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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2310.11121 | Mintu Karmakar | Mintu Karmakar | Quarantine as a delay, not a definitive solution | 10 pages | null | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | In the realm of pandemic dynamics, understanding the intricate interplay
between disease transmission, interventions, and immunity is pivotal for
effective control strategies. Through a rigorous agent-based simulation, we
embarked on a comprehensive exploration, traversing unmitigated spread,
lockdown scenarios, and the transformative potential of vaccination. we unveil
a paradoxical trend: while quarantine unquestionably delays the pandemic peak,
it does not act as an impenetrable barrier to halt the progression of
infectious diseases. Vaccination factor revealed a potent weapon against
outbreaks higher vaccination percentage not only delayed infection peaks but
also substantially curtailed their impact. Our investigation into bond dilution
below the percolation threshold presents an additional dimension to pandemic
control. We observed that localized isolation through bond dilution offers a
targeted control strategy that can be more resource-efficient compared to
blanket lockdowns or large-scale vaccination campaigns.
| [
{
"created": "Tue, 17 Oct 2023 10:14:03 GMT",
"version": "v1"
}
] | 2023-10-18 | [
[
"Karmakar",
"Mintu",
""
]
] | In the realm of pandemic dynamics, understanding the intricate interplay between disease transmission, interventions, and immunity is pivotal for effective control strategies. Through a rigorous agent-based simulation, we embarked on a comprehensive exploration, traversing unmitigated spread, lockdown scenarios, and the transformative potential of vaccination. we unveil a paradoxical trend: while quarantine unquestionably delays the pandemic peak, it does not act as an impenetrable barrier to halt the progression of infectious diseases. Vaccination factor revealed a potent weapon against outbreaks higher vaccination percentage not only delayed infection peaks but also substantially curtailed their impact. Our investigation into bond dilution below the percolation threshold presents an additional dimension to pandemic control. We observed that localized isolation through bond dilution offers a targeted control strategy that can be more resource-efficient compared to blanket lockdowns or large-scale vaccination campaigns. |
1910.09491 | Kabir Husain | Kabir Husain, Arvind Murugan | Physical constraints on epistasis | null | null | null | null | q-bio.PE physics.bio-ph q-bio.BM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Living systems evolve one mutation at a time, but a single mutation can alter
the effect of subsequent mutations. The underlying mechanistic determinants of
such epistasis are unclear. Here, we demonstrate that the physical dynamics of
a biological system can generically constrain epistasis. We analyze models and
experimental data on proteins and regulatory networks. In each, we find that if
the long-time physical dynamics is dominated by a slow, collective mode, then
the dimensionality of mutational effects is reduced. Consequently, epistatic
coefficients for different combinations of mutations are no longer independent,
even if individually strong. Such epistasis can be summarized as resulting from
a global non-linearity applied to an underlying linear trait, i.e., as global
epistasis. This constraint, in turn, reduces the ruggedness of the
sequence-to-function map. By providing a generic mechanistic origin for
experimentally observed global epistasis, our work suggests that slow
collective physical modes can make biological systems evolvable.
| [
{
"created": "Mon, 21 Oct 2019 16:28:38 GMT",
"version": "v1"
}
] | 2019-10-22 | [
[
"Husain",
"Kabir",
""
],
[
"Murugan",
"Arvind",
""
]
] | Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis. We analyze models and experimental data on proteins and regulatory networks. In each, we find that if the long-time physical dynamics is dominated by a slow, collective mode, then the dimensionality of mutational effects is reduced. Consequently, epistatic coefficients for different combinations of mutations are no longer independent, even if individually strong. Such epistasis can be summarized as resulting from a global non-linearity applied to an underlying linear trait, i.e., as global epistasis. This constraint, in turn, reduces the ruggedness of the sequence-to-function map. By providing a generic mechanistic origin for experimentally observed global epistasis, our work suggests that slow collective physical modes can make biological systems evolvable. |
1912.12553 | Yang Shen | Mostafa Karimi, Di Wu, Zhangyang Wang, Yang Shen | Explainable Deep Relational Networks for Predicting Compound-Protein
Affinities and Contacts | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Predicting compound-protein affinity is critical for accelerating drug
discovery. Recent progress made by machine learning focuses on accuracy but
leaves much to be desired for interpretability. Through molecular contacts
underlying affinities, our large-scale interpretability assessment finds
commonly-used attention mechanisms inadequate. We thus formulate a hierarchical
multi-objective learning problem whose predicted contacts form the basis for
predicted affinities. We further design a physics-inspired deep relational
network, DeepRelations, with intrinsically explainable architecture.
Specifically, various atomic-level contacts or "relations" lead to
molecular-level affinity prediction. And the embedded attentions are
regularized with predicted structural contexts and supervised with partially
available training contacts. DeepRelations shows superior interpretability to
the state-of-the-art: without compromising affinity prediction, it boosts the
AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test,
compound-unique, protein-unique, and both-unique sets, respectively. Our study
represents the first dedicated model development and systematic model
assessment for interpretable machine learning of compound-protein affinity.
| [
{
"created": "Sun, 29 Dec 2019 00:14:07 GMT",
"version": "v1"
}
] | 2020-01-01 | [
[
"Karimi",
"Mostafa",
""
],
[
"Wu",
"Di",
""
],
[
"Wang",
"Zhangyang",
""
],
[
"Shen",
"Yang",
""
]
] | Predicting compound-protein affinity is critical for accelerating drug discovery. Recent progress made by machine learning focuses on accuracy but leaves much to be desired for interpretability. Through molecular contacts underlying affinities, our large-scale interpretability assessment finds commonly-used attention mechanisms inadequate. We thus formulate a hierarchical multi-objective learning problem whose predicted contacts form the basis for predicted affinities. We further design a physics-inspired deep relational network, DeepRelations, with intrinsically explainable architecture. Specifically, various atomic-level contacts or "relations" lead to molecular-level affinity prediction. And the embedded attentions are regularized with predicted structural contexts and supervised with partially available training contacts. DeepRelations shows superior interpretability to the state-of-the-art: without compromising affinity prediction, it boosts the AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test, compound-unique, protein-unique, and both-unique sets, respectively. Our study represents the first dedicated model development and systematic model assessment for interpretable machine learning of compound-protein affinity. |
2110.03339 | Johann Summhammer | Johann Summhammer | Morphology and high frequency bio-electric fields | 13 pages, including 4 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate possible shapes of the electric field, which oscillating
dipoles in a certain region of biological tissue can produce in a neighboring
region, or outside the tissue boundaries. We find that a wide range of shapes,
including the typical morphology of limbs and appendages, can be generated as a
zone of extremely low field amplitudes embedded in a zone of much larger field
amplitudes. Neutral molecules with a resonance close to the frequency of the
oscillating field may be attracted to this zone or be repelled from it, while
the driving effect on molecules with an electric charge is only extremely weak.
The forces would be sufficient for the controlled deposition of molecules
during growth or regeneration. They could also serve as a method of information
transfer.
| [
{
"created": "Thu, 7 Oct 2021 11:08:43 GMT",
"version": "v1"
},
{
"created": "Tue, 1 Feb 2022 10:21:04 GMT",
"version": "v2"
}
] | 2022-02-02 | [
[
"Summhammer",
"Johann",
""
]
] | We investigate possible shapes of the electric field, which oscillating dipoles in a certain region of biological tissue can produce in a neighboring region, or outside the tissue boundaries. We find that a wide range of shapes, including the typical morphology of limbs and appendages, can be generated as a zone of extremely low field amplitudes embedded in a zone of much larger field amplitudes. Neutral molecules with a resonance close to the frequency of the oscillating field may be attracted to this zone or be repelled from it, while the driving effect on molecules with an electric charge is only extremely weak. The forces would be sufficient for the controlled deposition of molecules during growth or regeneration. They could also serve as a method of information transfer. |
1706.10106 | Tanja Stadler | Tanja Stadler, Alexandra Gavryushkina, Rachel C.M. Warnock, Alexei J.
Drummond, Tracy A. Heath | The fossilized birth-death model for the analysis of stratigraphic range
data under different speciation concepts | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A birth-death-sampling model gives rise to phylogenetic trees with samples
from the past and the present. Interpreting "birth" as branching speciation,
"death" as extinction, and "sampling" as fossil preservation and recovery, this
model -- also referred to as the fossilized birth-death (FBD) model -- gives
rise to phylogenetic trees on extant and fossil samples. The model has been
mathematically analyzed and successfully applied to a range of datasets on
different taxonomic levels, such as penguins, plants, and insects. However, the
current mathematical treatment of this model does not allow for a group of
temporally distinct fossil specimens to be assigned to the same species. In
this paper, we provide a general mathematical FBD modeling framework that
explicitly takes "stratigraphic ranges" into account, with a stratigraphic
range being defined as the lineage interval associated with a single species,
ranging through time from the first to the last fossil appearance of the
species. To assign a sequence of fossil samples in the phylogenetic tree to the
same species, i.e., to specify a stratigraphic range, we need to define the
mode of speciation. We provide expressions to account for three common
speciation modes: budding (or asymmetric) speciation, bifurcating (or
symmetric) speciation, and anagenetic speciation. Our equations allow for
flexible joint Bayesian analysis of paleontological and neontological data.
Furthermore, our framework is directly applicable to epidemiology, where a
stratigraphic range is the observed duration of infection of a single patient,
"birth" via budding is transmission, "death" is recovery, and "sampling" is
sequencing the pathogen of a patient. Thus, we present a model that allows for
incorporation of multiple observations through time from a single patient.
| [
{
"created": "Fri, 30 Jun 2017 10:29:04 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Mar 2018 13:19:33 GMT",
"version": "v2"
}
] | 2018-03-12 | [
[
"Stadler",
"Tanja",
""
],
[
"Gavryushkina",
"Alexandra",
""
],
[
"Warnock",
"Rachel C. M.",
""
],
[
"Drummond",
"Alexei J.",
""
],
[
"Heath",
"Tracy A.",
""
]
] | A birth-death-sampling model gives rise to phylogenetic trees with samples from the past and the present. Interpreting "birth" as branching speciation, "death" as extinction, and "sampling" as fossil preservation and recovery, this model -- also referred to as the fossilized birth-death (FBD) model -- gives rise to phylogenetic trees on extant and fossil samples. The model has been mathematically analyzed and successfully applied to a range of datasets on different taxonomic levels, such as penguins, plants, and insects. However, the current mathematical treatment of this model does not allow for a group of temporally distinct fossil specimens to be assigned to the same species. In this paper, we provide a general mathematical FBD modeling framework that explicitly takes "stratigraphic ranges" into account, with a stratigraphic range being defined as the lineage interval associated with a single species, ranging through time from the first to the last fossil appearance of the species. To assign a sequence of fossil samples in the phylogenetic tree to the same species, i.e., to specify a stratigraphic range, we need to define the mode of speciation. We provide expressions to account for three common speciation modes: budding (or asymmetric) speciation, bifurcating (or symmetric) speciation, and anagenetic speciation. Our equations allow for flexible joint Bayesian analysis of paleontological and neontological data. Furthermore, our framework is directly applicable to epidemiology, where a stratigraphic range is the observed duration of infection of a single patient, "birth" via budding is transmission, "death" is recovery, and "sampling" is sequencing the pathogen of a patient. Thus, we present a model that allows for incorporation of multiple observations through time from a single patient. |
2008.05903 | Stefan Klus | Kateryna Melnyk, Stefan Klus, Gr\'egoire Montavon, Tim Conrad | GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis | null | null | 10.1007/s41109-020-00339-2 | null | q-bio.QM cs.LG q-bio.GN stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | More and more diseases have been found to be strongly correlated with
disturbances in the microbiome constitution, e.g., obesity, diabetes, or some
cancer types. Thanks to modern high-throughput omics technologies, it becomes
possible to directly analyze human microbiome and its influence on the health
status. Microbial communities are monitored over long periods of time and the
associations between their members are explored. These relationships can be
described by a time-evolving graph. In order to understand responses of the
microbial community members to a distinct range of perturbations such as
antibiotics exposure or diseases and general dynamical properties, the
time-evolving graph of the human microbial communities has to be analyzed. This
becomes especially challenging due to dozens of complex interactions among
microbes and metastable dynamics. The key to solving this problem is the
representation of the time-evolving graphs as fixed-length feature vectors
preserving the original dynamics. We propose a method for learning the
embedding of the time-evolving graph that is based on the spectral analysis of
transfer operators and graph kernels. We demonstrate that our method can
capture temporary changes in the time-evolving graph on both created synthetic
data and real-world data. Our experiments demonstrate the efficacy of the
method. Furthermore, we show that our method can be applied to human microbiome
data to study dynamic processes.
| [
{
"created": "Wed, 12 Aug 2020 10:57:02 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Sep 2020 09:35:33 GMT",
"version": "v2"
},
{
"created": "Thu, 19 Nov 2020 12:06:13 GMT",
"version": "v3"
}
] | 2021-04-06 | [
[
"Melnyk",
"Kateryna",
""
],
[
"Klus",
"Stefan",
""
],
[
"Montavon",
"Grégoire",
""
],
[
"Conrad",
"Tim",
""
]
] | More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both created synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes. |
2310.04317 | Alexander Fleischmann | Andrea Pierr\'e, Tuan Pham, Jonah Pearl, Sandeep Robert Datta, Jason
T. Ritt, and Alexander Fleischmann | A perspective on neuroscience data standardization with Neurodata
Without Borders | 32 pages, 9 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Neuroscience research has evolved to generate increasingly large and complex
experimental data sets, and advanced data science tools are taking on central
roles in neuroscience research. Neurodata Without Borders (NWB), a standard
language for neurophysiology data, has recently emerged as a powerful solution
for data management, analysis, and sharing. We here discuss our efforts to
implement NWB data science pipelines. We describe general principles and
specific use cases that illustrate successes, challenges, and non-trivial
decisions in software engineering. We hope that our experience can provide
guidance for the neuroscience community and help bridge the gap between
experimental neuroscience and data science.
| [
{
"created": "Fri, 6 Oct 2023 15:28:51 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Jan 2024 17:45:24 GMT",
"version": "v2"
}
] | 2024-01-23 | [
[
"Pierré",
"Andrea",
""
],
[
"Pham",
"Tuan",
""
],
[
"Pearl",
"Jonah",
""
],
[
"Datta",
"Sandeep Robert",
""
],
[
"Ritt",
"Jason T.",
""
],
[
"Fleischmann",
"Alexander",
""
]
] | Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science. |
1007.2689 | Alistair Forrest | A. R. R. Forrest, M. Kanamori-Katayama, Y. Tomaru, T. Lassmann, N.
Ninomiya, Y. Takahashi, M. J. L. de Hoon, A. Kubosaki, A. Kaiho, M. Suzuki,
J. Yasuda, J. Kawai, Y. Hayashizaki, D. A. Hume and H. Suzuki | Induction of microRNAs, mir-155, mir-222, mir-424 and mir-503, promotes
monocytic differentiation through combinatorial regulation | 45 pages 5 figures | Leukemia. 24 (2010) 460-6 | 10.1038/leu.2009.246 | null | q-bio.GN q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Acute myeloid leukemia (AML) involves a block in terminal differentiation of
the myeloid lineage and uncontrolled proliferation of a progenitor state. Using
phorbol myristate acetate (PMA), it is possible to overcome this block in THP-1
cells (an M5-AML containing the MLL-MLLT3 fusion), resulting in differentiation
to an adherent monocytic phenotype. As part of FANTOM4, we used microarrays to
identify 23 microRNAs that are regulated by PMA. We identify four PMA-induced
micro- RNAs (mir-155, mir-222, mir-424 and mir-503) that when overexpressed
cause cell-cycle arrest and partial differentiation and when used in
combination induce additional changes not seen by any individual microRNA. We
further characterize these prodifferentiative microRNAs and show that mir-155
and mir-222 induce G2 arrest and apoptosis, respectively. We find mir-424 and
mir-503 are derived from a polycistronic precursor mir-424-503 that is under
repression by the MLL-MLLT3 leukemogenic fusion. Both of these microRNAs
directly target cell-cycle regulators and induce G1 cell-cycle arrest when
overexpressed in THP-1. We also find that the pro-differentiative mir-424 and
mir-503 downregulate the anti-differentiative mir-9 by targeting a site in its
primary transcript. Our study highlights the combinatorial effects of multiple
microRNAs within cellular systems.
| [
{
"created": "Fri, 16 Jul 2010 02:22:59 GMT",
"version": "v1"
}
] | 2010-07-19 | [
[
"Forrest",
"A. R. R.",
""
],
[
"Kanamori-Katayama",
"M.",
""
],
[
"Tomaru",
"Y.",
""
],
[
"Lassmann",
"T.",
""
],
[
"Ninomiya",
"N.",
""
],
[
"Takahashi",
"Y.",
""
],
[
"de Hoon",
"M. J. L.",
""
],
[
"Kubosaki",
"A.",
""
],
[
"Kaiho",
"A.",
""
],
[
"Suzuki",
"M.",
""
],
[
"Yasuda",
"J.",
""
],
[
"Kawai",
"J.",
""
],
[
"Hayashizaki",
"Y.",
""
],
[
"Hume",
"D. A.",
""
],
[
"Suzuki",
"H.",
""
]
] | Acute myeloid leukemia (AML) involves a block in terminal differentiation of the myeloid lineage and uncontrolled proliferation of a progenitor state. Using phorbol myristate acetate (PMA), it is possible to overcome this block in THP-1 cells (an M5-AML containing the MLL-MLLT3 fusion), resulting in differentiation to an adherent monocytic phenotype. As part of FANTOM4, we used microarrays to identify 23 microRNAs that are regulated by PMA. We identify four PMA-induced micro- RNAs (mir-155, mir-222, mir-424 and mir-503) that when overexpressed cause cell-cycle arrest and partial differentiation and when used in combination induce additional changes not seen by any individual microRNA. We further characterize these prodifferentiative microRNAs and show that mir-155 and mir-222 induce G2 arrest and apoptosis, respectively. We find mir-424 and mir-503 are derived from a polycistronic precursor mir-424-503 that is under repression by the MLL-MLLT3 leukemogenic fusion. Both of these microRNAs directly target cell-cycle regulators and induce G1 cell-cycle arrest when overexpressed in THP-1. We also find that the pro-differentiative mir-424 and mir-503 downregulate the anti-differentiative mir-9 by targeting a site in its primary transcript. Our study highlights the combinatorial effects of multiple microRNAs within cellular systems. |
2303.11494 | Patricia Suriana | Patricia Suriana, Joseph M. Paggi, Ron O. Dror | FlexVDW: A machine learning approach to account for protein flexibility
in ligand docking | Published at the MLDD workshop, International Conference on Learning
Representations (ICLR) 2023 | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Most widely used ligand docking methods assume a rigid protein structure.
This leads to problems when the structure of the target protein deforms upon
ligand binding. In particular, the ligand's true binding pose is often scored
very unfavorably due to apparent clashes between ligand and protein atoms,
which lead to extremely high values of the calculated van der Waals energy
term. Traditionally, this problem has been addressed by explicitly searching
for receptor conformations to account for the flexibility of the receptor in
ligand binding. Here we present a deep learning model trained to take receptor
flexibility into account implicitly when predicting van der Waals energy. We
show that incorporating this machine-learned energy term into a
state-of-the-art physics-based scoring function improves small molecule ligand
pose prediction results in cases with substantial protein deformation, without
degrading performance in cases with minimal protein deformation. This work
demonstrates the feasibility of learning effects of protein flexibility on
ligand binding without explicitly modeling changes in protein structure.
| [
{
"created": "Mon, 20 Mar 2023 23:19:05 GMT",
"version": "v1"
}
] | 2023-03-22 | [
[
"Suriana",
"Patricia",
""
],
[
"Paggi",
"Joseph M.",
""
],
[
"Dror",
"Ron O.",
""
]
] | Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular, the ligand's true binding pose is often scored very unfavorably due to apparent clashes between ligand and protein atoms, which lead to extremely high values of the calculated van der Waals energy term. Traditionally, this problem has been addressed by explicitly searching for receptor conformations to account for the flexibility of the receptor in ligand binding. Here we present a deep learning model trained to take receptor flexibility into account implicitly when predicting van der Waals energy. We show that incorporating this machine-learned energy term into a state-of-the-art physics-based scoring function improves small molecule ligand pose prediction results in cases with substantial protein deformation, without degrading performance in cases with minimal protein deformation. This work demonstrates the feasibility of learning effects of protein flexibility on ligand binding without explicitly modeling changes in protein structure. |
2208.11293 | Dexuan Xie | Dexuan Xie | An Extension of Goldman-Hodgkin-Katz Equations by Charges from Ionic
Solution and Ion Channel Protein | 18 pages, 3 figures, two tables | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Goldman-Hodgkin-Katz (GHK) equations have been widely applied to ion
channel studies, simulations, and model developments. However, they are
constructed under a constant electric field, causing them to have a low degree
of approximation in the prediction of ionic fluxes, electric currents, and
membrane potentials. In this paper, the equations are extended from the
constant electric field to the nonlinear electric field induced by charges from
an ionic solution and an ion channel protein. Furthermore, a novel numerical
quadrature scheme is developed to estimate one major parameter, called the
extension parameter, of the extended GHK equations in terms of a set of
electrostatic potential values. To this end, the extended GHK equations become
a bridge between the "macroscopic" ion channel kinetics and the "microscopic"
electrostatic potential values across a cell membrane. To generate a set of
required electrostatic potential values, a nonlinear finite element iterative
scheme for solving a one-dimensional Poisson-Nernst-Planck ion channel model is
developed and implemented as a Python software package. This package is then
used to do numerical studies on the extended GHK equations, the numerical
quadrature scheme, and the nonlinear iterative scheme. Numerical results
confirm the importance of considering charge effects in the calculation of
ionic fluxes. They also validate the high numerical accuracy of the numerical
quadrature scheme, the fast convergence rate of the nonlinear iterative scheme,
and the high performance of the software package.
| [
{
"created": "Wed, 24 Aug 2022 04:12:18 GMT",
"version": "v1"
}
] | 2022-08-25 | [
[
"Xie",
"Dexuan",
""
]
] | The Goldman-Hodgkin-Katz (GHK) equations have been widely applied to ion channel studies, simulations, and model developments. However, they are constructed under a constant electric field, causing them to have a low degree of approximation in the prediction of ionic fluxes, electric currents, and membrane potentials. In this paper, the equations are extended from the constant electric field to the nonlinear electric field induced by charges from an ionic solution and an ion channel protein. Furthermore, a novel numerical quadrature scheme is developed to estimate one major parameter, called the extension parameter, of the extended GHK equations in terms of a set of electrostatic potential values. To this end, the extended GHK equations become a bridge between the "macroscopic" ion channel kinetics and the "microscopic" electrostatic potential values across a cell membrane. To generate a set of required electrostatic potential values, a nonlinear finite element iterative scheme for solving a one-dimensional Poisson-Nernst-Planck ion channel model is developed and implemented as a Python software package. This package is then used to do numerical studies on the extended GHK equations, the numerical quadrature scheme, and the nonlinear iterative scheme. Numerical results confirm the importance of considering charge effects in the calculation of ionic fluxes. They also validate the high numerical accuracy of the numerical quadrature scheme, the fast convergence rate of the nonlinear iterative scheme, and the high performance of the software package. |
2202.01682 | James Whittington | James C.R. Whittington, David McCaffary, Jacob J.W. Bakermans, Timothy
E.J. Behrens | How to build a cognitive map: insights from models of the hippocampal
formation | null | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Learning and interpreting the structure of the environment is an innate
feature of biological systems, and is integral to guiding flexible behaviours
for evolutionary viability. The concept of a cognitive map has emerged as one
of the leading metaphors for these capacities, and unravelling the learning and
neural representation of such a map has become a central focus of neuroscience.
While experimentalists are providing a detailed picture of the neural substrate
of cognitive maps in hippocampus and beyond, theorists have been busy building
models to bridge the divide between neurons, computation, and behaviour. These
models can account for a variety of known representations and neural phenomena,
but often provide a differing understanding of not only the underlying
principles of cognitive maps, but also the respective roles of hippocampus and
cortex. In this Perspective, we bring many of these models into a common
language, distil their underlying principles of constructing cognitive maps,
provide novel (re)interpretations for neural phenomena, suggest how the
principles can be extended to account for prefrontal cortex representations
and, finally, speculate on the role of cognitive maps in higher cognitive
capacities.
| [
{
"created": "Thu, 3 Feb 2022 16:49:37 GMT",
"version": "v1"
}
] | 2022-02-04 | [
[
"Whittington",
"James C. R.",
""
],
[
"McCaffary",
"David",
""
],
[
"Bakermans",
"Jacob J. W.",
""
],
[
"Behrens",
"Timothy E. J.",
""
]
] | Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviours for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unravelling the learning and neural representation of such a map has become a central focus of neuroscience. While experimentalists are providing a detailed picture of the neural substrate of cognitive maps in hippocampus and beyond, theorists have been busy building models to bridge the divide between neurons, computation, and behaviour. These models can account for a variety of known representations and neural phenomena, but often provide a differing understanding of not only the underlying principles of cognitive maps, but also the respective roles of hippocampus and cortex. In this Perspective, we bring many of these models into a common language, distil their underlying principles of constructing cognitive maps, provide novel (re)interpretations for neural phenomena, suggest how the principles can be extended to account for prefrontal cortex representations and, finally, speculate on the role of cognitive maps in higher cognitive capacities. |
1002.2745 | Jose A. Cuesta | Susanna C. Manrubia and Jose A. Cuesta | Neutral networks of genotypes: Evolution behind the curtain | 7 pages, 7 color figures, uses a modification of pnastwo.cls called
pnastwo-modified.cls (included) | ARBOR, 186, 1051-1064 (2010) | 10.3989/arbor.2010.746n1253 | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our understanding of the evolutionary process has gone a long way since the
publication, 150 years ago, of "On the origin of species" by Charles R. Darwin.
The XXth Century witnessed great efforts to embrace replication, mutation, and
selection within the framework of a formal theory, able eventually to predict
the dynamics and fate of evolving populations. However, a large body of
empirical evidence collected over the last decades strongly suggests that some
of the assumptions of those classical models necessitate a deep revision. The
viability of organisms is not dependent on a unique and optimal genotype. The
discovery of huge sets of genotypes (or neutral networks) yielding the same
phenotype --in the last term the same organism--, reveals that, most likely,
very different functional solutions can be found, accessed and fixed in a
population through a low-cost exploration of the space of genomes. The
'evolution behind the curtain' may be the answer to some of the current puzzles
that evolutionary theory faces, like the fast speciation process that is
observed in the fossil record after very long stasis periods.
| [
{
"created": "Sun, 14 Feb 2010 02:49:09 GMT",
"version": "v1"
}
] | 2012-02-02 | [
[
"Manrubia",
"Susanna C.",
""
],
[
"Cuesta",
"Jose A.",
""
]
] | Our understanding of the evolutionary process has gone a long way since the publication, 150 years ago, of "On the origin of species" by Charles R. Darwin. The XXth Century witnessed great efforts to embrace replication, mutation, and selection within the framework of a formal theory, able eventually to predict the dynamics and fate of evolving populations. However, a large body of empirical evidence collected over the last decades strongly suggests that some of the assumptions of those classical models necessitate a deep revision. The viability of organisms is not dependent on a unique and optimal genotype. The discovery of huge sets of genotypes (or neutral networks) yielding the same phenotype --in the last term the same organism--, reveals that, most likely, very different functional solutions can be found, accessed and fixed in a population through a low-cost exploration of the space of genomes. The 'evolution behind the curtain' may be the answer to some of the current puzzles that evolutionary theory faces, like the fast speciation process that is observed in the fossil record after very long stasis periods. |
2207.05335 | Dimitri Loutchko | Dimitri Loutchko | An algebraic characterization of self-generating chemical reaction
networks using semigroup models | 33 pages, 6 figures | null | 10.1007/s00285-023-01899-4 | null | q-bio.MN math.CO math.RA physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | The ability of a chemical reaction network to generate itself by catalyzed
reactions from constantly present environmental food sources is considered a
fundamental property in origin-of-life research. Based on Kaufmann's
autocatalytic sets, Hordijk and Steel have constructed the versatile formalism
of catalytic reaction systems (CRS) to model and to analyze such
self-generating networks, which they named reflexively autocatalytic and food
generated (RAF). Previously, it was established that the subsequent and
simultaenous catalytic functions of the chemicals of a CRS give rise to an
algebraic structure, termed a semigroup model. The semigroup model allows to
naturally consider the function of any subset of chemicals on the whole CRS.
This gives rise to a generative dynamics by iteratively applying the function
of a subset to the externally supplied food set. The fixed point of this
dynamics yields the maximal self-generating set of chemicals. Moreover, the
lattice of all functionally closed self-generating sets of chemicals is
discussed and a structure theorem for this lattice is proven. It is also shown
that a CRS which contains self-generating sets of chemicals cannot be nilpotent
and thus a useful link to the combinatorial theory of finite semigroups is
established. The main technical tool introduced and utilized in this work is
the representation of the semigroup elements as decorated rooted trees,
allowing to translate the generation of chemicals from a given set of resources
into the semigroup language.
| [
{
"created": "Tue, 12 Jul 2022 06:27:22 GMT",
"version": "v1"
}
] | 2023-08-16 | [
[
"Loutchko",
"Dimitri",
""
]
] | The ability of a chemical reaction network to generate itself by catalyzed reactions from constantly present environmental food sources is considered a fundamental property in origin-of-life research. Based on Kaufmann's autocatalytic sets, Hordijk and Steel have constructed the versatile formalism of catalytic reaction systems (CRS) to model and to analyze such self-generating networks, which they named reflexively autocatalytic and food generated (RAF). Previously, it was established that the subsequent and simultaenous catalytic functions of the chemicals of a CRS give rise to an algebraic structure, termed a semigroup model. The semigroup model allows to naturally consider the function of any subset of chemicals on the whole CRS. This gives rise to a generative dynamics by iteratively applying the function of a subset to the externally supplied food set. The fixed point of this dynamics yields the maximal self-generating set of chemicals. Moreover, the lattice of all functionally closed self-generating sets of chemicals is discussed and a structure theorem for this lattice is proven. It is also shown that a CRS which contains self-generating sets of chemicals cannot be nilpotent and thus a useful link to the combinatorial theory of finite semigroups is established. The main technical tool introduced and utilized in this work is the representation of the semigroup elements as decorated rooted trees, allowing to translate the generation of chemicals from a given set of resources into the semigroup language. |
1707.09046 | Ali Yousefi | Ali Yousefi, Theodore W. Berger | Time Divergence-Convergence Learning Scheme in Multi-Layer Dynamic
Synapse Neural Networks | 13 pages | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new learning scheme called time divergence-convergence (TDC) is proposed
for two-layer dynamic synapse neural networks (DSNN). DSNN is an artificial
neural network model, in which the synaptic transmission is modeled by a
dynamic process and the information between neurons are transmitted through
spike timing. In TDC, the intra-layer neurons of a DSNN are trained to map
input spike trains to a higher dimension of spike trains called a
feature-domain, and the output neurons are trained to build the desired spike
trains by processing the spike timing of intralayer neurons. The DSNN
performance was examined in a jittered spike train classification task which
shows more than 92\% accuracy in classifying different spike trains. The DSNN
performance is comparable with the recurrent multi-layer neural networks and
surpasses a single-layer DSNN with a 22\% margin. Synaptic dynamics have been
proposed as the neural substrate for sub-second temporal processing; we can
utilize TDC to train a DSNN to perform diverse forms of sub-second temporal
processing. The TDC learning proposed here is scalable in terms of the synaptic
adaptation of deeper layers of multi-layer DSNNs. The DSNN along with TDC
learning proposed here can be used in to replicate the processing observed in
neural circuitry and in pattern recognition tasks.
| [
{
"created": "Tue, 25 Jul 2017 23:35:17 GMT",
"version": "v1"
}
] | 2017-07-31 | [
[
"Yousefi",
"Ali",
""
],
[
"Berger",
"Theodore W.",
""
]
] | A new learning scheme called time divergence-convergence (TDC) is proposed for two-layer dynamic synapse neural networks (DSNN). DSNN is an artificial neural network model, in which the synaptic transmission is modeled by a dynamic process and the information between neurons are transmitted through spike timing. In TDC, the intra-layer neurons of a DSNN are trained to map input spike trains to a higher dimension of spike trains called a feature-domain, and the output neurons are trained to build the desired spike trains by processing the spike timing of intralayer neurons. The DSNN performance was examined in a jittered spike train classification task which shows more than 92\% accuracy in classifying different spike trains. The DSNN performance is comparable with the recurrent multi-layer neural networks and surpasses a single-layer DSNN with a 22\% margin. Synaptic dynamics have been proposed as the neural substrate for sub-second temporal processing; we can utilize TDC to train a DSNN to perform diverse forms of sub-second temporal processing. The TDC learning proposed here is scalable in terms of the synaptic adaptation of deeper layers of multi-layer DSNNs. The DSNN along with TDC learning proposed here can be used in to replicate the processing observed in neural circuitry and in pattern recognition tasks. |
q-bio/0402021 | William Chen | William W. Chen, Jeremy L. England, Eugene I. Shakhnovich | An Exact Model of Fluctuations in Gene Expression | 15 pages, 2 figures, RevTeX4 | null | null | null | q-bio.MN cond-mat.soft | null | Fluctuations in the measured mRNA levels of unperturbed cells under fixed
conditions have often been viewed as an impediment to the extraction of
information from expression profiles. Here, we argue that such expression
fluctuations should themselves be studied as a source of valuable information
about the underlying dynamics of genetic networks. By analyzing microarray data
taken from Saccharomyces cerevisiae, we demonstrate that correlations in
expression fluctuations have a highly statistically significant dependence on
gene function, and furthermore exhibit a remarkable scale-free network
structure. We therefore present what we view to be the simplest
phenomenological model of a genetic network which can account for the presence
of biological information in transcript level fluctuations. We proceed to
exactly solve this model using a path integral technique and derive several
quantitative predictions. Finally, we propose several experiments by which
these predictions might be rigorously tested.
| [
{
"created": "Tue, 10 Feb 2004 20:21:47 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Chen",
"William W.",
""
],
[
"England",
"Jeremy L.",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] | Fluctuations in the measured mRNA levels of unperturbed cells under fixed conditions have often been viewed as an impediment to the extraction of information from expression profiles. Here, we argue that such expression fluctuations should themselves be studied as a source of valuable information about the underlying dynamics of genetic networks. By analyzing microarray data taken from Saccharomyces cerevisiae, we demonstrate that correlations in expression fluctuations have a highly statistically significant dependence on gene function, and furthermore exhibit a remarkable scale-free network structure. We therefore present what we view to be the simplest phenomenological model of a genetic network which can account for the presence of biological information in transcript level fluctuations. We proceed to exactly solve this model using a path integral technique and derive several quantitative predictions. Finally, we propose several experiments by which these predictions might be rigorously tested. |
1604.01308 | Carsten Mehring | Luke Bashford, Jing Wu, Devapratim Sarma, Kelly Collins, Jeff Ojemann,
Carsten Mehring | Natural movement with concurrent brain-computer interface control
induces persistent dissociation of neural activity | 2 pages, 3 figures, submitted to the annual BCI research award 2016
http://www.bci-award.com/ | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As Brain-computer interface (BCI) technology develops it is likely it may be
incorporated into protocols that complement and supplement existing movements
of the user. Two possible scenarios for such a control could be: the increasing
interest to control artificial supernumerary prosthetics, or in cases following
brain injury where BCI can be incorporated alongside residual movements to
recover ability. In this study we explore the extent to which the human motor
cortex is able to concurrently control movements via a BCI and overtly executed
movements. Crucially both movement types are driven from the same cortical
site. With this we aim to dissociate the activity at this cortical site from
the movements being made and instead allow the representation and control for
the BCI to develop alongside motor cortex activity. We investigated both BCI
performance and its effect on the movement evoked potentials originally
associated with overt execution.
| [
{
"created": "Tue, 5 Apr 2016 15:53:28 GMT",
"version": "v1"
}
] | 2016-04-06 | [
[
"Bashford",
"Luke",
""
],
[
"Wu",
"Jing",
""
],
[
"Sarma",
"Devapratim",
""
],
[
"Collins",
"Kelly",
""
],
[
"Ojemann",
"Jeff",
""
],
[
"Mehring",
"Carsten",
""
]
] | As Brain-computer interface (BCI) technology develops it is likely it may be incorporated into protocols that complement and supplement existing movements of the user. Two possible scenarios for such a control could be: the increasing interest to control artificial supernumerary prosthetics, or in cases following brain injury where BCI can be incorporated alongside residual movements to recover ability. In this study we explore the extent to which the human motor cortex is able to concurrently control movements via a BCI and overtly executed movements. Crucially both movement types are driven from the same cortical site. With this we aim to dissociate the activity at this cortical site from the movements being made and instead allow the representation and control for the BCI to develop alongside motor cortex activity. We investigated both BCI performance and its effect on the movement evoked potentials originally associated with overt execution. |
1405.5993 | Kohaku H. Z. So | Kohaku H. Z. So, Hisashi Ohtsuki, Takeo Kato | Spatial effect on stochastic dynamics of bistable evolutionary games | null | J. Stat. Mech. (2014) P10020 | 10.1088/1742-5468/2014/10/P10020 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the lifetimes of metastable states in bistable evolutionary games
(coordination games), and examine how they are affected by spatial structure. A
semiclassical approximation based on a path integral method is applied to
stochastic evolutionary game dynamics with and without spatial structure, and
the lifetimes of the metastable states are evaluated. It is shown that the
population dependence of the lifetimes is qualitatively different in these two
models. Our result indicates that spatial structure can accelerate the
transitions between metastable states.
| [
{
"created": "Fri, 23 May 2014 09:06:36 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Aug 2014 03:57:36 GMT",
"version": "v2"
}
] | 2014-12-01 | [
[
"So",
"Kohaku H. Z.",
""
],
[
"Ohtsuki",
"Hisashi",
""
],
[
"Kato",
"Takeo",
""
]
] | We consider the lifetimes of metastable states in bistable evolutionary games (coordination games), and examine how they are affected by spatial structure. A semiclassical approximation based on a path integral method is applied to stochastic evolutionary game dynamics with and without spatial structure, and the lifetimes of the metastable states are evaluated. It is shown that the population dependence of the lifetimes is qualitatively different in these two models. Our result indicates that spatial structure can accelerate the transitions between metastable states. |
1211.0194 | Chaitanya A. Athale | Saurabh Mahajan and Chaitanya A. Athale | Spatial and Temporal Sensing Limits of Microtubule Polarization in
Neuronal Growth Cones by Intracellular Gradients and Forces | 7 figures and supplementary material | Biophys. J. 103(12) 2432-2445, 19 December 2012 | 10.1016/j.bpj.2012.10.021 | null | q-bio.SC physics.bio-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuronal growth cones are the most sensitive amongst eukaryotic cells in
responding to directional chemical cues. Although a dynamic microtubule
cytoskeleton has been shown to be essential for growth cone turning, the
precise nature of coupling of the spatial cue with microtubule polarization is
less understood. Here we present a computational model of microtubule
polarization in a turning neuronal growth cone (GC). We explore the limits of
directional cues in modifying the spatial polarization of microtubules by
testing the role of microtubule dynamics, gradients of regulators and
retrograde forces along filopodia. We analyze the steady state and transition
behavior of microtubules on being presented with a directional stimulus. The
model makes novel predictions about the minimal angular spread of the chemical
signal at the growth cone and the fastest polarization times. A regulatory
reaction-diffusion network based on the cyclic
phosphorylation-dephosphorylation of a regulator predicts that the receptor
signal magnitude can generate the maximal polarization of microtubules and not
feedback loops or amplifications in the network. Using both the
phenomenological and network models we have demonstrated some of the physical
limits within which the MT polarization system works in turning neuron.
| [
{
"created": "Thu, 1 Nov 2012 14:41:41 GMT",
"version": "v1"
}
] | 2019-05-16 | [
[
"Mahajan",
"Saurabh",
""
],
[
"Athale",
"Chaitanya A.",
""
]
] | Neuronal growth cones are the most sensitive amongst eukaryotic cells in responding to directional chemical cues. Although a dynamic microtubule cytoskeleton has been shown to be essential for growth cone turning, the precise nature of coupling of the spatial cue with microtubule polarization is less understood. Here we present a computational model of microtubule polarization in a turning neuronal growth cone (GC). We explore the limits of directional cues in modifying the spatial polarization of microtubules by testing the role of microtubule dynamics, gradients of regulators and retrograde forces along filopodia. We analyze the steady state and transition behavior of microtubules on being presented with a directional stimulus. The model makes novel predictions about the minimal angular spread of the chemical signal at the growth cone and the fastest polarization times. A regulatory reaction-diffusion network based on the cyclic phosphorylation-dephosphorylation of a regulator predicts that the receptor signal magnitude can generate the maximal polarization of microtubules and not feedback loops or amplifications in the network. Using both the phenomenological and network models we have demonstrated some of the physical limits within which the MT polarization system works in turning neuron. |
2405.10993 | Thomas Li | Thomas Z. Li, Kaiwen Xu, Aravind Krishnan, Riqiang Gao, Michael N.
Kammer, Sanja Antic, David Xiao, Michael Knight, Yency Martinez, Rafael Paez,
Robert J. Lentz, Stephen Deppen, Eric L. Grogan, Thomas A. Lasko, Kim L.
Sandler, Fabien Maldonado, Bennett A. Landman | No winners: Performance of lung cancer prediction models depends on
screening-detected, incidental, and biopsied pulmonary nodule use cases | Submitted to Radiology: AI | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Statistical models for predicting lung cancer have the potential to
facilitate earlier diagnosis of malignancy and avoid invasive workup of benign
disease. Many models have been published, but comparative studies of their
utility in different clinical settings in which patients would arguably most
benefit are scarce. This study retrospectively evaluated promising predictive
models for lung cancer prediction in three clinical settings: lung cancer
screening with low-dose computed tomography, incidentally detected pulmonary
nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged
9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple
institutions to assess the area under the receiver operating characteristic
curve (AUC) of validated models including logistic regressions on clinical
variables and radiologist nodule characterizations, artificial intelligence on
chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented
each model from their published literature, re-training the models if
necessary, and curated each cohort from primary data sources. We observed that
model performance varied greatly across clinical use cases. No single
predictive model emerged as a clear winner across all cohorts, but certain
models excelled in specific clinical contexts. Single timepoint chest CT AI
performed well in lung screening, but struggled to generalize to other clinical
settings. Longitudinal imaging and multimodal models demonstrated comparatively
promising performance on incidentally-detected nodules. However, when applied
to nodules that underwent biopsy, all models underperformed. These results
underscore the strengths and limitations of 8 validated predictive models and
highlight promising directions towards personalized, noninvasive lung cancer
diagnosis.
| [
{
"created": "Thu, 16 May 2024 14:16:47 GMT",
"version": "v1"
}
] | 2024-05-21 | [
[
"Li",
"Thomas Z.",
""
],
[
"Xu",
"Kaiwen",
""
],
[
"Krishnan",
"Aravind",
""
],
[
"Gao",
"Riqiang",
""
],
[
"Kammer",
"Michael N.",
""
],
[
"Antic",
"Sanja",
""
],
[
"Xiao",
"David",
""
],
[
"Knight",
"Michael",
""
],
[
"Martinez",
"Yency",
""
],
[
"Paez",
"Rafael",
""
],
[
"Lentz",
"Robert J.",
""
],
[
"Deppen",
"Stephen",
""
],
[
"Grogan",
"Eric L.",
""
],
[
"Lasko",
"Thomas A.",
""
],
[
"Sandler",
"Kim L.",
""
],
[
"Maldonado",
"Fabien",
""
],
[
"Landman",
"Bennett A.",
""
]
] | Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis. |
1111.5165 | Burak Erman | Nazan B. Walpoth and Burak Erman | The effect of point mutations on energy conduction pathways in proteins | 14 pages, 8 figures, a supplementary section | null | null | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Energetically responsive residues of the 173 amino acid N-terminal domain of
the cardiac Ryanodine receptor RyR2 are identified by a simple elastic net
model. Residues that respond in a correlated way to fluctuations of spatially
neighboring residues specify a hydrogen bonded path through the protein. The
evolutionarily conserved residues of the protein are all located on this path
or in its close proximity. All of the residues of the path are either located
on the two Mir domains of the protein or are hydrogen bonded them. Two calcium
binding residues, E171 and E173, are proposed as potential binding region,
based on insights gained from the elastic net analysis of another calcium
channel receptor, the inositol 1,4,5-triphosphate receptor, IP3R. Analysis of
the disease causing A77V mutated RyR2 showed that the path is disrupted by the
loss of energy responsiveness of certain residues.
| [
{
"created": "Tue, 22 Nov 2011 11:51:49 GMT",
"version": "v1"
}
] | 2011-11-23 | [
[
"Walpoth",
"Nazan B.",
""
],
[
"Erman",
"Burak",
""
]
] | Energetically responsive residues of the 173 amino acid N-terminal domain of the cardiac Ryanodine receptor RyR2 are identified by a simple elastic net model. Residues that respond in a correlated way to fluctuations of spatially neighboring residues specify a hydrogen bonded path through the protein. The evolutionarily conserved residues of the protein are all located on this path or in its close proximity. All of the residues of the path are either located on the two Mir domains of the protein or are hydrogen bonded them. Two calcium binding residues, E171 and E173, are proposed as potential binding region, based on insights gained from the elastic net analysis of another calcium channel receptor, the inositol 1,4,5-triphosphate receptor, IP3R. Analysis of the disease causing A77V mutated RyR2 showed that the path is disrupted by the loss of energy responsiveness of certain residues. |
2210.05649 | Suman Bhowmick | Suman Bhowmick, Igor M. Sokolov, Hartmut H. K. Lentz | Decoding the double trouble: A mathematical modelling of co-infection
dynamics of SARS-CoV-2 and influenza-like illness | null | null | null | null | q-bio.PE physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | After the detection of coronavirus disease 2019 (Covid-19), caused by the
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, Hubei
Province, China in late December, the cases of Covid-19 have spiralled out
around the globe. Due to the clinical similarity of Covid-19 with other flulike
syndromes, patients are assayed for other pathogens of influenza like illness.
There have been reported cases of co-infection amongst patients with Covid-19.
Bacteria for example Streptococcus pneumoniae, Staphylococcus aureus,
Klebsiella pneumoniae, Mycoplasma pneumoniae, Chlamydia pneumonia, Legionella
pneumophila etc and viruses such as influenza, coronavirus,
rhinovirus/enterovirus, parainfluenza, metapneumovirus, influenza B virus etc
are identified as co-pathogens. In our current effort, we develop and analysed
a compartmental based Ordinary Differential Equation (ODE) type mathematical
model to understand the co-infection dynamics of Covid-19 and other influenza
type illness. In this work we have incorporated the saturated treatment rate to
take account of the impact of limited treatment resources to control the
possible Covid-19 cases. As results, we formulate the basic reproduction number
of the model system. Finally, we have performed numerical simulations of the
co-infection model to examine the solutions in different zones of parameter
space.
| [
{
"created": "Tue, 11 Oct 2022 17:48:27 GMT",
"version": "v1"
}
] | 2022-10-12 | [
[
"Bhowmick",
"Suman",
""
],
[
"Sokolov",
"Igor M.",
""
],
[
"Lentz",
"Hartmut H. K.",
""
]
] | After the detection of coronavirus disease 2019 (Covid-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, Hubei Province, China in late December, the cases of Covid-19 have spiralled out around the globe. Due to the clinical similarity of Covid-19 with other flulike syndromes, patients are assayed for other pathogens of influenza like illness. There have been reported cases of co-infection amongst patients with Covid-19. Bacteria for example Streptococcus pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae, Mycoplasma pneumoniae, Chlamydia pneumonia, Legionella pneumophila etc and viruses such as influenza, coronavirus, rhinovirus/enterovirus, parainfluenza, metapneumovirus, influenza B virus etc are identified as co-pathogens. In our current effort, we develop and analysed a compartmental based Ordinary Differential Equation (ODE) type mathematical model to understand the co-infection dynamics of Covid-19 and other influenza type illness. In this work we have incorporated the saturated treatment rate to take account of the impact of limited treatment resources to control the possible Covid-19 cases. As results, we formulate the basic reproduction number of the model system. Finally, we have performed numerical simulations of the co-infection model to examine the solutions in different zones of parameter space. |
1310.7226 | Ricardo V\^encio | Ricardo R. Silva, Fabien Jourdan, Diego M. Salvanha, Fabien Letisse,
Emilien L. Jamin, Simone Guidetti-Gonzalez, Carlos A. Labate, Ricardo Z.N.
V\^encio | ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS
based metabolomics | Manuscript to be submitted very soon. 7 pages, 3 color figures. There
is a companion material, the two case studies, which are going to be posted
here together with the main text in next updated version | null | 10.1093/bioinformatics/btu019 | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present ProbMetab, an R package which promotes substantial improvement in
automatic probabilistic LC-MS based metabolome annotation. The inference engine
core is based on a Bayesian model implemented to: (i) allow diverse source of
experimental data and metadata to be systematically incorporated into the model
with alternative ways to calculate the likelihood function and; (ii) allow
sensitive selection of biologically meaningful biochemical reactions databases
as Dirichlet-categorical prior distribution. Additionally, to ensure result
interpretation by system biologists, we display the annotation in a network
where observed mass peaks are connected if their candidate metabolites are
substrate/product of known biochemical reactions. This graph can be overlaid
with other graph-based analysis, such as partial correlation networks, in a
visualization scheme exported to Cytoscape, with web and stand alone versions.
ProbMetab was implemented in a modular fashion to fit together with established
upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools
(GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive
documentation and case studies, is freely available under GNU license at:
http://labpib.fmrp.usp.br/methods/probmetab/.
| [
{
"created": "Sun, 27 Oct 2013 18:31:30 GMT",
"version": "v1"
}
] | 2014-02-05 | [
[
"Silva",
"Ricardo R.",
""
],
[
"Jourdan",
"Fabien",
""
],
[
"Salvanha",
"Diego M.",
""
],
[
"Letisse",
"Fabien",
""
],
[
"Jamin",
"Emilien L.",
""
],
[
"Guidetti-Gonzalez",
"Simone",
""
],
[
"Labate",
"Carlos A.",
""
],
[
"Vêncio",
"Ricardo Z. N.",
""
]
] | We present ProbMetab, an R package which promotes substantial improvement in automatic probabilistic LC-MS based metabolome annotation. The inference engine core is based on a Bayesian model implemented to: (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and; (ii) allow sensitive selection of biologically meaningful biochemical reactions databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand alone versions. ProbMetab was implemented in a modular fashion to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/. |
1209.4254 | Daniela Delneri | Elzbieta M. Piatkowska, David Knight and Daniela Delneri | Chimeric protein complexes in hybrid species generate novel evolutionary
phenotypes | 20 pages, 4 figures, for supplementary files email
d.delneri@manchester.ac.uk | PLoS Genet. 2013 Oct;9(10):e1003836 | 10.1371/journal.pgen.1003836 | null | q-bio.GN q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | Hybridization between species is an important mechanism for the origin of
novel lineages and adaptation to new environments. Increased allelic variation
and modification of the transcriptional network are the two recognized forces
currently deemed to be responsible for the phenotypic properties seen in
hybrids. However, since the majority of the biological functions in a cell are
carried out by protein complexes, inter-specific protein assemblies therefore
represent another important source of natural variation upon which evolutionary
forces can act. Here we studied the composition of six protein complexes in two
different Saccharomyces "sensu strictu" hybrids, to understand whether chimeric
interactions can be freely formed in the cell in spite of species-specific
co-evolutionary forces, and whether the different types of complexes cause a
change in hybrid fitness. The protein assemblies were isolated from the hybrids
via affinity chromatography and identified via mass spectrometry. We found
evidence of spontaneous chimericity for four of the six protein assemblies
tested and we showed that different types of complexes can cause a variety of
phenotypes in selected environments. In the case of TRP2/TRP3 complex, the
effect of such chimeric formation resulted in the fitness advantage of the
hybrid in an environment lacking tryptophan, while only one type of parental
combination of the MBF complex could confer viability to the hybrid under
respiratory conditions. This study provides empirical evidence that chimeric
protein complexes can freely assemble in cells and reveals a new mechanism to
generate phenotypic novelty and plasticity in hybrids to complement the genomic
innovation resulting from gene duplication. The ability to exchange orthologous
members has also important implications for the adaptation and subsequent
genome evolution of the hybrids in terms of pattern of gene loss.
| [
{
"created": "Wed, 19 Sep 2012 14:24:04 GMT",
"version": "v1"
}
] | 2013-10-24 | [
[
"Piatkowska",
"Elzbieta M.",
""
],
[
"Knight",
"David",
""
],
[
"Delneri",
"Daniela",
""
]
] | Hybridization between species is an important mechanism for the origin of novel lineages and adaptation to new environments. Increased allelic variation and modification of the transcriptional network are the two recognized forces currently deemed to be responsible for the phenotypic properties seen in hybrids. However, since the majority of the biological functions in a cell are carried out by protein complexes, inter-specific protein assemblies therefore represent another important source of natural variation upon which evolutionary forces can act. Here we studied the composition of six protein complexes in two different Saccharomyces "sensu strictu" hybrids, to understand whether chimeric interactions can be freely formed in the cell in spite of species-specific co-evolutionary forces, and whether the different types of complexes cause a change in hybrid fitness. The protein assemblies were isolated from the hybrids via affinity chromatography and identified via mass spectrometry. We found evidence of spontaneous chimericity for four of the six protein assemblies tested and we showed that different types of complexes can cause a variety of phenotypes in selected environments. In the case of TRP2/TRP3 complex, the effect of such chimeric formation resulted in the fitness advantage of the hybrid in an environment lacking tryptophan, while only one type of parental combination of the MBF complex could confer viability to the hybrid under respiratory conditions. This study provides empirical evidence that chimeric protein complexes can freely assemble in cells and reveals a new mechanism to generate phenotypic novelty and plasticity in hybrids to complement the genomic innovation resulting from gene duplication. The ability to exchange orthologous members has also important implications for the adaptation and subsequent genome evolution of the hybrids in terms of pattern of gene loss. |
2105.13582 | Yanzheng Meng | Yanzheng Meng, Lei Li | Cysteine post-translational modifications: ten years from chemical
proteomics to bioinformatics | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As the only thiol-bearing amino acid, cysteine (Cys) residues in proteins
have the reactive thiol side chain, which is susceptible to a series of
post-translational modifications (PTMs). These PTMs participate in a wide range
of biological activities including the alteration of enzymatic reactions,
protein-protein interactions and protein stability. Here we summarize the
advance of cysteine PTM identification technologies and the features of the
various kinds of the PTMs. We also discuss in silico approaches for the
prediction of the different types of cysteine modified sites, giving directions
for future study.
| [
{
"created": "Fri, 28 May 2021 04:25:54 GMT",
"version": "v1"
}
] | 2021-05-31 | [
[
"Meng",
"Yanzheng",
""
],
[
"Li",
"Lei",
""
]
] | As the only thiol-bearing amino acid, cysteine (Cys) residues in proteins have the reactive thiol side chain, which is susceptible to a series of post-translational modifications (PTMs). These PTMs participate in a wide range of biological activities including the alteration of enzymatic reactions, protein-protein interactions and protein stability. Here we summarize the advance of cysteine PTM identification technologies and the features of the various kinds of the PTMs. We also discuss in silico approaches for the prediction of the different types of cysteine modified sites, giving directions for future study. |
1105.4242 | Uwe C. T\"auber | Uwe C. Tauber (Virginia Tech) | Stochastic population oscillations in spatial predator-prey models | 14 pages, 6 figures, submitted to J. Phys C: Conf. Ser. (2011) | J. Phys.: Conf. Ser. 319 (2011) 012019 | 10.1088/1742-6596/319/1/012019 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is well-established that including spatial structure and stochastic noise
in models for predator-prey interactions invalidates the classical
deterministic Lotka-Volterra picture of neutral population cycles. In contrast,
stochastic models yield long-lived, but ultimately decaying erratic population
oscillations, which can be understood through a resonant amplification
mechanism for density fluctuations. In Monte Carlo simulations of spatial
stochastic predator-prey systems, one observes striking complex spatio-temporal
structures. These spreading activity fronts induce persistent correlations
between predators and prey. In the presence of local particle density
restrictions (finite prey carrying capacity), there exists an extinction
threshold for the predator population. The accompanying continuous
non-equilibrium phase transition is governed by the directed-percolation
universality class. We employ field-theoretic methods based on the Doi-Peliti
representation of the master equation for stochastic particle interaction
models to (i) map the ensuing action in the vicinity of the absorbing state
phase transition to Reggeon field theory, and (ii) to quantitatively address
fluctuation-induced renormalizations of the population oscillation frequency,
damping, and diffusion coefficients in the species coexistence phase.
| [
{
"created": "Sat, 21 May 2011 09:31:08 GMT",
"version": "v1"
}
] | 2011-09-20 | [
[
"Tauber",
"Uwe C.",
"",
"Virginia Tech"
]
] | It is well-established that including spatial structure and stochastic noise in models for predator-prey interactions invalidates the classical deterministic Lotka-Volterra picture of neutral population cycles. In contrast, stochastic models yield long-lived, but ultimately decaying erratic population oscillations, which can be understood through a resonant amplification mechanism for density fluctuations. In Monte Carlo simulations of spatial stochastic predator-prey systems, one observes striking complex spatio-temporal structures. These spreading activity fronts induce persistent correlations between predators and prey. In the presence of local particle density restrictions (finite prey carrying capacity), there exists an extinction threshold for the predator population. The accompanying continuous non-equilibrium phase transition is governed by the directed-percolation universality class. We employ field-theoretic methods based on the Doi-Peliti representation of the master equation for stochastic particle interaction models to (i) map the ensuing action in the vicinity of the absorbing state phase transition to Reggeon field theory, and (ii) to quantitatively address fluctuation-induced renormalizations of the population oscillation frequency, damping, and diffusion coefficients in the species coexistence phase. |
0706.2516 | Bhalchandra Thatte | Bhalchandra D. Thatte and Mike Steel | Reconstructing pedigrees: a stochastic perspective | 20 pages, 3 figures | null | null | null | q-bio.PE | null | A pedigree is a directed graph that describes how individuals are related
through ancestry in a sexually-reproducing population. In this paper we explore
the question of whether one can reconstruct a pedigree by just observing
sequence data for present day individuals. This is motivated by the increasing
availability of genomic sequences, but in this paper we take a more theoretical
approach and consider what models of sequence evolution might allow pedigree
reconstruction (given sufficiently long sequences). Our results complement
recent work that showed that pedigree reconstruction may be fundamentally
impossible if one uses just the degrees of relatedness between different extant
individuals. We find that for certain stochastic processes, pedigrees can be
recovered up to isomorphism from sufficiently long sequences.
| [
{
"created": "Mon, 18 Jun 2007 00:16:14 GMT",
"version": "v1"
}
] | 2007-06-19 | [
[
"Thatte",
"Bhalchandra D.",
""
],
[
"Steel",
"Mike",
""
]
] | A pedigree is a directed graph that describes how individuals are related through ancestry in a sexually-reproducing population. In this paper we explore the question of whether one can reconstruct a pedigree by just observing sequence data for present day individuals. This is motivated by the increasing availability of genomic sequences, but in this paper we take a more theoretical approach and consider what models of sequence evolution might allow pedigree reconstruction (given sufficiently long sequences). Our results complement recent work that showed that pedigree reconstruction may be fundamentally impossible if one uses just the degrees of relatedness between different extant individuals. We find that for certain stochastic processes, pedigrees can be recovered up to isomorphism from sufficiently long sequences. |
1602.04258 | Christophe Dessimoz | Oscar Robinson, David Dylus, Christophe Dessimoz | Phylo.io: interactive viewing and comparison of large phylogenetic trees
on the web | null | null | 10.1093/molbev/msw080 | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic trees are pervasively used to depict evolutionary relationships.
Increasingly, researchers need to visualize large trees and compare multiple
large trees inferred for the same set of taxa (reflecting uncertainty in the
tree inference or genuine discordance among the loci analysed). Existing tree
visualization tools are however not well suited to these tasks. In particular,
side-by-side comparison of trees can prove challenging beyond a few dozen taxa.
Here, we introduce Phylo.io, a web application to visualize and compare
phylogenetic trees side-by-side. Its distinctive features are: highlighting of
similarities and differences between two trees, automatic identification of the
best matching rooting and leaf order, scalability to very large trees, high
usability, multiplatform support via standard HTML5 implementation, and
possibility to store and share visualisations. The tool can be freely accessed
at http://phylo.io. The code for the associated JavaScript library is available
at https://github.com/DessimozLab/phylo-io under an MIT open source license.
| [
{
"created": "Fri, 12 Feb 2016 23:01:54 GMT",
"version": "v1"
}
] | 2016-04-21 | [
[
"Robinson",
"Oscar",
""
],
[
"Dylus",
"David",
""
],
[
"Dessimoz",
"Christophe",
""
]
] | Phylogenetic trees are pervasively used to depict evolutionary relationships. Increasingly, researchers need to visualize large trees and compare multiple large trees inferred for the same set of taxa (reflecting uncertainty in the tree inference or genuine discordance among the loci analysed). Existing tree visualization tools are however not well suited to these tasks. In particular, side-by-side comparison of trees can prove challenging beyond a few dozen taxa. Here, we introduce Phylo.io, a web application to visualize and compare phylogenetic trees side-by-side. Its distinctive features are: highlighting of similarities and differences between two trees, automatic identification of the best matching rooting and leaf order, scalability to very large trees, high usability, multiplatform support via standard HTML5 implementation, and possibility to store and share visualisations. The tool can be freely accessed at http://phylo.io. The code for the associated JavaScript library is available at https://github.com/DessimozLab/phylo-io under an MIT open source license. |
1105.5816 | Polina Kurbatova | Stephan Fischer (LIRIS / INRIA Grenoble Rh\^one-Alpes / INSA Lyon /
UCB Lyon), Polina Kurbatova (ICJ), Nikolai Bessonov (IPME), Olivier
Gandrillon, Vitaly Volpert (ICJ), Fabien Crauste (ICJ) | Modelling Erythroblastic Islands: Using a Hybrid Model to Assess the
Function of Central Macrophage | null | null | null | UMR5208, UMR5208 | q-bio.QM physics.bio-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The production and regulation of red blood cells, erythropoiesis, occurs in
the bone marrow where erythroid cells proliferate and differentiate within
particular structures, called erythroblastic islands. A typical structure of
these islands consists in a macrophage (white cell) surrounded by immature
erythroid cells (progenitors), with more mature cells on the periphery of the
island, ready to leave the bone marrow and enter the bloodstream. A hybrid
model, coupling a continuous model (ordinary differential equations) describing
intracellular regulation through competition of two key proteins, to a discrete
spatial model describing cell-cell interactions, with growth factor diffusion
in the medium described by a continuous model (partial differential equations),
is proposed to investigate the role of the central macrophage in normal
erythropoiesis. Intracellular competition of the two proteins leads the
erythroid cell to either proliferation, differentiation, or death by apoptosis.
This approach allows considering spatial aspects of erythropoiesis, involved
for instance in the occurrence of cellular interactions or the access to
external factors, as well as dynamics of intracellular and extracellular scales
of this complex cellular process, accounting for stochasticity in cell cycle
durations and orientation of the mitotic spindle. The analysis of the model
shows a strong effect of the central macrophage on the stability of an
erythroblastic island, when assuming the macrophage releases pro-survival
cytokines. Even though it is not clear whether or not erythroblastic island
stability must be required, investigation of the model concludes that stability
improves responsiveness of the model, hence stressing out the potential
relevance of the central macrophage in normal erythropoiesis.
| [
{
"created": "Sun, 29 May 2011 19:19:59 GMT",
"version": "v1"
}
] | 2011-06-01 | [
[
"Fischer",
"Stephan",
"",
"LIRIS / INRIA Grenoble Rhône-Alpes / INSA Lyon /\n UCB Lyon"
],
[
"Kurbatova",
"Polina",
"",
"ICJ"
],
[
"Bessonov",
"Nikolai",
"",
"IPME"
],
[
"Gandrillon",
"Olivier",
"",
"ICJ"
],
[
"Volpert",
"Vitaly",
"",
"ICJ"
],
[
"Crauste",
"Fabien",
"",
"ICJ"
]
] | The production and regulation of red blood cells, erythropoiesis, occurs in the bone marrow where erythroid cells proliferate and differentiate within particular structures, called erythroblastic islands. A typical structure of these islands consists in a macrophage (white cell) surrounded by immature erythroid cells (progenitors), with more mature cells on the periphery of the island, ready to leave the bone marrow and enter the bloodstream. A hybrid model, coupling a continuous model (ordinary differential equations) describing intracellular regulation through competition of two key proteins, to a discrete spatial model describing cell-cell interactions, with growth factor diffusion in the medium described by a continuous model (partial differential equations), is proposed to investigate the role of the central macrophage in normal erythropoiesis. Intracellular competition of the two proteins leads the erythroid cell to either proliferation, differentiation, or death by apoptosis. This approach allows considering spatial aspects of erythropoiesis, involved for instance in the occurrence of cellular interactions or the access to external factors, as well as dynamics of intracellular and extracellular scales of this complex cellular process, accounting for stochasticity in cell cycle durations and orientation of the mitotic spindle. The analysis of the model shows a strong effect of the central macrophage on the stability of an erythroblastic island, when assuming the macrophage releases pro-survival cytokines. Even though it is not clear whether or not erythroblastic island stability must be required, investigation of the model concludes that stability improves responsiveness of the model, hence stressing out the potential relevance of the central macrophage in normal erythropoiesis. |
2312.17506 | Giulia Di Teodoro | Giulia Di Teodoro, Federico Siciliano, Valerio Guarrasi, Anne-Mieke
Vandamme, Valeria Ghisetti, Anders S\"onnerborg, Maurizio Zazzi, Fabrizio
Silvestri, Laura Palagi | A graph neural network-based model with Out-of-Distribution Robustness
for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1 | 32 pages, 2 figures | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/publicdomain/zero/1.0/ | Predicting the outcome of antiretroviral therapies for HIV-1 is a pressing
clinical challenge, especially when the treatment regimen includes drugs for
which limited effectiveness data is available. This scarcity of data can arise
either due to the introduction of a new drug to the market or due to limited
use in clinical settings. To tackle this issue, we introduce a novel joint
fusion model, which combines features from a Fully Connected (FC) Neural
Network and a Graph Neural Network (GNN). The FC network employs tabular data
with a feature vector made up of viral mutations identified in the most recent
genotypic resistance test, along with the drugs used in therapy. Conversely,
the GNN leverages knowledge derived from Stanford drug-resistance mutation
tables, which serve as benchmark references for deducing in-vivo treatment
efficacy based on the viral genetic sequence, to build informative graphs. We
evaluated these models' robustness against Out-of-Distribution drugs in the
test set, with a specific focus on the GNN's role in handling such scenarios.
Our comprehensive analysis demonstrates that the proposed model consistently
outperforms the FC model, especially when considering Out-of-Distribution
drugs. These results underscore the advantage of integrating Stanford scores in
the model, thereby enhancing its generalizability and robustness, but also
extending its utility in real-world applications with limited data
availability. This research highlights the potential of our approach to inform
antiretroviral therapy outcome prediction and contribute to more informed
clinical decisions.
| [
{
"created": "Fri, 29 Dec 2023 08:02:13 GMT",
"version": "v1"
}
] | 2024-01-01 | [
[
"Di Teodoro",
"Giulia",
""
],
[
"Siciliano",
"Federico",
""
],
[
"Guarrasi",
"Valerio",
""
],
[
"Vandamme",
"Anne-Mieke",
""
],
[
"Ghisetti",
"Valeria",
""
],
[
"Sönnerborg",
"Anders",
""
],
[
"Zazzi",
"Maurizio",
""
],
[
"Silvestri",
"Fabrizio",
""
],
[
"Palagi",
"Laura",
""
]
] | Predicting the outcome of antiretroviral therapies for HIV-1 is a pressing clinical challenge, especially when the treatment regimen includes drugs for which limited effectiveness data is available. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN). The FC network employs tabular data with a feature vector made up of viral mutations identified in the most recent genotypic resistance test, along with the drugs used in therapy. Conversely, the GNN leverages knowledge derived from Stanford drug-resistance mutation tables, which serve as benchmark references for deducing in-vivo treatment efficacy based on the viral genetic sequence, to build informative graphs. We evaluated these models' robustness against Out-of-Distribution drugs in the test set, with a specific focus on the GNN's role in handling such scenarios. Our comprehensive analysis demonstrates that the proposed model consistently outperforms the FC model, especially when considering Out-of-Distribution drugs. These results underscore the advantage of integrating Stanford scores in the model, thereby enhancing its generalizability and robustness, but also extending its utility in real-world applications with limited data availability. This research highlights the potential of our approach to inform antiretroviral therapy outcome prediction and contribute to more informed clinical decisions. |
1805.12491 | Christopher Lynn | Christopher W. Lynn, Ari E. Kahn, Nathaniel Nyema, and Danielle S.
Bassett | Abstract representations of events arise from mental errors in learning
and memory | 73 pages, 11 figures, 11 tables | null | null | null | q-bio.NC physics.bio-ph physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans are adept at uncovering abstract associations in the world around
them, yet the underlying mechanisms remain poorly understood. Intuitively,
learning the higher-order structure of statistical relationships should involve
complex mental processes. Here we propose an alternative perspective: that
higher-order associations instead arise from natural errors in learning and
memory. Combining ideas from information theory and reinforcement learning, we
derive a maximum entropy (or minimum complexity) model of people's internal
representations of the transitions between stimuli. Importantly, our model (i)
affords a concise analytic form, (ii) qualitatively explains the effects of
transition network structure on human expectations, and (iii) quantitatively
predicts human reaction times in probabilistic sequential motor tasks.
Together, these results suggest that mental errors influence our abstract
representations of the world in significant and predictable ways, with direct
implications for the study and design of optimally learnable information
sources.
| [
{
"created": "Thu, 31 May 2018 14:30:40 GMT",
"version": "v1"
},
{
"created": "Thu, 31 Jan 2019 15:47:29 GMT",
"version": "v2"
},
{
"created": "Wed, 25 Mar 2020 17:41:13 GMT",
"version": "v3"
}
] | 2020-03-26 | [
[
"Lynn",
"Christopher W.",
""
],
[
"Kahn",
"Ari E.",
""
],
[
"Nyema",
"Nathaniel",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) model of people's internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks. Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources. |
1803.04085 | Andrew Leifer | Mochi Liu, Anuj K Sharma, Joshua W Shaevitz, Andrew M Leifer | Temporal processing and context dependency in C. elegans
mechanosensation | 40 pages, 8 main figures, 19 supplementary figures | eLife 2018;7:e36419 | 10.7554/eLife.36419 | null | q-bio.NC physics.bio-ph | http://creativecommons.org/licenses/by-sa/4.0/ | A quantitative understanding of how sensory signals are transformed into
motor outputs places useful constraints on brain function and helps reveal the
brain's underlying computations. We investigate how the nematode C. elegans
responds to time-varying mechanosensory signals using a high-throughput
optogenetic assay and automated behavior quantification. In the prevailing
picture of the touch circuit, the animal's behavior is determined by which
neurons are stimulated and by the stimulus amplitude. In contrast, we find that
the behavioral response is tuned to temporal properties of mechanosensory
signals, like its integral and derivative, that extend over many seconds.
Mechanosensory signals, even in the same neurons, can be tailored to elicit
different behavioral responses. Moreover, we find that the animal's response
also depends on its behavioral context. Most dramatically, the animal ignores
all tested mechanosensory stimuli during turns. Finally, we present a
linear-nonlinear model that predicts the animal's behavioral response to
stimulus.
| [
{
"created": "Mon, 12 Mar 2018 01:42:26 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Mar 2018 16:30:34 GMT",
"version": "v2"
}
] | 2020-08-18 | [
[
"Liu",
"Mochi",
""
],
[
"Sharma",
"Anuj K",
""
],
[
"Shaevitz",
"Joshua W",
""
],
[
"Leifer",
"Andrew M",
""
]
] | A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps reveal the brain's underlying computations. We investigate how the nematode C. elegans responds to time-varying mechanosensory signals using a high-throughput optogenetic assay and automated behavior quantification. In the prevailing picture of the touch circuit, the animal's behavior is determined by which neurons are stimulated and by the stimulus amplitude. In contrast, we find that the behavioral response is tuned to temporal properties of mechanosensory signals, like its integral and derivative, that extend over many seconds. Mechanosensory signals, even in the same neurons, can be tailored to elicit different behavioral responses. Moreover, we find that the animal's response also depends on its behavioral context. Most dramatically, the animal ignores all tested mechanosensory stimuli during turns. Finally, we present a linear-nonlinear model that predicts the animal's behavioral response to stimulus. |
1610.09696 | Natalie Sanborn | Natalie E. Sanborn, N. Robert Hayre, Rajiv R.P Singh, and Daniel L.
Cox | All-atom Molecular Dynamics Simulations of the Projection Domain of the
Intrinsically Disordered htau40 Protein | 12 pages with 8 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have performed all atom molecular dynamics simulations on the projection
domain of the intrinsically disordered htau40 protein. After generating a
suitable ensemble of starting conformations at high temperatures, at room
temperature in an adaptive box algorithm we have generated histograms for the
radius of gyration, secondary structure time series, generated model small
angle x-ray scattering intensities, and model chemical shift plots for
comparison to nuclear magnetic resonance data for solvated and filamentous tau.
Significantly, we find that the chemical shift spectrum is more consistent with
filamentous tau than full length solution based tau. We have also carried out
principle component analysis and find three basics groups: compact globules,
tadpoles, and extended hinging structures. To validate the adaptive box and our
force field choice, we have run limited simulations in a large conventional box
with varying force fields and find that our essential results are unchanged. We
also performed two simulations with the TIP4P-D water model, the effects of
which depended on whether the initial configuration was compact or extended.
| [
{
"created": "Sun, 30 Oct 2016 19:32:45 GMT",
"version": "v1"
}
] | 2016-11-01 | [
[
"Sanborn",
"Natalie E.",
""
],
[
"Hayre",
"N. Robert",
""
],
[
"Singh",
"Rajiv R. P",
""
],
[
"Cox",
"Daniel L.",
""
]
] | We have performed all atom molecular dynamics simulations on the projection domain of the intrinsically disordered htau40 protein. After generating a suitable ensemble of starting conformations at high temperatures, at room temperature in an adaptive box algorithm we have generated histograms for the radius of gyration, secondary structure time series, generated model small angle x-ray scattering intensities, and model chemical shift plots for comparison to nuclear magnetic resonance data for solvated and filamentous tau. Significantly, we find that the chemical shift spectrum is more consistent with filamentous tau than full length solution based tau. We have also carried out principle component analysis and find three basics groups: compact globules, tadpoles, and extended hinging structures. To validate the adaptive box and our force field choice, we have run limited simulations in a large conventional box with varying force fields and find that our essential results are unchanged. We also performed two simulations with the TIP4P-D water model, the effects of which depended on whether the initial configuration was compact or extended. |
2007.06150 | Teresa Head-Gordon | Meili Liu, Akshaya K. Das, James Lincoff, Sukanya Sasmal, Sara Y.
Cheng, Robert Vernon, Julie Forman-Kay, Teresa Head-Gordon | Configurational Entropy of Folded Proteins and its Importance for
Intrinsically Disordered Proteins | null | null | null | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many pairwise additive force fields are in active use for intrinsically
disordered proteins (IDPs) and regions (IDRs), some of which modify energetic
terms to improve description of IDPs/IDRs, but are largely in disagreement with
solution experiments for the disordered states. We have evaluated
representative pairwise and many-body protein and water force fields against
experimental data on representative IDPs and IDRs, a peptide that undergoes a
disorder-to-order transition, and for seven globular proteins ranging in size
from 130-266 amino acids. We find that force fields with the largest
statistical fluctuations consistent with the radius of gyration and universal
Lindemann values for folded states simultaneously better describe IDPs and IDRs
and disorder to order transitions. Hence the crux of what a force field should
exhibit to well describe IDRs/IDPs is not just the balance between protein and
water energetics, but the balance between energetic effects and configurational
entropy of folded states of globular proteins.
| [
{
"created": "Mon, 13 Jul 2020 02:00:40 GMT",
"version": "v1"
},
{
"created": "Fri, 31 Jul 2020 14:42:14 GMT",
"version": "v2"
},
{
"created": "Thu, 12 Nov 2020 01:48:24 GMT",
"version": "v3"
}
] | 2020-11-13 | [
[
"Liu",
"Meili",
""
],
[
"Das",
"Akshaya K.",
""
],
[
"Lincoff",
"James",
""
],
[
"Sasmal",
"Sukanya",
""
],
[
"Cheng",
"Sara Y.",
""
],
[
"Vernon",
"Robert",
""
],
[
"Forman-Kay",
"Julie",
""
],
[
"Head-Gordon",
"Teresa",
""
]
] | Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve description of IDPs/IDRs, but are largely in disagreement with solution experiments for the disordered states. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, and for seven globular proteins ranging in size from 130-266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder to order transitions. Hence the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics, but the balance between energetic effects and configurational entropy of folded states of globular proteins. |
2112.09244 | Joseph Johnson | Joseph D. Johnson, Nathan L. White, Alain Kangabire and Daniel M.
Abrams | A Dynamical Model for the Origin of Anisogamy | 8 pages, 8 figures | Journal of Theoretical Biology, 521, 110669 (2021) | 10.1016/j.jtbi.2021.110669 | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The vast majority of multi-cellular organisms are anisogamous, meaning that
male and female sex cells differ in size. It remains an open question how this
asymmetric state evolved, presumably from the symmetric isogamous state where
all gametes are roughly the same size (drawn from the same distribution). Here,
we use tools from the study of nonlinear dynamical systems to develop a simple
mathematical model for this phenomenon. Using theoretical analysis and
numerical simulation, we demonstrate that competition between individuals that
is linked to the mean gamete size will almost inevitably result in a stable
anisogamous equilibrium, and thus isogamy may naturally lead to anisogamy.
| [
{
"created": "Thu, 16 Dec 2021 23:00:50 GMT",
"version": "v1"
}
] | 2021-12-20 | [
[
"Johnson",
"Joseph D.",
""
],
[
"White",
"Nathan L.",
""
],
[
"Kangabire",
"Alain",
""
],
[
"Abrams",
"Daniel M.",
""
]
] | The vast majority of multi-cellular organisms are anisogamous, meaning that male and female sex cells differ in size. It remains an open question how this asymmetric state evolved, presumably from the symmetric isogamous state where all gametes are roughly the same size (drawn from the same distribution). Here, we use tools from the study of nonlinear dynamical systems to develop a simple mathematical model for this phenomenon. Using theoretical analysis and numerical simulation, we demonstrate that competition between individuals that is linked to the mean gamete size will almost inevitably result in a stable anisogamous equilibrium, and thus isogamy may naturally lead to anisogamy. |
1602.03046 | Deniz Akdemir | Deniz Akdemir, Julio Isidro Sanchez | Efficient Breeding by Genomic Mating | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we propose an approach to breeding which focuses on mating
instead of truncation selection, our method uses genome-wide marker information
in a similar fashion to genomic selection so we refer it to as genomic mating.
Using concepts of estimated breeding values, risk (usefulness) and inbreeding,
an efficient mating approach is formulated for improvement of breeding values
in the long run. We have used a genetic algorithm to find solutions to this
optimization problem. Results from our simulations point to the efficiency of
genomic mating for breeding complex traits compared to truncation selection.
| [
{
"created": "Mon, 8 Feb 2016 20:43:39 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Jun 2016 15:47:07 GMT",
"version": "v2"
}
] | 2016-06-28 | [
[
"Akdemir",
"Deniz",
""
],
[
"Sanchez",
"Julio Isidro",
""
]
] | In this article, we propose an approach to breeding which focuses on mating instead of truncation selection, our method uses genome-wide marker information in a similar fashion to genomic selection so we refer it to as genomic mating. Using concepts of estimated breeding values, risk (usefulness) and inbreeding, an efficient mating approach is formulated for improvement of breeding values in the long run. We have used a genetic algorithm to find solutions to this optimization problem. Results from our simulations point to the efficiency of genomic mating for breeding complex traits compared to truncation selection. |
1702.00768 | Xerxes D. Arsiwalla | Riccardo Zucca, Xerxes D. Arsiwalla, Hoang Le, Mikail Rubinov, Paul
Verschure | Scaling Properties of Human Brain Functional Networks | International Conference on Artificial Neural Networks - ICANN 2016 | Artificial Neural Networks and Machine Learning, Lecture Notes in
Computer Science, vol 9886, 2016 | 10.1007/978-3-319-44778-0_13 | null | q-bio.NC cond-mat.dis-nn cs.NE physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate scaling properties of human brain functional networks in the
resting-state. Analyzing network degree distributions, we statistically test
whether their tails scale as power-law or not. Initial studies, based on
least-squares fitting, were shown to be inadequate for precise estimation of
power-law distributions. Subsequently, methods based on maximum-likelihood
estimators have been proposed and applied to address this question.
Nevertheless, no clear consensus has emerged, mainly because results have shown
substantial variability depending on the data-set used or its resolution. In
this study, we work with high-resolution data (10K nodes) from the Human
Connectome Project and take into account network weights. We test for the
power-law, exponential, log-normal and generalized Pareto distributions. Our
results show that the statistics generally do not support a power-law, but
instead these degree distributions tend towards the thin-tail limit of the
generalized Pareto model. This may have implications for the number of hubs in
human brain functional networks.
| [
{
"created": "Thu, 2 Feb 2017 18:01:07 GMT",
"version": "v1"
}
] | 2017-02-03 | [
[
"Zucca",
"Riccardo",
""
],
[
"Arsiwalla",
"Xerxes D.",
""
],
[
"Le",
"Hoang",
""
],
[
"Rubinov",
"Mikail",
""
],
[
"Verschure",
"Paul",
""
]
] | We investigate scaling properties of human brain functional networks in the resting-state. Analyzing network degree distributions, we statistically test whether their tails scale as power-law or not. Initial studies, based on least-squares fitting, were shown to be inadequate for precise estimation of power-law distributions. Subsequently, methods based on maximum-likelihood estimators have been proposed and applied to address this question. Nevertheless, no clear consensus has emerged, mainly because results have shown substantial variability depending on the data-set used or its resolution. In this study, we work with high-resolution data (10K nodes) from the Human Connectome Project and take into account network weights. We test for the power-law, exponential, log-normal and generalized Pareto distributions. Our results show that the statistics generally do not support a power-law, but instead these degree distributions tend towards the thin-tail limit of the generalized Pareto model. This may have implications for the number of hubs in human brain functional networks. |
1312.3028 | Daniel Balick | Daniel J. Balick, Ron Do, David Reich, and Shamil R. Sunyaev | Response to a population bottleneck can be used to infer recessive
selection | 35 pages including supplement, 7 figures including supplement | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here we present the first genome wide statistical test for recessive
selection. This test uses explicitly non-equilibrium demographic differences
between populations to infer the mode of selection. By analyzing the transient
response to a population bottleneck and subsequent re-expansion, we
qualitatively distinguish between alleles under additive and recessive
selection. We analyze the response of the average number of deleterious
mutations per haploid individual and describe time dependence of this quantity.
We introduce a statistic, $B_R$, to compare the number of mutations in
different populations and detail its functional dependence on the strength of
selection and the intensity of the population bottleneck. This test can be used
to detect the predominant mode of selection on the genome wide or regional
level, as well as among a sufficiently large set of medically or functionally
relevant alleles.
| [
{
"created": "Wed, 11 Dec 2013 03:22:54 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Mar 2014 04:05:00 GMT",
"version": "v2"
}
] | 2014-03-24 | [
[
"Balick",
"Daniel J.",
""
],
[
"Do",
"Ron",
""
],
[
"Reich",
"David",
""
],
[
"Sunyaev",
"Shamil R.",
""
]
] | Here we present the first genome wide statistical test for recessive selection. This test uses explicitly non-equilibrium demographic differences between populations to infer the mode of selection. By analyzing the transient response to a population bottleneck and subsequent re-expansion, we qualitatively distinguish between alleles under additive and recessive selection. We analyze the response of the average number of deleterious mutations per haploid individual and describe time dependence of this quantity. We introduce a statistic, $B_R$, to compare the number of mutations in different populations and detail its functional dependence on the strength of selection and the intensity of the population bottleneck. This test can be used to detect the predominant mode of selection on the genome wide or regional level, as well as among a sufficiently large set of medically or functionally relevant alleles. |
1608.00985 | Artem Kaznatcheev | Artem Kaznatcheev, Robert Vander Velde, Jacob G. Scott, David Basanta | Cancer treatment scheduling and dynamic heterogeneity in social dilemmas
of tumour acidity and vasculature | 14 main pages (+10 pg appendix), 3 figures | null | null | null | q-bio.PE q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Tumours are diverse ecosystems with persistent heterogeneity in
various cancer hallmarks like self-sufficiency of growth factor production for
angiogenesis and reprogramming of energy-metabolism for aerobic glycolysis.
This heterogeneity has consequences for diagnosis, treatment, and disease
progression.
Methods: We introduce the double goods game to study the dynamics of these
traits using evolutionary game theory. We model glycolytic acid production as a
public good for all tumour cells and oxygen from vascularization via VEGF
production as a club good benefiting non-glycolytic tumour cells. This results
in three viable phenotypic strategies: glycolytic, angiogenic, and aerobic
non-angiogenic.
Results: We classify the dynamics into three qualitatively distinct regimes:
(1) fully glycolytic, (2) fully angiogenic, or (3) polyclonal in all three cell
types. The third regime allows for dynamic heterogeneity even with linear
goods, something that was not possible in prior public good models that
considered glycolysis or growth-factor production in isolation.
Conclusion: The cyclic dynamics of the polyclonal regime stress the
importance of timing for anti-glycolysis treatments like lonidamine. The
existence of qualitatively different dynamic regimes highlights the order
effects of treatments. In particular, we consider the potential of vascular
renormalization as a neoadjuvant therapy before follow up with interventions
like buffer therapy.
| [
{
"created": "Tue, 2 Aug 2016 20:07:48 GMT",
"version": "v1"
}
] | 2016-08-04 | [
[
"Kaznatcheev",
"Artem",
""
],
[
"Velde",
"Robert Vander",
""
],
[
"Scott",
"Jacob G.",
""
],
[
"Basanta",
"David",
""
]
] | Background: Tumours are diverse ecosystems with persistent heterogeneity in various cancer hallmarks like self-sufficiency of growth factor production for angiogenesis and reprogramming of energy-metabolism for aerobic glycolysis. This heterogeneity has consequences for diagnosis, treatment, and disease progression. Methods: We introduce the double goods game to study the dynamics of these traits using evolutionary game theory. We model glycolytic acid production as a public good for all tumour cells and oxygen from vascularization via VEGF production as a club good benefiting non-glycolytic tumour cells. This results in three viable phenotypic strategies: glycolytic, angiogenic, and aerobic non-angiogenic. Results: We classify the dynamics into three qualitatively distinct regimes: (1) fully glycolytic, (2) fully angiogenic, or (3) polyclonal in all three cell types. The third regime allows for dynamic heterogeneity even with linear goods, something that was not possible in prior public good models that considered glycolysis or growth-factor production in isolation. Conclusion: The cyclic dynamics of the polyclonal regime stress the importance of timing for anti-glycolysis treatments like lonidamine. The existence of qualitatively different dynamic regimes highlights the order effects of treatments. In particular, we consider the potential of vascular renormalization as a neoadjuvant therapy before follow up with interventions like buffer therapy. |
1503.08324 | Corey S. O'Hern | Manuel Mai, Kun Wang, Greg Huber, Michael Kirby, Mark D. Shattuck, and
Corey S. O'Hern | Outcome prediction in mathematical models of immune response to
infection | 14 pages, 7 figures | PLoS ONE 10 (2015) e0135861 | 10.1371/journal.pone.0135861 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clinicians need to predict patient outcomes with high accuracy as early as
possible after disease inception. In this manuscript, we show that
patient-to-patient variability sets a fundamental limit on outcome prediction
accuracy for a general class of mathematical models for the immune response to
infection. However, accuracy can be increased at the expense of delayed
prognosis. We investigate several systems of ordinary differential equations
(ODEs) that model the host immune response to a pathogen load. Advantages of
systems of ODEs for investigating the immune response to infection include the
ability to collect data on large numbers of `virtual patients', each with a
given set of model parameters, and obtain many time points during the course of
the infection. We implement patient-to-patient variability $v$ in the ODE
models by randomly selecting the model parameters from Gaussian distributions
with variance $v$ that are centered on physiological values. We use logistic
regression with one-versus-all classification to predict the discrete
steady-state outcomes of the system. We find that the prediction algorithm
achieves near $100\%$ accuracy for $v=0$, and the accuracy decreases with
increasing $v$ for all ODE models studied. The fact that multiple steady-state
outcomes can be obtained for a given initial condition, i.e. the basins of
attraction overlap in the space of initial conditions, limits the prediction
accuracy for $v>0$. Increasing the elapsed time of the variables used to train
and test the classifier, increases the prediction accuracy, while adding
explicit external noise to the ODE models decreases the prediction accuracy.
Our results quantify the competition between early prognosis and high
prediction accuracy that is frequently encountered by clinicians.
| [
{
"created": "Sat, 28 Mar 2015 16:36:04 GMT",
"version": "v1"
}
] | 2016-02-17 | [
[
"Mai",
"Manuel",
""
],
[
"Wang",
"Kun",
""
],
[
"Huber",
"Greg",
""
],
[
"Kirby",
"Michael",
""
],
[
"Shattuck",
"Mark D.",
""
],
[
"O'Hern",
"Corey S.",
""
]
] | Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs) that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of `virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability $v$ in the ODE models by randomly selecting the model parameters from Gaussian distributions with variance $v$ that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near $100\%$ accuracy for $v=0$, and the accuracy decreases with increasing $v$ for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for $v>0$. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians. |
2407.16721 | Ashad Kabir | Sheikh Mohammed Shariful Islam, Moloud Abrar, Teketo Tegegne, Liliana
Loranjo, Chandan Karmakar, Md Abdul Awal, Md. Shahadat Hossain, Muhammad
Ashad Kabir, Mufti Mahmud, Abbas Khosravi, George Siopis, Jeban C Moses,
Ralph Maddison | Machine Learning Models for the Identification of Cardiovascular
Diseases Using UK Biobank Data | 19 pages, 3 figures | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Machine learning models have the potential to identify cardiovascular
diseases (CVDs) early and accurately in primary healthcare settings, which is
crucial for delivering timely treatment and management. Although
population-based CVD risk models have been used traditionally, these models
often do not consider variations in lifestyles, socioeconomic conditions, or
genetic predispositions. Therefore, we aimed to develop machine learning models
for CVD detection using primary healthcare data, compare the performance of
different models, and identify the best models. We used data from the UK
Biobank study, which included over 500,000 middle-aged participants from
different primary healthcare centers in the UK. Data collected at baseline
(2006--2010) and during imaging visits after 2014 were used in this study.
Baseline characteristics, including sex, age, and the Townsend Deprivation
Index, were included. Participants were classified as having CVD if they
reported at least one of the following conditions: heart attack, angina,
stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram
and echocardiography data, including left ventricular size and function,
cardiac output, and stroke volume, were also used. We used 9 machine learning
models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are
explainable and easily interpretable. We reported the accuracy, precision,
recall, and F-1 scores; confusion matrices; and area under the curve (AUC)
curves.
| [
{
"created": "Tue, 23 Jul 2024 11:05:20 GMT",
"version": "v1"
}
] | 2024-07-25 | [
[
"Islam",
"Sheikh Mohammed Shariful",
""
],
[
"Abrar",
"Moloud",
""
],
[
"Tegegne",
"Teketo",
""
],
[
"Loranjo",
"Liliana",
""
],
[
"Karmakar",
"Chandan",
""
],
[
"Awal",
"Md Abdul",
""
],
[
"Hossain",
"Md. Shahadat",
""
],
[
"Kabir",
"Muhammad Ashad",
""
],
[
"Mahmud",
"Mufti",
""
],
[
"Khosravi",
"Abbas",
""
],
[
"Siopis",
"George",
""
],
[
"Moses",
"Jeban C",
""
],
[
"Maddison",
"Ralph",
""
]
] | Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models. We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics, including sex, age, and the Townsend Deprivation Index, were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data, including left ventricular size and function, cardiac output, and stroke volume, were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F-1 scores; confusion matrices; and area under the curve (AUC) curves. |
q-bio/0702010 | Michael Meyer-Hermann | Michael Meyer-Hermann | The electrophysiology of the betacell based on single transmembrane
protein characteristics | 28 pages, 5 figures, 54 references, 14 pages supplementary material | null | null | null | q-bio.CB q-bio.QM | null | The electrophysiology of betacells is at the origin of insulin secretion.
Betacells exhibit a complex behaviour upon stimulation with glucose including
repeated and uninterrupted bursting. Mathematical modelling is most suitable to
improve knowledge about the function of various transmembrane currents provided
the model is based on reliable data. This is the first attempt to build a
mathematical model for the betacell-electrophysiology in a bottom-up approach
which relies on single protein conductivity data. The results of previous
whole-cell-based models are reconsidered. The full simulation including all
prominent transmembrane proteins in betacells is used to provide a functional
interpretation of their role in betacell-bursting and an updated vantage point
of betacell-electrophysiology. As a result of a number of in silico knock-out-
and block-experiments the novel model makes some unexpected predictions:
Single-channel conductivity data imply that calcium-gated potassium currents
are rather small. Thus, their role in burst interruption has to be revisited.
An alternative role in high calcium level oscillations is proposed and an
alternative burst interruption model is presented. It also turns out that
sodium currents are more relevant than expected so far. Experiments are
proposed to verify these predictions.
| [
{
"created": "Wed, 7 Feb 2007 13:10:05 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Meyer-Hermann",
"Michael",
""
]
] | The electrophysiology of betacells is at the origin of insulin secretion. Betacells exhibit a complex behaviour upon stimulation with glucose including repeated and uninterrupted bursting. Mathematical modelling is most suitable to improve knowledge about the function of various transmembrane currents provided the model is based on reliable data. This is the first attempt to build a mathematical model for the betacell-electrophysiology in a bottom-up approach which relies on single protein conductivity data. The results of previous whole-cell-based models are reconsidered. The full simulation including all prominent transmembrane proteins in betacells is used to provide a functional interpretation of their role in betacell-bursting and an updated vantage point of betacell-electrophysiology. As a result of a number of in silico knock-out- and block-experiments the novel model makes some unexpected predictions: Single-channel conductivity data imply that calcium-gated potassium currents are rather small. Thus, their role in burst interruption has to be revisited. An alternative role in high calcium level oscillations is proposed and an alternative burst interruption model is presented. It also turns out that sodium currents are more relevant than expected so far. Experiments are proposed to verify these predictions. |
0708.3627 | Walter Nadler | Walter Nadler, Ulrich H. E. Hansmann | Optimizing Replica Exchange Moves For Molecular Dynamics | 4 pages, 3 figures; revised version (1 figure added), PRE in press | null | 10.1103/PhysRevE.76.057102 | null | q-bio.QM cond-mat.stat-mech physics.comp-ph q-bio.BM | null | In this short note we sketch the statistical physics framework of the replica
exchange technique when applied to molecular dynamics simulations. In
particular, we draw attention to generalized move sets that allow a variety of
optimizations as well as new applications of the method.
| [
{
"created": "Mon, 27 Aug 2007 15:18:03 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Aug 2007 09:18:00 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Oct 2007 09:26:27 GMT",
"version": "v3"
}
] | 2009-11-13 | [
[
"Nadler",
"Walter",
""
],
[
"Hansmann",
"Ulrich H. E.",
""
]
] | In this short note we sketch the statistical physics framework of the replica exchange technique when applied to molecular dynamics simulations. In particular, we draw attention to generalized move sets that allow a variety of optimizations as well as new applications of the method. |
1902.08357 | Xiaotao Li | Xiaotao Li, Xuejing Chen, Fangfang Fan, Li Ning, Kangguang Lin, Zan
Chen, Zhenyun Qin, Albert S. Yeung, Liping Wang, Xiaojian Li, Kwok-Fai So | Cognitive computation of brain disorders based primarily on ocular
responses | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The present review presents multiple techniques in which ocular assessments
may serve as a noninvasive approach for the early diagnoses of various
cognitive and psychiatric disorders, such as Alzheimer's disease (AD), autism
spectrum disorder (ASD), schizophrenia (SZ), and major depressive disorder
(MDD). Real-time ocular responses are tightly associated with emotional and
cognitive processing within the central nervous system. Patterns seen in
saccades, pupillary responses, and blinking, as well as retinal
microvasculature and morphology visualized via office-based ophthalmic imaging,
are potential biomarkers for the screening and evaluation of cognitive and
psychiatric disorders. Additionally, rapid advances in artificial intelligence
(AI) present a growing opportunity to use machine-learning-based AI, especially
deep-learning neural networks, to shed new light on the field of cognitive
neuroscience, which may lead to novel evaluations and interventions via ocular
approaches for cognitive and psychiatric disorders.
| [
{
"created": "Fri, 22 Feb 2019 04:18:16 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Mar 2019 14:40:24 GMT",
"version": "v2"
},
{
"created": "Fri, 3 Apr 2020 17:23:03 GMT",
"version": "v3"
}
] | 2020-04-06 | [
[
"Li",
"Xiaotao",
""
],
[
"Chen",
"Xuejing",
""
],
[
"Fan",
"Fangfang",
""
],
[
"Ning",
"Li",
""
],
[
"Lin",
"Kangguang",
""
],
[
"Chen",
"Zan",
""
],
[
"Qin",
"Zhenyun",
""
],
[
"Yeung",
"Albert S.",
""
],
[
"Wang",
"Liping",
""
],
[
"Li",
"Xiaojian",
""
],
[
"So",
"Kwok-Fai",
""
]
] | The present review presents multiple techniques in which ocular assessments may serve as a noninvasive approach for the early diagnoses of various cognitive and psychiatric disorders, such as Alzheimer's disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ), and major depressive disorder (MDD). Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and blinking, as well as retinal microvasculature and morphology visualized via office-based ophthalmic imaging, are potential biomarkers for the screening and evaluation of cognitive and psychiatric disorders. Additionally, rapid advances in artificial intelligence (AI) present a growing opportunity to use machine-learning-based AI, especially deep-learning neural networks, to shed new light on the field of cognitive neuroscience, which may lead to novel evaluations and interventions via ocular approaches for cognitive and psychiatric disorders. |
2005.12390 | Constantinos Siettos | Ioannis Gallos, Evangelos Galaris, Constantinos Siettos | Construction of embedded fMRI resting state functional connectivity
networks using manifold learning | null | Cogn Neurodyn 15, 585-608 (2021) | 10.1007/s11571-020-09645-y | null | q-bio.NC cs.LG cs.NA math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations.
| [
{
"created": "Mon, 25 May 2020 20:39:29 GMT",
"version": "v1"
}
] | 2023-03-24 | [
[
"Gallos",
"Ioannis",
""
],
[
"Galaris",
"Evangelos",
""
],
[
"Siettos",
"Constantinos",
""
]
] | We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations. |
2108.01765 | John Rhodes | Elizabeth S. Allman, Hector Ba\~nos, John A. Rhodes | Identifiability of species network topologies from genomic sequences
using the logDet distance | 25 pages | null | null | null | q-bio.PE math.ST stat.TH | http://creativecommons.org/licenses/by/4.0/ | Inference of network-like evolutionary relationships between species from
genomic data must address the interwoven signals from both gene flow and
incomplete lineage sorting. The heavy computational demands of standard
approaches to this problem severely limit the size of datasets that may be
analyzed, in both the number of species and the number of genetic loci. Here we
provide a theoretical pointer to more efficient methods, by showing that logDet
distances computed from genomic-scale sequences retain sufficient information
to recover network relationships in the level-1 ultrametric case. This result
is obtained under the Network Multispecies Coalescent model combined with a
mixture of General Time-Reversible sequence evolution models across individual
gene trees, but does not depend on partitioning sequences by genes. Thus under
standard stochastic models statistically justifiable inference of network
relationships from sequences can be accomplished without consideration of
individual genes or gene trees.
| [
{
"created": "Tue, 3 Aug 2021 21:58:19 GMT",
"version": "v1"
}
] | 2021-08-05 | [
[
"Allman",
"Elizabeth S.",
""
],
[
"Baños",
"Hector",
""
],
[
"Rhodes",
"John A.",
""
]
] | Inference of network-like evolutionary relationships between species from genomic data must address the interwoven signals from both gene flow and incomplete lineage sorting. The heavy computational demands of standard approaches to this problem severely limit the size of datasets that may be analyzed, in both the number of species and the number of genetic loci. Here we provide a theoretical pointer to more efficient methods, by showing that logDet distances computed from genomic-scale sequences retain sufficient information to recover network relationships in the level-1 ultrametric case. This result is obtained under the Network Multispecies Coalescent model combined with a mixture of General Time-Reversible sequence evolution models across individual gene trees, but does not depend on partitioning sequences by genes. Thus under standard stochastic models statistically justifiable inference of network relationships from sequences can be accomplished without consideration of individual genes or gene trees. |
1806.05547 | Erik Doty | E. Doty, N. McCague, D.J. Stone, L.A. Celi | Analyzing counterintuitive data | 11 pages, 2 figures, 4 tables | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: To explore the issue of counterintuitive data via analysis of a
representative case and further discussion of those situations in which the
data appear to be inconsistent with current knowledge. Case: 844 postoperative
CABG patients, who were extubated within 24 hours of surgery were identified in
a critical care database (MIMIC-III). Nurse elicited pain scores were
documented throughout their hospital stay on a scale of 0 to 10. Levels were
tracked as mean, median, and maximum values, and categorized as no (0/10), mild
(1-3), moderate (4-6) and severe pain (7-10). Regression analysis was employed
to analyze the relationship between pain scores and outcomes of interest
(mortality and hospital LOS). After covariate adjustment, increased levels of
pain were found to be associated with lower mortality rates and reduced
hospital LOS. Conclusion: These counterintuitive results for post-CABG pain
related outcomes have not been previously reported. While not representing
strong enough evidence to alter clinical practice, confirmed and reliable
results such as these should serve as a research trigger and prompt further
studies into unexpected associations between pain and patient outcomes. With
the advent of frequent secondary analysis of electronic health records, such
counterintuitive data results are likely to become more frequent. We discuss
the issue of counterintuitive data in extended fashion, including possible
reasons for, and approaches to, this phenomenon.
| [
{
"created": "Tue, 12 Jun 2018 23:40:07 GMT",
"version": "v1"
}
] | 2018-06-15 | [
[
"Doty",
"E.",
""
],
[
"McCague",
"N.",
""
],
[
"Stone",
"D. J.",
""
],
[
"Celi",
"L. A.",
""
]
] | Purpose: To explore the issue of counterintuitive data via analysis of a representative case and further discussion of those situations in which the data appear to be inconsistent with current knowledge. Case: 844 postoperative CABG patients, who were extubated within 24 hours of surgery were identified in a critical care database (MIMIC-III). Nurse elicited pain scores were documented throughout their hospital stay on a scale of 0 to 10. Levels were tracked as mean, median, and maximum values, and categorized as no (0/10), mild (1-3), moderate (4-6) and severe pain (7-10). Regression analysis was employed to analyze the relationship between pain scores and outcomes of interest (mortality and hospital LOS). After covariate adjustment, increased levels of pain were found to be associated with lower mortality rates and reduced hospital LOS. Conclusion: These counterintuitive results for post-CABG pain related outcomes have not been previously reported. While not representing strong enough evidence to alter clinical practice, confirmed and reliable results such as these should serve as a research trigger and prompt further studies into unexpected associations between pain and patient outcomes. With the advent of frequent secondary analysis of electronic health records, such counterintuitive data results are likely to become more frequent. We discuss the issue of counterintuitive data in extended fashion, including possible reasons for, and approaches to, this phenomenon. |
q-bio/0402008 | Tom Chou | Kevin Klapstein, Tom Chou, and Robijn Bruinsma | Physics of RecA-mediated homologous recognition | 12pp, 10 figures | null | 10.1529/biophysj.104.039578 | null | q-bio.BM cond-mat.stat-mech q-bio.GN | null | Most proteins involved in processing DNA accomplish their activities as a
monomer or as a component of a multimer containing a relatively small number of
other elements. They generally act locally, binding to one or a few small
regions of the DNA substrate. Striking exceptions are the \textit{E. coli}
protein RecA and its homologues in other species, whose activities are
associated with homologous DNA recombination. The active form of RecA in DNA
recombination is a stiff nucleoprotein filament formed by RecA and DNA, within
which the DNA is extended by 50%. Invoking physical and geometrical ideas, we
show that the filamentary organization greatly enhances the rate of homologous
recognition while preventing the formation of topological traps originating
from multi-site recognition.
| [
{
"created": "Wed, 4 Feb 2004 21:47:42 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Klapstein",
"Kevin",
""
],
[
"Chou",
"Tom",
""
],
[
"Bruinsma",
"Robijn",
""
]
] | Most proteins involved in processing DNA accomplish their activities as a monomer or as a component of a multimer containing a relatively small number of other elements. They generally act locally, binding to one or a few small regions of the DNA substrate. Striking exceptions are the \textit{E. coli} protein RecA and its homologues in other species, whose activities are associated with homologous DNA recombination. The active form of RecA in DNA recombination is a stiff nucleoprotein filament formed by RecA and DNA, within which the DNA is extended by 50%. Invoking physical and geometrical ideas, we show that the filamentary organization greatly enhances the rate of homologous recognition while preventing the formation of topological traps originating from multi-site recognition. |
1202.6505 | Ralf Metzler | Leila Esmaeili Sereshki, Michael A. Lomholt, and Ralf Metzler | A solution to the subdiffusion-efficiency paradox: Inactive states
enhance reaction efficiency at subdiffusion conditions in living cells | 6 plus epsilon pages, 6 figures | EPL 97, 20008 (2012) | null | null | q-bio.SC cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Macromolecular crowding in living biological cells effects subdiffusion of
larger biomolecules such as proteins and enzymes. Mimicking this subdiffusion
in terms of random walks on a critical percolation cluster, we here present a
case study of EcoRV restriction enzymes involved in vital cellular defence. We
show that due to its so far elusive propensity to an inactive state the enzyme
avoids non-specific binding and remains well-distributed in the bulk cytoplasm
of the cell. Despite the reduced volume exploration capability of subdiffusion
processes, this mechanism guarantees a high efficiency of the enzyme. By
variation of the non-specific binding constant and the bond occupation
probability on the percolation network, we demonstrate that reduced
non-specific binding are beneficial for efficient subdiffusive enzyme activity
even in relatively small bacteria cells. Our results corroborate a more local
picture of cellular regulation.
| [
{
"created": "Wed, 29 Feb 2012 10:22:55 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Mar 2012 17:03:58 GMT",
"version": "v2"
}
] | 2012-03-09 | [
[
"Sereshki",
"Leila Esmaeili",
""
],
[
"Lomholt",
"Michael A.",
""
],
[
"Metzler",
"Ralf",
""
]
] | Macromolecular crowding in living biological cells effects subdiffusion of larger biomolecules such as proteins and enzymes. Mimicking this subdiffusion in terms of random walks on a critical percolation cluster, we here present a case study of EcoRV restriction enzymes involved in vital cellular defence. We show that due to its so far elusive propensity to an inactive state the enzyme avoids non-specific binding and remains well-distributed in the bulk cytoplasm of the cell. Despite the reduced volume exploration capability of subdiffusion processes, this mechanism guarantees a high efficiency of the enzyme. By variation of the non-specific binding constant and the bond occupation probability on the percolation network, we demonstrate that reduced non-specific binding are beneficial for efficient subdiffusive enzyme activity even in relatively small bacteria cells. Our results corroborate a more local picture of cellular regulation. |
1106.6344 | Joel Miller | Joel C. Miller and Erik M. Volz | Edge-Based Compartmental Modeling for Infectious Disease Spread Part
III: Disease and Population Structure | null | PLoS ONE 8(8): e69162. 2013 | 10.1371/journal.pone.0069162 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the edge-based compartmental models for infectious disease spread
introduced in Part I. These models allow us to consider standard SIR diseases
spreading in random populations. In this paper we show how to handle deviations
of the disease or population from the simplistic assumptions of Part I. We
allow the population to have structure due to effects such as demographic
detail or multiple types of risk behavior the disease to have more complicated
natural history. We introduce these modifications in the static network
context, though it is straightforward to incorporate them into dynamic
networks. We also consider serosorting, which requires using the dynamic
network models. The basic methods we use to derive these generalizations are
widely applicable, and so it is straightforward to introduce many other
generalizations not considered here.
| [
{
"created": "Thu, 30 Jun 2011 19:11:29 GMT",
"version": "v1"
}
] | 2015-09-03 | [
[
"Miller",
"Joel C.",
""
],
[
"Volz",
"Erik M.",
""
]
] | We consider the edge-based compartmental models for infectious disease spread introduced in Part I. These models allow us to consider standard SIR diseases spreading in random populations. In this paper we show how to handle deviations of the disease or population from the simplistic assumptions of Part I. We allow the population to have structure due to effects such as demographic detail or multiple types of risk behavior the disease to have more complicated natural history. We introduce these modifications in the static network context, though it is straightforward to incorporate them into dynamic networks. We also consider serosorting, which requires using the dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here. |
2407.09355 | Aaron Ge | Aaron Ge, Jeya Balasubramanian, Xueyao Wu, Peter Kraft, and Jonas S.
Almeida | FastImpute: A Baseline for Open-source, Reference-Free Genotype
Imputation Methods -- A Case Study in PRS313 | This paper is 16 pages long and contains 7 figures. For more
information and to access related resources: * Web application:
https://aaronge-2020.github.io/DeepImpute/ * Code repository:
https://github.com/aaronge-2020/DeepImpute | null | null | null | q-bio.GN cs.AI | http://creativecommons.org/licenses/by/4.0/ | Genotype imputation enhances genetic data by predicting missing SNPs using
reference haplotype information. Traditional methods leverage linkage
disequilibrium (LD) to infer untyped SNP genotypes, relying on the similarity
of LD structures between genotyped target sets and fully sequenced reference
panels. Recently, reference-free deep learning-based methods have emerged,
offering a promising alternative by predicting missing genotypes without
external databases, thereby enhancing privacy and accessibility. However, these
methods often produce models with tens of millions of parameters, leading to
challenges such as the need for substantial computational resources to train
and inefficiency for client-sided deployment. Our study addresses these
limitations by introducing a baseline for a novel genotype imputation pipeline
that supports client-sided imputation models generalizable across any
genotyping chip and genomic region. This approach enhances patient privacy by
performing imputation directly on edge devices. As a case study, we focus on
PRS313, a polygenic risk score comprising 313 SNPs used for breast cancer risk
prediction. Utilizing consumer genetic panels such as 23andMe, our model
democratizes access to personalized genetic insights by allowing 23andMe users
to obtain their PRS313 score. We demonstrate that simple linear regression can
significantly improve the accuracy of PRS313 scores when calculated using SNPs
imputed from consumer gene panels, such as 23andMe. Our linear regression model
achieved an R^2 of 0.86, compared to 0.33 without imputation and 0.28 with
simple imputation (substituting missing SNPs with the minor allele frequency).
These findings suggest that popular SNP analysis libraries could benefit from
integrating linear regression models for genotype imputation, providing a
viable and light-weight alternative to reference based imputation.
| [
{
"created": "Fri, 12 Jul 2024 15:28:13 GMT",
"version": "v1"
}
] | 2024-07-15 | [
[
"Ge",
"Aaron",
""
],
[
"Balasubramanian",
"Jeya",
""
],
[
"Wu",
"Xueyao",
""
],
[
"Kraft",
"Peter",
""
],
[
"Almeida",
"Jonas S.",
""
]
] | Genotype imputation enhances genetic data by predicting missing SNPs using reference haplotype information. Traditional methods leverage linkage disequilibrium (LD) to infer untyped SNP genotypes, relying on the similarity of LD structures between genotyped target sets and fully sequenced reference panels. Recently, reference-free deep learning-based methods have emerged, offering a promising alternative by predicting missing genotypes without external databases, thereby enhancing privacy and accessibility. However, these methods often produce models with tens of millions of parameters, leading to challenges such as the need for substantial computational resources to train and inefficiency for client-sided deployment. Our study addresses these limitations by introducing a baseline for a novel genotype imputation pipeline that supports client-sided imputation models generalizable across any genotyping chip and genomic region. This approach enhances patient privacy by performing imputation directly on edge devices. As a case study, we focus on PRS313, a polygenic risk score comprising 313 SNPs used for breast cancer risk prediction. Utilizing consumer genetic panels such as 23andMe, our model democratizes access to personalized genetic insights by allowing 23andMe users to obtain their PRS313 score. We demonstrate that simple linear regression can significantly improve the accuracy of PRS313 scores when calculated using SNPs imputed from consumer gene panels, such as 23andMe. Our linear regression model achieved an R^2 of 0.86, compared to 0.33 without imputation and 0.28 with simple imputation (substituting missing SNPs with the minor allele frequency). These findings suggest that popular SNP analysis libraries could benefit from integrating linear regression models for genotype imputation, providing a viable and light-weight alternative to reference based imputation. |
1212.2658 | Etienne Rajon | Etienne Rajon and Joanna Masel | Compensatory evolution and the origins of innovations | null | Genetics 193 (2013) 1209-1220 | 10.1534/genetics.112.148627 | null | q-bio.PE q-bio.GN q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cryptic genetic sequences have attenuated effects on phenotypes. In the
classic view, relaxed selection allows cryptic genetic diversity to build up
across individuals in a population, providing alleles that may later contribute
to adaptation when co-opted - e.g. following a mutation increasing expression
from a low, attenuated baseline. This view is described, for example, by the
metaphor of the spread of a population across a neutral network in genotype
space. As an alternative view, consider the fact that most phenotypic traits
are affected by multiple sequences, including cryptic ones. Even in a strictly
clonal population, the co-option of cryptic sequences at different loci may
have different phenotypic effects and offer the population multiple adaptive
possibilities. Here, we model the evolution of quantitative phenotypic
characters encoded by cryptic sequences, and compare the relative contributions
of genetic diversity and of variation across sites to the phenotypic potential
of a population. We show that most of the phenotypic variation accessible
through co-option would exist even in populations with no polymorphism. This is
made possible by a history of compensatory evolution, whereby the phenotypic
effect of a cryptic mutation at one site was balanced by mutations elsewhere in
the genome, leading to a diversity of cryptic effect sizes across sites rather
than across individuals. Cryptic sequences might accelerate adaptation and
facilitate large phenotypic changes even in the absence of genetic diversity,
as traditionally defined in terms of alternative alleles.
| [
{
"created": "Tue, 11 Dec 2012 21:48:57 GMT",
"version": "v1"
}
] | 2013-05-23 | [
[
"Rajon",
"Etienne",
""
],
[
"Masel",
"Joanna",
""
]
] | Cryptic genetic sequences have attenuated effects on phenotypes. In the classic view, relaxed selection allows cryptic genetic diversity to build up across individuals in a population, providing alleles that may later contribute to adaptation when co-opted - e.g. following a mutation increasing expression from a low, attenuated baseline. This view is described, for example, by the metaphor of the spread of a population across a neutral network in genotype space. As an alternative view, consider the fact that most phenotypic traits are affected by multiple sequences, including cryptic ones. Even in a strictly clonal population, the co-option of cryptic sequences at different loci may have different phenotypic effects and offer the population multiple adaptive possibilities. Here, we model the evolution of quantitative phenotypic characters encoded by cryptic sequences, and compare the relative contributions of genetic diversity and of variation across sites to the phenotypic potential of a population. We show that most of the phenotypic variation accessible through co-option would exist even in populations with no polymorphism. This is made possible by a history of compensatory evolution, whereby the phenotypic effect of a cryptic mutation at one site was balanced by mutations elsewhere in the genome, leading to a diversity of cryptic effect sizes across sites rather than across individuals. Cryptic sequences might accelerate adaptation and facilitate large phenotypic changes even in the absence of genetic diversity, as traditionally defined in terms of alternative alleles. |
0708.0572 | Eduardo Candelario-Jalil | E. Candelario-Jalil, N. H. Mhadu, S. M. Al-Dalain, G. Martinez, O. S.
Leon | Time course of oxidative damage in different brain regions following
transient cerebral ischemia in gerbils | null | Neuroscience Research 41(3): 233-241 (2001) | null | null | q-bio.TO | null | The time course of oxidative damage in different brain regions was
investigated in the gerbil model of transient cerebral ischemia. Animals were
subjected to both common carotid arteries occlusion for 5 min. After the end of
ischemia and at different reperfusion times (2, 6, 12, 24, 48, 72, 96 h and 7
days), markers of lipid peroxidation, reduced and oxidized glutathione levels,
glutathione peroxidase, glutathione reductase, manganese-dependent superoxide
dismutase (MnSOD) and copper/zinc containing SOD (Cu/ZnSOD) activities were
measured in hippocampus, cortex and striatum. Oxidative damage in hippocampus
was maximal at late stages after ischemia (48-96 h) coincident with a
significant impairment in glutathione homeostasis. MnSOD increased in
hippocampus at 24, 48 and 72 h after ischemia, coincident with the marked
reduction in the activity of glutathione-related enzymes. The late disturbance
in oxidant-antioxidant balance corresponds with the time course of delayed
neuronal loss in the hippocampal CA1 sector. Cerebral cortex showed early
changes in oxidative damage with no significant impairment in antioxidant
capacity. Striatal lipid peroxidation significantly increased as early as 2 h
after ischemia and persisted until 48 h with respect to the sham-operated
group. These results contribute significant information on the timing and
factors that influence free radical formation following ischemic brain injury,
an essential step in determining effective antioxidant intervention.
| [
{
"created": "Fri, 3 Aug 2007 19:42:03 GMT",
"version": "v1"
}
] | 2007-08-06 | [
[
"Candelario-Jalil",
"E.",
""
],
[
"Mhadu",
"N. H.",
""
],
[
"Al-Dalain",
"S. M.",
""
],
[
"Martinez",
"G.",
""
],
[
"Leon",
"O. S.",
""
]
] | The time course of oxidative damage in different brain regions was investigated in the gerbil model of transient cerebral ischemia. Animals were subjected to both common carotid arteries occlusion for 5 min. After the end of ischemia and at different reperfusion times (2, 6, 12, 24, 48, 72, 96 h and 7 days), markers of lipid peroxidation, reduced and oxidized glutathione levels, glutathione peroxidase, glutathione reductase, manganese-dependent superoxide dismutase (MnSOD) and copper/zinc containing SOD (Cu/ZnSOD) activities were measured in hippocampus, cortex and striatum. Oxidative damage in hippocampus was maximal at late stages after ischemia (48-96 h) coincident with a significant impairment in glutathione homeostasis. MnSOD increased in hippocampus at 24, 48 and 72 h after ischemia, coincident with the marked reduction in the activity of glutathione-related enzymes. The late disturbance in oxidant-antioxidant balance corresponds with the time course of delayed neuronal loss in the hippocampal CA1 sector. Cerebral cortex showed early changes in oxidative damage with no significant impairment in antioxidant capacity. Striatal lipid peroxidation significantly increased as early as 2 h after ischemia and persisted until 48 h with respect to the sham-operated group. These results contribute significant information on the timing and factors that influence free radical formation following ischemic brain injury, an essential step in determining effective antioxidant intervention. |
2405.19159 | Hideaki Yamamoto | Hakuba Murota, Hideaki Yamamoto, Nobuaki Monma, Shigeo Sato, Ayumi
Hirano-Iwata | Precision microfluidic control of neuronal ensembles in cultured
cortical networks | 30 pages, 6 figures, 6 supplementary figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In vitro neuronal culture is an important research platform in cellular and
network neuroscience. However, neurons cultured on a homogeneous scaffold form
dense, randomly connected networks and display excessively synchronized
activity; this phenomenon has limited their applications in network-level
studies, such as studies of neuronal ensembles, or coordinated activity by a
group of neurons. Herein, we develop polydimethylsiloxane-based microfluidic
devices to create small neuronal networks exhibiting a hierarchically modular
structure resembling the connectivity observed in the mammalian cortex. The
strength of intermodular coupling was manipulated by varying the width and
height of the microchannels that connect the modules. Using fluorescent calcium
imaging, we observe that the spontaneous activity in networks with smaller
microchannels (2.2$-$5.5 $\mu$m$^2$) had lower synchrony and exhibit a
threefold variety of neuronal ensembles. Optogenetic stimulation demonstrates
that a reduction in intermodular coupling enriches evoked neuronal activity
patterns and that repeated stimulation induces plasticity in neuronal ensembles
in these networks. These findings suggest that cell engineering technologies
based on microfluidic devices enable in vitro reconstruction of the intricate
dynamics of neuronal ensembles, thus providing a robust platform for studying
neuronal ensembles in a well-defined physicochemical environment.
| [
{
"created": "Wed, 29 May 2024 15:02:28 GMT",
"version": "v1"
}
] | 2024-05-30 | [
[
"Murota",
"Hakuba",
""
],
[
"Yamamoto",
"Hideaki",
""
],
[
"Monma",
"Nobuaki",
""
],
[
"Sato",
"Shigeo",
""
],
[
"Hirano-Iwata",
"Ayumi",
""
]
] | In vitro neuronal culture is an important research platform in cellular and network neuroscience. However, neurons cultured on a homogeneous scaffold form dense, randomly connected networks and display excessively synchronized activity; this phenomenon has limited their applications in network-level studies, such as studies of neuronal ensembles, or coordinated activity by a group of neurons. Herein, we develop polydimethylsiloxane-based microfluidic devices to create small neuronal networks exhibiting a hierarchically modular structure resembling the connectivity observed in the mammalian cortex. The strength of intermodular coupling was manipulated by varying the width and height of the microchannels that connect the modules. Using fluorescent calcium imaging, we observe that the spontaneous activity in networks with smaller microchannels (2.2$-$5.5 $\mu$m$^2$) had lower synchrony and exhibit a threefold variety of neuronal ensembles. Optogenetic stimulation demonstrates that a reduction in intermodular coupling enriches evoked neuronal activity patterns and that repeated stimulation induces plasticity in neuronal ensembles in these networks. These findings suggest that cell engineering technologies based on microfluidic devices enable in vitro reconstruction of the intricate dynamics of neuronal ensembles, thus providing a robust platform for studying neuronal ensembles in a well-defined physicochemical environment. |
1308.1252 | Marc H. E. de Lussanet PhD | Marc H.E. de Lussanet | The human and mammalian cerebrum scale by computational power and
information resistance | There are some crucial flaws in the calculations | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The cerebrum of mammals spans a vast range of sizes and yet has a very
regular structure. The amount of folding of the cortical surface and the
proportion of white matter gradually increase with size, but the underlying
mechanisms remain elusive. Here, two laws are derived to fully explain these
cerebral scaling relations. The first law holds that the long-range information
flow in the cerebrum is determined by the total cortical surface (i.e., the
number of neurons) and the increasing information resistance of long-range
connections. Despite having just one free parameter, the first law fits the
mammalian cerebrum better than any existing function, both across species and
within humans. According to the second law, the white matter volume scales,
with a few minor corrections, to the cortical surface area. It follows from the
first law that large cerebrums have much local processing and little global
information flow. Moreover, paradoxically, a further increase in long-range
connections would decrease the efficiency of information flow.
| [
{
"created": "Tue, 6 Aug 2013 12:13:18 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Jun 2022 19:52:09 GMT",
"version": "v2"
}
] | 2022-06-14 | [
[
"de Lussanet",
"Marc H. E.",
""
]
] | The cerebrum of mammals spans a vast range of sizes and yet has a very regular structure. The amount of folding of the cortical surface and the proportion of white matter gradually increase with size, but the underlying mechanisms remain elusive. Here, two laws are derived to fully explain these cerebral scaling relations. The first law holds that the long-range information flow in the cerebrum is determined by the total cortical surface (i.e., the number of neurons) and the increasing information resistance of long-range connections. Despite having just one free parameter, the first law fits the mammalian cerebrum better than any existing function, both across species and within humans. According to the second law, the white matter volume scales, with a few minor corrections, to the cortical surface area. It follows from the first law that large cerebrums have much local processing and little global information flow. Moreover, paradoxically, a further increase in long-range connections would decrease the efficiency of information flow. |
2308.12585 | Il Memming Park | Il Memming Park and \'Abel S\'agodi and Piotr Aleksander Sok\'o\l | Persistent learning signals and working memory without continuous
attractors | null | null | null | null | q-bio.NC cs.LG cs.NE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural dynamical systems with stable attractor structures, such as point
attractors and continuous attractors, are hypothesized to underlie meaningful
temporal behavior that requires working memory. However, working memory may not
support useful learning signals necessary to adapt to changes in the temporal
structure of the environment. We show that in addition to the continuous
attractors that are widely implicated, periodic and quasi-periodic attractors
can also support learning arbitrarily long temporal relationships. Unlike the
continuous attractors that suffer from the fine-tuning problem, the less
explored quasi-periodic attractors are uniquely qualified for learning to
produce temporally structured behavior. Our theory has broad implications for
the design of artificial learning systems and makes predictions about
observable signatures of biological neural dynamics that can support temporal
dependence learning and working memory. Based on our theory, we developed a new
initialization scheme for artificial recurrent neural networks that outperforms
standard methods for tasks that require learning temporal dynamics. Moreover,
we propose a robust recurrent memory mechanism for integrating and maintaining
head direction without a ring attractor.
| [
{
"created": "Thu, 24 Aug 2023 06:12:41 GMT",
"version": "v1"
}
] | 2023-08-25 | [
[
"Park",
"Il Memming",
""
],
[
"Ságodi",
"Ábel",
""
],
[
"Sokół",
"Piotr Aleksander",
""
]
] | Neural dynamical systems with stable attractor structures, such as point attractors and continuous attractors, are hypothesized to underlie meaningful temporal behavior that requires working memory. However, working memory may not support useful learning signals necessary to adapt to changes in the temporal structure of the environment. We show that in addition to the continuous attractors that are widely implicated, periodic and quasi-periodic attractors can also support learning arbitrarily long temporal relationships. Unlike the continuous attractors that suffer from the fine-tuning problem, the less explored quasi-periodic attractors are uniquely qualified for learning to produce temporally structured behavior. Our theory has broad implications for the design of artificial learning systems and makes predictions about observable signatures of biological neural dynamics that can support temporal dependence learning and working memory. Based on our theory, we developed a new initialization scheme for artificial recurrent neural networks that outperforms standard methods for tasks that require learning temporal dynamics. Moreover, we propose a robust recurrent memory mechanism for integrating and maintaining head direction without a ring attractor. |
0912.2366 | Rafael Dias Vilela | Rafael D. Vilela and Benjamin Lindner | Are the input parameters of white-noise-driven integrate-and-fire
neurons uniquely determined by rate and CV? | null | J. Theor. Biol. 257, 90 (2009) | null | null | q-bio.NC nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Integrate-and-fire (IF) neurons have found widespread applications in
computational neuroscience. Particularly important are stochastic versions of
these models where the driving consists of a synaptic input modeled as white
Gaussian noise with mean $\mu$ and noise intensity $D$. Different IF models
have been proposed, the firing statistics of which depends nontrivially on the
input parameters $\mu$ and $D$. In order to compare these models among each
other, one must first specify the correspondence between their parameters. This
can be done by determining which set of parameters ($\mu$, $D$) of each model
is associated to a given set of basic firing statistics as, for instance, the
firing rate and the coefficient of variation (CV) of the interspike interval
(ISI). However, it is not clear {\em a priori} whether for a given firing rate
and CV there is only one unique choice of input parameters for each model. Here
we review the dependence of rate and CV on input parameters for the perfect,
leaky, and quadratic IF neuron models and show analytically that indeed in
these three models the firing rate and the CV uniquely determine the input
parameters.
| [
{
"created": "Fri, 11 Dec 2009 22:00:45 GMT",
"version": "v1"
}
] | 2009-12-15 | [
[
"Vilela",
"Rafael D.",
""
],
[
"Lindner",
"Benjamin",
""
]
] | Integrate-and-fire (IF) neurons have found widespread applications in computational neuroscience. Particularly important are stochastic versions of these models where the driving consists of a synaptic input modeled as white Gaussian noise with mean $\mu$ and noise intensity $D$. Different IF models have been proposed, the firing statistics of which depends nontrivially on the input parameters $\mu$ and $D$. In order to compare these models among each other, one must first specify the correspondence between their parameters. This can be done by determining which set of parameters ($\mu$, $D$) of each model is associated to a given set of basic firing statistics as, for instance, the firing rate and the coefficient of variation (CV) of the interspike interval (ISI). However, it is not clear {\em a priori} whether for a given firing rate and CV there is only one unique choice of input parameters for each model. Here we review the dependence of rate and CV on input parameters for the perfect, leaky, and quadratic IF neuron models and show analytically that indeed in these three models the firing rate and the CV uniquely determine the input parameters. |
1609.00032 | Qing Wan | Chang Jin Wan, Wei Wang, Li Qiang Zhu, Yang Hui Liu, Ping Feng, Zhao
Ping Liu, Yi Shi, and Qing Wan | Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Devices on
Flexible Conducting Graphene Substrates | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Flexible metal oxide/graphene oxide hybrid multi-gate neuron transistors were
fabricated on flexible graphene substrates. Dendritic integrations in both
spatial and temporal modes were successfully emulated, and spatiotemporal
correlated logics were obtained. A proof-of-principle visual system model for
emulating lobula giant motion detector neuron was investigated. Our results are
of great interest for flexible neuromorphic cognitive systems.
| [
{
"created": "Mon, 7 Mar 2016 07:02:51 GMT",
"version": "v1"
}
] | 2016-09-02 | [
[
"Wan",
"Chang Jin",
""
],
[
"Wang",
"Wei",
""
],
[
"Zhu",
"Li Qiang",
""
],
[
"Liu",
"Yang Hui",
""
],
[
"Feng",
"Ping",
""
],
[
"Liu",
"Zhao Ping",
""
],
[
"Shi",
"Yi",
""
],
[
"Wan",
"Qing",
""
]
] | Flexible metal oxide/graphene oxide hybrid multi-gate neuron transistors were fabricated on flexible graphene substrates. Dendritic integrations in both spatial and temporal modes were successfully emulated, and spatiotemporal correlated logics were obtained. A proof-of-principle visual system model for emulating lobula giant motion detector neuron was investigated. Our results are of great interest for flexible neuromorphic cognitive systems. |
2211.03774 | Puttipong Pongtanapaisan | Isabel K. Darcy, Garrett Jones and Puttipong Pongtanapaisan | Modeling knotted proteins with tangles | null | null | null | null | q-bio.BM math.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although rare, an increasing number of proteins have been observed to contain
entanglements in their native structures. To gain more insight into the
significance of protein knotting, researchers have been investigating protein
knot formation using both experimental and theoretical methods. Motivated by
the hypothesized folding pathway of $\alpha$-haloacid dehalogenase (DehI)
protein, Flapan, He, and Wong proposed a theory of how protein knots form,
which includes existing folding pathways described by Taylor and B\"olinger et
al. as special cases. In their topological descriptions, two loops in an
unknotted open protein chain containing at most two twists each come close
together, and one end of the protein eventually passes through the two loops.
In this paper, we build on Flapan, He, and Wong's theory where we pay attention
to the crossing signs of the threading process and assume that the unknotted
protein chain may arrange itself into a more complicated configuration before
threading occurs. We then apply tangle calculus, originally developed by Ernst
and Sumners to analyze the action of specific proteins on DNA, to give all
possible knots or knotoids that may be discovered in the future according to
our model and give recipes for engineering specific knots in proteins from
simpler pieces. We show why twists knots are the most likely knots to occur in
proteins. We use chirality to show that the most likely knots to occur in
proteins via Taylor's twisted hairpin model are the knots $+3_1$, $4_1$, and
$-5_2$.
| [
{
"created": "Mon, 7 Nov 2022 18:50:17 GMT",
"version": "v1"
}
] | 2022-11-08 | [
[
"Darcy",
"Isabel K.",
""
],
[
"Jones",
"Garrett",
""
],
[
"Pongtanapaisan",
"Puttipong",
""
]
] | Although rare, an increasing number of proteins have been observed to contain entanglements in their native structures. To gain more insight into the significance of protein knotting, researchers have been investigating protein knot formation using both experimental and theoretical methods. Motivated by the hypothesized folding pathway of $\alpha$-haloacid dehalogenase (DehI) protein, Flapan, He, and Wong proposed a theory of how protein knots form, which includes existing folding pathways described by Taylor and B\"olinger et al. as special cases. In their topological descriptions, two loops in an unknotted open protein chain containing at most two twists each come close together, and one end of the protein eventually passes through the two loops. In this paper, we build on Flapan, He, and Wong's theory where we pay attention to the crossing signs of the threading process and assume that the unknotted protein chain may arrange itself into a more complicated configuration before threading occurs. We then apply tangle calculus, originally developed by Ernst and Sumners to analyze the action of specific proteins on DNA, to give all possible knots or knotoids that may be discovered in the future according to our model and give recipes for engineering specific knots in proteins from simpler pieces. We show why twists knots are the most likely knots to occur in proteins. We use chirality to show that the most likely knots to occur in proteins via Taylor's twisted hairpin model are the knots $+3_1$, $4_1$, and $-5_2$. |
1509.08409 | Alexander Gates | Alexander J. Gates and Luis M. Rocha | Control of complex networks requires both structure and dynamics | 15 pages, 6 figures | Scientific Reports 6, Article number: 24456 (2016) | 10.1038/srep24456 | null | q-bio.MN cs.SY math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study of network structure has uncovered signatures of the organization
of complex systems. However, there is also a need to understand how to control
them; for example, identifying strategies to revert a diseased cell to a
healthy state, or a mature cell to a pluripotent state. Two recent
methodologies suggest that the controllability of complex systems can be
predicted solely from the graph of interactions between variables, without
considering their dynamics: structural controllability and minimum dominating
sets. We demonstrate that such structure-only methods fail to characterize
controllability when dynamics are introduced. We study Boolean network
ensembles of network motifs as well as three models of biochemical regulation:
the segment polarity network in Drosophila melanogaster, the cell cycle of
budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in
Arabidopsis thaliana. We demonstrate that structure-only methods both
undershoot and overshoot the number and which sets of critical variables best
control the dynamics of these models, highlighting the importance of the actual
system dynamics in determining control. Our analysis further shows that the
logic of automata transition functions, namely how canalizing they are, plays
an important role in the extent to which structure predicts dynamics.
| [
{
"created": "Mon, 28 Sep 2015 17:40:29 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Apr 2016 17:23:45 GMT",
"version": "v2"
}
] | 2016-04-19 | [
[
"Gates",
"Alexander J.",
""
],
[
"Rocha",
"Luis M.",
""
]
] | The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics. |
1411.4980 | David Sterratt | David C. Sterratt and Oksana Sorokina and J. Douglas Armstrong | Integration of rule-based models and compartmental models of neurons | Presented to the Third International Workshop on Hybrid Systems
Biology Vienna, Austria, July 23-24, 2014 at the International Conference on
Computer-Aided Verification 2014 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synaptic plasticity depends on the interaction between electrical activity in
neurons and the synaptic proteome, the collection of over 1000 proteins in the
post-synaptic density (PSD) of synapses. To construct models of synaptic
plasticity with realistic numbers of proteins, we aim to combine rule-based
models of molecular interactions in the synaptic proteome with compartmental
models of the electrical activity of neurons. Rule-based models allow
interactions between the combinatorially large number of protein complexes in
the postsynaptic proteome to be expressed straightforwardly. Simulations of
rule-based models are stochastic and thus can deal with the small copy numbers
of proteins and complexes in the PSD. Compartmental models of neurons are
expressed as systems of coupled ordinary differential equations and solved
deterministically. We present an algorithm which incorporates stochastic
rule-based models into deterministic compartmental models and demonstrate an
implementation ("KappaNEURON") of this hybrid system using the SpatialKappa and
NEURON simulators.
| [
{
"created": "Tue, 18 Nov 2014 19:43:09 GMT",
"version": "v1"
}
] | 2014-11-19 | [
[
"Sterratt",
"David C.",
""
],
[
"Sorokina",
"Oksana",
""
],
[
"Armstrong",
"J. Douglas",
""
]
] | Synaptic plasticity depends on the interaction between electrical activity in neurons and the synaptic proteome, the collection of over 1000 proteins in the post-synaptic density (PSD) of synapses. To construct models of synaptic plasticity with realistic numbers of proteins, we aim to combine rule-based models of molecular interactions in the synaptic proteome with compartmental models of the electrical activity of neurons. Rule-based models allow interactions between the combinatorially large number of protein complexes in the postsynaptic proteome to be expressed straightforwardly. Simulations of rule-based models are stochastic and thus can deal with the small copy numbers of proteins and complexes in the PSD. Compartmental models of neurons are expressed as systems of coupled ordinary differential equations and solved deterministically. We present an algorithm which incorporates stochastic rule-based models into deterministic compartmental models and demonstrate an implementation ("KappaNEURON") of this hybrid system using the SpatialKappa and NEURON simulators. |
2011.05859 | Sitabhra Sinha | Richa Tripathi, Shakti N. Menon and Sitabhra Sinha | The nonlinearity of interactions drives networks of neural oscillators
to decoherence at strong coupling | 6 pages, 3 figures (+ 6 pages SI) | null | null | null | q-bio.NC nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While phase oscillators are often used to model neuronal populations, in
contrast to the Kuramoto paradigm, strong interactions between brain areas can
be associated with loss of synchrony. Using networks of coupled oscillators
described by neural mass models, we find that a transition to decoherence at
increased coupling strength results from the fundamental nonlinearity, e.g.,
arising from refractoriness, of the interactions between the nodes. The
nonlinearity-driven transition also depends on the connection topology,
underlining the role of network structure in shaping brain activity.
| [
{
"created": "Thu, 29 Oct 2020 17:58:47 GMT",
"version": "v1"
}
] | 2020-11-12 | [
[
"Tripathi",
"Richa",
""
],
[
"Menon",
"Shakti N.",
""
],
[
"Sinha",
"Sitabhra",
""
]
] | While phase oscillators are often used to model neuronal populations, in contrast to the Kuramoto paradigm, strong interactions between brain areas can be associated with loss of synchrony. Using networks of coupled oscillators described by neural mass models, we find that a transition to decoherence at increased coupling strength results from the fundamental nonlinearity, e.g., arising from refractoriness, of the interactions between the nodes. The nonlinearity-driven transition also depends on the connection topology, underlining the role of network structure in shaping brain activity. |
1312.7111 | Michael Tress | Iakes Ezkurdia, David Juan, Jose Manuel Rodriguez, Adam Frankish, Mark
Diekhans, Jennifer Harrow, Jesus Vazquez, Alfonso Valencia, Michael L. Tress | The shrinking human protein coding complement: are there now fewer than
20,000 genes? | Main article 34 pages, 1 1/2 spaced, five figures, one table.
Supplementary info, 13 pages, 10 figures Update no 1: Version 4: small change
in numbers between version 1 and 2 was because we removed the pseudoautosomal
genes. The number 11,838 in version 2 is a typo | null | null | null | q-bio.GN q-bio.MN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Determining the full complement of protein-coding genes is a key goal of
genome annotation. The most powerful approach for confirming protein coding
potential is the detection of cellular protein expression through peptide mass
spectrometry experiments. Here we map the peptides detected in 7 large-scale
proteomics studies to almost 60% of the protein coding genes in the GENCODE
annotation the human genome. We find that conservation across vertebrate
species and the age of the gene family are key indicators of whether a peptide
will be detected in proteomics experiments. We find peptides for most highly
conserved genes and for practically all genes that evolved before bilateria. At
the same time there is almost no evidence of protein expression for genes that
have appeared since primates, or for genes that do not have any protein-like
features or cross-species conservation. We identify 19 non-protein-like
features such as weak conservation, no protein features or ambiguous
annotations in major databases that are indicators of low peptide detection
rates. We use these features to describe a set of 2,001 genes that are
potentially non-coding, and show that many of these genes behave more like
non-coding genes than protein-coding genes. We detect peptides for just 3% of
these genes. We suggest that many of these 2,001 genes do not code for proteins
under normal circumstances and that they should not be included in the human
protein coding gene catalogue. These potential non-coding genes will be revised
as part of the ongoing human genome annotation effort.
| [
{
"created": "Thu, 26 Dec 2013 14:22:23 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Jan 2014 21:20:56 GMT",
"version": "v2"
},
{
"created": "Thu, 23 Jan 2014 12:59:37 GMT",
"version": "v3"
},
{
"created": "Tue, 11 Feb 2014 19:23:44 GMT",
"version": "v4"
}
] | 2014-02-12 | [
[
"Ezkurdia",
"Iakes",
""
],
[
"Juan",
"David",
""
],
[
"Rodriguez",
"Jose Manuel",
""
],
[
"Frankish",
"Adam",
""
],
[
"Diekhans",
"Mark",
""
],
[
"Harrow",
"Jennifer",
""
],
[
"Vazquez",
"Jesus",
""
],
[
"Valencia",
"Alfonso",
""
],
[
"Tress",
"Michael L.",
""
]
] | Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein coding potential is the detection of cellular protein expression through peptide mass spectrometry experiments. Here we map the peptides detected in 7 large-scale proteomics studies to almost 60% of the protein coding genes in the GENCODE annotation the human genome. We find that conservation across vertebrate species and the age of the gene family are key indicators of whether a peptide will be detected in proteomics experiments. We find peptides for most highly conserved genes and for practically all genes that evolved before bilateria. At the same time there is almost no evidence of protein expression for genes that have appeared since primates, or for genes that do not have any protein-like features or cross-species conservation. We identify 19 non-protein-like features such as weak conservation, no protein features or ambiguous annotations in major databases that are indicators of low peptide detection rates. We use these features to describe a set of 2,001 genes that are potentially non-coding, and show that many of these genes behave more like non-coding genes than protein-coding genes. We detect peptides for just 3% of these genes. We suggest that many of these 2,001 genes do not code for proteins under normal circumstances and that they should not be included in the human protein coding gene catalogue. These potential non-coding genes will be revised as part of the ongoing human genome annotation effort. |
0801.4301 | Laurent Jacob | Laurent Jacob (CB), Brice Hoffmann (CB), V\'eronique Stoven (CB),
Jean-Philippe Vert (CB) | Virtual screening of GPCRs: an in silico chemogenomics approach | null | null | null | null | q-bio.QM | null | The G-protein coupled receptor (GPCR) superfamily is currently the largest
class of therapeutic targets. \textit{In silico} prediction of interactions
between GPCRs and small molecules is therefore a crucial step in the drug
discovery process, which remains a daunting task due to the difficulty to
characterize the 3D structure of most GPCRs, and to the limited amount of known
ligands for some members of the superfamily. Chemogenomics, which attempts to
characterize interactions between all members of a target class and all small
molecules simultaneously, has recently been proposed as an interesting
alternative to traditional docking or ligand-based virtual screening
strategies. We propose new methods for in silico chemogenomics and validate
them on the virtual screening of GPCRs. The methods represent an extension of a
recently proposed machine learning strategy, based on support vector machines
(SVM), which provides a flexible framework to incorporate various information
sources on the biological space of targets and on the chemical space of small
molecules. We investigate the use of 2D and 3D descriptors for small molecules,
and test a variety of descriptors for GPCRs. We show fo instance that
incorporating information about the known hierarchical classification of the
target family and about key residues in their inferred binding pockets
significantly improves the prediction accuracy of our model. In particular we
are able to predict ligands of orphan GPCRs with an estimated accuracy of
78.1%.
| [
{
"created": "Mon, 28 Jan 2008 15:03:47 GMT",
"version": "v1"
}
] | 2008-01-29 | [
[
"Jacob",
"Laurent",
"",
"CB"
],
[
"Hoffmann",
"Brice",
"",
"CB"
],
[
"Stoven",
"Véronique",
"",
"CB"
],
[
"Vert",
"Jean-Philippe",
"",
"CB"
]
] | The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. \textit{In silico} prediction of interactions between GPCRs and small molecules is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies. We propose new methods for in silico chemogenomics and validate them on the virtual screening of GPCRs. The methods represent an extension of a recently proposed machine learning strategy, based on support vector machines (SVM), which provides a flexible framework to incorporate various information sources on the biological space of targets and on the chemical space of small molecules. We investigate the use of 2D and 3D descriptors for small molecules, and test a variety of descriptors for GPCRs. We show fo instance that incorporating information about the known hierarchical classification of the target family and about key residues in their inferred binding pockets significantly improves the prediction accuracy of our model. In particular we are able to predict ligands of orphan GPCRs with an estimated accuracy of 78.1%. |
2401.06629 | Marianna Karapitta | Marianna Karapitta, Andreas Kasis, Charithea Stylianides, Kleanthis
Malialis, Panayiotis Kolios | Pandemic infection forecasting through compartmental model and
learning-based approaches | 13 pages, 7 figures, 12 tables | null | null | null | q-bio.PE math.OC q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | The emergence and spread of deadly pandemics has repeatedly occurred
throughout history, causing widespread infections and loss of life. The rapid
spread of pandemics have made governments across the world adopt a range of
actions, including non-pharmaceutical measures to contain its impact. However,
the dynamic nature of pandemics makes selecting intervention strategies
challenging. Hence, the development of suitable monitoring and forecasting
tools for tracking infected cases is crucial for designing and implementing
effective measures. Motivated by this, we present a hybrid pandemic infection
forecasting methodology that integrates compartmental model and learning-based
approaches. In particular, we develop a compartmental model that includes
time-varying infection rates, which are the key parameters that determine the
pandemic's evolution. To identify the time-dependent infection rates, we
establish a hybrid methodology that combines the developed compartmental model
and tools from optimization and neural networks. Specifically, the proposed
methodology estimates the infection rates by fitting the model to available
data, regarding the COVID-19 pandemic in Cyprus, and then predicting their
future values through either a) extrapolation, or b) feeding them to neural
networks. The developed approach exhibits strong accuracy in predicting
infections seven days in advance, achieving low average percentage errors both
using the extrapolation (9.90%) and neural network (5.04%) approaches.
| [
{
"created": "Fri, 12 Jan 2024 15:23:07 GMT",
"version": "v1"
}
] | 2024-01-15 | [
[
"Karapitta",
"Marianna",
""
],
[
"Kasis",
"Andreas",
""
],
[
"Stylianides",
"Charithea",
""
],
[
"Malialis",
"Kleanthis",
""
],
[
"Kolios",
"Panayiotis",
""
]
] | The emergence and spread of deadly pandemics has repeatedly occurred throughout history, causing widespread infections and loss of life. The rapid spread of pandemics have made governments across the world adopt a range of actions, including non-pharmaceutical measures to contain its impact. However, the dynamic nature of pandemics makes selecting intervention strategies challenging. Hence, the development of suitable monitoring and forecasting tools for tracking infected cases is crucial for designing and implementing effective measures. Motivated by this, we present a hybrid pandemic infection forecasting methodology that integrates compartmental model and learning-based approaches. In particular, we develop a compartmental model that includes time-varying infection rates, which are the key parameters that determine the pandemic's evolution. To identify the time-dependent infection rates, we establish a hybrid methodology that combines the developed compartmental model and tools from optimization and neural networks. Specifically, the proposed methodology estimates the infection rates by fitting the model to available data, regarding the COVID-19 pandemic in Cyprus, and then predicting their future values through either a) extrapolation, or b) feeding them to neural networks. The developed approach exhibits strong accuracy in predicting infections seven days in advance, achieving low average percentage errors both using the extrapolation (9.90%) and neural network (5.04%) approaches. |
1711.05517 | Alfred Anwander | Christa M\"uller-Axt, Alfred Anwander, Katharina von Kriegstein | Altered structural connectivity of the left visual thalamus in
developmental dyslexia | 31 pages, 5 figures, 2 tables | Current Biology (2017) | 10.1016/j.cub.2017.10.034 | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Developmental dyslexia is characterized by persistent reading and spelling
deficits. Partly due to technical challenges with investigating subcortical
sensory structures, current research on dyslexia in humans by-and-large focuses
on the cerebral cortex. These studies found that dyslexia is typically
associated with functional and structural alterations of a distributed
left-hemispheric cerebral cortex network. However, findings from animal models
and post-mortem studies in humans suggest that developmental dyslexia might
also be associated with structural alterations in subcortical sensory pathways.
Whether these alterations also exist in developmental dyslexia in-vivo and how
they relate to dyslexia symptoms is currently unknown. Here we used ultra-high
resolution structural magnetic resonance imaging (MRI), diffusion MRI and
probabilistic tractography to investigate the structural connections of the
visual sensory pathway in dyslexia in-vivo. We discovered that individuals with
developmental dyslexia have reduced structural connections in the direct
pathway between the left visual thalamus (LGN) and left middle temporal area
V5/MT, but not between the left LGN and left primary visual cortex (V1). In
addition, left V5/MT-LGN connectivity strength correlated with rapid naming
abilities - a key deficit in dyslexia [14]. These findings provide the first
evidence of specific structural alterations in the connections between the
sensory thalamus and cortex in developmental dyslexia. The results challenge
current standard models and provide novel evidence for the importance of
cortico-thalamic interactions in explaining dyslexia.
| [
{
"created": "Wed, 15 Nov 2017 12:21:22 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Nov 2017 23:56:00 GMT",
"version": "v2"
}
] | 2017-11-20 | [
[
"Müller-Axt",
"Christa",
""
],
[
"Anwander",
"Alfred",
""
],
[
"von Kriegstein",
"Katharina",
""
]
] | Developmental dyslexia is characterized by persistent reading and spelling deficits. Partly due to technical challenges with investigating subcortical sensory structures, current research on dyslexia in humans by-and-large focuses on the cerebral cortex. These studies found that dyslexia is typically associated with functional and structural alterations of a distributed left-hemispheric cerebral cortex network. However, findings from animal models and post-mortem studies in humans suggest that developmental dyslexia might also be associated with structural alterations in subcortical sensory pathways. Whether these alterations also exist in developmental dyslexia in-vivo and how they relate to dyslexia symptoms is currently unknown. Here we used ultra-high resolution structural magnetic resonance imaging (MRI), diffusion MRI and probabilistic tractography to investigate the structural connections of the visual sensory pathway in dyslexia in-vivo. We discovered that individuals with developmental dyslexia have reduced structural connections in the direct pathway between the left visual thalamus (LGN) and left middle temporal area V5/MT, but not between the left LGN and left primary visual cortex (V1). In addition, left V5/MT-LGN connectivity strength correlated with rapid naming abilities - a key deficit in dyslexia [14]. These findings provide the first evidence of specific structural alterations in the connections between the sensory thalamus and cortex in developmental dyslexia. The results challenge current standard models and provide novel evidence for the importance of cortico-thalamic interactions in explaining dyslexia. |
1012.3124 | Ernest Barreto | Ernest Barreto and John R. Cressman | Ion Concentration Dynamics as a Mechanism for Neuronal Bursting | The most recent version now contains citation information: Journal of
Biological Physics 37, 361-373 (2011) | null | 10.1007/s10867-010-9212-6 | null | q-bio.CB q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a simple conductance-based model neuron that includes intra- and
extra-cellular ion concentration dynamics and show that this model exhibits
periodic bursting. The bursting arises as the fast spiking behavior of the
neuron is modulated by the slow oscillatory behavior in the ion concentration
variables, and vice versa. By separating these time scales and studying the
bifurcation structure of the neuron, we catalog several qualitatively different
bursting profiles that are strikingly similar to those seen in experimental
preparations. Our work suggests that ion concentration dynamics may play an
important role in modulating neuronal excitability in real biological systems.
| [
{
"created": "Tue, 14 Dec 2010 18:33:39 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Dec 2010 21:04:36 GMT",
"version": "v2"
},
{
"created": "Wed, 21 Sep 2011 19:38:05 GMT",
"version": "v3"
}
] | 2011-09-22 | [
[
"Barreto",
"Ernest",
""
],
[
"Cressman",
"John R.",
""
]
] | We describe a simple conductance-based model neuron that includes intra- and extra-cellular ion concentration dynamics and show that this model exhibits periodic bursting. The bursting arises as the fast spiking behavior of the neuron is modulated by the slow oscillatory behavior in the ion concentration variables, and vice versa. By separating these time scales and studying the bifurcation structure of the neuron, we catalog several qualitatively different bursting profiles that are strikingly similar to those seen in experimental preparations. Our work suggests that ion concentration dynamics may play an important role in modulating neuronal excitability in real biological systems. |
1812.10602 | Zheng Zhao | Zheng Zhao, Lei Xie, and Philip E. Bourne | Structural insights into characterizing binding sites in EGFR kinase
mutants | 32 pages, 7 figures | Journal of Chemical Information and Modeling, 2018 | 10.1021/acs.jcim.8b00458 | null | q-bio.MN | http://creativecommons.org/licenses/by-sa/4.0/ | Over the last two decades epidermal growth factor receptor (EGFR) kinase has
become an important target to treat non-small cell lung cancer (NSCLC).
Currently, three generations of EGFR kinase-targeted small molecule drugs have
been FDA approved. They nominally produce a response at the start of treatment
and lead to a substantial survival benefit for patients. However, long-term
treatment results in acquired drug resistance and further vulnerability to
NSCLC. Therefore, novel EGFR kinase inhibitors that specially overcome acquired
mutations are urgently needed. To this end, we carried out a comprehensive
study of different EGFR kinase mutants using a structural systems pharmacology
strategy. Our analysis shows that both wild-type and mutated structures exhibit
multiple conformational states that have not been observed in solved crystal
structures. We show that this conformational flexibility accommodates diverse
types of ligands with multiple types of binding modes. These results provide
insights for designing a new-generation of EGFR kinase inhibitor that combats
acquired drug-resistant mutations through a multi-conformation-based drug
design strategy.
| [
{
"created": "Thu, 27 Dec 2018 02:51:34 GMT",
"version": "v1"
}
] | 2018-12-31 | [
[
"Zhao",
"Zheng",
""
],
[
"Xie",
"Lei",
""
],
[
"Bourne",
"Philip E.",
""
]
] | Over the last two decades epidermal growth factor receptor (EGFR) kinase has become an important target to treat non-small cell lung cancer (NSCLC). Currently, three generations of EGFR kinase-targeted small molecule drugs have been FDA approved. They nominally produce a response at the start of treatment and lead to a substantial survival benefit for patients. However, long-term treatment results in acquired drug resistance and further vulnerability to NSCLC. Therefore, novel EGFR kinase inhibitors that specially overcome acquired mutations are urgently needed. To this end, we carried out a comprehensive study of different EGFR kinase mutants using a structural systems pharmacology strategy. Our analysis shows that both wild-type and mutated structures exhibit multiple conformational states that have not been observed in solved crystal structures. We show that this conformational flexibility accommodates diverse types of ligands with multiple types of binding modes. These results provide insights for designing a new-generation of EGFR kinase inhibitor that combats acquired drug-resistant mutations through a multi-conformation-based drug design strategy. |
1906.07354 | Huilin Wei | Huilin Wei, Amirhossein Jafarian, Peter Zeidman, Vladimir Litvak,
Adeel Razi, Dewen Hu, Karl J. Friston | Bayesian fusion and multimodal DCM for EEG and fMRI | null | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper asks whether integrating multimodal EEG and fMRI data offers a
better characterisation of functional brain architectures than either modality
alone. This evaluation rests upon a dynamic causal model that generates both
EEG and fMRI data from the same neuronal dynamics. We introduce the use of
Bayesian fusion to provide informative (empirical) neuronal priors - derived
from dynamic causal modelling (DCM) of EEG data - for subsequent DCM of fMRI
data. To illustrate this procedure, we generated synthetic EEG and fMRI
timeseries for a mismatch negativity (or auditory oddball) paradigm, using
biologically plausible model parameters (i.e., posterior expectations from a
DCM of empirical, open access, EEG data). Using model inversion, we found that
Bayesian fusion provided a substantial improvement in marginal likelihood or
model evidence, indicating a more efficient estimation of model parameters, in
relation to inverting fMRI data alone. We quantified the benefits of multimodal
fusion with the information gain pertaining to neuronal and haemodynamic
parameters - as measured by the Kullback-Leibler divergence between their prior
and posterior densities. Remarkably, this analysis suggested that EEG data can
improve estimates of haemodynamic parameters; thereby furnishing
proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve
conditional dependencies between neuronal and haemodynamic estimators. These
results suggest that Bayesian fusion may offer a useful approach that exploits
the complementary temporal (EEG) and spatial (fMRI) precision of different data
modalities. We envisage the procedure could be applied to any multimodal
dataset that can be explained by a DCM with a common neuronal parameterisation.
| [
{
"created": "Tue, 18 Jun 2019 02:59:47 GMT",
"version": "v1"
}
] | 2019-06-19 | [
[
"Wei",
"Huilin",
""
],
[
"Jafarian",
"Amirhossein",
""
],
[
"Zeidman",
"Peter",
""
],
[
"Litvak",
"Vladimir",
""
],
[
"Razi",
"Adeel",
""
],
[
"Hu",
"Dewen",
""
],
[
"Friston",
"Karl J.",
""
]
] | This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors - derived from dynamic causal modelling (DCM) of EEG data - for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters - as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation. |
0807.1699 | Swarnendu Tripathi | Swarnendu Tripathi and John J. Portman | Inherent flexibility determines the transition mechanisms of the
EF-hands of Calmodulin | 17 pages, 7 figures | null | 10.1073/pnas.0806872106 | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore how inherent flexibility of a protein molecule influences the
mechanism controlling the kinetics of allosteric transitions using a
variational model inspired from work in protein folding. The striking
differences in the predicted transition mechanism for the opening of the two
domains of calmodulin (CaM) emphasizes that inherent flexibility is key to
understanding the complex conformational changes that occur in proteins. In
particular, the C-terminal domain of CaM (cCaM) which is inherently less
flexible than its N-terminal domain (nCaM) reveals "cracking" or local partial
unfolding during the open/closed transition. This result is in harmony with the
picture that cracking relieves local stresses due to conformational
deformations of a sufficiently rigid protein. We also compare the
conformational transition in a recently studied "even-odd" paired fragment of
CaM. Our results rationalize the different relative binding affinities of the
EF-hands in the engineered fragment compared to the intact "odd-even" paired
EF-hands (nCaM and cCaM) in terms of changes in flexibility along the
transition route. Aside from elucidating general theoretical ideas about the
cracking mechanism, these studies also emphasize how the remarkable intrinsic
plasticity of CaM underlies conformational dynamics essential for its diverse
functions.
| [
{
"created": "Thu, 10 Jul 2008 16:44:12 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Tripathi",
"Swarnendu",
""
],
[
"Portman",
"John J.",
""
]
] | We explore how inherent flexibility of a protein molecule influences the mechanism controlling the kinetics of allosteric transitions using a variational model inspired from work in protein folding. The striking differences in the predicted transition mechanism for the opening of the two domains of calmodulin (CaM) emphasizes that inherent flexibility is key to understanding the complex conformational changes that occur in proteins. In particular, the C-terminal domain of CaM (cCaM) which is inherently less flexible than its N-terminal domain (nCaM) reveals "cracking" or local partial unfolding during the open/closed transition. This result is in harmony with the picture that cracking relieves local stresses due to conformational deformations of a sufficiently rigid protein. We also compare the conformational transition in a recently studied "even-odd" paired fragment of CaM. Our results rationalize the different relative binding affinities of the EF-hands in the engineered fragment compared to the intact "odd-even" paired EF-hands (nCaM and cCaM) in terms of changes in flexibility along the transition route. Aside from elucidating general theoretical ideas about the cracking mechanism, these studies also emphasize how the remarkable intrinsic plasticity of CaM underlies conformational dynamics essential for its diverse functions. |
0801.0797 | Wang Weiming | Weiming Wang, Lei Zhang, Yakui Xue, Zhen Jin | Spatiotemporal pattern formation of Beddington-DeAngelis-type
predator-prey model | null | null | null | null | q-bio.PE | null | In this paper, we investigate the emergence of a predator-prey model with
Beddington-DeAngelis-type functional response and reaction-diffusion. We derive
the conditions for Hopf and Turing bifurcation on the spatial domain. Based on
the stability and bifurcation analysis, we give the spatial pattern formation
via numerical simulation, i.e., the evolution process of the model near the
coexistence equilibrium point. We find that for the model we consider, pure
Turing instability gives birth to the spotted pattern, pure Hopf instability
gives birth to the spiral wave pattern, and both Hopf and Turing instability
give birth to stripe-like pattern. Our results show that reaction-diffusion
model is an appropriate tool for investigating fundamental mechanism of complex
spatiotemporal dynamics. It will be useful for studying the dynamic complexity
of ecosystems.
| [
{
"created": "Sat, 5 Jan 2008 09:36:45 GMT",
"version": "v1"
}
] | 2008-01-08 | [
[
"Wang",
"Weiming",
""
],
[
"Zhang",
"Lei",
""
],
[
"Xue",
"Yakui",
""
],
[
"Jin",
"Zhen",
""
]
] | In this paper, we investigate the emergence of a predator-prey model with Beddington-DeAngelis-type functional response and reaction-diffusion. We derive the conditions for Hopf and Turing bifurcation on the spatial domain. Based on the stability and bifurcation analysis, we give the spatial pattern formation via numerical simulation, i.e., the evolution process of the model near the coexistence equilibrium point. We find that for the model we consider, pure Turing instability gives birth to the spotted pattern, pure Hopf instability gives birth to the spiral wave pattern, and both Hopf and Turing instability give birth to stripe-like pattern. Our results show that reaction-diffusion model is an appropriate tool for investigating fundamental mechanism of complex spatiotemporal dynamics. It will be useful for studying the dynamic complexity of ecosystems. |
q-bio/0701001 | Edwin Wang Dr. | Qinghua Cui, Zhenbao Yu, Youlian Pan, Enrico Purisima and Edwin Wang | MicroRNAs preferentially target the genes with high transcriptional
regulation complexity | supplementary data available at http://www.bri.nrc.ca/wang | Biochem Biophys Res Commun., 352:733-738, 2007 | 10.1016/j.bbrc.2006.11.080 | null | q-bio.GN q-bio.MN | null | Over the past few years, microRNAs (miRNAs) have emerged as a new prominent
class of gene regulatory factors that negatively regulate expression of
approximately one-third of the genes in animal genomes at post-transcriptional
level. However, it is still unclear why some genes are regulated by miRNAs but
others are not, i.e. what principles govern miRNA regulation in animal genomes.
In this study, we systematically analyzed the relationship between
transcription factors (TFs) and miRNAs in gene regulation. We found that the
genes with more TF-binding sites have a higher probability of being targeted by
miRNAs and have more miRNA-binding sites on average. This observation reveals
that the genes with higher cis-regulation complexity are more coordinately
regulated by TFs at the transcriptional level and by miRNAs at the
post-transcriptional level. This is a potentially novel discovery of mechanism
for coordinated regulation of gene expression. Gene ontology analysis further
demonstrated that such coordinated regulation is more popular in the
developmental genes.
| [
{
"created": "Sat, 30 Dec 2006 04:20:26 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Cui",
"Qinghua",
""
],
[
"Yu",
"Zhenbao",
""
],
[
"Pan",
"Youlian",
""
],
[
"Purisima",
"Enrico",
""
],
[
"Wang",
"Edwin",
""
]
] | Over the past few years, microRNAs (miRNAs) have emerged as a new prominent class of gene regulatory factors that negatively regulate expression of approximately one-third of the genes in animal genomes at post-transcriptional level. However, it is still unclear why some genes are regulated by miRNAs but others are not, i.e. what principles govern miRNA regulation in animal genomes. In this study, we systematically analyzed the relationship between transcription factors (TFs) and miRNAs in gene regulation. We found that the genes with more TF-binding sites have a higher probability of being targeted by miRNAs and have more miRNA-binding sites on average. This observation reveals that the genes with higher cis-regulation complexity are more coordinately regulated by TFs at the transcriptional level and by miRNAs at the post-transcriptional level. This is a potentially novel discovery of mechanism for coordinated regulation of gene expression. Gene ontology analysis further demonstrated that such coordinated regulation is more popular in the developmental genes. |
1705.10001 | Marco Kienzle | M.K. Broadhurst, M. Kienzle and J. Stewart | Natural mortality of Trachurus novaezelandiae and their size selection
by purse seines off south-eastern Australia | null | null | 10.1111/fme.12286 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The natural mortality (M) and purse-seine catchability and selectivity were
estimated for Trachurus novaezelandiae, Richardson, 1843 (yellowtail scad)-a
small inshore pelagic species harvested off south-eastern Australia. Hazard
functions were applied to two decades of data describing catches (mostly stable
at a mean +- SE of 315 +- 14 t p.a.) and effort (declining from a maximum of
2289 to 642 boat days between 1999/00 and 2015/16) and inter-dispersed (over
nine years) annual estimates of size-at-age (0+ to 18 years) to enable survival
analysis. The data were best described by a model with eight parameters,
including catchability (estimated at < 0.1 x 10-7 boat day-1), M (0.22 year-1)
and variable age-specific selection up to 6 years with a 50% retention among
5-year olds (larger than the estimated age at maturation). The low catchability
implied minimal fishing mortality by the purse-seine fleet. Ongoing monitoring
and applied gear-based studies are required to validate purse-seine
catchability and selectivity, but the data nevertheless imply T. novaezelandiae
could incur substantial additional fishing effort and, in doing, so alleviate
pressure on other regional small pelagics.
| [
{
"created": "Mon, 29 May 2017 00:00:17 GMT",
"version": "v1"
}
] | 2018-07-10 | [
[
"Broadhurst",
"M. K.",
""
],
[
"Kienzle",
"M.",
""
],
[
"Stewart",
"J.",
""
]
] | The natural mortality (M) and purse-seine catchability and selectivity were estimated for Trachurus novaezelandiae, Richardson, 1843 (yellowtail scad)-a small inshore pelagic species harvested off south-eastern Australia. Hazard functions were applied to two decades of data describing catches (mostly stable at a mean +- SE of 315 +- 14 t p.a.) and effort (declining from a maximum of 2289 to 642 boat days between 1999/00 and 2015/16) and inter-dispersed (over nine years) annual estimates of size-at-age (0+ to 18 years) to enable survival analysis. The data were best described by a model with eight parameters, including catchability (estimated at < 0.1 x 10-7 boat day-1), M (0.22 year-1) and variable age-specific selection up to 6 years with a 50% retention among 5-year olds (larger than the estimated age at maturation). The low catchability implied minimal fishing mortality by the purse-seine fleet. Ongoing monitoring and applied gear-based studies are required to validate purse-seine catchability and selectivity, but the data nevertheless imply T. novaezelandiae could incur substantial additional fishing effort and, in doing, so alleviate pressure on other regional small pelagics. |
1707.08240 | Peter Taylor | Peter N Taylor, Nishant Sinha, Yujiang Wang, Sjoerd B Vos, Jane de
Tisi, Anna Miserocchi, Andrew W McEvoy, Gavin P Winston, John S Duncan | The impact of epilepsy surgery on the structural connectome and its
relation to outcome | null | NeuroImage.Clinical 18 (2018) 202-214 | 10.1016/j.nicl.2018.01.028 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal lobe surgical resection brings seizure remission in up to 80% of
patients, with long-term complete seizure freedom in 41%. However, it is
unclear how surgery impacts on the structural white matter network, and how the
network changes relate to seizure outcome. We used white matter fibre
tractography on preoperative diffusion MRI to generate a structural white
matter network, and postoperative T1-weighted MRI to retrospectively infer the
impact of surgical resection on this network. We then applied graph theory and
machine learning to investigate the properties of change between the
preoperative and predicted postoperative networks. Temporal lobe surgery had a
modest impact on global network efficiency, despite the disruption caused. This
was due to alternative shortest paths in the network leading to widespread
increases in betweenness centrality post-surgery. Measurements of network
change could retrospectively predict seizure outcomes with 79% accuracy and 65%
specificity, which is twice as high as the empirical distribution. Fifteen
connections which changed due to surgery were identified as useful for
prediction of outcome, eight of which connected to the ipsilateral temporal
pole. Our results suggest that the use of network change metrics may have
clinical value for predicting seizure outcome. This approach could be used to
prospectively predict outcomes given a suggested resection mask using
preoperative data only.
| [
{
"created": "Tue, 25 Jul 2017 22:18:09 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Mar 2018 19:05:12 GMT",
"version": "v2"
}
] | 2020-09-30 | [
[
"Taylor",
"Peter N",
""
],
[
"Sinha",
"Nishant",
""
],
[
"Wang",
"Yujiang",
""
],
[
"Vos",
"Sjoerd B",
""
],
[
"de Tisi",
"Jane",
""
],
[
"Miserocchi",
"Anna",
""
],
[
"McEvoy",
"Andrew W",
""
],
[
"Winston",
"Gavin P",
""
],
[
"Duncan",
"John S",
""
]
] | Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome. We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks. Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole. Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only. |
q-bio/0309001 | Eduardo D. Sontag | David Angeli and Eduardo D. Sontag | Monotone Systems with Inputs and Outputs | null | null | null | null | q-bio.QM q-bio.MN | null | Monotone systems constitute one of the most important classes of dynamical
systems used in mathematical biology modeling. The objective of this paper is
to extend the notion of monotonicity to systems with inputs and outputs, a
necessary first step in trying to understand interconnections, especially
including feedback loops, built up out of monotone components. Basic
definitions and theorems are provided, as well as an application of a theorem
regarding negative feedback loops to the study of a model of one of the cell's
most important subsystems (MAPK cascades) .
| [
{
"created": "Tue, 16 Sep 2003 13:59:15 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Angeli",
"David",
""
],
[
"Sontag",
"Eduardo D.",
""
]
] | Monotone systems constitute one of the most important classes of dynamical systems used in mathematical biology modeling. The objective of this paper is to extend the notion of monotonicity to systems with inputs and outputs, a necessary first step in trying to understand interconnections, especially including feedback loops, built up out of monotone components. Basic definitions and theorems are provided, as well as an application of a theorem regarding negative feedback loops to the study of a model of one of the cell's most important subsystems (MAPK cascades) . |
2003.09403 | Raj Abhijit Dandekar | Raj Dandekar and George Barbastathis | Neural Network aided quarantine control model estimation of COVID spread
in Wuhan, China | 9 pages including references. 8 figures | null | null | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a move described as unprecedented in public health history, starting 24
January 2020, China imposed quarantine and isolation restrictions in Wuhan, a
city of more than 10 million people. This raised the question: is mass
quarantine and isolation effective as a social tool in addition to its
scientific use as a medical tool? In an effort to address this question, using
a epidemiological model driven approach augmented by machine learning, we show
that the quarantine and isolation measures implemented in Wuhan brought down
the effective reproduction number R(t) of the CoVID-19 spread from R(t) > 1 to
R(t) <1 within a month after the imposition of quarantine control measures in
Wuhan, China. This ultimately resulted in a stagnation phase in the infected
case count in Wuhan. Our results indicate that the strict public health
policies implemented in Wuhan may have played a crucial role in halting down
the spread of infection and such measures should potentially be implemented in
other highly affected countries such as South Korea, Italy and Iran to curtail
spread of the disease. Finally, our forecasting results predict a stagnation in
the quarantine control measures implemented in Wuhan towards the end of March
2020; this would lead to a subsequent stagnation in the effective reproduction
number at R(t) <1. We warn that immediate relaxation of the quarantine measures
in Wuhan may lead to a relapse in the infection spread and a subsequent
increase in the effective reproduction number to R(t) >1. Thus, it may be wise
to relax quarantine measures after sufficient time has elapsed, during which
maximum of the quarantined/isolated individuals are recovered.
| [
{
"created": "Wed, 18 Mar 2020 16:28:18 GMT",
"version": "v1"
}
] | 2020-03-23 | [
[
"Dandekar",
"Raj",
""
],
[
"Barbastathis",
"George",
""
]
] | In a move described as unprecedented in public health history, starting 24 January 2020, China imposed quarantine and isolation restrictions in Wuhan, a city of more than 10 million people. This raised the question: is mass quarantine and isolation effective as a social tool in addition to its scientific use as a medical tool? In an effort to address this question, using a epidemiological model driven approach augmented by machine learning, we show that the quarantine and isolation measures implemented in Wuhan brought down the effective reproduction number R(t) of the CoVID-19 spread from R(t) > 1 to R(t) <1 within a month after the imposition of quarantine control measures in Wuhan, China. This ultimately resulted in a stagnation phase in the infected case count in Wuhan. Our results indicate that the strict public health policies implemented in Wuhan may have played a crucial role in halting down the spread of infection and such measures should potentially be implemented in other highly affected countries such as South Korea, Italy and Iran to curtail spread of the disease. Finally, our forecasting results predict a stagnation in the quarantine control measures implemented in Wuhan towards the end of March 2020; this would lead to a subsequent stagnation in the effective reproduction number at R(t) <1. We warn that immediate relaxation of the quarantine measures in Wuhan may lead to a relapse in the infection spread and a subsequent increase in the effective reproduction number to R(t) >1. Thus, it may be wise to relax quarantine measures after sufficient time has elapsed, during which maximum of the quarantined/isolated individuals are recovered. |
1809.05969 | Marie Li | Marie Li | Missing Value Estimation Algorithms on Cluster and Representativeness
Preservation of Gene Expression Microarray Data | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Missing values are largely inevitable in gene expression microarray studies.
Data sets often have significant omissions due to individuals dropping out of
experiments, errors in data collection, image corruptions, and so on. Missing
data could potentially undermine the validity of research results - leading to
inaccurate predictive models and misleading conclusions. Imputation - a
relatively flexible, general purpose approach towards dealing with missing data
- is now available in massive numbers, making it possible to handle missing
data. While these estimation methods are becoming increasingly more effective
in resolving the discrepancies between true and estimated values, its effect on
clustering outcomes is largely disregarded.
This study seeks to reveal the vast differences in agglomerative hierarchal
clustering outcomes estimation methods can construct in comparison to the
precision exhibited (presented through the cophenetic correlation coefficient)
in comparison to their high efficiency and effectivity in value preservation of
true and imputed values (presented through the root-mean-squared error). We
argue against the traditional approach towards the development of imputation
methods and instead, advocate towards methods that reproduce a data set's
original, natural cluster.
By using a number of advanced imputation methods, we reveal extensive
differences between original and reconstructed clusters that could
significantly transform the interpretations of the data as a whole.
| [
{
"created": "Sun, 16 Sep 2018 22:21:46 GMT",
"version": "v1"
}
] | 2018-09-18 | [
[
"Li",
"Marie",
""
]
] | Missing values are largely inevitable in gene expression microarray studies. Data sets often have significant omissions due to individuals dropping out of experiments, errors in data collection, image corruptions, and so on. Missing data could potentially undermine the validity of research results - leading to inaccurate predictive models and misleading conclusions. Imputation - a relatively flexible, general purpose approach towards dealing with missing data - is now available in massive numbers, making it possible to handle missing data. While these estimation methods are becoming increasingly more effective in resolving the discrepancies between true and estimated values, its effect on clustering outcomes is largely disregarded. This study seeks to reveal the vast differences in agglomerative hierarchal clustering outcomes estimation methods can construct in comparison to the precision exhibited (presented through the cophenetic correlation coefficient) in comparison to their high efficiency and effectivity in value preservation of true and imputed values (presented through the root-mean-squared error). We argue against the traditional approach towards the development of imputation methods and instead, advocate towards methods that reproduce a data set's original, natural cluster. By using a number of advanced imputation methods, we reveal extensive differences between original and reconstructed clusters that could significantly transform the interpretations of the data as a whole. |
1111.0379 | Jakub Truszkowski | Daniel G. Brown and Jakub Truszkowski | Fast reconstruction of phylogenetic trees using locality-sensitive
hashing | null | null | null | null | q-bio.PE cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the first sub-quadratic time algorithm that with high probability
correctly reconstructs phylogenetic trees for short sequences generated by a
Markov model of evolution. Due to rapid expansion in sequence databases, such
very fast algorithms are becoming necessary. Other fast heuristics have been
developed for building trees from very large alignments (Price et al, and Brown
et al), but they lack theoretical performance guarantees. Our new algorithm
runs in $O(n^{1+\gamma(g)}\log^2n)$ time, where $\gamma$ is an increasing
function of an upper bound on the branch lengths in the phylogeny, the upper
bound $g$ must be below$1/2-\sqrt{1/8} \approx 0.15$, and $\gamma(g)<1$ for all
$g$. For phylogenies with very short branches, the running time of our
algorithm is close to linear. For example, if all branch lengths correspond to
a mutation probability of less than 0.02, the running time of our algorithm is
roughly $O(n^{1.2}\log^2n)$. Via a prototype and a sequence of large-scale
experiments, we show that many large phylogenies can be reconstructed fast,
without compromising reconstruction accuracy.
| [
{
"created": "Wed, 2 Nov 2011 04:21:19 GMT",
"version": "v1"
},
{
"created": "Thu, 31 May 2012 07:28:25 GMT",
"version": "v2"
}
] | 2012-06-01 | [
[
"Brown",
"Daniel G.",
""
],
[
"Truszkowski",
"Jakub",
""
]
] | We present the first sub-quadratic time algorithm that with high probability correctly reconstructs phylogenetic trees for short sequences generated by a Markov model of evolution. Due to rapid expansion in sequence databases, such very fast algorithms are becoming necessary. Other fast heuristics have been developed for building trees from very large alignments (Price et al, and Brown et al), but they lack theoretical performance guarantees. Our new algorithm runs in $O(n^{1+\gamma(g)}\log^2n)$ time, where $\gamma$ is an increasing function of an upper bound on the branch lengths in the phylogeny, the upper bound $g$ must be below$1/2-\sqrt{1/8} \approx 0.15$, and $\gamma(g)<1$ for all $g$. For phylogenies with very short branches, the running time of our algorithm is close to linear. For example, if all branch lengths correspond to a mutation probability of less than 0.02, the running time of our algorithm is roughly $O(n^{1.2}\log^2n)$. Via a prototype and a sequence of large-scale experiments, we show that many large phylogenies can be reconstructed fast, without compromising reconstruction accuracy. |
2001.07841 | Ye Lin | Ye Lin, Sean B. Andersson | Simultaneous Localization and Parameter Estimation for Single Particle
Tracking via Sigma Points based EM | Accepted by 58th Conference on Decision and Control (CDC) | null | null | null | q-bio.BM math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single Particle Tracking (SPT) is a powerful class of tools for analyzing the
dynamics of individual biological macromolecules moving inside living cells.
The acquired data is typically in the form of a sequence of camera images that
are then post-processed to reveal details about the motion. In this work, we
develop an algorithm for jointly estimating both particle trajectory and motion
model parameters from the data. Our approach uses Expectation Maximization (EM)
combined with an Unscented Kalman filter (UKF) and an Unscented
Rauch-Tung-Striebel smoother (URTSS), allowing us to use an accurate, nonlinear
model of the observations acquired by the camera. Due to the shot noise
characteristics of the photon generation process, this model uses a Poisson
distribution to capture the measurement noise inherent in imaging. In order to
apply a UKF, we first must transform the measurements into a model with
additive Gaussian noise. We consider two approaches, one based on variance
stabilizing transformations (where we compare the Anscombe and Freeman-Tukey
transforms) and one on a Gaussian approximation to the Poisson distribution.
Through simulations, we demonstrate efficacy of the approach and explore the
differences among these measurement transformations.
| [
{
"created": "Wed, 22 Jan 2020 01:42:01 GMT",
"version": "v1"
}
] | 2020-01-23 | [
[
"Lin",
"Ye",
""
],
[
"Andersson",
"Sean B.",
""
]
] | Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop an algorithm for jointly estimating both particle trajectory and motion model parameters from the data. Our approach uses Expectation Maximization (EM) combined with an Unscented Kalman filter (UKF) and an Unscented Rauch-Tung-Striebel smoother (URTSS), allowing us to use an accurate, nonlinear model of the observations acquired by the camera. Due to the shot noise characteristics of the photon generation process, this model uses a Poisson distribution to capture the measurement noise inherent in imaging. In order to apply a UKF, we first must transform the measurements into a model with additive Gaussian noise. We consider two approaches, one based on variance stabilizing transformations (where we compare the Anscombe and Freeman-Tukey transforms) and one on a Gaussian approximation to the Poisson distribution. Through simulations, we demonstrate efficacy of the approach and explore the differences among these measurement transformations. |
2104.04567 | Kaare Mikkelsen | Kaare B. Mikkelsen, Huy Phan, Mike L. Rank, Martin C. Hemmsen, Maarten
de Vos, Preben Kidmose | Light-weight sleep monitoring: electrode distance matters more than
placement for automatic scoring | 8 pages, 8 figures | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Modern sleep monitoring development is shifting towards the use of
unobtrusive sensors combined with algorithms for automatic sleep scoring. Many
different combinations of wet and dry electrodes, ear-centered,
forehead-mounted or headband-inspired designs have been proposed, alongside an
ever growing variety of machine learning algorithms for automatic sleep
scoring. In this paper, we compare 13 different, realistic sensor setups
derived from the same data set and analysed with the same pipeline. We find
that all setups which include both a lateral and an EOG derivation show
similar, state-of-the-art performance, with average Cohen's kappa values of at
least 0.80. This indicates that electrode distance, rather than position, is
important for accurate sleep scoring. Finally, based on the results presented,
we argue that with the current competitive performance of automated staging
approaches, there is an urgent need for establishing an improved benchmark
beyond current single human rater scoring.
| [
{
"created": "Fri, 9 Apr 2021 18:52:23 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Apr 2021 08:45:06 GMT",
"version": "v2"
}
] | 2021-04-14 | [
[
"Mikkelsen",
"Kaare B.",
""
],
[
"Phan",
"Huy",
""
],
[
"Rank",
"Mike L.",
""
],
[
"Hemmsen",
"Martin C.",
""
],
[
"de Vos",
"Maarten",
""
],
[
"Kidmose",
"Preben",
""
]
] | Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, ear-centered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring. In this paper, we compare 13 different, realistic sensor setups derived from the same data set and analysed with the same pipeline. We find that all setups which include both a lateral and an EOG derivation show similar, state-of-the-art performance, with average Cohen's kappa values of at least 0.80. This indicates that electrode distance, rather than position, is important for accurate sleep scoring. Finally, based on the results presented, we argue that with the current competitive performance of automated staging approaches, there is an urgent need for establishing an improved benchmark beyond current single human rater scoring. |
1311.5652 | Kevin Liu | Kevin J. Liu, Ethan Steinberg, Alexander Yozzo, Ying Song, Michael H.
Kohn, Luay Nakhleh | Interspecific Introgressive Origin of Genomic Diversity in the House
Mouse | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report on a genome-wide scan for introgression in the house mouse (Mus
musculus domesticus) involving the Algerian mouse (Mus spretus), using samples
from the ranges of sympatry and allopatry in Africa and Europe. Our analysis
reveals wide variability in introgression signatures along the genomes, as well
as across the samples. We find that fewer than half of the autosomes in each
genome harbor all detectable introgression, while the X chromosome has none.
Further, European mice carry more M. spretus alleles than the sympatric African
ones. Using the length distribution and sharing patterns of introgressed
genomic tracts across the samples, we infer, first, that at least three
distinct hybridization events involving M. spretus have occurred, one of which
is ancient, and the other two are recent (one presumably due to warfarin
rodenticide selection). Second, several of the inferred introgressed tracts
contain genes that are likely to confer adaptive advantage. Third, introgressed
tracts might contain driver genes that determine the evolutionary fate of those
tracts. Further, functional analysis revealed introgressed genes that are
essential to fitness, including the Vkorc1 gene, which is implicated in
rodenticide resistance, and olfactory receptor genes. Our findings highlight
the extent and role of introgression in nature, and call for careful analysis
and interpretation of house mouse data in evolutionary and genetic studies.
| [
{
"created": "Fri, 22 Nov 2013 04:35:47 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Sep 2014 16:41:34 GMT",
"version": "v2"
}
] | 2014-09-22 | [
[
"Liu",
"Kevin J.",
""
],
[
"Steinberg",
"Ethan",
""
],
[
"Yozzo",
"Alexander",
""
],
[
"Song",
"Ying",
""
],
[
"Kohn",
"Michael H.",
""
],
[
"Nakhleh",
"Luay",
""
]
] | We report on a genome-wide scan for introgression in the house mouse (Mus musculus domesticus) involving the Algerian mouse (Mus spretus), using samples from the ranges of sympatry and allopatry in Africa and Europe. Our analysis reveals wide variability in introgression signatures along the genomes, as well as across the samples. We find that fewer than half of the autosomes in each genome harbor all detectable introgression, while the X chromosome has none. Further, European mice carry more M. spretus alleles than the sympatric African ones. Using the length distribution and sharing patterns of introgressed genomic tracts across the samples, we infer, first, that at least three distinct hybridization events involving M. spretus have occurred, one of which is ancient, and the other two are recent (one presumably due to warfarin rodenticide selection). Second, several of the inferred introgressed tracts contain genes that are likely to confer adaptive advantage. Third, introgressed tracts might contain driver genes that determine the evolutionary fate of those tracts. Further, functional analysis revealed introgressed genes that are essential to fitness, including the Vkorc1 gene, which is implicated in rodenticide resistance, and olfactory receptor genes. Our findings highlight the extent and role of introgression in nature, and call for careful analysis and interpretation of house mouse data in evolutionary and genetic studies. |
2005.02211 | Alessandro Salatiello | Alessandro Salatiello and Martin A. Giese | Recurrent Neural Network Learning of Performance and Intrinsic
Population Dynamics from Sparse Neural Data | null | Artificial Neural Networks and Machine Learning - ICANN 2020.
ICANN 2020. Lecture Notes in Computer Science, vol 12396. Springer,
Cham.:874-86 | 10.1007/978-3-030-61609-0_69 | null | q-bio.NC cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recurrent Neural Networks (RNNs) are popular models of brain function. The
typical training strategy is to adjust their input-output behavior so that it
matches that of the biological circuit of interest. Even though this strategy
ensures that the biological and artificial networks perform the same
computational task, it does not guarantee that their internal activity dynamics
match. This suggests that the trained RNNs might end up performing the task
employing a different internal computational mechanism, which would make them a
suboptimal model of the biological circuit. In this work, we introduce a novel
training strategy that allows learning not only the input-output behavior of an
RNN but also its internal network dynamics, based on sparse neural recordings.
We test the proposed method by training an RNN to simultaneously reproduce
internal dynamics and output signals of a physiologically-inspired neural
model. Specifically, this model generates the multiphasic muscle-like activity
patterns typically observed during the execution of reaching movements, based
on the oscillatory activation patterns concurrently observed in the motor
cortex. Remarkably, we show that the reproduction of the internal dynamics is
successful even when the training algorithm relies on the activities of a small
subset of neurons sampled from the biological network. Furthermore, we show
that training the RNNs with this method significantly improves their
generalization performance. Overall, our results suggest that the proposed
method is suitable for building powerful functional RNN models, which
automatically capture important computational properties of the biological
circuit of interest from sparse neural recordings.
| [
{
"created": "Tue, 5 May 2020 14:16:54 GMT",
"version": "v1"
}
] | 2020-11-09 | [
[
"Salatiello",
"Alessandro",
""
],
[
"Giese",
"Martin A.",
""
]
] | Recurrent Neural Networks (RNNs) are popular models of brain function. The typical training strategy is to adjust their input-output behavior so that it matches that of the biological circuit of interest. Even though this strategy ensures that the biological and artificial networks perform the same computational task, it does not guarantee that their internal activity dynamics match. This suggests that the trained RNNs might end up performing the task employing a different internal computational mechanism, which would make them a suboptimal model of the biological circuit. In this work, we introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics, based on sparse neural recordings. We test the proposed method by training an RNN to simultaneously reproduce internal dynamics and output signals of a physiologically-inspired neural model. Specifically, this model generates the multiphasic muscle-like activity patterns typically observed during the execution of reaching movements, based on the oscillatory activation patterns concurrently observed in the motor cortex. Remarkably, we show that the reproduction of the internal dynamics is successful even when the training algorithm relies on the activities of a small subset of neurons sampled from the biological network. Furthermore, we show that training the RNNs with this method significantly improves their generalization performance. Overall, our results suggest that the proposed method is suitable for building powerful functional RNN models, which automatically capture important computational properties of the biological circuit of interest from sparse neural recordings. |
1511.04769 | Yun Kang | Yun Kang and Guy Theraulaz | Dynamical models of task organization in social insect colonies | null | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The organizations of insect societies, such as division of labor, task
allocation, collective regulation, mass action responses, have been considered
as main reasons for the ecological success. In this article, we propose and
study a general modeling framework that includes the following three features:
(a) the average internal response threshold for each task (the internal
factor); (b) social network communications that could lead to task switching
(the environmental factor); and (c) dynamical changes of task demands (the
external factor). Since workers in many social insect species exhibit \emph{age
polyethism}, we also extend our model to incorporate \emph{age polyethism} in
which worker task preferences change with age. We apply our general modeling
framework to the cases of two task groups: the inside colony task versus the
outside colony task. Our analytical study of the models provides important
insights and predictions on the effects of colony size, social communication,
and age related task preferences on task allocation and division of labor in
the adaptive dynamical environment. Our study implies that the smaller size
colony invests its resource for the colony growth and allocates more workers in
the risky tasks such as foraging while the larger colony shifts more workers to
perform the safer tasks inside the colony. Social interactions among different
task groups play an important role in shaping task allocation depending on the
relative cost and demands of the tasks.
| [
{
"created": "Sun, 15 Nov 2015 21:30:26 GMT",
"version": "v1"
}
] | 2015-11-17 | [
[
"Kang",
"Yun",
""
],
[
"Theraulaz",
"Guy",
""
]
] | The organizations of insect societies, such as division of labor, task allocation, collective regulation, mass action responses, have been considered as main reasons for the ecological success. In this article, we propose and study a general modeling framework that includes the following three features: (a) the average internal response threshold for each task (the internal factor); (b) social network communications that could lead to task switching (the environmental factor); and (c) dynamical changes of task demands (the external factor). Since workers in many social insect species exhibit \emph{age polyethism}, we also extend our model to incorporate \emph{age polyethism} in which worker task preferences change with age. We apply our general modeling framework to the cases of two task groups: the inside colony task versus the outside colony task. Our analytical study of the models provides important insights and predictions on the effects of colony size, social communication, and age related task preferences on task allocation and division of labor in the adaptive dynamical environment. Our study implies that the smaller size colony invests its resource for the colony growth and allocates more workers in the risky tasks such as foraging while the larger colony shifts more workers to perform the safer tasks inside the colony. Social interactions among different task groups play an important role in shaping task allocation depending on the relative cost and demands of the tasks. |
1605.09682 | Jose A Capitan | Jose A. Capitan, Sara Cuenda, Alejandro Ordo\~nez, David Alonso | A signal of competitive dominance in mid-latitude herbaceous plant
communities | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the main determinants of species coexistence across space and
time is a central question in ecology. However, ecologists still know little
about the scales and conditions at which biotic interactions matter and how
these interact with the environment to structure species assemblages. Here we
use recent theory developments to analyze plant distribution and trait data
across Europe and find that plant height clustering is related to both
evapotranspiration and gross primary productivity. This clustering is a signal
of interspecies competition between plants, which is most evident in
mid-latitude ecoregions, where conditions for growth (reflected in actual
evapotranspiration rates and gross primary productivities) are optimal. Away
from this optimum, climate severity likely overrides the effect of competition,
or other interactions become increasingly important. Our approach bridges the
gap between species-rich competition theories and large-scale species
distribution data analysis.
| [
{
"created": "Tue, 31 May 2016 15:45:50 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Sep 2016 09:48:56 GMT",
"version": "v2"
},
{
"created": "Thu, 19 Aug 2021 08:49:22 GMT",
"version": "v3"
}
] | 2021-08-20 | [
[
"Capitan",
"Jose A.",
""
],
[
"Cuenda",
"Sara",
""
],
[
"Ordoñez",
"Alejandro",
""
],
[
"Alonso",
"David",
""
]
] | Understanding the main determinants of species coexistence across space and time is a central question in ecology. However, ecologists still know little about the scales and conditions at which biotic interactions matter and how these interact with the environment to structure species assemblages. Here we use recent theory developments to analyze plant distribution and trait data across Europe and find that plant height clustering is related to both evapotranspiration and gross primary productivity. This clustering is a signal of interspecies competition between plants, which is most evident in mid-latitude ecoregions, where conditions for growth (reflected in actual evapotranspiration rates and gross primary productivities) are optimal. Away from this optimum, climate severity likely overrides the effect of competition, or other interactions become increasingly important. Our approach bridges the gap between species-rich competition theories and large-scale species distribution data analysis. |
2111.06969 | Reimann Stefan | Stefan Reimann | Computing with Cognitive States | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Basic experimental findings about human working memory can be described by an
algebra built on high-dimensional binary states, representing information
items, and two operations: multiplication for binding and addition for
bundling. In contrast to common VSA algebras, bundling is not associative.
Consequently bundling a sequence of items preserves their sequential ordering.
The cognitive states representing a memorised list exhibit a primacy as well as
a recency gradient. The typical concave-up and asymmetrically shaped serial
position curve is derived as a linear combination of those gradients.
Quantitative implications of the algebra are shown to agree well with empirical
data from basic cognitive tasks including storage and retrieval of information
in human working memory.
| [
{
"created": "Wed, 3 Nov 2021 20:23:58 GMT",
"version": "v1"
}
] | 2021-11-16 | [
[
"Reimann",
"Stefan",
""
]
] | Basic experimental findings about human working memory can be described by an algebra built on high-dimensional binary states, representing information items, and two operations: multiplication for binding and addition for bundling. In contrast to common VSA algebras, bundling is not associative. Consequently bundling a sequence of items preserves their sequential ordering. The cognitive states representing a memorised list exhibit a primacy as well as a recency gradient. The typical concave-up and asymmetrically shaped serial position curve is derived as a linear combination of those gradients. Quantitative implications of the algebra are shown to agree well with empirical data from basic cognitive tasks including storage and retrieval of information in human working memory. |
2305.14388 | Zuzanna Szyma\'nska Ph.D. | Zuzanna Szyma\'nska and Miros{\l}aw Lachowicz and Nikolaos Sfakianakis
and Mark A. J. Chaplain | Mathematical modelling of cancer invasion: Phenotypic transitioning
provides insight into multifocal foci formation | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | The transition from the epithelial to mesenchymal phenotype and its reverse
(from mesenchymal to epithelial) are crucial processes necessary for the
progression and spread of cancer. In this paper, we investigate how phenotypic
switching at the cancer cell level impacts on behaviour at the tissue level,
specifically on the emergence of isolated foci of the invading solid tumour
mass leading to a multifocal tumour. To this end, we propose a new mathematical
model of cancer invasion that includes the influence of cancer cell phenotype
on the rate of invasion and metastasis. The implications of model are explored
through numerical simulations revealing that the plasticity of tumour cell
phenotypes appears to be crucial for disease progression and local invasive
spread. The computational simulations show the progression of the invasive
spread of a primary cancer reminiscent of in vivo multifocal breast carcinomas,
where multiple, synchronous, ipsilateral neoplastic foci are frequently
observed and are associated with a poorer patient prognosis.
| [
{
"created": "Mon, 22 May 2023 19:43:30 GMT",
"version": "v1"
}
] | 2023-05-25 | [
[
"Szymańska",
"Zuzanna",
""
],
[
"Lachowicz",
"Mirosław",
""
],
[
"Sfakianakis",
"Nikolaos",
""
],
[
"Chaplain",
"Mark A. J.",
""
]
] | The transition from the epithelial to mesenchymal phenotype and its reverse (from mesenchymal to epithelial) are crucial processes necessary for the progression and spread of cancer. In this paper, we investigate how phenotypic switching at the cancer cell level impacts on behaviour at the tissue level, specifically on the emergence of isolated foci of the invading solid tumour mass leading to a multifocal tumour. To this end, we propose a new mathematical model of cancer invasion that includes the influence of cancer cell phenotype on the rate of invasion and metastasis. The implications of model are explored through numerical simulations revealing that the plasticity of tumour cell phenotypes appears to be crucial for disease progression and local invasive spread. The computational simulations show the progression of the invasive spread of a primary cancer reminiscent of in vivo multifocal breast carcinomas, where multiple, synchronous, ipsilateral neoplastic foci are frequently observed and are associated with a poorer patient prognosis. |
1604.04801 | Ehtibar Dzhafarov | Ru Zhang and Ehtibar N. Dzhafarov | Testing Contextuality in Cyclic Psychophysical Systems of High Ranks | to appear in Lecture Notes in Computer Science, based on Quantum
Interaction 2016 conference, version 2 is a minor revision | null | null | null | q-bio.NC quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Contextuality-by-Default (CbD) theory allows one to separate
contextuality from context-dependent errors and violations of selective
influences (aka "no-signaling" or "no-disturbance" principles). This makes the
theory especially applicable to behavioral systems, where violations of
selective influences are ubiquitous. For cyclic systems with binary random
variables, CbD provides necessary and sufficient conditions for
noncontextuality, and these conditions are known to be breached in certain
quantum systems. We apply the theory of cyclic systems to a psychophysical
double-detection experiment, in which observers were asked to determine
presence or absence of a signal property in each of two simultaneously
presented stimuli. The results, as in all other behavioral and social systems
previous analyzed, indicate lack of contextuality. The role of context in
double-detection is confined to lack of selectiveness: the distribution of
responses to one of the stimuli is influenced by the state of the other
stimulus.
| [
{
"created": "Sat, 16 Apr 2016 21:13:26 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Aug 2016 02:55:04 GMT",
"version": "v2"
}
] | 2016-08-25 | [
[
"Zhang",
"Ru",
""
],
[
"Dzhafarov",
"Ehtibar N.",
""
]
] | The Contextuality-by-Default (CbD) theory allows one to separate contextuality from context-dependent errors and violations of selective influences (aka "no-signaling" or "no-disturbance" principles). This makes the theory especially applicable to behavioral systems, where violations of selective influences are ubiquitous. For cyclic systems with binary random variables, CbD provides necessary and sufficient conditions for noncontextuality, and these conditions are known to be breached in certain quantum systems. We apply the theory of cyclic systems to a psychophysical double-detection experiment, in which observers were asked to determine presence or absence of a signal property in each of two simultaneously presented stimuli. The results, as in all other behavioral and social systems previous analyzed, indicate lack of contextuality. The role of context in double-detection is confined to lack of selectiveness: the distribution of responses to one of the stimuli is influenced by the state of the other stimulus. |
2308.01402 | Daisy Yi Ding | Daisy Yi Ding, Yuhui Zhang, Yuan Jia, Jiuzhi Sun | Machine Learning-guided Lipid Nanoparticle Design for mRNA Delivery | The 2023 ICML Workshop on Computational Biology | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While RNA technologies hold immense therapeutic potential in a range of
applications from vaccination to gene editing, the broad implementation of
these technologies is hindered by the challenge of delivering these agents
effectively. Lipid nanoparticles have emerged as one of the most widely used
delivery agents, but their design optimization relies on laborious and costly
experimental methods. We propose to in silico optimize LNP design with machine
learning models. On a curated dataset of 622 LNPs from published studies, we
demonstrate the effectiveness of our model in predicting the transfection
efficiency of unseen LNPs, with the multilayer perceptron achieving a
classification accuracy of 98% on the test set. Our work represents a
pioneering effort in combining ML and LNP design, offering significant
potential for improving screening efficiency by computationally prioritizing
LNP candidates for experimental validation and accelerating the development of
effective mRNA delivery systems.
| [
{
"created": "Wed, 2 Aug 2023 19:53:04 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Aug 2023 01:48:31 GMT",
"version": "v2"
}
] | 2023-08-30 | [
[
"Ding",
"Daisy Yi",
""
],
[
"Zhang",
"Yuhui",
""
],
[
"Jia",
"Yuan",
""
],
[
"Sun",
"Jiuzhi",
""
]
] | While RNA technologies hold immense therapeutic potential in a range of applications from vaccination to gene editing, the broad implementation of these technologies is hindered by the challenge of delivering these agents effectively. Lipid nanoparticles have emerged as one of the most widely used delivery agents, but their design optimization relies on laborious and costly experimental methods. We propose to in silico optimize LNP design with machine learning models. On a curated dataset of 622 LNPs from published studies, we demonstrate the effectiveness of our model in predicting the transfection efficiency of unseen LNPs, with the multilayer perceptron achieving a classification accuracy of 98% on the test set. Our work represents a pioneering effort in combining ML and LNP design, offering significant potential for improving screening efficiency by computationally prioritizing LNP candidates for experimental validation and accelerating the development of effective mRNA delivery systems. |
q-bio/0407033 | Ambarish Kunwar | Ambarish Kunwar | Evolution of Spatially Inhomogeneous Eco-Systems: An Unified Model Based
Approach | Latex, 10 pages, 8 figures | International Journal of Modern Physics C, Vol. 15, 1449 (2004) | 10.1142/S0129183104006856 | null | q-bio.PE | null | Recently we have extended our the "unified" model of evolutionary ecology to
incorporate the {\it spatial inhomogeneities} of the eco-system and the {\it
migration} of individual organisms from one patch to another within the same
eco-system. In this paper an extension of our recent model is investigated so
as to describe the {\it migration} and {\it speciation} in a more realistic
way.
| [
{
"created": "Sun, 25 Jul 2004 09:25:48 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Kunwar",
"Ambarish",
""
]
] | Recently we have extended our the "unified" model of evolutionary ecology to incorporate the {\it spatial inhomogeneities} of the eco-system and the {\it migration} of individual organisms from one patch to another within the same eco-system. In this paper an extension of our recent model is investigated so as to describe the {\it migration} and {\it speciation} in a more realistic way. |
2111.15424 | Markus Pfeil | Markus Pfeil and Thomas Slawig | Unique steady annual cycle in marine ecosystem model simulations | null | null | null | null | q-bio.PE physics.ao-ph | http://creativecommons.org/licenses/by/4.0/ | Marine ecosystem models are an important tool to assess the role of the ocean
biota in climate change and to identify relevant biogeochemical processes by
validating the model outputs against observational data. For the assessment of
the marine ecosystem models, the existence and uniqueness of an annual periodic
solution (i.e., a steady annual cycle) is desirable. To analyze the uniqueness
of a steady annual cycle, we performed a larger number of simulations starting
from different initial concentrations for a hierarchy of biogeochemical models
with an increasing complexity. The numerical results suggested that the
simulations finished always with the same steady annual cycle regardless of the
initial concentration. Due to numerical instabilities, some inadmissible
approximations of the steady annual cycle, however, occurred in some cases for
the three most complex biogeochemical models. Our numerical results indicate a
unique steady annual cycle for practical applications.
| [
{
"created": "Tue, 30 Nov 2021 14:15:37 GMT",
"version": "v1"
}
] | 2021-12-01 | [
[
"Pfeil",
"Markus",
""
],
[
"Slawig",
"Thomas",
""
]
] | Marine ecosystem models are an important tool to assess the role of the ocean biota in climate change and to identify relevant biogeochemical processes by validating the model outputs against observational data. For the assessment of the marine ecosystem models, the existence and uniqueness of an annual periodic solution (i.e., a steady annual cycle) is desirable. To analyze the uniqueness of a steady annual cycle, we performed a larger number of simulations starting from different initial concentrations for a hierarchy of biogeochemical models with an increasing complexity. The numerical results suggested that the simulations finished always with the same steady annual cycle regardless of the initial concentration. Due to numerical instabilities, some inadmissible approximations of the steady annual cycle, however, occurred in some cases for the three most complex biogeochemical models. Our numerical results indicate a unique steady annual cycle for practical applications. |
1802.05774 | Michael Manhart | Michael Manhart and Eugene I. Shakhnovich | Growth tradeoffs produce complex microbial communities on a single
limiting resource | null | null | 10.1038/s41467-018-05703-6 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The relationship between the dynamics of a community and its constituent
pairwise interactions is a fundamental problem in ecology. Higher-order
ecological effects beyond pairwise interactions may be key to complex
ecosystems, but mechanisms to produce these effects remain poorly understood.
Here we show that higher-order effects can arise from variation in multiple
microbial growth traits, such as lag times and growth rates, on a single
limiting resource with no other interactions. These effects produce a range of
ecological phenomena: an unlimited number of strains can exhibit multistability
and neutral coexistence, potentially with a single keystone strain; strains
that coexist in pairs do not coexist all together; and the champion of all
pairwise competitions may not dominate in a mixed community. Since variation in
multiple growth traits is ubiquitous in microbial populations due to pleiotropy
and non-genetic variation, our results indicate these higher-order effects may
also be widespread, especially in laboratory ecology and evolution experiments.
| [
{
"created": "Thu, 15 Feb 2018 21:51:32 GMT",
"version": "v1"
},
{
"created": "Thu, 31 May 2018 15:19:44 GMT",
"version": "v2"
}
] | 2018-09-05 | [
[
"Manhart",
"Michael",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] | The relationship between the dynamics of a community and its constituent pairwise interactions is a fundamental problem in ecology. Higher-order ecological effects beyond pairwise interactions may be key to complex ecosystems, but mechanisms to produce these effects remain poorly understood. Here we show that higher-order effects can arise from variation in multiple microbial growth traits, such as lag times and growth rates, on a single limiting resource with no other interactions. These effects produce a range of ecological phenomena: an unlimited number of strains can exhibit multistability and neutral coexistence, potentially with a single keystone strain; strains that coexist in pairs do not coexist all together; and the champion of all pairwise competitions may not dominate in a mixed community. Since variation in multiple growth traits is ubiquitous in microbial populations due to pleiotropy and non-genetic variation, our results indicate these higher-order effects may also be widespread, especially in laboratory ecology and evolution experiments. |
1212.3932 | Li Xiaoguang | Cheng Lv, Xiaoguang Li, Fangting Li, and Tiejun Li | Transition Path, Quasi-potential Energy Landscape and Stability of
Genetic Switches | 5 pages, 6 figures | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by-nc-sa/3.0/ | One of the fundamental cellular processes governed by genetic regulatory
networks in cells is the transition among different states under the intrinsic
and extrinsic noise. Based on a two-state genetic switching model with positive
feedback, we develop a framework to understand the metastability in gene
expressions. This framework is comprised of identifying the transition path,
reconstructing the global quasi-potential energy landscape, analyzing the
uphill and downhill transition paths, etc. It is successfully utilized to
investigate the stability of genetic switching models and fluctuation
properties in different regimes of gene expression with positive feedback. The
quasi-potential energy landscape, which is the rationalized version of
Waddington potential, provides a quantitative tool to understand the
metastability in more general biological processes with intrinsic noise.
| [
{
"created": "Mon, 17 Dec 2012 08:32:58 GMT",
"version": "v1"
}
] | 2012-12-18 | [
[
"Lv",
"Cheng",
""
],
[
"Li",
"Xiaoguang",
""
],
[
"Li",
"Fangting",
""
],
[
"Li",
"Tiejun",
""
]
] | One of the fundamental cellular processes governed by genetic regulatory networks in cells is the transition among different states under the intrinsic and extrinsic noise. Based on a two-state genetic switching model with positive feedback, we develop a framework to understand the metastability in gene expressions. This framework is comprised of identifying the transition path, reconstructing the global quasi-potential energy landscape, analyzing the uphill and downhill transition paths, etc. It is successfully utilized to investigate the stability of genetic switching models and fluctuation properties in different regimes of gene expression with positive feedback. The quasi-potential energy landscape, which is the rationalized version of Waddington potential, provides a quantitative tool to understand the metastability in more general biological processes with intrinsic noise. |
2011.13462 | Andreas Reichmuth | Ilaria Incaviglia, Andreas Frutiger, Yves Blickenstorfer, Fridolin
Treindl, Giulia Ammirati, Ines L\"uchtefeld, Birgit Dreier, Andreas
Pl\"uckthun, Janos V\"or\"os, Andreas M Reichmuth | A promising approach for the real-time quantification of cytosolic
protein-protein interactions in living cells | 22 pages, 4 figures | null | 10.1021/acssensors.0c02480 | null | q-bio.QM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, cell-based assays have been frequently used in molecular
interaction analysis. Cell-based assays complement traditional biochemical and
biophysical methods, as they allow for molecular interaction analysis, mode of
action studies and even drug screening processes to be performed under
physiologically relevant conditions. In most cellular assays, biomolecules are
usually labeled to achieve specificity. In order to overcome some of the
drawbacks associated with label-based assays, we have recently introduced
cell-based molography as a biosensor for the analysis of specific molecular
interactions involving native membrane receptors in living cells. Here, we
expand this assay to cytosolic protein-protein interactions. First, we created
a biomimetic membrane receptor by tethering one cytosolic interaction partner
to the plasma membrane. The artificial construct is then coherently arranged
into a two-dimensional pattern within the cytosol of living cells. Thanks to
the molographic sensor, the specific interactions between the coherently
arranged protein and its endogenous interaction partners become visible in
real-time without the use of a fluorescent label. This method turns out to be
an important extension of cell-based molography because it expands the range of
interactions that can be analyzed by molography to those in the cytosol of
living cells.
| [
{
"created": "Thu, 26 Nov 2020 20:13:02 GMT",
"version": "v1"
}
] | 2021-04-13 | [
[
"Incaviglia",
"Ilaria",
""
],
[
"Frutiger",
"Andreas",
""
],
[
"Blickenstorfer",
"Yves",
""
],
[
"Treindl",
"Fridolin",
""
],
[
"Ammirati",
"Giulia",
""
],
[
"Lüchtefeld",
"Ines",
""
],
[
"Dreier",
"Birgit",
""
],
[
"Plückthun",
"Andreas",
""
],
[
"Vörös",
"Janos",
""
],
[
"Reichmuth",
"Andreas M",
""
]
] | In recent years, cell-based assays have been frequently used in molecular interaction analysis. Cell-based assays complement traditional biochemical and biophysical methods, as they allow for molecular interaction analysis, mode of action studies and even drug screening processes to be performed under physiologically relevant conditions. In most cellular assays, biomolecules are usually labeled to achieve specificity. In order to overcome some of the drawbacks associated with label-based assays, we have recently introduced cell-based molography as a biosensor for the analysis of specific molecular interactions involving native membrane receptors in living cells. Here, we expand this assay to cytosolic protein-protein interactions. First, we created a biomimetic membrane receptor by tethering one cytosolic interaction partner to the plasma membrane. The artificial construct is then coherently arranged into a two-dimensional pattern within the cytosol of living cells. Thanks to the molographic sensor, the specific interactions between the coherently arranged protein and its endogenous interaction partners become visible in real-time without the use of a fluorescent label. This method turns out to be an important extension of cell-based molography because it expands the range of interactions that can be analyzed by molography to those in the cytosol of living cells. |
2107.09056 | Madiha Hameed Awan | Madiha Hameed, Abdul Majiid, Asifullah Khan | FANCA: In-Silico deleterious mutation analysis for early prediction of
leukemia | 22 pages, 09 figure | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | As a novel biomarker from the Fanconi anemia complementation group (FANC)
family, FANCA is antigens to Leukemia cancer. The overexpression of FANCA has
predicted the second most common cancer in the world that is responsible for
cancer-related deaths. Non-synonymous SNPs are an essential group of SNPs that
lead to alterations in encoded polypeptides. Changes in the amino acid
sequences of gene products lead to Leukemia. First, we study individual SNPs in
the coding region of FANCA and computational tools like PROVEAN, PolyPhen2,
MuPro, and PANTHER to compute deleterious mutation scores. The
three-dimensional structural and functional prediction conducted using
I-TASSER. Further, the predicted structure refined using the GlaxyWeb tool. In
the study, the proteomic data has been retrieved from the UniProtKB. The coding
region of the dataset contains 100 non-synonymous single nucleotide
polymorphisms (nsSNPs), and 24 missense SNPs have been determined as
deleterious by all analyses. In this work, six well-known computational tools
were employed to study Leukemia-associated nsSNPs. It is inferred that these
nsSNPs could play their role in the up-regulation of FANCA, which further leads
to provoke leukemia advancement. The current research would benefit researchers
and practitioners in handling cancer-associated diseases related to FANCA. The
proposed study would also help to develop precision medicine in the field of
drug discovery.
| [
{
"created": "Mon, 19 Jul 2021 05:54:08 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Oct 2021 05:16:53 GMT",
"version": "v2"
}
] | 2021-11-01 | [
[
"Hameed",
"Madiha",
""
],
[
"Majiid",
"Abdul",
""
],
[
"Khan",
"Asifullah",
""
]
] | As a novel biomarker from the Fanconi anemia complementation group (FANC) family, FANCA is antigens to Leukemia cancer. The overexpression of FANCA has predicted the second most common cancer in the world that is responsible for cancer-related deaths. Non-synonymous SNPs are an essential group of SNPs that lead to alterations in encoded polypeptides. Changes in the amino acid sequences of gene products lead to Leukemia. First, we study individual SNPs in the coding region of FANCA and computational tools like PROVEAN, PolyPhen2, MuPro, and PANTHER to compute deleterious mutation scores. The three-dimensional structural and functional prediction conducted using I-TASSER. Further, the predicted structure refined using the GlaxyWeb tool. In the study, the proteomic data has been retrieved from the UniProtKB. The coding region of the dataset contains 100 non-synonymous single nucleotide polymorphisms (nsSNPs), and 24 missense SNPs have been determined as deleterious by all analyses. In this work, six well-known computational tools were employed to study Leukemia-associated nsSNPs. It is inferred that these nsSNPs could play their role in the up-regulation of FANCA, which further leads to provoke leukemia advancement. The current research would benefit researchers and practitioners in handling cancer-associated diseases related to FANCA. The proposed study would also help to develop precision medicine in the field of drug discovery. |
2203.10179 | Xavier Michalet | Donald Ferschweiler, Maya Segal, Shimon Weiss, Xavier Michalet | A user-friendly tool to convert photon counting data to the open-source
Photon-HDF5 file format | null | Proceedings of SPIE Vol. 11967 (2022) art. 1196703 | 10.1117/12.2608487 | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Photon-HDF5 is an open-source and open file format for storing
photon-counting data from single molecule microscopy experiments, introduced to
simplify data exchange and increase the reproducibility of data analysis. Part
of the Photon-HDF5 ecosystem, is phconvert, an extensible python library that
allows converting proprietary formats into Photon-HDF5 files. However, its use
requires some proficiency with command line instructions, the python
programming language, and the YAML markup format. This creates a significant
barrier for potential users without that expertise, but who want to benefit
from the advantages of releasing their files in an open format. In this work,
we present a GUI that lowers this barrier, thus simplifying the use of
Photon-HDF5. This tool uses the phconvert python library to convert data files
originally saved in proprietary data formats to Photon-HDF5 files, without
users having to write a single line of code. Because reproducible analyses
depend on essential experimental information, such as laser power or sample
description, the GUI also includes (currently limited) functionality to
associate valid metadata with the converted file, without having to write any
YAML. Finally, the GUI includes several productivity-enhancing features such as
whole-directory batch conversion and the ability to re-run a failed batch, only
converting the files that could not be converted in the previous run.
| [
{
"created": "Fri, 18 Mar 2022 22:24:14 GMT",
"version": "v1"
}
] | 2022-03-22 | [
[
"Ferschweiler",
"Donald",
""
],
[
"Segal",
"Maya",
""
],
[
"Weiss",
"Shimon",
""
],
[
"Michalet",
"Xavier",
""
]
] | Photon-HDF5 is an open-source and open file format for storing photon-counting data from single molecule microscopy experiments, introduced to simplify data exchange and increase the reproducibility of data analysis. Part of the Photon-HDF5 ecosystem, is phconvert, an extensible python library that allows converting proprietary formats into Photon-HDF5 files. However, its use requires some proficiency with command line instructions, the python programming language, and the YAML markup format. This creates a significant barrier for potential users without that expertise, but who want to benefit from the advantages of releasing their files in an open format. In this work, we present a GUI that lowers this barrier, thus simplifying the use of Photon-HDF5. This tool uses the phconvert python library to convert data files originally saved in proprietary data formats to Photon-HDF5 files, without users having to write a single line of code. Because reproducible analyses depend on essential experimental information, such as laser power or sample description, the GUI also includes (currently limited) functionality to associate valid metadata with the converted file, without having to write any YAML. Finally, the GUI includes several productivity-enhancing features such as whole-directory batch conversion and the ability to re-run a failed batch, only converting the files that could not be converted in the previous run. |
2008.03067 | Antonia Mey | Antonia S. J. S. Mey, Bryce Allen, Hannah E. Bruce Macdonald, John D.
Chodera, Maximilian Kuhn, Julien Michel, David L. Mobley, Levi N. Naden,
Samarjeet Prasad, Andrea Rizzi, Jenke Scheen, Michael R. Shirts, Gary
Tresadern, Huafeng Xu | Best Practices for Alchemical Free Energy Calculations | 48 pages, 14 figures | null | 10.33011/livecoms.2.1.18378 | null | q-bio.BM stat.CO | http://creativecommons.org/licenses/by-sa/4.0/ | Alchemical free energy calculations are a useful tool for predicting free
energy differences associated with the transfer of molecules from one
environment to another. The hallmark of these methods is the use of "bridging"
potential energy functions representing \emph{alchemical} intermediate states
that cannot exist as real chemical species. The data collected from these
bridging alchemical thermodynamic states allows the efficient computation of
transfer free energies (or differences in transfer free energies) with orders
of magnitude less simulation time than simulating the transfer process
directly. While these methods are highly flexible, care must be taken in
avoiding common pitfalls to ensure that computed free energy differences can be
robust and reproducible for the chosen force field, and that appropriate
corrections are included to permit direct comparison with experimental data. In
this paper, we review current best practices for several popular application
domains of alchemical free energy calculations, including relative and absolute
small molecule binding free energy calculations to biomolecular targets.
| [
{
"created": "Fri, 7 Aug 2020 10:01:31 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Aug 2020 14:27:19 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Aug 2020 07:41:34 GMT",
"version": "v3"
}
] | 2021-04-02 | [
[
"Mey",
"Antonia S. J. S.",
""
],
[
"Allen",
"Bryce",
""
],
[
"Macdonald",
"Hannah E. Bruce",
""
],
[
"Chodera",
"John D.",
""
],
[
"Kuhn",
"Maximilian",
""
],
[
"Michel",
"Julien",
""
],
[
"Mobley",
"David L.",
""
],
[
"Naden",
"Levi N.",
""
],
[
"Prasad",
"Samarjeet",
""
],
[
"Rizzi",
"Andrea",
""
],
[
"Scheen",
"Jenke",
""
],
[
"Shirts",
"Michael R.",
""
],
[
"Tresadern",
"Gary",
""
],
[
"Xu",
"Huafeng",
""
]
] | Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing \emph{alchemical} intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations, including relative and absolute small molecule binding free energy calculations to biomolecular targets. |
1602.00600 | Steven Schiff | Steven J. Schiff, Julius Kiwanuka, Gina Riggio, Lan Nguyen, Kevin Mu,
Emily Sproul, Joel Bazira, Juliet Mwanga, Dickson Tumusiime, Eunice
Nyesigire, Nkangi Lwanga, Kaleb T. Bogale, Vivek Kapur, James Broach, Sarah
Morton, Benjamin C. Warf, Mary Poss | Separating Putative Pathogens from Background Contamination with
Principal Orthogonal Decomposition: Evidence for Leptospira in the Ugandan
Neonatal Septisome | 23 pages, 2 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neonatal sepsis (NS) is responsible for over a 1 million yearly deaths
worldwide. In the developing world NS is often treated without an identified
microbial pathogen. Amplicon sequencing of the bacterial 16S rRNA gene can be
used to identify organisms that are difficult to detect by routine
microbiological methods. However, contaminating bacteria are ubiquitous in both
hospital settings and research reagents, and must be accounted for to make
effective use of these data. In the present study, we sequenced the bacterial
16S rRNA gene obtained from blood and cerebrospinal fluid (CSF) of 80 neonates
presenting with NS to the Mbarara Regional Hospital in Uganda. Assuming that
patterns of background contamination would be independent of pathogenic
microorganism DNA, we applied a novel quantitative approach using principal
orthogonal decomposition to separate background contamination from potential
pathogens in sequencing data. We designed our quantitative approach contrasting
blood, CSF, and control specimens, and employed a variety of statistical random
matrix bootstrap hypotheses to estimate statistical significance. These
analyses demonstrate that Leptospira appears present in some infants presenting
within 48 hr of birth, indicative of infection in utero, and up to 28 days of
age, suggesting environmental exposure. This organism cannot be cultured in
routine bacteriological settings, and is enzootic in the cattle that the rural
peoples of western Uganda often live in close proximity. Our findings
demonstrate that statistical approaches to remove background organisms common
in 16S sequence data can reveal putative pathogens in small volume biological
samples from newborns. This computational analysis thus reveals an important
medical finding that has the potential to alter therapy and prevention efforts
in a critically ill population.
| [
{
"created": "Mon, 1 Feb 2016 17:17:04 GMT",
"version": "v1"
}
] | 2016-02-02 | [
[
"Schiff",
"Steven J.",
""
],
[
"Kiwanuka",
"Julius",
""
],
[
"Riggio",
"Gina",
""
],
[
"Nguyen",
"Lan",
""
],
[
"Mu",
"Kevin",
""
],
[
"Sproul",
"Emily",
""
],
[
"Bazira",
"Joel",
""
],
[
"Mwanga",
"Juliet",
""
],
[
"Tumusiime",
"Dickson",
""
],
[
"Nyesigire",
"Eunice",
""
],
[
"Lwanga",
"Nkangi",
""
],
[
"Bogale",
"Kaleb T.",
""
],
[
"Kapur",
"Vivek",
""
],
[
"Broach",
"James",
""
],
[
"Morton",
"Sarah",
""
],
[
"Warf",
"Benjamin C.",
""
],
[
"Poss",
"Mary",
""
]
] | Neonatal sepsis (NS) is responsible for over a 1 million yearly deaths worldwide. In the developing world NS is often treated without an identified microbial pathogen. Amplicon sequencing of the bacterial 16S rRNA gene can be used to identify organisms that are difficult to detect by routine microbiological methods. However, contaminating bacteria are ubiquitous in both hospital settings and research reagents, and must be accounted for to make effective use of these data. In the present study, we sequenced the bacterial 16S rRNA gene obtained from blood and cerebrospinal fluid (CSF) of 80 neonates presenting with NS to the Mbarara Regional Hospital in Uganda. Assuming that patterns of background contamination would be independent of pathogenic microorganism DNA, we applied a novel quantitative approach using principal orthogonal decomposition to separate background contamination from potential pathogens in sequencing data. We designed our quantitative approach contrasting blood, CSF, and control specimens, and employed a variety of statistical random matrix bootstrap hypotheses to estimate statistical significance. These analyses demonstrate that Leptospira appears present in some infants presenting within 48 hr of birth, indicative of infection in utero, and up to 28 days of age, suggesting environmental exposure. This organism cannot be cultured in routine bacteriological settings, and is enzootic in the cattle that the rural peoples of western Uganda often live in close proximity. Our findings demonstrate that statistical approaches to remove background organisms common in 16S sequence data can reveal putative pathogens in small volume biological samples from newborns. This computational analysis thus reveals an important medical finding that has the potential to alter therapy and prevention efforts in a critically ill population. |
0808.2204 | Michael Hagan | Michael F. Hagan | A theory for viral capsid assembly around electrostatic cores | This version has been updated from v1 as follows. The calculation
accounts for curvature, explicitly represents the polymeric nature of surface
functionalization molecules, and determines the dissociation equilibrium of
the functionalized carboxylate groups. 13 pages main text, 4 pages appendix,
14 figures | Hagan, M.F., A theory for viral capsid assembly around
electrostatic cores, J. Chem. Phys., 2009, v130, 114902 | 10.1063/1.3086041 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop equilibrium and kinetic theories that describe the assembly of
viral capsid proteins on a charged central core, as seen in recent experiments
in which brome mosaic virus (BMV) capsids assemble around nanoparticles
functionalized with polyelectrolyte. We model interactions between capsid
proteins and nanoparticle surfaces as the interaction of polyelectrolyte
brushes with opposite charge, using the nonlinear Poisson Boltzmann equation.
The models predict that there is a threshold density of functionalized charge,
above which capsids efficiently assemble around nanoparticles, and that light
scatter intensity increases rapidly at early times, without the lag phase
characteristic of empty capsid assembly. These predictions are consistent with,
and enable interpretation of, preliminary experimental data. However, the
models predict a stronger dependence of nanoparticle incorporation efficiency
on functionalized charge density than measured in experiments, and do not
completely capture a logarithmic growth phase seen in experimental light
scatter. These discrepancies may suggest the presence of metastable disordered
states in the experimental system. In addition to discussing future experiments
for nanoparticle-capsid systems, we discuss broader implications for
understanding assembly around charged cores such as nucleic acids.
| [
{
"created": "Fri, 15 Aug 2008 21:29:48 GMT",
"version": "v1"
},
{
"created": "Wed, 6 May 2009 18:38:20 GMT",
"version": "v2"
}
] | 2009-05-06 | [
[
"Hagan",
"Michael F.",
""
]
] | We develop equilibrium and kinetic theories that describe the assembly of viral capsid proteins on a charged central core, as seen in recent experiments in which brome mosaic virus (BMV) capsids assemble around nanoparticles functionalized with polyelectrolyte. We model interactions between capsid proteins and nanoparticle surfaces as the interaction of polyelectrolyte brushes with opposite charge, using the nonlinear Poisson Boltzmann equation. The models predict that there is a threshold density of functionalized charge, above which capsids efficiently assemble around nanoparticles, and that light scatter intensity increases rapidly at early times, without the lag phase characteristic of empty capsid assembly. These predictions are consistent with, and enable interpretation of, preliminary experimental data. However, the models predict a stronger dependence of nanoparticle incorporation efficiency on functionalized charge density than measured in experiments, and do not completely capture a logarithmic growth phase seen in experimental light scatter. These discrepancies may suggest the presence of metastable disordered states in the experimental system. In addition to discussing future experiments for nanoparticle-capsid systems, we discuss broader implications for understanding assembly around charged cores such as nucleic acids. |
1802.00864 | Fatima Zohra Smaili | Fatima Zohra Smaili, Xin Gao, and Robert Hoehndorf | Onto2Vec: joint vector-based representation of biological entities and
their ontology-based annotations | null | null | 10.1093/bioinformatics/bty259 | null | q-bio.QM cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose the Onto2Vec method, an approach to learn feature vectors for
biological entities based on their annotations to biomedical ontologies. Our
method can be applied to a wide range of bioinformatics research problems such
as similarity-based prediction of interactions between proteins, classification
of interaction types using supervised learning, or clustering.
| [
{
"created": "Wed, 31 Jan 2018 08:23:45 GMT",
"version": "v1"
}
] | 2018-07-13 | [
[
"Smaili",
"Fatima Zohra",
""
],
[
"Gao",
"Xin",
""
],
[
"Hoehndorf",
"Robert",
""
]
] | We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. |
1906.11546 | Matthias Fischer | Matthias M. Fischer and Matthias Bild | Gut microbiome composition: back to baseline? | 4 pages, 2 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Nature Microbiology, Palleja and colleagues studied the changes in gut
microbiome composition in twelve healthy men over a period of six months
following an antibiotic intervention. The authors argued that the 'gut
microbiota of the subjects recovered to near-baseline composition within 1.5
months' and only exhibited a 'mild yet long-lasting imprint following
antibiotics exposure.' We here present a series of re-analyses of their
original data which demonstrate a significant loss of microbial taxa even after
the complete study period of 180 days. Additionally we show that the
composition of the microbiomes after the complete study period only moderately
correlates with the initial baseline states. Taken together with the lack of
significant compositional differences between day 42 and day 180, we think that
these findings suggest the convergence of the microbiomes to another stable
composition, which is different from the pre-treatment states, instead of a
recovery of the baseline state. Given the accumulating evidence of the role of
microbiome perturbations in a variety of infectious and non-infectious
diseases, as well as the crucial role antibiotics play in modern medicine, we
consider these differences in compositional states worthy of further
investigation.
| [
{
"created": "Thu, 27 Jun 2019 11:08:39 GMT",
"version": "v1"
}
] | 2019-06-28 | [
[
"Fischer",
"Matthias M.",
""
],
[
"Bild",
"Matthias",
""
]
] | In Nature Microbiology, Palleja and colleagues studied the changes in gut microbiome composition in twelve healthy men over a period of six months following an antibiotic intervention. The authors argued that the 'gut microbiota of the subjects recovered to near-baseline composition within 1.5 months' and only exhibited a 'mild yet long-lasting imprint following antibiotics exposure.' We here present a series of re-analyses of their original data which demonstrate a significant loss of microbial taxa even after the complete study period of 180 days. Additionally we show that the composition of the microbiomes after the complete study period only moderately correlates with the initial baseline states. Taken together with the lack of significant compositional differences between day 42 and day 180, we think that these findings suggest the convergence of the microbiomes to another stable composition, which is different from the pre-treatment states, instead of a recovery of the baseline state. Given the accumulating evidence of the role of microbiome perturbations in a variety of infectious and non-infectious diseases, as well as the crucial role antibiotics play in modern medicine, we consider these differences in compositional states worthy of further investigation. |
2006.10265 | Joann Jasiak | Christian Gourieroux, Joann Jasiak | Analysis of Virus Propagation: A Transition Model Representation of
Stochastic Epidemiological Models | 38 pages, 9 figures | null | null | null | q-bio.PE physics.soc-ph stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The growing literature on the propagation of COVID-19 relies on various
dynamic SIR-type models (Susceptible-Infected-Recovered) which yield
model-dependent results. For transparency and ease of comparing the results, we
introduce a common representation of the SIR-type stochastic epidemiological
models. This representation is a discrete time transition model, which allows
us to classify the epidemiological models with respect to the number of states
(compartments) and their interpretation. Additionally, the transition model
eliminates several limitations of the deterministic continuous time
epidemiological models which are pointed out in the paper. We also show that
all SIR-type models have a nonlinear (pseudo) state space representation and
are easily estimable from an extended Kalman filter.
| [
{
"created": "Thu, 18 Jun 2020 04:03:05 GMT",
"version": "v1"
}
] | 2020-06-19 | [
[
"Gourieroux",
"Christian",
""
],
[
"Jasiak",
"Joann",
""
]
] | The growing literature on the propagation of COVID-19 relies on various dynamic SIR-type models (Susceptible-Infected-Recovered) which yield model-dependent results. For transparency and ease of comparing the results, we introduce a common representation of the SIR-type stochastic epidemiological models. This representation is a discrete time transition model, which allows us to classify the epidemiological models with respect to the number of states (compartments) and their interpretation. Additionally, the transition model eliminates several limitations of the deterministic continuous time epidemiological models which are pointed out in the paper. We also show that all SIR-type models have a nonlinear (pseudo) state space representation and are easily estimable from an extended Kalman filter. |
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