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 steady-state probability distribution and all steady-state 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 coarse-grained 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.

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Cited by in corpus (11)

• Fluctuating Entropy Production on the Coarse-Grained Level: Inference and Localization of Irreversibility
• Modeling of biomolecular machines in non-equilibrium steady states
• An estimator of entropy production for partially accessible Markov networks based on the observation of blurred transitions
• Thermodynamic inference in partially accessible Markov networks: A unifying perspective from transition-based waiting time distributions
• Dynamics of Micro and Nanoscale Systems in the Weak-Memory Regime
• Waiting time distributions in hybrid models of motor-bead assays: A concept and tool for inference
• Stochastic paths controlling speed and dissipation
• Dynamics of micro and nanoscale systems in the weak-memory regime: A mathematical framework beyond the Markov approximation
• Inferring entropy production rate from partially observed Langevin dynamics under coarse-graining
• Weak-Memory Dynamics in Discrete Time
• Excess and loss of entropy production for different levels of coarse-graining