Strong convergence for split-step methods in stochastic jump kinetics

Abstract

Mesoscopic models in the reaction-diffusion framework have gained recognition as a viable approach to describing chemical processes in cell biology. The resulting computational problem is a continuous-time Markov chain on a discrete and typically very large state space. Due to the many temporal and spatial scales involved many different types of computationally more effective multiscale models have been proposed, typically coupling different types of descriptions within the Markov chain framework. In this work we look at the strong convergence properties of the basic first order Strang, or Lie-Trotter, split-step method, which is formed by decoupling the dynamics in finite time-steps. Thanks to its simplicity and flexibility, this approach has been tried in many different combinations. We develop explicit sufficient conditions for path-wise well-posedness and convergence of the method, including error estimates, and we illustrate our findings with numerical examples. In doing so, we also suggest a certain partition of unity representation for the split-step method, which in turn implies a concrete simulation algorithm under which trajectories may be compared in a path-wise sense.

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Cited by in corpus (5)

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• Stochastic simulation of pattern formation in growing tissue: a multilevel approach
• Pathwise error bounds in Multiscale variable splitting methods for spatial stochastic kinetics
• Slow-scale split-step tau-leap method for stiff stochastic chemical systems
• Multiscale modeling via split-step methods in neural firing