Coarse graining of biochemical systems described by discrete stochastic dynamics
Abstract
Many biological systems can be described by finite Markov models. A general method for simplifying master equations is presented that is based on merging adjacent states. The approach preserves the steadystate probability distribution and all steadystate fluxes except the one between the merged states. Different levels of coarse graining of the underlying microscopic dynamics can be obtained by iteration, with the result being independent of the order in which states are merged. A criterion for the optimal level of coarse graining or resolution of the process is proposed via a tradeoff between the simplicity of the coarsegrained model and the information loss relative to the original model. As a case study, the method is applied to the cycle kinetics of the molecular motor kinesin.
 Publication:

Physical Review E
 Pub Date:
 December 2020
 DOI:
 10.1103/PhysRevE.102.062149
 arXiv:
 arXiv:2102.13394
 Bibcode:
 2020PhRvE.102f2149S
 Keywords:

 Physics  Biological Physics;
 Condensed Matter  Statistical Mechanics;
 Physics  Data Analysis;
 Statistics and Probability
 EPrint:
 Phys. Rev. E 102, 062149. December 2020