Efficient stochastic simulation of metastable Markov chains

We address the problem of metastable Markov chain simulation, a class of systems characterized by the existence of two or more “pseudo-equilibrium” states and very slow convergence towards global equilibrium [1]. For such systems, approximation of the stationary distribution by direct application of the Stochastic Simulation Algorithm (SSA) [2] would be very inefficient. In this paper we propose a new method for steady-state simulation of metastable chains that is centered around the concept of stochastic complementation [3]. The use of this mathematical device along with SSA results in an algorithm with much better convergence properties, that facilitates the analysis of rarely switching stochastic biochemical systems.