Fluctuation preserving coarse graining for biochemical systems

TL;DR

This paper introduces a novel coarse-graining method for biochemical Markov models that preserves fluctuations and the connection between microscopic and macroscopic levels, improving over naive approaches.

Contribution

The authors propose a fluctuation-preserving coarse-graining technique that maintains local and cycle-based properties of biochemical stochastic models.

Findings

Better preservation of fluctuations compared to naive methods

Maintains meso-micro and meso-macro connections

Effective on single- and multicycle examples

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 na\"ive approaches.