vasily",
""
],
[
"Hänggi",
"Peter",
""
]
] | Mating preferences of many biological species are not constant but season-dependent. Within the framework of evolutionary game theory this can be modeled with two finite opposite-sex populations playing against each other following the rules that are periodically changing. By combining Floquet theory and the concept of quasi-stationary distributions, we reveal existence of metastable time-periodic states in the evolution of finite game-driven populations. The evolutionary Floquet states correspond to time-periodic probability flows in the strategy space which cannot be resolved within the mean-field framework. The lifetime of metastable Floquet states increases with the size $N$ of populations so that they become attractors in the limit $N \rightarrow \infty$. |
1404.0196 | Salva Duran-Nebreda | Salva Duran-Nebreda and Ricard V. Sol\'e | Emergence of multicellularity in a model of cell growth, death and
aggregation under size-dependent selection | 7 pages, 5 figures | null | 10.1098/rsif.2014.0982 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How multicellular life forms evolved out from unicellular ones constitutes a
major problem in our understanding of the evolution of our biosphere. A recent
set of experiments involving yeast cell populations has shown that selection
for faster sedimenting cells leads to the appearance of stable aggregates of
cells that are able to split into smaller clusters. It was suggested that the
observed evolutionary patterns could be the result of evolved programs
affecting cell death. Here we show, using a simple model of cell-cell
interactions and evolving adhesion rates, that the observed patterns in cluster
size and localized mortality can be easily interpreted in terms of waste
accumulation and toxicity driven apoptosis. This simple mechanism would have
played a key role in the early evolution of multicellular life forms based on
both aggregative and clonal development. The potential extensions of this work
and its implications for natural and synthetic multicellularity are discussed.
| [
{
"created": "Tue, 1 Apr 2014 10:53:32 GMT",
"version": "v1"
},
{
"created": "Sun, 8 Nov 2015 17:11:39 GMT",
"version": "v2"
}
] | 2015-11-10 | [
[
"Duran-Nebreda",
"Salva",
""
],
[
"Solé",
"Ricard V.",
""
]
] | How multicellular life forms evolved out from unicellular ones constitutes a major problem in our understanding of the evolution of our biosphere. A recent set of experiments involving yeast cell populations has shown that selection for faster sedimenting cells leads to the appearance of stable aggregates of cells that are able to split into smaller clusters. It was suggested that the observed evolutionary patterns could be the result of evolved programs affecting cell death. Here we show, using a simple model of cell-cell interactions and evolving adhesion rates, that the observed patterns in cluster size and localized mortality can be easily interpreted in terms of waste accumulation and toxicity driven apoptosis. This simple mechanism would have played a key role in the early evolution of multicellular life forms based on both aggregative and clonal development. The potential extensions of this work and its implications for natural and synthetic multicellularity are discussed. |
q-bio/0401031 | Paul Smolen | Paul Smolen, Paul E. Hardin, Brian S. Lo, Douglas A. Baxter, John H.
Byrne | Simulation of Drosophila Circadian Oscillations, Mutations, and Light
Responses by a Model with VRI, PDP-1, and CLK | Accepted to Biophysical Journal, 1/16/04. Single PDF file, 7 figures
at end | null | 10.1016/S0006-3495(04)74332-5 | null | q-bio.MN q-bio.SC | null | A model of Drosophila circadian rhythm generation was developed to represent
feedback loops based on transcriptional regulation of per, Clk (dclock), Pdp-1,
and vri (vrille). The model postulates that histone acetylation kinetics make
transcriptional activation a nonlinear function of [CLK]. Such a nonlinearity
is essential to simulate robust circadian oscillations of transcription in our
model and in previous models. Simulations suggest two positive feedback loops
involving Clk are not essential for oscillations, because oscillations of [PER]
were preserved when Clk, vri, or Pdp-1 expression was fixed. Eliminating the
negative feedback loop in which PER represses per expression abolished
oscillations. Simulations of per or Clk null mutations and of vri, Clk, or
Pdp-1 heterozygous null mutations altered model behavior in ways similar to
experimental data. The model simulated a photic phase-response curve resembling
experimental curves, and oscillations entrained to simulated light-dark cycles.
The model makes experimental predictions, some of which could be tested in
transgenic Drosophila.
| [
{
"created": "Fri, 23 Jan 2004 20:09:48 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Smolen",
"Paul",
""
],
[
"Hardin",
"Paul E.",
""
],
[
"Lo",
"Brian S.",
""
],
[
"Baxter",
"Douglas A.",
""
],
[
"Byrne",
"John H.",
""
]
] | A model of Drosophila circadian rhythm generation was developed to represent feedback loops based on transcriptional regulation of per, Clk (dclock), Pdp-1, and vri (vrille). The model postulates that histone acetylation kinetics make transcriptional activation a nonlinear function of [CLK]. Such a nonlinearity is essential to simulate robust circadian oscillations of transcription in our model and in previous models. Simulations suggest two positive feedback loops involving Clk are not essential for oscillations, because oscillations of [PER] were preserved when Clk, vri, or Pdp-1 expression was fixed. Eliminating the negative feedback loop in which PER represses per expression abolished oscillations. Simulations of per or Clk null mutations and of vri, Clk, or Pdp-1 heterozygous null mutations altered model behavior in ways similar to experimental data. The model simulated a photic phase-response curve resembling experimental curves, and oscillations entrained to simulated light-dark cycles. The model makes experimental predictions, some of which could be tested in transgenic Drosophila. |
2403.02603 | Dong Chen | Dong Chen, Gengzhuo Liu, Hongyan Du, Junjie Wee, Rui Wang, Jiahui
Chen, Jana Shen, and Guo-Wei Wei | Drug resistance revealed by in silico deep mutational scanning and
mutation tracker | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As COVID-19 enters its fifth year, it continues to pose a significant global
health threat, with the constantly mutating SARS-CoV-2 virus challenging drug
effectiveness. A comprehensive understanding of virus-drug interactions is
essential for predicting and improving drug effectiveness, especially in
combating drug resistance during the pandemic. In response, the Path Laplacian
Transformer-based Prospective Analysis Framework (PLFormer-PAF) has been
proposed, integrating historical data analysis and predictive modeling
strategies. This dual-strategy approach utilizes path topology to transform
protein-ligand complexes into topological sequences, enabling the use of
advanced large language models for analyzing protein-ligand interactions and
enhancing its reliability with factual insights garnered from historical data.
It has shown unparalleled performance in predicting binding affinity tasks
across various benchmarks, including specific evaluations related to
SARS-CoV-2, and assesses the impact of virus mutations on drug efficacy,
offering crucial insights into potential drug resistance. The predictions align
with observed mutation patterns in SARS-CoV-2, indicating that the widespread
use of the Pfizer drug has lead to viral evolution and reduced drug efficacy.
PLFormer-PAF's capabilities extend beyond identifying drug-resistant strains,
positioning it as a key tool in drug discovery research and the development of
new therapeutic strategies against fast-mutating viruses like COVID-19.
| [
{
"created": "Tue, 5 Mar 2024 02:35:47 GMT",
"version": "v1"
}
] | 2024-03-06 | [
[
"Chen",
"Dong",
""
],
[
"Liu",
"Gengzhuo",
""
],
[
"Du",
"Hongyan",
""
],
[
"Wee",
"Junjie",
""
],
[
"Wang",
"Rui",
""
],
[
"Chen",
"Jiahui",
""
],
[
"Shen",
"Jana",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | As COVID-19 enters its fifth year, it continues to pose a significant global health threat, with the constantly mutating SARS-CoV-2 virus challenging drug effectiveness. A comprehensive understanding of virus-drug interactions is essential for predicting and improving drug effectiveness, especially in combating drug resistance during the pandemic. In response, the Path Laplacian Transformer-based Prospective Analysis Framework (PLFormer-PAF) has been proposed, integrating historical data analysis and predictive modeling strategies. This dual-strategy approach utilizes path topology to transform protein-ligand complexes into topological sequences, enabling the use of advanced large language models for analyzing protein-ligand interactions and enhancing its reliability with factual insights garnered from historical data. It has shown unparalleled performance in predicting binding affinity tasks across various benchmarks, including specific evaluations related to SARS-CoV-2, and assesses the impact of virus mutations on drug efficacy, offering crucial insights into potential drug resistance. The predictions align with observed mutation patterns in SARS-CoV-2, indicating that the widespread use of the Pfizer drug has lead to viral evolution and reduced drug efficacy. PLFormer-PAF's capabilities extend beyond identifying drug-resistant strains, positioning it as a key tool in drug discovery research and the development of new therapeutic strategies against fast-mutating viruses like COVID-19. |
0807.1061 | Michel Aoun | Michel Aoun, Gilbert Charles, Annick Hourmant | Micropropagation of three genotypes of Indian mustard [{Brassica juncea}
(L.) Czern.] using seedling-derived transverse thin cell layer (tTCL)
explants | 12 pages, 2 figures and 2 tables | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Micropropagation of three genotypes of Indian mustard [\textit{Brassica
juncea} (L.) Czern.] using 7-days old seedling-derived transverse thin cell
layer (tTCL) explants was accomplished.
The genotype, explant source and addition of silver nitrate to the medium
significantly influenced shoot bud induction. MS medium with 26.6 $\mu$M of
6-Benzylaminopurin (BAP) and 3.22 $\mu$M of 1-naphtaleneacetic acid (NAA) was
identical (in the case of cotyledon tTCLs whatever the organ) and superior for
the induction of buds (in the cases of petiole tTCL explants of genotypes 1 and
2 and hypocotyl tTCL explants of genotypes 1 and 3) than 53.3 $\mu$M of BAP and
3.22 $\mu$M of NAA. However, 53.3 $\mu$M of BAP was superior for the induction
of buds than 26.6 $\mu$M in the presence of the same concentration of NAA for
petiole tTCL explants of genotype 3 and hypocotyl tTCL explants of genotype 2.
The addition of silver nitrate significantly enhanced the rate of shoot
induction in all genotypes. Cotyledon-derived tTCL explants exhibited the
highest shoot bud induction potential and was followed by petiole- and
hypocotyl-derived ones. Addition of 10 $\mu$M of silver nitrate to BAP and NAA
supplemented medium induced higher frequency shoot bud induction (up to 100 %)
with the highest means of 4.45 shoots per cotyledon-derived tTCL explants
obtained with the genotype 2. Shoot regenerated were rooted on MS basal medium
without PGRs which induced 99 % of roots per shoot. The plantlets established
in greenhouse conditions with 99 % survival, flowered normally and set seeds.
| [
{
"created": "Mon, 7 Jul 2008 16:17:23 GMT",
"version": "v1"
}
] | 2008-07-08 | [
[
"Aoun",
"Michel",
""
],
[
"Charles",
"Gilbert",
""
],
[
"Hourmant",
"Annick",
""
]
] | Micropropagation of three genotypes of Indian mustard [\textit{Brassica juncea} (L.) Czern.] using 7-days old seedling-derived transverse thin cell layer (tTCL) explants was accomplished. The genotype, explant source and addition of silver nitrate to the medium significantly influenced shoot bud induction. MS medium with 26.6 $\mu$M of 6-Benzylaminopurin (BAP) and 3.22 $\mu$M of 1-naphtaleneacetic acid (NAA) was identical (in the case of cotyledon tTCLs whatever the organ) and superior for the induction of buds (in the cases of petiole tTCL explants of genotypes 1 and 2 and hypocotyl tTCL explants of genotypes 1 and 3) than 53.3 $\mu$M of BAP and 3.22 $\mu$M of NAA. However, 53.3 $\mu$M of BAP was superior for the induction of buds than 26.6 $\mu$M in the presence of the same concentration of NAA for petiole tTCL explants of genotype 3 and hypocotyl tTCL explants of genotype 2. The addition of silver nitrate significantly enhanced the rate of shoot induction in all genotypes. Cotyledon-derived tTCL explants exhibited the highest shoot bud induction potential and was followed by petiole- and hypocotyl-derived ones. Addition of 10 $\mu$M of silver nitrate to BAP and NAA supplemented medium induced higher frequency shoot bud induction (up to 100 %) with the highest means of 4.45 shoots per cotyledon-derived tTCL explants obtained with the genotype 2. Shoot regenerated were rooted on MS basal medium without PGRs which induced 99 % of roots per shoot. The plantlets established in greenhouse conditions with 99 % survival, flowered normally and set seeds. |
1506.06925 | Tamar Friedlander | Tamar Friedlander and Roshan Prizak and C\u{a}lin C. Guet and Nicholas
H. Barton and Ga\v{s}per Tka\v{c}ik | Intrinsic limits to gene regulation by global crosstalk | null | Nature Communications 7:12307 (2016) | 10.1038/ncomms12307 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene regulation relies on the specificity of transcription factor (TF) - DNA
interactions. In equilibrium, limited specificity may lead to crosstalk: a
regulatory state in which a gene is either incorrectly activated due to
noncognate TF-DNA interactions or remains erroneously inactive. We present a
tractable biophysical model of global crosstalk, where many genes are
simultaneously regulated by many TFs. We show that in the simplest regulatory
scenario, a lower bound on crosstalk severity can be analytically derived
solely from the number of (co)regulated genes and a suitable parameter that
describes binding site similarity. Estimates show that crosstalk could present
a significant challenge for organisms with low-specificity TFs, such as
metazoans, unless they use appropriate regulation schemes. Strong cooperativity
substantially decreases crosstalk, while joint regulation by activators and
repressors, surprisingly, does not; moreover, certain microscopic details about
promoter architecture emerge as globally important determinants of crosstalk
strength. Our results suggest that crosstalk imposes a new type of global
constraint on the functioning and evolution of regulatory networks, which is
qualitatively distinct from the known constraints acting at the level of
individual gene regulatory elements.
| [
{
"created": "Tue, 23 Jun 2015 09:43:17 GMT",
"version": "v1"
},
{
"created": "Tue, 3 May 2016 15:21:59 GMT",
"version": "v2"
}
] | 2016-10-31 | [
[
"Friedlander",
"Tamar",
""
],
[
"Prizak",
"Roshan",
""
],
[
"Guet",
"Călin C.",
""
],
[
"Barton",
"Nicholas H.",
""
],
[
"Tkačik",
"Gašper",
""
]
] | Gene regulation relies on the specificity of transcription factor (TF) - DNA interactions. In equilibrium, limited specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to noncognate TF-DNA interactions or remains erroneously inactive. We present a tractable biophysical model of global crosstalk, where many genes are simultaneously regulated by many TFs. We show that in the simplest regulatory scenario, a lower bound on crosstalk severity can be analytically derived solely from the number of (co)regulated genes and a suitable parameter that describes binding site similarity. Estimates show that crosstalk could present a significant challenge for organisms with low-specificity TFs, such as metazoans, unless they use appropriate regulation schemes. Strong cooperativity substantially decreases crosstalk, while joint regulation by activators and repressors, surprisingly, does not; moreover, certain microscopic details about promoter architecture emerge as globally important determinants of crosstalk strength. Our results suggest that crosstalk imposes a new type of global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints acting at the level of individual gene regulatory elements. |
0801.3832 | Michael Schnabel | Michael Schnabel, Matthias Kaschube and Fred Wolf | Pinwheel stability, pattern selection and the geometry of visual space | 5 pages, 5 figures | null | null | null | q-bio.NC nlin.PS physics.bio-ph | null | It has been proposed that the dynamical stability of topological defects in
the visual cortex reflects the Euclidean symmetry of the visual world. We
analyze defect stability and pattern selection in a generalized Swift-Hohenberg
model of visual cortical development symmetric under the Euclidean group E(2).
Euclidean symmetry strongly influences the geometry and multistability of model
solutions but does not directly impact on defect stability.
| [
{
"created": "Thu, 24 Jan 2008 20:42:26 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Jan 2008 23:07:20 GMT",
"version": "v2"
}
] | 2008-01-30 | [
[
"Schnabel",
"Michael",
""
],
[
"Kaschube",
"Matthias",
""
],
[
"Wolf",
"Fred",
""
]
] | It has been proposed that the dynamical stability of topological defects in the visual cortex reflects the Euclidean symmetry of the visual world. We analyze defect stability and pattern selection in a generalized Swift-Hohenberg model of visual cortical development symmetric under the Euclidean group E(2). Euclidean symmetry strongly influences the geometry and multistability of model solutions but does not directly impact on defect stability. |
1802.04347 | Yuriria Cortes-Poza | Yuriria Cortes Poza, Pablo Padilla Longoria, Elena Alvarez Buylla | Spatial dynamics of flower organ formation | 32 pages, 11 figures | null | null | null | q-bio.TO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the emergence of biological structures and their changes is a
complex problem. On a biochemical level, it is based on gene regulatory
networks (GRN) consisting on interactions between the genes responsible for
cell differentiation and coupled in a greater scale with external factors. In
this work we provide a systematic methodological framework to construct
Waddington's epigenetic landscape of the GRN involved in cellular determination
during the early stages of development of angiosperms. As a specific example we
consider the flower of the plant \textit{Arabidopsis thaliana}. Our model,
which is based on experimental data, recovers accurately the spatial
configuration of the flower during cell fate determination, not only for the
wild type, but for its homeotic mutants as well. The method developed in this
project is general enough to be used in the study of the relationship between
genotype-phenotype in other living organisms.
| [
{
"created": "Mon, 29 Jan 2018 18:26:01 GMT",
"version": "v1"
}
] | 2018-02-14 | [
[
"Poza",
"Yuriria Cortes",
""
],
[
"Longoria",
"Pablo Padilla",
""
],
[
"Buylla",
"Elena Alvarez",
""
]
] | Understanding the emergence of biological structures and their changes is a complex problem. On a biochemical level, it is based on gene regulatory networks (GRN) consisting on interactions between the genes responsible for cell differentiation and coupled in a greater scale with external factors. In this work we provide a systematic methodological framework to construct Waddington's epigenetic landscape of the GRN involved in cellular determination during the early stages of development of angiosperms. As a specific example we consider the flower of the plant \textit{Arabidopsis thaliana}. Our model, which is based on experimental data, recovers accurately the spatial configuration of the flower during cell fate determination, not only for the wild type, but for its homeotic mutants as well. The method developed in this project is general enough to be used in the study of the relationship between genotype-phenotype in other living organisms. |
1304.1985 | Jiankui He | Jiankui He, Luwen Ning, Yin Tong | Origins and evolutionary genomics of the novel 2013 avian-origin H7N9
influenza A virus in China: Early findings | 8 pages, 5 figures, 2 table | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In March and early April 2013, a new avian-origin influenza A (H7N9) virus
(A-OIV) emerged in the eastern China. During the first week of April, the 18
infection cases have been confirmed and 6 people have died since March. This
virus has caused global concern as a potential pandemic threat. Here we use
evolutionary analysis to reconstruct the origins and early development of the
A-OIV viruses. We found that A-OIV was derived from a reassortment of three
avian flu virus strains, and substantial mutations have been detected. Our
results highlight the need for systematic surveillance of influenza in birds,
and provide evidence that the mixing of new genetic elements in avian can
result in the emergence of viruses with pandemic potential in humans.
| [
{
"created": "Sun, 7 Apr 2013 11:42:46 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Apr 2013 13:31:28 GMT",
"version": "v2"
}
] | 2013-04-10 | [
[
"He",
"Jiankui",
""
],
[
"Ning",
"Luwen",
""
],
[
"Tong",
"Yin",
""
]
] | In March and early April 2013, a new avian-origin influenza A (H7N9) virus (A-OIV) emerged in the eastern China. During the first week of April, the 18 infection cases have been confirmed and 6 people have died since March. This virus has caused global concern as a potential pandemic threat. Here we use evolutionary analysis to reconstruct the origins and early development of the A-OIV viruses. We found that A-OIV was derived from a reassortment of three avian flu virus strains, and substantial mutations have been detected. Our results highlight the need for systematic surveillance of influenza in birds, and provide evidence that the mixing of new genetic elements in avian can result in the emergence of viruses with pandemic potential in humans. |
2405.00333 | Sarah Vollert | Sarah A. Vollert, Christopher Drovandi, Matthew P. Adams | Reevaluating coexistence and stability in ecosystem networks to address
ecological transients: methods and implications | null | null | null | null | q-bio.PE stat.AP | http://creativecommons.org/licenses/by/4.0/ | Representing ecosystems at equilibrium has been foundational for building
ecological theories, forecasting species populations and planning conservation
actions. The equilibrium "balance of nature" ideal suggests that populations
will eventually stabilise to a coexisting balance of species. However, a
growing body of literature argues that the equilibrium ideal is inappropriate
for ecosystems. Here, we develop and demonstrate a new framework for
representing ecosystems without considering equilibrium dynamics. Instead, far
more pragmatic ecosystem models are constructed by considering population
trajectories, regardless of whether they exhibit equilibrium or transient (i.e.
non-equilibrium) behaviour. This novel framework maximally utilises readily
available, but often overlooked, knowledge from field observations and expert
elicitation, rather than relying on theoretical ecosystem properties. We
developed innovative Bayesian algorithms to generate ecosystem models in this
new statistical framework, without excessive computational burden. Our results
reveal that our pragmatic framework could have a dramatic impact on
conservation decision-making and enhance the realism of ecosystem models and
forecasts.
| [
{
"created": "Wed, 1 May 2024 05:52:15 GMT",
"version": "v1"
}
] | 2024-05-02 | [
[
"Vollert",
"Sarah A.",
""
],
[
"Drovandi",
"Christopher",
""
],
[
"Adams",
"Matthew P.",
""
]
] | Representing ecosystems at equilibrium has been foundational for building ecological theories, forecasting species populations and planning conservation actions. The equilibrium "balance of nature" ideal suggests that populations will eventually stabilise to a coexisting balance of species. However, a growing body of literature argues that the equilibrium ideal is inappropriate for ecosystems. Here, we develop and demonstrate a new framework for representing ecosystems without considering equilibrium dynamics. Instead, far more pragmatic ecosystem models are constructed by considering population trajectories, regardless of whether they exhibit equilibrium or transient (i.e. non-equilibrium) behaviour. This novel framework maximally utilises readily available, but often overlooked, knowledge from field observations and expert elicitation, rather than relying on theoretical ecosystem properties. We developed innovative Bayesian algorithms to generate ecosystem models in this new statistical framework, without excessive computational burden. Our results reveal that our pragmatic framework could have a dramatic impact on conservation decision-making and enhance the realism of ecosystem models and forecasts. |
2204.04573 | David Vulakh | Elchanan Mossel, David Vulakh | Efficient Reconstruction of Stochastic Pedigrees: Some Steps From Theory
to Practice | To appear in PSB 2023 | PSB '23: Proceedings of the 2023 Pacific Symposium on
Biocomputing. November 2022. Pp. 133-144 | 10.1142/9789811270611_0013 | null | q-bio.PE cs.DS cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In an extant population, how much information do extant individuals provide
on the pedigree of their ancestors? Recent work by Kim, Mossel, Ramnarayan and
Turner (2020) studied this question under a number of simplifying assumptions,
including random mating, fixed length inheritance blocks and sufficiently large
founding population. They showed that under these conditions if the average
number of offspring is a sufficiently large constant, then it is possible to
recover a large fraction of the pedigree structure and genetic content by an
algorithm they named REC-GEN.
We are interested in studying the performance of REC-GEN on simulated data
generated according to the model. As a first step, we improve the running time
of the algorithm. However, we observe that even the faster version of the
algorithm does not do well in any simulations in recovering the pedigree beyond
2 generations. We claim that this is due to the inbreeding present in any
setting where the algorithm can be run, even on simulated data. To support the
claim we show that a main step of the algorithm, called ancestral
reconstruction, performs accurately in a idealized setting with no inbreeding
but performs poorly in random mating populations.
To overcome the poor behavior of REC-GEN we introduce a Belief-Propagation
based heuristic that accounts for the inbreeding and performs much better in
our simulations.
| [
{
"created": "Sun, 10 Apr 2022 01:08:39 GMT",
"version": "v1"
},
{
"created": "Sun, 25 Sep 2022 01:26:17 GMT",
"version": "v2"
}
] | 2022-11-29 | [
[
"Mossel",
"Elchanan",
""
],
[
"Vulakh",
"David",
""
]
] | In an extant population, how much information do extant individuals provide on the pedigree of their ancestors? Recent work by Kim, Mossel, Ramnarayan and Turner (2020) studied this question under a number of simplifying assumptions, including random mating, fixed length inheritance blocks and sufficiently large founding population. They showed that under these conditions if the average number of offspring is a sufficiently large constant, then it is possible to recover a large fraction of the pedigree structure and genetic content by an algorithm they named REC-GEN. We are interested in studying the performance of REC-GEN on simulated data generated according to the model. As a first step, we improve the running time of the algorithm. However, we observe that even the faster version of the algorithm does not do well in any simulations in recovering the pedigree beyond 2 generations. We claim that this is due to the inbreeding present in any setting where the algorithm can be run, even on simulated data. To support the claim we show that a main step of the algorithm, called ancestral reconstruction, performs accurately in a idealized setting with no inbreeding but performs poorly in random mating populations. To overcome the poor behavior of REC-GEN we introduce a Belief-Propagation based heuristic that accounts for the inbreeding and performs much better in our simulations. |
1809.05621 | Ludmil Zikatanov | Katherine Y. Zipp, Yangqingxiang Wu, Kaiyi Wu, and Ludmil T. Zikatanov | Optimal spatial-dynamic management to minimize the damages caused by
aquatic invasive species | 10 pages, 4 algorithms | null | null | null | q-bio.PE math.NA math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Invasive species have been recognized as a leading threat to biodiversity. In
particular, lakes are especially affected by species invasions because they are
closed systems sensitive to disruption. Accurately controlling the spread of
invasive species requires solving a complex spatial-dynamic optimization
problem. In this work we propose a novel framework for determining the optimal
management strategy to maximize the value of a lake system net of damages from
invasive species, including an endogenous diffusion mechanism for the spread of
invasive species through boaters' trips between lakes. The proposed method
includes a combined global iterative process which determines the optimal
number of trips to each lake in each season and the spatial-dynamic optimal
boat ramp fee.
| [
{
"created": "Sat, 15 Sep 2018 00:37:54 GMT",
"version": "v1"
}
] | 2018-09-18 | [
[
"Zipp",
"Katherine Y.",
""
],
[
"Wu",
"Yangqingxiang",
""
],
[
"Wu",
"Kaiyi",
""
],
[
"Zikatanov",
"Ludmil T.",
""
]
] | Invasive species have been recognized as a leading threat to biodiversity. In particular, lakes are especially affected by species invasions because they are closed systems sensitive to disruption. Accurately controlling the spread of invasive species requires solving a complex spatial-dynamic optimization problem. In this work we propose a novel framework for determining the optimal management strategy to maximize the value of a lake system net of damages from invasive species, including an endogenous diffusion mechanism for the spread of invasive species through boaters' trips between lakes. The proposed method includes a combined global iterative process which determines the optimal number of trips to each lake in each season and the spatial-dynamic optimal boat ramp fee. |
1504.01298 | Christian Lyngby Vestergaard | Christian L. Vestergaard, Mathieu G\'enois | Temporal Gillespie algorithm: Fast simulation of contagion processes on
time-varying networks | Minor changes and updates to references | PLoS Comput. Biol. 11 (2015) e1004579 | 10.1371/journal.pcbi.1004579 | null | q-bio.QM physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic simulations are one of the cornerstones of the analysis of
dynamical processes on complex networks, and are often the only accessible way
to explore their behavior. The development of fast algorithms is paramount to
allow large-scale simulations. The Gillespie algorithm can be used for fast
simulation of stochastic processes, and variants of it have been applied to
simulate dynamical processes on static networks. However, its adaptation to
temporal networks remains non-trivial. We here present a temporal Gillespie
algorithm that solves this problem. Our method is applicable to general Poisson
(constant-rate) processes on temporal networks, stochastically exact, and up to
multiple orders of magnitude faster than traditional simulation schemes based
on rejection sampling. We also show how it can be extended to simulate
non-Markovian processes. The algorithm is easily applicable in practice, and as
an illustration we detail how to simulate both Poissonian and non-Markovian
models of epidemic spreading. Namely, we provide pseudocode and its
implementation in C++ for simulating the paradigmatic
Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and
a Susceptible-Infected-Recovered model with non-constant recovery rates. For
empirical networks, the temporal Gillespie algorithm is here typically from 10
to 100 times faster than rejection sampling.
| [
{
"created": "Fri, 3 Apr 2015 15:55:03 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Sep 2015 14:38:48 GMT",
"version": "v2"
},
{
"created": "Mon, 26 Oct 2015 08:50:54 GMT",
"version": "v3"
}
] | 2015-11-09 | [
[
"Vestergaard",
"Christian L.",
""
],
[
"Génois",
"Mathieu",
""
]
] | Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling. |
2406.05058 | Yuan Yin | Yuan Yin, Jennifer A. Flegg, Mark B. Flegg | Accurate stochastic simulation algorithm for multiscale models of
infectious diseases | 23 pages, 7 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | In the infectious disease literature, significant effort has been devoted to
studying dynamics at a single scale. For example, compartmental models
describing population-level dynamics are often formulated using differential
equations. In cases where small numbers or noise play a crucial role, these
differential equations are replaced with memoryless Markovian models, where
discrete individuals can be members of a compartment and transition
stochastically. Classic stochastic simulation algorithms, such as Gillespie's
algorithm and the next reaction method, can be employed to solve these
Markovian models exactly. The intricate coupling between models at different
scales underscores the importance of multiscale modelling in infectious
diseases. However, several computational challenges arise when the multiscale
model becomes non-Markovian. In this paper, we address these challenges by
developing a novel exact stochastic simulation algorithm. We apply it to a
showcase multiscale system where all individuals share the same deterministic
within-host model while the population-level dynamics are governed by a
stochastic formulation. We demonstrate that as long as the within-host
information is harvested at a reasonable resolution, the novel algorithm we
develop will always be accurate. Moreover, the novel algorithm we develop is
general and can be easily applied to other multiscale models in (or outside)
the realm of infectious diseases.
| [
{
"created": "Fri, 7 Jun 2024 16:28:19 GMT",
"version": "v1"
}
] | 2024-06-10 | [
[
"Yin",
"Yuan",
""
],
[
"Flegg",
"Jennifer A.",
""
],
[
"Flegg",
"Mark B.",
""
]
] | In the infectious disease literature, significant effort has been devoted to studying dynamics at a single scale. For example, compartmental models describing population-level dynamics are often formulated using differential equations. In cases where small numbers or noise play a crucial role, these differential equations are replaced with memoryless Markovian models, where discrete individuals can be members of a compartment and transition stochastically. Classic stochastic simulation algorithms, such as Gillespie's algorithm and the next reaction method, can be employed to solve these Markovian models exactly. The intricate coupling between models at different scales underscores the importance of multiscale modelling in infectious diseases. However, several computational challenges arise when the multiscale model becomes non-Markovian. In this paper, we address these challenges by developing a novel exact stochastic simulation algorithm. We apply it to a showcase multiscale system where all individuals share the same deterministic within-host model while the population-level dynamics are governed by a stochastic formulation. We demonstrate that as long as the within-host information is harvested at a reasonable resolution, the novel algorithm we develop will always be accurate. Moreover, the novel algorithm we develop is general and can be easily applied to other multiscale models in (or outside) the realm of infectious diseases. |
1501.01338 | Richard McMurtrey | Richard J. McMurtrey | Patterned and Functionalized Nanofiber Scaffolds in Three-Dimensional
Hydrogel Constructs Enhance Neurite Outgrowth and Directional Control | null | J Neural Eng. 2014 Dec;11(6):066009 | 10.1088/1741-2560/11/6/066009 | null | q-bio.TO | http://creativecommons.org/licenses/by/3.0/ | Neural tissue engineering holds incredible potential to restore functional
capabilities to damaged neural tissue. It was hypothesized that patterned and
functionalized nanofiber scaffolds could control neurite direction and enhance
neurite outgrowth. Aligned nanofibers were created according to a mathematical
model and were shown to enable significant control over the direction of
neurite outgrowth in both two-dimensional (2D) and three-dimensional (3D)
neuronal cultures. Laminin-functionalized nanofibers in 3D hyaluronic acid (HA)
hydrogels enabled significant alignment of neurites with nanofibers, enabled
significant neurite tracking of nanofibers, and significantly increased the
distance over which neurites could extend. This work demonstrates the ability
to create unique 3D neural tissue constructs using a combined system of
hydrogel and nanofiber scaffolding. Importantly, patterned and
biofunctionalized nanofiber scaffolds that can control direction and increase
length of neurite outgrowth in three-dimensions hold much potential for neural
tissue engineering. This approach offers advancements in the development of
implantable neural tissue constructs that enable control of neural development
and reproduction of neuroanatomical pathways, with the ultimate goal being the
achievement of functional neural regeneration.
| [
{
"created": "Wed, 7 Jan 2015 00:16:22 GMT",
"version": "v1"
}
] | 2016-01-05 | [
[
"McMurtrey",
"Richard J.",
""
]
] | Neural tissue engineering holds incredible potential to restore functional capabilities to damaged neural tissue. It was hypothesized that patterned and functionalized nanofiber scaffolds could control neurite direction and enhance neurite outgrowth. Aligned nanofibers were created according to a mathematical model and were shown to enable significant control over the direction of neurite outgrowth in both two-dimensional (2D) and three-dimensional (3D) neuronal cultures. Laminin-functionalized nanofibers in 3D hyaluronic acid (HA) hydrogels enabled significant alignment of neurites with nanofibers, enabled significant neurite tracking of nanofibers, and significantly increased the distance over which neurites could extend. This work demonstrates the ability to create unique 3D neural tissue constructs using a combined system of hydrogel and nanofiber scaffolding. Importantly, patterned and biofunctionalized nanofiber scaffolds that can control direction and increase length of neurite outgrowth in three-dimensions hold much potential for neural tissue engineering. This approach offers advancements in the development of implantable neural tissue constructs that enable control of neural development and reproduction of neuroanatomical pathways, with the ultimate goal being the achievement of functional neural regeneration. |
2003.08913 | Giulia Bassignana | Giulia Bassignana, Jennifer Fransson, Vincent Henry, Olivier Colliot,
Violetta Zujovic, Fabrizio De Vico Fallani | Step-wise target controllability identifies dysregulated pathways of
macrophage networks in multiple sclerosis | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying the nodes that have the potential to influence the state of a
network is a relevant question for many complex systems. In many applications
it is often essential to test the ability of an individual node to control a
specific target subset of the network. In biological networks, this might
provide precious information on how single genes regulate the expression of
specific groups of molecules in the cell. Taking into account these
constraints, we propose an optimized heuristic based on the Kalman rank
condition to quantify the centrality of a node as the number of target nodes it
can control. By introducing a hierarchy among the nodes in the target set, and
performing a step-wise research, we ensure for sparse and directed networks the
identification of a controllable driver-target configuration in a significantly
reduced space and time complexity. We show how the method works for simple
network configurations, then we use it to characterize the inflammatory
pathways in molecular gene networks associated with macrophage dysfunction in
patients with multiple sclerosis. Results indicate that the targeted secreted
molecules can in general be controlled by a large number of driver nodes (51%)
involved in different cell functions, i.e. sensing, signaling and
transcription. However, during the inflammatory response only a moderate
fraction of all the possible driver-target pairs are significantly coactivated,
as measured by gene expression data obtained from human blood samples. Notably,
they differ between multiple sclerosis patients and healthy controls, and we
find that this is related to the presence of dysregulated genes along the
controllable walks. Our method, that we name step-wise target controllability,
represents a practical solution to identify controllable driver-target
configurations in directed complex networks and test their relevance from a
functional perspective.
| [
{
"created": "Thu, 19 Mar 2020 17:24:07 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Mar 2020 14:54:43 GMT",
"version": "v2"
},
{
"created": "Tue, 24 Mar 2020 10:12:25 GMT",
"version": "v3"
},
{
"created": "Mon, 7 Dec 2020 14:01:57 GMT",
"version": "v4"
}
] | 2020-12-08 | [
[
"Bassignana",
"Giulia",
""
],
[
"Fransson",
"Jennifer",
""
],
[
"Henry",
"Vincent",
""
],
[
"Colliot",
"Olivier",
""
],
[
"Zujovic",
"Violetta",
""
],
[
"Fallani",
"Fabrizio De Vico",
""
]
] | Identifying the nodes that have the potential to influence the state of a network is a relevant question for many complex systems. In many applications it is often essential to test the ability of an individual node to control a specific target subset of the network. In biological networks, this might provide precious information on how single genes regulate the expression of specific groups of molecules in the cell. Taking into account these constraints, we propose an optimized heuristic based on the Kalman rank condition to quantify the centrality of a node as the number of target nodes it can control. By introducing a hierarchy among the nodes in the target set, and performing a step-wise research, we ensure for sparse and directed networks the identification of a controllable driver-target configuration in a significantly reduced space and time complexity. We show how the method works for simple network configurations, then we use it to characterize the inflammatory pathways in molecular gene networks associated with macrophage dysfunction in patients with multiple sclerosis. Results indicate that the targeted secreted molecules can in general be controlled by a large number of driver nodes (51%) involved in different cell functions, i.e. sensing, signaling and transcription. However, during the inflammatory response only a moderate fraction of all the possible driver-target pairs are significantly coactivated, as measured by gene expression data obtained from human blood samples. Notably, they differ between multiple sclerosis patients and healthy controls, and we find that this is related to the presence of dysregulated genes along the controllable walks. Our method, that we name step-wise target controllability, represents a practical solution to identify controllable driver-target configurations in directed complex networks and test their relevance from a functional perspective. |
1507.02716 | Pierre Sacr\'e | Pierre Sacr\'e, Sridevi V. Sarma, Yun Guan, William S. Anderson | Electrical neurostimulation for chronic pain: on selective relay of
sensory neural activities in myelinated nerve fibers | null | null | 10.1109/EMBC.2015.7319444 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chronic pain affects about 100 million adults in the US. Despite their great
need, neuropharmacology and neurostimulation therapies for chronic pain have
been associated with suboptimal efficacy and limited long-term success, as
their mechanisms of action are unclear. Yet current computational models of
pain transmission suffer from several limitations. In particular, dorsal column
models do not include the fundamental underlying sensory activity traveling in
these nerve fibers. We developed a (simple) simulation test bed of electrical
neurostimulation of myelinated nerve fibers with underlying sensory activity.
This paper reports our findings so far. Interactions between stimulation-evoked
and underlying activities are mainly due to collisions of action potentials and
losses of excitability due to the refractory period following an action
potential. In addition, intuitively, the reliability of sensory activity
decreases as the stimulation frequency increases. This first step opens the
door to a better understanding of pain transmission and its modulation by
neurostimulation therapies.
| [
{
"created": "Thu, 9 Jul 2015 21:11:41 GMT",
"version": "v1"
}
] | 2024-05-03 | [
[
"Sacré",
"Pierre",
""
],
[
"Sarma",
"Sridevi V.",
""
],
[
"Guan",
"Yun",
""
],
[
"Anderson",
"William S.",
""
]
] | Chronic pain affects about 100 million adults in the US. Despite their great need, neuropharmacology and neurostimulation therapies for chronic pain have been associated with suboptimal efficacy and limited long-term success, as their mechanisms of action are unclear. Yet current computational models of pain transmission suffer from several limitations. In particular, dorsal column models do not include the fundamental underlying sensory activity traveling in these nerve fibers. We developed a (simple) simulation test bed of electrical neurostimulation of myelinated nerve fibers with underlying sensory activity. This paper reports our findings so far. Interactions between stimulation-evoked and underlying activities are mainly due to collisions of action potentials and losses of excitability due to the refractory period following an action potential. In addition, intuitively, the reliability of sensory activity decreases as the stimulation frequency increases. This first step opens the door to a better understanding of pain transmission and its modulation by neurostimulation therapies. |
2312.12062 | Rati Sharma | Binil Shyam T V and Rati Sharma | mRNA translation from a unidirectional traffic perspective | 39 pages, 5 figures, review article | null | null | null | q-bio.SC cond-mat.soft q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | mRNA translation is a crucial process that leads to protein synthesis in
living cells. Therefore, it is a process that needs to work optimally for a
cell to stay healthy and alive. With advancements in microscopy and novel
experimental techniques, a lot of the intricate details about the translation
mechanism are now known. However, the why and how of this mechanism are still
ill understood, and therefore, is an active area of research. Theoretical
studies of mRNA translation typically view it in terms of the Totally
Asymmetric Simple Exclusion Process or TASEP. Various works have used the TASEP
model in order to study a wide range of phenomena and factors affecting
translation, such as ribosome traffic on an mRNA under noisy (codon-dependent
or otherwise) conditions, ribosome stalling, premature termination, ribosome
reinitiation and dropoff, codon-dependent elongation and competition among mRNA
for ribosomes, among others. In this review, we relay the history and physics
of the translation process in terms of the TASEP framework. In particular, we
discuss the viability and evolution of this model and its limitations while
also formulating the reasons behind its success. Finally, we also identify gaps
in the existing literature and suggest possible extensions and applications
that will lead to a better understanding of ribosome traffic on the mRNA.
| [
{
"created": "Tue, 19 Dec 2023 11:28:24 GMT",
"version": "v1"
}
] | 2023-12-20 | [
[
"T",
"Binil Shyam",
"V"
],
[
"Sharma",
"Rati",
""
]
] | mRNA translation is a crucial process that leads to protein synthesis in living cells. Therefore, it is a process that needs to work optimally for a cell to stay healthy and alive. With advancements in microscopy and novel experimental techniques, a lot of the intricate details about the translation mechanism are now known. However, the why and how of this mechanism are still ill understood, and therefore, is an active area of research. Theoretical studies of mRNA translation typically view it in terms of the Totally Asymmetric Simple Exclusion Process or TASEP. Various works have used the TASEP model in order to study a wide range of phenomena and factors affecting translation, such as ribosome traffic on an mRNA under noisy (codon-dependent or otherwise) conditions, ribosome stalling, premature termination, ribosome reinitiation and dropoff, codon-dependent elongation and competition among mRNA for ribosomes, among others. In this review, we relay the history and physics of the translation process in terms of the TASEP framework. In particular, we discuss the viability and evolution of this model and its limitations while also formulating the reasons behind its success. Finally, we also identify gaps in the existing literature and suggest possible extensions and applications that will lead to a better understanding of ribosome traffic on the mRNA. |
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