Fluctuation preserving coarse graining for biochemical systems

Abstract

Finite stochastic Markov models play a major role for modelling biochemical pathways. Such models are a coarse-grained description of the underlying microscopic dynamics and can be considered mesoscopic. The level of coarse-graining is to a certain extend arbitrary since it depends on the resolution of accomodating measurements. Here, we present a way to simplify such stochastic descriptions, which preserves both the meso-micro and the meso-macro connection. The former is achieved by demanding locality, the latter by considering cycles on the network of states. Using single- and multicycle examples we demonstrate how our new method preserves fluctuations of observables much better than naïve approaches.

PACS: 87.10.Mn, 05.40.-a, 05.70.Ln, 87.18.Tt (4 pages, 4 figures)

References in corpus (4)

• The large deviation approach to statistical mechanics
• The noisy edge of traveling waves
• Entropy production and coarse-graining in Markov processes
• Gallavotti-Cohen-Type symmetry related to cycle decompositions for Markov chains and biochemical applications

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