Stochastic distinguishability of Markovian trajectories release_el4rwnk7sjhs5bbuyjiyaz2ooi

by Asawari Pagare, Zhongmin Zhang, Jiming Zheng, Zhiyue Lu

Published in Journal of Chemical Physics by AIP Publishing.

2024   Volume 160, Issue 17

Abstract

The ability to distinguish between stochastic systems based on their trajectories is crucial in thermodynamics, chemistry, and biophysics. The Kullback–Leibler (KL) divergence, DKLAB(0,τ), quantifies the distinguishability between the two ensembles of length-τ trajectories from Markov processes A and B. However, evaluating DKLAB(0,τ) from histograms of trajectories faces sufficient sampling difficulties, and no theory explicitly reveals what dynamical features contribute to the distinguishability. This work provides a general formula that decomposes DKLAB(0,τ) in space and time for any Markov processes, arbitrarily far from equilibrium or steady state. It circumvents the sampling difficulty of evaluating DKLAB(0,τ). Furthermore, it explicitly connects trajectory KL divergence with individual transition events and their waiting time statistics. The results provide insights into understanding distinguishability between Markov processes, leading to new theoretical frameworks for designing biological sensors and optimizing signal transduction.
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Type  article-journal
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Date   2024-05-03
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DOI  10.1063/5.0203335
PubMed  38748023
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