A Stochastic Markov Model for Coordinated Molecular Motors

TL;DR

This paper develops an exact probabilistic model to analyze the coordinated behavior of multiple molecular motors, advancing understanding beyond single-motor models and Monte Carlo simulations.

Contribution

It introduces a stochastic Markov model for multiple interacting molecular motors, providing an exact theoretical framework instead of simulation-based analysis.

Findings

Derivation of the probability density function for motor configurations

Enhanced understanding of multi-motor transport dynamics

Analytical insights into motor coordination mechanisms

Abstract

Many cell functions are accomplished thanks to intracellular transport mechanisms of macromolecules along filaments. Molecular motors such as dynein or kinesin are proteins playing a primary role in these processes. The behavior of such proteins is quite well understood when there is only one of them moving a cargo particle. Indeed, numerous in vitro experiments have been performed to derive accurate models for a single molecular motor. However, in vivo macromolecules are often carried by multiple motors. The main focus of this paper is to provide an analysis of the behavior of more molecular motors interacting together in order to improve the understanding of their actual physiological behavior. Previous studies provide analyses based on results obtained from Monte Carlo simulations. Different from these studies, we derive an equipollent probabilistic model to describe the dynamics of multiple proteins coupled together and provide an exact theoretical analysis. We are capable of obtaining the probability density function of the motor protein configurations, thus enabling a deeper understanding of their behavior.