Structured Projection-Based Model Reduction with Application to Stochastic Biochemical Networks

Abstract

The chemical master equation (CME) is well known to provide the highest resolution models of a biochemical reaction network. Unfortunately, even simulating the CME can be a challenging task. For this reason, simpler approximations to the CME have been proposed. In this paper, we focus on one such model, the linear noise approximation (LNA). Specifically, we consider implications of a recently proposed LNA time-scale separation method. We show that the reduced-order LNA converges to the full-order model in the mean square sense. Using this as motivation, we derive a network structure-preserving reduction algorithm based on structured projections. We discuss when these structured projections exist and we present convex optimization algorithms that describe how such projections can be computed. The algorithms are then applied to a linearized stochastic LNA model of the yeast glycolysis pathway.

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