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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0411001 | Angel Garcia | Hugh Nymeyer, Thomas B. Woolf, and Angel E. Garcia | Folding is Not Required for Bilayer Insertion: Replica Exchange
Simulations of an a-Helical Peptide with an Explicit Lipid Bilayer | 30 pages, 5 figures | null | null | null | q-bio.BM | null | We implement the replica exchange molecular dynamics algorithm to study the
interactions of a model peptide (WALP-16) with an explicitly represented DPPC
membrane bilayer. We observe the spontaneous, unbiased insertion of WALP-16
into the DPPC bilayer and its folding into an a-helix with a trans-bilayer
orientation. We observe that the insertion of the peptide into the DPPC bilayer
precedes secondary structure formation. Although the peptide has some
propensity to form a partially helical structure in the interfacial region of
the DPPC/water system, this state is not a productive intermediate but rather
an off-pathway trap for WALP-16 insertion. Equilibrium simulations show that
the observed insertion/folding pathway mirrors the potential of mean force
(PMF). Calculation of the enthalpic and entropic contributions to this PMF show
that the surface bound conformation of WALP-16 is significantly lower in energy
than other conformations, and that the insertion of WALP-16 into the bilayer
without regular secondary structure is enthalpically unfavorable by 5-10
kcal/mol/residue. The observed insertion/folding pathway disagrees with the
dominant conceptual model, which is that a surface bound helix is an obligatory
intermediate for the insertion of a-helical peptides into lipid bilayers. In
our simulations, the observed insertion/folding pathway is favored because of a
large (> 100 kcal/mol) increase in system entropy that occurs when the
unstructured WALP-16 peptide enters the lipid bilayer interior. The
insertion/folding pathway that is lowest in free energy depends sensitively on
the near cancellation of large enthalpic and entropic terms. This suggests that
intrinsic membrane peptides may have a diversity of insertion/folding behaviors
depending on the exact system of peptide and lipid under consideration.
| [
{
"created": "Fri, 29 Oct 2004 22:01:27 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Nymeyer",
"Hugh",
""
],
[
"Woolf",
"Thomas B.",
""
],
[
"Garcia",
"Angel E.",
""
]
] | We implement the replica exchange molecular dynamics algorithm to study the interactions of a model peptide (WALP-16) with an explicitly represented DPPC membrane bilayer. We observe the spontaneous, unbiased insertion of WALP-16 into the DPPC bilayer and its folding into an a-helix with a trans-bilayer orientation. We observe that the insertion of the peptide into the DPPC bilayer precedes secondary structure formation. Although the peptide has some propensity to form a partially helical structure in the interfacial region of the DPPC/water system, this state is not a productive intermediate but rather an off-pathway trap for WALP-16 insertion. Equilibrium simulations show that the observed insertion/folding pathway mirrors the potential of mean force (PMF). Calculation of the enthalpic and entropic contributions to this PMF show that the surface bound conformation of WALP-16 is significantly lower in energy than other conformations, and that the insertion of WALP-16 into the bilayer without regular secondary structure is enthalpically unfavorable by 5-10 kcal/mol/residue. The observed insertion/folding pathway disagrees with the dominant conceptual model, which is that a surface bound helix is an obligatory intermediate for the insertion of a-helical peptides into lipid bilayers. In our simulations, the observed insertion/folding pathway is favored because of a large (> 100 kcal/mol) increase in system entropy that occurs when the unstructured WALP-16 peptide enters the lipid bilayer interior. The insertion/folding pathway that is lowest in free energy depends sensitively on the near cancellation of large enthalpic and entropic terms. This suggests that intrinsic membrane peptides may have a diversity of insertion/folding behaviors depending on the exact system of peptide and lipid under consideration. |
2012.06556 | Julie Rowlett | Susanne Menden-Deuer and Julie Rowlett | Many ways to stay in the game: Individual variability maintains high
biodiversity in planktonic micro-organisms | This is a preliminary version of the manuscript published in Journal
of the Royal Society Interfaces and freely available online! | J. R. Soc. Interface, vol. 11, issue 95, 1120140031 (2014) | 10.1098/rsif.2014.0031 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In apparent contradiction to competition theory, the number of known,
co-existing plankton species far exceeds their explicable biodiversity - a
discrepancy termed the Paradox of the Plankton. We introduce a new
game-theoretic model for competing micro-organisms in which one player consists
of all organisms of one species. The stable points for the population dynamics
in our model, known as strategic behavior distributions (SBDs), are probability
distributions of behaviors across all organisms which imply a stable population
of the species as a whole. We find that intra-specific variability is the key
characteristic that ultimately allows co-existence because the outcomes of
competitions between individuals with variable competitive abilities is
unpredictable. Our simulations based on the theoretical model show that up to
100 species can coexist for at least 10000 generations, and that even small
population sizes or species with inferior competitive ability can survive when
there is intra-specific variability. In nature, this variability can be
observed as niche differentiation, variability in environmental and ecological
factors, and variability of individual behaviors or physiology. Therefore
previous specific explanations of the paradox are consistent with and provide
specific examples of our suggestion that individual variability is the
mechanism which solves the paradox.
| [
{
"created": "Fri, 11 Dec 2020 18:37:30 GMT",
"version": "v1"
}
] | 2020-12-14 | [
[
"Menden-Deuer",
"Susanne",
""
],
[
"Rowlett",
"Julie",
""
]
] | In apparent contradiction to competition theory, the number of known, co-existing plankton species far exceeds their explicable biodiversity - a discrepancy termed the Paradox of the Plankton. We introduce a new game-theoretic model for competing micro-organisms in which one player consists of all organisms of one species. The stable points for the population dynamics in our model, known as strategic behavior distributions (SBDs), are probability distributions of behaviors across all organisms which imply a stable population of the species as a whole. We find that intra-specific variability is the key characteristic that ultimately allows co-existence because the outcomes of competitions between individuals with variable competitive abilities is unpredictable. Our simulations based on the theoretical model show that up to 100 species can coexist for at least 10000 generations, and that even small population sizes or species with inferior competitive ability can survive when there is intra-specific variability. In nature, this variability can be observed as niche differentiation, variability in environmental and ecological factors, and variability of individual behaviors or physiology. Therefore previous specific explanations of the paradox are consistent with and provide specific examples of our suggestion that individual variability is the mechanism which solves the paradox. |
2103.10976 | Sidney Redner | Jordi Pi\~nero, S. Redner, Ricard Sol\'e | Fixation and Fluctuations in Two-Species Cooperation | 12 pages, 6 figures. Version 2 is almost completely rewritten, with
various new results. Now 22 pages and 7 figures in IOP format. Version 3 has
some additional minor changes in response to referee comments and now extends
to 23 pages. For publication in J Phys Complexity | null | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Cooperative interactions pervade in a broad range of many-body populations,
such as ecological communities, social organizations, and economic webs. We
investigate the dynamics of a population of two equivalent species A and B that
are driven by cooperative and symmetric interactions between these species. For
an isolated population, we determine the probability to reach fixation, where
only one species remains, as a function of the initial concentrations of the
two species, as well as the time to reach fixation. The latter scales
exponentially with the population size. When members of each species migrate
into the population at rate $\lambda$ and replace a randomly selected
individual, surprisingly rich dynamics ensues. Ostensibly, the population
reaches a steady state, but the steady-state population distribution undergoes
a unimodal to trimodal transition as the migration rate decreases below a
critical value $\lambda_c$. In the low-migration regime, $\lambda<\lambda_c$,
the steady state is not truly steady, but instead strongly fluctuates between
near-fixation states, where the population consists of mostly A's or of mostly
B's. The characteristic time scale of these fluctuations diverges as
$\lambda^{-1}$. Thus in spite of the cooperative interaction, a typical
snapshot of the population will contain almost all A's or almost all B's.
| [
{
"created": "Fri, 19 Mar 2021 18:22:09 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Sep 2021 01:29:43 GMT",
"version": "v2"
},
{
"created": "Tue, 1 Feb 2022 21:48:03 GMT",
"version": "v3"
}
] | 2022-02-03 | [
[
"Piñero",
"Jordi",
""
],
[
"Redner",
"S.",
""
],
[
"Solé",
"Ricard",
""
]
] | Cooperative interactions pervade in a broad range of many-body populations, such as ecological communities, social organizations, and economic webs. We investigate the dynamics of a population of two equivalent species A and B that are driven by cooperative and symmetric interactions between these species. For an isolated population, we determine the probability to reach fixation, where only one species remains, as a function of the initial concentrations of the two species, as well as the time to reach fixation. The latter scales exponentially with the population size. When members of each species migrate into the population at rate $\lambda$ and replace a randomly selected individual, surprisingly rich dynamics ensues. Ostensibly, the population reaches a steady state, but the steady-state population distribution undergoes a unimodal to trimodal transition as the migration rate decreases below a critical value $\lambda_c$. In the low-migration regime, $\lambda<\lambda_c$, the steady state is not truly steady, but instead strongly fluctuates between near-fixation states, where the population consists of mostly A's or of mostly B's. The characteristic time scale of these fluctuations diverges as $\lambda^{-1}$. Thus in spite of the cooperative interaction, a typical snapshot of the population will contain almost all A's or almost all B's. |
1003.3551 | Andre X. C. N. Valente | A. X. C. N. Valente | Prediction in the Hypothesis-Rich Regime | null | In book "Science and Engineering in High-Throughput Biology"
(2011) ISBN 978-1-257-11175-6 | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe the fundamental difference between the nature of problems in
traditional physics and that of many problems arising today in systems biology
and other complex settings. The difference hinges on the much larger number of
a priori plausible alternative laws for explaining the phenomena at hand in the
latter case. An approach and a mathematical framework for prediction in this
hypothesis-rich regime are introduced.
| [
{
"created": "Thu, 18 Mar 2010 11:27:47 GMT",
"version": "v1"
}
] | 2011-04-22 | [
[
"Valente",
"A. X. C. N.",
""
]
] | We describe the fundamental difference between the nature of problems in traditional physics and that of many problems arising today in systems biology and other complex settings. The difference hinges on the much larger number of a priori plausible alternative laws for explaining the phenomena at hand in the latter case. An approach and a mathematical framework for prediction in this hypothesis-rich regime are introduced. |
2301.00968 | Nai Ding | Nai Ding | Interpretation and Analysis of the Steady-State Neural Response to
Complex Sequential Structures: a Methodological Note | null | null | null | null | q-bio.NC eess.SP | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Frequency tagging is a powerful approach to investigate the neural processing
of sensory features, and is recently adapted to study the neural correlates of
superordinate structures, i.e., chunks, in complex sequences such as speech and
music. The nesting of sequence structures, the necessity to control the
periodicity in sensory features, and the low-frequency nature of sequence
structures pose new challenges for data analysis and interpretation. Here, I
discuss how to interpret the frequency of a sequential structure, and factors
that need to be considered when analyzing the periodicity in a signal. Finally,
a safe procedure is recommended for the analysis of frequency-tagged responses.
| [
{
"created": "Tue, 3 Jan 2023 06:38:58 GMT",
"version": "v1"
}
] | 2023-01-04 | [
[
"Ding",
"Nai",
""
]
] | Frequency tagging is a powerful approach to investigate the neural processing of sensory features, and is recently adapted to study the neural correlates of superordinate structures, i.e., chunks, in complex sequences such as speech and music. The nesting of sequence structures, the necessity to control the periodicity in sensory features, and the low-frequency nature of sequence structures pose new challenges for data analysis and interpretation. Here, I discuss how to interpret the frequency of a sequential structure, and factors that need to be considered when analyzing the periodicity in a signal. Finally, a safe procedure is recommended for the analysis of frequency-tagged responses. |
2006.15974 | Alejandro Rodr\'iguez-Collado | Cristina Rueda, Alejandro Rodr\'iguez-Collado and Yolanda Larriba | A novel wave decomposition for oscillatory signals | null | null | 10.1371/journal.pone.0254152 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Oscillatory systems arise in the different science fields. Complex
mathematical formulations with differential equations have been proposed to
model the dynamics of these systems. While they have the advantage of having a
direct physiological meaning, they are not useful in practice as a result of
the parameter adjustment complexity and the presence of noise. In this paper, a
signal plus error model is proposed to analyze oscillations, where the signal
is a multicomponent $FMM$ and the noise is assumed Gaussian. The signal
formulation is also a novel decomposition approach in AM-FM components,
competing with Fourier and other decompositions. Several interesting
theoretical properties are derived including the Ordinary Differential
Equations describing the signal. Furthermore, the usefulness in real practice
is demonstrate to analyze signals associated to neuron synapses and by
addressing other questions in Neuroscience.
| [
{
"created": "Wed, 10 Jun 2020 14:45:50 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Jul 2020 14:24:35 GMT",
"version": "v2"
}
] | 2022-05-02 | [
[
"Rueda",
"Cristina",
""
],
[
"Rodríguez-Collado",
"Alejandro",
""
],
[
"Larriba",
"Yolanda",
""
]
] | Oscillatory systems arise in the different science fields. Complex mathematical formulations with differential equations have been proposed to model the dynamics of these systems. While they have the advantage of having a direct physiological meaning, they are not useful in practice as a result of the parameter adjustment complexity and the presence of noise. In this paper, a signal plus error model is proposed to analyze oscillations, where the signal is a multicomponent $FMM$ and the noise is assumed Gaussian. The signal formulation is also a novel decomposition approach in AM-FM components, competing with Fourier and other decompositions. Several interesting theoretical properties are derived including the Ordinary Differential Equations describing the signal. Furthermore, the usefulness in real practice is demonstrate to analyze signals associated to neuron synapses and by addressing other questions in Neuroscience. |
2004.13256 | Ze Wang | Ze Wang | Assessing the neurocognitive correlates of resting brain entropy | Part of the work is accepted for presentation in ISMRM 2020 | Neuroimage 2021 | 10.1016/j.neuroimage.2021.117893 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human brain exhibits large-scale spontaneous fluctuations that account
for most of its total energy metabolism. Independent of any overt function,
this immense ongoing activity likely creates or maintains a potential
functional brain reserve to facilitate normal brain function. An important
property of spontaneous brain activity is the long-range temporal coherence,
which can be characterized by resting state fMRI-based brain entropy mapping
(BEN), a relatively new method that has gained increasing research interest.
The purpose of this study was to leverage the large resting state fMRI and
behavioral data publicly available from the human connectome project to address
three important but still unknown questions: temporal stability of
rsfMRI-derived BEN; the relationship of resting BEN to latent functional
reserve; associations of resting BEN to neurocognition. Our results showed that
rsfMRI-derived BEN was highly stable across time; resting BEN in the default
mode network (DMN) and executive control network (ECN) was related to brain
reserve in a negative correlation to education years; and lower DMN/ECN BEN
corresponds to higher fluid intelligence and better task performance. These
results suggest that resting BEN is a temporally stable brain trait; BEN in
DMN/ECN may provide a means to measure the latent functional reserve that
bestows better brain functionality and may be enhanced by education.
| [
{
"created": "Tue, 28 Apr 2020 02:55:36 GMT",
"version": "v1"
}
] | 2021-02-23 | [
[
"Wang",
"Ze",
""
]
] | The human brain exhibits large-scale spontaneous fluctuations that account for most of its total energy metabolism. Independent of any overt function, this immense ongoing activity likely creates or maintains a potential functional brain reserve to facilitate normal brain function. An important property of spontaneous brain activity is the long-range temporal coherence, which can be characterized by resting state fMRI-based brain entropy mapping (BEN), a relatively new method that has gained increasing research interest. The purpose of this study was to leverage the large resting state fMRI and behavioral data publicly available from the human connectome project to address three important but still unknown questions: temporal stability of rsfMRI-derived BEN; the relationship of resting BEN to latent functional reserve; associations of resting BEN to neurocognition. Our results showed that rsfMRI-derived BEN was highly stable across time; resting BEN in the default mode network (DMN) and executive control network (ECN) was related to brain reserve in a negative correlation to education years; and lower DMN/ECN BEN corresponds to higher fluid intelligence and better task performance. These results suggest that resting BEN is a temporally stable brain trait; BEN in DMN/ECN may provide a means to measure the latent functional reserve that bestows better brain functionality and may be enhanced by education. |
2211.09186 | Guillermo Lorenzo | St\'ephane Urcun, Guillermo Lorenzo, Davide Baroli, Pierre-Yves Rohan,
Giuseppe Scium\`e, Wafa Skalli, Vincent Lubrano, St\'ephane P.A. Bordas | Oncology and mechanics: landmark studies and promising clinical
applications | null | Advances in Applied Mechanics, Volume 55, 2022, Pages 513-571 | 10.1016/bs.aams.2022.05.003 | null | q-bio.TO cs.CE q-bio.CB | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Clinical management of cancer has continuously evolved for several decades.
Biochemical, molecular and genomics approaches have brought and still bring
numerous insights into cancerous diseases. It is now accepted that some
phenomena, allowed by favorable biological conditions, emerge via mechanical
signaling at the cellular scale and via mechanical forces at the macroscale.
Mechanical phenomena in cancer have been studied in-depth over the last
decades, and their clinical applications are starting to be understood. If
numerous models and experimental setups have been proposed, only a few have led
to clinical applications. The objective of this contribution is to propose to
review a large scope of mechanical findings which have consequences on the
clinical management of cancer. This review is mainly addressed to doctoral
candidates in mechanics and applied mathematics who are faced with the
challenge of the mechanics-based modeling of cancer with the aim of clinical
applications. We show that the collaboration of the biological and mechanical
approaches has led to promising advances in terms of modeling, experimental
design and therapeutic targets. Additionally, a specific focus is brought on
imaging-informed mechanics-based models, which we believe can further the
development of new therapeutic targets and the advent of personalized medicine.
We study in detail several successful workflows on patient-specific targeted
therapies based on mechanistic modeling.
| [
{
"created": "Wed, 16 Nov 2022 20:10:27 GMT",
"version": "v1"
}
] | 2022-11-18 | [
[
"Urcun",
"Stéphane",
""
],
[
"Lorenzo",
"Guillermo",
""
],
[
"Baroli",
"Davide",
""
],
[
"Rohan",
"Pierre-Yves",
""
],
[
"Sciumè",
"Giuseppe",
""
],
[
"Skalli",
"Wafa",
""
],
[
"Lubrano",
"Vincent",
""
],
[
"Bordas",
"Stéphane P. A.",
""
]
] | Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to propose to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design and therapeutic targets. Additionally, a specific focus is brought on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling. |
1905.00235 | Ovidiu Radulescu | Guilherme C.P. Innocentini, Fernando Antoneli, Arran Hodgkinson and
Ovidiu Radulescu | Effective computational methods for hybrid stochastic gene networks | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | At the scale of the individual cell, protein production is a stochastic
process with multiple time scales, combining quick and slow random steps with
discontinuous and smooth variation. Hybrid stochastic processes, in particular
piecewise-deterministic Markov processes (PDMP), are well adapted for
describing such situations. PDMPs approximate the jump Markov processes
traditionally used as models for stochastic chemical reaction networks.
Although hybrid modelling is now well established in biology, these models
remain computationally challenging. We propose several improved methods for
computing time dependent multivariate probability distributions (MPD) of PDMP
models of gene networks. In these models, the promoter dynamics is described by
a finite state, continuous time Markov process, whereas the mRNA and protein
levels follow ordinary differential equations (ODEs). The Monte-Carlo method
combines direct simulation of the PDMP with analytic solutions of the ODEs. The
push-forward method numerically computes the probability measure advected by
the deterministic ODE flow, through the use of analytic expressions of the
corresponding semigroup. Compared to earlier versions of this method, the
probability of the promoter states sequence is computed beyond the naive mean
field theory and adapted for non-linear regulation functions.
| [
{
"created": "Wed, 1 May 2019 09:45:32 GMT",
"version": "v1"
}
] | 2019-05-02 | [
[
"Innocentini",
"Guilherme C. P.",
""
],
[
"Antoneli",
"Fernando",
""
],
[
"Hodgkinson",
"Arran",
""
],
[
"Radulescu",
"Ovidiu",
""
]
] | At the scale of the individual cell, protein production is a stochastic process with multiple time scales, combining quick and slow random steps with discontinuous and smooth variation. Hybrid stochastic processes, in particular piecewise-deterministic Markov processes (PDMP), are well adapted for describing such situations. PDMPs approximate the jump Markov processes traditionally used as models for stochastic chemical reaction networks. Although hybrid modelling is now well established in biology, these models remain computationally challenging. We propose several improved methods for computing time dependent multivariate probability distributions (MPD) of PDMP models of gene networks. In these models, the promoter dynamics is described by a finite state, continuous time Markov process, whereas the mRNA and protein levels follow ordinary differential equations (ODEs). The Monte-Carlo method combines direct simulation of the PDMP with analytic solutions of the ODEs. The push-forward method numerically computes the probability measure advected by the deterministic ODE flow, through the use of analytic expressions of the corresponding semigroup. Compared to earlier versions of this method, the probability of the promoter states sequence is computed beyond the naive mean field theory and adapted for non-linear regulation functions. |
0709.4425 | Benjamin Audit | Emmanuel D. Levy (LMB), Christos A. Ouzounis, Walter R. Gilks,
Benjamin Audit (Phys-ENS) | Probabilistic annotation of protein sequences based on functional
classifications | null | BMC Bioinformatics 6 (2005) 302 | 10.1186/1471-2105-6-302 | null | q-bio.QM | null | BACKGROUND: One of the most evident achievements of bioinformatics is the
development of methods that transfer biological knowledge from characterised
proteins to uncharacterised sequences. This mode of protein function assignment
is mostly based on the detection of sequence similarity and the premise that
functional properties are conserved during evolution. Most automatic approaches
developed to date rely on the identification of clusters of homologous proteins
and the mapping of new proteins onto these clusters, which are expected to
share functional characteristics. RESULTS: Here, we inverse the logic of this
process, by considering the mapping of sequences directly to a functional
classification instead of mapping functions to a sequence clustering. In this
mode, the starting point is a database of labelled proteins according to a
functional classification scheme, and the subsequent use of sequence similarity
allows defining the membership of new proteins to these functional classes. In
this framework, we define the Correspondence Indicators as measures of
relationship between sequence and function and further formulate two Bayesian
approaches to estimate the probability for a sequence of unknown function to
belong to a functional class. This approach allows the parametrisation of
different sequence search strategies and provides a direct measure of
annotation error rates. We validate this approach with a database of enzymes
labelled by their corresponding four-digit EC numbers and analyse specific
cases. CONCLUSION: The performance of this method is significantly higher than
the simple strategy consisting in transferring the annotation from the highest
scoring BLAST match and is expected to find applications in automated
functional annotation pipelines.
| [
{
"created": "Thu, 27 Sep 2007 15:22:08 GMT",
"version": "v1"
}
] | 2007-09-28 | [
[
"Levy",
"Emmanuel D.",
"",
"LMB"
],
[
"Ouzounis",
"Christos A.",
"",
"Phys-ENS"
],
[
"Gilks",
"Walter R.",
"",
"Phys-ENS"
],
[
"Audit",
"Benjamin",
"",
"Phys-ENS"
]
] | BACKGROUND: One of the most evident achievements of bioinformatics is the development of methods that transfer biological knowledge from characterised proteins to uncharacterised sequences. This mode of protein function assignment is mostly based on the detection of sequence similarity and the premise that functional properties are conserved during evolution. Most automatic approaches developed to date rely on the identification of clusters of homologous proteins and the mapping of new proteins onto these clusters, which are expected to share functional characteristics. RESULTS: Here, we inverse the logic of this process, by considering the mapping of sequences directly to a functional classification instead of mapping functions to a sequence clustering. In this mode, the starting point is a database of labelled proteins according to a functional classification scheme, and the subsequent use of sequence similarity allows defining the membership of new proteins to these functional classes. In this framework, we define the Correspondence Indicators as measures of relationship between sequence and function and further formulate two Bayesian approaches to estimate the probability for a sequence of unknown function to belong to a functional class. This approach allows the parametrisation of different sequence search strategies and provides a direct measure of annotation error rates. We validate this approach with a database of enzymes labelled by their corresponding four-digit EC numbers and analyse specific cases. CONCLUSION: The performance of this method is significantly higher than the simple strategy consisting in transferring the annotation from the highest scoring BLAST match and is expected to find applications in automated functional annotation pipelines. |
1203.3037 | Sebastiano Stramaglia | S. Stramaglia, Guo-Rong Wu, M. Pellicoro and D. Marinazzo | Expanding the Transfer Entropy to Identify Information Subgraphs in
Complex Systems | null | null | 10.1103/PhysRevE.86.066211 | null | q-bio.QM cs.IT math.IT physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a formal expansion of the transfer entropy to put in evidence
irreducible sets of variables which provide information for the future state of
each assigned target. Multiplets characterized by a large contribution to the
expansion are associated to informational circuits present in the system, with
an informational character which can be associated to the sign of the
contribution. For the sake of computational complexity, we adopt the assumption
of Gaussianity and use the corresponding exact formula for the conditional
mutual information. We report the application of the proposed methodology on
two EEG data sets.
| [
{
"created": "Wed, 14 Mar 2012 10:28:16 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Sep 2012 10:23:46 GMT",
"version": "v2"
}
] | 2015-06-04 | [
[
"Stramaglia",
"S.",
""
],
[
"Wu",
"Guo-Rong",
""
],
[
"Pellicoro",
"M.",
""
],
[
"Marinazzo",
"D.",
""
]
] | We propose a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to informational circuits present in the system, with an informational character which can be associated to the sign of the contribution. For the sake of computational complexity, we adopt the assumption of Gaussianity and use the corresponding exact formula for the conditional mutual information. We report the application of the proposed methodology on two EEG data sets. |
1905.06534 | David Lusseau | Eilidh Stirrup and David Lusseau | Getting a head start: the slime mold, Physarum polycephalum, tune
foraging decision to motivational asymmetry when faced with competition | 6 pages, 2 figures | null | null | null | q-bio.PE q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Slime mould plasmodia can adjust their behaviour in response to chemical
trails left by themselves and other Physarum plasmodia. This simple feedback
process increases their foraging efficiency. We still do not know whether other
factors influence plasmodium behaviour in realistic competition settings. Here
we designed a competition experiment where two plasmodia had to find one food
source in a common environment. As previously shown, the time it took plasmodia
to find food depended on their hunger motivation. However, the time it took a
plasmodium to start looking for food depended on its motivation and the
motivation of its competitor. Plasmodia always initiated foraging quicker if
they were in the presence of a competitor and the quickest if they were hungry
and in the presence of a satiated competitor. The time it took to arrive to the
food was not influenced by whether they were alone or with a competitor.
Ultimately, this complex competition response benefited the hungry plasmodia as
they had a 4:1 chance of finding the food first. The sensory ecology of
Physarum polycephalum is more complex than previously thought and yields
complex behaviour in a simple organism.
| [
{
"created": "Thu, 16 May 2019 05:41:02 GMT",
"version": "v1"
}
] | 2019-05-17 | [
[
"Stirrup",
"Eilidh",
""
],
[
"Lusseau",
"David",
""
]
] | Slime mould plasmodia can adjust their behaviour in response to chemical trails left by themselves and other Physarum plasmodia. This simple feedback process increases their foraging efficiency. We still do not know whether other factors influence plasmodium behaviour in realistic competition settings. Here we designed a competition experiment where two plasmodia had to find one food source in a common environment. As previously shown, the time it took plasmodia to find food depended on their hunger motivation. However, the time it took a plasmodium to start looking for food depended on its motivation and the motivation of its competitor. Plasmodia always initiated foraging quicker if they were in the presence of a competitor and the quickest if they were hungry and in the presence of a satiated competitor. The time it took to arrive to the food was not influenced by whether they were alone or with a competitor. Ultimately, this complex competition response benefited the hungry plasmodia as they had a 4:1 chance of finding the food first. The sensory ecology of Physarum polycephalum is more complex than previously thought and yields complex behaviour in a simple organism. |
2009.09589 | Petr Sulc | Jonah Procyk, Erik Poppleton, Petr \v{S}ulc | Coarse-Grained Nucleic Acid-Protein Model for Hybrid Nanotechnology | 8 pages, 6 figures | null | null | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The emerging field of hybrid DNA - protein nanotechnology brings with it the
potential for many novel materials which combine the addressability of DNA
nanotechnology with versatility of protein interactions. However, the design
and computational study of these hybrid structures is difficult due to the
system sizes involved. To aid in the design and in silico analysis process, we
introduce here a coarse-grained DNA/RNA-protein model that extends the
oxDNA/oxRNA models of DNA/RNA with a coarse-grained model of proteins based on
an anisotropic network model representation. Fully equipped with analysis
scripts and visualization, our model aims to facilitate hybrid nanomaterial
design towards eventual experimental realization, as well as enabling study of
biological complexes. We further demonstrate its usage by simulating
DNA-protein nanocage, DNA wrapped around histones, and a nascent RNA in
polymerase.
| [
{
"created": "Mon, 21 Sep 2020 03:02:11 GMT",
"version": "v1"
}
] | 2020-09-22 | [
[
"Procyk",
"Jonah",
""
],
[
"Poppleton",
"Erik",
""
],
[
"Šulc",
"Petr",
""
]
] | The emerging field of hybrid DNA - protein nanotechnology brings with it the potential for many novel materials which combine the addressability of DNA nanotechnology with versatility of protein interactions. However, the design and computational study of these hybrid structures is difficult due to the system sizes involved. To aid in the design and in silico analysis process, we introduce here a coarse-grained DNA/RNA-protein model that extends the oxDNA/oxRNA models of DNA/RNA with a coarse-grained model of proteins based on an anisotropic network model representation. Fully equipped with analysis scripts and visualization, our model aims to facilitate hybrid nanomaterial design towards eventual experimental realization, as well as enabling study of biological complexes. We further demonstrate its usage by simulating DNA-protein nanocage, DNA wrapped around histones, and a nascent RNA in polymerase. |
1705.08663 | Leonardo Victor De Knegt | Nana Dupont (1), Mette Fertner (1), Anna Camilla Birkegaard (2), Vibe
Dalhoff Andersen (3), Gitte Blach Nielsen (1), Amanda Brinch Kruse (1),
Leonardo Victor de Knegt (1 and 3) ((1) Department of Veterinary and Animal
Sciences, Faculty of Health and Medical Sciences, University of Copenhagen,
Frederiksberg, Denmark, (2) Section for Diagnostics and Scientific Advice,
National Veterinary Institute, Technical University of Denmark, Kongens
Lyngby, Denmark, (3) Research group for Genomic Epidemiology, National Food
Institute, Technical University of Denmark, Kongens Lyngby, Denmark.) | Improving institutional memory on challenges and methods for estimation
of pig herd antimicrobial exposure based on data from the Danish Veterinary
Medicines Statistics Program (VetStat) | 25 pages, including two Appendices (pages not numbered). Title page,
including abstract, is on page 1. Body of text, including references,
abbreviation list and disclaimers for conflict of interest and funding, are
on pages 2-18. Two figures embedded in the text on pages 3 and 5. Appendix 1
starts on page 19, and Appendix 2 on page 25 | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | With the increasing occurrence of antimicrobial resistance, more attention
has been directed towards surveillance of both human and veterinary
antimicrobial use. Since the early 2000s, several research papers on Danish pig
antimicrobial usage have been published, based on data from the Danish
Veterinary Medicines Statistics Program (VetStat). VetStat was established in
2000, as a national database containing detailed information on purchases of
veterinary medicine. This paper presents a critical set of challenges
originating from static system features, which researchers must address when
estimating antimicrobial exposure in Danish pig herds. Most challenges
presented are followed by at least one robust solution. A set of challenges
requiring awareness from the researcher, but for which no immediate solution
was available, were also presented. The selection of challenges and solutions
was based on a consensus by a cross-institutional group of researchers working
in projects using VetStat data. No quantitative data quality evaluations were
performed, as the frequency of errors and inconsistencies in a dataset will
vary, depending on the period covered in the data. Instead, this paper focuses
on clarifying how VetStat data may be translated to an estimation of the
antimicrobial exposure at herd level, by suggesting uniform methods of
extracting and editing data, in order to obtain reliable and comparable
estimates on pig antimicrobial consumption for research purposes.
| [
{
"created": "Wed, 24 May 2017 08:57:52 GMT",
"version": "v1"
}
] | 2017-05-25 | [
[
"Dupont",
"Nana",
"",
"1 and 3"
],
[
"Fertner",
"Mette",
"",
"1 and 3"
],
[
"Birkegaard",
"Anna Camilla",
"",
"1 and 3"
],
[
"Andersen",
"Vibe Dalhoff",
"",
"1 and 3"
],
[
"Nielsen",
"Gitte Blach",
"",
"1 and 3"
],
[
"Kruse",
"Amanda Brinch",
"",
"1 and 3"
],
[
"de Knegt",
"Leonardo Victor",
"",
"1 and 3"
]
] | With the increasing occurrence of antimicrobial resistance, more attention has been directed towards surveillance of both human and veterinary antimicrobial use. Since the early 2000s, several research papers on Danish pig antimicrobial usage have been published, based on data from the Danish Veterinary Medicines Statistics Program (VetStat). VetStat was established in 2000, as a national database containing detailed information on purchases of veterinary medicine. This paper presents a critical set of challenges originating from static system features, which researchers must address when estimating antimicrobial exposure in Danish pig herds. Most challenges presented are followed by at least one robust solution. A set of challenges requiring awareness from the researcher, but for which no immediate solution was available, were also presented. The selection of challenges and solutions was based on a consensus by a cross-institutional group of researchers working in projects using VetStat data. No quantitative data quality evaluations were performed, as the frequency of errors and inconsistencies in a dataset will vary, depending on the period covered in the data. Instead, this paper focuses on clarifying how VetStat data may be translated to an estimation of the antimicrobial exposure at herd level, by suggesting uniform methods of extracting and editing data, in order to obtain reliable and comparable estimates on pig antimicrobial consumption for research purposes. |
1412.0894 | Christian Geier | Christian Geier, Stephan Bialonski, Christian E. Elger, Klaus Lehnertz | How important is the seizure onset zone for seizure dynamics? | In press (Seizure) | null | 10.1016/j.seizure.2014.10.013 | null | q-bio.NC physics.data-an physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: Research into epileptic networks has recently allowed deeper
insights into the epileptic process. Here we investigated the importance of
individual network nodes for seizure dynamics.
Methods: We analysed intracranial electroencephalographic recordings of 86
focal seizures with different anatomical onset locations. With time-resolved
correlation analyses, we derived a sequence of weighted epileptic networks
spanning the pre-ictal, ictal, and post-ictal period, and each recording site
represents a network node. We assessed node importance with commonly used
centrality indices that take into account different network properties.
Results: A high variability of temporal evolution of node importance was
observed, both intra- and interindividually. Nevertheless, nodes near and far
off the seizure onset zone (SOZ) were rated as most important for seizure
dynamics more often (65% of cases) than nodes from within the SOZ (35% of
cases).
Conclusion: Our findings underline the high relevance of brain outside of the
SOZ but within the large-scale epileptic network for seizure dynamics.
Knowledge about these network constituents may elucidate targets for
individualised therapeutic interventions that aim at preventing seizure
generation and spread.
| [
{
"created": "Tue, 2 Dec 2014 12:44:06 GMT",
"version": "v1"
}
] | 2014-12-03 | [
[
"Geier",
"Christian",
""
],
[
"Bialonski",
"Stephan",
""
],
[
"Elger",
"Christian E.",
""
],
[
"Lehnertz",
"Klaus",
""
]
] | Purpose: Research into epileptic networks has recently allowed deeper insights into the epileptic process. Here we investigated the importance of individual network nodes for seizure dynamics. Methods: We analysed intracranial electroencephalographic recordings of 86 focal seizures with different anatomical onset locations. With time-resolved correlation analyses, we derived a sequence of weighted epileptic networks spanning the pre-ictal, ictal, and post-ictal period, and each recording site represents a network node. We assessed node importance with commonly used centrality indices that take into account different network properties. Results: A high variability of temporal evolution of node importance was observed, both intra- and interindividually. Nevertheless, nodes near and far off the seizure onset zone (SOZ) were rated as most important for seizure dynamics more often (65% of cases) than nodes from within the SOZ (35% of cases). Conclusion: Our findings underline the high relevance of brain outside of the SOZ but within the large-scale epileptic network for seizure dynamics. Knowledge about these network constituents may elucidate targets for individualised therapeutic interventions that aim at preventing seizure generation and spread. |
1105.3758 | Benedikt Obermayer | B. Obermayer, H. Krammer, D. Braun, U. Gerland | Emergence of information transmission in a prebiotic RNA reactor | accepted at Phys. Rev. Lett | Phys. Rev. Lett. 107, 018101 (2011) | 10.1103/PhysRevLett.107.018101 | null | q-bio.BM physics.bio-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A poorly understood step in the transition from a chemical to a biological
world is the emergence of self-replicating molecular systems. We study how a
precursor for such a replicator might arise in a hydrothermal RNA reactor,
which accumulates longer sequences from unbiased monomer influx and random
ligation. In the reactor, intra- and inter-molecular basepairing locally
protects from random cleavage. By analyzing stochastic simulations, we find
temporal sequence correlations that constitute a signature of information
transmission, weaker but of the same form as in a true replicator.
| [
{
"created": "Wed, 18 May 2011 22:21:59 GMT",
"version": "v1"
}
] | 2011-06-30 | [
[
"Obermayer",
"B.",
""
],
[
"Krammer",
"H.",
""
],
[
"Braun",
"D.",
""
],
[
"Gerland",
"U.",
""
]
] | A poorly understood step in the transition from a chemical to a biological world is the emergence of self-replicating molecular systems. We study how a precursor for such a replicator might arise in a hydrothermal RNA reactor, which accumulates longer sequences from unbiased monomer influx and random ligation. In the reactor, intra- and inter-molecular basepairing locally protects from random cleavage. By analyzing stochastic simulations, we find temporal sequence correlations that constitute a signature of information transmission, weaker but of the same form as in a true replicator. |
1704.00351 | Nina Golyandina E. | Theodore Alexandrov, Nina Golyandina, David Holloway, Alex Shlemov,
and Alexander Spirov | Two-exponential models of gene expression patterns for noisy
experimental data | null | Journal of Computational Biology (2018) Vol. 25, No. 11, p.
1220-1230 | 10.1089/cmb.2017.0063 | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Spatial pattern formation of the primary anterior-posterior
morphogenetic gradient of the transcription factor Bicoid (Bcd) has been
studied experimentally and computationally for many years. Bcd specifies
positional information for the downstream segmentation genes, affecting the fly
body plan. More recently, a number of researchers have focused on the
patterning dynamics of the underlying bcd mRNA gradient, which is translated
into Bcd protein. New, more accurate techniques for visualizing bcd mRNA need
to be combined with quantitative signal extraction techniques to reconstruct
the bcd mRNA distribution.
Results: Here, we present a robust technique for quantifying gradients with a
two-exponential model. This approach: 1) has natural, biologically relevant
parameters; and 2) is invariant to linear transformations of the data which can
arise due to variation in experimental conditions (e.g. microscope settings,
non-specific background signal). This allows us to quantify bcd mRNA gradient
variability from embryo to embryo (important for studying the robustness of
developmental regulatory networks); sort out atypical gradients; and classify
embryos to developmental stage by quantitative gradient parameters.
| [
{
"created": "Sun, 2 Apr 2017 19:09:39 GMT",
"version": "v1"
}
] | 2019-06-27 | [
[
"Alexandrov",
"Theodore",
""
],
[
"Golyandina",
"Nina",
""
],
[
"Holloway",
"David",
""
],
[
"Shlemov",
"Alex",
""
],
[
"Spirov",
"Alexander",
""
]
] | Motivation: Spatial pattern formation of the primary anterior-posterior morphogenetic gradient of the transcription factor Bicoid (Bcd) has been studied experimentally and computationally for many years. Bcd specifies positional information for the downstream segmentation genes, affecting the fly body plan. More recently, a number of researchers have focused on the patterning dynamics of the underlying bcd mRNA gradient, which is translated into Bcd protein. New, more accurate techniques for visualizing bcd mRNA need to be combined with quantitative signal extraction techniques to reconstruct the bcd mRNA distribution. Results: Here, we present a robust technique for quantifying gradients with a two-exponential model. This approach: 1) has natural, biologically relevant parameters; and 2) is invariant to linear transformations of the data which can arise due to variation in experimental conditions (e.g. microscope settings, non-specific background signal). This allows us to quantify bcd mRNA gradient variability from embryo to embryo (important for studying the robustness of developmental regulatory networks); sort out atypical gradients; and classify embryos to developmental stage by quantitative gradient parameters. |
2105.01570 | Martin Miguel | Martin Miguel, Pablo Riera and Diego Fernandez Slezak | Simple and Cheap Setup for Timing Tapping Responses Synchronized to
Auditory Stimuli | null | null | null | null | q-bio.NC cs.SD eess.AS | http://creativecommons.org/licenses/by/4.0/ | Measuring human capabilities to synchronize in time, adapt to perturbations
to timing sequences or reproduce time intervals often require experimental
setups that allow recording response times with millisecond precision. Most
setups present auditory stimuli using either MIDI devices or specialized
hardware such as Arduino and are often expensive or require calibration and
advanced programming skills. Here, we present in detail an experimental setup
that only requires an external sound card and minor electronic skills, works on
a conventional PC, is cheaper than alternatives and requires almost no
programming skills. It is intended for presenting any auditory stimuli and
recording tapping response times with within 2 milliseconds precision (up to
-2ms lag). This paper shows why desired accuracy in recording response times
against auditory stimuli is difficult to achieve in conventional computer
setups, presents an experimental setup to overcome this and explains in detail
how to set it up and use the provided code. Finally, the code for analyzing the
recorded tapping responses was evaluated, showing that no spurious or missing
events were found in 94% of the analyzed recordings.
| [
{
"created": "Fri, 30 Apr 2021 21:30:40 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Jul 2021 23:40:23 GMT",
"version": "v2"
}
] | 2021-07-20 | [
[
"Miguel",
"Martin",
""
],
[
"Riera",
"Pablo",
""
],
[
"Slezak",
"Diego Fernandez",
""
]
] | Measuring human capabilities to synchronize in time, adapt to perturbations to timing sequences or reproduce time intervals often require experimental setups that allow recording response times with millisecond precision. Most setups present auditory stimuli using either MIDI devices or specialized hardware such as Arduino and are often expensive or require calibration and advanced programming skills. Here, we present in detail an experimental setup that only requires an external sound card and minor electronic skills, works on a conventional PC, is cheaper than alternatives and requires almost no programming skills. It is intended for presenting any auditory stimuli and recording tapping response times with within 2 milliseconds precision (up to -2ms lag). This paper shows why desired accuracy in recording response times against auditory stimuli is difficult to achieve in conventional computer setups, presents an experimental setup to overcome this and explains in detail how to set it up and use the provided code. Finally, the code for analyzing the recorded tapping responses was evaluated, showing that no spurious or missing events were found in 94% of the analyzed recordings. |
1805.09001 | Sizhong Lan | Sizhong Lan | One-to-one Mapping between Stimulus and Neural State: Memory and
Classification | 8 pages, 15 figures, final for AIP Advances | null | null | null | q-bio.NC cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Synaptic strength can be seen as probability to propagate impulse, and
according to synaptic plasticity, function could exist from propagation
activity to synaptic strength. If the function satisfies constraints such as
continuity and monotonicity, neural network under external stimulus will always
go to fixed point, and there could be one-to-one mapping between external
stimulus and synaptic strength at fixed point. In other words, neural network
"memorizes" external stimulus in its synapses. A biological classifier is
proposed to utilize this mapping.
| [
{
"created": "Wed, 23 May 2018 08:08:23 GMT",
"version": "v1"
},
{
"created": "Sun, 17 Jun 2018 16:52:34 GMT",
"version": "v2"
},
{
"created": "Sun, 19 Aug 2018 17:00:10 GMT",
"version": "v3"
},
{
"created": "Mon, 3 Dec 2018 17:19:11 GMT",
"version": "v4"
},
{
"created": "Tue, 8 Jan 2019 05:27:46 GMT",
"version": "v5"
},
{
"created": "Wed, 24 Apr 2019 03:07:16 GMT",
"version": "v6"
}
] | 2019-04-25 | [
[
"Lan",
"Sizhong",
""
]
] | Synaptic strength can be seen as probability to propagate impulse, and according to synaptic plasticity, function could exist from propagation activity to synaptic strength. If the function satisfies constraints such as continuity and monotonicity, neural network under external stimulus will always go to fixed point, and there could be one-to-one mapping between external stimulus and synaptic strength at fixed point. In other words, neural network "memorizes" external stimulus in its synapses. A biological classifier is proposed to utilize this mapping. |
1602.04005 | Ciprian Palaghianu Dr. | Ciprian Palaghianu | A tool for computing diversity and consideration on differences between
diversity indices | 5 pages, Journal of Landscape Management, Brno | Journal of Landscape Management, 5 (2), 78-82 (2014) | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diversity represents a key concept in ecology, and there are various methods
of assessing it. The multitude of diversity indices are quite puzzling and
sometimes difficult to compute for a large volume of data. This paper promotes
a computational tool used to assess the diversity of different entities. The
BIODIV software is a user-friendly tool, developed using Microsoft Visual
Basic. It is capable to compute several diversity indices such as: Shannon,
Simpson, Pielou, Brillouin, Berger-Parker, McIntosh, Margalef, Menhinick and
Gleason. The software tool was tested using real data sets and the results were
analysed in order to make assumption on the indices behaviour. The results
showed a clear segregation of indices in two major groups with similar
expressivity.
| [
{
"created": "Fri, 12 Feb 2016 10:32:54 GMT",
"version": "v1"
}
] | 2016-02-15 | [
[
"Palaghianu",
"Ciprian",
""
]
] | Diversity represents a key concept in ecology, and there are various methods of assessing it. The multitude of diversity indices are quite puzzling and sometimes difficult to compute for a large volume of data. This paper promotes a computational tool used to assess the diversity of different entities. The BIODIV software is a user-friendly tool, developed using Microsoft Visual Basic. It is capable to compute several diversity indices such as: Shannon, Simpson, Pielou, Brillouin, Berger-Parker, McIntosh, Margalef, Menhinick and Gleason. The software tool was tested using real data sets and the results were analysed in order to make assumption on the indices behaviour. The results showed a clear segregation of indices in two major groups with similar expressivity. |
1504.00906 | Sebastian Schreiber | Sebastian J. Schreiber and Swati Patel | Evolutionarily induced alternative states and coexistence in systems
with apparent competition | 17 pages, 3 figures | Natural Resource Modeling Volume 28, Issue 4 (2015) pgs. 475-496 | 10.1111/nrm.12076 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predators often consume multiple prey and by mutually subsidizing a shared
predator, the prey may reciprocally harm each other. When predation levels are
high, this apparent competition can culminate in a prey species being
displaced. Coupling quantitative genetics and Lotka-Volterra models, we study
how predator evolution alters this and other ecological outcomes. These models
account for a trade-off between the predator's attack rates on two prey
species. We provide a mathematical characterization of a strong form of
persistence--permanence--for which there is a global attractor bounded away
from extinction. When the evolutionary dynamics occur at a sufficiently slower
time scale than the ecological dynamics, we also characterize attractors and
their basins' of attraction using singular perturbation theory and a graphical
approach to the eco-evolutionary dynamics. Our results show that
eco-evolutionary feedbacks can mediate permanence at intermediate trade-offs in
the attack rates. However, at strong trade-offs, permanence is lost. Despite
this loss of permanence, there can be attractors supporting coexistence. These
attractors, however, may coincide with attractors at which the predator is
excluded. Our results highlight that evo-evolutionary feedbacks can alter
community structure by mediating coexistence or leading to trait-dependent
alternative stable states.
| [
{
"created": "Fri, 3 Apr 2015 18:20:36 GMT",
"version": "v1"
}
] | 2019-02-12 | [
[
"Schreiber",
"Sebastian J.",
""
],
[
"Patel",
"Swati",
""
]
] | Predators often consume multiple prey and by mutually subsidizing a shared predator, the prey may reciprocally harm each other. When predation levels are high, this apparent competition can culminate in a prey species being displaced. Coupling quantitative genetics and Lotka-Volterra models, we study how predator evolution alters this and other ecological outcomes. These models account for a trade-off between the predator's attack rates on two prey species. We provide a mathematical characterization of a strong form of persistence--permanence--for which there is a global attractor bounded away from extinction. When the evolutionary dynamics occur at a sufficiently slower time scale than the ecological dynamics, we also characterize attractors and their basins' of attraction using singular perturbation theory and a graphical approach to the eco-evolutionary dynamics. Our results show that eco-evolutionary feedbacks can mediate permanence at intermediate trade-offs in the attack rates. However, at strong trade-offs, permanence is lost. Despite this loss of permanence, there can be attractors supporting coexistence. These attractors, however, may coincide with attractors at which the predator is excluded. Our results highlight that evo-evolutionary feedbacks can alter community structure by mediating coexistence or leading to trait-dependent alternative stable states. |
1710.05745 | Prakash Narayan PhD | Rithika Narayan, Prakash Narayan | Premature Unilateral Ripening in Euonymus alatus: Two Hits Leave(s) a
Red Face | 8 pages, 8 figures and 1 table | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An empirical 2-hit hypothesis is presented to account for premature and
unilateral ripening in Euonymus alatus.
| [
{
"created": "Thu, 12 Oct 2017 18:00:26 GMT",
"version": "v1"
}
] | 2017-10-17 | [
[
"Narayan",
"Rithika",
""
],
[
"Narayan",
"Prakash",
""
]
] | An empirical 2-hit hypothesis is presented to account for premature and unilateral ripening in Euonymus alatus. |
2102.09146 | Dalton Sakthivadivel | Dalton A R Sakthivadivel | Characterising the Non-Equilibrium Dynamics of a Neural Cell | Seven pages and one of references; two figures | null | null | null | q-bio.NC cond-mat.stat-mech math.DS nlin.AO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We examine the dynamical evolution of the state of a neurone, with particular
care to the non-equilibrium nature of the forces influencing its movement in
state space. We combine non-equilibrium statistical mechanics and dynamical
systems theory to characterise the nature of the neural resting state, and its
relationship to firing. The stereotypical shape of the action potential arises
from this model, as well as bursting dynamics, and the non-equilibrium phase
transition from resting to spiking. Geometric properties of the system are
discussed, such as the birth and shape of the neural limit cycle, which provide
a complementary understanding of these dynamics. This provides a multiscale
model of the neural cell, from molecules to spikes, and explains various
phenomena in a unified manner. Some more general notions for damped
oscillators, birth-death processes, and stationary non-equilibrium systems are
included.
| [
{
"created": "Thu, 18 Feb 2021 03:54:16 GMT",
"version": "v1"
}
] | 2021-02-19 | [
[
"Sakthivadivel",
"Dalton A R",
""
]
] | We examine the dynamical evolution of the state of a neurone, with particular care to the non-equilibrium nature of the forces influencing its movement in state space. We combine non-equilibrium statistical mechanics and dynamical systems theory to characterise the nature of the neural resting state, and its relationship to firing. The stereotypical shape of the action potential arises from this model, as well as bursting dynamics, and the non-equilibrium phase transition from resting to spiking. Geometric properties of the system are discussed, such as the birth and shape of the neural limit cycle, which provide a complementary understanding of these dynamics. This provides a multiscale model of the neural cell, from molecules to spikes, and explains various phenomena in a unified manner. Some more general notions for damped oscillators, birth-death processes, and stationary non-equilibrium systems are included. |
1301.5054 | Alexandre Bouchard-C\^ot\'e | Alexandre Bouchard-C\^ot\'e | A Note on Probabilistic Models over Strings: the Linear Algebra Approach | 17 pages, 7 figures | null | null | null | q-bio.PE cs.FL stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic models over strings have played a key role in developing
methods allowing indels to be treated as phylogenetically informative events.
There is an extensive literature on using automata and transducers on
phylogenies to do inference on these probabilistic models, in which an
important theoretical question in the field is the complexity of computing the
normalization of a class of string-valued graphical models. This question has
been investigated using tools from combinatorics, dynamic programming, and
graph theory, and has practical applications in Bayesian phylogenetics. In this
work, we revisit this theoretical question from a different point of view,
based on linear algebra. The main contribution is a new proof of a known result
on the complexity of inference on TKF91, a well-known probabilistic model over
strings. Our proof uses a different approach based on classical linear algebra
results, and is in some cases easier to extend to other models. The proving
method also has consequences on the implementation and complexity of inference
algorithms.
| [
{
"created": "Tue, 22 Jan 2013 01:46:25 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Jul 2013 23:54:48 GMT",
"version": "v2"
}
] | 2013-07-15 | [
[
"Bouchard-Côté",
"Alexandre",
""
]
] | Probabilistic models over strings have played a key role in developing methods allowing indels to be treated as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question in the field is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a new proof of a known result on the complexity of inference on TKF91, a well-known probabilistic model over strings. Our proof uses a different approach based on classical linear algebra results, and is in some cases easier to extend to other models. The proving method also has consequences on the implementation and complexity of inference algorithms. |
1811.07012 | Shashaank Vattikuti | Benjamin P Cohen, Carson C Chow, Shashaank Vattikuti | Multi-scale variability in neuronal competition | 35 pages, 10 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | We examine whether a single biophysical cortical circuit model can explain
both spiking and perceptual variability. We consider perceptual rivalry, which
provides a window into intrinsic neural processing since neural activity in
some brain areas is correlated to the alternating perception rather than the
constant ambiguous stimulus. The prevalent theory for spiking variability is a
chaotic attractor called the balanced state; whereas, the source of perceptual
variability is an open question. We present a dynamical model with a chaotic
attractor that explains both spiking and perceptual variability and adheres to
a broad set of strict experimental constraints. The model makes quantitative
predictions for how both spiking and perceptual variability will change as the
stimulus changes.
| [
{
"created": "Fri, 16 Nov 2018 20:05:41 GMT",
"version": "v1"
}
] | 2018-11-20 | [
[
"Cohen",
"Benjamin P",
""
],
[
"Chow",
"Carson C",
""
],
[
"Vattikuti",
"Shashaank",
""
]
] | We examine whether a single biophysical cortical circuit model can explain both spiking and perceptual variability. We consider perceptual rivalry, which provides a window into intrinsic neural processing since neural activity in some brain areas is correlated to the alternating perception rather than the constant ambiguous stimulus. The prevalent theory for spiking variability is a chaotic attractor called the balanced state; whereas, the source of perceptual variability is an open question. We present a dynamical model with a chaotic attractor that explains both spiking and perceptual variability and adheres to a broad set of strict experimental constraints. The model makes quantitative predictions for how both spiking and perceptual variability will change as the stimulus changes. |
0704.3071 | Karina Mazzitello | K. I. Mazzitello, C. M. Arizmendi, and H. G. E. Hentschel | Converting genetic network oscillations into somite spatial pattern | 7 pages, 7 figures | null | 10.1103/PhysRevE.78.021906 | null | q-bio.QM | null | In most vertebrate species, the body axis is generated by the formation of
repeated transient structures called somites. This spatial periodicity in
somitogenesis has been related to the temporally sustained oscillations in
certain mRNAs and their associated gene products in the cells forming the
presomatic mesoderm. The mechanism underlying these oscillations have been
identified as due to the delays involved in the synthesis of mRNA and
translation into protein molecules [J. Lewis, Current Biol. {\bf 13}, 1398
(2003)]. In addition, in the zebrafish embryo intercellular Notch signalling
couples these oscillators and a longitudinal positional information signal in
the form of an Fgf8 gradient exists that could be used to transform these
coupled temporal oscillations into the observed spatial periodicity of somites.
Here we consider a simple model based on this known biology and study its
consequences for somitogenesis. Comparison is made with the known properties of
somite formation in the zebrafish embryo . We also study the effects of
localized Fgf8 perturbations on somite patterning.
| [
{
"created": "Mon, 23 Apr 2007 22:20:53 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Mazzitello",
"K. I.",
""
],
[
"Arizmendi",
"C. M.",
""
],
[
"Hentschel",
"H. G. E.",
""
]
] | In most vertebrate species, the body axis is generated by the formation of repeated transient structures called somites. This spatial periodicity in somitogenesis has been related to the temporally sustained oscillations in certain mRNAs and their associated gene products in the cells forming the presomatic mesoderm. The mechanism underlying these oscillations have been identified as due to the delays involved in the synthesis of mRNA and translation into protein molecules [J. Lewis, Current Biol. {\bf 13}, 1398 (2003)]. In addition, in the zebrafish embryo intercellular Notch signalling couples these oscillators and a longitudinal positional information signal in the form of an Fgf8 gradient exists that could be used to transform these coupled temporal oscillations into the observed spatial periodicity of somites. Here we consider a simple model based on this known biology and study its consequences for somitogenesis. Comparison is made with the known properties of somite formation in the zebrafish embryo . We also study the effects of localized Fgf8 perturbations on somite patterning. |
1605.06086 | Bartlomiej Waclaw Dr | Rosalind Allen and Bartlomiej Waclaw | Antibiotic resistance: a physicist's view | 7 pages, 1 figure | null | 10.1088/1478-3975/13/4/045001 | null | q-bio.PE q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of antibiotic resistance poses challenges across many
disciplines. One such challenge is to understand the fundamental science of how
antibiotics work, and how resistance to them can emerge. This is an area where
physicists can make important contributions. Here, we highlight cases where
this is already happening, and suggest directions for further physics
involvement in antimicrobial research.
| [
{
"created": "Thu, 19 May 2016 19:22:54 GMT",
"version": "v1"
}
] | 2016-08-24 | [
[
"Allen",
"Rosalind",
""
],
[
"Waclaw",
"Bartlomiej",
""
]
] | The problem of antibiotic resistance poses challenges across many disciplines. One such challenge is to understand the fundamental science of how antibiotics work, and how resistance to them can emerge. This is an area where physicists can make important contributions. Here, we highlight cases where this is already happening, and suggest directions for further physics involvement in antimicrobial research. |
2203.06649 | William Berrios | William Berrios, Arturo Deza | Joint rotational invariance and adversarial training of a dual-stream
Transformer yields state of the art Brain-Score for Area V4 | Under review | null | null | null | q-bio.NC cs.AI cs.CV cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern high-scoring models of vision in the brain score competition do not
stem from Vision Transformers. However, in this paper, we provide evidence
against the unexpected trend of Vision Transformers (ViT) being not
perceptually aligned with human visual representations by showing how a
dual-stream Transformer, a CrossViT$~\textit{a la}$ Chen et al. (2021), under a
joint rotationally-invariant and adversarial optimization procedure yields 2nd
place in the aggregate Brain-Score 2022 competition(Schrimpf et al., 2020b)
averaged across all visual categories, and at the time of the competition held
1st place for the highest explainable variance of area V4. In addition, our
current Transformer-based model also achieves greater explainable variance for
areas V4, IT and Behaviour than a biologically-inspired CNN (ResNet50) that
integrates a frontal V1-like computation module (Dapello et al.,2020). To
assess the contribution of the optimization scheme with respect to the CrossViT
architecture, we perform several additional experiments on differently
optimized CrossViT's regarding adversarial robustness, common corruption
benchmarks, mid-ventral stimuli interpretation and feature inversion. Against
our initial expectations, our family of results provides tentative support for
an $\textit{"All roads lead to Rome"}$ argument enforced via a joint
optimization rule even for non biologically-motivated models of vision such as
Vision Transformers. Code is available at
https://github.com/williamberrios/BrainScore-Transformers
| [
{
"created": "Tue, 8 Mar 2022 23:08:35 GMT",
"version": "v1"
},
{
"created": "Thu, 26 May 2022 21:53:52 GMT",
"version": "v2"
},
{
"created": "Mon, 17 Oct 2022 20:49:10 GMT",
"version": "v3"
}
] | 2022-10-19 | [
[
"Berrios",
"William",
""
],
[
"Deza",
"Arturo",
""
]
] | Modern high-scoring models of vision in the brain score competition do not stem from Vision Transformers. However, in this paper, we provide evidence against the unexpected trend of Vision Transformers (ViT) being not perceptually aligned with human visual representations by showing how a dual-stream Transformer, a CrossViT$~\textit{a la}$ Chen et al. (2021), under a joint rotationally-invariant and adversarial optimization procedure yields 2nd place in the aggregate Brain-Score 2022 competition(Schrimpf et al., 2020b) averaged across all visual categories, and at the time of the competition held 1st place for the highest explainable variance of area V4. In addition, our current Transformer-based model also achieves greater explainable variance for areas V4, IT and Behaviour than a biologically-inspired CNN (ResNet50) that integrates a frontal V1-like computation module (Dapello et al.,2020). To assess the contribution of the optimization scheme with respect to the CrossViT architecture, we perform several additional experiments on differently optimized CrossViT's regarding adversarial robustness, common corruption benchmarks, mid-ventral stimuli interpretation and feature inversion. Against our initial expectations, our family of results provides tentative support for an $\textit{"All roads lead to Rome"}$ argument enforced via a joint optimization rule even for non biologically-motivated models of vision such as Vision Transformers. Code is available at https://github.com/williamberrios/BrainScore-Transformers |
2209.02592 | Gerardo Ortiz | L. J. Fosque, A. Alipour, M. Zare, R. V. Williams-Garcia, J. M. Beggs
and G. Ortiz | Quasicriticality explains variability of human neural dynamics across
life span | 23 pages, 15 figures | null | null | null | q-bio.NC cond-mat.soft nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ageing impacts the brain's structural and functional organization and over
time leads to various disorders, such as Alzheimer's disease and cognitive
impairment. The process also impacts sensory function, bringing about a general
slowing in various perceptual and cognitive functions. Here, we analyze the
Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state
magnetoencephalography (MEG) dataset -- the largest ageing cohort available --
in light of the quasicriticality framework, a novel organizing principle for
brain functionality which relates information processing and scaling properties
of brain activity to brain connectivity and stimulus. Examination of the data
using this framework reveals interesting correlations with age and gender of
test subjects. Using simulated data as verification, our results suggest a link
between changes to brain connectivity due to ageing, and increased
vulnerability to distraction from irrelevant information. Our findings suggest
a platform to develop biomarkers of neurological health.
| [
{
"created": "Tue, 6 Sep 2022 15:47:12 GMT",
"version": "v1"
}
] | 2022-09-07 | [
[
"Fosque",
"L. J.",
""
],
[
"Alipour",
"A.",
""
],
[
"Zare",
"M.",
""
],
[
"Williams-Garcia",
"R. V.",
""
],
[
"Beggs",
"J. M.",
""
],
[
"Ortiz",
"G.",
""
]
] | Ageing impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset -- the largest ageing cohort available -- in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to ageing, and increased vulnerability to distraction from irrelevant information. Our findings suggest a platform to develop biomarkers of neurological health. |
2108.13752 | Mark Leake | Jack W Shepherd, Mark C Leake | The End Restraint Method for Mechanically Perturbing Nucleic Acids in
silico | null | null | null | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Far from being a passive information store, the genome is a mechanically
dynamic and diverse system in which torsion and tension fluctuate and combine
to determine structure and help regulate gene expression. Much of this
mechanical perturbation is due to molecular machines such as topoisomerases
which must stretch and twist DNA as part of various functions including DNA
repair and replication. While the broad-scale mechanical response of nucleic
acids to tension and torsion is well characterized, detail at the single base
pair level is beyond the limits of even super-resolution imaging. Here, we
present a straightforward, flexible, and extensible umbrella-sampling protocol
to twist and stretch nucleic acids in silico using the popular biomolecular
simulation package Amber -- though the principles we describe are applicable
also to other packages such as GROMACS. We discuss how to set up the simulation
system, decide forcefields and solvation models, and equilibrate. We then
introduce the torsionally-constrained stretching protocol, and finally we
present some analysis techniques we have used to characterize structural motif
formation. Rather than define forces or fictional pseudoatoms, we instead
define a fixed translation of specified atoms between each umbrella sampling
step, which allows comparison with experiment without needing to estimate
applied forces by simply using the fractional end-to-end displacement as a
comparison metric. We hope that this easy to implement solution will be
valuable for interrogating optical and magnetic tweezers data on nucleic acids
at base pair resolution.
| [
{
"created": "Tue, 31 Aug 2021 11:08:25 GMT",
"version": "v1"
}
] | 2021-09-01 | [
[
"Shepherd",
"Jack W",
""
],
[
"Leake",
"Mark C",
""
]
] | Far from being a passive information store, the genome is a mechanically dynamic and diverse system in which torsion and tension fluctuate and combine to determine structure and help regulate gene expression. Much of this mechanical perturbation is due to molecular machines such as topoisomerases which must stretch and twist DNA as part of various functions including DNA repair and replication. While the broad-scale mechanical response of nucleic acids to tension and torsion is well characterized, detail at the single base pair level is beyond the limits of even super-resolution imaging. Here, we present a straightforward, flexible, and extensible umbrella-sampling protocol to twist and stretch nucleic acids in silico using the popular biomolecular simulation package Amber -- though the principles we describe are applicable also to other packages such as GROMACS. We discuss how to set up the simulation system, decide forcefields and solvation models, and equilibrate. We then introduce the torsionally-constrained stretching protocol, and finally we present some analysis techniques we have used to characterize structural motif formation. Rather than define forces or fictional pseudoatoms, we instead define a fixed translation of specified atoms between each umbrella sampling step, which allows comparison with experiment without needing to estimate applied forces by simply using the fractional end-to-end displacement as a comparison metric. We hope that this easy to implement solution will be valuable for interrogating optical and magnetic tweezers data on nucleic acids at base pair resolution. |
1902.04166 | Hakime \"Ozt\"urk | Hakime \"Ozt\"urk, Elif Ozkirimli, Arzucan \"Ozg\"ur | WideDTA: prediction of drug-target binding affinity | null | null | null | null | q-bio.QM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Prediction of the interaction affinity between proteins and
compounds is a major challenge in the drug discovery process. WideDTA is a
deep-learning based prediction model that employs chemical and biological
textual sequence information to predict binding affinity.
Results: WideDTA uses four text-based information sources, namely the protein
sequence, ligand SMILES, protein domains and motifs, and maximum common
substructure words to predict binding affinity. WideDTA outperformed one of the
state of the art deep learning methods for drug-target binding affinity
prediction, DeepDTA on the KIBA dataset with a statistical significance. This
indicates that the word-based sequence representation adapted by WideDTA is a
promising alternative to the character-based sequence representation approach
in deep learning models for binding affinity prediction, such as the one used
in DeepDTA. In addition, the results showed that, given the protein sequence
and ligand SMILES, the inclusion of protein domain and motif information as
well as ligand maximum common substructure words do not provide additional
useful information for the deep learning model. Interestingly, however, using
only domain and motif information to represent proteins achieved similar
performance to using the full protein sequence, suggesting that important
binding relevant information is contained within the protein motifs and
domains.
| [
{
"created": "Mon, 4 Feb 2019 08:24:41 GMT",
"version": "v1"
}
] | 2019-02-13 | [
[
"Öztürk",
"Hakime",
""
],
[
"Ozkirimli",
"Elif",
""
],
[
"Özgür",
"Arzucan",
""
]
] | Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity. Results: WideDTA uses four text-based information sources, namely the protein sequence, ligand SMILES, protein domains and motifs, and maximum common substructure words to predict binding affinity. WideDTA outperformed one of the state of the art deep learning methods for drug-target binding affinity prediction, DeepDTA on the KIBA dataset with a statistical significance. This indicates that the word-based sequence representation adapted by WideDTA is a promising alternative to the character-based sequence representation approach in deep learning models for binding affinity prediction, such as the one used in DeepDTA. In addition, the results showed that, given the protein sequence and ligand SMILES, the inclusion of protein domain and motif information as well as ligand maximum common substructure words do not provide additional useful information for the deep learning model. Interestingly, however, using only domain and motif information to represent proteins achieved similar performance to using the full protein sequence, suggesting that important binding relevant information is contained within the protein motifs and domains. |
1409.0906 | Brian Moore | Brian R. Moore, Jim McGuire, Fredrik Ronquist, and John P. Huelsenbeck | Bayesian Analysis of Partitioned Data | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Variation in the evolutionary process across the sites of nucleotide sequence
alignments is well established, and is an increasingly pervasive feature of
datasets composed of gene regions sampled from multiple loci and/or different
genomes. Inference of phylogeny from these data demands that we adequately
model the underlying process heterogeneity; failure to do so can lead to biased
estimates of phylogeny and other parameters. Traditionally, process
heterogeneity has been accommodated by first assigning sites to data subsets
based on relevant prior information (reflecting codon positions in
protein-coding DNA, stem and loop regions of ribosomal DNA, etc.), and then
estimating the phylogeny and other model parameters under the resulting mixed
model. Here, we consider an alternative approach for accommodating process
heterogeneity that is similar in spirit to this conventional mixed-model
approach. However, rather than treating the partitioning scheme as a fixed
assumption of the analysis, we treat the process partition as a random variable
using a Dirichlet process prior model, where the phylogeny is estimated by
integrating over all possible process partitions for the specified data
subsets. We apply this method to simulated and empirical datasets, and compare
our results to those estimated previously using conventional mixed-model
selection criteria based on Bayes factors. We find that estimation under the
Dirichlet process prior model discovers novel process partitions that may more
effectively balance error variance and estimation bias, while rendering
phylogenetic inference more robust to process heterogeneity by virtue of
integrating estimates over all possible partition schemes. (Keywords: Bayesian
phylogenetic inference; Dirichlet process prior; Markov chain Monte Carlo;
maximum likelihood; partitioned analyses; process heterogeneity.)
| [
{
"created": "Tue, 2 Sep 2014 22:27:41 GMT",
"version": "v1"
}
] | 2014-09-04 | [
[
"Moore",
"Brian R.",
""
],
[
"McGuire",
"Jim",
""
],
[
"Ronquist",
"Fredrik",
""
],
[
"Huelsenbeck",
"John P.",
""
]
] | Variation in the evolutionary process across the sites of nucleotide sequence alignments is well established, and is an increasingly pervasive feature of datasets composed of gene regions sampled from multiple loci and/or different genomes. Inference of phylogeny from these data demands that we adequately model the underlying process heterogeneity; failure to do so can lead to biased estimates of phylogeny and other parameters. Traditionally, process heterogeneity has been accommodated by first assigning sites to data subsets based on relevant prior information (reflecting codon positions in protein-coding DNA, stem and loop regions of ribosomal DNA, etc.), and then estimating the phylogeny and other model parameters under the resulting mixed model. Here, we consider an alternative approach for accommodating process heterogeneity that is similar in spirit to this conventional mixed-model approach. However, rather than treating the partitioning scheme as a fixed assumption of the analysis, we treat the process partition as a random variable using a Dirichlet process prior model, where the phylogeny is estimated by integrating over all possible process partitions for the specified data subsets. We apply this method to simulated and empirical datasets, and compare our results to those estimated previously using conventional mixed-model selection criteria based on Bayes factors. We find that estimation under the Dirichlet process prior model discovers novel process partitions that may more effectively balance error variance and estimation bias, while rendering phylogenetic inference more robust to process heterogeneity by virtue of integrating estimates over all possible partition schemes. (Keywords: Bayesian phylogenetic inference; Dirichlet process prior; Markov chain Monte Carlo; maximum likelihood; partitioned analyses; process heterogeneity.) |
2112.00507 | Seungho Choe | Seungho Choe | Free energy analyses of cell-penetrating peptides using the weighted
ensemble method | 9 pages, 6 figures (w/ Suppl. Info 5 pages, 3 figures) | Membranes 11, 974 (2021) | null | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell-penetrating peptides (CPPs) have been widely used for drug-delivery
agents; however, it has not been fully understood how they translocate across
cell membranes. The Weighted Ensemble (WE) method, one of powerful and flexible
path sampling techniques, can be helpful to reveal translocation paths and free
energy barriers along those paths. Within the WE approach we show how Arg9
(nona-arginine) and Tat interact with a DOPC/DOPG (4:1) model membrane, and we
present free energy (or potential mean of forces, PMFs) profiles of
penetration, although a translocation across the membrane has not been observed
in the current simulations. Two different compositions of lipid molecules were
also tried and compared. Our approach can be applied to any CPPs interacting
with various model membranes, and it will provide useful information regarding
the transport mechanisms of CPPs.
| [
{
"created": "Wed, 1 Dec 2021 14:05:11 GMT",
"version": "v1"
}
] | 2021-12-20 | [
[
"Choe",
"Seungho",
""
]
] | Cell-penetrating peptides (CPPs) have been widely used for drug-delivery agents; however, it has not been fully understood how they translocate across cell membranes. The Weighted Ensemble (WE) method, one of powerful and flexible path sampling techniques, can be helpful to reveal translocation paths and free energy barriers along those paths. Within the WE approach we show how Arg9 (nona-arginine) and Tat interact with a DOPC/DOPG (4:1) model membrane, and we present free energy (or potential mean of forces, PMFs) profiles of penetration, although a translocation across the membrane has not been observed in the current simulations. Two different compositions of lipid molecules were also tried and compared. Our approach can be applied to any CPPs interacting with various model membranes, and it will provide useful information regarding the transport mechanisms of CPPs. |
2407.06833 | Min Xu | Yizhou Zhao, Hengwei Bian, Michael Mu, Mostofa R. Uddin, Zhenyang Li,
Xiang Li, Tianyang Wang, Min Xu | Training-free CryoET Tomogram Segmentation | This preprint has not undergone peer review or any post-submission
improvements or corrections. The Version of Record of this contribution will
be published in MICCAI 2024 | null | null | null | q-bio.QM cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | Cryogenic Electron Tomography (CryoET) is a useful imaging technology in
structural biology that is hindered by its need for manual annotations,
especially in particle picking. Recent works have endeavored to remedy this
issue with few-shot learning or contrastive learning techniques. However,
supervised training is still inevitable for them. We instead choose to leverage
the power of existing 2D foundation models and present a novel, training-free
framework, CryoSAM. In addition to prompt-based single-particle instance
segmentation, our approach can automatically search for similar features,
facilitating full tomogram semantic segmentation with only one prompt. CryoSAM
is composed of two major parts: 1) a prompt-based 3D segmentation system that
uses prompts to complete single-particle instance segmentation recursively with
Cross-Plane Self-Prompting, and 2) a Hierarchical Feature Matching mechanism
that efficiently matches relevant features with extracted tomogram features.
They collaborate to enable the segmentation of all particles of one category
with just one particle-specific prompt. Our experiments show that CryoSAM
outperforms existing works by a significant margin and requires even fewer
annotations in particle picking. Further visualizations demonstrate its ability
when dealing with full tomogram segmentation for various subcellular
structures. Our code is available at: https://github.com/xulabs/aitom
| [
{
"created": "Mon, 8 Jul 2024 02:51:41 GMT",
"version": "v1"
}
] | 2024-07-10 | [
[
"Zhao",
"Yizhou",
""
],
[
"Bian",
"Hengwei",
""
],
[
"Mu",
"Michael",
""
],
[
"Uddin",
"Mostofa R.",
""
],
[
"Li",
"Zhenyang",
""
],
[
"Li",
"Xiang",
""
],
[
"Wang",
"Tianyang",
""
],
[
"Xu",
"Min",
""
]
] | Cryogenic Electron Tomography (CryoET) is a useful imaging technology in structural biology that is hindered by its need for manual annotations, especially in particle picking. Recent works have endeavored to remedy this issue with few-shot learning or contrastive learning techniques. However, supervised training is still inevitable for them. We instead choose to leverage the power of existing 2D foundation models and present a novel, training-free framework, CryoSAM. In addition to prompt-based single-particle instance segmentation, our approach can automatically search for similar features, facilitating full tomogram semantic segmentation with only one prompt. CryoSAM is composed of two major parts: 1) a prompt-based 3D segmentation system that uses prompts to complete single-particle instance segmentation recursively with Cross-Plane Self-Prompting, and 2) a Hierarchical Feature Matching mechanism that efficiently matches relevant features with extracted tomogram features. They collaborate to enable the segmentation of all particles of one category with just one particle-specific prompt. Our experiments show that CryoSAM outperforms existing works by a significant margin and requires even fewer annotations in particle picking. Further visualizations demonstrate its ability when dealing with full tomogram segmentation for various subcellular structures. Our code is available at: https://github.com/xulabs/aitom |
2406.01880 | Sarah Lawson Ms | Sarah Lawson, Diane Donovan, James Lefevre | An application of node and edge nonlinear hypergraph centrality to a
protein complex hypernetwork | 14 pages, 7 figures | null | null | null | q-bio.QM math.CO | http://creativecommons.org/licenses/by/4.0/ | The use of graph centrality measures applied to biological networks, such as
protein interaction networks, underpins much research into identifying key
players within biological processes. This approach however is restricted to
dyadic interactions and it is well-known that in many instances interactions
are polyadic. In this study we illustrate the merit of using hypergraph
centrality applied to a hypernetwork as an alternative. Specifically, we review
and propose an extension to a recently introduced node and edge nonlinear
hypergraph centrality model which provides mutually dependent node and edge
centralities. A Saccharomyces Cerevisiae protein complex hypernetwork is used
as an example application with nodes representing proteins and hyperedges
representing protein complexes. The resulting rankings of the nodes and edges
are considered to see if they provide insight into the essentiality of the
proteins and complexes. We find that certain variations of the model predict
essentiality more accurately and that the degree-based variation illustrates
that the centrality-lethality rule extends to a hypergraph setting. In
particular, through exploitation of the models flexibility, we identify small
sets of proteins densely populated with essential proteins.
| [
{
"created": "Tue, 4 Jun 2024 01:26:30 GMT",
"version": "v1"
}
] | 2024-06-05 | [
[
"Lawson",
"Sarah",
""
],
[
"Donovan",
"Diane",
""
],
[
"Lefevre",
"James",
""
]
] | The use of graph centrality measures applied to biological networks, such as protein interaction networks, underpins much research into identifying key players within biological processes. This approach however is restricted to dyadic interactions and it is well-known that in many instances interactions are polyadic. In this study we illustrate the merit of using hypergraph centrality applied to a hypernetwork as an alternative. Specifically, we review and propose an extension to a recently introduced node and edge nonlinear hypergraph centrality model which provides mutually dependent node and edge centralities. A Saccharomyces Cerevisiae protein complex hypernetwork is used as an example application with nodes representing proteins and hyperedges representing protein complexes. The resulting rankings of the nodes and edges are considered to see if they provide insight into the essentiality of the proteins and complexes. We find that certain variations of the model predict essentiality more accurately and that the degree-based variation illustrates that the centrality-lethality rule extends to a hypergraph setting. In particular, through exploitation of the models flexibility, we identify small sets of proteins densely populated with essential proteins. |
2109.06525 | Giacomo Cacciapaglia | Giacomo Cacciapaglia, Stefan Hohenegger, Francesco Sannino | Effective Mathematical Modelling of Health Passes during a Pandemic | 40 pages, 39 figures | null | null | LYCEN 2021-04 | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | We study the impact on the epidemiological dynamics of a class of restrictive
measures that are aimed at reducing the number of contacts of individuals who
have a higher risk of being infected with a transmittable disease. Such
measures are currently either implemented or at least discussed in numerous
countries worldwide to ward off a potential new wave of COVID-19 across Europe.
They come in the form of Health Passes (HP), which grant full access to public
life only to individuals with a certificate that proves that they have either
been fully vaccinated, have recovered from a previous infection or have
recently tested negative to SARS-Cov-19 . We develop both a compartmental model
as well as an epidemic Renormalisation Group approach, which is capable of
describing the dynamics over a longer period of time, notably an entire
epidemiological wave. Introducing different versions of HPs in this model, we
are capable of providing quantitative estimates on the effectiveness of the
underlying measures as a function of the fraction of the population that is
vaccinated and the vaccination rate. We apply our models to the latest COVID-19
wave in several European countries, notably Germany and Austria, which validate
our theoretical findings.
| [
{
"created": "Tue, 14 Sep 2021 08:44:53 GMT",
"version": "v1"
}
] | 2021-09-15 | [
[
"Cacciapaglia",
"Giacomo",
""
],
[
"Hohenegger",
"Stefan",
""
],
[
"Sannino",
"Francesco",
""
]
] | We study the impact on the epidemiological dynamics of a class of restrictive measures that are aimed at reducing the number of contacts of individuals who have a higher risk of being infected with a transmittable disease. Such measures are currently either implemented or at least discussed in numerous countries worldwide to ward off a potential new wave of COVID-19 across Europe. They come in the form of Health Passes (HP), which grant full access to public life only to individuals with a certificate that proves that they have either been fully vaccinated, have recovered from a previous infection or have recently tested negative to SARS-Cov-19 . We develop both a compartmental model as well as an epidemic Renormalisation Group approach, which is capable of describing the dynamics over a longer period of time, notably an entire epidemiological wave. Introducing different versions of HPs in this model, we are capable of providing quantitative estimates on the effectiveness of the underlying measures as a function of the fraction of the population that is vaccinated and the vaccination rate. We apply our models to the latest COVID-19 wave in several European countries, notably Germany and Austria, which validate our theoretical findings. |
1703.03127 | Yasser A. Ahmed | Y. A. Ahmed, L. Tatarczuch, A. El-Hafez, A. E. Zayed, H. M. Davies and
E. J. Mackie | Are Paralysed Chondrocytes Really Dying? | 7 pages, 4 figures | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aims of the current study were to establish a system of culture for
induction of paralysed chondrocytes and to investigate if these cells are
really dying. Chondrocytes were isolated from the growth cartilage of fetal
equines, centrifuged and cultured as pellets in either 10% fetal calf serum or
10% horse serum for 28 days and processed for light and electron microscopy.
Different cell types were counted and expressed as a percentage to the total
cell number. Growth kinetics including the pellet weight and thickness and the
cellular density were evaluated. After 7 days in culture, paralysed
chondrocytes with similar morphology to those described in the rabbit growth
cartilage could be identified in pellets in each serum type, however, the
proportion of the cells was different. In pellet cultured with 10% fetal calf
serum, more than 50% of the cells were paralysed chondrocytes but in 10% horse
serum, less than 10% of cells were of paralysed type. At day 14, about 50% of
the cells in pellets cultured in either serum type differentiated into
hypertrophic dark chondrocytes and the proportion of paralysed cells was
markedly decreased. After 21 days in each culture, more than 70% of the cells
were hypertrophic dark chondrocytes and no paralysed chondrocytes could be
observed. The paralysed chondrocytes may be not dying and they likely to be an
immature form of hypertrophic dark chondrocytes. It is better to use the term
immature dark chondrocytes instead of paralysed cells. This culture system will
be useful for further molecular studies on paralysed chondrocytes and to
explore the functions of these cells.
| [
{
"created": "Thu, 9 Mar 2017 04:28:34 GMT",
"version": "v1"
}
] | 2017-03-10 | [
[
"Ahmed",
"Y. A.",
""
],
[
"Tatarczuch",
"L.",
""
],
[
"El-Hafez",
"A.",
""
],
[
"Zayed",
"A. E.",
""
],
[
"Davies",
"H. M.",
""
],
[
"Mackie",
"E. J.",
""
]
] | The aims of the current study were to establish a system of culture for induction of paralysed chondrocytes and to investigate if these cells are really dying. Chondrocytes were isolated from the growth cartilage of fetal equines, centrifuged and cultured as pellets in either 10% fetal calf serum or 10% horse serum for 28 days and processed for light and electron microscopy. Different cell types were counted and expressed as a percentage to the total cell number. Growth kinetics including the pellet weight and thickness and the cellular density were evaluated. After 7 days in culture, paralysed chondrocytes with similar morphology to those described in the rabbit growth cartilage could be identified in pellets in each serum type, however, the proportion of the cells was different. In pellet cultured with 10% fetal calf serum, more than 50% of the cells were paralysed chondrocytes but in 10% horse serum, less than 10% of cells were of paralysed type. At day 14, about 50% of the cells in pellets cultured in either serum type differentiated into hypertrophic dark chondrocytes and the proportion of paralysed cells was markedly decreased. After 21 days in each culture, more than 70% of the cells were hypertrophic dark chondrocytes and no paralysed chondrocytes could be observed. The paralysed chondrocytes may be not dying and they likely to be an immature form of hypertrophic dark chondrocytes. It is better to use the term immature dark chondrocytes instead of paralysed cells. This culture system will be useful for further molecular studies on paralysed chondrocytes and to explore the functions of these cells. |
1208.2250 | Iain Johnston | Iain G. Johnston | The chaos within: exploring noise in cellular biology | 5 pages, 4 figures | Significance 9 (4) 17 2012 | 10.1111/j.1740-9713.2012.00586.x | null | q-bio.CB q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cellular biology exists embedded in a world dominated by random dynamics and
chance. Many vital molecules and pieces of cellular machinery diffuse within
cells, moving along random trajectories as they collide with the other
biomolecular inhabitants of the cell. Cellular components may block each
other's progress, be produced or degraded at random times, and become unevenly
separated as cells grow and divide. Cellular behaviour, including important
features of stem cells, tumours and infectious bacteria, is profoundly
influenced by the chaos which is the environment within the cell walls. Here we
will look at some important causes and effects of randomness in cellular
biology, and some ways in which researchers, helped by the vast amounts of data
that are now flowing in, have made progress in describing the randomness of
nature.
| [
{
"created": "Fri, 10 Aug 2012 19:01:38 GMT",
"version": "v1"
}
] | 2012-08-13 | [
[
"Johnston",
"Iain G.",
""
]
] | Cellular biology exists embedded in a world dominated by random dynamics and chance. Many vital molecules and pieces of cellular machinery diffuse within cells, moving along random trajectories as they collide with the other biomolecular inhabitants of the cell. Cellular components may block each other's progress, be produced or degraded at random times, and become unevenly separated as cells grow and divide. Cellular behaviour, including important features of stem cells, tumours and infectious bacteria, is profoundly influenced by the chaos which is the environment within the cell walls. Here we will look at some important causes and effects of randomness in cellular biology, and some ways in which researchers, helped by the vast amounts of data that are now flowing in, have made progress in describing the randomness of nature. |
1505.05179 | Xiao-Jun Tian | Xiao-Jun Tian, Hang Zhang, Jens Sannerud, and Jianhua Xing | Achieving Diverse and Monoallelic Olfactory Receptor Selection Through
Dual-Objective Optimization Design | 10 pages, 6 figures, Proceedings of the National Academy of Sciences
2016 | null | 10.1073/pnas.1601722113 | null | q-bio.MN q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Multiple-objective optimization is common in biological systems. In the
mammalian olfactory system, each sensory neuron stochastically expresses only
one out of up to thousands of olfactory receptor (OR) gene alleles; at organism
level the types of expressed ORs need to be maximized. Existing models focus
only on monoallele activation, and cannot explain recent observations in
mutants, especially the reduced global diversity of expressed ORs in G9a/GLP
knockouts. In this work we integrated existing information on OR expression,
and constructed a comprehensive model that has all its components based on
physical interactions. Analyzing the model reveals an evolutionarily optimized
three-layer regulation mechanism, which includes zonal segregation, epigenetic
barrier crossing coupled to a negative feedback loop that mechanistically
differs from previous theoretical proposals, and a previously unidentified
enhancer competition step. This model not only recapitulates monoallelic OR
expression, but also elucidates how the olfactory system maximizes and
maintains the diversity of OR expression, and has multiple predictions
validated by existing experimental results. Through making analogy to a
physical system with thermally activated barrier crossing and comparative
reverse engineering analyses, the study reveals that the olfactory receptor
selection system is optimally designed, and particularly underscores
cooperativity and synergy as a general design principle for multi-objective
optimization in biology.
| [
{
"created": "Tue, 19 May 2015 20:41:43 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Jun 2015 18:49:41 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Jun 2015 19:40:42 GMT",
"version": "v3"
},
{
"created": "Sat, 16 Jan 2016 14:08:04 GMT",
"version": "v4"
},
{
"created": "Mon, 9 May 2016 20:41:02 GMT",
"version": "v5"
}
] | 2016-05-11 | [
[
"Tian",
"Xiao-Jun",
""
],
[
"Zhang",
"Hang",
""
],
[
"Sannerud",
"Jens",
""
],
[
"Xing",
"Jianhua",
""
]
] | Multiple-objective optimization is common in biological systems. In the mammalian olfactory system, each sensory neuron stochastically expresses only one out of up to thousands of olfactory receptor (OR) gene alleles; at organism level the types of expressed ORs need to be maximized. Existing models focus only on monoallele activation, and cannot explain recent observations in mutants, especially the reduced global diversity of expressed ORs in G9a/GLP knockouts. In this work we integrated existing information on OR expression, and constructed a comprehensive model that has all its components based on physical interactions. Analyzing the model reveals an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a previously unidentified enhancer competition step. This model not only recapitulates monoallelic OR expression, but also elucidates how the olfactory system maximizes and maintains the diversity of OR expression, and has multiple predictions validated by existing experimental results. Through making analogy to a physical system with thermally activated barrier crossing and comparative reverse engineering analyses, the study reveals that the olfactory receptor selection system is optimally designed, and particularly underscores cooperativity and synergy as a general design principle for multi-objective optimization in biology. |
1103.2070 | Dante Chialvo | Enzo Tagliazucchi, Dante R. Chialvo | The collective brain is critical | null | null | null | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The unique dynamical features of the critical state endow the brain with
properties which are fundamental for adaptive behavior. This proposal, put
forward with Per Bak several years ago, is now supported by a wide body of
empirical evidence at different scales demonstrating that the spatiotemporal
brain dynamics exhibits key signatures of critical dynamics previously
recognized in other complex systems. The rationale behind this program is
discussed in these notes, followed by an account of the most recent results,
together with a discussion of the physiological significance of these ideas.
| [
{
"created": "Thu, 10 Mar 2011 16:23:58 GMT",
"version": "v1"
}
] | 2011-03-15 | [
[
"Tagliazucchi",
"Enzo",
""
],
[
"Chialvo",
"Dante R.",
""
]
] | The unique dynamical features of the critical state endow the brain with properties which are fundamental for adaptive behavior. This proposal, put forward with Per Bak several years ago, is now supported by a wide body of empirical evidence at different scales demonstrating that the spatiotemporal brain dynamics exhibits key signatures of critical dynamics previously recognized in other complex systems. The rationale behind this program is discussed in these notes, followed by an account of the most recent results, together with a discussion of the physiological significance of these ideas. |
1807.08852 | Anne-Florence Bitbol | Anne-Florence Bitbol | Inferring interaction partners from protein sequences using mutual
information | 26 pages, 11 figures, published version | PLoS Comput. Biol. 14(11): e1006401 (2018) | 10.1371/journal.pcbi.1006401 | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Functional protein-protein interactions are crucial in most cellular
processes. They enable multi-protein complexes to assemble and to remain
stable, and they allow signal transduction in various pathways. Functional
interactions between proteins result in coevolution between the interacting
partners, and thus in correlations between their sequences. Pairwise
maximum-entropy based models have enabled successful inference of pairs of
amino-acid residues that are in contact in the three-dimensional structure of
multi-protein complexes, starting from the correlations in the sequence data of
known interaction partners. Recently, algorithms inspired by these methods have
been developed to identify which proteins are functional interaction partners
among the paralogous proteins of two families, starting from sequence data
alone. Here, we demonstrate that a slightly higher performance for partner
identification can be reached by an approximate maximization of the mutual
information between the sequence alignments of the two protein families. Our
mutual information-based method also provides signatures of the existence of
interactions between protein families. These results stand in contrast with
structure prediction of proteins and of multi-protein complexes from sequence
data, where pairwise maximum-entropy based global statistical models
substantially improve performance compared to mutual information. Our findings
entail that the statistical dependences allowing interaction partner prediction
from sequence data are not restricted to the residue pairs that are in direct
contact at the interface between the partner proteins.
| [
{
"created": "Mon, 23 Jul 2018 22:33:17 GMT",
"version": "v1"
},
{
"created": "Sat, 27 Oct 2018 21:54:24 GMT",
"version": "v2"
},
{
"created": "Tue, 13 Nov 2018 14:36:35 GMT",
"version": "v3"
}
] | 2018-11-14 | [
[
"Bitbol",
"Anne-Florence",
""
]
] | Functional protein-protein interactions are crucial in most cellular processes. They enable multi-protein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are functional interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. Our mutual information-based method also provides signatures of the existence of interactions between protein families. These results stand in contrast with structure prediction of proteins and of multi-protein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins. |
2203.14296 | Rohan Kalahasty Mr | Rohan Kalahasty, Lakshmi Sritan Motati | StrokeSight: A Novel EEG-Based Diagnostic System for Strokes Using
Spectral Analysis and Deep Learning | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | A stroke is defined as a neurologic deficit arising from an interruption in
blood supply to the brain. According to the World Health Organization, over 15
million people suffer from strokes annually, of which almost 70% die or are
permanently disabled. Effective treatment must be administered within one hour
to prevent irreversible brain damage. Unfortunately, the current gold standards
for diagnosis, CT and MRI, are time-consuming, expensive, and immobile.
Electroencephalograms reveal biomarkers of strokes while being inexpensive and
available for remote use, but no system exists that utilizes them for this
purpose. To address this issue, we created StrokeSight, a novel, open-source
web application that automatically provides a full diagnosis and visualization
of ischemic and hemorrhagic strokes in under 50 seconds using 60-second
electroencephalograms. We first calculated the averaged power spectral
densities for 132, 60-second electroencephalogram readings, which we then used
to train three deep neural networks that respectively predict a stroke type
(control/ischemic/hemorrhagic), location (left/right hemisphere), and severity
(small/large) with accuracies of 97.5%, 94.4%, and 100%. StrokeSight also
implements a novel process to visualize spectral abnormalities caused by
strokes. Azimuthal equidistant projection and multivariate spline interpolation
are used to reshape 3D electrodes onto a head-shaped 2D plane and then a
contour map of each frequency band power is created, allowing neurologists to
quickly and accurately interpret electroencephalogram data. StrokeSight could
act as a revolutionary solution for stroke care that drastically improves the
speed, cost efficiency, and accessibility of stroke diagnosis while allowing
for personalized treatment and interpretation.
| [
{
"created": "Sun, 27 Mar 2022 13:09:10 GMT",
"version": "v1"
}
] | 2022-03-29 | [
[
"Kalahasty",
"Rohan",
""
],
[
"Motati",
"Lakshmi Sritan",
""
]
] | A stroke is defined as a neurologic deficit arising from an interruption in blood supply to the brain. According to the World Health Organization, over 15 million people suffer from strokes annually, of which almost 70% die or are permanently disabled. Effective treatment must be administered within one hour to prevent irreversible brain damage. Unfortunately, the current gold standards for diagnosis, CT and MRI, are time-consuming, expensive, and immobile. Electroencephalograms reveal biomarkers of strokes while being inexpensive and available for remote use, but no system exists that utilizes them for this purpose. To address this issue, we created StrokeSight, a novel, open-source web application that automatically provides a full diagnosis and visualization of ischemic and hemorrhagic strokes in under 50 seconds using 60-second electroencephalograms. We first calculated the averaged power spectral densities for 132, 60-second electroencephalogram readings, which we then used to train three deep neural networks that respectively predict a stroke type (control/ischemic/hemorrhagic), location (left/right hemisphere), and severity (small/large) with accuracies of 97.5%, 94.4%, and 100%. StrokeSight also implements a novel process to visualize spectral abnormalities caused by strokes. Azimuthal equidistant projection and multivariate spline interpolation are used to reshape 3D electrodes onto a head-shaped 2D plane and then a contour map of each frequency band power is created, allowing neurologists to quickly and accurately interpret electroencephalogram data. StrokeSight could act as a revolutionary solution for stroke care that drastically improves the speed, cost efficiency, and accessibility of stroke diagnosis while allowing for personalized treatment and interpretation. |
2307.06393 | Guilhem Doulcier | Guilhem Doulcier, Amaury Lambert | Neutral Diversity in Experimental Metapopulations | 51 pages, 25 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-sa/4.0/ | New automated and high-throughput methods allow the manipulation and
selection of numerous bacterial populations. In this manuscript we are
interested in the neutral diversity patterns that emerge from such a setup in
which many bacterial populations are grown in parallel serial transfers, in
some cases with population-wide extinction and splitting events. We model
bacterial growth by a birth-death process and use the theory of coalescent
point processes. We show that there is a dilution factor that optimises the
expected amount of neutral diversity for a given amount of cycles, and study
the power law behaviour of the mutation frequency spectrum for different
experimental regimes. We also explore how neutral variation diverges between
two recently split populations by establishing a new formula for the expected
number of shared and private mutations. Finally, we show the interest of such a
setup to select a phenotype of interest that requires multiple mutations.
| [
{
"created": "Wed, 12 Jul 2023 18:23:46 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Mar 2024 05:40:29 GMT",
"version": "v2"
}
] | 2024-03-13 | [
[
"Doulcier",
"Guilhem",
""
],
[
"Lambert",
"Amaury",
""
]
] | New automated and high-throughput methods allow the manipulation and selection of numerous bacterial populations. In this manuscript we are interested in the neutral diversity patterns that emerge from such a setup in which many bacterial populations are grown in parallel serial transfers, in some cases with population-wide extinction and splitting events. We model bacterial growth by a birth-death process and use the theory of coalescent point processes. We show that there is a dilution factor that optimises the expected amount of neutral diversity for a given amount of cycles, and study the power law behaviour of the mutation frequency spectrum for different experimental regimes. We also explore how neutral variation diverges between two recently split populations by establishing a new formula for the expected number of shared and private mutations. Finally, we show the interest of such a setup to select a phenotype of interest that requires multiple mutations. |
0911.0188 | Kaushik Majumdar | Kaushik Majumdar | Differential Operator in Seizure Detection | 15 pages, 3 figures, two tables. Submitted to Computers in Biology
and Medicine (Elsevier) | null | null | null | q-bio.QM physics.med-ph q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differential operators can detect significant changes in signals. This has
been utilized to enhance the contrast of the seizure signatures in depth EEG or
ECoG. We have actually taken normalized exponential of absolute value of single
or double derivative of epileptic ECoG. Variance operation has been performed
to automatically detect seizures. A novel method for determining the duration
of seizure has also been proposed. Since all operations take only linear time,
the whole method is extremely fast. Seven novel parameters have been introduced
whose patient specific thresholding brings down the rate of false detection to
a bare minimum. Results of implementation on the ECoG data of four epileptic
patients have been reported with an ROC curve analysis. High value of the area
under the ROC curve indicates excellent detection performance.
| [
{
"created": "Sun, 1 Nov 2009 18:47:05 GMT",
"version": "v1"
}
] | 2009-11-03 | [
[
"Majumdar",
"Kaushik",
""
]
] | Differential operators can detect significant changes in signals. This has been utilized to enhance the contrast of the seizure signatures in depth EEG or ECoG. We have actually taken normalized exponential of absolute value of single or double derivative of epileptic ECoG. Variance operation has been performed to automatically detect seizures. A novel method for determining the duration of seizure has also been proposed. Since all operations take only linear time, the whole method is extremely fast. Seven novel parameters have been introduced whose patient specific thresholding brings down the rate of false detection to a bare minimum. Results of implementation on the ECoG data of four epileptic patients have been reported with an ROC curve analysis. High value of the area under the ROC curve indicates excellent detection performance. |
2304.05199 | Peter Taylor | Thomas W. Owen, Vytene Janiukstyte, Gerard R. Hall, Fahmida A.
Chowdhury, Beate Diehl, Andrew McEvoy, Anna Miserocchi, Jane de Tisi, John S.
Duncan, Fergus Rugg-Gunn, Yujiang Wang, Peter N. Taylor | Interictal MEG abnormalities to guide intracranial electrode
implantation and predict surgical outcome | 22 pages, 6 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Intracranial EEG (iEEG) is the gold standard technique for epileptogenic zone
(EZ) localisation, but requires a hypothesis of which tissue is epileptogenic,
guided by qualitative analysis of seizure semiology and other imaging
modalities such as magnetoencephalography (MEG). We hypothesised that if
quantifiable MEG band power abnormalities were sampled by iEEG, then patients'
post-resection seizure outcome were better.
Thirty-two individuals with neocortical epilepsy underwent MEG and iEEG
recordings as part of pre-surgical evaluation. Interictal MEG band power
abnormalities were derived using 70 healthy controls as a normative baseline.
MEG abnormality maps were compared to electrode implantation, with the spatial
overlap of iEEG electrodes and MEG abnormalities recorded. Finally, we assessed
if the implantation of electrodes in abnormal tissue, and resection of the
strongest abnormalities determined by MEG and iEEG explained surgical outcome.
Intracranial electrodes were implanted in brain tissue with the most abnormal
MEG findings in individuals that were seizure-free post-resection (T=3.9,
p=0.003). The overlap between MEG abnormalities and iEEG electrodes
distinguished outcome groups moderately well (AUC=0.68). In isolation, the
resection of the strongest MEG and iEEG abnormalities separated surgical
outcome groups well (AUC=0.71, AUC=0.74 respectively). A model incorporating
all three features separated outcome groups best (AUC=0.80).
Intracranial EEG is a key tool to delineate the EZ and help render patients
seizure-free after resection. We showed that data-driven abnormalities derived
from interictal MEG recordings have clinical value and may help guide electrode
placement in individuals with neocortical epilepsy. Finally, our predictive
model of post-operative seizure-freedom, which leverages both MEG and iEEG
recordings, may aid patient counselling of expected outcome.
| [
{
"created": "Tue, 11 Apr 2023 13:09:00 GMT",
"version": "v1"
}
] | 2023-04-12 | [
[
"Owen",
"Thomas W.",
""
],
[
"Janiukstyte",
"Vytene",
""
],
[
"Hall",
"Gerard R.",
""
],
[
"Chowdhury",
"Fahmida A.",
""
],
[
"Diehl",
"Beate",
""
],
[
"McEvoy",
"Andrew",
""
],
[
"Miserocchi",
"Anna",
""
],
[
"de Tisi",
"Jane",
""
],
[
"Duncan",
"John S.",
""
],
[
"Rugg-Gunn",
"Fergus",
""
],
[
"Wang",
"Yujiang",
""
],
[
"Taylor",
"Peter N.",
""
]
] | Intracranial EEG (iEEG) is the gold standard technique for epileptogenic zone (EZ) localisation, but requires a hypothesis of which tissue is epileptogenic, guided by qualitative analysis of seizure semiology and other imaging modalities such as magnetoencephalography (MEG). We hypothesised that if quantifiable MEG band power abnormalities were sampled by iEEG, then patients' post-resection seizure outcome were better. Thirty-two individuals with neocortical epilepsy underwent MEG and iEEG recordings as part of pre-surgical evaluation. Interictal MEG band power abnormalities were derived using 70 healthy controls as a normative baseline. MEG abnormality maps were compared to electrode implantation, with the spatial overlap of iEEG electrodes and MEG abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue, and resection of the strongest abnormalities determined by MEG and iEEG explained surgical outcome. Intracranial electrodes were implanted in brain tissue with the most abnormal MEG findings in individuals that were seizure-free post-resection (T=3.9, p=0.003). The overlap between MEG abnormalities and iEEG electrodes distinguished outcome groups moderately well (AUC=0.68). In isolation, the resection of the strongest MEG and iEEG abnormalities separated surgical outcome groups well (AUC=0.71, AUC=0.74 respectively). A model incorporating all three features separated outcome groups best (AUC=0.80). Intracranial EEG is a key tool to delineate the EZ and help render patients seizure-free after resection. We showed that data-driven abnormalities derived from interictal MEG recordings have clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Finally, our predictive model of post-operative seizure-freedom, which leverages both MEG and iEEG recordings, may aid patient counselling of expected outcome. |
1901.05652 | Angelique Stephanou | Ang\'elique St\'ephanou (TIMC-IMAG-DyCTiM), Pascal Ballet, Gibin
Powathil | Hybrid Modelling in Oncology: Sucesses, Challenges and Hopes | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this review we make the statement that hybrid models in oncology are
required as a mean for enhanced data integration. In the context of systems
oncology, experimental and clinical data need to be at the heart of the models
developments from conception to validation to ensure a relevant use of the
models in the clinical context. The main applications pursued are to improve
diagnosis and to optimize therapies.We first present the Successes achieved
thanks to hybrid modelling approaches to advance knowledge, treatments or drug
discovery. Then we present the Challenges than need to be addressed to allow
for a better integration of the model parts and of the data into the models.
And Finally, the Hopes with a focus towards making personalised medicine a
reality. Mathematics Subject Classification. 35Q92, 68U20, 68T05, 92-08, 92B05.
| [
{
"created": "Thu, 17 Jan 2019 07:14:37 GMT",
"version": "v1"
}
] | 2019-01-18 | [
[
"Stéphanou",
"Angélique",
"",
"TIMC-IMAG-DyCTiM"
],
[
"Ballet",
"Pascal",
""
],
[
"Powathil",
"Gibin",
""
]
] | In this review we make the statement that hybrid models in oncology are required as a mean for enhanced data integration. In the context of systems oncology, experimental and clinical data need to be at the heart of the models developments from conception to validation to ensure a relevant use of the models in the clinical context. The main applications pursued are to improve diagnosis and to optimize therapies.We first present the Successes achieved thanks to hybrid modelling approaches to advance knowledge, treatments or drug discovery. Then we present the Challenges than need to be addressed to allow for a better integration of the model parts and of the data into the models. And Finally, the Hopes with a focus towards making personalised medicine a reality. Mathematics Subject Classification. 35Q92, 68U20, 68T05, 92-08, 92B05. |
2204.10286 | Hanwen Xu | Hanwen Xu and Sheng Wang | ProTranslator: zero-shot protein function prediction using textual
description | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Accurately finding proteins and genes that have a certain function is the
prerequisite for a broad range of biomedical applications. Despite the
encouraging progress of existing computational approaches in protein function
prediction, it remains challenging to annotate proteins to a novel function
that is not collected in the Gene Ontology and does not have any annotated
proteins. This limitation, a side effect from the widely-used multi-label
classification problem setting of protein function prediction, hampers the
progress of studying new pathways and biological processes, and further slows
down research in various biomedical areas. Here, we tackle this problem by
annotating proteins to a function only based on its textual description so that
we do not need to know any associated proteins for this function. The key idea
of our method ProTranslator is to redefine protein function prediction as a
machine translation problem, which translates the description word sequence of
a function to the amino acid sequence of a protein. We can then transfer
annotations from functions that have similar textual description to annotate a
novel function. We observed substantial improvement in annotating novel
functions and sparsely annotated functions on CAFA3, SwissProt and GOA
datasets. We further demonstrated how our method accurately predicted gene
members for a given pathway in Reactome, KEGG and MSigDB only based on the
pathway description. Finally, we showed how ProTranslator enabled us to
generate the textual description instead of the function label for a set of
proteins, providing a new scheme for protein function prediction. We envision
ProTranslator will give rise to a protein function "search engine" that returns
a list of proteins based on the free text queried by the user.
| [
{
"created": "Wed, 20 Apr 2022 03:55:32 GMT",
"version": "v1"
}
] | 2022-04-22 | [
[
"Xu",
"Hanwen",
""
],
[
"Wang",
"Sheng",
""
]
] | Accurately finding proteins and genes that have a certain function is the prerequisite for a broad range of biomedical applications. Despite the encouraging progress of existing computational approaches in protein function prediction, it remains challenging to annotate proteins to a novel function that is not collected in the Gene Ontology and does not have any annotated proteins. This limitation, a side effect from the widely-used multi-label classification problem setting of protein function prediction, hampers the progress of studying new pathways and biological processes, and further slows down research in various biomedical areas. Here, we tackle this problem by annotating proteins to a function only based on its textual description so that we do not need to know any associated proteins for this function. The key idea of our method ProTranslator is to redefine protein function prediction as a machine translation problem, which translates the description word sequence of a function to the amino acid sequence of a protein. We can then transfer annotations from functions that have similar textual description to annotate a novel function. We observed substantial improvement in annotating novel functions and sparsely annotated functions on CAFA3, SwissProt and GOA datasets. We further demonstrated how our method accurately predicted gene members for a given pathway in Reactome, KEGG and MSigDB only based on the pathway description. Finally, we showed how ProTranslator enabled us to generate the textual description instead of the function label for a set of proteins, providing a new scheme for protein function prediction. We envision ProTranslator will give rise to a protein function "search engine" that returns a list of proteins based on the free text queried by the user. |
0908.1515 | Kanury Rao | Virendra K. Chaudhri, Dhiraj Kumar, Manjari Misra, Raina Dua and
Kanury V.S. Rao | Integration of a Phosphatase Cascade with the MAP Kinase Pathway
provides for a Novel Signal Processing Function | Whole Manuscript 33 pages inclduing Main text, 7 Figures and
Supporting Information | J Biol Chem 285,(2), 2010 | 10.1074/jbc.M109.055863 | null | q-bio.MN q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We mathematically modeled the receptor-activated MAP kinase signaling by
incorporating the regulation through cellular phosphatases. Activation induced
the alignment of a phosphatase cascade in parallel with the MAP kinase pathway.
A novel regulatory motif was thus generated, providing for the combinatorial
control of each MAPK intermediate. This ensured a non-linear mode of signal
transmission with the output being shaped by the balance between the strength
of input signal, and the activity gradient along the phosphatase axis. Shifts
in this balance yielded modulations in topology of the motif, thereby expanding
the repertoire of output responses. Thus we identify an added dimension to
signal processing, wherein the output response to an external stimulus is
additionally filtered through indicators that define the phenotypic status of
the cell.
| [
{
"created": "Tue, 11 Aug 2009 15:34:41 GMT",
"version": "v1"
}
] | 2010-01-20 | [
[
"Chaudhri",
"Virendra K.",
""
],
[
"Kumar",
"Dhiraj",
""
],
[
"Misra",
"Manjari",
""
],
[
"Dua",
"Raina",
""
],
[
"Rao",
"Kanury V. S.",
""
]
] | We mathematically modeled the receptor-activated MAP kinase signaling by incorporating the regulation through cellular phosphatases. Activation induced the alignment of a phosphatase cascade in parallel with the MAP kinase pathway. A novel regulatory motif was thus generated, providing for the combinatorial control of each MAPK intermediate. This ensured a non-linear mode of signal transmission with the output being shaped by the balance between the strength of input signal, and the activity gradient along the phosphatase axis. Shifts in this balance yielded modulations in topology of the motif, thereby expanding the repertoire of output responses. Thus we identify an added dimension to signal processing, wherein the output response to an external stimulus is additionally filtered through indicators that define the phenotypic status of the cell. |
2405.00810 | Herbert Sauro Dr | Herbert M Sauro | A Simple Comparison of Biochemical Systems Theory and Metabolic Control
Analysis | null | null | null | null | q-bio.MN q-bio.QM q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | This paper explores some basic concepts of Biochemical Systems Theory (BST)
and Metabolic Control Analysis (MCA), two frameworks developed to understand
the behavior of biochemical networks. Initially introduced by Savageau, BST
focuses on system stability and employs power laws in modeling biochemical
systems. On the other hand, MCA, pioneered by authors such as Kacser and Burns
and Heinrich and Rapoport, emphasizes linearization of the governing equations
and describes relationships (known as theorems) between different measures.
Despite apparent differences, both frameworks are shown to be equivalent in
many respects. Through a simple example of a linear chain, the paper
demonstrates how BST and MCA yield identical results when analyzing
steady-state behavior and logarithmic gains within biochemical pathways. This
comparative analysis highlights the interchangeability of concepts such as
kinetic orders, elasticities and other logarithmic gains.
| [
{
"created": "Wed, 1 May 2024 18:50:18 GMT",
"version": "v1"
},
{
"created": "Fri, 3 May 2024 15:42:03 GMT",
"version": "v2"
}
] | 2024-05-06 | [
[
"Sauro",
"Herbert M",
""
]
] | This paper explores some basic concepts of Biochemical Systems Theory (BST) and Metabolic Control Analysis (MCA), two frameworks developed to understand the behavior of biochemical networks. Initially introduced by Savageau, BST focuses on system stability and employs power laws in modeling biochemical systems. On the other hand, MCA, pioneered by authors such as Kacser and Burns and Heinrich and Rapoport, emphasizes linearization of the governing equations and describes relationships (known as theorems) between different measures. Despite apparent differences, both frameworks are shown to be equivalent in many respects. Through a simple example of a linear chain, the paper demonstrates how BST and MCA yield identical results when analyzing steady-state behavior and logarithmic gains within biochemical pathways. This comparative analysis highlights the interchangeability of concepts such as kinetic orders, elasticities and other logarithmic gains. |
1811.03620 | Andrey Chuhutin | Andrey Chuhutin, Brian Hansen, Agnieszka Wlodarczyk, Trevor Owens,
Noam Shemesh, Sune N{\o}rh{\o}j Jespersen | Diffusion Kurtosis Imaging maps neural damage in the EAE model of
multiple sclerosis | 40 pages, 6 figures, 4 tables | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diffusion kurtosis imaging (DKI), is an imaging modality that yields novel
disease biomarkers and in combination with nervous tissue modeling, provides
access to microstructural parameters. Recently, DKI and subsequent estimation
of microstructural model parameters has been used for assessment of tissue
changes in neurodegenerative diseases and their animal models. In this study,
mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE)
model of multiple sclerosis (MS) were investigated for the first time using DKI
in combination with biophysical modeling to study the relationship between
microstructural metrics and degree of animal dysfunction. Thirteen spinal cords
were extracted from animals of variable disability and scanned in a high-field
MRI scanner along with five control specimen. Diffusion weighted data were
acquired together with high resolution T2* images. Diffusion data were fit to
estimate diffusion and kurtosis tensors and white matter modeling parameters,
which were all used for subsequent statistical analysis using a linear mixed
effects model. T2* images were used to delineate focal
demyelination/inflammation. Our results unveil a strong relationship between
disability and measured microstructural parameters in normal appearing white
matter and gray matter. The changes we found in biophysical modeling parameters
and in particular in extra-axonal axial diffusivity were clearly different from
previous studies employing other animal models of MS. In conclusion, our data
suggest that DKI and microstructural modeling can provide a unique contrast
capable of detecting EAE-specific changes correlating with clinical disability.
These findings could close the gap between MRI findings and clinical
presentation in patients and deepen our understanding of EAE and the MS
mechanisms.
| [
{
"created": "Wed, 7 Nov 2018 11:06:09 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Apr 2019 08:43:16 GMT",
"version": "v2"
}
] | 2019-04-09 | [
[
"Chuhutin",
"Andrey",
""
],
[
"Hansen",
"Brian",
""
],
[
"Wlodarczyk",
"Agnieszka",
""
],
[
"Owens",
"Trevor",
""
],
[
"Shemesh",
"Noam",
""
],
[
"Jespersen",
"Sune Nørhøj",
""
]
] | Diffusion kurtosis imaging (DKI), is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and their animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals of variable disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T2* images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T2* images were used to delineate focal demyelination/inflammation. Our results unveil a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. The changes we found in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability. These findings could close the gap between MRI findings and clinical presentation in patients and deepen our understanding of EAE and the MS mechanisms. |
1611.09544 | Dmitri Voronine | Siyu He, Hongyuan Li, Zhe He, and Dmitri V. Voronine | Tip-enhanced Raman spectroscopic detection of aptamers | null | null | null | null | q-bio.BM physics.bio-ph physics.optics | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single molecule detection, sequencing and conformational mapping of aptamers
are important for improving medical and biosensing technologies and for better
understanding of biological processes at the molecular level. We obtain
vibrational signals of single aptamers immobilized on gold substrates using
tip-enhanced Raman spectroscopy (TERS). We compare topographic and optical
signals and investigate the fluctuations of the position-dependent TERS
spectra. TERS mapping provides information about the chemical composition and
conformation of aptamers, and paves the way to future single-molecule
label-free sequencing.
| [
{
"created": "Tue, 29 Nov 2016 10:00:25 GMT",
"version": "v1"
}
] | 2016-12-05 | [
[
"He",
"Siyu",
""
],
[
"Li",
"Hongyuan",
""
],
[
"He",
"Zhe",
""
],
[
"Voronine",
"Dmitri V.",
""
]
] | Single molecule detection, sequencing and conformational mapping of aptamers are important for improving medical and biosensing technologies and for better understanding of biological processes at the molecular level. We obtain vibrational signals of single aptamers immobilized on gold substrates using tip-enhanced Raman spectroscopy (TERS). We compare topographic and optical signals and investigate the fluctuations of the position-dependent TERS spectra. TERS mapping provides information about the chemical composition and conformation of aptamers, and paves the way to future single-molecule label-free sequencing. |
2407.00847 | Leonid Rubchinsky | Quynh-Anh Nguyen, Leonid L Rubchinsky | Synaptic effects on the intermittent synchronization of gamma rhythms | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synchronization of neural activity in the gamma frequency band is associated
with various cognitive phenomena. Abnormalities of gamma synchronization may
underlie symptoms of several neurological and psychiatric disorders such as
schizophrenia and autism spectrum disorder. Properties of neural oscillations
in the gamma band depend critically on the synaptic properties of the
underlying circuits. This study explores how synaptic properties in
pyramidal-interneuronal circuits affect not only the average synchronization
strength but also the fine temporal patterning of neural synchrony. If two
signals show only moderate synchrony strength, it may be possible to consider
these dynamics as alternating between synchronized and desynchronized states.
We use a model of connected circuits that produces pyramidal-interneuronal
gamma (PING) oscillations to explore the temporal patterning of synchronized
and desynchronized intervals. Changes in synaptic strength may alter the
temporal patterning of synchronized dynamics (even if the average synchrony
strength is not changed). Larger values of local synaptic connections promote
longer desynchronization durations, while larger values of long-range synaptic
connections promote shorter desynchronization durations. Furthermore, we show
that circuits with different temporal patterning of synchronization may have
different sensitivity to synaptic input. Thus, the alterations of synaptic
strength may mediate physiological properties of neural circuits not only
through change in the average synchrony level of gamma oscillations, but also
through change in how synchrony is patterned in time over very short time
scales.
| [
{
"created": "Sun, 30 Jun 2024 22:52:39 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Nguyen",
"Quynh-Anh",
""
],
[
"Rubchinsky",
"Leonid L",
""
]
] | Synchronization of neural activity in the gamma frequency band is associated with various cognitive phenomena. Abnormalities of gamma synchronization may underlie symptoms of several neurological and psychiatric disorders such as schizophrenia and autism spectrum disorder. Properties of neural oscillations in the gamma band depend critically on the synaptic properties of the underlying circuits. This study explores how synaptic properties in pyramidal-interneuronal circuits affect not only the average synchronization strength but also the fine temporal patterning of neural synchrony. If two signals show only moderate synchrony strength, it may be possible to consider these dynamics as alternating between synchronized and desynchronized states. We use a model of connected circuits that produces pyramidal-interneuronal gamma (PING) oscillations to explore the temporal patterning of synchronized and desynchronized intervals. Changes in synaptic strength may alter the temporal patterning of synchronized dynamics (even if the average synchrony strength is not changed). Larger values of local synaptic connections promote longer desynchronization durations, while larger values of long-range synaptic connections promote shorter desynchronization durations. Furthermore, we show that circuits with different temporal patterning of synchronization may have different sensitivity to synaptic input. Thus, the alterations of synaptic strength may mediate physiological properties of neural circuits not only through change in the average synchrony level of gamma oscillations, but also through change in how synchrony is patterned in time over very short time scales. |
1308.5037 | Liane Gabora | Liane Gabora | Revenge of the 'Neurds': Characterizing Creative Thought in terms of the
Structure and Dynamics of Memory | 25 pages including 3 figures. arXiv admin note: substantial text
overlap with arXiv:1106.3600 | Creativity Research Journal, 22(1), 1-13 (2010) | 10.1080/10400410903579494 | null | q-bio.NC nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is cognitive, neurological, and computational support for the
hypothesis that defocusing attention results in divergent or associative
thought, conducive to insight and finding unusual connections, while focusing
attention results in convergent or analytic thought, conducive to rule-based
operations. Creativity appears to involve both. It is widely believed that it
is possible to escape mental fixation by spontaneously and temporarily engaging
in a more associative mode of thought. The resulting insight (if found) may be
refined in a more analytic mode of thought. The questions addressed here are:
(1) how does the architecture of memory support these two modes of thought, and
(2) what is happening at the neural level when one shifts between them? Recent
advances in neuroscience shed light on this. Activated cell assemblies are
composed of multiple neural cliques, groups of neurons that respond
differentially to general or context-specific aspects of a situation. I refer
to neural cliques that would not be included in the assembly if one were in an
analytic mode, but would be if one were in an associative mode, as neurds. It
is posited that the shift to a more associative mode of thought is accomplished
by recruiting neurds that respond to abstract or atypical microfeatures of the
problem or task. Since memory is distributed and content-addressable, this
fosters the forging of associations to potentially relevant items previously
encoded in those neurons. Thus it is proposed that creative thought not by
searching a space of predefined alternatives and blindly tweaking those that
hold promise, but by evoking remotely associated items through the recruitment
of neurds in a distributed, content-addressable memory.
| [
{
"created": "Fri, 23 Aug 2013 03:32:15 GMT",
"version": "v1"
},
{
"created": "Sun, 30 Jun 2019 02:10:08 GMT",
"version": "v2"
}
] | 2019-07-02 | [
[
"Gabora",
"Liane",
""
]
] | There is cognitive, neurological, and computational support for the hypothesis that defocusing attention results in divergent or associative thought, conducive to insight and finding unusual connections, while focusing attention results in convergent or analytic thought, conducive to rule-based operations. Creativity appears to involve both. It is widely believed that it is possible to escape mental fixation by spontaneously and temporarily engaging in a more associative mode of thought. The resulting insight (if found) may be refined in a more analytic mode of thought. The questions addressed here are: (1) how does the architecture of memory support these two modes of thought, and (2) what is happening at the neural level when one shifts between them? Recent advances in neuroscience shed light on this. Activated cell assemblies are composed of multiple neural cliques, groups of neurons that respond differentially to general or context-specific aspects of a situation. I refer to neural cliques that would not be included in the assembly if one were in an analytic mode, but would be if one were in an associative mode, as neurds. It is posited that the shift to a more associative mode of thought is accomplished by recruiting neurds that respond to abstract or atypical microfeatures of the problem or task. Since memory is distributed and content-addressable, this fosters the forging of associations to potentially relevant items previously encoded in those neurons. Thus it is proposed that creative thought not by searching a space of predefined alternatives and blindly tweaking those that hold promise, but by evoking remotely associated items through the recruitment of neurds in a distributed, content-addressable memory. |
1111.4643 | Marcus Aguiar de | Elizabeth M. Baptestini, Marcus A.M. de Aguiar, Yaneer Bar-Yam | The role of sex separation in neutral speciation | 18 pages + 8 figures | J. Theor. Ecol. (2012) | 10.1007/s12080-012-0172-2 | null | q-bio.PE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neutral speciation mechanisms based on isolation by distance and sexual
selection, termed topopatric, have recently been shown to describe the observed
patterns of abundance distributions and species-area relationships. Previous
works have considered this type of process only in the context of hermaphrodic
populations. In this work we extend a hermaphroditic model of topopatric
speciation to populations where individuals are explicitly separated into males
and females. We show that for a particular carrying capacity speciation occurs
under similar conditions, but the number of species generated decreases as
compared to the hermaphroditic case. Evolution results in fewer species having
more abundant populations.
| [
{
"created": "Sun, 20 Nov 2011 16:08:59 GMT",
"version": "v1"
}
] | 2012-10-24 | [
[
"Baptestini",
"Elizabeth M.",
""
],
[
"de Aguiar",
"Marcus A. M.",
""
],
[
"Bar-Yam",
"Yaneer",
""
]
] | Neutral speciation mechanisms based on isolation by distance and sexual selection, termed topopatric, have recently been shown to describe the observed patterns of abundance distributions and species-area relationships. Previous works have considered this type of process only in the context of hermaphrodic populations. In this work we extend a hermaphroditic model of topopatric speciation to populations where individuals are explicitly separated into males and females. We show that for a particular carrying capacity speciation occurs under similar conditions, but the number of species generated decreases as compared to the hermaphroditic case. Evolution results in fewer species having more abundant populations. |
2011.05136 | Ankit Singhal | Ankit Singhal | Predicting Hydroxyl Mediated Nucleophilic Degradation and Molecular
Stability of RNA Sequences through the Application of Deep Learning Methods | 12 pages, 13 figures - Updated Third Version after Review | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synthesis and efficient implementation mRNA strands has been shown to have
wide utility, especially recently in the development of COVID vaccines.
However, the intrinsic chemical stability of mRNA poses a challenge due to the
presence of 2'-hydroxyl groups in ribose sugars. The -OH group in the backbone
structure enables a base-catalyzed nucleophilic attack by the deprotonated
hydroxyl on the adjacent phosphorous and consequent self-hydrolysis of the
phosphodiester bond. As expected for in-line hydrolytic cleavage reactions, the
chemical stability of mRNA strands is highly dependent on external
environmental factors, e.g. pH, temperature, oxidizers, etc. Predicting this
chemical instability using a computational model will reduce the number of
sequences synthesized and tested through identifying the most promising
candidates, aiding the development of mRNA related therapies. This paper
proposes and evaluates three deep learning models (Long Short Term Memory,
Gated Recurrent Unit, and Graph Convolutional Networks) as methods to predict
the reactivity and risk of degradation of mRNA sequences. The Stanford Open
Vaccine dataset of 6034 mRNA sequences was used in this study. The training set
consisted of 3029 of these sequences (length of 107 nucleotide bases) while the
testing dataset consisted of 3005 sequences (length of 130 nucleotide bases),
in structured (Lowest Entropy Base Pair Probability Matrix) and unstructured
(Nodes and Edges) forms. The stability of mRNA strands was accurately
generated, with the Graph Convolutional Network being the best predictor of
reactivity ($RMSE = 0.249$) while the Gated Recurrent Unit Network was the best
at predicting risks of degradation ($RMSE = 0.266$). Combining all target
variables, the GRU performed the best with 76% accuracy. Results suggest these
models can be applied to understand and predict the chemical stability of mRNA
in the near future.
| [
{
"created": "Mon, 9 Nov 2020 10:42:53 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Apr 2021 14:55:55 GMT",
"version": "v2"
},
{
"created": "Sun, 26 Sep 2021 16:42:24 GMT",
"version": "v3"
}
] | 2021-09-28 | [
[
"Singhal",
"Ankit",
""
]
] | Synthesis and efficient implementation mRNA strands has been shown to have wide utility, especially recently in the development of COVID vaccines. However, the intrinsic chemical stability of mRNA poses a challenge due to the presence of 2'-hydroxyl groups in ribose sugars. The -OH group in the backbone structure enables a base-catalyzed nucleophilic attack by the deprotonated hydroxyl on the adjacent phosphorous and consequent self-hydrolysis of the phosphodiester bond. As expected for in-line hydrolytic cleavage reactions, the chemical stability of mRNA strands is highly dependent on external environmental factors, e.g. pH, temperature, oxidizers, etc. Predicting this chemical instability using a computational model will reduce the number of sequences synthesized and tested through identifying the most promising candidates, aiding the development of mRNA related therapies. This paper proposes and evaluates three deep learning models (Long Short Term Memory, Gated Recurrent Unit, and Graph Convolutional Networks) as methods to predict the reactivity and risk of degradation of mRNA sequences. The Stanford Open Vaccine dataset of 6034 mRNA sequences was used in this study. The training set consisted of 3029 of these sequences (length of 107 nucleotide bases) while the testing dataset consisted of 3005 sequences (length of 130 nucleotide bases), in structured (Lowest Entropy Base Pair Probability Matrix) and unstructured (Nodes and Edges) forms. The stability of mRNA strands was accurately generated, with the Graph Convolutional Network being the best predictor of reactivity ($RMSE = 0.249$) while the Gated Recurrent Unit Network was the best at predicting risks of degradation ($RMSE = 0.266$). Combining all target variables, the GRU performed the best with 76% accuracy. Results suggest these models can be applied to understand and predict the chemical stability of mRNA in the near future. |
1610.09406 | Ramu Anandakrishnan | Ramu Anandakrishnan and Daniel M. Zuckerman | Biophysical comparison of ATP-driven proton pumping mechanisms suggests
a kinetic advantage for the rotary process depending on coupling ratio | null | null | 10.1371/journal.pone.0173500 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ATP-driven proton pumps, which are critical to the operation of a cell,
maintain cytosolic and organellar pH levels within a narrow functional range.
These pumps employ two very different mechanisms: an elaborate rotary mechanism
used by V-ATPase H+ pumps, and a simpler alternating access mechanism used by
P-ATPase H+ pumps. Why are two different mechanisms used to perform the same
function? Systematic analysis, without parameter fitting, of kinetic models of
the rotary, alternating access and other possible mechanisms suggest that, when
the ratio of protons transported per ATP hydrolyzed exceeds one, the
one-at-a-time proton transport by the rotary mechanism is faster than other
possible mechanisms across a wide range of driving conditions. When the ratio
is one, there is no intrinsic difference in the free energy landscape between
mechanisms, and therefore all mechanisms can exhibit the same kinetic
performance. To our knowledge all known rotary pumps have an H+:ATP ratio
greater than one, and all known alternating access ATP-driven proton pumps have
a ratio of one. Our analysis suggests a possible explanation for this apparent
relationship between coupling ratio and mechanism. When the conditions under
which the pump must operate permit a coupling ratio greater than one, the
rotary mechanism may have been selected for its kinetic advantage. On the other
hand, when conditions require a coupling ratio of one or less, the alternating
access mechanism may have been selected for other possible advantages resulting
from its structural and functional simplicity.
| [
{
"created": "Fri, 28 Oct 2016 21:23:17 GMT",
"version": "v1"
}
] | 2017-11-01 | [
[
"Anandakrishnan",
"Ramu",
""
],
[
"Zuckerman",
"Daniel M.",
""
]
] | ATP-driven proton pumps, which are critical to the operation of a cell, maintain cytosolic and organellar pH levels within a narrow functional range. These pumps employ two very different mechanisms: an elaborate rotary mechanism used by V-ATPase H+ pumps, and a simpler alternating access mechanism used by P-ATPase H+ pumps. Why are two different mechanisms used to perform the same function? Systematic analysis, without parameter fitting, of kinetic models of the rotary, alternating access and other possible mechanisms suggest that, when the ratio of protons transported per ATP hydrolyzed exceeds one, the one-at-a-time proton transport by the rotary mechanism is faster than other possible mechanisms across a wide range of driving conditions. When the ratio is one, there is no intrinsic difference in the free energy landscape between mechanisms, and therefore all mechanisms can exhibit the same kinetic performance. To our knowledge all known rotary pumps have an H+:ATP ratio greater than one, and all known alternating access ATP-driven proton pumps have a ratio of one. Our analysis suggests a possible explanation for this apparent relationship between coupling ratio and mechanism. When the conditions under which the pump must operate permit a coupling ratio greater than one, the rotary mechanism may have been selected for its kinetic advantage. On the other hand, when conditions require a coupling ratio of one or less, the alternating access mechanism may have been selected for other possible advantages resulting from its structural and functional simplicity. |
2003.05350 | Takashi Nozoe | Takashi Nozoe and Edo Kussell | Cell cycle heritability and localization phase transition in growing
populations | null | Phys. Rev. Lett. 125, 268103 (2020) | 10.1103/PhysRevLett.125.268103 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The cell cycle duration is a variable cellular phenotype that underlies
long-term population growth and age structures. By analyzing the stationary
solutions of a branching process with heritable cell division times, we
demonstrate existence of a phase transition, which can be continuous or
first-order, by which a non-zero fraction of the population becomes localized
at a minimal division time. Just below the transition, we demonstrate
coexistence of localized and delocalized age-structure phases, and power law
decay of correlation functions. Above it, we observe self-synchronization of
cell cycles, collective divisions, and slow 'aging' of population growth rates.
| [
{
"created": "Wed, 11 Mar 2020 15:11:47 GMT",
"version": "v1"
}
] | 2021-01-04 | [
[
"Nozoe",
"Takashi",
""
],
[
"Kussell",
"Edo",
""
]
] | The cell cycle duration is a variable cellular phenotype that underlies long-term population growth and age structures. By analyzing the stationary solutions of a branching process with heritable cell division times, we demonstrate existence of a phase transition, which can be continuous or first-order, by which a non-zero fraction of the population becomes localized at a minimal division time. Just below the transition, we demonstrate coexistence of localized and delocalized age-structure phases, and power law decay of correlation functions. Above it, we observe self-synchronization of cell cycles, collective divisions, and slow 'aging' of population growth rates. |
q-bio/0612025 | Sungho Hong | Sungho Hong (University of Washington), Blaise Aguera y Arcas
(Princeton), and Adrienne L. Fairhall (University of Washington) | Single neuron computation: from dynamical system to feature detector | 33 pages. LaTeX + 12 figures. Submitted to Neural Computation | null | null | null | q-bio.NC physics.bio-ph physics.data-an | null | White noise methods are a powerful tool for characterizing the computation
performed by neural systems. These methods allow one to identify the feature or
features that a neural system extracts from a complex input, and to determine
how these features are combined to drive the system's spiking response. These
methods have also been applied to characterize the input/output relations of
single neurons driven by synaptic inputs, simulated by direct current
injection. To interpret the results of white noise analysis of single neurons,
we would like to understand how the obtained feature space of a single neuron
maps onto the biophysical properties of the membrane, in particular the
dynamics of ion channels. Here, through analysis of a simple dynamical model
neuron, we draw explicit connections between the output of a white noise
analysis and the underlying dynamical system. We find that under certain
assumptions, the form of the relevant features is well defined by the
parameters of the dynamical system. Further, we show that under some
conditions, the feature space is spanned by the spike-triggered average and its
successive order time derivatives.
| [
{
"created": "Wed, 13 Dec 2006 22:24:31 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Hong",
"Sungho",
"",
"University of Washington"
],
[
"Arcas",
"Blaise Aguera y",
"",
"Princeton"
],
[
"Fairhall",
"Adrienne L.",
"",
"University of Washington"
]
] | White noise methods are a powerful tool for characterizing the computation performed by neural systems. These methods allow one to identify the feature or features that a neural system extracts from a complex input, and to determine how these features are combined to drive the system's spiking response. These methods have also been applied to characterize the input/output relations of single neurons driven by synaptic inputs, simulated by direct current injection. To interpret the results of white noise analysis of single neurons, we would like to understand how the obtained feature space of a single neuron maps onto the biophysical properties of the membrane, in particular the dynamics of ion channels. Here, through analysis of a simple dynamical model neuron, we draw explicit connections between the output of a white noise analysis and the underlying dynamical system. We find that under certain assumptions, the form of the relevant features is well defined by the parameters of the dynamical system. Further, we show that under some conditions, the feature space is spanned by the spike-triggered average and its successive order time derivatives. |
q-bio/0407020 | Katja Lindenberg | C. Escudero, J. Buceta, F. J. de la Rubia, and Katja Lindenberg | Effects of internal fluctuations on the spreading of Hantavirus | null | null | 10.1103/PhysRevE.70.061907 | null | q-bio.PE | null | We study the spread of Hantavirus over a host population of deer mice using a
population dynamics model. We show that taking into account the internal
fluctuations in the mouse population due to its discrete character strongly
alters the behaviour of the system. In addition to the familiar transition
present in the deterministic model, the inclusion of internal fluctuations
leads to the emergence of an additional deterministically hidden transition. We
determine parameter values that lead to maximal propagation of the disease, and
discuss some implications for disease prevention policies.
| [
{
"created": "Wed, 14 Jul 2004 20:24:40 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Escudero",
"C.",
""
],
[
"Buceta",
"J.",
""
],
[
"de la Rubia",
"F. J.",
""
],
[
"Lindenberg",
"Katja",
""
]
] | We study the spread of Hantavirus over a host population of deer mice using a population dynamics model. We show that taking into account the internal fluctuations in the mouse population due to its discrete character strongly alters the behaviour of the system. In addition to the familiar transition present in the deterministic model, the inclusion of internal fluctuations leads to the emergence of an additional deterministically hidden transition. We determine parameter values that lead to maximal propagation of the disease, and discuss some implications for disease prevention policies. |
1005.2913 | Gabor Szederkenyi | Gabor Szederkenyi, Katalin M. Hangos, Tamas Peni | Maximal and minimal realizations of reaction kinetic systems:
computation and properties | 23 pages, 4 figures | MATCH Commun. Math. Comput. Chem., vol. 65 (2): 309-332, 2011 | null | PCRG_002/2010 | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents new results about the optimization based generation of
chemical reaction networks (CRNs) of higher deficiency. Firstly, it is shown
that the graph structure of the realization containing the maximal number of
reactions is unique if the set of possible complexes is fixed. Secondly, a
mixed integer programming based numerical procedure is given for computing a
realization containing the minimal/maximal number of complexes. Moreover, the
linear inequalities corresponding to full reversibility of the CRN realization
are also described. The theoretical results are illustrated on meaningful
examples.
| [
{
"created": "Mon, 17 May 2010 13:09:58 GMT",
"version": "v1"
}
] | 2011-03-23 | [
[
"Szederkenyi",
"Gabor",
""
],
[
"Hangos",
"Katalin M.",
""
],
[
"Peni",
"Tamas",
""
]
] | This paper presents new results about the optimization based generation of chemical reaction networks (CRNs) of higher deficiency. Firstly, it is shown that the graph structure of the realization containing the maximal number of reactions is unique if the set of possible complexes is fixed. Secondly, a mixed integer programming based numerical procedure is given for computing a realization containing the minimal/maximal number of complexes. Moreover, the linear inequalities corresponding to full reversibility of the CRN realization are also described. The theoretical results are illustrated on meaningful examples. |
1405.1462 | Peter Hinow | Peter Hinow, Edward A. Rietman, Jack A. Tuszynski | Algebraic and Topological Indices of Molecular Pathway Networks in Human
Cancers | 15 pages, 4 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein-protein interaction networks associated with diseases have gained
prominence as an area of research. We investigate algebraic and topological
indices for protein-protein interaction networks of 11 human cancers derived
from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a
strong correlation between relative automorphism group sizes and topological
network complexities on the one hand and five year survival probabilities on
the other hand. Moreover, we identify several protein families (e.g. PIK, ITG,
AKT families) that are repeated motifs in many of the cancer pathways.
Interestingly, these sources of symmetry are often central rather than
peripheral. Our results can aide in identification of promising targets for
anti-cancer drugs. Beyond that, we provide a unifying framework to study
protein-protein interaction networks of families of related diseases (e.g.
neurodegenerative diseases, viral diseases, substance abuse disorders).
| [
{
"created": "Tue, 6 May 2014 21:58:50 GMT",
"version": "v1"
}
] | 2014-05-08 | [
[
"Hinow",
"Peter",
""
],
[
"Rietman",
"Edward A.",
""
],
[
"Tuszynski",
"Jack A.",
""
]
] | Protein-protein interaction networks associated with diseases have gained prominence as an area of research. We investigate algebraic and topological indices for protein-protein interaction networks of 11 human cancers derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a strong correlation between relative automorphism group sizes and topological network complexities on the one hand and five year survival probabilities on the other hand. Moreover, we identify several protein families (e.g. PIK, ITG, AKT families) that are repeated motifs in many of the cancer pathways. Interestingly, these sources of symmetry are often central rather than peripheral. Our results can aide in identification of promising targets for anti-cancer drugs. Beyond that, we provide a unifying framework to study protein-protein interaction networks of families of related diseases (e.g. neurodegenerative diseases, viral diseases, substance abuse disorders). |
1409.8275 | William Softky | William Softky | Elastic Nanocomputation in an Ideal Brain (1p abstract + 36 pages + 49
endnotes) | 1 page abstract, 36 pages content incl. 12 figures, 49 endnotes | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This explanation of what a brain is and does rests on informational first
principles, because information theory, like its parent theory thermodynamics,
is mathematically sacrosanct, itself resting on real-valued probability.Just as
thermodynamics has enabled hyper-potent physical technologies from the internal
combustion engine to the hydrogen bomb, so information theory has enabled
hyper-persuasive technologies, from color television to addictive video games.
Only a theory of what a brain is and does based on those same principles makes
legible and transparent the mechanisms by which such hyper-persuasion works. In
information-theoretic terms, a brain is a specialized real-valued real-time 3D
processor detecting discontinuities in spacetime outside itself and
reconstituting in itself a continuous reality based on them. This continuous
approach is difficult to reconcile with any computational architecture based on
separate neurons, and in fact the vast discrepancy in efficiency (of order at
least a hundred million) between those architectures constitutes the
calculations of this paper. This remarkable signal-processing requires strong
prior hypotheses embedded in 3D edge-detecting algorithms, priors which
unfortunately also open an unpatchable security hole to automated persuasion.
So a 3D model of the brain is essential for understanding how and why
persuasive technologies alter our perception of reality, and for protecting us
against systemic, systematic cognitive manipulation.
| [
{
"created": "Sat, 27 Sep 2014 19:28:22 GMT",
"version": "v1"
}
] | 2014-10-01 | [
[
"Softky",
"William",
""
]
] | This explanation of what a brain is and does rests on informational first principles, because information theory, like its parent theory thermodynamics, is mathematically sacrosanct, itself resting on real-valued probability.Just as thermodynamics has enabled hyper-potent physical technologies from the internal combustion engine to the hydrogen bomb, so information theory has enabled hyper-persuasive technologies, from color television to addictive video games. Only a theory of what a brain is and does based on those same principles makes legible and transparent the mechanisms by which such hyper-persuasion works. In information-theoretic terms, a brain is a specialized real-valued real-time 3D processor detecting discontinuities in spacetime outside itself and reconstituting in itself a continuous reality based on them. This continuous approach is difficult to reconcile with any computational architecture based on separate neurons, and in fact the vast discrepancy in efficiency (of order at least a hundred million) between those architectures constitutes the calculations of this paper. This remarkable signal-processing requires strong prior hypotheses embedded in 3D edge-detecting algorithms, priors which unfortunately also open an unpatchable security hole to automated persuasion. So a 3D model of the brain is essential for understanding how and why persuasive technologies alter our perception of reality, and for protecting us against systemic, systematic cognitive manipulation. |
2302.10800 | Justin Reese | J Harry Caufield, Tim Putman, Kevin Schaper, Deepak R Unni, Harshad
Hegde, Tiffany J Callahan, Luca Cappelletti, Sierra AT Moxon, Vida Ravanmehr,
Seth Carbon, Lauren E Chan, Katherina Cortes, Kent A Shefchek, Glass
Elsarboukh, James P Balhoff, Tommaso Fontana, Nicolas Matentzoglu, Richard M
Bruskiewich, Anne E Thessen, Nomi L Harris, Monica C Munoz-Torres, Melissa A
Haendel, Peter N Robinson, Marcin P Joachimiak, Christopher J Mungall, Justin
T Reese | KG-Hub -- Building and Exchanging Biological Knowledge Graphs | null | null | null | null | q-bio.QM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous
data and making inferences in biology and many other domains, but a coherent
solution for constructing, exchanging, and facilitating the downstream use of
knowledge graphs is lacking. Here we present KG-Hub, a platform that enables
standardized construction, exchange, and reuse of knowledge graphs. Features
include a simple, modular extract-transform-load (ETL) pattern for producing
graphs compliant with Biolink Model (a high-level data model for standardizing
biological data), easy integration of any OBO (Open Biological and Biomedical
Ontologies) ontology, cached downloads of upstream data sources, versioned and
automatically updated builds with stable URLs, web-browsable storage of KG
artifacts on cloud infrastructure, and easy reuse of transformed subgraphs
across projects. Current KG-Hub projects span use cases including COVID-19
research, drug repurposing, microbial-environmental interactions, and rare
disease research. KG-Hub is equipped with tooling to easily analyze and
manipulate knowledge graphs. KG-Hub is also tightly integrated with graph
machine learning (ML) tools which allow automated graph machine learning,
including node embeddings and training of models for link prediction and node
classification.
| [
{
"created": "Tue, 31 Jan 2023 21:29:35 GMT",
"version": "v1"
}
] | 2023-02-22 | [
[
"Caufield",
"J Harry",
""
],
[
"Putman",
"Tim",
""
],
[
"Schaper",
"Kevin",
""
],
[
"Unni",
"Deepak R",
""
],
[
"Hegde",
"Harshad",
""
],
[
"Callahan",
"Tiffany J",
""
],
[
"Cappelletti",
"Luca",
""
],
[
"Moxon",
"Sierra AT",
""
],
[
"Ravanmehr",
"Vida",
""
],
[
"Carbon",
"Seth",
""
],
[
"Chan",
"Lauren E",
""
],
[
"Cortes",
"Katherina",
""
],
[
"Shefchek",
"Kent A",
""
],
[
"Elsarboukh",
"Glass",
""
],
[
"Balhoff",
"James P",
""
],
[
"Fontana",
"Tommaso",
""
],
[
"Matentzoglu",
"Nicolas",
""
],
[
"Bruskiewich",
"Richard M",
""
],
[
"Thessen",
"Anne E",
""
],
[
"Harris",
"Nomi L",
""
],
[
"Munoz-Torres",
"Monica C",
""
],
[
"Haendel",
"Melissa A",
""
],
[
"Robinson",
"Peter N",
""
],
[
"Joachimiak",
"Marcin P",
""
],
[
"Mungall",
"Christopher J",
""
],
[
"Reese",
"Justin T",
""
]
] | Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph machine learning, including node embeddings and training of models for link prediction and node classification. |
1703.07654 | Roberto D. Pascual-Marqui | RD Pascual-Marqui, P Faber, S Ikeda, R Ishii, T Kinoshita, Y Kitaura,
K Kochi, P Milz, K Nishida, M Yoshimura | The cross-frequency mediation mechanism of intracortical information
transactions | https://doi.org/10.1101/119362 licensed as CC-BY-NC-ND 4.0
International license: http://creativecommons.org/licenses/by-nc-nd/4.0/ | null | 10.1101/119362 | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In a seminal paper by von Stein and Sarnthein (2000), it was hypothesized
that "bottom-up" information processing of "content" elicits local, high
frequency (beta-gamma) oscillations, whereas "top-down" processing is
"contextual", characterized by large scale integration spanning distant
cortical regions, and implemented by slower frequency (theta-alpha)
oscillations. This corresponds to a mechanism of cortical information
transactions, where synchronization of beta-gamma oscillations between distant
cortical regions is mediated by widespread theta-alpha oscillations. It is the
aim of this paper to express this hypothesis quantitatively, in terms of a
model that will allow testing this type of information transaction mechanism.
The basic methodology used here corresponds to statistical mediation analysis,
originally developed by (Baron and Kenny 1986). We generalize the classical
mediator model to the case of multivariate complex-valued data, consisting of
the discrete Fourier transform coefficients of signals of electric neuronal
activity, at different frequencies, and at different cortical locations. The
"mediation effect" is quantified here in a novel way, as the product of "dual
frequency RV-coupling coefficients", that were introduced in (Pascual-Marqui et
al 2016, http://arxiv.org/abs/1603.05343). Relevant statistical procedures are
presented for testing the cross-frequency mediation mechanism in general, and
in particular for testing the von Stein & Sarnthein hypothesis.
| [
{
"created": "Wed, 22 Mar 2017 13:47:00 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Mar 2017 23:10:16 GMT",
"version": "v2"
}
] | 2017-03-29 | [
[
"Pascual-Marqui",
"RD",
""
],
[
"Faber",
"P",
""
],
[
"Ikeda",
"S",
""
],
[
"Ishii",
"R",
""
],
[
"Kinoshita",
"T",
""
],
[
"Kitaura",
"Y",
""
],
[
"Kochi",
"K",
""
],
[
"Milz",
"P",
""
],
[
"Nishida",
"K",
""
],
[
"Yoshimura",
"M",
""
]
] | In a seminal paper by von Stein and Sarnthein (2000), it was hypothesized that "bottom-up" information processing of "content" elicits local, high frequency (beta-gamma) oscillations, whereas "top-down" processing is "contextual", characterized by large scale integration spanning distant cortical regions, and implemented by slower frequency (theta-alpha) oscillations. This corresponds to a mechanism of cortical information transactions, where synchronization of beta-gamma oscillations between distant cortical regions is mediated by widespread theta-alpha oscillations. It is the aim of this paper to express this hypothesis quantitatively, in terms of a model that will allow testing this type of information transaction mechanism. The basic methodology used here corresponds to statistical mediation analysis, originally developed by (Baron and Kenny 1986). We generalize the classical mediator model to the case of multivariate complex-valued data, consisting of the discrete Fourier transform coefficients of signals of electric neuronal activity, at different frequencies, and at different cortical locations. The "mediation effect" is quantified here in a novel way, as the product of "dual frequency RV-coupling coefficients", that were introduced in (Pascual-Marqui et al 2016, http://arxiv.org/abs/1603.05343). Relevant statistical procedures are presented for testing the cross-frequency mediation mechanism in general, and in particular for testing the von Stein & Sarnthein hypothesis. |
2301.00552 | Ignacio Rodr\'iguez-Rodr\'iguez Dr. | I. Rodr\'Iguez-Rodr\'Iguez, A. Ortiz, N.J. Gallego-Molina, M.A.
Formoso, W.L. Woo | Neural source/sink phase connectivity in developmental dyslexia by means
of interchannel causality | null | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | While the brain connectivity network can inform the understanding and
diagnosis of developmental dyslexia, its cause-effect relationships have not
yet enough been examined. Employing electroencephalography signals and
band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we
measure the phase Granger causalities among channels to identify differences
between dyslexic learners and controls, thereby proposing a method to calculate
directional connectivity. As causal relationships run in both directions, we
explore three scenarios, namely channels' activity as sources, as sinks, and in
total. Our proposed method can be used for both classification and exploratory
analysis. In all scenarios, we find confirmation of the established
right-lateralized Theta sampling network anomaly, in line with the temporal
sampling framework's assumption of oscillatory differences in the Theta and
Gamma bands. Further, we show that this anomaly primarily occurs in the causal
relationships of channels acting as sinks, where it is significantly more
pronounced than when only total activity is observed. In the sink scenario, our
classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta
and Gamma bands, respectively.
| [
{
"created": "Mon, 2 Jan 2023 07:56:03 GMT",
"version": "v1"
}
] | 2023-01-03 | [
[
"RodrÍguez-RodrÍguez",
"I.",
""
],
[
"Ortiz",
"A.",
""
],
[
"Gallego-Molina",
"N. J.",
""
],
[
"Formoso",
"M. A.",
""
],
[
"Woo",
"W. L.",
""
]
] | While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively. |
0704.3826 | Mark Ya. Azbel' | Mark Ya. Azbel | Non-coding DNA programs express adaptation and its universal law | Refined version 19 pages, 10 figs | null | null | null | q-bio.GN cond-mat.other nlin.AO q-bio.OT q-bio.PE q-bio.QM | null | Significant fraction (98.5% in humans) of most animal genomes is non- coding
dark matter. Its largely unknown function (1-5) is related to programming
(rather than to spontaneous mutations) of accurate adaptation to rapidly
changing environment. Programmed adaptation to the same universal law for
non-competing animals from anaerobic yeast to human is revealed in the study of
their extensively quantified mortality (6-21). Adaptation of animals with
removed non-coding DNA fractions may specify their contribution to genomic
programming. Emergence of new adaptation programs and their (non-Mendelian)
heredity may be studied in antibiotic mini-extinctions (22-24). On a large
evolutionary scale rapid universal adaptation was vital for survival, and
evolved, in otherwise lethal for diverse species major mass extinctions
(25-28). Evolutionary and experimental data corroborate these conclusions
(6-21, 29-32). Universal law implies certain biological universality of diverse
species, thus quantifies applicability of animal models to humans). Genomic
adaptation programming calls for unusual approach to its study and implies
unanticipated perspectives, in particular, directed biological changes.
| [
{
"created": "Sun, 29 Apr 2007 05:32:50 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Aug 2007 16:23:54 GMT",
"version": "v2"
}
] | 2007-08-02 | [
[
"Azbel",
"Mark Ya.",
""
]
] | Significant fraction (98.5% in humans) of most animal genomes is non- coding dark matter. Its largely unknown function (1-5) is related to programming (rather than to spontaneous mutations) of accurate adaptation to rapidly changing environment. Programmed adaptation to the same universal law for non-competing animals from anaerobic yeast to human is revealed in the study of their extensively quantified mortality (6-21). Adaptation of animals with removed non-coding DNA fractions may specify their contribution to genomic programming. Emergence of new adaptation programs and their (non-Mendelian) heredity may be studied in antibiotic mini-extinctions (22-24). On a large evolutionary scale rapid universal adaptation was vital for survival, and evolved, in otherwise lethal for diverse species major mass extinctions (25-28). Evolutionary and experimental data corroborate these conclusions (6-21, 29-32). Universal law implies certain biological universality of diverse species, thus quantifies applicability of animal models to humans). Genomic adaptation programming calls for unusual approach to its study and implies unanticipated perspectives, in particular, directed biological changes. |
2204.06714 | Qiaolan Deng | Qiaolan Deng, Jin Hyun Nam, Ayse Selen Yilmaz, Won Chang, Maciej
Pietrzak, Lang Li, Hang J. Kim, Dongjun Chung | graph-GPA 2.0: A Graphical Model for Multi-disease Analysis of GWAS
Results with Integration of Functional Annotation Data | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Genome-wide association studies (GWAS) have successfully identified a large
number of genetic variants associated with traits and diseases. However, it
still remains challenging to fully understand functional mechanisms underlying
many associated variants. This is especially the case when we are interested in
variants shared across multiple phenotypes. To address this challenge, we
propose graph-GPA 2.0 (GGPA 2.0), a novel statistical framework to integrate
GWAS datasets for multiple phenotypes and incorporate functional annotations
within a unified framework. We conducted simulation studies to evaluate GGPA
2.0. The results indicate that incorporating functional annotation data using
GGPA 2.0 does not only improve detection of disease-associated variants, but
also allows to identify more accurate relationships among diseases. We analyzed
five autoimmune diseases and five psychiatric disorders with the functional
annotations derived from GenoSkyline and GenoSkyline-Plus and the prior disease
graph generated by biomedical literature mining. For autoimmune diseases, GGPA
2.0 identified enrichment for blood, especially B cells and regulatory T cells
across multiple diseases. Psychiatric disorders were enriched for brain,
especially prefrontal cortex and inferior temporal lobe for bipolar disorder
(BIP) and schizophrenia (SCZ), respectively. Finally, GGPA 2.0 successfully
identified the pleiotropy between BIP and SCZ. These results demonstrate that
GGPA 2.0 can be a powerful tool to identify associated variants associated with
each phenotype or those shared across multiple phenotypes, while also promoting
understanding of functional mechanisms underlying the associated variants.
| [
{
"created": "Thu, 14 Apr 2022 02:54:28 GMT",
"version": "v1"
}
] | 2022-04-15 | [
[
"Deng",
"Qiaolan",
""
],
[
"Nam",
"Jin Hyun",
""
],
[
"Yilmaz",
"Ayse Selen",
""
],
[
"Chang",
"Won",
""
],
[
"Pietrzak",
"Maciej",
""
],
[
"Li",
"Lang",
""
],
[
"Kim",
"Hang J.",
""
],
[
"Chung",
"Dongjun",
""
]
] | Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a novel statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. We conducted simulation studies to evaluate GGPA 2.0. The results indicate that incorporating functional annotation data using GGPA 2.0 does not only improve detection of disease-associated variants, but also allows to identify more accurate relationships among diseases. We analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus and the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood, especially B cells and regulatory T cells across multiple diseases. Psychiatric disorders were enriched for brain, especially prefrontal cortex and inferior temporal lobe for bipolar disorder (BIP) and schizophrenia (SCZ), respectively. Finally, GGPA 2.0 successfully identified the pleiotropy between BIP and SCZ. These results demonstrate that GGPA 2.0 can be a powerful tool to identify associated variants associated with each phenotype or those shared across multiple phenotypes, while also promoting understanding of functional mechanisms underlying the associated variants. |
2309.12906 | Yang Wang | Yang Wang, Zanyu Shi, Timothy Richardson, Kun Huang, Pathum
Weerawarna, Yijie Wang | Building explainable graph neural network by sparse learning for the
drug-protein binding prediction | null | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explainable Graph Neural Networks (GNNs) have been developed and applied to
drug-protein binding prediction to identify the key chemical structures in a
drug that have active interactions with the target proteins. However, the key
structures identified by the current explainable GNN models are typically
chemically invalid. Furthermore, a threshold needs to be manually selected to
pinpoint the key structures from the rest. To overcome the limitations of the
current explainable GNN models, we propose our SLGNN, which stands for using
Sparse Learning to Graph Neural Networks. Our SLGNN relies on using a
chemical-substructure-based graph (where nodes are chemical substructures) to
represent a drug molecule. Furthermore, SLGNN incorporates generalized fussed
lasso with message-passing algorithms to identify connected subgraphs that are
critical for the drug-protein binding prediction. Due to the use of the
chemical-substructure-based graph, it is guaranteed that any subgraphs in a
drug identified by our SLGNN are chemically valid structures. These structures
can be further interpreted as the key chemical structures for the drug to bind
to the target protein. We demonstrate the explanatory power of our SLGNN by
first showing all the key structures identified by our SLGNN are chemically
valid. In addition, we illustrate that the key structures identified by our
SLGNN have more predictive power than the key structures identified by the
competing methods. At last, we use known drug-protein binding data to show the
key structures identified by our SLGNN contain most of the binding sites.
| [
{
"created": "Sun, 27 Aug 2023 02:20:30 GMT",
"version": "v1"
}
] | 2023-09-25 | [
[
"Wang",
"Yang",
""
],
[
"Shi",
"Zanyu",
""
],
[
"Richardson",
"Timothy",
""
],
[
"Huang",
"Kun",
""
],
[
"Weerawarna",
"Pathum",
""
],
[
"Wang",
"Yijie",
""
]
] | Explainable Graph Neural Networks (GNNs) have been developed and applied to drug-protein binding prediction to identify the key chemical structures in a drug that have active interactions with the target proteins. However, the key structures identified by the current explainable GNN models are typically chemically invalid. Furthermore, a threshold needs to be manually selected to pinpoint the key structures from the rest. To overcome the limitations of the current explainable GNN models, we propose our SLGNN, which stands for using Sparse Learning to Graph Neural Networks. Our SLGNN relies on using a chemical-substructure-based graph (where nodes are chemical substructures) to represent a drug molecule. Furthermore, SLGNN incorporates generalized fussed lasso with message-passing algorithms to identify connected subgraphs that are critical for the drug-protein binding prediction. Due to the use of the chemical-substructure-based graph, it is guaranteed that any subgraphs in a drug identified by our SLGNN are chemically valid structures. These structures can be further interpreted as the key chemical structures for the drug to bind to the target protein. We demonstrate the explanatory power of our SLGNN by first showing all the key structures identified by our SLGNN are chemically valid. In addition, we illustrate that the key structures identified by our SLGNN have more predictive power than the key structures identified by the competing methods. At last, we use known drug-protein binding data to show the key structures identified by our SLGNN contain most of the binding sites. |
2005.14472 | Sanjukta Krishnagopal | Sanjukta Krishnagopal | Multi-layer Trajectory Clustering: A Network Algorithm for Disease
Subtyping | 20 pages, 8 figures | null | null | null | q-bio.QM cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many diseases display heterogeneity in clinical features and their
progression, indicative of the existence of disease subtypes. Extracting
patterns of disease variable progression for subtypes has tremendous
application in medicine, for example, in early prognosis and personalized
medical therapy. This work present a novel, data-driven, network-based
Trajectory Clustering (TC) algorithm for identifying Parkinson's subtypes based
on disease trajectory. Modeling patient-variable interactions as a bipartite
network, TC first extracts communities of co-expressing disease variables at
different stages of progression. Then, it identifies Parkinson's subtypes by
clustering similar patient trajectories that are characterized by severity of
disease variables through a multi-layer network. Determination of trajectory
similarity accounts for direct overlaps between trajectories as well as
second-order similarities, i.e., common overlap with a third set of
trajectories. This work clusters trajectories across two types of layers: (a)
temporal, and (b) ranges of independent outcome variable (representative of
disease severity), both of which yield four distinct subtypes. The former
subtypes exhibit differences in progression of disease domains (Cognitive,
Mental Health etc.), whereas the latter subtypes exhibit different degrees of
progression, i.e., some remain mild, whereas others show significant
deterioration after 5 years. The TC approach is validated through statistical
analyses and consistency of the identified subtypes with medical literature.
This generalizable and robust method can easily be extended to other
progressive multi-variate disease datasets, and can effectively assist in
targeted subtype-specific treatment in the field of personalized medicine.
| [
{
"created": "Fri, 29 May 2020 09:44:31 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Aug 2020 15:12:38 GMT",
"version": "v2"
}
] | 2020-08-04 | [
[
"Krishnagopal",
"Sanjukta",
""
]
] | Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for example, in early prognosis and personalized medical therapy. This work present a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson's subtypes based on disease trajectory. Modeling patient-variable interactions as a bipartite network, TC first extracts communities of co-expressing disease variables at different stages of progression. Then, it identifies Parkinson's subtypes by clustering similar patient trajectories that are characterized by severity of disease variables through a multi-layer network. Determination of trajectory similarity accounts for direct overlaps between trajectories as well as second-order similarities, i.e., common overlap with a third set of trajectories. This work clusters trajectories across two types of layers: (a) temporal, and (b) ranges of independent outcome variable (representative of disease severity), both of which yield four distinct subtypes. The former subtypes exhibit differences in progression of disease domains (Cognitive, Mental Health etc.), whereas the latter subtypes exhibit different degrees of progression, i.e., some remain mild, whereas others show significant deterioration after 5 years. The TC approach is validated through statistical analyses and consistency of the identified subtypes with medical literature. This generalizable and robust method can easily be extended to other progressive multi-variate disease datasets, and can effectively assist in targeted subtype-specific treatment in the field of personalized medicine. |
1709.06165 | Yi Shang | Chao Fang, Yi Shang, and Dong Xu | MUFold-SS: Protein Secondary Structure Prediction Using Deep
Inception-Inside-Inception Networks | null | null | null | null | q-bio.QM cs.CV cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Protein secondary structure prediction can provide important
information for protein 3D structure prediction and protein functions. Deep
learning, which has been successfully applied to various research fields such
as image classification and voice recognition, provides a new opportunity to
significantly improve the secondary structure prediction accuracy. Although
several deep-learning methods have been developed for secondary structure
prediction, there is room for improvement. MUFold-SS was developed to address
these issues. Results: Here, a very deep neural network, the deep
inception-inside-inception networks (Deep3I), is proposed for protein secondary
structure prediction and a software tool was implemented using this network.
This network takes two inputs: a protein sequence and a profile generated by
PSI-BLAST. The output is the predicted eight states (Q8) or three states (Q3)
of secondary structures. The proposed Deep3I not only achieves the
state-of-the-art performance but also runs faster than other tools. Deep3I
achieves Q3 82.8% and Q8 71.1% accuracies on the CB513 benchmark.
| [
{
"created": "Tue, 12 Sep 2017 20:59:27 GMT",
"version": "v1"
}
] | 2017-09-20 | [
[
"Fang",
"Chao",
""
],
[
"Shang",
"Yi",
""
],
[
"Xu",
"Dong",
""
]
] | Motivation: Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning, which has been successfully applied to various research fields such as image classification and voice recognition, provides a new opportunity to significantly improve the secondary structure prediction accuracy. Although several deep-learning methods have been developed for secondary structure prediction, there is room for improvement. MUFold-SS was developed to address these issues. Results: Here, a very deep neural network, the deep inception-inside-inception networks (Deep3I), is proposed for protein secondary structure prediction and a software tool was implemented using this network. This network takes two inputs: a protein sequence and a profile generated by PSI-BLAST. The output is the predicted eight states (Q8) or three states (Q3) of secondary structures. The proposed Deep3I not only achieves the state-of-the-art performance but also runs faster than other tools. Deep3I achieves Q3 82.8% and Q8 71.1% accuracies on the CB513 benchmark. |
1205.5857 | Fredrik Persson Dr. | Arash Sanamrad, Fredrik Persson, and Johan Elf | Isotropic diffusion of the small ribosomal subunit in Escherichia coli | 5 pages, 5 figures | null | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ribosome is one of the most important macromolecular complexes in a
living cell. We have tracked individual 30S ribosomal subunits in exponentially
growing E. coli cells using three-dimensional single-particle tracking, where
the z-position is estimated using astigmatism. The 30S subunits are
stochiometrically labeled with S2-mEos2 by replacing the rpsB gene in the E.
coli chromosome by rpsB-mEos2. The spatial precision in tracking is 20 nm in xy
and 70 nm in z. The average trajectory consists of 4 steps corresponding to 80
ms. The trajectories are excluded from parts of the cell, consistent with
nucleoid exclusion, and display isotropic diffusion with nearly identical
apparent diffusion coefficients (0.05 +/- 0.01 um^2/s) in x, y and z. The
tracking data fits well to a two-state diffusion model where 46% of the
molecules are diffusing at 0.02 um^2/s and 54% are diffusing at 0.14 um^2/s.
These states could correspond to translating 70S ribosomes and free 30S
subunits.
| [
{
"created": "Sat, 26 May 2012 07:48:38 GMT",
"version": "v1"
},
{
"created": "Thu, 31 May 2012 06:26:52 GMT",
"version": "v2"
},
{
"created": "Wed, 13 Jun 2012 16:38:11 GMT",
"version": "v3"
}
] | 2012-06-14 | [
[
"Sanamrad",
"Arash",
""
],
[
"Persson",
"Fredrik",
""
],
[
"Elf",
"Johan",
""
]
] | The ribosome is one of the most important macromolecular complexes in a living cell. We have tracked individual 30S ribosomal subunits in exponentially growing E. coli cells using three-dimensional single-particle tracking, where the z-position is estimated using astigmatism. The 30S subunits are stochiometrically labeled with S2-mEos2 by replacing the rpsB gene in the E. coli chromosome by rpsB-mEos2. The spatial precision in tracking is 20 nm in xy and 70 nm in z. The average trajectory consists of 4 steps corresponding to 80 ms. The trajectories are excluded from parts of the cell, consistent with nucleoid exclusion, and display isotropic diffusion with nearly identical apparent diffusion coefficients (0.05 +/- 0.01 um^2/s) in x, y and z. The tracking data fits well to a two-state diffusion model where 46% of the molecules are diffusing at 0.02 um^2/s and 54% are diffusing at 0.14 um^2/s. These states could correspond to translating 70S ribosomes and free 30S subunits. |
q-bio/0702008 | Laurent Jacob | Laurent Jacob (CB), Jean-Philippe Vert (CB) | Epitope prediction improved by multitask support vector machines | null | We use various multitask kernels in order to improve MHC-I-peptide
binding prediction, in particular for MHC alleles for which few training data
is available. (05/02/2007) | null | null | q-bio.QM | null | Motivation: In silico methods for the prediction of antigenic peptides
binding to MHC class I molecules play an increasingly important role in the
identification of T-cell epitopes. Statistical and machine learning methods, in
particular, are widely used to score candidate epitopes based on their
similarity with known epitopes and non epitopes. The genes coding for the MHC
molecules, however, are highly polymorphic, and statistical methods have
difficulties to build models for alleles with few known epitopes. In this case,
recent works have demonstrated the utility of leveraging information across
alleles to improve the performance of the prediction. Results: We design a
support vector machine algorithm that is able to learn epitope models for all
alleles simultaneously, by sharing information across similar alleles. The
sharing of information across alleles is controlled by a user-defined measure
of similarity between alleles. We show that this similarity can be defined in
terms of supertypes, or more directly by comparing key residues known to play a
role in the peptide-MHC binding. We illustrate the potential of this approach
on various benchmark experiments where it outperforms other state-of-the-art
methods.
| [
{
"created": "Tue, 6 Feb 2007 13:03:54 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Jacob",
"Laurent",
"",
"CB"
],
[
"Vert",
"Jean-Philippe",
"",
"CB"
]
] | Motivation: In silico methods for the prediction of antigenic peptides binding to MHC class I molecules play an increasingly important role in the identification of T-cell epitopes. Statistical and machine learning methods, in particular, are widely used to score candidate epitopes based on their similarity with known epitopes and non epitopes. The genes coding for the MHC molecules, however, are highly polymorphic, and statistical methods have difficulties to build models for alleles with few known epitopes. In this case, recent works have demonstrated the utility of leveraging information across alleles to improve the performance of the prediction. Results: We design a support vector machine algorithm that is able to learn epitope models for all alleles simultaneously, by sharing information across similar alleles. The sharing of information across alleles is controlled by a user-defined measure of similarity between alleles. We show that this similarity can be defined in terms of supertypes, or more directly by comparing key residues known to play a role in the peptide-MHC binding. We illustrate the potential of this approach on various benchmark experiments where it outperforms other state-of-the-art methods. |
1608.03520 | Ann Sizemore | Ann Sizemore, Chad Giusti, Ari Kahn, Richard F. Betzel, and Danielle
S. Bassett | Cliques and Cavities in the Human Connectome | 19 pages, 6 figures | null | null | null | q-bio.NC math.AT math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Encoding brain regions and their connections as a network of nodes and edges
captures many of the possible paths along which information can be transmitted
as humans process and perform complex behaviors. Because cognitive processes
involve large and distributed networks of brain areas, examinations of
multi-node routes within larger connection patterns can offer fundamental
insights into the complexities of brain function. Here, we investigate both
densely connected groups of nodes that could perform local computations as well
as larger patterns of interactions that would allow for parallel processing.
Finding such structures necessitates we move from considering pairwise
interactions to capturing higher order relations, concepts naturally expressed
in the language of algebraic topology. These tools can be used to study
mesoscale structures arising from the arrangement of densely connected
substructures called cliques in otherwise sparsely connected brain networks. We
detect cliques (all-to-all connected sets of brain regions) in the average
structural connectomes of 8 healthy adults and discover the presence of more
large cliques than expected in null networks constructed via wiring
minimization, providing architecture through which brain network can perform
rapid, local processing. We then locate topological cavities of different
dimensions, around which information may flow in either diverging or converging
patterns. These cavities exist consistently across subjects, differ from those
observed in null model networks, and link regions of early and late
evolutionary origin in long loops, underscoring their unique role in
controlling brain function. These results offer a first demonstration that
techniques from algebraic topology offer a novel perspective on structural
connectomics, highlighting loop-like paths as crucial features in the human
brain's structural architecture.
| [
{
"created": "Thu, 11 Aug 2016 16:17:57 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Dec 2016 14:04:30 GMT",
"version": "v2"
}
] | 2016-12-21 | [
[
"Sizemore",
"Ann",
""
],
[
"Giusti",
"Chad",
""
],
[
"Kahn",
"Ari",
""
],
[
"Betzel",
"Richard F.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans process and perform complex behaviors. Because cognitive processes involve large and distributed networks of brain areas, examinations of multi-node routes within larger connection patterns can offer fundamental insights into the complexities of brain function. Here, we investigate both densely connected groups of nodes that could perform local computations as well as larger patterns of interactions that would allow for parallel processing. Finding such structures necessitates we move from considering pairwise interactions to capturing higher order relations, concepts naturally expressed in the language of algebraic topology. These tools can be used to study mesoscale structures arising from the arrangement of densely connected substructures called cliques in otherwise sparsely connected brain networks. We detect cliques (all-to-all connected sets of brain regions) in the average structural connectomes of 8 healthy adults and discover the presence of more large cliques than expected in null networks constructed via wiring minimization, providing architecture through which brain network can perform rapid, local processing. We then locate topological cavities of different dimensions, around which information may flow in either diverging or converging patterns. These cavities exist consistently across subjects, differ from those observed in null model networks, and link regions of early and late evolutionary origin in long loops, underscoring their unique role in controlling brain function. These results offer a first demonstration that techniques from algebraic topology offer a novel perspective on structural connectomics, highlighting loop-like paths as crucial features in the human brain's structural architecture. |
2209.09075 | Katsushi Kagaya | Katsushi Kagaya, Tomoyuki Kubota, Kohei Nakajima | Self-Organized Criticality Explains Readiness Potential | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Readiness potential is a widely observed brain activity in several species
including crayfish before the spontaneous behavioral initiation. However, it is
poorly understood how this spontaneous activity is generated. The hypothesis
that some specific, dedicated site is responsible for the spontaneity has been
questioned. Here, by using intracellular recording and staining of the brain
neurons in crayfish and modeling using the sandpile, which is the original
model of self-organized criticality (SOC), we show that readiness potential can
emerge everywhere in the brain because it is a SOC system. Despite the
diversity in neurons and their morphology, brain neurons showed signatures of
criticality and readiness potential. We find that the previously known
readiness potential in a neuron is a consequence of the critical behavior of
the entire network. Indeed, seemingly unrelated membrane potential activity in
neurons in different animals can shape readiness potential when its time series
are averaged after their alignment with respect to the spontaneous behavioral
initiation. We show that the sandpile model not made for the potential, can
form the premovement buildup activity similar to readiness potential. Scaling
properties of the synaptic avalanches are in line with those of vertebrate
species; thus, not only is the critical brain hypothesis supported in crayfish,
but our findings might also provide a unified view of the basis of spontaneity
in animal behavior.
| [
{
"created": "Mon, 19 Sep 2022 15:06:36 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Aug 2024 15:52:40 GMT",
"version": "v2"
}
] | 2024-08-05 | [
[
"Kagaya",
"Katsushi",
""
],
[
"Kubota",
"Tomoyuki",
""
],
[
"Nakajima",
"Kohei",
""
]
] | Readiness potential is a widely observed brain activity in several species including crayfish before the spontaneous behavioral initiation. However, it is poorly understood how this spontaneous activity is generated. The hypothesis that some specific, dedicated site is responsible for the spontaneity has been questioned. Here, by using intracellular recording and staining of the brain neurons in crayfish and modeling using the sandpile, which is the original model of self-organized criticality (SOC), we show that readiness potential can emerge everywhere in the brain because it is a SOC system. Despite the diversity in neurons and their morphology, brain neurons showed signatures of criticality and readiness potential. We find that the previously known readiness potential in a neuron is a consequence of the critical behavior of the entire network. Indeed, seemingly unrelated membrane potential activity in neurons in different animals can shape readiness potential when its time series are averaged after their alignment with respect to the spontaneous behavioral initiation. We show that the sandpile model not made for the potential, can form the premovement buildup activity similar to readiness potential. Scaling properties of the synaptic avalanches are in line with those of vertebrate species; thus, not only is the critical brain hypothesis supported in crayfish, but our findings might also provide a unified view of the basis of spontaneity in animal behavior. |
2304.09693 | Pablo Moreno-Spiegelberg | Pablo Moreno-Spiegelberg and Dami\`a Gomila | A model for seagrass species competition: dynamics of the symmetric case | 17 pages, 8 figures and 2 tables | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | We propose a general population dynamics model for two seagrass species
growing and interacting in two spatial dimensions. The model includes spatial
terms accounting for the clonal growth characteristics of seagrasses, and
coupling between species through the net mortality rate. We consider both
intraspecies and interspecies facilitative and competitive interactions,
allowing density-dependent interaction mechanisms. Here we study the case of
very similar species with reciprocal interactions, which allows reducing the
number of the model parameters to just four, and whose bifurcation structure
can be considered the backbone of the complete system. We find that the
parameter space can be divided into ten regions with qualitatively different
bifurcation diagrams. These regimes can be further grouped into just five
regimes with different ecological interpretations. Our analysis allows the
classifying of all possible density distributions and dynamical behaviors of
meadows with two coexisting species.
| [
{
"created": "Wed, 19 Apr 2023 14:35:11 GMT",
"version": "v1"
}
] | 2023-04-20 | [
[
"Moreno-Spiegelberg",
"Pablo",
""
],
[
"Gomila",
"Damià",
""
]
] | We propose a general population dynamics model for two seagrass species growing and interacting in two spatial dimensions. The model includes spatial terms accounting for the clonal growth characteristics of seagrasses, and coupling between species through the net mortality rate. We consider both intraspecies and interspecies facilitative and competitive interactions, allowing density-dependent interaction mechanisms. Here we study the case of very similar species with reciprocal interactions, which allows reducing the number of the model parameters to just four, and whose bifurcation structure can be considered the backbone of the complete system. We find that the parameter space can be divided into ten regions with qualitatively different bifurcation diagrams. These regimes can be further grouped into just five regimes with different ecological interpretations. Our analysis allows the classifying of all possible density distributions and dynamical behaviors of meadows with two coexisting species. |
1908.11261 | Nathan Baker | Joseph Laureanti, Juan Brandi, Elvis Offor, David Engel, Robert Rallo,
Bojana Ginovska, Xavier Martinez, Marc Baaden, Nathan A. Baker | Visualizing biomolecular electrostatics in virtual reality with
UnityMol-APBS | null | Protein Science, 2019 | 10.1002/pro.3773 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Virtual reality is a powerful tool with the ability to immerse a user within
a completely external environment. This immersion is particularly useful when
visualizing and analyzing interactions between small organic molecules,
molecular inorganic complexes, and biomolecular systems such as redox proteins
and enzymes. A common tool used in the biomedical community to analyze such
interactions is the APBS software, which was developed to solve the equations
of continuum electrostatics for large biomolecular assemblages. Numerous
applications exist for using APBS in the biomedical community including
analysis of protein ligand interactions and APBS has enjoyed widespread
adoption throughout the biomedical community. Currently, typical use of the
full APBS toolset is completed via the command line followed by visualization
using a variety of two-dimensional external molecular visualization software.
This process has inherent limitations: visualization of three-dimensional
objects using a two-dimensional interface masks important information within
the depth component. Herein, we have developed a single application,
UnityMol-APBS, that provides a dual experience where users can utilize the full
range of the APBS toolset, without the use of a command line interface, by use
of a simple \ac{GUI} for either a standard desktop or immersive virtual reality
experience.
| [
{
"created": "Thu, 29 Aug 2019 14:36:04 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Nov 2019 02:47:22 GMT",
"version": "v2"
}
] | 2019-11-14 | [
[
"Laureanti",
"Joseph",
""
],
[
"Brandi",
"Juan",
""
],
[
"Offor",
"Elvis",
""
],
[
"Engel",
"David",
""
],
[
"Rallo",
"Robert",
""
],
[
"Ginovska",
"Bojana",
""
],
[
"Martinez",
"Xavier",
""
],
[
"Baaden",
"Marc",
""
],
[
"Baker",
"Nathan A.",
""
]
] | Virtual reality is a powerful tool with the ability to immerse a user within a completely external environment. This immersion is particularly useful when visualizing and analyzing interactions between small organic molecules, molecular inorganic complexes, and biomolecular systems such as redox proteins and enzymes. A common tool used in the biomedical community to analyze such interactions is the APBS software, which was developed to solve the equations of continuum electrostatics for large biomolecular assemblages. Numerous applications exist for using APBS in the biomedical community including analysis of protein ligand interactions and APBS has enjoyed widespread adoption throughout the biomedical community. Currently, typical use of the full APBS toolset is completed via the command line followed by visualization using a variety of two-dimensional external molecular visualization software. This process has inherent limitations: visualization of three-dimensional objects using a two-dimensional interface masks important information within the depth component. Herein, we have developed a single application, UnityMol-APBS, that provides a dual experience where users can utilize the full range of the APBS toolset, without the use of a command line interface, by use of a simple \ac{GUI} for either a standard desktop or immersive virtual reality experience. |
1811.11423 | Corentin Vallee | Corouge Isabelle (VisAGeS), Corentin Vall\'ee (VisAGeS), Pierre Maurel
(VisAGeS), Isabelle Corouge, Christian Barillot (VisAGeS) | Resting-state ASL : Toward an optimal sequence duration | null | International Society for Magnetic Resonance in Medicine, 2018,
Paris, France. https://www.ismrm.org/ | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Resting-state functional Arterial Spin Labeling (rs-fASL) in clinical daily
practice and academic research stay discreet compared to resting-state BOLD.
However, by giving direct access to cerebral blood flow maps, rs-fASL leads to
significant clinical subject scaled application as CBF can be considered as a
biomarker in common neuropathology. Our work here focuses on the link between
overall quality of rs-fASL and duration of acquisition. To this end, we
consider subject self-Default Mode Network (DMN), and assess DMN quality
depletion compared to a gold standard DMN depending on the duration of
acquisition.
| [
{
"created": "Wed, 28 Nov 2018 07:48:36 GMT",
"version": "v1"
}
] | 2018-11-29 | [
[
"Isabelle",
"Corouge",
"",
"VisAGeS"
],
[
"Vallée",
"Corentin",
"",
"VisAGeS"
],
[
"Maurel",
"Pierre",
"",
"VisAGeS"
],
[
"Corouge",
"Isabelle",
"",
"VisAGeS"
],
[
"Barillot",
"Christian",
"",
"VisAGeS"
]
] | Resting-state functional Arterial Spin Labeling (rs-fASL) in clinical daily practice and academic research stay discreet compared to resting-state BOLD. However, by giving direct access to cerebral blood flow maps, rs-fASL leads to significant clinical subject scaled application as CBF can be considered as a biomarker in common neuropathology. Our work here focuses on the link between overall quality of rs-fASL and duration of acquisition. To this end, we consider subject self-Default Mode Network (DMN), and assess DMN quality depletion compared to a gold standard DMN depending on the duration of acquisition. |
0711.1304 | Alexander Iomin | Sergei Fedotov and Alexander Iomin | Probabilistic approach to a proliferation and migration dichotomy in the
tumor cell invasion | Accepted for publication as a Regular Article in Physical Review E | null | 10.1103/PhysRevE.77.031911 | null | q-bio.CB q-bio.PE | null | The proliferation and migration dichotomy of the tumor cell invasion is
examined within a two-component continuous time random walk (CTRW) model. The
balance equations for the cancer cells of two phenotypes with random switching
between cell proliferation and migration are derived. The transport of tumor
cells is formulated in terms of the CTRW with an arbitrary waiting time
distribution law, while proliferation is modelled by a logistic growth. The
overall rate of tumor cell invasion for normal diffusion and subdiffusion is
determined.
| [
{
"created": "Thu, 8 Nov 2007 16:00:52 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Feb 2008 12:07:06 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Fedotov",
"Sergei",
""
],
[
"Iomin",
"Alexander",
""
]
] | The proliferation and migration dichotomy of the tumor cell invasion is examined within a two-component continuous time random walk (CTRW) model. The balance equations for the cancer cells of two phenotypes with random switching between cell proliferation and migration are derived. The transport of tumor cells is formulated in terms of the CTRW with an arbitrary waiting time distribution law, while proliferation is modelled by a logistic growth. The overall rate of tumor cell invasion for normal diffusion and subdiffusion is determined. |
2202.00234 | Michael Nestor | Michael Nestor, Bingtuan Li | Periodic Traveling Waves in an Integro-Difference Equation With
Non-Monotonic Growth and Strong Allee Effect | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | We derive sufficient conditions for the existence of a periodic traveling
wave solution to an integro-difference equation with a piecewise constant
growth function exhibiting a stable period2 cycle and strong Allee effect. The
mean traveling wave speed is shown to be the asymptotic spreading speed of
solutions with compactly supported initial data under appropriate conditions.
We then conduct case studies for the Laplace kernel and uniform kernel.
| [
{
"created": "Tue, 1 Feb 2022 06:03:16 GMT",
"version": "v1"
}
] | 2022-02-02 | [
[
"Nestor",
"Michael",
""
],
[
"Li",
"Bingtuan",
""
]
] | We derive sufficient conditions for the existence of a periodic traveling wave solution to an integro-difference equation with a piecewise constant growth function exhibiting a stable period2 cycle and strong Allee effect. The mean traveling wave speed is shown to be the asymptotic spreading speed of solutions with compactly supported initial data under appropriate conditions. We then conduct case studies for the Laplace kernel and uniform kernel. |
1811.12933 | Heeralal Janwa | Heeralal Janwa, Steven E. Massey, Julian Velev, and Bud Mishra | Origin of Biomolecular Networks | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biomolecular networks have already found great utility in characterizing
complex biological systems arising from pair-wise interactions amongst
biomolecules. Here, we review how graph theoretical approaches can be applied
not only for a better understanding of various proximate (mechanistic)
relations, but also, ultimate (evolutionary) structures encoded in such
networks. A central question deals with the evolutionary dynamics by which
different topologies of biomolecular networks might have evolved, as well as
the biological principles that can be hypothesized from a deeper understanding
of the induced network dynamics. We emphasize the role of gene duplication in
terms of signaling game theory, whereby sender and receiver gene players accrue
benefit from gene duplication, leading to a preferential attachment mode of
network growth. Information asymmetry between sender and receiver genes is
hypothesized as a key driver of the resulting network topology. The study of
the resulting dynamics suggests many mathematical/computational problems, the
majority of which are intractable but yield to efficient approximation
algorithms, when studied through an algebraic graph theoretic lens.
| [
{
"created": "Fri, 30 Nov 2018 18:39:15 GMT",
"version": "v1"
}
] | 2018-12-03 | [
[
"Janwa",
"Heeralal",
""
],
[
"Massey",
"Steven E.",
""
],
[
"Velev",
"Julian",
""
],
[
"Mishra",
"Bud",
""
]
] | Biomolecular networks have already found great utility in characterizing complex biological systems arising from pair-wise interactions amongst biomolecules. Here, we review how graph theoretical approaches can be applied not only for a better understanding of various proximate (mechanistic) relations, but also, ultimate (evolutionary) structures encoded in such networks. A central question deals with the evolutionary dynamics by which different topologies of biomolecular networks might have evolved, as well as the biological principles that can be hypothesized from a deeper understanding of the induced network dynamics. We emphasize the role of gene duplication in terms of signaling game theory, whereby sender and receiver gene players accrue benefit from gene duplication, leading to a preferential attachment mode of network growth. Information asymmetry between sender and receiver genes is hypothesized as a key driver of the resulting network topology. The study of the resulting dynamics suggests many mathematical/computational problems, the majority of which are intractable but yield to efficient approximation algorithms, when studied through an algebraic graph theoretic lens. |
1810.09852 | Maria Kleshnina | Maria Kleshnina, Jerzy A. Filar, Cecilia Gonzalez Tokman | Nonlinear learning and learning advantages in evolutionary games | 20 pages, 5 figures | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The idea of incompetence as a learning or adaptation function was introduced
in the context of evolutionary games as a fixed parameter. However, live
organisms usually perform different nonlinear adaptation functions such as a
power law or exponential fitness growth. Here, we examine how the functional
form of the learning process may affect the social competition between
different behavioral types. Further, we extend our results for the evolutionary
games where fluctuations in the environment affect the behavioral adaptation of
competing species and demonstrate importance of the starting level of
incompetence for survival. Hence, we define a new concept of learning
advantages that becomes crucial when environments are constantly changing and
requiring rapid adaptation from species. This may lead to the evolutionarily
weak phase when even evolutionary stable populations become vulnerable to
invasions.
| [
{
"created": "Mon, 22 Oct 2018 01:30:56 GMT",
"version": "v1"
}
] | 2018-10-24 | [
[
"Kleshnina",
"Maria",
""
],
[
"Filar",
"Jerzy A.",
""
],
[
"Tokman",
"Cecilia Gonzalez",
""
]
] | The idea of incompetence as a learning or adaptation function was introduced in the context of evolutionary games as a fixed parameter. However, live organisms usually perform different nonlinear adaptation functions such as a power law or exponential fitness growth. Here, we examine how the functional form of the learning process may affect the social competition between different behavioral types. Further, we extend our results for the evolutionary games where fluctuations in the environment affect the behavioral adaptation of competing species and demonstrate importance of the starting level of incompetence for survival. Hence, we define a new concept of learning advantages that becomes crucial when environments are constantly changing and requiring rapid adaptation from species. This may lead to the evolutionarily weak phase when even evolutionary stable populations become vulnerable to invasions. |
1505.01968 | Tobias Ambjornsson | Michaela Reiter-Schad, Erik Werner, Jonas O. Tegenfeldt, Bernhard
Mehlig, Tobias Ambjornsson | How nanochannel confinement affects the DNA melting transition within
the Poland-Scheraga model | 15 pages | J. Chem. Phys. 143, 115101 (2015) | 10.1063/1.4930220 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When double-stranded DNA molecules are heated, or exposed to denaturing
agents, the two strands get separated. The statistical physics of this process
has a long history, and is commonly described in term of the Poland-Scheraga
(PS) model. Crucial to this model is the configurational entropy for a melted
region (compared to the entropy of an intact region of the same size),
quantified by the loop factor. In this study we investigate how confinement
affects the DNA melting transition, by using the loop factor for an ideal
Gaussian chain. By subsequent numerical solutions of the PS model, we
demonstrate that the melting temperature depends on the persistence lengths of
single-stranded and double-stranded DNA. For realistic values of the
persistence lengths the melting temperature is predicted to decrease with
decreasing channel diameter. We also demonstrate that confinement broadens the
melting transition. These general findings hold for the three scenarios
investigated: namely 1. homo-DNA, i.e. identical basepairs along the DNA
molecule; 2. random sequence DNA, and 3. "real" DNA, here T4 phage DNA. We show
that cases 2 and 3 in general give rise to broader transitions than case 1.
Case 3 exhibits a similar phase transition as case 2 provided the random
sequence DNA has the same ratio of AT to GC basepairs. A simple analytical
estimate for the shift in melting temperature is provided as a function of
nanochannel diameter. For homo-DNA, we also present an analytical prediction of
the melting probability as a function of temperature.
| [
{
"created": "Fri, 8 May 2015 09:41:22 GMT",
"version": "v1"
}
] | 2015-09-21 | [
[
"Reiter-Schad",
"Michaela",
""
],
[
"Werner",
"Erik",
""
],
[
"Tegenfeldt",
"Jonas O.",
""
],
[
"Mehlig",
"Bernhard",
""
],
[
"Ambjornsson",
"Tobias",
""
]
] | When double-stranded DNA molecules are heated, or exposed to denaturing agents, the two strands get separated. The statistical physics of this process has a long history, and is commonly described in term of the Poland-Scheraga (PS) model. Crucial to this model is the configurational entropy for a melted region (compared to the entropy of an intact region of the same size), quantified by the loop factor. In this study we investigate how confinement affects the DNA melting transition, by using the loop factor for an ideal Gaussian chain. By subsequent numerical solutions of the PS model, we demonstrate that the melting temperature depends on the persistence lengths of single-stranded and double-stranded DNA. For realistic values of the persistence lengths the melting temperature is predicted to decrease with decreasing channel diameter. We also demonstrate that confinement broadens the melting transition. These general findings hold for the three scenarios investigated: namely 1. homo-DNA, i.e. identical basepairs along the DNA molecule; 2. random sequence DNA, and 3. "real" DNA, here T4 phage DNA. We show that cases 2 and 3 in general give rise to broader transitions than case 1. Case 3 exhibits a similar phase transition as case 2 provided the random sequence DNA has the same ratio of AT to GC basepairs. A simple analytical estimate for the shift in melting temperature is provided as a function of nanochannel diameter. For homo-DNA, we also present an analytical prediction of the melting probability as a function of temperature. |
1204.6023 | Michael Deem | Dirk M. Lorenz, Jeong-Man Park, and Michael W. Deem | Evolutionary Processes in Finite Populations | 23 pages, 11 figures, to appear in Phys. Rev. E | null | 10.1103/PhysRevE.87.022704 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the evolution of large but finite populations on arbitrary
fitness landscapes. We describe the evolutionary process by a Markov, Moran
process. We show that to $\mathcal O(1/N)$, the time-averaged fitness is lower
for the finite population than it is for the infinite population. We also show
that fluctuations in the number of individuals for a given genotype can be
proportional to a power of the inverse of the mutation rate. Finally, we show
that the probability for the system to take a given path through the fitness
landscape can be non-monotonic in system size.
| [
{
"created": "Thu, 26 Apr 2012 19:38:46 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Dec 2012 19:44:45 GMT",
"version": "v2"
},
{
"created": "Fri, 25 Jan 2013 15:57:12 GMT",
"version": "v3"
}
] | 2015-06-04 | [
[
"Lorenz",
"Dirk M.",
""
],
[
"Park",
"Jeong-Man",
""
],
[
"Deem",
"Michael W.",
""
]
] | We consider the evolution of large but finite populations on arbitrary fitness landscapes. We describe the evolutionary process by a Markov, Moran process. We show that to $\mathcal O(1/N)$, the time-averaged fitness is lower for the finite population than it is for the infinite population. We also show that fluctuations in the number of individuals for a given genotype can be proportional to a power of the inverse of the mutation rate. Finally, we show that the probability for the system to take a given path through the fitness landscape can be non-monotonic in system size. |
1905.10923 | Mingyue Liu | Shaolong Chen, Jun Xu, Mingyue Liu, A.L.N. Rao, Roya Zandi, Sarjeet S.
Gill, Umar Mohideen | Investigation of HIV-1 Gag binding with RNAs and Lipids using Atomic
Force Microscopy | null | null | 10.1371/journal.pone.0228036 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Atomic Force Microscopy was utilized to study the morphology of Gag,
{\Psi}RNA, and their binding complexes with lipids in a solution environment
with 0.1{\AA} vertical and 1nm lateral resolution. TARpolyA RNA was used as a
RNA control. The lipid used was phospha-tidylinositol-(4,5)-bisphosphate
(PI(4,5)P2). The morphology of specific complexes Gag-{\Psi}RNA, Gag-TARpolyA
RNA, Gag-PI(4,5)P2 and PI(4,5)P2-{\Psi}RNA-Gag were studied. They were imaged
on either positively or negatively charged mica substrates depending on the net
charges carried. Gag and its complexes consist of monomers, dimers and
tetramers, which was confirmed by gel electrophoresis. The addition of specific
{\Psi}RNA to Gag is found to increase Gag multimerization. Non-specific
TARpolyA RNA was found not to lead to an increase in Gag multimerization. The
addition PI(4,5)P2 to Gag increases Gag multimerization, but to a lesser extent
than {\Psi}RNA. When both {\Psi}RNA and PI(4,5)P2 are present Gag undergoes
comformational changes and an even higher degree of multimerization.
| [
{
"created": "Mon, 27 May 2019 01:27:26 GMT",
"version": "v1"
}
] | 2020-07-01 | [
[
"Chen",
"Shaolong",
""
],
[
"Xu",
"Jun",
""
],
[
"Liu",
"Mingyue",
""
],
[
"Rao",
"A. L. N.",
""
],
[
"Zandi",
"Roya",
""
],
[
"Gill",
"Sarjeet S.",
""
],
[
"Mohideen",
"Umar",
""
]
] | Atomic Force Microscopy was utilized to study the morphology of Gag, {\Psi}RNA, and their binding complexes with lipids in a solution environment with 0.1{\AA} vertical and 1nm lateral resolution. TARpolyA RNA was used as a RNA control. The lipid used was phospha-tidylinositol-(4,5)-bisphosphate (PI(4,5)P2). The morphology of specific complexes Gag-{\Psi}RNA, Gag-TARpolyA RNA, Gag-PI(4,5)P2 and PI(4,5)P2-{\Psi}RNA-Gag were studied. They were imaged on either positively or negatively charged mica substrates depending on the net charges carried. Gag and its complexes consist of monomers, dimers and tetramers, which was confirmed by gel electrophoresis. The addition of specific {\Psi}RNA to Gag is found to increase Gag multimerization. Non-specific TARpolyA RNA was found not to lead to an increase in Gag multimerization. The addition PI(4,5)P2 to Gag increases Gag multimerization, but to a lesser extent than {\Psi}RNA. When both {\Psi}RNA and PI(4,5)P2 are present Gag undergoes comformational changes and an even higher degree of multimerization. |
2307.11735 | Gianmarco Tiddia | Gianmarco Tiddia, Luca Sergi and Bruno Golosio | A theoretical framework for learning through structural plasticity | null | null | null | null | q-bio.NC physics.bio-ph physics.comp-ph | http://creativecommons.org/licenses/by/4.0/ | A growing body of research indicates that structural plasticity mechanisms
are crucial for learning and memory consolidation. Starting from a simple
phenomenological model, we exploit a mean-field approach to develop a
theoretical framework of learning through this kind of plasticity, capable of
taking into account several features of the connectivity and pattern of
activity of biological neural networks, including probability distributions of
neuron firing rates, selectivity of the responses of single neurons to multiple
stimuli, probabilistic connection rules and noisy stimuli. More importantly, it
describes the effects of stabilization, pruning and reorganization of synaptic
connections. This framework is used to compute the values of some relevant
quantities used to characterize the learning and memory capabilities of the
neuronal network in training and testing procedures as the number of training
patterns and other model parameters vary. The results are then compared with
those obtained through simulations with firing-rate-based neuronal network
models.
| [
{
"created": "Fri, 21 Jul 2023 17:47:06 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Aug 2023 09:22:43 GMT",
"version": "v2"
},
{
"created": "Thu, 24 Aug 2023 20:34:18 GMT",
"version": "v3"
},
{
"created": "Tue, 19 Mar 2024 15:18:19 GMT",
"version": "v4"
},
{
"created": "Wed, 29 May 2024 10:37:35 GMT",
"version": "v5"
},
{
"created": "Tue, 18 Jun 2024 07:59:57 GMT",
"version": "v6"
}
] | 2024-06-19 | [
[
"Tiddia",
"Gianmarco",
""
],
[
"Sergi",
"Luca",
""
],
[
"Golosio",
"Bruno",
""
]
] | A growing body of research indicates that structural plasticity mechanisms are crucial for learning and memory consolidation. Starting from a simple phenomenological model, we exploit a mean-field approach to develop a theoretical framework of learning through this kind of plasticity, capable of taking into account several features of the connectivity and pattern of activity of biological neural networks, including probability distributions of neuron firing rates, selectivity of the responses of single neurons to multiple stimuli, probabilistic connection rules and noisy stimuli. More importantly, it describes the effects of stabilization, pruning and reorganization of synaptic connections. This framework is used to compute the values of some relevant quantities used to characterize the learning and memory capabilities of the neuronal network in training and testing procedures as the number of training patterns and other model parameters vary. The results are then compared with those obtained through simulations with firing-rate-based neuronal network models. |
1302.4326 | Jakub Otwinowski | Jakub Otwinowski, Joachim Krug | Clonal interference and Muller's ratchet in spatial habitats | null | Physical Biology, 11(5), 056003, 2014 | 10.1088/1478-3975/11/5/056003 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Competition between independently arising beneficial mutations is enhanced in
spatial populations due to the linear rather than exponential growth of clones.
Recent theoretical studies have pointed out that the resulting fitness dynamics
is analogous to a surface growth process, where new layers nucleate and spread
stochastically, leading to the build up of scale-invariant roughness. This
scenario differs qualitatively from the standard view of adaptation in that the
speed of adaptation becomes independent of population size while the fitness
variance does not. Here we exploit recent progress in the understanding of
surface growth processes to obtain precise predictions for the universal,
non-Gaussian shape of the fitness distribution for one-dimensional habitats,
which are verified by simulations. When the mutations are deleterious rather
than beneficial the problem becomes a spatial version of Muller's ratchet. In
contrast to the case of well-mixed populations, the rate of fitness decline
remains finite even in the limit of an infinite habitat, provided the ratio
$U_d/s^2$ between the deleterious mutation rate and the square of the
(negative) selection coefficient is sufficiently large. Using again an analogy
to surface growth models we show that the transition between the stationary and
the moving state of the ratchet is governed by directed percolation.
| [
{
"created": "Mon, 18 Feb 2013 15:57:00 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Mar 2014 19:58:27 GMT",
"version": "v2"
},
{
"created": "Wed, 23 Jul 2014 14:56:23 GMT",
"version": "v3"
}
] | 2014-11-11 | [
[
"Otwinowski",
"Jakub",
""
],
[
"Krug",
"Joachim",
""
]
] | Competition between independently arising beneficial mutations is enhanced in spatial populations due to the linear rather than exponential growth of clones. Recent theoretical studies have pointed out that the resulting fitness dynamics is analogous to a surface growth process, where new layers nucleate and spread stochastically, leading to the build up of scale-invariant roughness. This scenario differs qualitatively from the standard view of adaptation in that the speed of adaptation becomes independent of population size while the fitness variance does not. Here we exploit recent progress in the understanding of surface growth processes to obtain precise predictions for the universal, non-Gaussian shape of the fitness distribution for one-dimensional habitats, which are verified by simulations. When the mutations are deleterious rather than beneficial the problem becomes a spatial version of Muller's ratchet. In contrast to the case of well-mixed populations, the rate of fitness decline remains finite even in the limit of an infinite habitat, provided the ratio $U_d/s^2$ between the deleterious mutation rate and the square of the (negative) selection coefficient is sufficiently large. Using again an analogy to surface growth models we show that the transition between the stationary and the moving state of the ratchet is governed by directed percolation. |
0911.4074 | Olga Isaeva | O.G. Isaeva and V.A. Osipov | Photodynamic therapy influence on anti-cancer immunity | 9 pages, 5 figures | null | 10.1117/12.853588 | null | q-bio.CB q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The system of partial differential equations describing tumor-immune dynamics
with angiogenesis taken into account is presented. For spatially homogeneous
case, the steady state analysis of the model is carried out. The effects of
single photodynamic impact are numerically simulated. In the case of strong
immune response we found that the photodynamic therapy (PDT) gives rise to the
substantial shrinkage of tumor size which is accompanied by the increase of
interleukin-2 concentration. On the contrary, the photodynamic stimulation of
weak immune response is shown to be insufficient to reduce the tumor. These
findings indicate the important role of anti-cancer immune response in the
long-term tumor control after PDT.
| [
{
"created": "Fri, 20 Nov 2009 16:22:38 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Dec 2009 15:06:12 GMT",
"version": "v2"
}
] | 2015-05-14 | [
[
"Isaeva",
"O. G.",
""
],
[
"Osipov",
"V. A.",
""
]
] | The system of partial differential equations describing tumor-immune dynamics with angiogenesis taken into account is presented. For spatially homogeneous case, the steady state analysis of the model is carried out. The effects of single photodynamic impact are numerically simulated. In the case of strong immune response we found that the photodynamic therapy (PDT) gives rise to the substantial shrinkage of tumor size which is accompanied by the increase of interleukin-2 concentration. On the contrary, the photodynamic stimulation of weak immune response is shown to be insufficient to reduce the tumor. These findings indicate the important role of anti-cancer immune response in the long-term tumor control after PDT. |
1807.03881 | Anish Mukherjee | Anish Mukherjee, Joshua Hooks, J. Brandon Dixon | Entrainment of Lymphatic Contraction to Oscillatory Flow | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lymphedema, a disfiguring condition characterized by the asymmetrical
swelling of the limbs, is suspected to be caused by dysfunctions in the
lymphatic system. Lymphangions, the spontaneously contracting units of the
lymphatic system, are sensitive to luminal wall shear stress. In this study,
the response of the lymphangions to dynamically varying wall shear stress is
characterized in isolated rat thoracic ducts in relation to their shear
sensitivity. The critical shear stress above which the thoracic duct shows
substantial inhibition of contraction was found to be significantly negatively
correlated to the diameter of the lymphangion. The entrainment of the
lymphangion to an applied oscillatory shear stress was found to be
significantly dependent on the difference between the applied frequency and
intrinsic frequency of contraction of the lymphangion. The strength of
entrainment was also positively correlated to the applied shear stress when
this shear was below the critical shear stress. The results suggest an
adaptation of the lymphangion contractility to the existing oscillatory
mechanical shear stress as a function of its intrinsic contractility and shear
sensitivity. These adaptations might be crucial to ensure synchronized
contraction of adjacent lymphangions through mechanosensitive means and might
help explain the lymphatic dysfunctions that result from impaired
mechanosensitivity.
| [
{
"created": "Tue, 10 Jul 2018 21:49:18 GMT",
"version": "v1"
}
] | 2018-07-12 | [
[
"Mukherjee",
"Anish",
""
],
[
"Hooks",
"Joshua",
""
],
[
"Dixon",
"J. Brandon",
""
]
] | Lymphedema, a disfiguring condition characterized by the asymmetrical swelling of the limbs, is suspected to be caused by dysfunctions in the lymphatic system. Lymphangions, the spontaneously contracting units of the lymphatic system, are sensitive to luminal wall shear stress. In this study, the response of the lymphangions to dynamically varying wall shear stress is characterized in isolated rat thoracic ducts in relation to their shear sensitivity. The critical shear stress above which the thoracic duct shows substantial inhibition of contraction was found to be significantly negatively correlated to the diameter of the lymphangion. The entrainment of the lymphangion to an applied oscillatory shear stress was found to be significantly dependent on the difference between the applied frequency and intrinsic frequency of contraction of the lymphangion. The strength of entrainment was also positively correlated to the applied shear stress when this shear was below the critical shear stress. The results suggest an adaptation of the lymphangion contractility to the existing oscillatory mechanical shear stress as a function of its intrinsic contractility and shear sensitivity. These adaptations might be crucial to ensure synchronized contraction of adjacent lymphangions through mechanosensitive means and might help explain the lymphatic dysfunctions that result from impaired mechanosensitivity. |
1610.02292 | Paola Causin Paola Causin | Paola Causin and Francesca Malgaroli | Mathematical modeling of local perfusion in large distensible
microvascular networks | null | null | 10.1016/j.cma.2017.05.015 | null | q-bio.TO math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Microvessels -blood vessels with diameter less than 200 microns- form large,
intricate networks organized into arterioles, capillaries and venules. In these
networks, the distribution of flow and pressure drop is a highly interlaced
function of single vessel resistances and mutual vessel interactions. In this
paper we propose a mathematical and computational model to study the behavior
of microcirculatory networks subjected to different conditions. The network
geometry is composed of a graph of connected straight cylinders, each one
representing a vessel. The blood flow and pressure drop across the single
vessel, further split into smaller elements, are related through a generalized
Ohm's law featuring a conductivity parameter, function of the vessel cross
section area and geometry, which undergo deformations under pressure loads. The
membrane theory is used to describe the deformation of vessel lumina, tailored
to the structure of thick-walled arterioles and thin-walled venules. In
addition, since venules can possibly experience negative transmural pressures,
a buckling model is also included to represent vessel collapse. The complete
model including arterioles, capillaries and venules represents a nonlinear
system of PDEs, which is approached numerically by finite element
discretization and linearization techniques. We use the model to simulate flow
in the microcirculation of the human eye retina, a terminal system with a
single inlet and outlet. After a phase of validation against experimental
measurements, we simulate the network response to different interstitial
pressure values. Such a study is carried out both for global and localized
variations of the interstitial pressure. In both cases, significant
redistributions of the blood flow in the network arise, highlighting the
importance of considering the single vessel behavior along with its position
and connectivity in the network.
| [
{
"created": "Wed, 28 Sep 2016 11:17:38 GMT",
"version": "v1"
}
] | 2017-08-02 | [
[
"Causin",
"Paola",
""
],
[
"Malgaroli",
"Francesca",
""
]
] | Microvessels -blood vessels with diameter less than 200 microns- form large, intricate networks organized into arterioles, capillaries and venules. In these networks, the distribution of flow and pressure drop is a highly interlaced function of single vessel resistances and mutual vessel interactions. In this paper we propose a mathematical and computational model to study the behavior of microcirculatory networks subjected to different conditions. The network geometry is composed of a graph of connected straight cylinders, each one representing a vessel. The blood flow and pressure drop across the single vessel, further split into smaller elements, are related through a generalized Ohm's law featuring a conductivity parameter, function of the vessel cross section area and geometry, which undergo deformations under pressure loads. The membrane theory is used to describe the deformation of vessel lumina, tailored to the structure of thick-walled arterioles and thin-walled venules. In addition, since venules can possibly experience negative transmural pressures, a buckling model is also included to represent vessel collapse. The complete model including arterioles, capillaries and venules represents a nonlinear system of PDEs, which is approached numerically by finite element discretization and linearization techniques. We use the model to simulate flow in the microcirculation of the human eye retina, a terminal system with a single inlet and outlet. After a phase of validation against experimental measurements, we simulate the network response to different interstitial pressure values. Such a study is carried out both for global and localized variations of the interstitial pressure. In both cases, significant redistributions of the blood flow in the network arise, highlighting the importance of considering the single vessel behavior along with its position and connectivity in the network. |
0706.1198 | Anirban Banerjee | Anirban Banerjee and J\"urgen Jost | Spectral plots and the representation and interpretation of biological
data | 15 pages, 7 figures | Theory in Biosciences, 126(1), 15-21, (2007) | 10.1007/s12064-007-0005-9 | null | q-bio.QM | null | It is basic question in biology and other fields to identify the char-
acteristic properties that on one hand are shared by structures from a
particular realm, like gene regulation, protein-protein interaction or neu- ral
networks or foodwebs, and that on the other hand distinguish them from other
structures. We introduce and apply a general method, based on the spectrum of
the normalized graph Laplacian, that yields repre- sentations, the spectral
plots, that allow us to find and visualize such properties systematically. We
present such visualizations for a wide range of biological networks and compare
them with those for networks derived from theoretical schemes. The differences
that we find are quite striking and suggest that the search for universal
properties of biological networks should be complemented by an understanding of
more specific features of biological organization principles at different
scales.
| [
{
"created": "Fri, 8 Jun 2007 16:52:48 GMT",
"version": "v1"
}
] | 2012-10-19 | [
[
"Banerjee",
"Anirban",
""
],
[
"Jost",
"Jürgen",
""
]
] | It is basic question in biology and other fields to identify the char- acteristic properties that on one hand are shared by structures from a particular realm, like gene regulation, protein-protein interaction or neu- ral networks or foodwebs, and that on the other hand distinguish them from other structures. We introduce and apply a general method, based on the spectrum of the normalized graph Laplacian, that yields repre- sentations, the spectral plots, that allow us to find and visualize such properties systematically. We present such visualizations for a wide range of biological networks and compare them with those for networks derived from theoretical schemes. The differences that we find are quite striking and suggest that the search for universal properties of biological networks should be complemented by an understanding of more specific features of biological organization principles at different scales. |
2401.09255 | Ardalan Aarabi | Mahshid Fouladivanda, Kamran Kazemi, Habibollah Danyali, Ardalan
Aarabi | Graph-based vulnerability assessment of resting-state functional brain
networks in full-term neonates | null | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Network disruption during early brain development can result in long-term
cognitive impairments. In this study, we investigated rich-club organization in
resting-state functional brain networks in full-term neonates using a
multiscale connectivity analysis. We further identified the most influential
nodes, also called spreaders, having higher impacts on the flow of information
throughout the network. The network vulnerability to damage to rich-club (RC)
connectivity within and between resting-state networks was also assessed using
a graph-based vulnerability analysis. Our results revealed a rich club
organization and small-world topology for resting-state functional brain
networks in full term neonates, regardless of the network size. Interconnected
mostly through short-range connections, functional rich-club hubs were confined
to sensory-motor, cognitive-attention-salience (CAS), default mode, and
language-auditory networks with an average cross-scale overlap of 36%, 20%, 15%
and 12%, respectively. The majority of the functional hubs also showed high
spreading potential, except for several non-RC spreaders within CAS and
temporal networks. The functional networks exhibited high vulnerability to loss
of RC nodes within sensorimotor cortices, resulting in a significant increase
and decrease in network segregation and integration, respectively. The network
vulnerability to damage to RC nodes within the language-auditory,
cognitive-attention-salience, and default mode networks was also significant
but relatively less prominent. Our findings suggest that the network
integration in neonates can be highly compromised by damage to RC connectivity
due to brain immaturity.
| [
{
"created": "Wed, 17 Jan 2024 15:00:31 GMT",
"version": "v1"
}
] | 2024-01-18 | [
[
"Fouladivanda",
"Mahshid",
""
],
[
"Kazemi",
"Kamran",
""
],
[
"Danyali",
"Habibollah",
""
],
[
"Aarabi",
"Ardalan",
""
]
] | Network disruption during early brain development can result in long-term cognitive impairments. In this study, we investigated rich-club organization in resting-state functional brain networks in full-term neonates using a multiscale connectivity analysis. We further identified the most influential nodes, also called spreaders, having higher impacts on the flow of information throughout the network. The network vulnerability to damage to rich-club (RC) connectivity within and between resting-state networks was also assessed using a graph-based vulnerability analysis. Our results revealed a rich club organization and small-world topology for resting-state functional brain networks in full term neonates, regardless of the network size. Interconnected mostly through short-range connections, functional rich-club hubs were confined to sensory-motor, cognitive-attention-salience (CAS), default mode, and language-auditory networks with an average cross-scale overlap of 36%, 20%, 15% and 12%, respectively. The majority of the functional hubs also showed high spreading potential, except for several non-RC spreaders within CAS and temporal networks. The functional networks exhibited high vulnerability to loss of RC nodes within sensorimotor cortices, resulting in a significant increase and decrease in network segregation and integration, respectively. The network vulnerability to damage to RC nodes within the language-auditory, cognitive-attention-salience, and default mode networks was also significant but relatively less prominent. Our findings suggest that the network integration in neonates can be highly compromised by damage to RC connectivity due to brain immaturity. |
2109.02041 | Ido Kanter | Roni Vardi, Yael Tugendhaft, Shira Sardi and Ido Kanter | Significant anisotropic neuronal refractory period plasticity | 16 pages, 11 figures | 2021 EPL 134 60007 | 10.1209/0295-5075/ac177a | https://iopscience.iop.org/article/10.1209/0295-5075/ac177a/meta | q-bio.NC physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Refractory periods are an unavoidable feature of excitable elements,
resulting in necessary time-lags for re-excitation. Herein, we measure neuronal
absolute refractory periods (ARPs) in synaptic blocked neuronal cultures. In so
doing, we show that their duration can be significantly extended by dozens of
milliseconds using preceding evoked spikes generated by extracellular
stimulations. The ARP increases with the frequency of preceding stimulations,
and saturates at the intermittent phase of the neuronal response latency, where
a short relative refractory period might appear. Nevertheless, preceding
stimulations via a different extracellular route does not affect the ARP. It is
also found to be independent of preceding intracellular stimulations. All these
features strongly suggest that the anisotropic ARPs originate in neuronal
dendrites. The results demonstrate the fast and significant plasticity of the
neuronal ARP, depending on the firing activity of its connecting neurons, which
is expected to affect network dynamics.
| [
{
"created": "Sun, 5 Sep 2021 11:38:21 GMT",
"version": "v1"
}
] | 2021-09-07 | [
[
"Vardi",
"Roni",
""
],
[
"Tugendhaft",
"Yael",
""
],
[
"Sardi",
"Shira",
""
],
[
"Kanter",
"Ido",
""
]
] | Refractory periods are an unavoidable feature of excitable elements, resulting in necessary time-lags for re-excitation. Herein, we measure neuronal absolute refractory periods (ARPs) in synaptic blocked neuronal cultures. In so doing, we show that their duration can be significantly extended by dozens of milliseconds using preceding evoked spikes generated by extracellular stimulations. The ARP increases with the frequency of preceding stimulations, and saturates at the intermittent phase of the neuronal response latency, where a short relative refractory period might appear. Nevertheless, preceding stimulations via a different extracellular route does not affect the ARP. It is also found to be independent of preceding intracellular stimulations. All these features strongly suggest that the anisotropic ARPs originate in neuronal dendrites. The results demonstrate the fast and significant plasticity of the neuronal ARP, depending on the firing activity of its connecting neurons, which is expected to affect network dynamics. |
q-bio/0501020 | Peter F. Arndt | Peter F Arndt, Terence Hwa, and Dmitri A Petrov | Substantial regional variation in substitution rates in the human
genome: importance of GC content, gene density and telomere-specific effects | 35 pages, 6 figures | null | null | null | q-bio.GN | null | This study presents the first global, 1 Mbp level analysis of patterns of
nucleotide substitutions along the human lineage. The study is based on the
analysis of a large amount of repetitive elements deposited into the human
genome since the mammalian radiation, yielding a number of results that would
have been difficult to obtain using the more conventional comparative method of
analysis. This analysis revealed substantial and consistent variability of
rates of substitution, with the variability ranging up to 2-fold among
different regions. The rates of substitutions of C or G nucleotides with A or T
nucleotides vary much more sharply than the reverse rates suggesting that much
of that variation is due to differences in mutation rates rather than in the
probabilities of fixation of C/G vs. A/T nucleotides across the genome. For all
types of substitution we observe substantially more hotspots than coldspots,
with hotspots showing substantial clustering over tens of Mbp's. Our analysis
revealed that GC-content of surrounding sequences is the best predictor of the
rates of substitution. The pattern of substitution appears very different near
telomeres compared to the rest of the genome and cannot be explained by the
genome-wide correlations of the substitution rates with GC content or exon
density. The telomere pattern of substitution is consistent with natural
selection or biased gene conversion acting to increase the GC-content of the
sequences that are within 10-15 Mbp away from the telomere.
| [
{
"created": "Thu, 13 Jan 2005 18:19:32 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Arndt",
"Peter F",
""
],
[
"Hwa",
"Terence",
""
],
[
"Petrov",
"Dmitri A",
""
]
] | This study presents the first global, 1 Mbp level analysis of patterns of nucleotide substitutions along the human lineage. The study is based on the analysis of a large amount of repetitive elements deposited into the human genome since the mammalian radiation, yielding a number of results that would have been difficult to obtain using the more conventional comparative method of analysis. This analysis revealed substantial and consistent variability of rates of substitution, with the variability ranging up to 2-fold among different regions. The rates of substitutions of C or G nucleotides with A or T nucleotides vary much more sharply than the reverse rates suggesting that much of that variation is due to differences in mutation rates rather than in the probabilities of fixation of C/G vs. A/T nucleotides across the genome. For all types of substitution we observe substantially more hotspots than coldspots, with hotspots showing substantial clustering over tens of Mbp's. Our analysis revealed that GC-content of surrounding sequences is the best predictor of the rates of substitution. The pattern of substitution appears very different near telomeres compared to the rest of the genome and cannot be explained by the genome-wide correlations of the substitution rates with GC content or exon density. The telomere pattern of substitution is consistent with natural selection or biased gene conversion acting to increase the GC-content of the sequences that are within 10-15 Mbp away from the telomere. |
1710.09459 | Ashish B. George | Ashish B. George and Kirill S. Korolev | Chirality provides a direct fitness advantage and facilitates
intermixing in cellular aggregates | null | null | null | null | q-bio.PE cond-mat.soft cond-mat.stat-mech nlin.PS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chirality in shape and motility can evolve rapidly in microbes and cancer
cells. To determine how chirality affects cell fitness, we developed a model of
chiral growth in compact aggregates such as microbial colonies and solid
tumors. Our model recapitulates previous experimental findings and shows that
mutant cells can invade by increasing their chirality or switching their
handedness. The invasion results either in a takeover or stable coexistence
between the mutant and the ancestor depending on their relative chirality. For
large chiralities, the coexistence is accompanied by strong intermixing between
the cells, while spatial segregation occurs otherwise. We show that the
competition within the aggregate is mediated by bulges in regions where the
cells with different chiralities meet. The two-way coupling between aggregate
shape and natural selection is described by the chiral Kardar-Parisi-Zhang
equation coupled to the Burgers' equation with multiplicative noise. We solve
for the key features of this theory to explain the origin of selection on
chirality. Overall, our work suggests that chirality could be an important
ecological trait that mediates competition, invasion, and spatial structure in
cellular populations.
| [
{
"created": "Wed, 25 Oct 2017 20:58:23 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Jun 2018 00:44:49 GMT",
"version": "v2"
},
{
"created": "Fri, 10 Jul 2020 20:26:38 GMT",
"version": "v3"
}
] | 2020-07-14 | [
[
"George",
"Ashish B.",
""
],
[
"Korolev",
"Kirill S.",
""
]
] | Chirality in shape and motility can evolve rapidly in microbes and cancer cells. To determine how chirality affects cell fitness, we developed a model of chiral growth in compact aggregates such as microbial colonies and solid tumors. Our model recapitulates previous experimental findings and shows that mutant cells can invade by increasing their chirality or switching their handedness. The invasion results either in a takeover or stable coexistence between the mutant and the ancestor depending on their relative chirality. For large chiralities, the coexistence is accompanied by strong intermixing between the cells, while spatial segregation occurs otherwise. We show that the competition within the aggregate is mediated by bulges in regions where the cells with different chiralities meet. The two-way coupling between aggregate shape and natural selection is described by the chiral Kardar-Parisi-Zhang equation coupled to the Burgers' equation with multiplicative noise. We solve for the key features of this theory to explain the origin of selection on chirality. Overall, our work suggests that chirality could be an important ecological trait that mediates competition, invasion, and spatial structure in cellular populations. |
2301.02242 | Nishant Rajadhyaksha | Nishant Rajadhyaksha and Aarushi Chitkara | Graph Contrastive Learning for Multi-omics Data | null | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by/4.0/ | Advancements in technologies related to working with omics data require novel
computation methods to fully leverage information and help develop a better
understanding of human diseases. This paper studies the effects of introducing
graph contrastive learning to help leverage graph structure and information to
produce better representations for downstream classification tasks for
multi-omics datasets. We present a learnining framework named Multi-Omics Graph
Contrastive Learner(MOGCL) which outperforms several aproaches for integrating
multi-omics data for supervised learning tasks. We show that pre-training graph
models with a contrastive methodology along with fine-tuning it in a supervised
manner is an efficient strategy for multi-omics data classification.
| [
{
"created": "Tue, 3 Jan 2023 10:03:08 GMT",
"version": "v1"
}
] | 2023-10-17 | [
[
"Rajadhyaksha",
"Nishant",
""
],
[
"Chitkara",
"Aarushi",
""
]
] | Advancements in technologies related to working with omics data require novel computation methods to fully leverage information and help develop a better understanding of human diseases. This paper studies the effects of introducing graph contrastive learning to help leverage graph structure and information to produce better representations for downstream classification tasks for multi-omics datasets. We present a learnining framework named Multi-Omics Graph Contrastive Learner(MOGCL) which outperforms several aproaches for integrating multi-omics data for supervised learning tasks. We show that pre-training graph models with a contrastive methodology along with fine-tuning it in a supervised manner is an efficient strategy for multi-omics data classification. |
2101.10746 | Liam Maher | Katharina T. Huber and Liam J. Maher | The hybrid number of a ploidy profile | 27 pages, 11 figures, 26 citations | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Polyploidization, whereby an organism inherits multiple copies of the genome
of their parents, is an important evolutionary event that has been observed in
plants and animals. One way to study such events is in terms of the ploidy
number of the species that make up a dataset of interest. It is therefore
natural to ask: How much information about the evolutionary past of the set of
species that form a dataset can be gleaned from the ploidy numbers of the
species? To help answer this question, we introduce and study the novel concept
of a ploidy profile which allows us to formalize it in terms of a multiplicity
vector indexed by the species the dataset is comprised of. Using the framework
of a phylogenetic network, we present a closed formula for computing the hybrid
number (i.e. the minimal number of polyploidization events required to explain
a ploidy profile) of a large class of ploidy profiles. This formula relies on
the construction of a certain phylogenetic network from the simplification
sequence of a ploidy profile and the hybrid number of the ploidy profile with
which this construction is initialized. Both of them can be computed easily in
case the ploidy numbers that make up the ploidy profile are not too large. To
help illustrate the applicability of our approach, we apply it to a simplified
version of a publicly available Viola dataset.
| [
{
"created": "Tue, 26 Jan 2021 12:37:20 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Aug 2022 11:52:25 GMT",
"version": "v2"
}
] | 2022-08-12 | [
[
"Huber",
"Katharina T.",
""
],
[
"Maher",
"Liam J.",
""
]
] | Polyploidization, whereby an organism inherits multiple copies of the genome of their parents, is an important evolutionary event that has been observed in plants and animals. One way to study such events is in terms of the ploidy number of the species that make up a dataset of interest. It is therefore natural to ask: How much information about the evolutionary past of the set of species that form a dataset can be gleaned from the ploidy numbers of the species? To help answer this question, we introduce and study the novel concept of a ploidy profile which allows us to formalize it in terms of a multiplicity vector indexed by the species the dataset is comprised of. Using the framework of a phylogenetic network, we present a closed formula for computing the hybrid number (i.e. the minimal number of polyploidization events required to explain a ploidy profile) of a large class of ploidy profiles. This formula relies on the construction of a certain phylogenetic network from the simplification sequence of a ploidy profile and the hybrid number of the ploidy profile with which this construction is initialized. Both of them can be computed easily in case the ploidy numbers that make up the ploidy profile are not too large. To help illustrate the applicability of our approach, we apply it to a simplified version of a publicly available Viola dataset. |
1604.02454 | Sergio Estay | Daniela N. Lopez, Patricio A. Camus, Nelson Valdivia and Sergio A.
Estay | High temporal variability in the occurrence of consumer-resource
interactions in ecological networks | 21 pages, 3 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ecological networks are theoretical abstractions that represent ecological
communities. These networks are usually defined as static entities, in which
the occurrence of a particular interaction between species is considered fixed
despite the intrinsic dynamics of ecological systems. However, empirical
analysis of the temporal variation of trophic interactions is constrained by
the lack of data with high spatial, temporal, and taxonomic resolution. Here,
we evaluate the spatiotemporal variability of multiple consumer-resource
interactions of large marine networks. The tropic interactions of all of the
analyzed networks had low temporal persistence, which was well described by a
common exponential decay in the rank-frequency relationship of
consumer-resource interactions. This common pattern of low temporal persistence
was evident despite the dissimilarities of environmental conditions among
sites. Between-site rank correlations of frequency of occurrence of
interactions ranged from 0.59 to 0.73. After removing the interactions with
<50% frequency, the between-site correlations decreased to values between 0.60
and 0.28, indicating that low-frequency interactions accounted for the apparent
similarities between sites. Our results showed that the communities studied
were characterized by few persistent interactions and a large number of
transient trophic interactions. We suggest that consumer-resource temporal
asynchrony in addition to varying local environmental conditions and
opportunistic foraging could be among the mechanisms generating the observed
rank-frequency relationship of trophic interactions. Therefore, our results
question the analysis of ecological communities as static and persistent
natural entities and stress the need for strengthening the analysis of temporal
variability in ecological networks and long-term studies.
| [
{
"created": "Fri, 8 Apr 2016 19:20:42 GMT",
"version": "v1"
}
] | 2016-04-12 | [
[
"Lopez",
"Daniela N.",
""
],
[
"Camus",
"Patricio A.",
""
],
[
"Valdivia",
"Nelson",
""
],
[
"Estay",
"Sergio A.",
""
]
] | Ecological networks are theoretical abstractions that represent ecological communities. These networks are usually defined as static entities, in which the occurrence of a particular interaction between species is considered fixed despite the intrinsic dynamics of ecological systems. However, empirical analysis of the temporal variation of trophic interactions is constrained by the lack of data with high spatial, temporal, and taxonomic resolution. Here, we evaluate the spatiotemporal variability of multiple consumer-resource interactions of large marine networks. The tropic interactions of all of the analyzed networks had low temporal persistence, which was well described by a common exponential decay in the rank-frequency relationship of consumer-resource interactions. This common pattern of low temporal persistence was evident despite the dissimilarities of environmental conditions among sites. Between-site rank correlations of frequency of occurrence of interactions ranged from 0.59 to 0.73. After removing the interactions with <50% frequency, the between-site correlations decreased to values between 0.60 and 0.28, indicating that low-frequency interactions accounted for the apparent similarities between sites. Our results showed that the communities studied were characterized by few persistent interactions and a large number of transient trophic interactions. We suggest that consumer-resource temporal asynchrony in addition to varying local environmental conditions and opportunistic foraging could be among the mechanisms generating the observed rank-frequency relationship of trophic interactions. Therefore, our results question the analysis of ecological communities as static and persistent natural entities and stress the need for strengthening the analysis of temporal variability in ecological networks and long-term studies. |
1504.07902 | Manoel Manghi | Anna\"el Brunet, S\'ebastien Chevalier, Nicolas Destainville, Manoel
Manghi, Philippe Rousseau, Maya Salhi, Laurence Salom\'e, Catherine Tardin | Probing a label-free local bend in DNA by single-molecule Tethered
Particle Motion | in Nucleic Acids Research published March 12, 2015 | null | 10.1016/j.bpj.2015.11.1027 | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Being capable of characterizing DNA local bending is essential to understand
thoroughly many biological processes because they involve a local bending of
the double helix axis, either intrinsic to the sequence or induced by the
binding of proteins. Developing a method to measure DNA bend angles that does
not perturb the conformation of the DNA itself or the DNA-protein complex is a
challenging task. Here, we propose a joint theory-experiment high throughput
approach to rigorously measure such bend angles using the Tethered Particle
Motion (TPM) technique. By carefully modeling the TPM geometry, we propose a
simple formula based on a kinked Worm-Like Chain model to extract the bend
angle from TPM measurements. Using constructs made of 575 base-pair DNAs with
in-phase assemblies of 1 to 7 6A-tracts, we find that the sequence CA6CGG
induces a bend angle of 19 [4] {\deg}. Our method is successfully compared to
more theoretically complex or experimentally invasive ones such as cyclization,
NMR, FRET or AFM. We further apply our procedure to TPM measurements from the
literature and demonstrate that the angles of bends induced by proteins, such
as Integration Host Factor (IHF) can be reliably evaluated as well.
| [
{
"created": "Wed, 29 Apr 2015 15:49:58 GMT",
"version": "v1"
}
] | 2016-04-20 | [
[
"Brunet",
"Annaël",
""
],
[
"Chevalier",
"Sébastien",
""
],
[
"Destainville",
"Nicolas",
""
],
[
"Manghi",
"Manoel",
""
],
[
"Rousseau",
"Philippe",
""
],
[
"Salhi",
"Maya",
""
],
[
"Salomé",
"Laurence",
""
],
[
"Tardin",
"Catherine",
""
]
] | Being capable of characterizing DNA local bending is essential to understand thoroughly many biological processes because they involve a local bending of the double helix axis, either intrinsic to the sequence or induced by the binding of proteins. Developing a method to measure DNA bend angles that does not perturb the conformation of the DNA itself or the DNA-protein complex is a challenging task. Here, we propose a joint theory-experiment high throughput approach to rigorously measure such bend angles using the Tethered Particle Motion (TPM) technique. By carefully modeling the TPM geometry, we propose a simple formula based on a kinked Worm-Like Chain model to extract the bend angle from TPM measurements. Using constructs made of 575 base-pair DNAs with in-phase assemblies of 1 to 7 6A-tracts, we find that the sequence CA6CGG induces a bend angle of 19 [4] {\deg}. Our method is successfully compared to more theoretically complex or experimentally invasive ones such as cyclization, NMR, FRET or AFM. We further apply our procedure to TPM measurements from the literature and demonstrate that the angles of bends induced by proteins, such as Integration Host Factor (IHF) can be reliably evaluated as well. |
q-bio/0610058 | Robert Clewley | Robert H. Clewley, John M. Guckenheimer, and Francisco J.
Valero-Cuevas | Estimating degrees of freedom in motor systems | 44 pages, 30 figures | null | null | null | q-bio.QM physics.data-an | null | Studies of the degrees of freedom or "synergies" in musculoskeletal systems
rely critically on algorithms to estimate the "dimension" of kinematic or
neural data. Linear algorithms such as principal component analysis (PCA) are
used almost exclusively for this purpose. However, biological systems tend to
possess nonlinearities and operate at multiple spatial and temporal scales so
that the set of reachable system states typically does not lie close to a
single linear subspace. We compare the performance of PCA to two alternative
nonlinear algorithms (Isomap and our novel pointwise dimension estimation
(PD-E)) using synthetic and motion capture data from a robotic arm with known
kinematic dimensions, as well as motion capture data from human hands. We find
that consideration of the spectral properties of the singular value
decomposition in PCA can lead to more accurate dimension estimates than the
dominant practice of using a fixed variance capture threshold. We investigate
methods for identifying a single integer dimension using PCA and Isomap. In
contrast, PD-E provides a range of estimates of fractal dimension. This helps
to identify heterogeneous geometric structure of data sets such as unions of
manifolds of differing dimensions, to which Isomap is less sensitive. Contrary
to common opinion regarding fractal dimension methods, PD-E yielded reasonable
results with reasonable amounts of data. We conclude that it is necessary and
feasible to complement PCA with other methods that take into consideration the
nonlinear properties of biological systems for a more robust estimation of
their degrees of freedom.
| [
{
"created": "Mon, 30 Oct 2006 20:29:03 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Clewley",
"Robert H.",
""
],
[
"Guckenheimer",
"John M.",
""
],
[
"Valero-Cuevas",
"Francisco J.",
""
]
] | Studies of the degrees of freedom or "synergies" in musculoskeletal systems rely critically on algorithms to estimate the "dimension" of kinematic or neural data. Linear algorithms such as principal component analysis (PCA) are used almost exclusively for this purpose. However, biological systems tend to possess nonlinearities and operate at multiple spatial and temporal scales so that the set of reachable system states typically does not lie close to a single linear subspace. We compare the performance of PCA to two alternative nonlinear algorithms (Isomap and our novel pointwise dimension estimation (PD-E)) using synthetic and motion capture data from a robotic arm with known kinematic dimensions, as well as motion capture data from human hands. We find that consideration of the spectral properties of the singular value decomposition in PCA can lead to more accurate dimension estimates than the dominant practice of using a fixed variance capture threshold. We investigate methods for identifying a single integer dimension using PCA and Isomap. In contrast, PD-E provides a range of estimates of fractal dimension. This helps to identify heterogeneous geometric structure of data sets such as unions of manifolds of differing dimensions, to which Isomap is less sensitive. Contrary to common opinion regarding fractal dimension methods, PD-E yielded reasonable results with reasonable amounts of data. We conclude that it is necessary and feasible to complement PCA with other methods that take into consideration the nonlinear properties of biological systems for a more robust estimation of their degrees of freedom. |
q-bio/0606016 | Wentian Li | Wentian Li and Pedro Miramontes | Large-scale Oscillation of Structure-Related DNA Sequence Features in
Human Chromosome 21 | submitted to Physical Review E | Physical Review E, 74:021912 (2006) | 10.1103/PhysRevE.74.021912 | null | q-bio.GN | null | Human chromosome 21 is the only chromosome in human genome that exhibits
oscillation of (G+C)-content of cycle length of hundreds kilobases (500 kb near
the right telomere). We aim at establishing the existence of similar
periodicity in structure-related sequence features in order to relate this
(G+C)% oscillation to other biological phenomena. The following quantities are
shown to oscillate with the same 500kb periodicity in human chromosome 21:
binding energy calculated by two sets of dinucleotide-based thermodynamic
parameters, AA/TT and AAA/TTT bi-/tri-nucleotide density, 5'-TA-3' dinucleotide
density, and signal for 10/11-base periodicity of AA/TT or AAA/TTT. These
intrinsic quantities are related to structural features of the double helix of
DNA molecules, such as base-pair binding, untwisting/unwinding, stiffness, and
a putative tendency for nucleosome formation.
| [
{
"created": "Wed, 14 Jun 2006 20:27:49 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Li",
"Wentian",
""
],
[
"Miramontes",
"Pedro",
""
]
] | Human chromosome 21 is the only chromosome in human genome that exhibits oscillation of (G+C)-content of cycle length of hundreds kilobases (500 kb near the right telomere). We aim at establishing the existence of similar periodicity in structure-related sequence features in order to relate this (G+C)% oscillation to other biological phenomena. The following quantities are shown to oscillate with the same 500kb periodicity in human chromosome 21: binding energy calculated by two sets of dinucleotide-based thermodynamic parameters, AA/TT and AAA/TTT bi-/tri-nucleotide density, 5'-TA-3' dinucleotide density, and signal for 10/11-base periodicity of AA/TT or AAA/TTT. These intrinsic quantities are related to structural features of the double helix of DNA molecules, such as base-pair binding, untwisting/unwinding, stiffness, and a putative tendency for nucleosome formation. |
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