TY - JOUR T1 - Introducing prescribed biases in out of equilibrium Markov models JF - bioRxiv DO - 10.1101/198697 SP - 198697 AU - Purushottam D. Dixit Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/10/09/198697.abstract N2 - 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 Markov models to make them consistent with constraints that are derived from experiments. Here, we present a framework based on the principle of maximum path entropy to update Markov models using stationary state and dynamical trajectory-based constraints. We illustrate the framework using a biochemical model network of growth factors-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. ER -