Hybrid Numerical Solution of the Chemical Master Equation

We present a numerical approximation technique for the analysis of continuous-time Markov chains that describe networks of biochemical reactions and play an important role in the stochastic modeling of biological systems. Our approach is based on the construction of a stochastic hybrid model in which certain discrete random variables of the original Markov chain are approximated by continuous deterministic variables. We compute the solution of the stochastic hybrid model using a numerical algorithm that discretizes time and in each step performs a mutual update of the transient probability distribution of the discrete stochastic variables and the values of the continuous deterministic variables. We implemented the algorithm and we demonstrate its usefulness and efficiency on several case studies from systems biology. Categories and Subject Descriptors G.3 [Mathematics of Computing]: Probability and Statistics--Markov processes; I.6 [Simulation and Modeling]: Model Validation and Analy...

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Thomas A. Henzinger, Maria Mateescu, Linar Mikeev, Verena Wolf
• Keywords: Continuous Deterministic Variables, CORR 2010, Education, Markov Chain, Stochastic Hybrid Model