Dynamic boundaries in asymmetric exclusion processes

Nowak, Sarah A.; Fok, Pak-Wing; Chou, Tom

doi:10.1103/physreve.76.031135

Cited by 30 publications

(41 citation statements)

References 33 publications

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“…In order to compute Q 100 and Q 001 (defined in the frame of the particle), we use finitesegment MFT in the moving frame of the transported particle. We consider a sliding window of m sites always centered about the driven particle [10]. As before, we explicitly enumerate all configurations of the m sites surrounding the particle and assign appropriate rates to create the transition matrix M. Transitions generated by the motion of the particle are reflected in the modifications of M to include terms corresponding to sliding of the m-site window.…”

Section: Prl 99 248302 (2007) P H Y S I C a L R E V I E W L E T T E R Smentioning

confidence: 99%

Brownian Ratchets Driven by Asymmetric Nucleation of Hydrolysis Waves

Lakhanpal

Chou

2007

Phys. Rev. Lett.

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We propose a stochastic process wherein molecular transport is mediated by asymmetric nucleation of domains on a one-dimensional substrate, in contrast with molecular motors that hydrolyze nucleotide triphosphates and undergo conformational change. We show that asymmetric nucleation of hydrolysis waves on a track can also result in directed motion of an attached particle. Asymmetrically cooperative kinetics between hydrolyzed and unhydrolyzed states on each lattice site generate moving domain walls that push a particle sitting on the track. We use a novel fluctuating-frame, finite-segment mean field theory to accurately compute steady-state velocities of the driven particle and to discover parameter regimes yielding maximal domain wall flux, leading to optimal particle drift.

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Section: Prl 99 248302 (2007) P H Y S I C a L R E V I E W L E T T E R Smentioning

confidence: 99%

Brownian Ratchets Driven by Asymmetric Nucleation of Hydrolysis Waves

Lakhanpal

Chou

2007

Phys. Rev. Lett.

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“…However, in practice there could be many active particles moving along the same track, which could interact with each other and exhibit some form of collective behavior. This has motivated a number of studies that model the movement of multiple motor particles as an asymmetric exclusion process (ASEP) (Evans et al, 2003;Klumpp and Lipowsky, 2003;Kolomeisky, 1998;Lipowsky et al, 2001;Nowak et al, 2007a;Parmeggiani et al, 2003Parmeggiani et al, , 2004Popkov et al, 2003;Pronina and Kolomeisky, 2007). In the simplest version of such models, each particle hops unidirectionally at a uniform rate along a 1D lattice; the only interaction between particles is a hard-core repulsion that prevents more than one particle occupying the same lattice site at the same time.…”

Section: G Exclusion Processesmentioning

confidence: 99%

“…This motivates an approach called the finite segment mean field theory (FSMFT) approximation (Nowak et al, 2007b). Consider a small segment within the track containing m sites so that the number of states in the system is M = 2 m .…”

Section: Local Target Signalingmentioning

confidence: 99%

Stochastic models of intracellular transport

2013

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The interior of a living cell is a crowded, heterogenuous, fluctuating environment. Hence, a major challenge in modeling intracellular transport is to analyze stochastic processes within complex environments. Broadly speaking, there are two basic mechanisms for intracellular transport: passive diffusion and motor-driven active transport. Diffusive transport can be formulated in terms of the motion of an over-damped Brownian particle. On the other hand, active transport requires chemical energy, usually in the form of ATP hydrolysis, and can be direction specific, allowing biomolecules to be transported long distances; this is particularly important in neurons due to their complex geometry. In this review we present a wide range of analytical methods and models of intracellular transport. In the case of diffusive transport, we consider narrow escape problems, diffusion to a small target, confined and single-file diffusion, homogenization theory, and fractional diffusion. In the case of active transport, we consider Brownian ratchets, random walk models, exclusion processes, random intermittent search processes, quasisteady-state reduction methods, and mean field approximations. Applications include receptor trafficking, axonal transport, membrane diffusion, nuclear transport, protein-DNA interactions, virus trafficking, and the self-organization of subcellular structures.

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“…Solving for the length L yields the main result of this Section, Eq. (13). From the analysis it becomes apparent that the relevant length scale, denoted by λ [Eq.…”

Section: B Mathematical Analysis: Adiabatic Limitmentioning

confidence: 99%

Self-organized system-size oscillation of a stochastic lattice-gas model

2018

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The totally asymmetric simple exclusion process (TASEP) is a paradigmatic stochastic model for nonequilibrium physics, and has been successfully applied to describe active transport of molecular motors along cytoskeletal filaments. Building on this simple model, we consider a two-lane lattice-gas model that couples directed transport (TASEP) to diffusive motion in a semiclosed geometry, and simultaneously accounts for spontaneous growth and particle-induced shrinkage of the system's size. This particular extension of the TASEP is motivated by the question of how active transport and diffusion might influence length regulation in confined systems. Surprisingly, we find that the size of our intrinsically stochastic system exhibits robust temporal patterns over a broad range of growth rates. More specifically, when particle diffusion is slow relative to the shrinkage dynamics, we observe quasiperiodic changes in length. We provide an intuitive explanation for the occurrence of these self-organized temporal patterns, which is based on the imbalance between the diffusion and shrinkage speed in the confined geometry. Finally, we formulate an effective theory for the oscillatory regime, which explains the origin of the oscillations and correctly predicts the dependence of key quantities, such as the oscillation frequency, on the growth rate.

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Dynamic boundaries in asymmetric exclusion processes

Cited by 30 publications

References 33 publications

Brownian Ratchets Driven by Asymmetric Nucleation of Hydrolysis Waves

Brownian Ratchets Driven by Asymmetric Nucleation of Hydrolysis Waves

Stochastic models of intracellular transport

Self-organized system-size oscillation of a stochastic lattice-gas model

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