Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · cond-mat/0106409

A Stochastic Analysis of a Brownian Ratchet Model for Actin-Based Motility and Integrate-and-Firing Neurons

Hong Qian
View original source ↗Read source document ↗Export metadata ↓

Abstract

In recent single-particle tracking (SPT) measurements on {\it Listeria monocytogenes} motility {\em in vitro}, the actin-based stochastic dynamics of the bacterium movement is analyzed statistically (Kuo and McGrath, 2000). The mean-square displacement (MSD) of the detrended trajectory exhibit a linear behavior; it has been suggested that a corresponding analysis for the Brownian ratchet model (Peskin, Odell, & Oster, 1993) leads to a non-monotonic MSD. A simplified version of the Brownian ratchet, when its motion is limited by the bacterium movement, is proposed and analyzed stochastically. Analytical results for the simple model are obtained and statistical data analysis is investigated. The MSD of the stochastic bacterium movement is a quadratic function while the MSD for the detrended trajectory is shown to be linear. The mean velocity and effective diffusion constant of the propelled bacterium in the long-time limit, and the short-time relaxation are obtained from the MSD analysis. The MSD of the gap between actin and the bacterium exhibits an oscillatory behavior when there is a large resistant force from the bacterium. The stochastic model for actin-based motility is also mathematically equivalent to a model for integrate-and-firing neurons. Hence our mathematical results have applications in other biological problems. For comparison, a continuous formalism of the BR model with great analytical simplicity is also studied.

Explore related subjects

cond-mat.stat-mechcond-mat.softq-bio

Keep this discovery

BibTeXRIS

Hong Qian. 2001-06-20. A Stochastic Analysis of a Brownian Ratchet Model for Actin-Based Motility and Integrate-and-Firing Neurons. https://doi.org/10.3970/mcb.2004.001.267

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Universal sampling of spin systems across quenched disorder

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

cond-mat.stat-mech↗
arXiv2026

Information-Theoretic Characterization of Macroscopic Chaos Emerging from the Chemical Master Equation

Open chemical reaction networks exhibit stochastic concentration dynamics at finite system sizes, whereas their macroscopic limit is governed by deterministic rate equations that can display chaos. In this Letter, we show theoretically that a rate of information loss constructed from two-time mutual information recovers the Kolmogorov-Sinai entropy in the deterministic limit. We verify this result through numerical simulations of a Markov jump process for a three-species system involving seven reactions.

cond-mat.stat-mech↗
arXiv2026

Orientational order on non-orientable domains

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

cond-mat.stat-mech↗