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• W2791467473 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." @default.
• W2791467473 created "2018-03-29" @default.
• W2791467473 creator A5036684007 @default.
• W2791467473 date "2018-03-01" @default.
• W2791467473 modified "2023-10-16" @default.
• W2791467473 title "Communication: Introducing prescribed biases in out-of-equilibrium Markov models" @default.
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• W2791467473 doi "https://doi.org/10.1063/1.5023232" @default.
• W2791467473 hasPublicationYear "2018" @default.
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