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Abstract: 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.



Author: Martin Mann, Konstantin Klemm

Source: https://arxiv.org/







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