Inferring Transition Rates of Networks from Populations in Continuous-Time Markov Processes

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• 作者：Purushottam D. Dixit ; Abhinav Jain ; Gerhard Stock ; Ken A. Dill
• 刊名：Journal of Chemical Theory and Computation
• 出版年：2015
• 出版时间：November 10, 2015
• 年：2015
• 卷：11
• 期：11
• 页码：5464-5472
• 全文大小：462K
• ISSN：1549-9626

文摘

We are interested inferring rate processes on networks. In particular, given a network鈥檚 topology, the stationary populations on its nodes, and a few global dynamical observables, can we infer all the transition rates between nodes? We draw inferences using the principle of maximum caliber (maximum path entropy). We have previously derived results for discrete-time Markov processes. Here, we treat continuous-time processes, such as dynamics among metastable states of proteins. The present work leads to a particularly important analytical result: namely, that when the network is constrained only by a mean jump rate, the rate matrix is given by a square-root dependence of the rate, k_ab 鈭?(蟺_b/蟺_a)^1/2, on 蟺_a and 蟺_b, the stationary-state populations at nodes a and b. This leads to a fast way to estimate all of the microscopic rates in the system. As an illustration, we show that the method accurately predicts the nonequilibrium transition rates in an in silico gene expression network and transition probabilities among the metastable states of a small peptide at equilibrium. We note also that the method makes sensible predictions for so-called extra-thermodynamic relationships, such as those of Bronsted, Hammond, and others.