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arXiv · 0910.2559

Efficient exploration of discrete energy landscapes

Martin Mann·Konstantin Klemm
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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.

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Martin Mann, Konstantin Klemm. 2009-10-14. Efficient exploration of discrete energy landscapes. https://doi.org/10.1103/physreve.83.011113

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