id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1711.00193 | Eli Shlizerman | Hexuan Liu, Jimin Kim and Eli Shlizerman | Functional Connectomics from Data: Probabilistic Graphical Models for
Neuronal Network of C. elegans | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a data-driven approach to represent neuronal network dynamics as a
Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by
employing dimension reduction to network response dynamics evoked by stimuli
applied to each neuron separately. The outcome model captures how stimuli
propagate through the network and thus represents functional dependencies
between neurons, i.e., functional connectome. The benefit of using a PGM as the
functional connectome is that posterior inference can be done efficiently and
circumvent the complexities in direct inference of response pathways in dynamic
neuronal networks. In particular, posterior inference reveals the relations
between known stimuli and downstream neurons or allows to query which stimuli
are associated with downstream neurons. For validation and as an example for
our approach we apply our methodology to a model of Caenorhabiditis elegans
nervous system which structure and dynamics are well-studied. From its
dynamical model we collect time series of the network response and use singular
value decomposition to obtain a low-dimensional projection of the time series
data. We then extract dominant patterns in each data matrix to get pairwise
dependency information and create a graphical model for the full somatic
nervous system. The PGM enables us to obtain and verify underlying neuronal
pathways dominant for known behavioral scenarios and to detect possible
pathways for novel scenarios.
| [
{
"created": "Wed, 1 Nov 2017 03:43:52 GMT",
"version": "v1"
}
] | 2017-11-02 | [
[
"Liu",
"Hexuan",
""
],
[
"Kim",
"Jimin",
""
],
[
"Shlizerman",
"Eli",
""
]
] | We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate through the network and thus represents functional dependencies between neurons, i.e., functional connectome. The benefit of using a PGM as the functional connectome is that posterior inference can be done efficiently and circumvent the complexities in direct inference of response pathways in dynamic neuronal networks. In particular, posterior inference reveals the relations between known stimuli and downstream neurons or allows to query which stimuli are associated with downstream neurons. For validation and as an example for our approach we apply our methodology to a model of Caenorhabiditis elegans nervous system which structure and dynamics are well-studied. From its dynamical model we collect time series of the network response and use singular value decomposition to obtain a low-dimensional projection of the time series data. We then extract dominant patterns in each data matrix to get pairwise dependency information and create a graphical model for the full somatic nervous system. The PGM enables us to obtain and verify underlying neuronal pathways dominant for known behavioral scenarios and to detect possible pathways for novel scenarios. |
1508.03454 | Conor Lawless | Jonathan Heydari, Conor Lawless, David A. Lydall and Darren J.
Wilkinson | Bayesian hierarchical modelling for inferring genetic interactions in
yeast | To appear in Journal of Royal Statistical Society, Series C | null | 10.1111/rssc.12126 | null | q-bio.QM q-bio.GN stat.AP | http://creativecommons.org/licenses/by/4.0/ | Quantitative Fitness Analysis (QFA) is a high-throughput experimental and
computational methodology for measuring the growth of microbial populations.
QFA screens can be used to compare the health of cell populations with and
without a mutation in a query gene in order to infer genetic interaction
strengths genome-wide, examining thousands of separate genotypes. We introduce
Bayesian, hierarchical models of population growth rates and genetic
interactions that better reflect QFA experimental design than current
approaches. Our new approach models population dynamics and genetic interaction
simultaneously, thereby avoiding passing information between models via a
univariate fitness summary. Matching experimental structure more closely,
Bayesian hierarchical approaches use data more efficiently and find new
evidence for genes which interact with yeast telomeres within a published
dataset.
| [
{
"created": "Fri, 14 Aug 2015 09:53:19 GMT",
"version": "v1"
}
] | 2016-01-18 | [
[
"Heydari",
"Jonathan",
""
],
[
"Lawless",
"Conor",
""
],
[
"Lydall",
"David A.",
""
],
[
"Wilkinson",
"Darren J.",
""
]
] | Quantitative Fitness Analysis (QFA) is a high-throughput experimental and computational methodology for measuring the growth of microbial populations. QFA screens can be used to compare the health of cell populations with and without a mutation in a query gene in order to infer genetic interaction strengths genome-wide, examining thousands of separate genotypes. We introduce Bayesian, hierarchical models of population growth rates and genetic interactions that better reflect QFA experimental design than current approaches. Our new approach models population dynamics and genetic interaction simultaneously, thereby avoiding passing information between models via a univariate fitness summary. Matching experimental structure more closely, Bayesian hierarchical approaches use data more efficiently and find new evidence for genes which interact with yeast telomeres within a published dataset. |
1712.08600 | Thomas Risler | Francesco Gianoli, Thomas Risler, Andrei S. Kozlov | Lipid bilayer mediates ion-channel cooperativity in a model of hair-cell
mechanotransduction | The Supporting Information is attached at the end as an included
image | Proc Natl Acad Sci USA 114:E11010--E11019 (2017) | 10.1073/pnas.1713135114 | null | q-bio.SC physics.bio-ph q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mechanoelectrical transduction in the inner ear is a biophysical process
underlying the senses of hearing and balance. The key players involved in this
process are mechanosensitive ion channels. They are located in the stereocilia
of hair cells and opened by the tension in specialized molecular springs, the
tip links, connecting adjacent stereocilia. When channels open, the tip links
relax, reducing the hair-bundle stiffness. This gating compliance makes hair
cells especially sensitive to small stimuli. The classical explanation for the
gating compliance is that the conformational rearrangement of a single channel
directly shortens the tip link. However, to reconcile theoretical models based
on this mechanism with experimental data, an unrealistically large structural
change of the channel is required. Experimental evidence indicates that each
tip link is a dimeric molecule, associated on average with two channels at its
lower end. It also indicates that the lipid bilayer modulates channel gating,
although it is not clear how. Here, we design and analyze a model of
mechanotransduction where each tip link attaches to two channels, mobile within
the membrane. Their states and positions are coupled by membrane-mediated
elastic forces arising from the interaction between the channels' hydrophobic
cores and that of the lipid bilayer. This coupling induces cooperative opening
and closing of the channels. The model reproduces the main properties of
hair-cell mechanotransduction using only realistic parameters constrained by
experimental evidence. This work provides an insight into the fundamental role
that membrane-mediated ion-channel cooperativity can play in sensory
physiology.
| [
{
"created": "Fri, 22 Dec 2017 18:11:15 GMT",
"version": "v1"
}
] | 2017-12-25 | [
[
"Gianoli",
"Francesco",
""
],
[
"Risler",
"Thomas",
""
],
[
"Kozlov",
"Andrei S.",
""
]
] | Mechanoelectrical transduction in the inner ear is a biophysical process underlying the senses of hearing and balance. The key players involved in this process are mechanosensitive ion channels. They are located in the stereocilia of hair cells and opened by the tension in specialized molecular springs, the tip links, connecting adjacent stereocilia. When channels open, the tip links relax, reducing the hair-bundle stiffness. This gating compliance makes hair cells especially sensitive to small stimuli. The classical explanation for the gating compliance is that the conformational rearrangement of a single channel directly shortens the tip link. However, to reconcile theoretical models based on this mechanism with experimental data, an unrealistically large structural change of the channel is required. Experimental evidence indicates that each tip link is a dimeric molecule, associated on average with two channels at its lower end. It also indicates that the lipid bilayer modulates channel gating, although it is not clear how. Here, we design and analyze a model of mechanotransduction where each tip link attaches to two channels, mobile within the membrane. Their states and positions are coupled by membrane-mediated elastic forces arising from the interaction between the channels' hydrophobic cores and that of the lipid bilayer. This coupling induces cooperative opening and closing of the channels. The model reproduces the main properties of hair-cell mechanotransduction using only realistic parameters constrained by experimental evidence. This work provides an insight into the fundamental role that membrane-mediated ion-channel cooperativity can play in sensory physiology. |
1704.04208 | Irina Biktasheva | Irina V. Biktasheva, Richard A. Anderson, Arun V. Holden, Eleftheria
Pervolaraki, Fengcai Wen | Cardiac re-entry dynamics & self-termination in DT-MRI based model of
Human Foetal Heart | submitted to Chaos: An Interdisciplinary Journal of Nonlinear
Science, Focus Issue on the topic of Complex Cardiac Dynamics | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The effect of heart geometry and anisotropy on cardiac re-entry dynamics and
self-termination is studied here in anatomically realistic computer simulations
of human foetal heart. 20 weeks of gestational age human foetal heart isotropic
and anisotropic anatomy models from diffusion tensor MRI data sets are used in
the computer simulations. The fibre orientation angles of the heart were
obtained from the DT-MRI primary eigenvalues. In a spatially homogeneous
electrophysiological mono domain model with the DT-MRI based heart geometries,
we initiate simplified Fitz-Hugh-Nagumo kinetics cardiac re-entry at a
prescribed location in a 2D slice, and in the full 3D anatomy model. In a slice
of the heart, the MRI based fibre anisotropy changes the re-entry dynamics from
pinned to anatomical re-entry. In the full 3D MRI based model, the foetal heart
fibre anisotropy changes the re-entry dynamics from a persistent re-entry to
the re-entry self-termination.
| [
{
"created": "Thu, 13 Apr 2017 16:46:43 GMT",
"version": "v1"
},
{
"created": "Sat, 19 Aug 2017 15:43:35 GMT",
"version": "v2"
}
] | 2017-08-22 | [
[
"Biktasheva",
"Irina V.",
""
],
[
"Anderson",
"Richard A.",
""
],
[
"Holden",
"Arun V.",
""
],
[
"Pervolaraki",
"Eleftheria",
""
],
[
"Wen",
"Fengcai",
""
]
] | The effect of heart geometry and anisotropy on cardiac re-entry dynamics and self-termination is studied here in anatomically realistic computer simulations of human foetal heart. 20 weeks of gestational age human foetal heart isotropic and anisotropic anatomy models from diffusion tensor MRI data sets are used in the computer simulations. The fibre orientation angles of the heart were obtained from the DT-MRI primary eigenvalues. In a spatially homogeneous electrophysiological mono domain model with the DT-MRI based heart geometries, we initiate simplified Fitz-Hugh-Nagumo kinetics cardiac re-entry at a prescribed location in a 2D slice, and in the full 3D anatomy model. In a slice of the heart, the MRI based fibre anisotropy changes the re-entry dynamics from pinned to anatomical re-entry. In the full 3D MRI based model, the foetal heart fibre anisotropy changes the re-entry dynamics from a persistent re-entry to the re-entry self-termination. |
2011.11109 | Luca Dede' | Nicola Parolini, Giovanni Ardenghi, Luca Dede', Alfio Quarteroni | A Mathematical Dashboard for the Analysis of Italian COVID-19 Epidemic
Data | null | null | null | null | q-bio.PE cs.NA math.NA stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An analysis of the COVID-19 epidemic is proposed on the basis of the epiMOX
dashboard (publicly accessible at https://www.epimox.polimi.it) that deals with
data of the epidemic trends and outbreaks in Italy from late February 2020. Our
analysis provides an immediate appreciation of the past epidemic development,
together with its current trends by fostering a deeper interpretation of
available data through several critical epidemic indicators. In addition, we
complement the epiMOX dashboard with a predictive tool based on an
epidemiological compartmental model, named SUIHTER, for the forecast on the
near future epidemic evolution.
| [
{
"created": "Sun, 22 Nov 2020 21:27:21 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Nov 2020 15:56:36 GMT",
"version": "v2"
},
{
"created": "Mon, 15 Mar 2021 11:20:12 GMT",
"version": "v3"
},
{
"created": "Mon, 24 May 2021 12:28:34 GMT",
"version": "v4"
}
] | 2021-05-25 | [
[
"Parolini",
"Nicola",
""
],
[
"Ardenghi",
"Giovanni",
""
],
[
"Dede'",
"Luca",
""
],
[
"Quarteroni",
"Alfio",
""
]
] | An analysis of the COVID-19 epidemic is proposed on the basis of the epiMOX dashboard (publicly accessible at https://www.epimox.polimi.it) that deals with data of the epidemic trends and outbreaks in Italy from late February 2020. Our analysis provides an immediate appreciation of the past epidemic development, together with its current trends by fostering a deeper interpretation of available data through several critical epidemic indicators. In addition, we complement the epiMOX dashboard with a predictive tool based on an epidemiological compartmental model, named SUIHTER, for the forecast on the near future epidemic evolution. |
1608.01161 | Richard Betzel | Richard F. Betzel, John D. Medaglia, Lia Papadopoulos, Graham Baum,
Ruben Gur, Raquel Gur, David Roalf, Theodore D. Satterthwaite, Danielle S.
Bassett | The modular organization of human anatomical brain networks: Accounting
for the cost of wiring | 22 pages, 7 figures, 9 supplemental figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brain networks are expected to be modular. However, existing techniques for
estimating a network's modules make it difficult to assess the influence of
organizational principles such as wiring cost reduction on the detected
modules. Here, we present a modification of an existing module detection
algorithm that allows us to focus on connections that are unexpected under a
cost-reduction wiring rule and to identify modules from among these
connections. We apply this technique to anatomical brain networks and show that
the modules we detect differ from those detected using the standard technique.
We demonstrate that these novel modules are spatially distributed, exhibit
unique functional fingerprints, and overlap considerably with rich clubs,
giving rise to an alternative and complementary interpretation of the
functional roles of specific brain regions. Finally, we demonstrate that, using
the modified module detection approach, we can detect modules in a
developmental dataset that track normative patterns of maturation.
Collectively, these findings support the hypothesis that brain networks are
composed of modules and provide additional insight into the function of those
modules.
| [
{
"created": "Wed, 3 Aug 2016 12:07:40 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Nov 2016 23:26:09 GMT",
"version": "v2"
}
] | 2016-11-15 | [
[
"Betzel",
"Richard F.",
""
],
[
"Medaglia",
"John D.",
""
],
[
"Papadopoulos",
"Lia",
""
],
[
"Baum",
"Graham",
""
],
[
"Gur",
"Ruben",
""
],
[
"Gur",
"Raquel",
""
],
[
"Roalf",
"David",
""
],
[
"Satterthwaite",
"Theodore D.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Brain networks are expected to be modular. However, existing techniques for estimating a network's modules make it difficult to assess the influence of organizational principles such as wiring cost reduction on the detected modules. Here, we present a modification of an existing module detection algorithm that allows us to focus on connections that are unexpected under a cost-reduction wiring rule and to identify modules from among these connections. We apply this technique to anatomical brain networks and show that the modules we detect differ from those detected using the standard technique. We demonstrate that these novel modules are spatially distributed, exhibit unique functional fingerprints, and overlap considerably with rich clubs, giving rise to an alternative and complementary interpretation of the functional roles of specific brain regions. Finally, we demonstrate that, using the modified module detection approach, we can detect modules in a developmental dataset that track normative patterns of maturation. Collectively, these findings support the hypothesis that brain networks are composed of modules and provide additional insight into the function of those modules. |
1301.2417 | Liaofu Luo | LiaoFu Luo | Quantum conformational transition in biological macromolecule | 23 pages, 1 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The conformational change of biological macromolecule is investigated from
the point of quantum transition. A quantum theory on protein folding is
proposed. Compared with other dynamical variables such as mobile electrons,
chemical bonds and stretching-bending vibrations the molecular torsion has the
lowest energy and can be looked as the slow variable of the system.
Simultaneously, from the multi-minima property of torsion potential the local
conformational states are well defined. Following the idea that the slow
variables slave the fast ones and using the nonadiabaticity operator method we
deduce the Hamiltonian describing conformational change. It is proved that the
influence of fast variables on the macromolecule can fully be taken into
account through a phase transformation of slow variable wave function. Starting
from the conformation- transition Hamiltonian the nonradiative matrix element
is calculated in two important cases: A, only electrons are fast variables and
the electronic state does not change in the transition process; B, fast
variables are not limited to electrons but the perturbation approximation can
be used. Then, the general formulas for protein folding rate are deduced. The
analytical form of the formula is utilized to study the temperature dependence
of protein folding rate and the curious non-Arrhenius temperature relation is
interpreted. The decoherence time of quantum torsion state is estimated and the
quantum coherence degree of torsional angles in the protein folding is studied
by using temperature dependence data. The proposed folding rate formula gives a
unifying approach for the study of a large class problems of biological
conformational change.
| [
{
"created": "Fri, 11 Jan 2013 08:33:41 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Feb 2013 14:32:33 GMT",
"version": "v2"
},
{
"created": "Mon, 5 Aug 2013 07:15:04 GMT",
"version": "v3"
}
] | 2013-08-06 | [
[
"Luo",
"LiaoFu",
""
]
] | The conformational change of biological macromolecule is investigated from the point of quantum transition. A quantum theory on protein folding is proposed. Compared with other dynamical variables such as mobile electrons, chemical bonds and stretching-bending vibrations the molecular torsion has the lowest energy and can be looked as the slow variable of the system. Simultaneously, from the multi-minima property of torsion potential the local conformational states are well defined. Following the idea that the slow variables slave the fast ones and using the nonadiabaticity operator method we deduce the Hamiltonian describing conformational change. It is proved that the influence of fast variables on the macromolecule can fully be taken into account through a phase transformation of slow variable wave function. Starting from the conformation- transition Hamiltonian the nonradiative matrix element is calculated in two important cases: A, only electrons are fast variables and the electronic state does not change in the transition process; B, fast variables are not limited to electrons but the perturbation approximation can be used. Then, the general formulas for protein folding rate are deduced. The analytical form of the formula is utilized to study the temperature dependence of protein folding rate and the curious non-Arrhenius temperature relation is interpreted. The decoherence time of quantum torsion state is estimated and the quantum coherence degree of torsional angles in the protein folding is studied by using temperature dependence data. The proposed folding rate formula gives a unifying approach for the study of a large class problems of biological conformational change. |
2210.02521 | Sunny Duan | Sunny Duan, Mikail Khona, Adrian Bertagnoli, Sarthak Chandra and Ila
Fiete | See and Copy: Generation of complex compositional movements from modular
and geometric RNN representations | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | A hallmark of biological intelligence and control is combinatorial
generalization: animals are able to learn various things, then piece them
together in new combinations to produce appropriate outputs for new tasks.
Inspired by the ability of primates to readily imitate seen movement sequences,
we present a model of motor control using a realistic model of arm dynamics,
tasked with imitating a guide that makes arbitrary two-segment drawings. We
hypothesize that modular organization is one of the keys to such flexible and
generalizable control. We construct a modular control model consisting of
separate encoding and motor RNNs and a scheduler, which we train end-to-end on
the task. We show that the modular structure allows the model to generalize not
only to unseen two-segment trajectories, but to new drawings consisting of many
more segments than it was trained on, and also allows for rapid adaptation to
perturbations. Finally, our model recapitulates experimental observations of
the preparatory and execution-related processes unfolding during motor control,
providing a normative explanation for functional segregation of preparatory and
execution-related activity within the motor cortex.
| [
{
"created": "Wed, 5 Oct 2022 19:39:12 GMT",
"version": "v1"
}
] | 2022-10-07 | [
[
"Duan",
"Sunny",
""
],
[
"Khona",
"Mikail",
""
],
[
"Bertagnoli",
"Adrian",
""
],
[
"Chandra",
"Sarthak",
""
],
[
"Fiete",
"Ila",
""
]
] | A hallmark of biological intelligence and control is combinatorial generalization: animals are able to learn various things, then piece them together in new combinations to produce appropriate outputs for new tasks. Inspired by the ability of primates to readily imitate seen movement sequences, we present a model of motor control using a realistic model of arm dynamics, tasked with imitating a guide that makes arbitrary two-segment drawings. We hypothesize that modular organization is one of the keys to such flexible and generalizable control. We construct a modular control model consisting of separate encoding and motor RNNs and a scheduler, which we train end-to-end on the task. We show that the modular structure allows the model to generalize not only to unseen two-segment trajectories, but to new drawings consisting of many more segments than it was trained on, and also allows for rapid adaptation to perturbations. Finally, our model recapitulates experimental observations of the preparatory and execution-related processes unfolding during motor control, providing a normative explanation for functional segregation of preparatory and execution-related activity within the motor cortex. |
q-bio/0702022 | Arvind Rao | Arvind Rao, Alfred O.Hero III, David J. States, James Douglas Engel | Finding Sequence Features in Tissue-specific Sequences | 11 pages,9 figures | null | null | null | q-bio.GN | null | The discovery of motifs underlying gene expression is a challenging one. Some
of these motifs are known transcription factors, but sequence inspection often
provides valuable clues, even discovery of novel motifs with uncharacterized
function in gene expression. Coupled with the complexity underlying
tissue-specific gene expression, there are several motifs that are putatively
responsible for expression in a certain cell type. This has important
implications in understanding fundamental biological processes, such as
development and disease progression. In this work, we present an approach to
the principled selection of motifs (not necessarily transcription factor sites)
and examine its application to several questions in current bioinformatics
research.
There are two main contributions of this work: Firstly, we introduce a new
metric for variable selection during classification, and secondly, we
investigate a problem of finding specific sequence motifs that underlie tissue
specific gene expression. In conjunction with the SVM classifier we find these
motifs and discover several novel motifs which have not yet been attributed
with any particular functional role (eg: TFBS binding motifs). We hypothesize
that the discovery of these motifs would enable the large-scale investigation
for the tissue specific regulatory potential of any conserved sequence element
identified from genome-wide studies.
Finally, we propose the utility of this developed framework to not only aid
discovery of discriminatory motifs, but also to examine the role of any motif
of choice in co-regulation or co-expression of gene groups.
| [
{
"created": "Fri, 9 Feb 2007 16:11:48 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Rao",
"Arvind",
""
],
[
"Hero",
"Alfred O.",
"III"
],
[
"States",
"David J.",
""
],
[
"Engel",
"James Douglas",
""
]
] | The discovery of motifs underlying gene expression is a challenging one. Some of these motifs are known transcription factors, but sequence inspection often provides valuable clues, even discovery of novel motifs with uncharacterized function in gene expression. Coupled with the complexity underlying tissue-specific gene expression, there are several motifs that are putatively responsible for expression in a certain cell type. This has important implications in understanding fundamental biological processes, such as development and disease progression. In this work, we present an approach to the principled selection of motifs (not necessarily transcription factor sites) and examine its application to several questions in current bioinformatics research. There are two main contributions of this work: Firstly, we introduce a new metric for variable selection during classification, and secondly, we investigate a problem of finding specific sequence motifs that underlie tissue specific gene expression. In conjunction with the SVM classifier we find these motifs and discover several novel motifs which have not yet been attributed with any particular functional role (eg: TFBS binding motifs). We hypothesize that the discovery of these motifs would enable the large-scale investigation for the tissue specific regulatory potential of any conserved sequence element identified from genome-wide studies. Finally, we propose the utility of this developed framework to not only aid discovery of discriminatory motifs, but also to examine the role of any motif of choice in co-regulation or co-expression of gene groups. |
2404.10354 | Ruifeng Li | Ruifeng Li, Dongzhan Zhou, Ancheng Shen, Ao Zhang, Mao Su, Mingqian
Li, Hongyang Chen, Gang Chen, Yin Zhang, Shufei Zhang, Yuqiang Li, Wanli
Ouyang | Physical formula enhanced multi-task learning for pharmacokinetics
prediction | null | null | null | null | q-bio.QM cs.CE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence (AI) technology has demonstrated remarkable potential
in drug dis-covery, where pharmacokinetics plays a crucial role in determining
the dosage, safety, and efficacy of new drugs. A major challenge for AI-driven
drug discovery (AIDD) is the scarcity of high-quality data, which often
requires extensive wet-lab work. A typical example of this is pharmacokinetic
experiments. In this work, we develop a physical formula enhanced mul-ti-task
learning (PEMAL) method that predicts four key parameters of pharmacokinetics
simultaneously. By incorporating physical formulas into the multi-task
framework, PEMAL facilitates effective knowledge sharing and target alignment
among the pharmacokinetic parameters, thereby enhancing the accuracy of
prediction. Our experiments reveal that PEMAL significantly lowers the data
demand, compared to typical Graph Neural Networks. Moreover, we demonstrate
that PEMAL enhances the robustness to noise, an advantage that conventional
Neural Networks do not possess. Another advantage of PEMAL is its high
flexibility, which can be potentially applied to other multi-task machine
learning scenarios. Overall, our work illustrates the benefits and potential of
using PEMAL in AIDD and other scenarios with data scarcity and noise.
| [
{
"created": "Tue, 16 Apr 2024 07:42:55 GMT",
"version": "v1"
}
] | 2024-04-17 | [
[
"Li",
"Ruifeng",
""
],
[
"Zhou",
"Dongzhan",
""
],
[
"Shen",
"Ancheng",
""
],
[
"Zhang",
"Ao",
""
],
[
"Su",
"Mao",
""
],
[
"Li",
"Mingqian",
""
],
[
"Chen",
"Hongyang",
""
],
[
"Chen",
"Gang",
""
],
[
"Zhang",
"Yin",
""
],
[
"Zhang",
"Shufei",
""
],
[
"Li",
"Yuqiang",
""
],
[
"Ouyang",
"Wanli",
""
]
] | Artificial intelligence (AI) technology has demonstrated remarkable potential in drug dis-covery, where pharmacokinetics plays a crucial role in determining the dosage, safety, and efficacy of new drugs. A major challenge for AI-driven drug discovery (AIDD) is the scarcity of high-quality data, which often requires extensive wet-lab work. A typical example of this is pharmacokinetic experiments. In this work, we develop a physical formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. By incorporating physical formulas into the multi-task framework, PEMAL facilitates effective knowledge sharing and target alignment among the pharmacokinetic parameters, thereby enhancing the accuracy of prediction. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks. Moreover, we demonstrate that PEMAL enhances the robustness to noise, an advantage that conventional Neural Networks do not possess. Another advantage of PEMAL is its high flexibility, which can be potentially applied to other multi-task machine learning scenarios. Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise. |
1704.02379 | Anita Mehta | Haleh Ebadi, Michael Perry, Keith Short, Konstantin Klemm, Claude
Desplan, Peter F. Stadler, Anita Mehta | Patterning the insect eye: from stochastic to deterministic mechanisms | 24 pages, 4 figures | PLoS Comput Biol 14(11): e1006363 (2018) | 10.1371/journal.pcbi.1006363 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While most processes in biology are highly deterministic, stochastic
mechanisms are sometimes used to increase cellular diversity, such as in the
specification of sensory receptors. In the human and Drosophila eye,
photoreceptors sensitive to various wavelengths of light are distributed
randomly across the retina. Mechanisms that underlie stochastic cell fate
specification have been analysed in detail in the Drosophila retina. In
contrast, the retinas of another group of dipteran flies exhibit highly ordered
patterns. Species in the Dolichopodidae, the "long-legged" flies, have regular
alternating columns of two types of ommatidia (unit eyes), each producing
corneal lenses of different colours. Individual flies sometimes exhibit
perturbations of this orderly pattern, with "mistakes" producing changes in
pattern that can propagate across the entire eye, suggesting that the
underlying developmental mechanisms follow local, cellular-automaton-like
rules. We hypothesize that the regulatory circuitry patterning the eye is
largely conserved among flies such that the difference between the Drosophila
and Dolichopodidae eyes should be explicable in terms of relative interaction
strengths, rather than requiring a rewiring of the regulatory network. We
present a simple stochastic model which, among its other predictions, is
capable of explaining both the random Drosophila eye and the ordered, striped
pattern of Dolichopodidae.
| [
{
"created": "Fri, 7 Apr 2017 21:34:42 GMT",
"version": "v1"
},
{
"created": "Fri, 30 Nov 2018 12:11:58 GMT",
"version": "v2"
}
] | 2019-03-06 | [
[
"Ebadi",
"Haleh",
""
],
[
"Perry",
"Michael",
""
],
[
"Short",
"Keith",
""
],
[
"Klemm",
"Konstantin",
""
],
[
"Desplan",
"Claude",
""
],
[
"Stadler",
"Peter F.",
""
],
[
"Mehta",
"Anita",
""
]
] | While most processes in biology are highly deterministic, stochastic mechanisms are sometimes used to increase cellular diversity, such as in the specification of sensory receptors. In the human and Drosophila eye, photoreceptors sensitive to various wavelengths of light are distributed randomly across the retina. Mechanisms that underlie stochastic cell fate specification have been analysed in detail in the Drosophila retina. In contrast, the retinas of another group of dipteran flies exhibit highly ordered patterns. Species in the Dolichopodidae, the "long-legged" flies, have regular alternating columns of two types of ommatidia (unit eyes), each producing corneal lenses of different colours. Individual flies sometimes exhibit perturbations of this orderly pattern, with "mistakes" producing changes in pattern that can propagate across the entire eye, suggesting that the underlying developmental mechanisms follow local, cellular-automaton-like rules. We hypothesize that the regulatory circuitry patterning the eye is largely conserved among flies such that the difference between the Drosophila and Dolichopodidae eyes should be explicable in terms of relative interaction strengths, rather than requiring a rewiring of the regulatory network. We present a simple stochastic model which, among its other predictions, is capable of explaining both the random Drosophila eye and the ordered, striped pattern of Dolichopodidae. |
2201.02756 | Matilde Marcolli | Matilde Marcolli | Categorical Hopfield Networks | 27 pages, LaTeX | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses a simple and explicit toy-model example of the
categorical Hopfield equations introduced in previous work of Manin and the
author. These describe dynamical assignments of resources to networks, where
resources are objects in unital symmetric monoidal categories and assignments
are realized by summing functors. The special case discussed here is based on
computational resources (computational models of neurons) as objects in a
category of DNNs, with a simple choice of the endofunctors defining the
Hopfield equations that reproduce the usual updating of the weights in DNNs by
gradient descent.
| [
{
"created": "Sat, 8 Jan 2022 04:06:50 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Jul 2022 16:18:35 GMT",
"version": "v2"
}
] | 2022-07-28 | [
[
"Marcolli",
"Matilde",
""
]
] | This paper discusses a simple and explicit toy-model example of the categorical Hopfield equations introduced in previous work of Manin and the author. These describe dynamical assignments of resources to networks, where resources are objects in unital symmetric monoidal categories and assignments are realized by summing functors. The special case discussed here is based on computational resources (computational models of neurons) as objects in a category of DNNs, with a simple choice of the endofunctors defining the Hopfield equations that reproduce the usual updating of the weights in DNNs by gradient descent. |
1212.3549 | Joel Zylberberg | Joel Zylberberg and Eric Shea-Brown | Input nonlinearities can shape beyond-pairwise correlations and improve
information transmission by neural populations | Matches the version to appear in Physical Review E. Main paper is 10
pages long with 4 figures. The .pdf includes a 14-page appendix with an
additional figure | Phys. Rev. E 92, 062707 (2015) | 10.1103/PhysRevE.92.062707 | null | q-bio.NC cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While recent recordings from neural populations show beyond-pairwise, or
higher-order correlations (HOC), we have little understanding of how HOC arise
from network interactions and of how they impact encoded information. Here, we
show that input nonlinearities imply HOC in spin-glass-type statistical models.
We then discuss one such model with parameterized pairwise- and higher-order
interactions, revealing conditions under which beyond-pairwise interactions
increase the mutual information between a given stimulus type and the
population responses. For jointly Gaussian stimuli, coding performance is
improved by shaping output HOC only when neural firing rates are constrained to
be low. For stimuli with skewed probability distributions (like natural image
luminances), performance improves for all firing rates. Our work suggests
surprising connections between nonlinear integration of neural inputs, stimulus
statistics, and normative theories of population coding. Moreover, it suggests
that the inclusion of beyond-pairwise interactions could improve the
performance of Boltzmann machines for machine learning and signal processing
applications.
| [
{
"created": "Fri, 14 Dec 2012 18:06:38 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Nov 2013 17:03:27 GMT",
"version": "v2"
},
{
"created": "Tue, 8 Sep 2015 23:20:48 GMT",
"version": "v3"
},
{
"created": "Fri, 20 Nov 2015 17:46:46 GMT",
"version": "v4"
}
] | 2015-12-15 | [
[
"Zylberberg",
"Joel",
""
],
[
"Shea-Brown",
"Eric",
""
]
] | While recent recordings from neural populations show beyond-pairwise, or higher-order correlations (HOC), we have little understanding of how HOC arise from network interactions and of how they impact encoded information. Here, we show that input nonlinearities imply HOC in spin-glass-type statistical models. We then discuss one such model with parameterized pairwise- and higher-order interactions, revealing conditions under which beyond-pairwise interactions increase the mutual information between a given stimulus type and the population responses. For jointly Gaussian stimuli, coding performance is improved by shaping output HOC only when neural firing rates are constrained to be low. For stimuli with skewed probability distributions (like natural image luminances), performance improves for all firing rates. Our work suggests surprising connections between nonlinear integration of neural inputs, stimulus statistics, and normative theories of population coding. Moreover, it suggests that the inclusion of beyond-pairwise interactions could improve the performance of Boltzmann machines for machine learning and signal processing applications. |
1112.2013 | Casey Bennett | Casey Bennett, Frederika Kaestle | A Reanalysis of Eurasian Population History: Ancient DNA Evidence of
Population Affinities | Keywords: d-loop, China, Mongolia, aDNA, mtDNA, Iranian | Human Biology. (2006). 78: 413-440 | 10.1353/hub.2006.0052 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mitochondrial hypervariable region I genetic data from ancient populations at
two sites from Asia, Linzi in Shandong (northern China) and Egyin Gol in
Mongolia, were reanalyzed to detect population affinities. Data from a total of
51 modern populations were used to generate distance measures (Fst's) to the
two ancient populations. The tests first analyzed relationships at the regional
level, and then compiled the top regional matches for an overall comparison to
the two probe populations. The reanalysis showed that the Egyin Gol and Linzi
populations have clear distinctions in genetic affinity. The Egyin Gol
population as a whole appears to bear close affinities with modern populations
of northern East Asia. The Linzi population does seem to have some genetic
affinities with the West as suggested by the original analysis, though the
original attribution of "European-like" seems to be misleading. This study
suggests that the Linzi individuals are potentially related to early Iranians,
who are thought to have been widespread in parts of Central Eurasia and the
steppe regions in the first millennium BC, though some significant admixture
between a number of populations of varying origin cannot be ruled out. The
study also examines the effect of sequence length on this type of genetic data
analysis and provides analysis and explanation for the results of previous
studies on the Linzi sample as compared to this one.
| [
{
"created": "Fri, 9 Dec 2011 04:08:18 GMT",
"version": "v1"
}
] | 2011-12-12 | [
[
"Bennett",
"Casey",
""
],
[
"Kaestle",
"Frederika",
""
]
] | Mitochondrial hypervariable region I genetic data from ancient populations at two sites from Asia, Linzi in Shandong (northern China) and Egyin Gol in Mongolia, were reanalyzed to detect population affinities. Data from a total of 51 modern populations were used to generate distance measures (Fst's) to the two ancient populations. The tests first analyzed relationships at the regional level, and then compiled the top regional matches for an overall comparison to the two probe populations. The reanalysis showed that the Egyin Gol and Linzi populations have clear distinctions in genetic affinity. The Egyin Gol population as a whole appears to bear close affinities with modern populations of northern East Asia. The Linzi population does seem to have some genetic affinities with the West as suggested by the original analysis, though the original attribution of "European-like" seems to be misleading. This study suggests that the Linzi individuals are potentially related to early Iranians, who are thought to have been widespread in parts of Central Eurasia and the steppe regions in the first millennium BC, though some significant admixture between a number of populations of varying origin cannot be ruled out. The study also examines the effect of sequence length on this type of genetic data analysis and provides analysis and explanation for the results of previous studies on the Linzi sample as compared to this one. |
0804.4551 | Vladik Avetisov A. | Vladik A. Avetisov and Albert Kh. Bikulov | Are proteins ultrametric? | 10 pages, 4 figures | null | null | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A simple and surprisingly accurate description of spectral diffusion in
deeply frozen globular proteins is constructed directly using the concept of
ultrametricity of protein dynamics. Earlier the similar concept has been used
for successful description of ligand-rebinding kinetics of myoglobin at
temperatures about of 200 K. Hence the ultrametricity offers a universal
background for the protein dynamics in a wide range of scales of protein
motions.
| [
{
"created": "Tue, 29 Apr 2008 08:22:40 GMT",
"version": "v1"
}
] | 2008-04-30 | [
[
"Avetisov",
"Vladik A.",
""
],
[
"Bikulov",
"Albert Kh.",
""
]
] | A simple and surprisingly accurate description of spectral diffusion in deeply frozen globular proteins is constructed directly using the concept of ultrametricity of protein dynamics. Earlier the similar concept has been used for successful description of ligand-rebinding kinetics of myoglobin at temperatures about of 200 K. Hence the ultrametricity offers a universal background for the protein dynamics in a wide range of scales of protein motions. |
1308.6546 | Ulrich Behn | Robert Schulz, Benjamin Werner, Ulrich Behn | Self tolerance in a minimal model of the idiotypic network | 7 pages, 6 figures, 1 table | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of self tolerance in the frame of a minimalistic
model of the idiotypic network. A node of this network represents a population
of B lymphocytes of the same idiotype which is encoded by a bit string. The
links of the network connect nodes with (nearly) complementary strings. The
population of a node survives if the number of occupied neighbours is not too
small and not too large. There is an influx of lymphocytes with random idiotype
from the bone marrow. Previous investigations have shown that this system
evolves toward highly organized architectures, where the nodes can be
classified into groups according to their statistical properties. The building
principles of these architectures can be analytically described and the
statistical results of simulations agree very well with results of a modular
mean field theory. In this paper we present simulation results for the case
that one or several nodes, playing the role of self, are permanently occupied.
We observe that the group structure of the architecture is very similar to the
case without self antigen, but organized such that the neighbours of the self
are only weakly occupied, thus providing self tolerance. We also treat this
situation in mean field theory which give results in good agreement with data
from simulation.
| [
{
"created": "Thu, 29 Aug 2013 18:25:36 GMT",
"version": "v1"
}
] | 2013-08-30 | [
[
"Schulz",
"Robert",
""
],
[
"Werner",
"Benjamin",
""
],
[
"Behn",
"Ulrich",
""
]
] | We consider the problem of self tolerance in the frame of a minimalistic model of the idiotypic network. A node of this network represents a population of B lymphocytes of the same idiotype which is encoded by a bit string. The links of the network connect nodes with (nearly) complementary strings. The population of a node survives if the number of occupied neighbours is not too small and not too large. There is an influx of lymphocytes with random idiotype from the bone marrow. Previous investigations have shown that this system evolves toward highly organized architectures, where the nodes can be classified into groups according to their statistical properties. The building principles of these architectures can be analytically described and the statistical results of simulations agree very well with results of a modular mean field theory. In this paper we present simulation results for the case that one or several nodes, playing the role of self, are permanently occupied. We observe that the group structure of the architecture is very similar to the case without self antigen, but organized such that the neighbours of the self are only weakly occupied, thus providing self tolerance. We also treat this situation in mean field theory which give results in good agreement with data from simulation. |
q-bio/0504018 | Igor Volkov | Igor Volkov, Jayanth R. Banavar, Stephen P. Hubbell and Amos Maritan | Neutral Theory and Relative Species Abundance in Ecology | 19 pages, 1 figure | Nature, 424, 1035-1037 (28 August 2003) | 10.1038/nature01883 | null | q-bio.PE q-bio.QM | null | The theory of island biogeography[1] asserts that an island or a local
community approaches an equilibrium species richness as a result of the
interplay between the immigration of species from the much larger metacommunity
source area and local extinction of species on the island (local community).
Hubbell[2] generalized this neutral theory to explore the expected steady-state
distribution of relative species abundance (RSA) in the local community under
restricted immigration. Here we present a theoretical framework for the unified
neutral theory of biodiversity[2] and an analytical solution for the
distribution of the RSA both in the metacommunity (Fisher's logseries) and in
the local community, where there are fewer rare species. Rare species are more
extinction-prone, and once they go locally extinct, they take longer to
re-immigrate than do common species. Contrary to recent assertions[3], we show
that the analytical solution provides a better fit, with fewer free parameters,
to the RSA distribution of tree species on Barro Colorado Island (BCI)[4] than
the lognormal distribution[5,6].
| [
{
"created": "Thu, 14 Apr 2005 00:19:09 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Volkov",
"Igor",
""
],
[
"Banavar",
"Jayanth R.",
""
],
[
"Hubbell",
"Stephen P.",
""
],
[
"Maritan",
"Amos",
""
]
] | The theory of island biogeography[1] asserts that an island or a local community approaches an equilibrium species richness as a result of the interplay between the immigration of species from the much larger metacommunity source area and local extinction of species on the island (local community). Hubbell[2] generalized this neutral theory to explore the expected steady-state distribution of relative species abundance (RSA) in the local community under restricted immigration. Here we present a theoretical framework for the unified neutral theory of biodiversity[2] and an analytical solution for the distribution of the RSA both in the metacommunity (Fisher's logseries) and in the local community, where there are fewer rare species. Rare species are more extinction-prone, and once they go locally extinct, they take longer to re-immigrate than do common species. Contrary to recent assertions[3], we show that the analytical solution provides a better fit, with fewer free parameters, to the RSA distribution of tree species on Barro Colorado Island (BCI)[4] than the lognormal distribution[5,6]. |
2004.07108 | Rene Warren | Rene L Warren and Inanc Birol | HLA predictions from the bronchoalveolar lavage fluid samples of five
patients at the early stage of the Wuhan seafood market COVID-19 outbreak | 4 pages, 2 tables | Bioinformatics, btaa756 (2020) | 10.1093/bioinformatics/btaa756 | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We are in the midst of a global viral pandemic, one with no cure and a high
mortality rate. The Human Leukocyte Antigen (HLA) gene complex plays a critical
role in host immunity. We predicted HLA class I and II alleles from the
transcriptome sequencing data prepared from the bronchoalveolar lavage fluid
samples of five patients at the early stage of the COVID-19 outbreak. We
identified the HLA-I allele A*24:02 in four out of five patients, which is
higher than the expected frequency (17.2%) in the South Han Chinese population.
The difference is statistically significant with a p-value less than $10^{-4}$.
Our analysis results may help provide future insights on disease
susceptibility.
| [
{
"created": "Wed, 15 Apr 2020 14:17:54 GMT",
"version": "v1"
},
{
"created": "Sun, 19 Apr 2020 21:43:10 GMT",
"version": "v2"
},
{
"created": "Mon, 27 Apr 2020 14:49:21 GMT",
"version": "v3"
}
] | 2020-10-15 | [
[
"Warren",
"Rene L",
""
],
[
"Birol",
"Inanc",
""
]
] | We are in the midst of a global viral pandemic, one with no cure and a high mortality rate. The Human Leukocyte Antigen (HLA) gene complex plays a critical role in host immunity. We predicted HLA class I and II alleles from the transcriptome sequencing data prepared from the bronchoalveolar lavage fluid samples of five patients at the early stage of the COVID-19 outbreak. We identified the HLA-I allele A*24:02 in four out of five patients, which is higher than the expected frequency (17.2%) in the South Han Chinese population. The difference is statistically significant with a p-value less than $10^{-4}$. Our analysis results may help provide future insights on disease susceptibility. |
2403.02124 | Oleg Kovalevskiy | Oleg Kovalevskiy, Juan Mateos-Garcia, Kathryn Tunyasuvunakool | AlphaFold two years on: validation and impact | 17 pages, no figures, a perspective paper submitted to the PNAS | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Two years on from the initial release of AlphaFold2 we have seen its
widespread adoption as a structure prediction tool. Here we discuss some of the
latest work based on AlphaFold2, with a particular focus on its use within the
structural biology community. This encompasses use cases like speeding up
structure determination itself, enabling new computational studies, and
building new tools and workflows. We also look at the ongoing validation of
AlphaFold2, as its predictions continue to be compared against large numbers of
experimental structures to further delineate the model's capabilities and
limitations.
| [
{
"created": "Mon, 4 Mar 2024 15:31:12 GMT",
"version": "v1"
}
] | 2024-03-05 | [
[
"Kovalevskiy",
"Oleg",
""
],
[
"Mateos-Garcia",
"Juan",
""
],
[
"Tunyasuvunakool",
"Kathryn",
""
]
] | Two years on from the initial release of AlphaFold2 we have seen its widespread adoption as a structure prediction tool. Here we discuss some of the latest work based on AlphaFold2, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold2, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations. |
1703.06071 | \'Alvaro Garc\'ia L\'opez | Alvaro G. Lopez, Kelly C. Iarosz, Antonio M. Batista, Jesus M. Seoane,
Ricardo L. Viana and Miguel A. F. Sanjuan | The dose-dense principle in chemotherapy | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chemotherapy is a class of cancer treatment that uses drugs to kill cancer
cells. A typical chemotherapeutic protocol consists of several drugs delivered
in cycles of three weeks. We present mathematical analyses demonstrating the
existence of a maximum time between cycles of chemotherapy for a protocol to be
effective. A mathematical equation is derived, which relates such a maximum
time with the variables that govern the kinetics of the tumor and those
characterizing the chemotherapeutic treatment. Our results suggest that there
are compelling arguments supporting the use of dose-dense protocols. Finally,
we discuss the limitations of these protocols and suggest an alternative.
| [
{
"created": "Thu, 16 Mar 2017 14:38:03 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Jun 2017 10:04:00 GMT",
"version": "v2"
},
{
"created": "Fri, 23 Jun 2017 11:25:42 GMT",
"version": "v3"
}
] | 2017-06-26 | [
[
"Lopez",
"Alvaro G.",
""
],
[
"Iarosz",
"Kelly C.",
""
],
[
"Batista",
"Antonio M.",
""
],
[
"Seoane",
"Jesus M.",
""
],
[
"Viana",
"Ricardo L.",
""
],
[
"Sanjuan",
"Miguel A. F.",
""
]
] | Chemotherapy is a class of cancer treatment that uses drugs to kill cancer cells. A typical chemotherapeutic protocol consists of several drugs delivered in cycles of three weeks. We present mathematical analyses demonstrating the existence of a maximum time between cycles of chemotherapy for a protocol to be effective. A mathematical equation is derived, which relates such a maximum time with the variables that govern the kinetics of the tumor and those characterizing the chemotherapeutic treatment. Our results suggest that there are compelling arguments supporting the use of dose-dense protocols. Finally, we discuss the limitations of these protocols and suggest an alternative. |
2308.11567 | Arthur Pellegrino | Arthur Pellegrino, N Alex Cayco-Gajic, Angus Chadwick | Low Tensor Rank Learning of Neural Dynamics | The last two authors contributed equally - Accepted at NeurIPS 2023 | null | null | null | q-bio.NC cs.LG cs.NE math.DS stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning relies on coordinated synaptic changes in recurrently connected
populations of neurons. Therefore, understanding the collective evolution of
synaptic connectivity over learning is a key challenge in neuroscience and
machine learning. In particular, recent work has shown that the weight matrices
of task-trained RNNs are typically low rank, but how this low rank structure
unfolds over learning is unknown. To address this, we investigate the rank of
the 3-tensor formed by the weight matrices throughout learning. By fitting RNNs
of varying rank to large-scale neural recordings during a motor learning task,
we find that the inferred weights are low-tensor-rank and therefore evolve over
a fixed low-dimensional subspace throughout the entire course of learning. We
next validate the observation of low-tensor-rank learning on an RNN trained to
solve the same task. Finally, we present a set of mathematical results bounding
the matrix and tensor ranks of gradient descent learning dynamics which show
that low-tensor-rank weights emerge naturally in RNNs trained to solve
low-dimensional tasks. Taken together, our findings provide insight on the
evolution of population connectivity over learning in both biological and
artificial neural networks, and enable reverse engineering of learning-induced
changes in recurrent dynamics from large-scale neural recordings.
| [
{
"created": "Tue, 22 Aug 2023 17:08:47 GMT",
"version": "v1"
},
{
"created": "Sat, 4 Nov 2023 11:47:43 GMT",
"version": "v2"
}
] | 2023-11-07 | [
[
"Pellegrino",
"Arthur",
""
],
[
"Cayco-Gajic",
"N Alex",
""
],
[
"Chadwick",
"Angus",
""
]
] | Learning relies on coordinated synaptic changes in recurrently connected populations of neurons. Therefore, understanding the collective evolution of synaptic connectivity over learning is a key challenge in neuroscience and machine learning. In particular, recent work has shown that the weight matrices of task-trained RNNs are typically low rank, but how this low rank structure unfolds over learning is unknown. To address this, we investigate the rank of the 3-tensor formed by the weight matrices throughout learning. By fitting RNNs of varying rank to large-scale neural recordings during a motor learning task, we find that the inferred weights are low-tensor-rank and therefore evolve over a fixed low-dimensional subspace throughout the entire course of learning. We next validate the observation of low-tensor-rank learning on an RNN trained to solve the same task. Finally, we present a set of mathematical results bounding the matrix and tensor ranks of gradient descent learning dynamics which show that low-tensor-rank weights emerge naturally in RNNs trained to solve low-dimensional tasks. Taken together, our findings provide insight on the evolution of population connectivity over learning in both biological and artificial neural networks, and enable reverse engineering of learning-induced changes in recurrent dynamics from large-scale neural recordings. |
1701.02287 | Peter Schuck | Sumit K. Chaturvedi, Huaying Zhao, and Peter Schuck | Sedimentation of Reversibly Interacting Macromolecules with Changes in
Fluorescence Quantum Yield | 22 pages, 5 figures | null | 10.1016/j.bpj.2017.02.020 | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sedimentation velocity analytical ultracentrifugation with fluorescence
detection has emerged as a powerful method for the study of interacting systems
of macromolecules. It combines picomolar sensitivity with high hydrodynamic
resolution, and can be carried out with photoswitchable fluorophores for
multi-component discrimination, to determine the stoichiometry, affinity, and
shape of macromolecular complexes with dissociation equilibrium constants from
picomolar to micromolar. A popular approach for data interpretation is the
determination of the binding affinity by isotherms of weight-average
sedimentation coefficients, sw. A prevailing dogma in sedimentation analysis is
that the weight-average sedimentation coefficient from the transport method
corresponds to the signal- and population-weighted average of all species. We
show that this does not always hold true for systems that exhibit significant
signal changes with complex formation - properties that may be readily
encountered in practice, e.g., from a change in fluorescence quantum yield.
Coupled transport in the reaction boundary of rapidly reversible systems can
make significant contributions to the observed migration in a way that cannot
be accounted for in the standard population-based average. Effective particle
theory provides a simple physical picture for the reaction-coupled migration
process. On this basis we develop a more general binding model that converges
to the well-known form of sw with constant signals, but can account
simultaneously for hydrodynamic co-transport in the presence of changes in
fluorescence quantum yield. We believe this will be useful when studying
interacting systems exhibiting fluorescence quenching, enhancement or Forster
resonance energy transfer with transport methods.
| [
{
"created": "Mon, 9 Jan 2017 18:18:14 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Feb 2017 17:58:00 GMT",
"version": "v2"
}
] | 2017-05-24 | [
[
"Chaturvedi",
"Sumit K.",
""
],
[
"Zhao",
"Huaying",
""
],
[
"Schuck",
"Peter",
""
]
] | Sedimentation velocity analytical ultracentrifugation with fluorescence detection has emerged as a powerful method for the study of interacting systems of macromolecules. It combines picomolar sensitivity with high hydrodynamic resolution, and can be carried out with photoswitchable fluorophores for multi-component discrimination, to determine the stoichiometry, affinity, and shape of macromolecular complexes with dissociation equilibrium constants from picomolar to micromolar. A popular approach for data interpretation is the determination of the binding affinity by isotherms of weight-average sedimentation coefficients, sw. A prevailing dogma in sedimentation analysis is that the weight-average sedimentation coefficient from the transport method corresponds to the signal- and population-weighted average of all species. We show that this does not always hold true for systems that exhibit significant signal changes with complex formation - properties that may be readily encountered in practice, e.g., from a change in fluorescence quantum yield. Coupled transport in the reaction boundary of rapidly reversible systems can make significant contributions to the observed migration in a way that cannot be accounted for in the standard population-based average. Effective particle theory provides a simple physical picture for the reaction-coupled migration process. On this basis we develop a more general binding model that converges to the well-known form of sw with constant signals, but can account simultaneously for hydrodynamic co-transport in the presence of changes in fluorescence quantum yield. We believe this will be useful when studying interacting systems exhibiting fluorescence quenching, enhancement or Forster resonance energy transfer with transport methods. |
q-bio/0609028 | Antonio Leon | Antonio Leon | Living beings as informed systems: towards a physical theory of
information | 19 pages, 1 figure | Journal of Biological Systems. 1996. v. 4. pp. 565-584 | null | null | q-bio.PE | null | I propose here a new concept of information based on two relevant aspects of
its expression. The first related to the undeniable fact that the expression of
information modifies the physical state of its receiver. The second to the
arbitrariness of such physical changes. In fact, these changes are not
deducible from physical laws but from a code established arbitrarily. Thus,
physical information is proposed here as the capacity of producing arbitrary
changes. Once defined physical information from this physical point of view, I
deduce some basic properties of informed systems. These properties (renewal,
self-reproducing, evolution, diversification) are immediately recognized as the
attributes most characteristic of living beings, the only natural informed
systems we know. I also propose here a double evaluation of information. The
former is an absolute measure of the physical effects of its expression based
on Einstein's probability. The second is a functional measure based on the
probability that an informed systems attain a given objective as consequence of
the expression of its information.
| [
{
"created": "Tue, 19 Sep 2006 10:43:59 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Leon",
"Antonio",
""
]
] | I propose here a new concept of information based on two relevant aspects of its expression. The first related to the undeniable fact that the expression of information modifies the physical state of its receiver. The second to the arbitrariness of such physical changes. In fact, these changes are not deducible from physical laws but from a code established arbitrarily. Thus, physical information is proposed here as the capacity of producing arbitrary changes. Once defined physical information from this physical point of view, I deduce some basic properties of informed systems. These properties (renewal, self-reproducing, evolution, diversification) are immediately recognized as the attributes most characteristic of living beings, the only natural informed systems we know. I also propose here a double evaluation of information. The former is an absolute measure of the physical effects of its expression based on Einstein's probability. The second is a functional measure based on the probability that an informed systems attain a given objective as consequence of the expression of its information. |
1903.02532 | Russell Taylor | Mariah Schrum, Amanda Canezin, Sumana Chakravarty, Michelle Laskowski,
Suat Comert, Yunuscan Sevimli, Gregory S. Chirikjian, Stephen L. Hoffman, and
Russell H. Taylor | An Efficient Production Process for Extracting Salivary Glands from
Mosquitoes | 5 pages, 5 figures | null | null | null | q-bio.QM cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Malaria is the one of the leading causes of morbidity and mortality in many
developing countries. The development of a highly effective and readily
deployable vaccine represents a major goal for world health. There has been
recent progress in developing a clinically effective vaccine manufactured using
Plasmodium falciparum sporozoites (PfSPZ) extracted from the salivary glands of
Anopheles sp. Mosquitoes. The harvesting of PfSPZ requires dissection of the
mosquito and manual removal of the salivary glands from each mosquito by
trained technicians. While PfSPZ-based vaccines have shown highly promising
results, the process of dissection of salivary glands is tedious and labor
intensive. We propose a mechanical device that will greatly increase the rate
of mosquito dissection and deskill the process to make malaria vaccines more
affordable and more readily available. This device consists of several
components: a sorting stage in which the mosquitoes are sorted into slots, a
cutting stage in which the heads are removed, and a squeezing stage in which
the salivary glands are extracted and collected. This method allows mosquitoes
to be dissected twenty at a time instead of one by one as previously done and
significantly reduces the dissection time per mosquito.
| [
{
"created": "Tue, 5 Mar 2019 17:08:06 GMT",
"version": "v1"
}
] | 2019-03-07 | [
[
"Schrum",
"Mariah",
""
],
[
"Canezin",
"Amanda",
""
],
[
"Chakravarty",
"Sumana",
""
],
[
"Laskowski",
"Michelle",
""
],
[
"Comert",
"Suat",
""
],
[
"Sevimli",
"Yunuscan",
""
],
[
"Chirikjian",
"Gregory S.",
""
],
[
"Hoffman",
"Stephen L.",
""
],
[
"Taylor",
"Russell H.",
""
]
] | Malaria is the one of the leading causes of morbidity and mortality in many developing countries. The development of a highly effective and readily deployable vaccine represents a major goal for world health. There has been recent progress in developing a clinically effective vaccine manufactured using Plasmodium falciparum sporozoites (PfSPZ) extracted from the salivary glands of Anopheles sp. Mosquitoes. The harvesting of PfSPZ requires dissection of the mosquito and manual removal of the salivary glands from each mosquito by trained technicians. While PfSPZ-based vaccines have shown highly promising results, the process of dissection of salivary glands is tedious and labor intensive. We propose a mechanical device that will greatly increase the rate of mosquito dissection and deskill the process to make malaria vaccines more affordable and more readily available. This device consists of several components: a sorting stage in which the mosquitoes are sorted into slots, a cutting stage in which the heads are removed, and a squeezing stage in which the salivary glands are extracted and collected. This method allows mosquitoes to be dissected twenty at a time instead of one by one as previously done and significantly reduces the dissection time per mosquito. |
1705.09426 | Esteban Vargas Bernal | Camilo Sanabria Malagon and Esteban Vargas Bernal | Competent hosts and endemicity of multi-host diseases | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose a method to study a general vector-hosts
mathematical model in order to explain how the changes in biodiversity could
influence the dynamics of vector-borne diseases. We find that under the
assumption of frequency-dependent transmission, i.e. the assumption that the
number of contacts are diluted by the total population of hosts, the presence
of a competent host is a necessary condition for the existence of an endemic
state. In addition, we obtain that in the case of an endemic disease with a
unique competent and resilient host, an increase in its density amplifies the
disease.
| [
{
"created": "Fri, 26 May 2017 03:59:57 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Nov 2018 05:52:43 GMT",
"version": "v2"
}
] | 2018-11-28 | [
[
"Malagon",
"Camilo Sanabria",
""
],
[
"Bernal",
"Esteban Vargas",
""
]
] | In this paper we propose a method to study a general vector-hosts mathematical model in order to explain how the changes in biodiversity could influence the dynamics of vector-borne diseases. We find that under the assumption of frequency-dependent transmission, i.e. the assumption that the number of contacts are diluted by the total population of hosts, the presence of a competent host is a necessary condition for the existence of an endemic state. In addition, we obtain that in the case of an endemic disease with a unique competent and resilient host, an increase in its density amplifies the disease. |
q-bio/0401027 | Ming S. Liu | Ming S. Liu, B. D. Todd, and Richard J. Sadus | Complex cooperativity of ATP hydrolysis in the F1-ATPase molecular motor | 18pages, 4 figures | null | null | null | q-bio.GN q-bio.BM | null | F1-ATPase catalyses ATP hydrolysis and converts the cellular chemical energy
into mechanical rotation. The hydrolysis reaction in F1-ATPase does not follow
the widely believed Michaelis-Menten mechanism. Instead, the hydrolysis
mechanism behaves in an ATP-dependent manner. We develop a model for enzyme
kinetics and hydrolysis cooperativity of F1-ATPase which involves the
binding-state changes to the coupling catalytic reactions. The quantitative
analysis and modeling suggest the existence of complex cooperative hydrolysis
between three different catalysis sites of F1-ATPase. This complexity may be
taken into account to resolve the arguments on the bindingchange mechanism in
F1-ATPase.
| [
{
"created": "Wed, 21 Jan 2004 01:12:22 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Liu",
"Ming S.",
""
],
[
"Todd",
"B. D.",
""
],
[
"Sadus",
"Richard J.",
""
]
] | F1-ATPase catalyses ATP hydrolysis and converts the cellular chemical energy into mechanical rotation. The hydrolysis reaction in F1-ATPase does not follow the widely believed Michaelis-Menten mechanism. Instead, the hydrolysis mechanism behaves in an ATP-dependent manner. We develop a model for enzyme kinetics and hydrolysis cooperativity of F1-ATPase which involves the binding-state changes to the coupling catalytic reactions. The quantitative analysis and modeling suggest the existence of complex cooperative hydrolysis between three different catalysis sites of F1-ATPase. This complexity may be taken into account to resolve the arguments on the bindingchange mechanism in F1-ATPase. |
0804.4125 | Marco Morelli | Marco J. Morelli, Pieter Rein ten Wolde | Reaction Brownian Dynamics and the effect of spatial fluctuations on the
gain of a push-pull network | 25 pages, 7 figures, submitted to Journal of Chemical Physics | null | 10.1063/1.2958287 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brownian Dynamics algorithms are widely used for simulating soft-matter and
biochemical systems. In recent times, their application has been extended to
the simulation of coarse-grained models of cellular networks in simple
organisms. In these models, components move by diffusion, and can react with
one another upon contact. However, when reactions are incorporated into a
Brownian Dynamics algorithm, attention must be paid to avoid violations of the
detailed-balance rule, and therefore introducing systematic errors in the
simulation. We present a Brownian Dynamics algorithm for reaction-diffusion
systems that rigorously obeys detailed balance for equilibrium reactions. By
comparing the simulation results to exact analytical results for a bimolecular
reaction, we show that the algorithm correctly reproduces both equilibrium and
dynamical quantities. We apply our scheme to a ``push-pull'' network in which
two antagonistic enzymes covalently modify a substrate. Our results highlight
that the diffusive behaviour of the reacting species can reduce the gain of the
response curve of this network.
| [
{
"created": "Fri, 25 Apr 2008 15:15:17 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Morelli",
"Marco J.",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | Brownian Dynamics algorithms are widely used for simulating soft-matter and biochemical systems. In recent times, their application has been extended to the simulation of coarse-grained models of cellular networks in simple organisms. In these models, components move by diffusion, and can react with one another upon contact. However, when reactions are incorporated into a Brownian Dynamics algorithm, attention must be paid to avoid violations of the detailed-balance rule, and therefore introducing systematic errors in the simulation. We present a Brownian Dynamics algorithm for reaction-diffusion systems that rigorously obeys detailed balance for equilibrium reactions. By comparing the simulation results to exact analytical results for a bimolecular reaction, we show that the algorithm correctly reproduces both equilibrium and dynamical quantities. We apply our scheme to a ``push-pull'' network in which two antagonistic enzymes covalently modify a substrate. Our results highlight that the diffusive behaviour of the reacting species can reduce the gain of the response curve of this network. |
2205.01939 | Maelle Moranges | M. Moranges (CRNL), C. Rouby (CRNL), M. Plantevit (LIRIS), M. Bensafi
(CRNL) | Explicit and implicit measures of emotions: Data-science might help to
account for data complexity and heterogeneity | null | Food Quality and Preference, Elsevier, 2021, 92, pp.104181 | 10.1016/j.foodqual.2021.104181 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Measuring emotions is a real challenge for fundamental and applied research,
especially in ecological contexts. de Wijk and Noldus propose combining two
types of measures-explicit to characterize a specific food, and
implicit-physiological-to capture the whole experience of a meal in real-life
situations. This raises several challenges including development of new and
miniaturized sensors and devices but also developing new ways of data analysis.
We suggest a path to follow for future studies regarding data analysis: to
include Data Science in the game. This field of research may enable developing
predictive but also explicative models that link subjective experience of
emotions and physiological responses in real-life contexts. We suggest that
food scientists should go out of their comfort zone by collaborating with
computer scientists and then be trained with the new tools of Data Science,
which will undoubtedly enable them 1/ to better manage complex and
heterogeneous data sets, 2/ to extract knowledge that will be essential to this
field of research.
| [
{
"created": "Wed, 4 May 2022 08:16:58 GMT",
"version": "v1"
}
] | 2022-05-05 | [
[
"Moranges",
"M.",
"",
"CRNL"
],
[
"Rouby",
"C.",
"",
"CRNL"
],
[
"Plantevit",
"M.",
"",
"LIRIS"
],
[
"Bensafi",
"M.",
"",
"CRNL"
]
] | Measuring emotions is a real challenge for fundamental and applied research, especially in ecological contexts. de Wijk and Noldus propose combining two types of measures-explicit to characterize a specific food, and implicit-physiological-to capture the whole experience of a meal in real-life situations. This raises several challenges including development of new and miniaturized sensors and devices but also developing new ways of data analysis. We suggest a path to follow for future studies regarding data analysis: to include Data Science in the game. This field of research may enable developing predictive but also explicative models that link subjective experience of emotions and physiological responses in real-life contexts. We suggest that food scientists should go out of their comfort zone by collaborating with computer scientists and then be trained with the new tools of Data Science, which will undoubtedly enable them 1/ to better manage complex and heterogeneous data sets, 2/ to extract knowledge that will be essential to this field of research. |
1509.06970 | J\'ozsef Z. Farkas | J\'ozsef Z. Farkas, Stephen A. Gourley, Rongsong Liu, Abdul-Aziz
Yakubu | Wolbachia infection in a sex-structured mosquito population carrying
West Nile virus | to appear in Journal of Mathematical Biology | Journal of Mathematical Biology 75 (2017) | 10.1007/s00285-017-1096-7 | null | q-bio.PE math.CA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wolbachia is possibly the most studied reproductive parasite of arthropod
species. It appears to be a promising candidate for biocontrol of some mosquito
borne diseases. We begin by developing a sex-structured model for a Wolbachia
infected mosquito population. Our model incorporates the key effects of
Wolbachia infection including cytoplasmic incompatibility and male killing. We
also allow the possibility of reduced reproductive output, incomplete maternal
transmission, and different mortality rates for uninfected/infected male/female
individuals. We study the existence and local stability of equilibria,
including the biologically relevant and interesting boundary equilibria. For
some biologically relevant parameter regimes there may be multiple coexistence
steady states including, very importantly, a coexistence steady state in which
Wolbachia infected individuals dominate. We also extend the model to
incorporate West Nile virus (WNv) dynamics, using an SEI modelling approach.
Recent evidence suggests that a particular strain of Wolbachia infection
significantly reduces WNv replication in Aedes aegypti. We model this via
increased time spent in the WNv-exposed compartment for Wolbachia infected
female mosquitoes. A basic reproduction number $R_0$ is computed for the WNv
infection. Our results suggest that, if the mosquito population consists mainly
of Wolbachia infected individuals, WNv eradication is likely if WNv replication
in Wolbachia infected individuals is sufficiently reduced.
| [
{
"created": "Thu, 17 Sep 2015 18:18:59 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Jan 2017 08:55:30 GMT",
"version": "v2"
}
] | 2019-03-06 | [
[
"Farkas",
"József Z.",
""
],
[
"Gourley",
"Stephen A.",
""
],
[
"Liu",
"Rongsong",
""
],
[
"Yakubu",
"Abdul-Aziz",
""
]
] | Wolbachia is possibly the most studied reproductive parasite of arthropod species. It appears to be a promising candidate for biocontrol of some mosquito borne diseases. We begin by developing a sex-structured model for a Wolbachia infected mosquito population. Our model incorporates the key effects of Wolbachia infection including cytoplasmic incompatibility and male killing. We also allow the possibility of reduced reproductive output, incomplete maternal transmission, and different mortality rates for uninfected/infected male/female individuals. We study the existence and local stability of equilibria, including the biologically relevant and interesting boundary equilibria. For some biologically relevant parameter regimes there may be multiple coexistence steady states including, very importantly, a coexistence steady state in which Wolbachia infected individuals dominate. We also extend the model to incorporate West Nile virus (WNv) dynamics, using an SEI modelling approach. Recent evidence suggests that a particular strain of Wolbachia infection significantly reduces WNv replication in Aedes aegypti. We model this via increased time spent in the WNv-exposed compartment for Wolbachia infected female mosquitoes. A basic reproduction number $R_0$ is computed for the WNv infection. Our results suggest that, if the mosquito population consists mainly of Wolbachia infected individuals, WNv eradication is likely if WNv replication in Wolbachia infected individuals is sufficiently reduced. |
1712.02270 | Xiaoyong Pan | Xiaoyong Pan and Junchi Yan | Attention based convolutional neural network for predicting RNA-protein
binding sites | null | NIPS 2017 Computational Biology Workshop | null | null | q-bio.GN cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | RNA-binding proteins (RBPs) play crucial roles in many biological processes,
e.g. gene regulation. Computational identification of RBP binding sites on RNAs
are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence
motifs. Thus, fast locating those motifs on RNA sequences is crucial and
time-efficient for determining whether the RNAs interact with the RBPs or not.
In this study, we present an attention based convolutional neural network,
iDeepA, to predict RNA-protein binding sites from raw RNA sequences. We first
encode RNA sequences into one-hot encoding. Next, we design a deep learning
model with a convolutional neural network (CNN) and an attention mechanism,
which automatically search for important positions, e.g. binding motifs, to
learn discriminant high-level features for predicting RBP binding sites. We
evaluate iDeepA on publicly gold-standard RBP binding sites derived from
CLIP-seq data. The results demonstrate iDeepA achieves comparable performance
with other state-of-the-art methods.
| [
{
"created": "Wed, 6 Dec 2017 16:33:29 GMT",
"version": "v1"
}
] | 2018-06-07 | [
[
"Pan",
"Xiaoyong",
""
],
[
"Yan",
"Junchi",
""
]
] | RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence motifs. Thus, fast locating those motifs on RNA sequences is crucial and time-efficient for determining whether the RNAs interact with the RBPs or not. In this study, we present an attention based convolutional neural network, iDeepA, to predict RNA-protein binding sites from raw RNA sequences. We first encode RNA sequences into one-hot encoding. Next, we design a deep learning model with a convolutional neural network (CNN) and an attention mechanism, which automatically search for important positions, e.g. binding motifs, to learn discriminant high-level features for predicting RBP binding sites. We evaluate iDeepA on publicly gold-standard RBP binding sites derived from CLIP-seq data. The results demonstrate iDeepA achieves comparable performance with other state-of-the-art methods. |
1401.0281 | Artem Badasyan | Artem Badasyan, Shushanik A. Tonoyan, Achille Giacometti, Rudolf
Podgornik, V. Adrian Parsegian, Yevgeni Sh. Mamasakhlisov and Vladimir F.
Morozov | A unified description of solvent effects in the helix-coil transition | 12 pages, 12 figures, to be submitted to PRE | null | 10.1103/PhysRevE.89.022723 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze the problem of the helix-coil transition in explicit solvents
analytically by using spin-based models incorporating two different mechanisms
of solvent action: explicit solvent action through the formation of
solvent-polymer hydrogen bonds that can compete with the intrinsic
intra-polymer hydrogen bonded configurations (competing interactions) and
implicit solvent action, where the solvent-polymer interactions tune biopolymer
configurations by changing the activity of the solvent (non-competing
interactions). The overall spin Hamiltonian is comprised of three terms: the
background \emph{in vacuo} Hamiltonian of the "Generalized Model of Polypeptide
Chain" type and two additive terms that account for the two above mechanisms of
solvent action. We show that on this level the solvent degrees of freedom can
be {\sl explicitly} and {\sl exactly} traced over, the ensuing effective
partition function combining all the solvent effects in a unified framework. In
this way we are able to address helix-coil transitions for polypeptides,
proteins, and DNA, with different buffers and different external constraints.
Our spin-based effective Hamiltonian is applicable for treatment of such
diverse phenomena as cold denaturation, effects of osmotic pressure on the cold
and warm denaturation, complicated temperature dependence of the hydrophobic
effect as well as providing a conceptual base for understanding the behavior of
Intrinsically Disordered Proteins and their analogues.
| [
{
"created": "Wed, 1 Jan 2014 13:29:29 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Badasyan",
"Artem",
""
],
[
"Tonoyan",
"Shushanik A.",
""
],
[
"Giacometti",
"Achille",
""
],
[
"Podgornik",
"Rudolf",
""
],
[
"Parsegian",
"V. Adrian",
""
],
[
"Mamasakhlisov",
"Yevgeni Sh.",
""
],
[
"Morozov",
"Vladimir F.",
""
]
] | We analyze the problem of the helix-coil transition in explicit solvents analytically by using spin-based models incorporating two different mechanisms of solvent action: explicit solvent action through the formation of solvent-polymer hydrogen bonds that can compete with the intrinsic intra-polymer hydrogen bonded configurations (competing interactions) and implicit solvent action, where the solvent-polymer interactions tune biopolymer configurations by changing the activity of the solvent (non-competing interactions). The overall spin Hamiltonian is comprised of three terms: the background \emph{in vacuo} Hamiltonian of the "Generalized Model of Polypeptide Chain" type and two additive terms that account for the two above mechanisms of solvent action. We show that on this level the solvent degrees of freedom can be {\sl explicitly} and {\sl exactly} traced over, the ensuing effective partition function combining all the solvent effects in a unified framework. In this way we are able to address helix-coil transitions for polypeptides, proteins, and DNA, with different buffers and different external constraints. Our spin-based effective Hamiltonian is applicable for treatment of such diverse phenomena as cold denaturation, effects of osmotic pressure on the cold and warm denaturation, complicated temperature dependence of the hydrophobic effect as well as providing a conceptual base for understanding the behavior of Intrinsically Disordered Proteins and their analogues. |
1604.00091 | Alex Clarke | Alex Clarke, Philip J. Pell, Charan Ranganath, Lorraine K. Tyler | Learning warps object representations in the ventral temporal cortex | In press at Journal of Cognitive Neuroscience | null | 10.1162/jocn_a_00951 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human ventral temporal cortex (VTC) plays a critical role in object
recognition. Although it is well established that visual experience shapes VTC
object representations, the impact of semantic and contextual learning is
unclear. In this study, we tracked changes in representations of novel visual
objects that emerged after learning meaningful information about each object.
Over multiple training sessions, participants learned to associate semantic
features (e.g. made of wood, floats) and spatial contextual associations (e.g.
found in gardens) with novel objects. Functional magnetic resonance imaging was
used to examine VTC activity for objects before and after learning.
Multivariate pattern similarity analyses revealed that, after learning, VTC
activity patterns carried information about the learned contextual associations
of the objects, such that objects with contextual associations exhibited higher
pattern similarity after learning. Further, these learning-induced increases in
pattern information about contextual associations were correlated with
reductions in pattern information about the objects visual features. In a
second experiment, we validated that these contextual effects translated to
real-life objects. Our findings demonstrate that visual object representations
in VTC are shaped by the knowledge we have about objects, and show that object
representations can flexibly adapt as a consequence of learning with the
changes related to the specific kind of newly acquired information.
| [
{
"created": "Fri, 1 Apr 2016 01:02:30 GMT",
"version": "v1"
}
] | 2016-04-04 | [
[
"Clarke",
"Alex",
""
],
[
"Pell",
"Philip J.",
""
],
[
"Ranganath",
"Charan",
""
],
[
"Tyler",
"Lorraine K.",
""
]
] | The human ventral temporal cortex (VTC) plays a critical role in object recognition. Although it is well established that visual experience shapes VTC object representations, the impact of semantic and contextual learning is unclear. In this study, we tracked changes in representations of novel visual objects that emerged after learning meaningful information about each object. Over multiple training sessions, participants learned to associate semantic features (e.g. made of wood, floats) and spatial contextual associations (e.g. found in gardens) with novel objects. Functional magnetic resonance imaging was used to examine VTC activity for objects before and after learning. Multivariate pattern similarity analyses revealed that, after learning, VTC activity patterns carried information about the learned contextual associations of the objects, such that objects with contextual associations exhibited higher pattern similarity after learning. Further, these learning-induced increases in pattern information about contextual associations were correlated with reductions in pattern information about the objects visual features. In a second experiment, we validated that these contextual effects translated to real-life objects. Our findings demonstrate that visual object representations in VTC are shaped by the knowledge we have about objects, and show that object representations can flexibly adapt as a consequence of learning with the changes related to the specific kind of newly acquired information. |
1507.00298 | Roeland M.H. Merks | Dimitrios Palachanis, Andr\'as Szab\'o and Roeland M.H. Merks | Particle-based simulation of ellipse-shaped particle aggregation as a
model for vascular network formation | 9 pages, 11 figures, 2 supplementary videos (on Youtube), submitted
to Computational Particle Mechanics, special issue: Jos\'e-Manuel Garcia
Aznar (Ed.) Particle-based simulations on cell and biomolecular mechanics | null | 10.1007/s40571-015-0064-5 | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational modelling is helpful for elucidating the cellular mechanisms
driving biological morphogenesis. Previous simulation studies of blood vessel
growth based on the Cellular Potts model (CPM) proposed that elongated,
adhesive or mutually attractive endothelial cells suffice for the formation of
blood vessel sprouts and vascular networks. Because each mathematical
representation of a model introduces potential artifacts, it is important that
model results are reproduced using alternative modelling paradigms. Here, we
present a lattice-free, particle-based simulation of the cell elongation model
of vasculogenesis. The new, particle-based simulations confirm the results
obtained from the previous Cellular Potts simulations. Furthermore, our current
findings suggest that the emergence of order is possible with the application
of a high enough attractive force or, alternatively, a longer attraction
radius. The methodology will be applicable to a range of problems in
morphogenesis and noisy particle aggregation in which cell shape is a key
determining factor.
| [
{
"created": "Wed, 1 Jul 2015 17:45:53 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Aug 2015 09:21:30 GMT",
"version": "v2"
}
] | 2015-09-07 | [
[
"Palachanis",
"Dimitrios",
""
],
[
"Szabó",
"András",
""
],
[
"Merks",
"Roeland M. H.",
""
]
] | Computational modelling is helpful for elucidating the cellular mechanisms driving biological morphogenesis. Previous simulation studies of blood vessel growth based on the Cellular Potts model (CPM) proposed that elongated, adhesive or mutually attractive endothelial cells suffice for the formation of blood vessel sprouts and vascular networks. Because each mathematical representation of a model introduces potential artifacts, it is important that model results are reproduced using alternative modelling paradigms. Here, we present a lattice-free, particle-based simulation of the cell elongation model of vasculogenesis. The new, particle-based simulations confirm the results obtained from the previous Cellular Potts simulations. Furthermore, our current findings suggest that the emergence of order is possible with the application of a high enough attractive force or, alternatively, a longer attraction radius. The methodology will be applicable to a range of problems in morphogenesis and noisy particle aggregation in which cell shape is a key determining factor. |
2112.09018 | Jasper Albers | Jasper Albers, Jari Pronold, Anno Christopher Kurth, Stine Brekke
Vennemo, Kaveh Haghighi Mood, Alexander Patronis, Dennis Terhorst, Jakob
Jordan, Susanne Kunkel, Tom Tetzlaff, Markus Diesmann, Johanna Senk | A Modular Workflow for Performance Benchmarking of Neuronal Network
Simulations | 32 pages, 8 figures, 1 listing | Front. Neuroinform. 16:837549 (2022) | 10.3389/fninf.2022.837549 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern computational neuroscience strives to develop complex network models
to explain dynamics and function of brains in health and disease. This process
goes hand in hand with advancements in the theory of neuronal networks and
increasing availability of detailed anatomical data on brain connectivity.
Large-scale models that study interactions between multiple brain areas with
intricate connectivity and investigate phenomena on long time scales such as
system-level learning require progress in simulation speed. The corresponding
development of state-of-the-art simulation engines relies on information
provided by benchmark simulations which assess the time-to-solution for
scientifically relevant, complementary network models using various
combinations of hardware and software revisions. However, maintaining
comparability of benchmark results is difficult due to a lack of standardized
specifications for measuring the scaling performance of simulators on
high-performance computing (HPC) systems. Motivated by the challenging
complexity of benchmarking, we define a generic workflow that decomposes the
endeavor into unique segments consisting of separate modules. As a reference
implementation for the conceptual workflow, we develop beNNch: an open-source
software framework for the configuration, execution, and analysis of benchmarks
for neuronal network simulations. The framework records benchmarking data and
metadata in a unified way to foster reproducibility. For illustration, we
measure the performance of various versions of the NEST simulator across
network models with different levels of complexity on a contemporary HPC
system, demonstrating how performance bottlenecks can be identified, ultimately
guiding the development toward more efficient simulation technology.
| [
{
"created": "Thu, 16 Dec 2021 17:00:10 GMT",
"version": "v1"
}
] | 2022-10-04 | [
[
"Albers",
"Jasper",
""
],
[
"Pronold",
"Jari",
""
],
[
"Kurth",
"Anno Christopher",
""
],
[
"Vennemo",
"Stine Brekke",
""
],
[
"Mood",
"Kaveh Haghighi",
""
],
[
"Patronis",
"Alexander",
""
],
[
"Terhorst",
"Dennis",
""
],
[
"Jordan",
"Jakob",
""
],
[
"Kunkel",
"Susanne",
""
],
[
"Tetzlaff",
"Tom",
""
],
[
"Diesmann",
"Markus",
""
],
[
"Senk",
"Johanna",
""
]
] | Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availability of detailed anatomical data on brain connectivity. Large-scale models that study interactions between multiple brain areas with intricate connectivity and investigate phenomena on long time scales such as system-level learning require progress in simulation speed. The corresponding development of state-of-the-art simulation engines relies on information provided by benchmark simulations which assess the time-to-solution for scientifically relevant, complementary network models using various combinations of hardware and software revisions. However, maintaining comparability of benchmark results is difficult due to a lack of standardized specifications for measuring the scaling performance of simulators on high-performance computing (HPC) systems. Motivated by the challenging complexity of benchmarking, we define a generic workflow that decomposes the endeavor into unique segments consisting of separate modules. As a reference implementation for the conceptual workflow, we develop beNNch: an open-source software framework for the configuration, execution, and analysis of benchmarks for neuronal network simulations. The framework records benchmarking data and metadata in a unified way to foster reproducibility. For illustration, we measure the performance of various versions of the NEST simulator across network models with different levels of complexity on a contemporary HPC system, demonstrating how performance bottlenecks can be identified, ultimately guiding the development toward more efficient simulation technology. |
1503.08168 | Eduardo Henrique Colombo | E.H. Colombo, C. Anteneodo | Metapopulation dynamics in a complex ecological landscape | null | Phys. Rev. E 92, 022714 (2015) | 10.1103/PhysRevE.92.022714 | null | q-bio.PE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a general model to study the interplay between spatial dispersal
and environment spatiotemporal fluctuations in metapopulation dynamics. An
ecological landscape of favorable patches is generated like a L\'{e}vy dust,
which allows to build a range of patterns, from dispersed to clustered ones.
Locally, the dynamics is driven by a canonical model for the evolution of the
population density, consisting of a logistic expression plus multiplicative
noises. Spatial coupling is introduced by means of two spreading mechanisms:
diffusive dispersion and selective migration driven by patch suitability. We
focus on the long-time population size as a function of habitat configurations,
environment fluctuations and coupling schemes. We obtain the conditions, that
the spatial distribution of favorable patches and the coupling mechanisms must
fulfill, to grant population survival. The fundamental phenomenon that we
observe is the positive feedback between environment fluctuations and spatial
spread preventing extinction.
| [
{
"created": "Fri, 27 Mar 2015 18:01:35 GMT",
"version": "v1"
}
] | 2015-08-20 | [
[
"Colombo",
"E. H.",
""
],
[
"Anteneodo",
"C.",
""
]
] | We propose a general model to study the interplay between spatial dispersal and environment spatiotemporal fluctuations in metapopulation dynamics. An ecological landscape of favorable patches is generated like a L\'{e}vy dust, which allows to build a range of patterns, from dispersed to clustered ones. Locally, the dynamics is driven by a canonical model for the evolution of the population density, consisting of a logistic expression plus multiplicative noises. Spatial coupling is introduced by means of two spreading mechanisms: diffusive dispersion and selective migration driven by patch suitability. We focus on the long-time population size as a function of habitat configurations, environment fluctuations and coupling schemes. We obtain the conditions, that the spatial distribution of favorable patches and the coupling mechanisms must fulfill, to grant population survival. The fundamental phenomenon that we observe is the positive feedback between environment fluctuations and spatial spread preventing extinction. |
2305.19276 | Suchindra Suchindra | Suchindra and Preetam Nagaraj | New Sequence Alignment Algorithm using AI Rules and Dynamic Seeds | 15. arXiv admin note: substantial text overlap with arXiv:2305.00329 | Bioscience & Engineering: An International Journal (BIOEJ), 2023 | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | DNA sequence alignment is important today as it is usually the first step in
finding gene mutation, evolutionary similarities, protein structure, drug
development and cancer treatment. Covid-19 is one recent example. There are
many sequencing algorithms developed over the past decades but the sequence
alignment using expert systems is quite new. To find DNA sequence alignment,
dynamic programming was used initially. Later faster algorithms used small DNA
sequence length of fixed size to find regions of similarity, and then build the
final alignment using these regions. Such systems were not sensitive but were
fast. To improve the sensitivity, we propose a new algorithm which is based on
finding maximal matches between two sequences, find seeds between them, employ
rules to find more seeds of varying length, and then employ a new stitching
algorithm, and weighted seeds to solve the problem
| [
{
"created": "Thu, 25 May 2023 21:21:06 GMT",
"version": "v1"
}
] | 2023-06-01 | [
[
"Suchindra",
"",
""
],
[
"Nagaraj",
"Preetam",
""
]
] | DNA sequence alignment is important today as it is usually the first step in finding gene mutation, evolutionary similarities, protein structure, drug development and cancer treatment. Covid-19 is one recent example. There are many sequencing algorithms developed over the past decades but the sequence alignment using expert systems is quite new. To find DNA sequence alignment, dynamic programming was used initially. Later faster algorithms used small DNA sequence length of fixed size to find regions of similarity, and then build the final alignment using these regions. Such systems were not sensitive but were fast. To improve the sensitivity, we propose a new algorithm which is based on finding maximal matches between two sequences, find seeds between them, employ rules to find more seeds of varying length, and then employ a new stitching algorithm, and weighted seeds to solve the problem |
1803.11386 | Nicola Galvanetto | Nicola Galvanetto, Andrea Perissinotto, Andrea Pedroni, Vincent Torre | Fodis: software for protein unfolding analysis | null | N. Galvanetto, A. Perissinotto, A. Pedroni, V. Torre, Fodis:
Software for Protein Unfolding Analysis, Biophysical Journal. 114 (2018)
1264-1266. doi:10.1016/j.bpj.2018.02.004 | 10.1016/j.bpj.2018.02.004 | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | The folding dynamics of proteins at the single molecule level has been
studied with single-molecule force spectroscopy (SMFS) experiments for twenty
years, but a common standardized method for the analysis of the collected data
and for the sharing among the scientific community members is still not
available. We have developed a new open source tool, Fodis, for the analysis of
the Force-distance curves obtained in SMFS experiments, providing an almost
automatic processing, analysis and classification of the obtained data. Our
method provides also a classification of the possible unfolding pathways and
structural heterogeneity, present during the unfolding of proteins.
| [
{
"created": "Fri, 30 Mar 2018 08:38:35 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Sep 2018 13:51:06 GMT",
"version": "v2"
}
] | 2018-09-28 | [
[
"Galvanetto",
"Nicola",
""
],
[
"Perissinotto",
"Andrea",
""
],
[
"Pedroni",
"Andrea",
""
],
[
"Torre",
"Vincent",
""
]
] | The folding dynamics of proteins at the single molecule level has been studied with single-molecule force spectroscopy (SMFS) experiments for twenty years, but a common standardized method for the analysis of the collected data and for the sharing among the scientific community members is still not available. We have developed a new open source tool, Fodis, for the analysis of the Force-distance curves obtained in SMFS experiments, providing an almost automatic processing, analysis and classification of the obtained data. Our method provides also a classification of the possible unfolding pathways and structural heterogeneity, present during the unfolding of proteins. |
2407.16077 | Eryk Kopczynski | Dorota Celi\'nska-Kopczy\'nska and Eryk Kopczy\'nski | Modelling brain connectomes networks: Solv is a worthy competitor to
hyperbolic geometry! | Full version of our paper accepted to ECAI 2024 | null | null | null | q-bio.NC cs.AI math.MG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Finding suitable embeddings for connectomes (spatially embedded complex
networks that map neural connections in the brain) is crucial for analyzing and
understanding cognitive processes. Recent studies have found two-dimensional
hyperbolic embeddings superior to Euclidean embeddings in modeling connectomes
across species, especially human connectomes. However, those studies had
limitations: geometries other than Euclidean, hyperbolic, or spherical were not
considered. Following William Thurston's suggestion that the networks of
neurons in the brain could be successfully represented in Solv geometry, we
study the goodness-of-fit of the embeddings for 21 connectome networks (8
species). To this end, we suggest an embedding algorithm based on Simulating
Annealing that allows us to embed connectomes to Euclidean, Spherical,
Hyperbolic, Solv, Nil, and product geometries. Our algorithm tends to find
better embeddings than the state-of-the-art, even in the hyperbolic case. Our
findings suggest that while three-dimensional hyperbolic embeddings yield the
best results in many cases, Solv embeddings perform reasonably well.
| [
{
"created": "Mon, 22 Jul 2024 22:36:04 GMT",
"version": "v1"
}
] | 2024-07-24 | [
[
"Celińska-Kopczyńska",
"Dorota",
""
],
[
"Kopczyński",
"Eryk",
""
]
] | Finding suitable embeddings for connectomes (spatially embedded complex networks that map neural connections in the brain) is crucial for analyzing and understanding cognitive processes. Recent studies have found two-dimensional hyperbolic embeddings superior to Euclidean embeddings in modeling connectomes across species, especially human connectomes. However, those studies had limitations: geometries other than Euclidean, hyperbolic, or spherical were not considered. Following William Thurston's suggestion that the networks of neurons in the brain could be successfully represented in Solv geometry, we study the goodness-of-fit of the embeddings for 21 connectome networks (8 species). To this end, we suggest an embedding algorithm based on Simulating Annealing that allows us to embed connectomes to Euclidean, Spherical, Hyperbolic, Solv, Nil, and product geometries. Our algorithm tends to find better embeddings than the state-of-the-art, even in the hyperbolic case. Our findings suggest that while three-dimensional hyperbolic embeddings yield the best results in many cases, Solv embeddings perform reasonably well. |
1011.4125 | Nigel Goldenfeld | Nigel Goldenfeld and Carl Woese | Life is physics: evolution as a collective phenomenon far from
equilibrium | To appear in Annual Reviews of Condensed Matter Physics (2011) | null | 10.1146/annurev-conmatphys-062910-140509 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolution is the fundamental physical process that gives rise to biological
phenomena. Yet it is widely treated as a subset of population genetics, and
thus its scope is artificially limited. As a result, the key issues of how
rapidly evolution occurs, and its coupling to ecology have not been
satisfactorily addressed and formulated. The lack of widespread appreciation
for, and understanding of, the evolutionary process has arguably retarded the
development of biology as a science, with disastrous consequences for its
applications to medicine, ecology and the global environment. This review
focuses on evolution as a problem in non-equilibrium statistical mechanics,
where the key dynamical modes are collective, as evidenced by the plethora of
mobile genetic elements whose role in shaping evolution has been revealed by
modern genomic surveys. We discuss how condensed matter physics concepts might
provide a useful perspective in evolutionary biology, the conceptual failings
of the modern evolutionary synthesis, the open-ended growth of complexity, and
the quintessentially self-referential nature of evolutionary dynamics.
| [
{
"created": "Thu, 18 Nov 2010 02:05:40 GMT",
"version": "v1"
}
] | 2017-08-23 | [
[
"Goldenfeld",
"Nigel",
""
],
[
"Woese",
"Carl",
""
]
] | Evolution is the fundamental physical process that gives rise to biological phenomena. Yet it is widely treated as a subset of population genetics, and thus its scope is artificially limited. As a result, the key issues of how rapidly evolution occurs, and its coupling to ecology have not been satisfactorily addressed and formulated. The lack of widespread appreciation for, and understanding of, the evolutionary process has arguably retarded the development of biology as a science, with disastrous consequences for its applications to medicine, ecology and the global environment. This review focuses on evolution as a problem in non-equilibrium statistical mechanics, where the key dynamical modes are collective, as evidenced by the plethora of mobile genetic elements whose role in shaping evolution has been revealed by modern genomic surveys. We discuss how condensed matter physics concepts might provide a useful perspective in evolutionary biology, the conceptual failings of the modern evolutionary synthesis, the open-ended growth of complexity, and the quintessentially self-referential nature of evolutionary dynamics. |
1807.05669 | Liaofu Luo | Liaofu Luo and Lirong Zhang | Quantum patterns of genome size variation in angiosperms | 16 pages, 1 figure, 4 tables | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The nuclear DNA amount in angiosperms is studied from the eigen-value
equation of the genome evolution operator H. The operator H is introduced by
physical simulation and it is defined as a function of the genome size N and
the derivative with respective to the size. The discontinuity of DNA size
distribution and its synergetic occurrence in related angiosperms species are
successfully deduced from the solution of the equation. The results agree well
with the existing experimental data of Aloe, Clarkia, Nicotiana, Lathyrus,
Allium and other genera. It may indicate that the evolutionary constrains on
angiosperm genome are essentially of quantum origin.
| [
{
"created": "Mon, 16 Jul 2018 03:50:05 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Jul 2019 02:24:07 GMT",
"version": "v2"
}
] | 2019-07-04 | [
[
"Luo",
"Liaofu",
""
],
[
"Zhang",
"Lirong",
""
]
] | The nuclear DNA amount in angiosperms is studied from the eigen-value equation of the genome evolution operator H. The operator H is introduced by physical simulation and it is defined as a function of the genome size N and the derivative with respective to the size. The discontinuity of DNA size distribution and its synergetic occurrence in related angiosperms species are successfully deduced from the solution of the equation. The results agree well with the existing experimental data of Aloe, Clarkia, Nicotiana, Lathyrus, Allium and other genera. It may indicate that the evolutionary constrains on angiosperm genome are essentially of quantum origin. |
1808.02195 | Milad Rafiee Vahid | Milad R. Vahid, Bernard Hanzon, Raimund J. Ober | Fisher information matrix for single molecules with stochastic
trajectories | null | SIAM Journal on Imaging Sciences, 2020, Vol. 13, No. 1 : pp.
234-264 | 10.1137/19M1242562 | null | q-bio.QM physics.bio-ph stat.AP | http://creativecommons.org/licenses/by/4.0/ | Tracking of objects in cellular environments has become a vital tool in
molecular cell biology. A particularly important example is single molecule
tracking which enables the study of the motion of a molecule in cellular
environments and provides quantitative information on the behavior of
individual molecules in cellular environments, which were not available before
through bulk studies. Here, we consider a dynamical system where the motion of
an object is modeled by stochastic differential equations (SDEs), and
measurements are the detected photons emitted by the moving fluorescently
labeled object, which occur at discrete time points, corresponding to the
arrival times of a Poisson process, in contrast to uniform time points which
have been commonly used in similar dynamical systems. The measurements are
distributed according to optical diffraction theory, and therefore, they would
be modeled by different distributions, e.g., a Born and Wolf profile for an
out-of-focus molecule. For some special circumstances, Gaussian image models
have been proposed. In this paper, we introduce a stochastic framework in which
we calculate the maximum likelihood estimates of the biophysical parameters of
the molecular interactions, e.g., diffusion and drift coefficients. More
importantly, we develop a general framework to calculate the Cram\'er-Rao lower
bound (CRLB), given by the inverse of the Fisher information matrix, for the
estimation of unknown parameters and use it as a benchmark in the evaluation of
the standard deviation of the estimates. There exists no established method,
even for Gaussian measurements, to systematically calculate the CRLB for the
general motion model that we consider in this paper. We apply the developed
methodology to simulated data of a molecule with linear trajectories and show
that the standard deviation of the estimates matches well with the square root
of the CRLB.
| [
{
"created": "Tue, 7 Aug 2018 03:32:16 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Feb 2020 07:50:16 GMT",
"version": "v2"
}
] | 2020-02-27 | [
[
"Vahid",
"Milad R.",
""
],
[
"Hanzon",
"Bernard",
""
],
[
"Ober",
"Raimund J.",
""
]
] | Tracking of objects in cellular environments has become a vital tool in molecular cell biology. A particularly important example is single molecule tracking which enables the study of the motion of a molecule in cellular environments and provides quantitative information on the behavior of individual molecules in cellular environments, which were not available before through bulk studies. Here, we consider a dynamical system where the motion of an object is modeled by stochastic differential equations (SDEs), and measurements are the detected photons emitted by the moving fluorescently labeled object, which occur at discrete time points, corresponding to the arrival times of a Poisson process, in contrast to uniform time points which have been commonly used in similar dynamical systems. The measurements are distributed according to optical diffraction theory, and therefore, they would be modeled by different distributions, e.g., a Born and Wolf profile for an out-of-focus molecule. For some special circumstances, Gaussian image models have been proposed. In this paper, we introduce a stochastic framework in which we calculate the maximum likelihood estimates of the biophysical parameters of the molecular interactions, e.g., diffusion and drift coefficients. More importantly, we develop a general framework to calculate the Cram\'er-Rao lower bound (CRLB), given by the inverse of the Fisher information matrix, for the estimation of unknown parameters and use it as a benchmark in the evaluation of the standard deviation of the estimates. There exists no established method, even for Gaussian measurements, to systematically calculate the CRLB for the general motion model that we consider in this paper. We apply the developed methodology to simulated data of a molecule with linear trajectories and show that the standard deviation of the estimates matches well with the square root of the CRLB. |
1908.09117 | Markus D Schirmer | Markus D. Schirmer and Ai Wern Chung | Heat kernels with functional connectomes reveal atypical energy
transport in peripheral subnetworks in autism | null | null | 10.1007/978-3-030-32391-2_6 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autism is increasing in prevalence and is a neurodevelopmental disorder
characterised by impairments in communication skills and social behaviour.
Connectomes enable a systems-level representation of the brain with recent
interests in understanding the distributed nature of higher order cognitive
function using modules or subnetworks. By dividing the connectome according to
a central component of the brain critical for its function (it's hub), we
investigate network organisation in autism from hub through to peripheral
subnetworks. We complement this analysis by extracting features of energy
transport computed from heat kernels fitted with increasing time steps. This
heat kernel framework is advantageous as it can capture the energy transported
in all direct and indirect pathways between pair-wise regions over 'time', with
features that have correspondence to small-world properties. We apply our
framework to resting-state functional MRI connectomes from a large, publically
available autism dataset, ABIDE. We show that energy propagating through the
brain over time are different between subnetworks, and that heat kernel
features significantly differ between autism and controls. Furthermore, the hub
was functionally preserved and similar to controls, however, increasing
statistical significance between groups was found in increasingly peripheral
subnetworks. Our results support the increasing opinion of non-hub regions
playing an important role in functional organisation. This work shows that
analysing autism by subnetworks with the heat kernel reflects the atypical
activations in peripheral regions as alterations in energy dispersion and may
provide useful features towards understanding the distributed impact of this
disorder on the functional connectome.
| [
{
"created": "Sat, 24 Aug 2019 09:48:36 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Sep 2019 09:08:25 GMT",
"version": "v2"
}
] | 2019-09-26 | [
[
"Schirmer",
"Markus D.",
""
],
[
"Chung",
"Ai Wern",
""
]
] | Autism is increasing in prevalence and is a neurodevelopmental disorder characterised by impairments in communication skills and social behaviour. Connectomes enable a systems-level representation of the brain with recent interests in understanding the distributed nature of higher order cognitive function using modules or subnetworks. By dividing the connectome according to a central component of the brain critical for its function (it's hub), we investigate network organisation in autism from hub through to peripheral subnetworks. We complement this analysis by extracting features of energy transport computed from heat kernels fitted with increasing time steps. This heat kernel framework is advantageous as it can capture the energy transported in all direct and indirect pathways between pair-wise regions over 'time', with features that have correspondence to small-world properties. We apply our framework to resting-state functional MRI connectomes from a large, publically available autism dataset, ABIDE. We show that energy propagating through the brain over time are different between subnetworks, and that heat kernel features significantly differ between autism and controls. Furthermore, the hub was functionally preserved and similar to controls, however, increasing statistical significance between groups was found in increasingly peripheral subnetworks. Our results support the increasing opinion of non-hub regions playing an important role in functional organisation. This work shows that analysing autism by subnetworks with the heat kernel reflects the atypical activations in peripheral regions as alterations in energy dispersion and may provide useful features towards understanding the distributed impact of this disorder on the functional connectome. |
2011.14255 | Yang Ye | Yang Ye, Qingpeng Zhang, Zhidong Cao, Daniel Dajun Zeng | Optimal vaccination program for two infectious diseases with cross
immunity | 5 pages, 3 figures | null | 10.1209/0295-5075/133/46001 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are often multiple diseases with cross immunity competing for
vaccination resources. Here we investigate the optimal vaccination program in a
two-layer Susceptible-Infected-Removed (SIR) model, where two diseases with
cross immunity spread in the same population, and vaccines for both diseases
are available. We identify three scenarios of the optimal vaccination program,
which prevents the outbreaks of both diseases at the minimum cost. We
analytically derive a criterion to specify the optimal program based on the
costs for different vaccines.
| [
{
"created": "Sun, 29 Nov 2020 02:07:36 GMT",
"version": "v1"
}
] | 2021-05-26 | [
[
"Ye",
"Yang",
""
],
[
"Zhang",
"Qingpeng",
""
],
[
"Cao",
"Zhidong",
""
],
[
"Zeng",
"Daniel Dajun",
""
]
] | There are often multiple diseases with cross immunity competing for vaccination resources. Here we investigate the optimal vaccination program in a two-layer Susceptible-Infected-Removed (SIR) model, where two diseases with cross immunity spread in the same population, and vaccines for both diseases are available. We identify three scenarios of the optimal vaccination program, which prevents the outbreaks of both diseases at the minimum cost. We analytically derive a criterion to specify the optimal program based on the costs for different vaccines. |
1709.00973 | Kristoffer Spricer | Kristoffer Spricer, Pieter Trapman | Characterizing the Initial Phase of Epidemic Growth on some Empirical
Networks | To be included in the conference proceedings for SPAS 2017
(International Conference on Stochastic Processes and Algebraic Structures),
October 4-6, 2017 | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key parameter in models for the spread of infectious diseases is the basic
reproduction number $R_0$, which is the expected number of secondary cases a
typical infected primary case infects during its infectious period in a large
mostly susceptible population. In order for this quantity to be meaningful, the
initial expected growth of the number of infectious individuals in the
large-population limit should be exponential.
We investigate to what extent this assumption is valid by performing repeated
simulations of epidemics on selected empirical networks, viewing each epidemic
as a random process in discrete time. The initial phase of each epidemic is
analyzed by fitting the number of infected people at each time step to a
generalised growth model, allowing for estimating the shape of the growth. For
reference, similar investigations are done on some elementary graphs such as
integer lattices in different dimensions and configuration model graphs, for
which the early epidemic behaviour is known.
We find that for the empirical networks tested in this paper, exponential
growth characterizes the early stages of the epidemic, except when the network
is restricted by a strong low-dimensional spacial constraint, such as is the
case for the two-dimensional square lattice. However, on finite integer
lattices of sufficiently high dimension, the early development of epidemics
shows exponential growth.
| [
{
"created": "Mon, 4 Sep 2017 14:22:13 GMT",
"version": "v1"
}
] | 2017-09-05 | [
[
"Spricer",
"Kristoffer",
""
],
[
"Trapman",
"Pieter",
""
]
] | A key parameter in models for the spread of infectious diseases is the basic reproduction number $R_0$, which is the expected number of secondary cases a typical infected primary case infects during its infectious period in a large mostly susceptible population. In order for this quantity to be meaningful, the initial expected growth of the number of infectious individuals in the large-population limit should be exponential. We investigate to what extent this assumption is valid by performing repeated simulations of epidemics on selected empirical networks, viewing each epidemic as a random process in discrete time. The initial phase of each epidemic is analyzed by fitting the number of infected people at each time step to a generalised growth model, allowing for estimating the shape of the growth. For reference, similar investigations are done on some elementary graphs such as integer lattices in different dimensions and configuration model graphs, for which the early epidemic behaviour is known. We find that for the empirical networks tested in this paper, exponential growth characterizes the early stages of the epidemic, except when the network is restricted by a strong low-dimensional spacial constraint, such as is the case for the two-dimensional square lattice. However, on finite integer lattices of sufficiently high dimension, the early development of epidemics shows exponential growth. |
2106.11070 | Sayak Chatterjee | Anik Burman, Sayak Chatterjee, Pramit Ghosh and Indranil Mukhokadhyay | A Flexible Agent-Based Model to Study COVID-19 Outbreak -- A Generic
Approach | 6 pages, 4 figures, 1 table, 1 video | null | null | null | q-bio.PE | http://creativecommons.org/publicdomain/zero/1.0/ | Understanding dynamics of an outbreak like that of COVID-19 is important in
designing effective control measures. This study aims to develop an agent based
model that compares changes in infection progression by manipulating different
parameters in a synthetic population. Model input includes population
characteristics like age, sex, working status etc. of each individual and other
factors influencing disease dynamics. Depending on number of epicentres of
infection, location of primary cases, sensitivity, proportion of asymptomatic
and frequency or duration of lockdown, our simulator tracks every individual
and hence infection progression through community over time.
In a closed community of 10000 people, it is seen that without any lockdown,
number of cases peak around 6th week and wanes off around 15th week. If primary
case is located inside dense population cluster like slums, cases peak early
and wane off slowly. With introduction of lockdown, cases peak at slower rate.
If sensitivity of identifying infection decreases, cases and deaths increase.
Number of cases declines with increase in proportion of asymptomatic cases. The
model is robust and provides reproducible estimates with realistic parameter
values. It also guides in identifying measures to control outbreak in a
community. It is flexible in accommodating different parameters like
infectivity period, yield of testing, socio-economic strata, daily travel,
awareness level, population density, social distancing, lockdown etc. and can
be tailored to study other infections with similar transmission pattern.
| [
{
"created": "Wed, 16 Jun 2021 20:36:08 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Jun 2021 16:59:04 GMT",
"version": "v2"
}
] | 2021-06-24 | [
[
"Burman",
"Anik",
""
],
[
"Chatterjee",
"Sayak",
""
],
[
"Ghosh",
"Pramit",
""
],
[
"Mukhokadhyay",
"Indranil",
""
]
] | Understanding dynamics of an outbreak like that of COVID-19 is important in designing effective control measures. This study aims to develop an agent based model that compares changes in infection progression by manipulating different parameters in a synthetic population. Model input includes population characteristics like age, sex, working status etc. of each individual and other factors influencing disease dynamics. Depending on number of epicentres of infection, location of primary cases, sensitivity, proportion of asymptomatic and frequency or duration of lockdown, our simulator tracks every individual and hence infection progression through community over time. In a closed community of 10000 people, it is seen that without any lockdown, number of cases peak around 6th week and wanes off around 15th week. If primary case is located inside dense population cluster like slums, cases peak early and wane off slowly. With introduction of lockdown, cases peak at slower rate. If sensitivity of identifying infection decreases, cases and deaths increase. Number of cases declines with increase in proportion of asymptomatic cases. The model is robust and provides reproducible estimates with realistic parameter values. It also guides in identifying measures to control outbreak in a community. It is flexible in accommodating different parameters like infectivity period, yield of testing, socio-economic strata, daily travel, awareness level, population density, social distancing, lockdown etc. and can be tailored to study other infections with similar transmission pattern. |
1404.0664 | Paulo Bandiera-Paiva | Jackson Cordeiro Lima, Paulo Bandiera-Paiva | Group A Rotavirus NSP4 is Under Negative Selective Pressure | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rotavirus (RV) is the major etiologic agent of severe infantile
gastroenteritis; its genome has 11 segments of double stranded RNA, encoding 12
proteins. The non-structural protein 4 (NSP4) encoded by segment 10 is
multifunctional. The aim of this study is to analyze the selective pressure
driving the NSP4 of RV, through the ratio of non-synonymous substitutions per
synonymous substitutions (dN/dS). Our results show that NSP4 is under negative
evolutionary pressure (84.57% of the amino acid sequence) and no site was found
under positive selection. This may support other evolutionary studies of
different RV proteins or viral agents.
| [
{
"created": "Wed, 2 Apr 2014 19:41:38 GMT",
"version": "v1"
}
] | 2014-04-03 | [
[
"Lima",
"Jackson Cordeiro",
""
],
[
"Bandiera-Paiva",
"Paulo",
""
]
] | Rotavirus (RV) is the major etiologic agent of severe infantile gastroenteritis; its genome has 11 segments of double stranded RNA, encoding 12 proteins. The non-structural protein 4 (NSP4) encoded by segment 10 is multifunctional. The aim of this study is to analyze the selective pressure driving the NSP4 of RV, through the ratio of non-synonymous substitutions per synonymous substitutions (dN/dS). Our results show that NSP4 is under negative evolutionary pressure (84.57% of the amino acid sequence) and no site was found under positive selection. This may support other evolutionary studies of different RV proteins or viral agents. |
1810.10440 | Mattia Bramini | Martina Chiacchiaretta, Mattia Bramini, Anna Rocchi, Andrea Armirotti,
Emanuele Giordano, Ester V\'azquez, Tiziano Bandiera, Stefano Ferroni,
Fabrizia Cesca and Fabio Benfenati | Graphene oxide upregulates the homeostatic functions of primary
astrocytes and modulates astrocyte-to-neuron communication | This document is the unedited Author's version of a Submitted Work
that was subsequently accepted for publication in Nano Letters. To access the
final edited and published work see
https://pubs.acs.org/articlesonrequest/AOR-wvjrggcBp7kC8NwEImXr | Nano Lett. 2018, 18, 9, 5827-5838 | 10.1021/acs.nanolett.8b02487 | null | q-bio.NC q-bio.CB | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Graphene-based materials are the focus of intense research efforts to devise
novel theranostic strategies for targeting the central nervous system. In this
work, we have investigated the consequences of long-term exposure of primary
rat astrocytes to pristine graphene (GR) and graphene oxide (GO) flakes. We
demonstrate that GR/GO interfere with a variety of intracellular processes as a
result of their internalization through the endo-lysosomal pathway.
Graphene-exposed astrocytes acquire a more differentiated morphological
phenotype associated with extensive cytoskeletal rearrangements. Profound
functional alterations are induced by GO internalization, including the
upregulation of inward-rectifying K+ channels and of Na+-dependent glutamate
uptake, which are linked to the astrocyte capacity to control the extracellular
homeostasis. Interestingly, GO-pretreated astrocytes promote the functional
maturation of co-cultured primary neurons by inducing an increase in intrinsic
excitability and in the density of GABAergic synapses. The results indicate
that graphene nanomaterials profoundly affect astrocyte physiology in vitro,
with consequences for neuronal network activity. This work supports the view
that GO-based materials could be of great interest to address pathologies of
the central nervous system associated to astrocyte dysfunctions.
| [
{
"created": "Wed, 24 Oct 2018 15:09:15 GMT",
"version": "v1"
}
] | 2018-10-25 | [
[
"Chiacchiaretta",
"Martina",
""
],
[
"Bramini",
"Mattia",
""
],
[
"Rocchi",
"Anna",
""
],
[
"Armirotti",
"Andrea",
""
],
[
"Giordano",
"Emanuele",
""
],
[
"Vázquez",
"Ester",
""
],
[
"Bandiera",
"Tiziano",
""
],
[
"Ferroni",
"Stefano",
""
],
[
"Cesca",
"Fabrizia",
""
],
[
"Benfenati",
"Fabio",
""
]
] | Graphene-based materials are the focus of intense research efforts to devise novel theranostic strategies for targeting the central nervous system. In this work, we have investigated the consequences of long-term exposure of primary rat astrocytes to pristine graphene (GR) and graphene oxide (GO) flakes. We demonstrate that GR/GO interfere with a variety of intracellular processes as a result of their internalization through the endo-lysosomal pathway. Graphene-exposed astrocytes acquire a more differentiated morphological phenotype associated with extensive cytoskeletal rearrangements. Profound functional alterations are induced by GO internalization, including the upregulation of inward-rectifying K+ channels and of Na+-dependent glutamate uptake, which are linked to the astrocyte capacity to control the extracellular homeostasis. Interestingly, GO-pretreated astrocytes promote the functional maturation of co-cultured primary neurons by inducing an increase in intrinsic excitability and in the density of GABAergic synapses. The results indicate that graphene nanomaterials profoundly affect astrocyte physiology in vitro, with consequences for neuronal network activity. This work supports the view that GO-based materials could be of great interest to address pathologies of the central nervous system associated to astrocyte dysfunctions. |
0910.1637 | Vlad Elgart | Vlad Elgart, Tao Jia, Rahul Kulkarni | Quantifying mRNA synthesis and decay rates using small RNAs | null | null | 10.1016/j.bpj.2010.03.022 | null | q-bio.CB q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Regulation of mRNA decay is a critical component of global cellular
adaptation to changing environments. The corresponding changes in mRNA
lifetimes can be coordinated with changes in mRNA transcription rates to
fine-tune gene expression. Current approaches for measuring mRNA lifetimes can
give rise to secondary effects due to transcription inhibition and require
separate experiments to estimate changes in mRNA transcription rates. Here, we
propose an approach for simultaneous determination of changes in mRNA
transcription rate and lifetime using regulatory small RNAs to control mRNA
decay. We analyze a stochastic model for coupled degradation of mRNAs and sRNAs
and derive exact results connecting RNA lifetimes and transcription rates to
mean abundances. The results obtained show how steady-state measurements of RNA
levels can be used to analyze factors and processes regulating changes in mRNA
transcription and decay.
| [
{
"created": "Fri, 9 Oct 2009 00:19:55 GMT",
"version": "v1"
},
{
"created": "Sun, 11 Oct 2009 23:42:31 GMT",
"version": "v2"
}
] | 2015-05-14 | [
[
"Elgart",
"Vlad",
""
],
[
"Jia",
"Tao",
""
],
[
"Kulkarni",
"Rahul",
""
]
] | Regulation of mRNA decay is a critical component of global cellular adaptation to changing environments. The corresponding changes in mRNA lifetimes can be coordinated with changes in mRNA transcription rates to fine-tune gene expression. Current approaches for measuring mRNA lifetimes can give rise to secondary effects due to transcription inhibition and require separate experiments to estimate changes in mRNA transcription rates. Here, we propose an approach for simultaneous determination of changes in mRNA transcription rate and lifetime using regulatory small RNAs to control mRNA decay. We analyze a stochastic model for coupled degradation of mRNAs and sRNAs and derive exact results connecting RNA lifetimes and transcription rates to mean abundances. The results obtained show how steady-state measurements of RNA levels can be used to analyze factors and processes regulating changes in mRNA transcription and decay. |
1311.2494 | Geoffrey Barton | Nick Schurch, Christian Cole, Alexander Sherstnev, Junfang Song,
C\'eline Duc, Kate G. Storey, W. H. Irwin McLean, Sara J. Brown, Gordon G.
Simpson, and Geoffrey J. Barton | Improved annotation of 3-prime untranslated regions and complex loci by
combination of strand-specific Direct RNA Sequencing, RNA-seq and ESTs | 44 pages, 9 figures | PLoS ONE 9(4) (2014): e94270 | 10.1371/journal.pone.0094270 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The reference annotations made for a genome sequence provide the framework
for all subsequent analyses of the genome. Correct annotation is particularly
important when interpreting the results of RNA-seq experiments where short
sequence reads are mapped against the genome and assigned to genes according to
the annotation. Inconsistencies in annotations between the reference and the
experimental system can lead to incorrect interpretation of the effect on RNA
expression of an experimental treatment or mutation in the system under study.
Until recently, the genome-wide annotation of 3-prime untranslated regions
received less attention than coding regions and the delineation of intron/exon
boundaries. In this paper, data produced for samples in Human, Chicken and A.
thaliana by the novel single-molecule, strand-specific, Direct RNA Sequencing
technology from Helicos Biosciences which locates 3-prime polyadenylation sites
to within +/- 2 nt, were combined with archival EST and RNA-Seq data. Nine
examples are illustrated where this combination of data allowed: (1) gene and
3-prime UTR re-annotation (including extension of one 3-prime UTR by 5.9 kb);
(2) disentangling of gene expression in complex regions; (3) clearer
interpretation of small RNA expression and (4) identification of novel genes.
While the specific examples displayed here may become obsolete as genome
sequences and their annotations are refined, the principles laid out in this
paper will be of general use both to those annotating genomes and those seeking
to interpret existing publically available annotations in the context of their
own experimental data
| [
{
"created": "Mon, 11 Nov 2013 16:46:46 GMT",
"version": "v1"
}
] | 2014-05-20 | [
[
"Schurch",
"Nick",
""
],
[
"Cole",
"Christian",
""
],
[
"Sherstnev",
"Alexander",
""
],
[
"Song",
"Junfang",
""
],
[
"Duc",
"Céline",
""
],
[
"Storey",
"Kate G.",
""
],
[
"McLean",
"W. H. Irwin",
""
],
[
"Brown",
"Sara J.",
""
],
[
"Simpson",
"Gordon G.",
""
],
[
"Barton",
"Geoffrey J.",
""
]
] | The reference annotations made for a genome sequence provide the framework for all subsequent analyses of the genome. Correct annotation is particularly important when interpreting the results of RNA-seq experiments where short sequence reads are mapped against the genome and assigned to genes according to the annotation. Inconsistencies in annotations between the reference and the experimental system can lead to incorrect interpretation of the effect on RNA expression of an experimental treatment or mutation in the system under study. Until recently, the genome-wide annotation of 3-prime untranslated regions received less attention than coding regions and the delineation of intron/exon boundaries. In this paper, data produced for samples in Human, Chicken and A. thaliana by the novel single-molecule, strand-specific, Direct RNA Sequencing technology from Helicos Biosciences which locates 3-prime polyadenylation sites to within +/- 2 nt, were combined with archival EST and RNA-Seq data. Nine examples are illustrated where this combination of data allowed: (1) gene and 3-prime UTR re-annotation (including extension of one 3-prime UTR by 5.9 kb); (2) disentangling of gene expression in complex regions; (3) clearer interpretation of small RNA expression and (4) identification of novel genes. While the specific examples displayed here may become obsolete as genome sequences and their annotations are refined, the principles laid out in this paper will be of general use both to those annotating genomes and those seeking to interpret existing publically available annotations in the context of their own experimental data |
1901.11015 | Mateo Rojas-Carulla Mr | Mateo Rojas-Carulla, Ilya Tolstikhin, Guillermo Luque, Nicholas
Youngblut, Ruth Ley, Bernhard Sch\"olkopf | GeNet: Deep Representations for Metagenomics | null | null | null | null | q-bio.GN cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce GeNet, a method for shotgun metagenomic classification from raw
DNA sequences that exploits the known hierarchical structure between labels for
training. We provide a comparison with state-of-the-art methods Kraken and
Centrifuge on datasets obtained from several sequencing technologies, in which
dataset shift occurs. We show that GeNet obtains competitive precision and good
recall, with orders of magnitude less memory requirements. Moreover, we show
that a linear model trained on top of representations learned by GeNet achieves
recall comparable to state-of-the-art methods on the aforementioned datasets,
and achieves over 90% accuracy in a challenging pathogen detection problem.
This provides evidence of the usefulness of the representations learned by
GeNet for downstream biological tasks.
| [
{
"created": "Wed, 30 Jan 2019 09:48:53 GMT",
"version": "v1"
}
] | 2019-02-01 | [
[
"Rojas-Carulla",
"Mateo",
""
],
[
"Tolstikhin",
"Ilya",
""
],
[
"Luque",
"Guillermo",
""
],
[
"Youngblut",
"Nicholas",
""
],
[
"Ley",
"Ruth",
""
],
[
"Schölkopf",
"Bernhard",
""
]
] | We introduce GeNet, a method for shotgun metagenomic classification from raw DNA sequences that exploits the known hierarchical structure between labels for training. We provide a comparison with state-of-the-art methods Kraken and Centrifuge on datasets obtained from several sequencing technologies, in which dataset shift occurs. We show that GeNet obtains competitive precision and good recall, with orders of magnitude less memory requirements. Moreover, we show that a linear model trained on top of representations learned by GeNet achieves recall comparable to state-of-the-art methods on the aforementioned datasets, and achieves over 90% accuracy in a challenging pathogen detection problem. This provides evidence of the usefulness of the representations learned by GeNet for downstream biological tasks. |
q-bio/0407038 | John Cain | John W. Cain, Elena G. Tolkacheva, David G. Schaeffer, and Daniel J.
Gauthier | Rate-dependent propagation of cardiac action potentials in a
one-dimensional fiber | 17 pages, 7 figures | null | 10.1103/PhysRevE.70.061906 | null | q-bio.QM | null | Action potential duration (APD) restitution, which relates APD to the
preceding diastolic interval (DI), is a useful tool for predicting the onset of
abnormal cardiac rhythms. However, it is known that different pacing protocols
lead to different APD restitution curves (RCs). This phenomenon, known as APD
rate-dependence, is a consequence of memory in the tissue. In addition to APD
restitution, conduction velocity restitution also plays an important role in
the spatiotemporal dynamics of cardiac tissue. We present new results
concerning rate-dependent restitution in the velocity of propagating action
potentials in a one-dimensional fiber. Our numerical simulations show that,
independent of the amount of memory in the tissue, waveback velocity exhibits
pronounced rate-dependence and the wavefront velocity does not. Moreover, the
discrepancy between waveback velocity RCs is most significant for small DI. We
provide an analytical explanation of these results, using a system of coupled
maps to relate the wavefront and waveback velocities. Our calculations show
that waveback velocity rate-dependence is due to APD restitution, not memory.
| [
{
"created": "Thu, 29 Jul 2004 13:53:38 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Cain",
"John W.",
""
],
[
"Tolkacheva",
"Elena G.",
""
],
[
"Schaeffer",
"David G.",
""
],
[
"Gauthier",
"Daniel J.",
""
]
] | Action potential duration (APD) restitution, which relates APD to the preceding diastolic interval (DI), is a useful tool for predicting the onset of abnormal cardiac rhythms. However, it is known that different pacing protocols lead to different APD restitution curves (RCs). This phenomenon, known as APD rate-dependence, is a consequence of memory in the tissue. In addition to APD restitution, conduction velocity restitution also plays an important role in the spatiotemporal dynamics of cardiac tissue. We present new results concerning rate-dependent restitution in the velocity of propagating action potentials in a one-dimensional fiber. Our numerical simulations show that, independent of the amount of memory in the tissue, waveback velocity exhibits pronounced rate-dependence and the wavefront velocity does not. Moreover, the discrepancy between waveback velocity RCs is most significant for small DI. We provide an analytical explanation of these results, using a system of coupled maps to relate the wavefront and waveback velocities. Our calculations show that waveback velocity rate-dependence is due to APD restitution, not memory. |
2202.08209 | Abdul Karim Obeid | Abdul Karim Obeid, Peter Bruza, Catarina Moreira, Axel Bruns, Daniel
Angus | An Extension Of Combinatorial Contextuality For Cognitive Protocols | 28 pages, 10 figures, 5 tables | null | null | null | q-bio.NC cs.AI quant-ph | http://creativecommons.org/licenses/by/4.0/ | This article extends the combinatorial approach to support the determination
of contextuality amidst causal influences. Contextuality is an active field of
study in Quantum Cognition, in systems relating to mental phenomena, such as
concepts in human memory [Aerts et al., 2013]. In the cognitive field of study,
a contemporary challenge facing the determination of whether a phenomenon is
contextual has been the identification and management of disturbances
[Dzhafarov et al., 2016]. Whether or not said disturbances are identified
through the modelling approach, constitute causal influences, or are
disregardableas as noise is important, as contextuality cannot be adequately
determined in the presence of causal influences [Gleason, 1957]. To address
this challenge, we first provide a formalisation of necessary elements of the
combinatorial approach within the language of canonical9 causal models. Through
this formalisation, we extend the combinatorial approach to support a
measurement and treatment of disturbance, and offer techniques to separately
distinguish noise and causal influences. Thereafter, we develop a protocol
through which these elements may be represented within a cognitive experiment.
As human cognition seems rife with causal influences, cognitive modellers may
apply the extended combinatorial approach to practically determine the
contextuality of cognitive phenomena.
| [
{
"created": "Tue, 15 Feb 2022 04:28:14 GMT",
"version": "v1"
}
] | 2022-02-17 | [
[
"Obeid",
"Abdul Karim",
""
],
[
"Bruza",
"Peter",
""
],
[
"Moreira",
"Catarina",
""
],
[
"Bruns",
"Axel",
""
],
[
"Angus",
"Daniel",
""
]
] | This article extends the combinatorial approach to support the determination of contextuality amidst causal influences. Contextuality is an active field of study in Quantum Cognition, in systems relating to mental phenomena, such as concepts in human memory [Aerts et al., 2013]. In the cognitive field of study, a contemporary challenge facing the determination of whether a phenomenon is contextual has been the identification and management of disturbances [Dzhafarov et al., 2016]. Whether or not said disturbances are identified through the modelling approach, constitute causal influences, or are disregardableas as noise is important, as contextuality cannot be adequately determined in the presence of causal influences [Gleason, 1957]. To address this challenge, we first provide a formalisation of necessary elements of the combinatorial approach within the language of canonical9 causal models. Through this formalisation, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences. Thereafter, we develop a protocol through which these elements may be represented within a cognitive experiment. As human cognition seems rife with causal influences, cognitive modellers may apply the extended combinatorial approach to practically determine the contextuality of cognitive phenomena. |
1203.4067 | Simone Linz | Leo van Iersel and Simone Linz | A quadratic kernel for computing the hybridization number of multiple
trees | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has recently been shown that the NP-hard problem of calculating the
minimum number of hybridization events that is needed to explain a set of
rooted binary phylogenetic trees by means of a hybridization network is
fixed-parameter tractable if an instance of the problem consists of precisely
two such trees. In this paper, we show that this problem remains
fixed-parameter tractable for an arbitrarily large set of rooted binary
phylogenetic trees. In particular, we present a quadratic kernel.
| [
{
"created": "Mon, 19 Mar 2012 09:50:43 GMT",
"version": "v1"
}
] | 2012-03-20 | [
[
"van Iersel",
"Leo",
""
],
[
"Linz",
"Simone",
""
]
] | It has recently been shown that the NP-hard problem of calculating the minimum number of hybridization events that is needed to explain a set of rooted binary phylogenetic trees by means of a hybridization network is fixed-parameter tractable if an instance of the problem consists of precisely two such trees. In this paper, we show that this problem remains fixed-parameter tractable for an arbitrarily large set of rooted binary phylogenetic trees. In particular, we present a quadratic kernel. |
2304.04903 | Akihito Maruya | Akihito Maruya and Qasim Zaidi | Object Rigidity: Competition and cooperation between motion-energy and
feature-tracking mechanisms and shape-based priors | 36 pages, 11 figures (10 main figures and 1 appendix figure) | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Why do moving objects appear rigid when projected retinal images are deformed
nonrigidly? We used rotating rigid objects that can appear rigid or non-rigid
to test whether shape features contribute to rigidity perception. When two
circular rings were rigidly linked at an angle and jointly rotated at moderate
speeds, observers reported that the rings wobbled and were not linked rigidly
but rigid rotation was reported at slow speeds. When gaps, paint or vertices
were added, the rings appeared rigidly rotating even at moderate speeds. At
high speeds, all configurations appeared non-rigid. Salient features thus
contribute to rigidity at slow and moderate speeds, but not at high speeds.
Simulated responses of arrays of motion-energy cells showed that motion flow
vectors are predominantly orthogonal to the contours of the rings, not parallel
to the rotation direction. A convolutional neural network trained to
distinguish flow patterns for wobbling versus rotation, gave a high probability
of wobbling for the motion-energy flows. However, the CNN gave high
probabilities of rotation for motion flows generated by tracking features with
arrays of MT pattern-motion cells and corner detectors. In addition, circular
rings can appear to spin and roll despite the absence of any sensory evidence,
and this illusion is prevented by vertices, gaps, and painted segments, showing
the effects of rotational symmetry and shape. Combining CNN outputs that give
greater weight to motion energy at fast speeds and to feature tracking at slow,
with the shape-based priors for wobbling and rolling, explained rigid and
nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate
how cooperation and competition between different neuronal classes lead to
specific states of visual perception and to transitions between the states.
| [
{
"created": "Tue, 11 Apr 2023 00:25:19 GMT",
"version": "v1"
}
] | 2023-04-12 | [
[
"Maruya",
"Akihito",
""
],
[
"Zaidi",
"Qasim",
""
]
] | Why do moving objects appear rigid when projected retinal images are deformed nonrigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly but rigid rotation was reported at slow speeds. When gaps, paint or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds, but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation, gave a high probability of wobbling for the motion-energy flows. However, the CNN gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining CNN outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow, with the shape-based priors for wobbling and rolling, explained rigid and nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate how cooperation and competition between different neuronal classes lead to specific states of visual perception and to transitions between the states. |
2103.02175 | Naoya Fujiwara | Naoya Fujiwara, Tomokatsu Onaga, Takayuki Wada, Shouhei Takeuchi,
Junji Seto, Tomoki Nakaya and Kazuyuki Aihara | Analytical estimation of maximum fraction of infected individuals with
one-shot non-pharmaceutical intervention in a hybrid epidemic model | null | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Facing a global epidemic of new infectious diseases such as COVID-19,
non-pharmaceutical interventions (NPIs), which reduce transmission rates
without medical actions, are being implemented around the world to mitigate
spreads. One of the problems in assessing the effects of NPIs is that different
NPIs have been implemented at different times based on the situation of each
country; therefore, few assumptions can be shared about how the introduction of
policies affects the patient population. Mathematical models can contribute to
further understanding these phenomena by obtaining analytical solutions as well
as numerical simulations. In this study, an NPI was introduced into the SIR
model for a conceptual study of infectious diseases under the condition that
the transmission rate was reduced to a fixed value only once within a finite
time duration, and its effect was analyzed numerically and theoretically. It
was analytically shown that the maximum fraction of infected individuals and
the final size could be larger if the intervention starts too early. The
analytical results also suggested that more individuals may be infected at the
peak of the second wave with a stronger intervention. This study provides
quantitative relationship between the strength of a one-shot intervention and
the reduction in the number of patients with no approximation. This suggests
the importance of the strength and time of NPIs, although detailed studies are
necessary for the implementation of NPIs in complicated real-world environments
as the model used in this study is based on various simplifications.
| [
{
"created": "Wed, 3 Mar 2021 04:49:36 GMT",
"version": "v1"
}
] | 2021-03-04 | [
[
"Fujiwara",
"Naoya",
""
],
[
"Onaga",
"Tomokatsu",
""
],
[
"Wada",
"Takayuki",
""
],
[
"Takeuchi",
"Shouhei",
""
],
[
"Seto",
"Junji",
""
],
[
"Nakaya",
"Tomoki",
""
],
[
"Aihara",
"Kazuyuki",
""
]
] | Facing a global epidemic of new infectious diseases such as COVID-19, non-pharmaceutical interventions (NPIs), which reduce transmission rates without medical actions, are being implemented around the world to mitigate spreads. One of the problems in assessing the effects of NPIs is that different NPIs have been implemented at different times based on the situation of each country; therefore, few assumptions can be shared about how the introduction of policies affects the patient population. Mathematical models can contribute to further understanding these phenomena by obtaining analytical solutions as well as numerical simulations. In this study, an NPI was introduced into the SIR model for a conceptual study of infectious diseases under the condition that the transmission rate was reduced to a fixed value only once within a finite time duration, and its effect was analyzed numerically and theoretically. It was analytically shown that the maximum fraction of infected individuals and the final size could be larger if the intervention starts too early. The analytical results also suggested that more individuals may be infected at the peak of the second wave with a stronger intervention. This study provides quantitative relationship between the strength of a one-shot intervention and the reduction in the number of patients with no approximation. This suggests the importance of the strength and time of NPIs, although detailed studies are necessary for the implementation of NPIs in complicated real-world environments as the model used in this study is based on various simplifications. |
2311.04238 | Maxwell Wang | Maxwell H. Wang and Jukka-Pekka Onnela | Flexible Bayesian Inference on Partially Observed Epidemics | 27 pages, 7 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Individual-based models of contagious processes are useful for predicting
epidemic trajectories and informing intervention strategies. In such models,
the incorporation of contact network information can capture the non-randomness
and heterogeneity of realistic contact dynamics. In this paper, we consider
Bayesian inference on the spreading parameters of an SIR contagion on a known,
static network, where information regarding individual disease status is known
only from a series of tests (positive or negative disease status). When the
contagion model is complex or information such as infection and removal times
is missing, the posterior distribution can be difficult to sample from.
Previous work has considered the use of Approximate Bayesian Computation (ABC),
which allows for simulation-based Bayesian inference on complex models.
However, ABC methods usually require the user to select reasonable summary
statistics. Here, we consider an inference scheme based on the Mixture Density
Network compressed ABC (MDN-ABC), which minimizes the expected posterior
entropy in order to learn informative summary statistics. This allows us to
conduct Bayesian inference on the parameters of a partially observed contagious
process while also circumventing the need for manual summary statistic
selection. This methodology can be extended to incorporate additional
simulation complexities, including behavioral change after positive tests or
false test results.
| [
{
"created": "Mon, 6 Nov 2023 17:57:32 GMT",
"version": "v1"
}
] | 2023-11-09 | [
[
"Wang",
"Maxwell H.",
""
],
[
"Onnela",
"Jukka-Pekka",
""
]
] | Individual-based models of contagious processes are useful for predicting epidemic trajectories and informing intervention strategies. In such models, the incorporation of contact network information can capture the non-randomness and heterogeneity of realistic contact dynamics. In this paper, we consider Bayesian inference on the spreading parameters of an SIR contagion on a known, static network, where information regarding individual disease status is known only from a series of tests (positive or negative disease status). When the contagion model is complex or information such as infection and removal times is missing, the posterior distribution can be difficult to sample from. Previous work has considered the use of Approximate Bayesian Computation (ABC), which allows for simulation-based Bayesian inference on complex models. However, ABC methods usually require the user to select reasonable summary statistics. Here, we consider an inference scheme based on the Mixture Density Network compressed ABC (MDN-ABC), which minimizes the expected posterior entropy in order to learn informative summary statistics. This allows us to conduct Bayesian inference on the parameters of a partially observed contagious process while also circumventing the need for manual summary statistic selection. This methodology can be extended to incorporate additional simulation complexities, including behavioral change after positive tests or false test results. |
1903.07920 | Manuela Geiss | Manuela Gei{\ss} and Peter F. Stadler and Marc Hellmuth | Reciprocal Best Match Graphs | null | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reciprocal best matches play an important role in numerous applications in
computational biology, in particular as the basis of many widely used tools for
orthology assessment. Nevertheless, very little is known about their
mathematical structure. Here, we investigate the structure of reciprocal best
match graphs (RBMGs). In order to abstract from the details of measuring
distances, we define reciprocal best matches here as pairwise most closely
related leaves in a gene tree, arguing that conceptually this is the notion
that is pragmatically approximated by distance- or similarity-based heuristics.
We start by showing that a graph $G$ is an RBMG if and only if its quotient
graph w.r.t.\ a certain thinness relation is an RBMG. Furthermore, it is
necessary and sufficient that all connected components of $G$ are RBMGs. The
main result of this contribution is a complete characterization of RBMGs with 3
colors/species that can be checked in polynomial time. For 3 colors, there are
three distinct classes of trees that are related to the structure of the
phylogenetic trees explaining them. We derive an approach to recognize RBMGs
with an arbitrary number of colors; it remains open however, whether a
polynomial-time for RBMG recognition exists. In addition, we show that RBMGs
that at the same time are cographs (co-RBMGs) can be recognized in polynomial
time. Co-RBMGs are characterized in terms of hierarchically colored cographs, a
particular class of vertex colored cographs that is introduced here. The (least
resolved) trees that explain co-RBMGs can be constructed in polynomial time.
| [
{
"created": "Tue, 19 Mar 2019 10:20:42 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Apr 2019 19:02:18 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Apr 2019 16:54:19 GMT",
"version": "v3"
},
{
"created": "Thu, 20 Jun 2019 14:53:51 GMT",
"version": "v4"
},
{
"created": "Thu, 29 Aug 2019 11:10:28 GMT",
"version": "v5"
}
] | 2019-08-30 | [
[
"Geiß",
"Manuela",
""
],
[
"Stadler",
"Peter F.",
""
],
[
"Hellmuth",
"Marc",
""
]
] | Reciprocal best matches play an important role in numerous applications in computational biology, in particular as the basis of many widely used tools for orthology assessment. Nevertheless, very little is known about their mathematical structure. Here, we investigate the structure of reciprocal best match graphs (RBMGs). In order to abstract from the details of measuring distances, we define reciprocal best matches here as pairwise most closely related leaves in a gene tree, arguing that conceptually this is the notion that is pragmatically approximated by distance- or similarity-based heuristics. We start by showing that a graph $G$ is an RBMG if and only if its quotient graph w.r.t.\ a certain thinness relation is an RBMG. Furthermore, it is necessary and sufficient that all connected components of $G$ are RBMGs. The main result of this contribution is a complete characterization of RBMGs with 3 colors/species that can be checked in polynomial time. For 3 colors, there are three distinct classes of trees that are related to the structure of the phylogenetic trees explaining them. We derive an approach to recognize RBMGs with an arbitrary number of colors; it remains open however, whether a polynomial-time for RBMG recognition exists. In addition, we show that RBMGs that at the same time are cographs (co-RBMGs) can be recognized in polynomial time. Co-RBMGs are characterized in terms of hierarchically colored cographs, a particular class of vertex colored cographs that is introduced here. The (least resolved) trees that explain co-RBMGs can be constructed in polynomial time. |
1902.08087 | Francoise Lazarini | S\'ebastien Wagner, Pierre-Marie Lledo, Fran\c{c}oise Lazarini | Assessing Olfaction Using Ultrasonic Vocalization Recordings in Mouse
Pups with a Sono-olfactometer | null | Bio-protocol , Bio-protocol LCC, 2019, 9 (4) | 10.21769/BioProtoc.3170 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Olfaction is the first sensory modality to develop during fetal life in
mammals, and plays a key role in the various behaviors of neonates such as
feeding and social interaction. Odorant cues (i.e., mother or predator scents)
can trigger potentiation or inhibition of ultrasonic vocalizations (USV)
emitted by pups following their isolation. Here, we report how USV are
inhibited by olfactory cues using a sono-olfactometer that has been designed to
quantify precisely olfaction in pups congenitally infected by cytomegalovirus.
This olfactory-driven behavioral test assesses the USV emitted in presence of
unfamiliar odorants such as citral scent or adult male mouse scent. We measure
the number of USV emitted as an index of odorant detection during the three
periods of the 5-min isolation time of the pup into the sono-olfactometer:
first period without any odorant, second period with odorant exposure and last
period with exhaust odorant. This protocol can be easily used to reveal
olfactory deficits in pups with altered olfactory system due to toxic lesions
or infectious diseases.
| [
{
"created": "Thu, 21 Feb 2019 15:04:31 GMT",
"version": "v1"
}
] | 2019-02-22 | [
[
"Wagner",
"Sébastien",
""
],
[
"Lledo",
"Pierre-Marie",
""
],
[
"Lazarini",
"Françoise",
""
]
] | Olfaction is the first sensory modality to develop during fetal life in mammals, and plays a key role in the various behaviors of neonates such as feeding and social interaction. Odorant cues (i.e., mother or predator scents) can trigger potentiation or inhibition of ultrasonic vocalizations (USV) emitted by pups following their isolation. Here, we report how USV are inhibited by olfactory cues using a sono-olfactometer that has been designed to quantify precisely olfaction in pups congenitally infected by cytomegalovirus. This olfactory-driven behavioral test assesses the USV emitted in presence of unfamiliar odorants such as citral scent or adult male mouse scent. We measure the number of USV emitted as an index of odorant detection during the three periods of the 5-min isolation time of the pup into the sono-olfactometer: first period without any odorant, second period with odorant exposure and last period with exhaust odorant. This protocol can be easily used to reveal olfactory deficits in pups with altered olfactory system due to toxic lesions or infectious diseases. |
2111.04103 | R.K. Brojen Singh | Utkarsh Upadhyay, Chandrima Barua, Shivani Devi, Jay Prakash Kumar and
R.K. Brojen Singh | Dynamics of unfolded protein aggregation | 9 pages, 1 figure | null | null | null | q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | Unfolded protein aggregation in cellular system is a problem causing various
types of diseases depending on which type unfolded proteins aggregate. This
phenomenon of aggregation may take place during production, storage, shipment
or delivery in the cellular medium. In the present work, we studied a
simplified and extended version of unfolded protein aggregation model by Lumry
and Eyring using stochastic approach. We solved analytically the Master
equation of the model for the probability distribution $P(x,t)$ of the unfolded
protein population and the solution was found to be time dependent complex
binomial distribution. In the large population limit $P(x,t)\sim
\Lambda(x,t)\times Pois(x,t)$. Further, the distribution became Normal
distribution at large population and mean of the distribution limit:
$P(x,t)\sim\Lambda(x,t)\times N(\langle qx\rangle,\langle qx\rangle)$. The
fluctuations inherent in the dynamics measured by Fano factor can have
sub-Poisson, Poisson and super-Poisson at different situations.
| [
{
"created": "Sun, 7 Nov 2021 15:03:57 GMT",
"version": "v1"
}
] | 2021-11-09 | [
[
"Upadhyay",
"Utkarsh",
""
],
[
"Barua",
"Chandrima",
""
],
[
"Devi",
"Shivani",
""
],
[
"Kumar",
"Jay Prakash",
""
],
[
"Singh",
"R. K. Brojen",
""
]
] | Unfolded protein aggregation in cellular system is a problem causing various types of diseases depending on which type unfolded proteins aggregate. This phenomenon of aggregation may take place during production, storage, shipment or delivery in the cellular medium. In the present work, we studied a simplified and extended version of unfolded protein aggregation model by Lumry and Eyring using stochastic approach. We solved analytically the Master equation of the model for the probability distribution $P(x,t)$ of the unfolded protein population and the solution was found to be time dependent complex binomial distribution. In the large population limit $P(x,t)\sim \Lambda(x,t)\times Pois(x,t)$. Further, the distribution became Normal distribution at large population and mean of the distribution limit: $P(x,t)\sim\Lambda(x,t)\times N(\langle qx\rangle,\langle qx\rangle)$. The fluctuations inherent in the dynamics measured by Fano factor can have sub-Poisson, Poisson and super-Poisson at different situations. |
2212.07466 | Gustavo Machado | Jason A. Galvis, Gustavo Machado | The role of vehicle movement in swine disease dissemination: novel
method accounting for pathogen stability and vehicle cleaning effectiveness
uncertainties | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The transmission dynamics of infectious diseases in animal production are
driven by several propagation routes. Contaminated vehicles traveling between
farms have been associated with indirect disease transmission. In this study,
we used transportation vehicle data to analyze the magnitude of farm visits by
different vehicles and to propose a methodology to reconstruct vehicle contact
networks considering pathogen stability and cleaning and disinfection
effectiveness. Here, we collected information from 6,363 farms and Global
Positioning System (GPS) records from 567 vehicles used to transport feed,
animals, and people. We reconstructed vehicle contacts among the farms,
conserving pathogen stability decay and different probabilities of cleaning and
disinfection. Results showed that vehicle movement networks were densely
connected, with up to 86% of farms connected by these movements. Movements of
vehicle transporting feed and pig among farms showed the highest network
connectivity. The cleaning effectiveness of was variable among the different
vehicle types and highly influenced by the frequency of vehicles stopping at
clean stations. A large number of between-farm contacts with a pathogen
stability >0.8 were present in the vehicle network even with 100% cleaning
effectiveness. Finally, we identified that vehicles contacted farms from
different companies. Thus, our results suggest the vehicle network is a
potential mechanism for spreading pathogens among farms. Moreover, even with
scenarios with high effectiveness of cleaning and disinfection, the risk of
vehicles spreading diseases was not completely eliminated.
| [
{
"created": "Wed, 14 Dec 2022 19:21:06 GMT",
"version": "v1"
},
{
"created": "Sat, 4 Mar 2023 19:45:39 GMT",
"version": "v2"
}
] | 2023-03-07 | [
[
"Galvis",
"Jason A.",
""
],
[
"Machado",
"Gustavo",
""
]
] | The transmission dynamics of infectious diseases in animal production are driven by several propagation routes. Contaminated vehicles traveling between farms have been associated with indirect disease transmission. In this study, we used transportation vehicle data to analyze the magnitude of farm visits by different vehicles and to propose a methodology to reconstruct vehicle contact networks considering pathogen stability and cleaning and disinfection effectiveness. Here, we collected information from 6,363 farms and Global Positioning System (GPS) records from 567 vehicles used to transport feed, animals, and people. We reconstructed vehicle contacts among the farms, conserving pathogen stability decay and different probabilities of cleaning and disinfection. Results showed that vehicle movement networks were densely connected, with up to 86% of farms connected by these movements. Movements of vehicle transporting feed and pig among farms showed the highest network connectivity. The cleaning effectiveness of was variable among the different vehicle types and highly influenced by the frequency of vehicles stopping at clean stations. A large number of between-farm contacts with a pathogen stability >0.8 were present in the vehicle network even with 100% cleaning effectiveness. Finally, we identified that vehicles contacted farms from different companies. Thus, our results suggest the vehicle network is a potential mechanism for spreading pathogens among farms. Moreover, even with scenarios with high effectiveness of cleaning and disinfection, the risk of vehicles spreading diseases was not completely eliminated. |
2203.13126 | Paul Bressloff | Ryan D. Schumm and Paul C. Bressloff | Local accumulation times in a diffusion-trapping model of synaptic
receptor dynamics | 20 pages, 9 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The lateral diffusion and trapping of neurotransmitter receptors within the
postsynaptic membrane of a neuron plays a key role in determining synaptic
strength and plasticity. Trapping is mediated by the reversible binding of
receptors to scaffolding proteins (slots) within a synapse. In this paper we
introduce a new method for analyzing the transient dynamics of synapses in a
diffusion-trapping model of receptor trafficking. Given a population of
spatially distributed synapses, each of which has a fixed number of slots, we
calculate the rate of relaxation to the steady-state distribution of bound
slots (synaptic weights) in terms of a set of local accumulation times.
Assuming that the rates of exocytosis and endocytosis are sufficiently slow, we
show that the steady-state synaptic weights are independent of each other
(purely local). On the other hand, the local accumulation time of a given
synapse depends on the number of slots and the spatial location of all the
synapses, indicating a form of transient heterosynaptic plasticity. This
suggests that local accumulation time measurements could provide useful
information regarding the distribution of synaptic weights within a dendrite.
| [
{
"created": "Thu, 24 Mar 2022 15:39:24 GMT",
"version": "v1"
}
] | 2022-03-25 | [
[
"Schumm",
"Ryan D.",
""
],
[
"Bressloff",
"Paul C.",
""
]
] | The lateral diffusion and trapping of neurotransmitter receptors within the postsynaptic membrane of a neuron plays a key role in determining synaptic strength and plasticity. Trapping is mediated by the reversible binding of receptors to scaffolding proteins (slots) within a synapse. In this paper we introduce a new method for analyzing the transient dynamics of synapses in a diffusion-trapping model of receptor trafficking. Given a population of spatially distributed synapses, each of which has a fixed number of slots, we calculate the rate of relaxation to the steady-state distribution of bound slots (synaptic weights) in terms of a set of local accumulation times. Assuming that the rates of exocytosis and endocytosis are sufficiently slow, we show that the steady-state synaptic weights are independent of each other (purely local). On the other hand, the local accumulation time of a given synapse depends on the number of slots and the spatial location of all the synapses, indicating a form of transient heterosynaptic plasticity. This suggests that local accumulation time measurements could provide useful information regarding the distribution of synaptic weights within a dendrite. |
1706.00085 | Kayhan Ozcimder | Kayhan Ozcimder, Biswadip Dey, Sebastian Musslick, Giovanni Petri,
Nesreen K. Ahmed, Theodore L. Willke, Jonathan D. Cohen | A Formal Approach to Modeling the Cost of Cognitive Control | 6 pages, 3 figures, Conference paper | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a formal method to model the level of demand on control
when executing cognitive processes. The cost of cognitive control is parsed
into an intensity cost which encapsulates how much additional input information
is required so as to get the specified response, and an interaction cost which
encapsulates the level of interference between individual processes in a
network. We develop a formal relationship between the probability of successful
execution of desired processes and the control signals (additive control
biases). This relationship is also used to specify optimal control policies to
achieve a desired probability of activation for processes. We observe that
there are boundary cases when finding such control policies which leads us to
introduce the interaction cost. We show that the interaction cost is influenced
by the relative strengths of individual processes, as well as the
directionality of the underlying competition between processes.
| [
{
"created": "Wed, 31 May 2017 20:58:20 GMT",
"version": "v1"
}
] | 2017-06-02 | [
[
"Ozcimder",
"Kayhan",
""
],
[
"Dey",
"Biswadip",
""
],
[
"Musslick",
"Sebastian",
""
],
[
"Petri",
"Giovanni",
""
],
[
"Ahmed",
"Nesreen K.",
""
],
[
"Willke",
"Theodore L.",
""
],
[
"Cohen",
"Jonathan D.",
""
]
] | This paper introduces a formal method to model the level of demand on control when executing cognitive processes. The cost of cognitive control is parsed into an intensity cost which encapsulates how much additional input information is required so as to get the specified response, and an interaction cost which encapsulates the level of interference between individual processes in a network. We develop a formal relationship between the probability of successful execution of desired processes and the control signals (additive control biases). This relationship is also used to specify optimal control policies to achieve a desired probability of activation for processes. We observe that there are boundary cases when finding such control policies which leads us to introduce the interaction cost. We show that the interaction cost is influenced by the relative strengths of individual processes, as well as the directionality of the underlying competition between processes. |
1610.00216 | Cameron Mura | James D. Tang, Charles E. McAnany, Cameron Mura, Kyle J. Lampe | Toward a Designable Extracellular Matrix: Molecular Dynamics Simulations
of an Engineered Laminin-mimetic, Elastin-like Fusion Protein | 53 pages, 7 figures in the main text; Supporting Information contains
1 table, 12 figures, 4 trajectory animations (videos) | Biomacromolecules, 2016 | 10.1021/acs.biomac.6b00951 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Native extracellular matrices (ECMs), such as those of the human brain and
other neural tissues, exhibit networks of molecular interactions between
specific matrix proteins and other tissue components. Guided by these naturally
self-assembling supramolecular systems, we have designed a matrix-derived
protein chimera that contains a laminin globular-like (LG) domain fused to an
elastin-like polypeptide (ELP). All-atom, classical molecular dynamics
simulations of our designed laminin-elastin fusion protein reveal
temperature-dependent conformational changes, in terms of secondary structure
composition, solvent accessible surface area, hydrogen bonding, and surface
hydration. These properties illuminate the phase behavior of this fusion
protein, via the emergence of $\beta$-sheet character in
physiologically-relevant temperature ranges.
| [
{
"created": "Sun, 2 Oct 2016 02:28:15 GMT",
"version": "v1"
}
] | 2018-10-01 | [
[
"Tang",
"James D.",
""
],
[
"McAnany",
"Charles E.",
""
],
[
"Mura",
"Cameron",
""
],
[
"Lampe",
"Kyle J.",
""
]
] | Native extracellular matrices (ECMs), such as those of the human brain and other neural tissues, exhibit networks of molecular interactions between specific matrix proteins and other tissue components. Guided by these naturally self-assembling supramolecular systems, we have designed a matrix-derived protein chimera that contains a laminin globular-like (LG) domain fused to an elastin-like polypeptide (ELP). All-atom, classical molecular dynamics simulations of our designed laminin-elastin fusion protein reveal temperature-dependent conformational changes, in terms of secondary structure composition, solvent accessible surface area, hydrogen bonding, and surface hydration. These properties illuminate the phase behavior of this fusion protein, via the emergence of $\beta$-sheet character in physiologically-relevant temperature ranges. |
2001.04794 | Francesco Bardozzo | Francesco Bardozzo, Pietro Lio', Roberto Tagliaferri | A machine learning approach to investigate regulatory control circuits
in bacterial metabolic pathways | 5 pages, 3 figures | null | 10.1093/bioinformatics/btaa966 | null | q-bio.MN cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, a machine learning approach for identifying the multi-omics
metabolic regulatory control circuits inside the pathways is described.
Therefore, the identification of bacterial metabolic pathways that are more
regulated than others in term of their multi-omics follows from the analysis of
these circuits . This is a consequence of the alternation of the omic values of
codon usage and protein abundance along with the circuits. In this work, the
E.Coli's Glycolysis and its multi-omic circuit features are shown as an
example.
| [
{
"created": "Mon, 13 Jan 2020 11:04:26 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Bardozzo",
"Francesco",
""
],
[
"Lio'",
"Pietro",
""
],
[
"Tagliaferri",
"Roberto",
""
]
] | In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . This is a consequence of the alternation of the omic values of codon usage and protein abundance along with the circuits. In this work, the E.Coli's Glycolysis and its multi-omic circuit features are shown as an example. |
2408.06367 | Sezer Sorgun | Sezer Sorgun and Kahraman B\.irg\.in | Vertex-Edge Weighted Molecular Graphs: A study on topological indices
and their relevance to physicochemical properties of drugs in use cancer
treatment | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantitative Structure-Property Relationship (QSPR) analysis plays a crucial
role in predicting physicochemical properties and biological activities of
pharmaceutical compounds, aiding in drug design and optimization. This study
focuses on leveraging QSPR within the framework of vertex and edge weighted
(VEW) molecular graphs, exploring their significance in drug research. By
examining 48 drugs in used in the treatment of various cancers and their
physicochemical properties, previous studies serve as a foundation for our
research. Introducing a novel methodology for computing vertex and edge
weights, exemplified by the drug Busulfan, we highlight the importance of
considering atomic properties and inter-bond dynamics. Statistical analysis,
employing linear regression models, reveals enhanced correlations between
topological indices and physicochemical properties of drugs. Comparison with
previous studies on unweighted molecular graphs highlights the enhancements
achieved with our approach.
| [
{
"created": "Sun, 28 Jul 2024 10:14:36 GMT",
"version": "v1"
}
] | 2024-08-14 | [
[
"Sorgun",
"Sezer",
""
],
[
"Birgin",
"Kahraman",
""
]
] | Quantitative Structure-Property Relationship (QSPR) analysis plays a crucial role in predicting physicochemical properties and biological activities of pharmaceutical compounds, aiding in drug design and optimization. This study focuses on leveraging QSPR within the framework of vertex and edge weighted (VEW) molecular graphs, exploring their significance in drug research. By examining 48 drugs in used in the treatment of various cancers and their physicochemical properties, previous studies serve as a foundation for our research. Introducing a novel methodology for computing vertex and edge weights, exemplified by the drug Busulfan, we highlight the importance of considering atomic properties and inter-bond dynamics. Statistical analysis, employing linear regression models, reveals enhanced correlations between topological indices and physicochemical properties of drugs. Comparison with previous studies on unweighted molecular graphs highlights the enhancements achieved with our approach. |
0709.2027 | Cheong Xin Chan | Cheong Xin Chan, Robert G. Beiko and Mark A. Ragan | Units of genetic transfer in prokaryotes | 22 pages, 5 figures, 1 table | null | null | null | q-bio.GN q-bio.PE | null | The transfer of genetic materials across species (lateral genetic transfer,
LGT) contributes to genomic and physiological innovation in prokaryotes. The
extent of LGT in prokaryotes has been examined in a number of studies, but the
unit of transfer has not been studied in a rigorous manner. Using a rigorous
phylogenetic approach, we analysed the units of LGT within families of
single-copy genes obtained from 144 fully sequenced prokaryote genomes. A total
of 30.3% of these gene families show evidence of LGT. We found that the
transfer of gene fragments has been more frequent than the transfer of entire
genes, suggesting the extent of LGT has been underestimated. We found little
functional bias between within-gene (fragmentary) and whole-gene
(non-fragmentary) genetic transfer, but non-fragmentary transfer has been more
frequent into pathogens than into non-pathogens. As gene families that contain
probable paralogs were excluded from the current study, our results may still
underestimate the extent of LGT; nonetheless this is the most-comprehensive
study to date of the unit of LGT among prokaryote genomes.
| [
{
"created": "Thu, 13 Sep 2007 15:58:32 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Jan 2008 09:35:17 GMT",
"version": "v2"
}
] | 2008-01-29 | [
[
"Chan",
"Cheong Xin",
""
],
[
"Beiko",
"Robert G.",
""
],
[
"Ragan",
"Mark A.",
""
]
] | The transfer of genetic materials across species (lateral genetic transfer, LGT) contributes to genomic and physiological innovation in prokaryotes. The extent of LGT in prokaryotes has been examined in a number of studies, but the unit of transfer has not been studied in a rigorous manner. Using a rigorous phylogenetic approach, we analysed the units of LGT within families of single-copy genes obtained from 144 fully sequenced prokaryote genomes. A total of 30.3% of these gene families show evidence of LGT. We found that the transfer of gene fragments has been more frequent than the transfer of entire genes, suggesting the extent of LGT has been underestimated. We found little functional bias between within-gene (fragmentary) and whole-gene (non-fragmentary) genetic transfer, but non-fragmentary transfer has been more frequent into pathogens than into non-pathogens. As gene families that contain probable paralogs were excluded from the current study, our results may still underestimate the extent of LGT; nonetheless this is the most-comprehensive study to date of the unit of LGT among prokaryote genomes. |
1312.6633 | Gerardo Gonz\'alez-Aguilar | Filipe Monteiro-Silva, Gerardo Gonz\'alez-Aguilar | Evolution through time of Pyrrolizidine Alkaloids detection and
quantification | 24 pages, 8 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/3.0/ | Pyrrolizidine Alkaloids (PAs) are a group of naturally occurring alkaloids
that are produced by plants as a defense mechanism against insect herbivores.
The analytical methodologies employed for their detection have come a long way
since the first analytical experiment and in the last 30 years had an enormous
development, both technological and experimental. It is notorious that before
the generalization of certain technologies, especially in a post-war
atmosphere, most scientific researches relied on what it is today thin-layer
chromatography. Nevertheless this technique was not sufficient for accurately
measure quantities and unambiguously identify compounds, therefore
spectroscopic techniques arose as well as chromatographic techniques. While the
first never really coped with PAs analysis requirements the latter, either as
gas or liquid chromatography allowed the analysis of complex sample matrices.
Simultaneously, nuclear magnetic resonance also suffered significant
developments while mass spectrometry has become an attractive technique due to
increasingly higher maximum resolutions. The observed tendency in recent years,
in pyrrolizidine detection and quantification, as well as in many other areas,
is that hyphenated techniques are the chosen methods. A large number of papers
report multihyphenated methodologies, and the overwhelming majority relies on
gas or liquid chromatography.
| [
{
"created": "Mon, 23 Dec 2013 18:25:49 GMT",
"version": "v1"
}
] | 2013-12-24 | [
[
"Monteiro-Silva",
"Filipe",
""
],
[
"González-Aguilar",
"Gerardo",
""
]
] | Pyrrolizidine Alkaloids (PAs) are a group of naturally occurring alkaloids that are produced by plants as a defense mechanism against insect herbivores. The analytical methodologies employed for their detection have come a long way since the first analytical experiment and in the last 30 years had an enormous development, both technological and experimental. It is notorious that before the generalization of certain technologies, especially in a post-war atmosphere, most scientific researches relied on what it is today thin-layer chromatography. Nevertheless this technique was not sufficient for accurately measure quantities and unambiguously identify compounds, therefore spectroscopic techniques arose as well as chromatographic techniques. While the first never really coped with PAs analysis requirements the latter, either as gas or liquid chromatography allowed the analysis of complex sample matrices. Simultaneously, nuclear magnetic resonance also suffered significant developments while mass spectrometry has become an attractive technique due to increasingly higher maximum resolutions. The observed tendency in recent years, in pyrrolizidine detection and quantification, as well as in many other areas, is that hyphenated techniques are the chosen methods. A large number of papers report multihyphenated methodologies, and the overwhelming majority relies on gas or liquid chromatography. |
2004.12055 | Estari Mamidala Dr | Estari Mamidala, Rakesh Davella, Swapna Gurrapu and Pujala
Shivakrishna | In silico identification of clinically approved medicines against the
main protease of SARS-CoV-2, causative agent of covid-19 | 17 pages, 5 Figures, 5 Tables | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The COVID-19 pandemic triggered by SARS-CoV-2 is a worldwide health disaster.
Main protease is an attractive drug target among coronaviruses, due to its
vital role in processing the polyproteins that are translated from the viral
RNA. There is presently no exact drug or treatment for this diseases caused by
SARS-CoV-2. In the present study, we report the potential inhibitory activity
of some FDA approved drugs against SARS-CoV-2 main protease by molecular
docking study to investigate their binding affinity in protease active site.
Docking studies revealed that drug Oseltamivir (anti-H1N1 drug), Rifampin
(anti-TB drug), Maraviroc, Etravirine, Indinavir, Rilpivirine (anti-HIV drugs)
and Atovaquone, Quinidine, Halofantrine, Amodiaquine, Tetracylcine,
Azithromycin, hydroxycholoroquine (anti-malarial drugs) among others binds in
the active site of the protease with similar or higher affinity. However, the
in-silico abilities of the drug molecules tested in this study, further needs
to be validated by carrying out in vitro and in vivo studies. Moreover, this
study spreads the potential use of current drugs to be considered and used to
comprise the fast expanding SARS-CoV-2 infection.
| [
{
"created": "Sat, 25 Apr 2020 04:48:37 GMT",
"version": "v1"
}
] | 2020-04-29 | [
[
"Mamidala",
"Estari",
""
],
[
"Davella",
"Rakesh",
""
],
[
"Gurrapu",
"Swapna",
""
],
[
"Shivakrishna",
"Pujala",
""
]
] | The COVID-19 pandemic triggered by SARS-CoV-2 is a worldwide health disaster. Main protease is an attractive drug target among coronaviruses, due to its vital role in processing the polyproteins that are translated from the viral RNA. There is presently no exact drug or treatment for this diseases caused by SARS-CoV-2. In the present study, we report the potential inhibitory activity of some FDA approved drugs against SARS-CoV-2 main protease by molecular docking study to investigate their binding affinity in protease active site. Docking studies revealed that drug Oseltamivir (anti-H1N1 drug), Rifampin (anti-TB drug), Maraviroc, Etravirine, Indinavir, Rilpivirine (anti-HIV drugs) and Atovaquone, Quinidine, Halofantrine, Amodiaquine, Tetracylcine, Azithromycin, hydroxycholoroquine (anti-malarial drugs) among others binds in the active site of the protease with similar or higher affinity. However, the in-silico abilities of the drug molecules tested in this study, further needs to be validated by carrying out in vitro and in vivo studies. Moreover, this study spreads the potential use of current drugs to be considered and used to comprise the fast expanding SARS-CoV-2 infection. |
q-bio/0703020 | Paulo Murilo Castro de Oliveira | Paulo Murilo Castro de Oliveira | Chromosome Length Scaling in Haploid, Asexual Reproduction | null | J. Phys. CM 19, 065147 (2007) | null | null | q-bio.PE cond-mat.stat-mech | null | We study the genetic behaviour of a population formed by haploid individuals
which reproduce asexually. The genetic information for each individual is
stored along a bit-string (or chromosome) with L bits, where 0-bits represent
the wild-type allele and 1-bits correspond to harmful mutations. Each newborn
inherits this chromosome from its parent with some few random mutations: on
average a fixed number m of bits are flipped. Selection is implemented
according to the number N of 1-bits counted along the individual's chromosome:
the smaller N the higher the probability an individual has to survive a new
time step. Such a population evolves, with births and deaths, and its genetic
distribution becomes stabilised after many enough generations have passed.
The question we pose concerns the procedure of increasing L. The aim is to
get the same distribution of relative genetic loads N/L among the equilibrated
population, in spite of a larger L. Should we keep the same mutation rate m/L
for different values of L? The answer is yes, which intuitively seems to be
plausible. However, this conclusion is not trivial, according to our
simulational results: the question involves also the population size.
| [
{
"created": "Thu, 8 Mar 2007 20:24:59 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"de Oliveira",
"Paulo Murilo Castro",
""
]
] | We study the genetic behaviour of a population formed by haploid individuals which reproduce asexually. The genetic information for each individual is stored along a bit-string (or chromosome) with L bits, where 0-bits represent the wild-type allele and 1-bits correspond to harmful mutations. Each newborn inherits this chromosome from its parent with some few random mutations: on average a fixed number m of bits are flipped. Selection is implemented according to the number N of 1-bits counted along the individual's chromosome: the smaller N the higher the probability an individual has to survive a new time step. Such a population evolves, with births and deaths, and its genetic distribution becomes stabilised after many enough generations have passed. The question we pose concerns the procedure of increasing L. The aim is to get the same distribution of relative genetic loads N/L among the equilibrated population, in spite of a larger L. Should we keep the same mutation rate m/L for different values of L? The answer is yes, which intuitively seems to be plausible. However, this conclusion is not trivial, according to our simulational results: the question involves also the population size. |
1510.02321 | Mauricio Girardi-Schappo | G. S. Bortolotto, M. Girardi-Schappo, J. J. Gonsalves, L. T. Pinto, M.
H. R. Tragtenberg | Information processing occurs via critical avalanches in a model of the
primary visual cortex | 11 pages, 5 figures, 2 tables, BMSP Conference Proceedings | J. Phys.: Conf. Ser. 686 012008 (2016) | 10.1088/1742-6596/686/1/012008 | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a new biologically motivated model for the Macaque monkey primary
visual cortex which presents power-law avalanches after a visual stimulus. The
signal propagates through all the layers of the model via avalanches that
depend on network structure and synaptic parameter. We identify four different
avalanche profiles as a function of the excitatory postsynaptic potential. The
avalanches follow a size-duration scaling relation and present critical
exponents that match experiments. The structure of the network gives rise to a
regime of two characteristic spatial scales, one of which vanishes in the
thermodynamic limit.
| [
{
"created": "Thu, 8 Oct 2015 14:01:11 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Feb 2016 13:07:54 GMT",
"version": "v2"
},
{
"created": "Fri, 18 Nov 2016 19:06:09 GMT",
"version": "v3"
}
] | 2016-11-21 | [
[
"Bortolotto",
"G. S.",
""
],
[
"Girardi-Schappo",
"M.",
""
],
[
"Gonsalves",
"J. J.",
""
],
[
"Pinto",
"L. T.",
""
],
[
"Tragtenberg",
"M. H. R.",
""
]
] | We study a new biologically motivated model for the Macaque monkey primary visual cortex which presents power-law avalanches after a visual stimulus. The signal propagates through all the layers of the model via avalanches that depend on network structure and synaptic parameter. We identify four different avalanche profiles as a function of the excitatory postsynaptic potential. The avalanches follow a size-duration scaling relation and present critical exponents that match experiments. The structure of the network gives rise to a regime of two characteristic spatial scales, one of which vanishes in the thermodynamic limit. |
2304.05938 | Jan Mohring | Jan Mohring, Michael Burger, Robert Fe{\ss}ler, Jochen Fiedler, Neele
Leith\"auser, Johanna Schneider, Michael Speckert, Jaroslaw Wlazlo | Starker Effekt von Schnelltests (Strong effect of rapid tests) | 40 pages, 21 figures, Report of Fraunhofer ITWM | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-sa/4.0/ | This article is a reproduction of a Fraunhofer ITWM report from 28 June 2021
on the contribution of various non-pharmaceutical measures in breaking the 3rd
Corona wave in Germany. The main finding is that testing contributed more to
the containment of the pandemic in this phase than vaccination or contact
restrictions. The analysis is based on a new epidemiological cohort model that
represents testing, vaccination and contact restrictions by time-varying rates
of detection, vaccination and contacts, respectively.
Only the effectiveness of different vaccines is taken from the literature.
All other parameters are automatically identified in such a way that the
simulated and the published incidences and death rates match. Among these
parameters are incubation time, mean duration of the infectious phase,
mortality rate, as well as two contact rates and one detection rate per week.
Note that we can reconstruct such a high number of parameters only because we
assume that the weekly wave patterns in new infections follow real infection
dynamics, periodically driven by high contact rates on weekdays and lower ones
on weekends. Usually, people assume that the weekly wave patterns are just
reporting artefacts and that weekly mean values are the finest usable data.
One focus of the paper is to quantify the increase in detection rate due to
the introduction of rapid testing in schools. For this purpose, we compare
federal states that differ in the start of school tests and Easter holidays.
There is a clear temporal correlation with the identified detection rates.
Finally, we compare the effect of the individual non-pharmaceutical measures
by replacing one by one the fitted rates of detection, vaccination and contacts
by neutral ones. The increase in the simulated number of actually infected
persons measures the effect of the measure ignored.
| [
{
"created": "Wed, 12 Apr 2023 16:01:03 GMT",
"version": "v1"
}
] | 2023-04-13 | [
[
"Mohring",
"Jan",
""
],
[
"Burger",
"Michael",
""
],
[
"Feßler",
"Robert",
""
],
[
"Fiedler",
"Jochen",
""
],
[
"Leithäuser",
"Neele",
""
],
[
"Schneider",
"Johanna",
""
],
[
"Speckert",
"Michael",
""
],
[
"Wlazlo",
"Jaroslaw",
""
]
] | This article is a reproduction of a Fraunhofer ITWM report from 28 June 2021 on the contribution of various non-pharmaceutical measures in breaking the 3rd Corona wave in Germany. The main finding is that testing contributed more to the containment of the pandemic in this phase than vaccination or contact restrictions. The analysis is based on a new epidemiological cohort model that represents testing, vaccination and contact restrictions by time-varying rates of detection, vaccination and contacts, respectively. Only the effectiveness of different vaccines is taken from the literature. All other parameters are automatically identified in such a way that the simulated and the published incidences and death rates match. Among these parameters are incubation time, mean duration of the infectious phase, mortality rate, as well as two contact rates and one detection rate per week. Note that we can reconstruct such a high number of parameters only because we assume that the weekly wave patterns in new infections follow real infection dynamics, periodically driven by high contact rates on weekdays and lower ones on weekends. Usually, people assume that the weekly wave patterns are just reporting artefacts and that weekly mean values are the finest usable data. One focus of the paper is to quantify the increase in detection rate due to the introduction of rapid testing in schools. For this purpose, we compare federal states that differ in the start of school tests and Easter holidays. There is a clear temporal correlation with the identified detection rates. Finally, we compare the effect of the individual non-pharmaceutical measures by replacing one by one the fitted rates of detection, vaccination and contacts by neutral ones. The increase in the simulated number of actually infected persons measures the effect of the measure ignored. |
2106.06752 | Sotirios Panagiotou | Sotirios Panagiotou, Harry Sidiropoulos, Mario Negrello, Dimitrios
Soudris, Christos Strydis | EDEN: A high-performance, general-purpose, NeuroML-based neural
simulator | 29 pages, 9 figures | Front. Neuroinform. 16 (2022) | 10.3389/fninf.2022.724336 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern neuroscience employs in silico experimentation on ever-increasing and
more detailed neural networks. The high modelling detail goes hand in hand with
the need for high model reproducibility, reusability and transparency. Besides,
the size of the models and the long timescales under study mandate the use of a
simulation system with high computational performance, so as to provide an
acceptable time to result. In this work, we present EDEN (Extensible Dynamics
Engine for Networks), a new general-purpose, NeuroML-based neural simulator
that achieves both high model flexibility and high computational performance,
through an innovative model-analysis and code-generation technique. The
simulator runs NeuroML v2 models directly, eliminating the need for users to
learn yet another simulator-specific, model-specification language. EDEN's
functional correctness and computational performance were assessed through
NeuroML models available on the NeuroML-DB and Open Source Brain model
repositories. In qualitative experiments, the results produced by EDEN were
verified against the established NEURON simulator, for a wide range of models.
At the same time, computational-performance benchmarks reveal that EDEN runs up
to 2 orders-of-magnitude faster than NEURON on a typical desktop computer, and
does so without additional effort from the user. Finally, and without added
user effort, EDEN has been built from scratch to scale seamlessly over multiple
CPUs and across computer clusters, when available.
| [
{
"created": "Sat, 12 Jun 2021 11:41:43 GMT",
"version": "v1"
}
] | 2022-05-25 | [
[
"Panagiotou",
"Sotirios",
""
],
[
"Sidiropoulos",
"Harry",
""
],
[
"Negrello",
"Mario",
""
],
[
"Soudris",
"Dimitrios",
""
],
[
"Strydis",
"Christos",
""
]
] | Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modelling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a simulation system with high computational performance, so as to provide an acceptable time to result. In this work, we present EDEN (Extensible Dynamics Engine for Networks), a new general-purpose, NeuroML-based neural simulator that achieves both high model flexibility and high computational performance, through an innovative model-analysis and code-generation technique. The simulator runs NeuroML v2 models directly, eliminating the need for users to learn yet another simulator-specific, model-specification language. EDEN's functional correctness and computational performance were assessed through NeuroML models available on the NeuroML-DB and Open Source Brain model repositories. In qualitative experiments, the results produced by EDEN were verified against the established NEURON simulator, for a wide range of models. At the same time, computational-performance benchmarks reveal that EDEN runs up to 2 orders-of-magnitude faster than NEURON on a typical desktop computer, and does so without additional effort from the user. Finally, and without added user effort, EDEN has been built from scratch to scale seamlessly over multiple CPUs and across computer clusters, when available. |
2007.00126 | Lucas Miranda | Lucas Miranda, Riya Paul, Benno P\"utz, Bertram M\"uller-Myhsok | Functional MRI applications for psychiatric disease subtyping: a review | 16 pages, 2 figures, 3 tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Psychiatric disorders have historically been classified using symptom
information alone. With the advent of new technologies that allowed researchers
to investigate brain mechanisms more directly, interest in the mechanistic
rationale behind defined pathologies and aetiology redefinition has greatly
increased. This is particularly appealing for the field of personalised
medicine, which searches for data-driven approaches to improve individual
diagnosis, prognosis and treatment selection. Here we intend to systematically
analyse the usage of functional MRI on both the elucidation of psychiatric
disease biotypes and the interpretation of subtypes obtained via unsupervised
learning applied to symptom or biomarker data. We searched the existing
literature for functional MRI applications to the obtention or interpretation
of psychiatric disease subtypes. The PRISMA guidelines were applied to filter
the retrieved studies, and the active learning framework ASReviews was applied
for article prioritization. From the 20 studies that met the inclusion
criteria, 5 used functional MRI data to interpret symptom-derived disease
clusters, 4 used it for the interpretation of clusters derived from biomarker
data other than fMRI itself, and 11 applied clustering to fMRI directly. Major
depression disorder and schizophrenia were the two most studied pathologies,
followed by ADHD, psychosis, autism disorder, and early violence. No
trans-diagnostic studies were retrieved. While interest in personalised
medicine and data-driven disease subtyping is on the rise and psychiatry is not
the exception, unsupervised analyses of functional MRI data are inconsistent to
date, and much remains to be done in terms of gathering and centralising data,
standardising pipelines and model validation, and method refinement. The usage
of fMRI in the field of trans-diagnostic psychiatry remains vastly unexplored.
| [
{
"created": "Tue, 30 Jun 2020 21:50:23 GMT",
"version": "v1"
}
] | 2020-07-02 | [
[
"Miranda",
"Lucas",
""
],
[
"Paul",
"Riya",
""
],
[
"Pütz",
"Benno",
""
],
[
"Müller-Myhsok",
"Bertram",
""
]
] | Psychiatric disorders have historically been classified using symptom information alone. With the advent of new technologies that allowed researchers to investigate brain mechanisms more directly, interest in the mechanistic rationale behind defined pathologies and aetiology redefinition has greatly increased. This is particularly appealing for the field of personalised medicine, which searches for data-driven approaches to improve individual diagnosis, prognosis and treatment selection. Here we intend to systematically analyse the usage of functional MRI on both the elucidation of psychiatric disease biotypes and the interpretation of subtypes obtained via unsupervised learning applied to symptom or biomarker data. We searched the existing literature for functional MRI applications to the obtention or interpretation of psychiatric disease subtypes. The PRISMA guidelines were applied to filter the retrieved studies, and the active learning framework ASReviews was applied for article prioritization. From the 20 studies that met the inclusion criteria, 5 used functional MRI data to interpret symptom-derived disease clusters, 4 used it for the interpretation of clusters derived from biomarker data other than fMRI itself, and 11 applied clustering to fMRI directly. Major depression disorder and schizophrenia were the two most studied pathologies, followed by ADHD, psychosis, autism disorder, and early violence. No trans-diagnostic studies were retrieved. While interest in personalised medicine and data-driven disease subtyping is on the rise and psychiatry is not the exception, unsupervised analyses of functional MRI data are inconsistent to date, and much remains to be done in terms of gathering and centralising data, standardising pipelines and model validation, and method refinement. The usage of fMRI in the field of trans-diagnostic psychiatry remains vastly unexplored. |
2309.03907 | Pengtao Xie | Youwei Liang, Ruiyi Zhang, Li Zhang, Pengtao Xie | DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule
Graphs | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | A ChatGPT-like system for drug compounds could be a game-changer in
pharmaceutical research, accelerating drug discovery, enhancing our
understanding of structure-activity relationships, guiding lead optimization,
aiding drug repurposing, reducing the failure rate, and streamlining clinical
trials. In this work, we make an initial attempt towards enabling ChatGPT-like
capabilities on drug molecule graphs, by developing a prototype system
DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound
molecule graph and ask various questions about this compound. DrugChat will
answer these questions in a multi-turn, interactive manner. The DrugChat system
consists of a graph neural network (GNN), a large language model (LLM), and an
adaptor. The GNN takes a compound molecule graph as input and learns a
representation for this graph. The adaptor transforms the graph representation
produced by the GNN into another representation that is acceptable to the LLM.
The LLM takes the compound representation transformed by the adaptor and users'
questions about this compound as inputs and generates answers. All these
components are trained end-to-end. To train DrugChat, we collected instruction
tuning datasets which contain 10,834 drug compounds and 143,517 question-answer
pairs. The code and data is available at
\url{https://github.com/UCSD-AI4H/drugchat}
| [
{
"created": "Thu, 18 May 2023 16:22:33 GMT",
"version": "v1"
}
] | 2023-09-11 | [
[
"Liang",
"Youwei",
""
],
[
"Zhang",
"Ruiyi",
""
],
[
"Zhang",
"Li",
""
],
[
"Xie",
"Pengtao",
""
]
] | A ChatGPT-like system for drug compounds could be a game-changer in pharmaceutical research, accelerating drug discovery, enhancing our understanding of structure-activity relationships, guiding lead optimization, aiding drug repurposing, reducing the failure rate, and streamlining clinical trials. In this work, we make an initial attempt towards enabling ChatGPT-like capabilities on drug molecule graphs, by developing a prototype system DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound molecule graph and ask various questions about this compound. DrugChat will answer these questions in a multi-turn, interactive manner. The DrugChat system consists of a graph neural network (GNN), a large language model (LLM), and an adaptor. The GNN takes a compound molecule graph as input and learns a representation for this graph. The adaptor transforms the graph representation produced by the GNN into another representation that is acceptable to the LLM. The LLM takes the compound representation transformed by the adaptor and users' questions about this compound as inputs and generates answers. All these components are trained end-to-end. To train DrugChat, we collected instruction tuning datasets which contain 10,834 drug compounds and 143,517 question-answer pairs. The code and data is available at \url{https://github.com/UCSD-AI4H/drugchat} |
1703.05691 | Vyacheslav Yukalov | V.I. Yukalov and D. Sornette | Quantum Probabilities as Behavioral Probabilities | Latex file, 32 pages | Entropy 19 (2017) 112 | 10.3390/e19030112 | null | q-bio.NC physics.soc-ph quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We demonstrate that behavioral probabilities of human decision makers share
many common features with quantum probabilities. This does not imply that
humans are some quantum objects, but just shows that the mathematics of quantum
theory is applicable to the description of human decision making. The
applicability of quantum rules for describing decision making is connected with
the nontrivial process of making decisions in the case of composite prospects
under uncertainty. Such a process involves deliberations of a decision maker
when making a choice. In addition to the evaluation of the utilities of
considered prospects, real decision makers also appreciate their respective
attractiveness. Therefore, human choice is not based solely on the utility of
prospects, but includes the necessity of resolving the utility-attraction
duality. In order to justify that human consciousness really functions
similarly to the rules of quantum theory, we develop an approach defining human
behavioral probabilities as the probabilities determined by quantum rules. We
show that quantum behavioral probabilities of humans not merely explain
qualitatively how human decisions are made, but they predict quantitative
values of the behavioral probabilities. Analyzing a large set of empirical
data, we find good quantitative agreement between theoretical predictions and
observed experimental data.
| [
{
"created": "Thu, 16 Mar 2017 16:04:45 GMT",
"version": "v1"
}
] | 2017-04-05 | [
[
"Yukalov",
"V. I.",
""
],
[
"Sornette",
"D.",
""
]
] | We demonstrate that behavioral probabilities of human decision makers share many common features with quantum probabilities. This does not imply that humans are some quantum objects, but just shows that the mathematics of quantum theory is applicable to the description of human decision making. The applicability of quantum rules for describing decision making is connected with the nontrivial process of making decisions in the case of composite prospects under uncertainty. Such a process involves deliberations of a decision maker when making a choice. In addition to the evaluation of the utilities of considered prospects, real decision makers also appreciate their respective attractiveness. Therefore, human choice is not based solely on the utility of prospects, but includes the necessity of resolving the utility-attraction duality. In order to justify that human consciousness really functions similarly to the rules of quantum theory, we develop an approach defining human behavioral probabilities as the probabilities determined by quantum rules. We show that quantum behavioral probabilities of humans not merely explain qualitatively how human decisions are made, but they predict quantitative values of the behavioral probabilities. Analyzing a large set of empirical data, we find good quantitative agreement between theoretical predictions and observed experimental data. |
1908.10522 | Jacob George | Jacob A. George, Tyler S. Davis, Mark R. Brinton, Gregory A. Clark | Intuitive Neuromyoelectric Control of a Dexterous Bionic Arm Using a
Modified Kalman Filter | 10 figures. Accepted in J. Neurosci. Methods (2019) | null | null | null | q-bio.NC cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Multi-articulate prostheses are capable of performing dexterous
hand movements. However, clinically available control strategies fail to
provide users with intuitive, independent and proportional control over
multiple degrees of freedom (DOFs) in real-time. New Method: We detail the use
of a modified Kalman filter (MKF) to provide intuitive, independent and
proportional control over six-DOF prostheses such as the DEKA "LUKE" Arm. Input
features include neural firing rates recorded from Utah Slanted Electrode
Arrays and mean absolute value of intramuscular electromyographic (EMG)
recordings. Ad-hoc modifications include thresholds and non-unity gains on the
output of a Kalman filter. Results: We demonstrate that both neural and EMG
data can be combined effectively. We also highlight that modifications can be
optimized to significantly improve performance relative to an unmodified Kalman
filter. Thresholds significantly reduced unintended movement and promoted more
independent control of the different DOFs. Gain were significantly greater than
one and served to amplify participant effort. Optimal modifications can be
determined quickly offline and translate to functional improvements online.
Using a portable take-home system, participants performed various activities of
daily living. Comparison with Existing Methods: In contrast to pattern
recognition, the MKF allows users to continuously modulate their force output,
which is critical for fine dexterity. The MKF is also computationally efficient
and can be trained in less than five minutes. Conclusions: The MKF can be used
to explore the functional and psychological benefits associated with long-term,
at-home control of dexterous prosthetic hands.
| [
{
"created": "Wed, 28 Aug 2019 02:24:35 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Oct 2019 16:03:52 GMT",
"version": "v2"
}
] | 2019-10-11 | [
[
"George",
"Jacob A.",
""
],
[
"Davis",
"Tyler S.",
""
],
[
"Brinton",
"Mark R.",
""
],
[
"Clark",
"Gregory A.",
""
]
] | Background: Multi-articulate prostheses are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over multiple degrees of freedom (DOFs) in real-time. New Method: We detail the use of a modified Kalman filter (MKF) to provide intuitive, independent and proportional control over six-DOF prostheses such as the DEKA "LUKE" Arm. Input features include neural firing rates recorded from Utah Slanted Electrode Arrays and mean absolute value of intramuscular electromyographic (EMG) recordings. Ad-hoc modifications include thresholds and non-unity gains on the output of a Kalman filter. Results: We demonstrate that both neural and EMG data can be combined effectively. We also highlight that modifications can be optimized to significantly improve performance relative to an unmodified Kalman filter. Thresholds significantly reduced unintended movement and promoted more independent control of the different DOFs. Gain were significantly greater than one and served to amplify participant effort. Optimal modifications can be determined quickly offline and translate to functional improvements online. Using a portable take-home system, participants performed various activities of daily living. Comparison with Existing Methods: In contrast to pattern recognition, the MKF allows users to continuously modulate their force output, which is critical for fine dexterity. The MKF is also computationally efficient and can be trained in less than five minutes. Conclusions: The MKF can be used to explore the functional and psychological benefits associated with long-term, at-home control of dexterous prosthetic hands. |
0705.3691 | David Hsu | David Hsu (1), Aonan Tang (2), Murielle Hsu (1), and John M. Beggs (2)
((1) Department of Neurology, University of Wisconsin, Madison WI, (2)
Department of Physics, Indiana University, Bloomington IN) | A simple spontaneously active Hebbian learning model: homeostasis of
activity and connectivity, and consequences for learning and epileptogenesis | 37 pages, 1 table, 7 figures | Phys Rev E vol 76, October 2007 | 10.1103/PhysRevE.76.041909 | null | q-bio.NC | null | A spontaneously active neural system that is capable of continual learning
should also be capable of homeostasis of both firing rate and connectivity.
Experimental evidence suggests that both types of homeostasis exist, and that
connectivity is maintained at a state that is optimal for information
transmission and storage. This state is referred to as the critical state. We
present a simple stochastic computational Hebbian learning model that
incorporates both firing rate and critical homeostasis, and we explore its
stability and connectivity properties. We also examine the behavior of our
model with a simulated seizure and with simulated acute deafferentation. We
argue that a neural system that is more highly connected than the critical
state (i.e., one that is "supercritical") is epileptogenic. Based on our
simulations, we predict that the post-seizural and post-deafferentation states
should be supercritical and epileptogenic. Furthermore, interventions that
boost spontaneous activity should be protective against epileptogenesis.
| [
{
"created": "Fri, 25 May 2007 02:55:15 GMT",
"version": "v1"
}
] | 2007-10-15 | [
[
"Hsu",
"David",
""
],
[
"Tang",
"Aonan",
""
],
[
"Hsu",
"Murielle",
""
],
[
"Beggs",
"John M.",
""
]
] | A spontaneously active neural system that is capable of continual learning should also be capable of homeostasis of both firing rate and connectivity. Experimental evidence suggests that both types of homeostasis exist, and that connectivity is maintained at a state that is optimal for information transmission and storage. This state is referred to as the critical state. We present a simple stochastic computational Hebbian learning model that incorporates both firing rate and critical homeostasis, and we explore its stability and connectivity properties. We also examine the behavior of our model with a simulated seizure and with simulated acute deafferentation. We argue that a neural system that is more highly connected than the critical state (i.e., one that is "supercritical") is epileptogenic. Based on our simulations, we predict that the post-seizural and post-deafferentation states should be supercritical and epileptogenic. Furthermore, interventions that boost spontaneous activity should be protective against epileptogenesis. |
1501.01440 | Vincent Niviere | Emilie Tremey (LCBM - UMR 5249), Florence Bonnot (LCBM - UMR 5249),
Yohann Moreau (LCBM - UMR 5249), Catherine Berthomieu, Alain Desbois (CEA),
Vincent Favaudon, Genevi\`eve Blondin (LCBM - UMR 5249), Chantal
Hou\'ee-Levin (LCPO), Vincent Nivi\`ere (LCBM - UMR 5249) | Hydrogen bonding to the cysteine ligand of superoxide reductase:
acid--base control of the reaction intermediates | null | Journal of biological inorganic chemistry : JBIC : a publication
of the Society of Biological Inorganic Chemistry, 2013, 18, pp.815 - 830 | 10.1007/s00775-013-1025-1 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Superoxide reductase SOR is a non-heme iron metalloenzyme that detoxifies
superoxide radical in microorganisms. Its active site consists of an unusual
non-heme Fe2+ center in a [His4 Cys1] square pyramidal pentacoordination, with
the axial cysteine ligand proposed to be an essential feature in catalysis. Two
NH peptide groups from isoleucine 118 and histidine 119 establish H-bondings
with the sulfur ligand (Desulfoarculus baarsii SOR numbering). In order to
investigate the catalytic role of these H-bonds, the isoleucine 118 residue of
the SOR from Desulfoarculus baarsii was mutated into alanine, aspartate or
serine residues. Resonance Raman spectroscopy showed that the mutations
specifically induced an increase of the strength of the Fe3+-S(Cys) and
S-C$\beta$(Cys) bonds as well as a change in conformation of the cysteinyl side
chain, which was associated with the alteration of the NH hydrogen bonding to
the sulfur ligand. The effects of the isoleucine mutations on the reactivity of
SOR with $O2\bullet$- were investigated by pulse radiolysis. These studies
showed that the mutations induced a specific increase of the pKa of the first
reaction intermediate, recently proposed to be an $Fe2+-O2\bullet-$ species.
These data were supported by DFT calculations carried out on three models of
the $Fe2+-O2\bullet-$ intermediate, with one, two or no H-bonds on the sulfur
ligand. Our results demonstrated that the hydrogen bonds between the NH
(peptide) and the cysteine ligand tightly control the rate of protonation of
the $Fe2+-O2\bullet-$ reaction intermediate to form an Fe3+-OOH species.
| [
{
"created": "Wed, 7 Jan 2015 10:56:18 GMT",
"version": "v1"
}
] | 2015-01-08 | [
[
"Tremey",
"Emilie",
"",
"LCBM - UMR 5249"
],
[
"Bonnot",
"Florence",
"",
"LCBM - UMR 5249"
],
[
"Moreau",
"Yohann",
"",
"LCBM - UMR 5249"
],
[
"Berthomieu",
"Catherine",
"",
"CEA"
],
[
"Desbois",
"Alain",
"",
"CEA"
],
[
"Favaudon",
"Vincent",
"",
"LCBM - UMR 5249"
],
[
"Blondin",
"Geneviève",
"",
"LCBM - UMR 5249"
],
[
"Houée-Levin",
"Chantal",
"",
"LCPO"
],
[
"Nivière",
"Vincent",
"",
"LCBM - UMR 5249"
]
] | Superoxide reductase SOR is a non-heme iron metalloenzyme that detoxifies superoxide radical in microorganisms. Its active site consists of an unusual non-heme Fe2+ center in a [His4 Cys1] square pyramidal pentacoordination, with the axial cysteine ligand proposed to be an essential feature in catalysis. Two NH peptide groups from isoleucine 118 and histidine 119 establish H-bondings with the sulfur ligand (Desulfoarculus baarsii SOR numbering). In order to investigate the catalytic role of these H-bonds, the isoleucine 118 residue of the SOR from Desulfoarculus baarsii was mutated into alanine, aspartate or serine residues. Resonance Raman spectroscopy showed that the mutations specifically induced an increase of the strength of the Fe3+-S(Cys) and S-C$\beta$(Cys) bonds as well as a change in conformation of the cysteinyl side chain, which was associated with the alteration of the NH hydrogen bonding to the sulfur ligand. The effects of the isoleucine mutations on the reactivity of SOR with $O2\bullet$- were investigated by pulse radiolysis. These studies showed that the mutations induced a specific increase of the pKa of the first reaction intermediate, recently proposed to be an $Fe2+-O2\bullet-$ species. These data were supported by DFT calculations carried out on three models of the $Fe2+-O2\bullet-$ intermediate, with one, two or no H-bonds on the sulfur ligand. Our results demonstrated that the hydrogen bonds between the NH (peptide) and the cysteine ligand tightly control the rate of protonation of the $Fe2+-O2\bullet-$ reaction intermediate to form an Fe3+-OOH species. |
1309.0237 | Cameron Browne | Cameron J. Browne | A Multi-Strain Virus Model with Infected Cell Age Structure: Application
to HIV | Minor revisions performed; reworked Figure 2 | null | null | null | q-bio.PE math.AP math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a general mathematical model of a within-host viral infection
with $n$ virus strains and explicit age-since-infection structure for infected
cells. In the model, multiple virus strains compete for a population of target
cells. Cells infected with virus strain $i\in\left\{1,...,n\right\}$ die at
per-capita rate $\delta_i(a)$ and produce virions at per-capita rate $p_i(a)$,
where $\delta_i(a)$ and $p_i(a)$ are functions of the age-since-infection of
the cell. Viral strain $i$ has a basic reproduction number, $\mathcal{R}_i$,
and a corresponding positive single strain equilibrium, $E_i$, when
$\mathcal{R}_i>1$. If $\mathcal{R}_i<1$, then the total concentration of virus
strain $i$ will converge to 0 asymptotically. The main result is that when
$\max_i \mathcal{R}_i>1$ and all of the reproduction numbers are distinct, i.e.
$\mathcal{R}_i\neq \mathcal{R}_j \ \forall i\neq j$, the viral strain with the
maximal basic reproduction number competitively excludes the other strains. As
an application of the model, HIV evolution is considered and simulations are
provided.
| [
{
"created": "Sun, 1 Sep 2013 16:13:30 GMT",
"version": "v1"
},
{
"created": "Tue, 27 May 2014 16:03:28 GMT",
"version": "v2"
}
] | 2014-05-28 | [
[
"Browne",
"Cameron J.",
""
]
] | We consider a general mathematical model of a within-host viral infection with $n$ virus strains and explicit age-since-infection structure for infected cells. In the model, multiple virus strains compete for a population of target cells. Cells infected with virus strain $i\in\left\{1,...,n\right\}$ die at per-capita rate $\delta_i(a)$ and produce virions at per-capita rate $p_i(a)$, where $\delta_i(a)$ and $p_i(a)$ are functions of the age-since-infection of the cell. Viral strain $i$ has a basic reproduction number, $\mathcal{R}_i$, and a corresponding positive single strain equilibrium, $E_i$, when $\mathcal{R}_i>1$. If $\mathcal{R}_i<1$, then the total concentration of virus strain $i$ will converge to 0 asymptotically. The main result is that when $\max_i \mathcal{R}_i>1$ and all of the reproduction numbers are distinct, i.e. $\mathcal{R}_i\neq \mathcal{R}_j \ \forall i\neq j$, the viral strain with the maximal basic reproduction number competitively excludes the other strains. As an application of the model, HIV evolution is considered and simulations are provided. |
1207.6721 | Peter Pfaffelhuber | Peter Pfaffelhuber and Benedikt Vogt | Finite populations with frequency-dependent selection: a genealogical
approach | 14 pages, 3 figures | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolutionary models for populations of constant size are frequently studied
using the Moran model, the Wright-Fisher model, or their diffusion limits. When
evolution is neutral, a random genealogy given through Kingman's coalescent is
used in order to understand basic properties of such models. Here, we address
the use of a genealogical perspective for models with weak frequency-dependent
selection, i.e. N s =: {\alpha} is small, and s is the fitness advantage of a
fit individual and N is the population size. When computing fixation
probabilities, this leads either to the approach proposed by Rousset (2003),
who argues how to use the Kingman's coalescent for weak selection, or to
extensions of the ancestral selection graph of Neuhauser and Krone (1997) and
Neuhauser (1999). As an application, we re-derive the one-third law of
evolutionary game theory (Nowak et al., 2004). In addition, we provide the
approximate distribution of the genealogical distance of two randomly sampled
individuals under linear frequency-dependence.
| [
{
"created": "Sat, 28 Jul 2012 19:05:44 GMT",
"version": "v1"
}
] | 2012-07-31 | [
[
"Pfaffelhuber",
"Peter",
""
],
[
"Vogt",
"Benedikt",
""
]
] | Evolutionary models for populations of constant size are frequently studied using the Moran model, the Wright-Fisher model, or their diffusion limits. When evolution is neutral, a random genealogy given through Kingman's coalescent is used in order to understand basic properties of such models. Here, we address the use of a genealogical perspective for models with weak frequency-dependent selection, i.e. N s =: {\alpha} is small, and s is the fitness advantage of a fit individual and N is the population size. When computing fixation probabilities, this leads either to the approach proposed by Rousset (2003), who argues how to use the Kingman's coalescent for weak selection, or to extensions of the ancestral selection graph of Neuhauser and Krone (1997) and Neuhauser (1999). As an application, we re-derive the one-third law of evolutionary game theory (Nowak et al., 2004). In addition, we provide the approximate distribution of the genealogical distance of two randomly sampled individuals under linear frequency-dependence. |
1502.00481 | Valerio Capraro | Valerio Capraro and Giorgia Cococcioni | Social setting, intuition, and experience in lab experiments interact to
shape cooperative decision-making | Forthcoming in Proceedings of the Royal Society B: Biological
Sciences | null | null | null | q-bio.PE cs.GT physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies suggest that cooperative decision-making in one-shot
interactions is a history-dependent dynamic process: promoting intuition versus
deliberation has typically a positive effect on cooperation (dynamism) among
people living in a cooperative setting and with no previous experience in
economic games on cooperation (history-dependence). Here we report on a lab
experiment exploring how these findings transfer to a non-cooperative setting.
We find two major results: (i) promoting intuition versus deliberation has no
effect on cooperative behavior among inexperienced subjects living in a
non-cooperative setting; (ii) experienced subjects cooperate more than
inexperienced subjects, but only under time pressure. These results suggest
that cooperation is a learning process, rather than an instinctive impulse or a
self-controlled choice, and that experience operates primarily via the channel
of intuition. In doing so, our findings shed further light on the cognitive
basis of human cooperative decision-making and provide further support for the
recently proposed Social Heuristics Hypothesis.
| [
{
"created": "Mon, 2 Feb 2015 14:03:45 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Apr 2015 22:44:53 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Jun 2015 17:40:58 GMT",
"version": "v3"
}
] | 2015-06-10 | [
[
"Capraro",
"Valerio",
""
],
[
"Cococcioni",
"Giorgia",
""
]
] | Recent studies suggest that cooperative decision-making in one-shot interactions is a history-dependent dynamic process: promoting intuition versus deliberation has typically a positive effect on cooperation (dynamism) among people living in a cooperative setting and with no previous experience in economic games on cooperation (history-dependence). Here we report on a lab experiment exploring how these findings transfer to a non-cooperative setting. We find two major results: (i) promoting intuition versus deliberation has no effect on cooperative behavior among inexperienced subjects living in a non-cooperative setting; (ii) experienced subjects cooperate more than inexperienced subjects, but only under time pressure. These results suggest that cooperation is a learning process, rather than an instinctive impulse or a self-controlled choice, and that experience operates primarily via the channel of intuition. In doing so, our findings shed further light on the cognitive basis of human cooperative decision-making and provide further support for the recently proposed Social Heuristics Hypothesis. |
1312.1632 | Jake Bouvrie | Jake Bouvrie, Jean-Jacques Slotine | Synchronization and Noise: A Mechanism for Regularization in Neural
Systems | 32 pages, 7 figures. under review | null | null | null | q-bio.NC math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To learn and reason in the presence of uncertainty, the brain must be capable
of imposing some form of regularization. Here we suggest, through theoretical
and computational arguments, that the combination of noise with synchronization
provides a plausible mechanism for regularization in the nervous system. The
functional role of regularization is considered in a general context in which
coupled computational systems receive inputs corrupted by correlated noise.
Noise on the inputs is shown to impose regularization, and when synchronization
upstream induces time-varying correlations across noise variables, the degree
of regularization can be calibrated over time. The proposed mechanism is
explored first in the context of a simple associative learning problem, and
then in the context of a hierarchical sensory coding task. The resulting
qualitative behavior coincides with experimental data from visual cortex.
| [
{
"created": "Thu, 5 Dec 2013 17:58:06 GMT",
"version": "v1"
}
] | 2013-12-06 | [
[
"Bouvrie",
"Jake",
""
],
[
"Slotine",
"Jean-Jacques",
""
]
] | To learn and reason in the presence of uncertainty, the brain must be capable of imposing some form of regularization. Here we suggest, through theoretical and computational arguments, that the combination of noise with synchronization provides a plausible mechanism for regularization in the nervous system. The functional role of regularization is considered in a general context in which coupled computational systems receive inputs corrupted by correlated noise. Noise on the inputs is shown to impose regularization, and when synchronization upstream induces time-varying correlations across noise variables, the degree of regularization can be calibrated over time. The proposed mechanism is explored first in the context of a simple associative learning problem, and then in the context of a hierarchical sensory coding task. The resulting qualitative behavior coincides with experimental data from visual cortex. |
2010.08523 | Alessandro Ravoni | Alessandro Ravoni | Long-term behaviours of Autocatalytic Sets | null | null | null | null | q-bio.MN nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autocatalytic Sets are reaction networks theorised as networks at the basis
of life. Their main feature is the ability of spontaneously emerging and
self-reproducing. The Reflexively and Food-generated theory provides a formal
definition of Autocatalytic Sets in terms of graphs with peculiar topological
properties. This formalisation has been proved to be a powerful tool for the
study of the chemical networks underlying life, and it was able to identify
autocatalytic structures in real metabolic networks. However, the dynamical
behaviour of such networks has not been yet complitely clarified. In this work,
we present a first attempt to connect the topology of an Autocatalytic Set with
its dynamics. For this purpose, we represent Autocatalytic Sets in terms of
Chemical Reaction Networks, and we use the Chemical Reaction Network theory to
detect motifs in the networks'structure, that allow us to determine the
long-term behaviour of the system.
| [
{
"created": "Fri, 16 Oct 2020 17:29:18 GMT",
"version": "v1"
}
] | 2020-10-19 | [
[
"Ravoni",
"Alessandro",
""
]
] | Autocatalytic Sets are reaction networks theorised as networks at the basis of life. Their main feature is the ability of spontaneously emerging and self-reproducing. The Reflexively and Food-generated theory provides a formal definition of Autocatalytic Sets in terms of graphs with peculiar topological properties. This formalisation has been proved to be a powerful tool for the study of the chemical networks underlying life, and it was able to identify autocatalytic structures in real metabolic networks. However, the dynamical behaviour of such networks has not been yet complitely clarified. In this work, we present a first attempt to connect the topology of an Autocatalytic Set with its dynamics. For this purpose, we represent Autocatalytic Sets in terms of Chemical Reaction Networks, and we use the Chemical Reaction Network theory to detect motifs in the networks'structure, that allow us to determine the long-term behaviour of the system. |
1601.04600 | Andrew Tadrowski | Andrew C. Tadrowski, Martin R. Evans, Bartlomiej Waclaw | Phenotypic switching can speed up biological evolution of microbes | 9 pages, 5 figures (main text) + 3 pages, 5 figures (supplementary
information) | null | null | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic phenotype switching has been suggested to play a beneficial role
in microbial populations by leading to the division of labour among cells, or
ensuring that at least some of the population survives an unexpected change in
environmental conditions. Here we use a computational model to investigate an
alternative possible function of stochastic phenotype switching - as a way to
adapt more quickly even in a static environment. We show that when a genetic
mutation causes a population to become less fit, switching to an alternative
phenotype with higher fitness (growth rate) may give the population enough time
to develop compensatory mutations that increase the fitness again. The
possibility of switching phenotypes can reduce the time to adaptation by orders
of magnitude if the "fitness valley" caused by the deleterious mutation is deep
enough. Our work has important implications for the emergence of
antibiotic-resistant bacteria. In line with recent experimental findings we
hypothesise that switching to a slower growing but less sensitive phenotype
helps bacteria to develop resistance by exploring a larger set of beneficial
mutations while avoiding deleterious ones.
| [
{
"created": "Mon, 18 Jan 2016 16:47:23 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Feb 2016 09:30:57 GMT",
"version": "v2"
}
] | 2016-02-02 | [
[
"Tadrowski",
"Andrew C.",
""
],
[
"Evans",
"Martin R.",
""
],
[
"Waclaw",
"Bartlomiej",
""
]
] | Stochastic phenotype switching has been suggested to play a beneficial role in microbial populations by leading to the division of labour among cells, or ensuring that at least some of the population survives an unexpected change in environmental conditions. Here we use a computational model to investigate an alternative possible function of stochastic phenotype switching - as a way to adapt more quickly even in a static environment. We show that when a genetic mutation causes a population to become less fit, switching to an alternative phenotype with higher fitness (growth rate) may give the population enough time to develop compensatory mutations that increase the fitness again. The possibility of switching phenotypes can reduce the time to adaptation by orders of magnitude if the "fitness valley" caused by the deleterious mutation is deep enough. Our work has important implications for the emergence of antibiotic-resistant bacteria. In line with recent experimental findings we hypothesise that switching to a slower growing but less sensitive phenotype helps bacteria to develop resistance by exploring a larger set of beneficial mutations while avoiding deleterious ones. |
1802.00334 | Joaquin Goni | Enrico Amico, Alex Arenas, Joaquin Goni | Centralized and distributed cognitive task processing in the human
connectome | 22 pages main, 6 pages supplementary, 6 figures, 5 supplementary
figures, 1 table, 1 supplementary table. arXiv admin note: text overlap with
arXiv:1710.02199 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key question in modern neuroscience is how cognitive changes in a human
brain can be quantified and captured by functional connectomes (FC) . A
systematic approach to measure pairwise functional distance at different brain
states is lacking. This would provide a straight-forward way to quantify
differences in cognitive processing across tasks; also, it would help in
relating these differences in task-based FCs to the underlying structural
network. Here we propose a framework, based on the concept of Jensen-Shannon
divergence, to map the task-rest connectivity distance between tasks and
resting-state FC. We show how this information theoretical measure allows for
quantifying connectivity changes in distributed and centralized processing in
functional networks. We study resting-state and seven tasks from the Human
Connectome Project dataset to obtain the most distant links across tasks. We
investigate how these changes are associated to different functional brain
networks, and use the proposed measure to infer changes in the information
processing regimes. Furthermore, we show how the FC distance from resting state
is shaped by structural connectivity, and to what extent this relationship
depends on the task. This framework provides a well grounded mathematical
quantification of connectivity changes associated to cognitive processing in
large-scale brain networks.
| [
{
"created": "Wed, 31 Jan 2018 17:59:36 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Jul 2018 18:44:00 GMT",
"version": "v2"
},
{
"created": "Mon, 24 Sep 2018 20:09:47 GMT",
"version": "v3"
}
] | 2018-09-26 | [
[
"Amico",
"Enrico",
""
],
[
"Arenas",
"Alex",
""
],
[
"Goni",
"Joaquin",
""
]
] | A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectomes (FC) . A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straight-forward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting-state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated to different functional brain networks, and use the proposed measure to infer changes in the information processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well grounded mathematical quantification of connectivity changes associated to cognitive processing in large-scale brain networks. |
1404.6836 | Jesus M Cortes | Asier Erramuzpe, Guillermo J. Ortega, Jesus Pastor, Rafael G. de Sola,
Daniele Marinazzo, Sebastiano Stramaglia, Jesus M. Cortes | Identification of redundant and synergetic circuits in triplets of
electrophysiological data | 31 pages, 6 figures, 3 supplementary figures. To appear in the
Journal of Neural Engineering in its current form | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural systems are comprised of interacting units, and relevant information
regarding their function or malfunction can be inferred by analyzing the
statistical dependencies between the activity of each unit. Whilst correlations
and mutual information are commonly used to characterize these dependencies,
our objective here is to extend interactions to triplets of variables to better
detect and characterize dynamic information transfer. Our approach relies on
the measure of interaction information (II). The sign of II provides
information as to the extent to which the interaction of variables in triplets
is redundant (R) or synergetic (S). Here, based on this approach, we calculated
the R and S status for triplets of electrophysiological data recorded from
drug-resistant patients with mesial temporal lobe epilepsy in order to study
the spatial organization and dynamics of R and S close to the epileptogenic
zone (the area responsible for seizure propagation). In terms of spatial
organization, our results show that R matched the epileptogenic zone while S
was distributed more in the surrounding area. In relation to dynamics, R made
the largest contribution to high frequency bands (14-100Hz), whilst S was
expressed more strongly at lower frequencies (1-7Hz). Thus, applying
interaction information to such clinical data reveals new aspects of
epileptogenic structure in terms of the nature (redundancy vs. synergy) and
dynamics (fast vs. slow rhythms) of the interactions. We expect this
methodology, robust and simple, can reveal new aspects beyond pair-interactions
in networks of interacting units in other setups with multi-recording data sets
(and thus, not necessarily in epilepsy, the pathology we have approached here).
| [
{
"created": "Sun, 27 Apr 2014 22:15:53 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Sep 2015 14:20:22 GMT",
"version": "v2"
}
] | 2015-09-11 | [
[
"Erramuzpe",
"Asier",
""
],
[
"Ortega",
"Guillermo J.",
""
],
[
"Pastor",
"Jesus",
""
],
[
"de Sola",
"Rafael G.",
""
],
[
"Marinazzo",
"Daniele",
""
],
[
"Stramaglia",
"Sebastiano",
""
],
[
"Cortes",
"Jesus M.",
""
]
] | Neural systems are comprised of interacting units, and relevant information regarding their function or malfunction can be inferred by analyzing the statistical dependencies between the activity of each unit. Whilst correlations and mutual information are commonly used to characterize these dependencies, our objective here is to extend interactions to triplets of variables to better detect and characterize dynamic information transfer. Our approach relies on the measure of interaction information (II). The sign of II provides information as to the extent to which the interaction of variables in triplets is redundant (R) or synergetic (S). Here, based on this approach, we calculated the R and S status for triplets of electrophysiological data recorded from drug-resistant patients with mesial temporal lobe epilepsy in order to study the spatial organization and dynamics of R and S close to the epileptogenic zone (the area responsible for seizure propagation). In terms of spatial organization, our results show that R matched the epileptogenic zone while S was distributed more in the surrounding area. In relation to dynamics, R made the largest contribution to high frequency bands (14-100Hz), whilst S was expressed more strongly at lower frequencies (1-7Hz). Thus, applying interaction information to such clinical data reveals new aspects of epileptogenic structure in terms of the nature (redundancy vs. synergy) and dynamics (fast vs. slow rhythms) of the interactions. We expect this methodology, robust and simple, can reveal new aspects beyond pair-interactions in networks of interacting units in other setups with multi-recording data sets (and thus, not necessarily in epilepsy, the pathology we have approached here). |
1312.0867 | Diego Ferreiro | Diego U. Ferreiro, Elizabeth A. Komives, Peter G. Wolynes | Frustration in Biomolecules | 97 pages, 30 figures | null | null | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biomolecules are the prime information processing elements of living matter.
Most of these inanimate systems are polymers that compute their structures and
dynamics using as input seemingly random character strings of their sequence,
following which they coalesce and perform integrated cellular functions. In
large computational systems with a finite interaction-codes, the appearance of
conflicting goals is inevitable. Simple conflicting forces can lead to quite
complex structures and behaviors, leading to the concept of "frustration" in
condensed matter. We present here some basic ideas about frustration in
biomolecules and how the frustration concept leads to a better appreciation of
many aspects of the architecture of biomolecules, and how structure connects to
function. These ideas are simultaneously both seductively simple and perilously
subtle to grasp completely. The energy landscape theory of protein folding
provides a framework for quantifying frustration in large systems and has been
implemented at many levels of description. We first review the notion of
frustration from the areas of abstract logic and its uses in simple condensed
matter systems. We discuss then how the frustration concept applies
specifically to heteropolymers, testing folding landscape theory in computer
simulations of protein models and in experimentally accessible systems.
Studying the aspects of frustration averaged over many proteins provides ways
to infer energy functions useful for reliable structure prediction. We discuss
how frustration affects folding, how a large part of the biological functions
of proteins are related to subtle local frustration effects and how frustration
influences the appearance of metastable states, the nature of binding
processes, catalysis and allosteric transitions. We hope to illustrate how
Frustration is a fundamental concept in relating function to structural
biology.
| [
{
"created": "Tue, 3 Dec 2013 16:00:58 GMT",
"version": "v1"
}
] | 2013-12-04 | [
[
"Ferreiro",
"Diego U.",
""
],
[
"Komives",
"Elizabeth A.",
""
],
[
"Wolynes",
"Peter G.",
""
]
] | Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology. |
1804.11005 | Sahil Garg | Emilia M. Wysocka, Valery Dzutsati, Tirthankar Bandyopadhyay, Laura
Condon, Sahil Garg | Building Models for Biopathway Dynamics Using Intrinsic Dimensionality
Analysis | Presented in Santa Fe Complex Systems Summer School (CSSS) 2015 | null | null | null | q-bio.MN cs.IT math.IT q-bio.QM stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An important task for many if not all the scientific domains is efficient
knowledge integration, testing and codification. It is often solved with model
construction in a controllable computational environment. In spite of that, the
throughput of in-silico simulation-based observations become similarly
intractable for thorough analysis. This is especially the case in molecular
biology, which served as a subject for this study. In this project, we aimed to
test some approaches developed to deal with the curse of dimensionality. Among
these we found dimension reduction techniques especially appealing. They can be
used to identify irrelevant variability and help to understand critical
processes underlying high-dimensional datasets. Additionally, we subjected our
data sets to nonlinear time series analysis, as those are well established
methods for results comparison. To investigate the usefulness of dimension
reduction methods, we decided to base our study on a concrete sample set. The
example was taken from the domain of systems biology concerning dynamic
evolution of sub-cellular signaling. Particularly, the dataset relates to the
yeast pheromone pathway and is studied in-silico with a stochastic model. The
model reconstructs signal propagation stimulated by a mating pheromone. In the
paper, we elaborate on the reason of multidimensional analysis problem in the
context of molecular signaling, and next, we introduce the model of choice,
simulation details and obtained time series dynamics. A description of used
methods followed by a discussion of results and their biological interpretation
finalize the paper.
| [
{
"created": "Sun, 29 Apr 2018 23:40:44 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Oct 2018 13:13:15 GMT",
"version": "v2"
},
{
"created": "Sat, 3 Nov 2018 03:33:57 GMT",
"version": "v3"
}
] | 2018-11-06 | [
[
"Wysocka",
"Emilia M.",
""
],
[
"Dzutsati",
"Valery",
""
],
[
"Bandyopadhyay",
"Tirthankar",
""
],
[
"Condon",
"Laura",
""
],
[
"Garg",
"Sahil",
""
]
] | An important task for many if not all the scientific domains is efficient knowledge integration, testing and codification. It is often solved with model construction in a controllable computational environment. In spite of that, the throughput of in-silico simulation-based observations become similarly intractable for thorough analysis. This is especially the case in molecular biology, which served as a subject for this study. In this project, we aimed to test some approaches developed to deal with the curse of dimensionality. Among these we found dimension reduction techniques especially appealing. They can be used to identify irrelevant variability and help to understand critical processes underlying high-dimensional datasets. Additionally, we subjected our data sets to nonlinear time series analysis, as those are well established methods for results comparison. To investigate the usefulness of dimension reduction methods, we decided to base our study on a concrete sample set. The example was taken from the domain of systems biology concerning dynamic evolution of sub-cellular signaling. Particularly, the dataset relates to the yeast pheromone pathway and is studied in-silico with a stochastic model. The model reconstructs signal propagation stimulated by a mating pheromone. In the paper, we elaborate on the reason of multidimensional analysis problem in the context of molecular signaling, and next, we introduce the model of choice, simulation details and obtained time series dynamics. A description of used methods followed by a discussion of results and their biological interpretation finalize the paper. |
2307.15166 | Samuel Martin | Elizabeth Gross, Robert Krone, Samuel Martin | Dimensions of Level-1 Group-Based Phylogenetic Networks | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic networks represent evolutionary histories of sets of taxa where
horizontal evolution or hybridization has occurred. Placing a Markov model of
evolution on a phylogenetic network gives a model that is particularly amenable
to algebraic study by representing it as an algebraic variety. In this paper,
we give a formula for the dimension of the variety corresponding to a
triangle-free level-1 phylogenetic network under a group-based evolutionary
model. On our way to this, we give a dimension formula for codimension zero
toric fiber products. We conclude by illustrating applications to
identifiability.
| [
{
"created": "Thu, 27 Jul 2023 19:50:06 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Sep 2023 09:14:48 GMT",
"version": "v2"
}
] | 2023-09-13 | [
[
"Gross",
"Elizabeth",
""
],
[
"Krone",
"Robert",
""
],
[
"Martin",
"Samuel",
""
]
] | Phylogenetic networks represent evolutionary histories of sets of taxa where horizontal evolution or hybridization has occurred. Placing a Markov model of evolution on a phylogenetic network gives a model that is particularly amenable to algebraic study by representing it as an algebraic variety. In this paper, we give a formula for the dimension of the variety corresponding to a triangle-free level-1 phylogenetic network under a group-based evolutionary model. On our way to this, we give a dimension formula for codimension zero toric fiber products. We conclude by illustrating applications to identifiability. |
1805.03792 | Catherine Davey E | Catherine E Davey and David B Grayden and Anthony N Burkitt | Impact of axonal delay on structure development in a multi-layered
network | 27 pages, 6 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The mechanisms underlying how activity in the visual pathway may give rise
through neural plasticity to many of the features observed experimentally in
the early stages of visual processing was provided by Linkser in a seminal,
three-paper series. Owing to the complexity of multi-layer models, an implicit
assumption in Linsker's and subsequent papers has been that propagation delay
is homogeneous and plays little functional role in neural behaviour. We relax
this assumption to examine the impact of distance-dependent axonal propagation
delay on neural learning. We show that propagation delay induces low-pass
filtering by dispersing the arrival times of spikes from presynaptic neurons,
providing a natural correlation cancellation mechanism for distal connections.
The cut-off frequency decreases as the radial propagation delay within a layer
increases relative to propagation delay between the layers, introducing an
upper limit on temporal resolution. Given that the PSP also acts as a low-pass
filter, we show that the effective time constant of each should enable the
processing of similar scales of temporal information. This result has
implications for the visual system, in which receptive field size and, thus,
radial propagation delay, increases with eccentricity. Furthermore, the network
response is frequency dependent since higher frequencies require increased
input amplitude to compensate for attenuation. This concords with
frequency-dependent contrast sensitivity in the visual system, which changes
with eccentricity and receptive field size. We further show that the proportion
of inhibition relative to excitation is larger where radial propagation delay
is long relative to inter-laminar propagation delay. We show that the addition
of propagation delay reduces the range in the cell's on-center size, providing
stability to variations in homeostatic parameters.
| [
{
"created": "Thu, 10 May 2018 02:40:57 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Nov 2020 06:49:34 GMT",
"version": "v2"
}
] | 2020-11-30 | [
[
"Davey",
"Catherine E",
""
],
[
"Grayden",
"David B",
""
],
[
"Burkitt",
"Anthony N",
""
]
] | The mechanisms underlying how activity in the visual pathway may give rise through neural plasticity to many of the features observed experimentally in the early stages of visual processing was provided by Linkser in a seminal, three-paper series. Owing to the complexity of multi-layer models, an implicit assumption in Linsker's and subsequent papers has been that propagation delay is homogeneous and plays little functional role in neural behaviour. We relax this assumption to examine the impact of distance-dependent axonal propagation delay on neural learning. We show that propagation delay induces low-pass filtering by dispersing the arrival times of spikes from presynaptic neurons, providing a natural correlation cancellation mechanism for distal connections. The cut-off frequency decreases as the radial propagation delay within a layer increases relative to propagation delay between the layers, introducing an upper limit on temporal resolution. Given that the PSP also acts as a low-pass filter, we show that the effective time constant of each should enable the processing of similar scales of temporal information. This result has implications for the visual system, in which receptive field size and, thus, radial propagation delay, increases with eccentricity. Furthermore, the network response is frequency dependent since higher frequencies require increased input amplitude to compensate for attenuation. This concords with frequency-dependent contrast sensitivity in the visual system, which changes with eccentricity and receptive field size. We further show that the proportion of inhibition relative to excitation is larger where radial propagation delay is long relative to inter-laminar propagation delay. We show that the addition of propagation delay reduces the range in the cell's on-center size, providing stability to variations in homeostatic parameters. |
q-bio/0609052 | Gergely J Sz\"oll\H{o}si | Gergely J. Szollosi, Imre Derenyi and Tibor Vellai | The maintenance of sex in bacteria is ensured by its potential to reload
genes | 16 pages with 3 color figures. Manuscript accepted for publication in
Genetics (www.genetics.org) | Genetics 174, 2173-2180 (2006). | 10.1534/genetics.106.063412 | q-bio/0609052 | q-bio.PE cond-mat.stat-mech physics.bio-ph | null | Why sex is maintained in nature is a fundamental question in biology. Natural
genetic transformation (NGT) is a sexual process by which bacteria actively
take up exogenous DNA and use it to replace homologous chromosomal sequences.
As it has been demonstrated, the role of NGT in repairing deleterious mutations
under constant selection is insufficient for its survival, and the lack of
other viable explanations have left no alternative except that DNA uptake
provides nucleotides for food. Here we develop a novel simulation approach for
the long-term dynamics of genome organization (involving the loss and
acquisition of genes) in a bacterial species consisting of a large number of
spatially distinct populations subject to independently fluctuating ecological
conditions. Our results show that in the presence of weak inter-population
migration NGT is able to subsist as a mechanism to reload locally lost,
intermittently selected genes from the collective gene pool of the species
through DNA uptake from migrants. Reloading genes and combining them with those
in locally adapted genomes allow individual cells to re-adapt faster to
environmental changes. The machinery of transformation survives under a wide
range of model parameters readily encompassing real-world biological
conditions. These findings imply that the primary role of NGT is not to serve
the cell with food, but to provide homologous sequences for restoring genes
that have disappeared from or become degraded in the local population.
| [
{
"created": "Thu, 28 Sep 2006 12:36:26 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Nov 2006 12:58:48 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Szollosi",
"Gergely J.",
""
],
[
"Derenyi",
"Imre",
""
],
[
"Vellai",
"Tibor",
""
]
] | Why sex is maintained in nature is a fundamental question in biology. Natural genetic transformation (NGT) is a sexual process by which bacteria actively take up exogenous DNA and use it to replace homologous chromosomal sequences. As it has been demonstrated, the role of NGT in repairing deleterious mutations under constant selection is insufficient for its survival, and the lack of other viable explanations have left no alternative except that DNA uptake provides nucleotides for food. Here we develop a novel simulation approach for the long-term dynamics of genome organization (involving the loss and acquisition of genes) in a bacterial species consisting of a large number of spatially distinct populations subject to independently fluctuating ecological conditions. Our results show that in the presence of weak inter-population migration NGT is able to subsist as a mechanism to reload locally lost, intermittently selected genes from the collective gene pool of the species through DNA uptake from migrants. Reloading genes and combining them with those in locally adapted genomes allow individual cells to re-adapt faster to environmental changes. The machinery of transformation survives under a wide range of model parameters readily encompassing real-world biological conditions. These findings imply that the primary role of NGT is not to serve the cell with food, but to provide homologous sequences for restoring genes that have disappeared from or become degraded in the local population. |
2306.01794 | Yangtian Zhang | Yangtian Zhang, Zuobai Zhang, Bozitao Zhong, Sanchit Misra, Jian Tang | DiffPack: A Torsional Diffusion Model for Autoregressive Protein
Side-Chain Packing | 37th Conference on Neural Information Processing Systems (NeurIPS
2023) | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Proteins play a critical role in carrying out biological functions, and their
3D structures are essential in determining their functions. Accurately
predicting the conformation of protein side-chains given their backbones is
important for applications in protein structure prediction, design and
protein-protein interactions. Traditional methods are computationally intensive
and have limited accuracy, while existing machine learning methods treat the
problem as a regression task and overlook the restrictions imposed by the
constant covalent bond lengths and angles. In this work, we present DiffPack, a
torsional diffusion model that learns the joint distribution of side-chain
torsional angles, the only degrees of freedom in side-chain packing, by
diffusing and denoising on the torsional space. To avoid issues arising from
simultaneous perturbation of all four torsional angles, we propose
autoregressively generating the four torsional angles from $\chi_1$ to $\chi_4$
and training diffusion models for each torsional angle. We evaluate the method
on several benchmarks for protein side-chain packing and show that our method
achieves improvements of $11.9\%$ and $13.5\%$ in angle accuracy on CASP13 and
CASP14, respectively, with a significantly smaller model size ($60\times$ fewer
parameters). Additionally, we show the effectiveness of our method in enhancing
side-chain predictions in the AlphaFold2 model. Code is available at
https://github.com/DeepGraphLearning/DiffPack.
| [
{
"created": "Thu, 1 Jun 2023 09:22:09 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Feb 2024 03:29:18 GMT",
"version": "v2"
}
] | 2024-02-19 | [
[
"Zhang",
"Yangtian",
""
],
[
"Zhang",
"Zuobai",
""
],
[
"Zhong",
"Bozitao",
""
],
[
"Misra",
"Sanchit",
""
],
[
"Tang",
"Jian",
""
]
] | Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Accurately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from $\chi_1$ to $\chi_4$ and training diffusion models for each torsional angle. We evaluate the method on several benchmarks for protein side-chain packing and show that our method achieves improvements of $11.9\%$ and $13.5\%$ in angle accuracy on CASP13 and CASP14, respectively, with a significantly smaller model size ($60\times$ fewer parameters). Additionally, we show the effectiveness of our method in enhancing side-chain predictions in the AlphaFold2 model. Code is available at https://github.com/DeepGraphLearning/DiffPack. |
1706.00383 | Dervis Vural | Uryan Isik Can, Neerajha Nagarajan, Dervis Can Vural, Pinar Zorlutuna | Muscle-Cell-Based "Living Diodes" | null | Can UI, Nagarajan N, Vural DC, Zorlutuna P. Muscle-Cell-Based
"Living Diodes". Advanced Biosystems. 2017 Jan 1 | null | null | q-bio.TO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new type of diode that is made entirely of electrically excitable muscle
cells and nonexcitable fibroblast cells is designed, fabricated, and
characterized. These two cell types in a rectangular pattern allow the signal
initiated on the excitable side to pass to the nonexcitable side, and not in
the opposite direction.
| [
{
"created": "Thu, 1 Jun 2017 16:50:25 GMT",
"version": "v1"
}
] | 2017-06-02 | [
[
"Can",
"Uryan Isik",
""
],
[
"Nagarajan",
"Neerajha",
""
],
[
"Vural",
"Dervis Can",
""
],
[
"Zorlutuna",
"Pinar",
""
]
] | A new type of diode that is made entirely of electrically excitable muscle cells and nonexcitable fibroblast cells is designed, fabricated, and characterized. These two cell types in a rectangular pattern allow the signal initiated on the excitable side to pass to the nonexcitable side, and not in the opposite direction. |
q-bio/0604031 | Robbe Wunschiers | R. Wunschiers, R. Axelsson, P. Lindblad | Effects of Growth on Dinitrogen on the Transcriptome and Predicted
Proteome of Nostoc PCC 7120 | 38 pages, including 9 figures and 6 tables | null | null | null | q-bio.GN | null | Upon growth on dinitrogen, the filamentous cyanobacterium Nostoc PCC 7120
initiates metabolic and morphological changes. We analyzed the expression of
1249 genes from major metabolic categories under nitrogen fixing and
non-nitrogen fixing growth. The expression data were correlated with potential
target secondary structures, probe GC-content, predicted operon structures, and
nitrogen content of gene products. Of the selected genes, 494 show a more than
2-fold difference in the two conditions analyzed. Under nitrogen-fixing
conditions 465 genes, mainly involved in energy metabolism, photosynthesis,
respiration and nitrogen-fixation, were found to be stronger expressed, whereas
29 genes showed a stronger expression under non-nitrogen fixing conditions.
Analysis of the nitrogen content of regulated genes shows that Nostoc PCC 7120
growing on dinitrogen is freed from any constraints to save nitrogen. For the
first time the expression of high light-induced stress proteins (HLIP-family)
is shown to be linked to the nitrogen availability.
| [
{
"created": "Tue, 25 Apr 2006 13:21:18 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Wunschiers",
"R.",
""
],
[
"Axelsson",
"R.",
""
],
[
"Lindblad",
"P.",
""
]
] | Upon growth on dinitrogen, the filamentous cyanobacterium Nostoc PCC 7120 initiates metabolic and morphological changes. We analyzed the expression of 1249 genes from major metabolic categories under nitrogen fixing and non-nitrogen fixing growth. The expression data were correlated with potential target secondary structures, probe GC-content, predicted operon structures, and nitrogen content of gene products. Of the selected genes, 494 show a more than 2-fold difference in the two conditions analyzed. Under nitrogen-fixing conditions 465 genes, mainly involved in energy metabolism, photosynthesis, respiration and nitrogen-fixation, were found to be stronger expressed, whereas 29 genes showed a stronger expression under non-nitrogen fixing conditions. Analysis of the nitrogen content of regulated genes shows that Nostoc PCC 7120 growing on dinitrogen is freed from any constraints to save nitrogen. For the first time the expression of high light-induced stress proteins (HLIP-family) is shown to be linked to the nitrogen availability. |
1711.08198 | Giuseppe Jurman | Valerio Maggio and Marco Chierici and Giuseppe Jurman and Cesare
Furlanello | A multiobjective deep learning approach for predictive classification in
Neuroblastoma | NIPS ML4H workshop 2017 & MAQC 2018 | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical
courses that may vary from spontaneous regression to fatal progression; an
accurate patient's risk estimation at diagnosis is essential to design
appropriate tumor treatment strategies. Neuroblastoma is a paradigm disease
where different diagnostic and prognostic endpoints should be predicted from
common molecular and clinical information, with increasing complexity, as shown
in the FDA MAQC-II study. Here we introduce the novel multiobjective deep
learning architecture CDRP (Concatenated Diagnostic Relapse Prognostic)
composed by 8 layers to obtain a combined diagnostic and prognostic prediction
from high-throughput transcriptomics data. Two distinct loss functions are
optimized for the Event Free Survival (EFS) and Overall Survival (OS)
prognosis, respectively. We use the High-Risk (HR) diagnostic information as an
additional input generated by an autoencoder embedding. The latter is used as
network regulariser, based on a clinical algorithm commonly adopted for
stratifying patients from cancer stage, age at insurgence of disease, and MYCN,
the specific molecular marker. The architecture was applied to Illumina
HiSeq2000 RNA-Seq for 498 neuroblastoma patients (176 at high risk) from the
Sequencing Quality Control (SEQC) study, obtaining state-of-art on the
diagnostic endpoint and improving prediction of prognosis over the HR cohort.
| [
{
"created": "Wed, 22 Nov 2017 09:54:48 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Dec 2017 13:38:22 GMT",
"version": "v2"
},
{
"created": "Thu, 22 Feb 2018 18:43:29 GMT",
"version": "v3"
}
] | 2018-02-23 | [
[
"Maggio",
"Valerio",
""
],
[
"Chierici",
"Marco",
""
],
[
"Jurman",
"Giuseppe",
""
],
[
"Furlanello",
"Cesare",
""
]
] | Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical courses that may vary from spontaneous regression to fatal progression; an accurate patient's risk estimation at diagnosis is essential to design appropriate tumor treatment strategies. Neuroblastoma is a paradigm disease where different diagnostic and prognostic endpoints should be predicted from common molecular and clinical information, with increasing complexity, as shown in the FDA MAQC-II study. Here we introduce the novel multiobjective deep learning architecture CDRP (Concatenated Diagnostic Relapse Prognostic) composed by 8 layers to obtain a combined diagnostic and prognostic prediction from high-throughput transcriptomics data. Two distinct loss functions are optimized for the Event Free Survival (EFS) and Overall Survival (OS) prognosis, respectively. We use the High-Risk (HR) diagnostic information as an additional input generated by an autoencoder embedding. The latter is used as network regulariser, based on a clinical algorithm commonly adopted for stratifying patients from cancer stage, age at insurgence of disease, and MYCN, the specific molecular marker. The architecture was applied to Illumina HiSeq2000 RNA-Seq for 498 neuroblastoma patients (176 at high risk) from the Sequencing Quality Control (SEQC) study, obtaining state-of-art on the diagnostic endpoint and improving prediction of prognosis over the HR cohort. |
1112.5463 | Marcus Kaiser | Sreedevi Varier and Marcus Kaiser and Rob Forsyth | Establishing, versus Maintaining, Brain Function: A Neuro-computational
Model of Cortical Reorganization after Injury to the Immature Brain | null | Journal of the International Neuropsychological Society,
17:1030-1038, 2011 | 10.1017/S1355617711000993 | null | q-bio.NC physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The effect of age at injury on outcome after acquired brain injury (ABI) has
been the subject of much debate. Many argue that young brains are relatively
tolerant of injury. A contrasting viewpoint due to Hebb argues that greater
system integrity may be required for the initial establishment of a function
than for preservation of an already-established function. A neuro-computational
model of cortical map formation was adapted to examine effects of focal and
distributed injury at various stages of development. This neural network model
requires a period of training during which it self-organizes to establish
cortical maps. Injuries were simulated by lesioning the model at various stages
of this process and network function was monitored as "development" progressed
to completion. Lesion effects are greater for larger, earlier, and distributed
(multifocal) lesions. The mature system is relatively robust, particularly to
focal injury. Activities in recovering systems injured at an early stage show
changes that emerge after an asymptomatic interval. Early injuries cause
qualitative changes in system behavior that emerge after a delay during which
the effects of the injury are latent. Functions that are incompletely
established at the time of injury may be vulnerable particularly to multifocal
injury.
| [
{
"created": "Thu, 22 Dec 2011 21:12:09 GMT",
"version": "v1"
}
] | 2011-12-26 | [
[
"Varier",
"Sreedevi",
""
],
[
"Kaiser",
"Marcus",
""
],
[
"Forsyth",
"Rob",
""
]
] | The effect of age at injury on outcome after acquired brain injury (ABI) has been the subject of much debate. Many argue that young brains are relatively tolerant of injury. A contrasting viewpoint due to Hebb argues that greater system integrity may be required for the initial establishment of a function than for preservation of an already-established function. A neuro-computational model of cortical map formation was adapted to examine effects of focal and distributed injury at various stages of development. This neural network model requires a period of training during which it self-organizes to establish cortical maps. Injuries were simulated by lesioning the model at various stages of this process and network function was monitored as "development" progressed to completion. Lesion effects are greater for larger, earlier, and distributed (multifocal) lesions. The mature system is relatively robust, particularly to focal injury. Activities in recovering systems injured at an early stage show changes that emerge after an asymptomatic interval. Early injuries cause qualitative changes in system behavior that emerge after a delay during which the effects of the injury are latent. Functions that are incompletely established at the time of injury may be vulnerable particularly to multifocal injury. |
1912.10491 | Federica Ferretti | Federica Ferretti, Victor Chard\`es, Thierry Mora, Aleksandra M.
Walczak, Irene Giardina | Building general Langevin models from discrete data sets | we correct previous inaccuracy about a reference; 29 pages, 9 figures | Phys. Rev. X 10, 031018 (2020) | 10.1103/PhysRevX.10.031018 | null | q-bio.QM cond-mat.stat-mech physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many living and complex systems exhibit second order emergent dynamics.
Limited experimental access to the configurational degrees of freedom results
in data that appears to be generated by a non-Markovian process. This poses a
challenge in the quantitative reconstruction of the model from experimental
data, even in the simple case of equilibrium Langevin dynamics of Hamiltonian
systems. We develop a novel Bayesian inference approach to learn the parameters
of such stochastic effective models from discrete finite length trajectories.
We first discuss the failure of naive inference approaches based on the
estimation of derivatives through finite differences, regardless of the time
resolution and the length of the sampled trajectories. We then derive, adopting
higher order discretization schemes, maximum likelihood estimators for the
model parameters that provide excellent results even with moderately long
trajectories. We apply our method to second order models of collective motion
and show that our results also hold in the presence of interactions.
| [
{
"created": "Sun, 22 Dec 2019 17:45:33 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Dec 2019 09:37:52 GMT",
"version": "v2"
},
{
"created": "Sat, 18 Apr 2020 12:03:59 GMT",
"version": "v3"
},
{
"created": "Wed, 13 May 2020 22:20:15 GMT",
"version": "v4"
}
] | 2020-07-29 | [
[
"Ferretti",
"Federica",
""
],
[
"Chardès",
"Victor",
""
],
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra M.",
""
],
[
"Giardina",
"Irene",
""
]
] | Many living and complex systems exhibit second order emergent dynamics. Limited experimental access to the configurational degrees of freedom results in data that appears to be generated by a non-Markovian process. This poses a challenge in the quantitative reconstruction of the model from experimental data, even in the simple case of equilibrium Langevin dynamics of Hamiltonian systems. We develop a novel Bayesian inference approach to learn the parameters of such stochastic effective models from discrete finite length trajectories. We first discuss the failure of naive inference approaches based on the estimation of derivatives through finite differences, regardless of the time resolution and the length of the sampled trajectories. We then derive, adopting higher order discretization schemes, maximum likelihood estimators for the model parameters that provide excellent results even with moderately long trajectories. We apply our method to second order models of collective motion and show that our results also hold in the presence of interactions. |
q-bio/0508004 | Ulrich Gerland | Ralf Bundschuh and Ulrich Gerland | Coupled dynamics of RNA folding and nanopore translocation | 4 pages, 3 figures, to appear in Physical Review Letters | null | 10.1103/PhysRevLett.95.208104 | null | q-bio.BM cond-mat.soft | null | The translocation of structured RNA or DNA molecules through narrow pores
necessitates the opening of all base pairs. Here, we study the interplay
between the dynamics of translocation and base-pairing theoretically, using
kinetic Monte Carlo simulations and analytical methods. We find that the
transient formation of basepairs that do not occur in the ground state can
significantly speed up translocation.
| [
{
"created": "Mon, 1 Aug 2005 17:01:23 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Sep 2005 08:04:23 GMT",
"version": "v2"
}
] | 2009-11-11 | [
[
"Bundschuh",
"Ralf",
""
],
[
"Gerland",
"Ulrich",
""
]
] | The translocation of structured RNA or DNA molecules through narrow pores necessitates the opening of all base pairs. Here, we study the interplay between the dynamics of translocation and base-pairing theoretically, using kinetic Monte Carlo simulations and analytical methods. We find that the transient formation of basepairs that do not occur in the ground state can significantly speed up translocation. |
1405.0369 | Victor M. Perez-Garcia | Rosa Pardo, Alicia Martinez-Gonzalez and Victor M. Perez-Garcia | Waves of cells with an unstable phenotype accelerate the progression of
high-grade brain tumors | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we study a reduced continuous model describing the local
evolution of high grade gliomas - a lethal type of primary brain tumor -
through the interplay of different cellular phenotypes. We show how hypoxic
events, even sporadic and/or limited in space may have a crucial role on the
acceleration of the growth speed of high grade gliomas. Our modeling approach
is based on two cellular phenotypes one of them being more migratory and the
second one more proliferative with transitions between them being driven by the
local oxygen values, assumed in this simple model to be uniform. Surprisingly
even acute hypoxia events (i.e. very localized in time) leading to the
appearance of migratory populations have the potential of accelerating the
invasion speed of the proliferative phenotype up to speeds close to those of
the migratory phenotype. The high invasion speed of the tumor persists for
times much longer than the lifetime of the hypoxic event and the phenomenon is
observed both when the migratory cells form a persistent wave of cells located
on the invasion front and when they form a evanecent wave dissapearing after a
short time by decay into the more proliferative phenotype.
Our findings are obtained through numerical simulations of the model
equations. We also provide a deeper mathematical analysis of some aspects of
the problem such as the conditions for the existence of persistent waves of
cells with a more migratory phenotype.
| [
{
"created": "Fri, 2 May 2014 10:09:49 GMT",
"version": "v1"
}
] | 2014-05-05 | [
[
"Pardo",
"Rosa",
""
],
[
"Martinez-Gonzalez",
"Alicia",
""
],
[
"Perez-Garcia",
"Victor M.",
""
]
] | In this paper we study a reduced continuous model describing the local evolution of high grade gliomas - a lethal type of primary brain tumor - through the interplay of different cellular phenotypes. We show how hypoxic events, even sporadic and/or limited in space may have a crucial role on the acceleration of the growth speed of high grade gliomas. Our modeling approach is based on two cellular phenotypes one of them being more migratory and the second one more proliferative with transitions between them being driven by the local oxygen values, assumed in this simple model to be uniform. Surprisingly even acute hypoxia events (i.e. very localized in time) leading to the appearance of migratory populations have the potential of accelerating the invasion speed of the proliferative phenotype up to speeds close to those of the migratory phenotype. The high invasion speed of the tumor persists for times much longer than the lifetime of the hypoxic event and the phenomenon is observed both when the migratory cells form a persistent wave of cells located on the invasion front and when they form a evanecent wave dissapearing after a short time by decay into the more proliferative phenotype. Our findings are obtained through numerical simulations of the model equations. We also provide a deeper mathematical analysis of some aspects of the problem such as the conditions for the existence of persistent waves of cells with a more migratory phenotype. |
1408.5517 | Adam Auton | Christopher L. Campbell, Nicholas A. Furlotte, Nick Eriksson, David
Hinds, Adam Auton | Escape from crossover interference increases with maternal age | 47 pages, 18 figures | null | 10.1038/ncomms7260 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recombination plays a fundamental role in meiosis, ensuring the proper
segregation of chromosomes and contributing to genetic diversity by generating
novel combinations of alleles. Using data derived from directUtoUconsumer
genetic testing, we investigated patterns of recombination in over 4,200
families. Our analysis revealed a number of sex differences in the distribution
of recombination. We find the fraction of male events occurring within hotspots
to be 4.6% higher than for females. We confirm that the recombination rate
increases with maternal age, while hotspot usage decreases, with no such
effects observed in males. Finally, we show that the placement of female
recombination events becomes increasingly deregulated with maternal age, with
an increasing fraction of events appearing to escape crossover interference.
| [
{
"created": "Sat, 23 Aug 2014 17:34:04 GMT",
"version": "v1"
}
] | 2015-02-20 | [
[
"Campbell",
"Christopher L.",
""
],
[
"Furlotte",
"Nicholas A.",
""
],
[
"Eriksson",
"Nick",
""
],
[
"Hinds",
"David",
""
],
[
"Auton",
"Adam",
""
]
] | Recombination plays a fundamental role in meiosis, ensuring the proper segregation of chromosomes and contributing to genetic diversity by generating novel combinations of alleles. Using data derived from directUtoUconsumer genetic testing, we investigated patterns of recombination in over 4,200 families. Our analysis revealed a number of sex differences in the distribution of recombination. We find the fraction of male events occurring within hotspots to be 4.6% higher than for females. We confirm that the recombination rate increases with maternal age, while hotspot usage decreases, with no such effects observed in males. Finally, we show that the placement of female recombination events becomes increasingly deregulated with maternal age, with an increasing fraction of events appearing to escape crossover interference. |
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