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Many physical and chemical processes, such as folding of biopolymers, arebest described as dynamics on large combinatorial energy landscapes. A conciseapproximate description of dynamics is obtained by partitioning themicro-states of the landscape into macro-states. Since most landscapes ofinterest are not tractable analytically, the probabilities of transitionsbetween macro-states need to be extracted numerically from the microscopicones, typically by full enumeration of the state space. Here we propose toapproximate transition probabilities by a Markov chain Monte-Carlo method. Forlandscapes of the number partitioning problem and an RNA switch molecule weshow that the method allows for accurate probability estimates withsignificantly reduced computational cost.

Efficient exploration of discrete energy landscapes