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Many biological systems can be described by finite Markov models. A generalmethod for simplifying master equations is presented that is based on mergingadjacent states. The approach preserves the steady-state probabilitydistribution and all steady-state fluxes except the one between the mergedstates. Different levels of coarse graining of the underlying microscopicdynamics can be obtained by iteration, with the result being independent of theorder in which states are merged. A criterion for the optimal level of coarsegraining or resolution of the process is proposed via a tradeoff between thesimplicity of the coarse-grained model and the information loss relative to theoriginal model. As a case study, the method is applied to the cycle kinetics ofthe molecular motor kinesin.

Coarse graining of biochemical systems described by discrete stochastic dynamics