Escape rate of transiently active Brownian particle in one dimension

Scacchi, Alberto; Brader, Joseph M.; Sharma, Abhinav

doi:10.1103/physreve.100.012601

Cited by 13 publications

(9 citation statements)

References 18 publications

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“…For example, reconstructing dynamical geneexpression information from static snapshots is sometimes possible in the presence of oscillatory dynamics caused by processes such as the cell cycle, but can fail for gene networks with large oscillations that are not orthogonal to the processes of interest 15 . Adapting the above protocol to reconstruct the dynamics in the latter case, and of far-from-equilibrium systems in general, will require incorporating more sophisticated theories that include time-resolved information [75][76][77][78] and improved expressions for non-equilibrium transition rates 79 , and account for probabilistic fluxes 80 .…”

Section: Discussionmentioning

confidence: 99%

Learning Dynamical Information from Static Protein and Sequencing Data

Pearce

Woodhouse

Forrow

et al. 2020

Biophysical Journal

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Section: Discussionmentioning

confidence: 99%

Learning Dynamical Information from Static Protein and Sequencing Data

Pearce

Woodhouse

Forrow

et al. 2020

Biophysical Journal

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“…For example, reconstructing dynamical gene-expression information from static snapshots is sometimes possible in the presence of oscillatory dynamics caused by processes such as the cell cycle, but can fail for gene networks with large oscillations that are not orthogonal to the processes of interest 15 . Adapting the above protocol to reconstruct the dynamics in the latter case, and of far-from-equilibrium systems in general, will require incorporating more sophisticated theories that include time-resolved information 75–78 and improved expressions for non-equilibrium transition rates 79 , and account for probabilistic fluxes 80 .…”

Section: Discussionmentioning

confidence: 99%

Learning dynamical information from static protein and sequencing data

et al. 2019

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Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. Although efficient algorithms for cluster detection in high-dimensional spaces have been developed over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein-folding transitions, gene-regulatory network motifs, and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations, and phylogenetic trees, respectively. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein-sequencing datasets, and future cryo-electron microscopy (cryo-EM) data.

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“…Several recent studies have been devoted to explore different aspects of the escape kinetics of self-propelled particles from metastable states [12,43,[47][48][49][50][51][52][53][54][55]. Aiming at nano-technological and biomedical applications, the escape dynamics of self-propelled particles from a compartment of entropic channels has been investigated in Refs [12,43].…”

Section: Introductionmentioning

confidence: 99%

“…This study shows that synchronisation between barrier crossing events and the rotational relaxation process can enhance the escape rate to a large extent. A different model of self-propulsion has been used in Refs [48,51] to explore features of exit rate from a metastable state. A very recent experimental study [52] on escape dynamics of active particles in bistable potentials shows that there exists an optimal correlation time of self-propulsion that It is assumed that self-propulsion velocity is directed along a certain symmetry axis of the particle.…”

Section: Introductionmentioning

confidence: 99%

Escape kinetics of self-propelled particles from a circular cavity

Debnath,

Chaudhury,

Mukherjee

et al. 2021

Preprint

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We numerically investigate the mean exit time of an inertial active Brownian particle from a circular cavity with single or multiple exit windows. Our simulation results witness distinct escape mechanisms depending upon the relative amplitudes of the thermal length and self-propulsion length compared to the cavity and pore sizes. For exceedingly large self-propulsion lengths, overdamped active particles diffuse on the cavity surface, and rotational dynamics solely governs the exit process. On the other hand, the escape kinetics of a very weakly damped active particle is largely dictated by bouncing effects on the cavity walls irrespective of the amplitude of self-propulsion persistence lengths. We show that the exit rate can be maximized for an optimal self-propulsion persistence length, which depends on the damping strength, self-propulsion velocity, and cavity size. However, the optimal persistence length is insensitive to the opening windows' size, number, and arrangement. Numerical results have been interpreted analytically based on qualitative arguments. The present analysis aims to understand the transport controlling mechanism of active matter in confined structures.

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Escape rate of transiently active Brownian particle in one dimension

Cited by 13 publications

References 18 publications

Learning Dynamical Information from Static Protein and Sequencing Data

Learning Dynamical Information from Static Protein and Sequencing Data

Learning dynamical information from static protein and sequencing data

Escape kinetics of self-propelled particles from a circular cavity

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