A finite state projection algorithm for the stationary solution of the chemical master equation

• Published in: The Journal of chemical physics Vol. 147; no. 15; pp. 154101 - 154123
• Main Authors: Gupta, Ankit, Mikelson, Jan, Khammash, Mustafa
• Format: Journal Article
• Language: English
• Published: United States American Institute of Physics 21.10.2017
• Subjects: Algorithms; Biology; Differential equations; Estimation; Markov chains; Mathematical analysis; Ordinary differential equations; Organic chemistry; Variations
• ISSN: 0021-9606; 1089-7690; 1089-7690
• DOI: 10.1063/1.5006484

The chemical master equation (CME) is frequently used in systems biology to quantify the effects of stochastic fluctuations that arise due to biomolecular species with low copy numbers. The CME is a system of ordinary differential equations that describes the evolution of probability density for each population vector in the state-space of the stochastic reaction dynamics. For many examples of interest, this state-space is infinite, making it difficult to obtain exact solutions of the CME. To deal with this problem, the Finite State Projection (FSP) algorithm was developed by Munsky and Khammash [J. Chem. Phys. 124(4), 044104 (2006)], to provide approximate solutions to the CME by truncating the state-space. The FSP works well for finite time-periods but it cannot be used for estimating the stationary solutions of CMEs, which are often of interest in systems biology. The aim of this paper is to develop a version of FSP which we refer to as the stationary FSP (sFSP) that allows one to obtain accurate approximations of the stationary solutions of a CME by solving a finite linear-algebraic system that yields the stationary distribution of a continuous-time Markov chain over the truncated state-space. We derive bounds for the approximation error incurred by sFSP and we establish that under certain stability conditions, these errors can be made arbitrarily small by appropriately expanding the truncated state-space. We provide several examples to illustrate our sFSP method and demonstrate its efficiency in estimating the stationary distributions. In particular, we show that using a quantized tensor-train implementation of our sFSP method, problems admitting more than 100 × 106 states can be efficiently solved.