Communication: Introducing prescribed biases in out-of-equilibrium Markov models

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Abstract

Markov models are often used in modeling complex out-of-equilibrium chemical and biochemical systems. However, many times their predictions do not agree with experiments. We need a systematic framework to update existing Markov models to make them consistent with constraints that are derived from experiments. Here, we present a framework based on the principle of maximum relative path entropy (minimum Kullback-Leibler divergence) to update Markov models using stationary state and dynamical trajectory-based constraints. We illustrate the framework using a biochemical model network of growth factor-based signaling. We also show how to find the closest detailed balanced Markov model to a given Markov model. Further applications and generalizations are discussed.

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CITATION STYLE

APA

Dixit, P. D. (2018). Communication: Introducing prescribed biases in out-of-equilibrium Markov models. Journal of Chemical Physics, 148(9). https://doi.org/10.1063/1.5023232

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