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Background: Stochastic biochemical reaction networks are commonly modelled bythe chemical master equation, and can be simulated as first order lineardifferential equations through a finite state projection. Due to the very highstate space dimension of these equations, numerical simulations arecomputationally expensive. This is a particular problem for analysis tasksrequiring repeated simulations for different parameter values. Such tasks arecomputationally expensive to the point of infeasibility with the chemicalmaster equation. Results: In this article, we apply parametric model orderreduction techniques in order to construct accurate low-dimensional parametricmodels of the chemical master equation. These surrogate models can be used invarious parametric analysis task such as identifiability analysis, parameterestimation, or sensitivity analysis. As biological examples, we consider twomodels for gene regulation networks, a bistable switch and a network displayingstochastic oscillations. Conclusions: The results show that the parametricmodel reduction yields efficient models of stochastic biochemical reactionnetworks, and that these models can be useful for systems biology applicationsinvolving parametric analysis problems such as parameter exploration,optimization, estimation or sensitivity analysis.

Efficient parametric analysis of the chemical master equation through model order reduction