Efficient exploration of discrete energy landscapes

TL;DR

This paper introduces a Markov chain Monte-Carlo approach to efficiently approximate transition probabilities in large energy landscapes, enabling better analysis of complex physical and chemical processes with reduced computational effort.

Contribution

The paper presents a novel Monte-Carlo method for estimating transition probabilities in large energy landscapes, improving computational efficiency over full enumeration.

Findings

Accurate probability estimates achieved with reduced computational cost.

Method successfully applied to number partitioning and RNA switch landscapes.

Significant reduction in computational resources compared to traditional methods.

Abstract

Many physical and chemical processes, such as folding of biopolymers, are best described as dynamics on large combinatorial energy landscapes. A concise approximate description of dynamics is obtained by partitioning the micro-states of the landscape into macro-states. Since most landscapes of interest are not tractable analytically, the probabilities of transitions between macro-states need to be extracted numerically from the microscopic ones, typically by full enumeration of the state space. Here we propose to approximate transition probabilities by a Markov chain Monte-Carlo method. For landscapes of the number partitioning problem and an RNA switch molecule we show that the method allows for accurate probability estimates with significantly reduced computational cost.