  Background: Stochastic biochemical reaction networks are commonly modelled by
the chemical master equation, and can be simulated as first order linear
differential equations through a finite state projection. Due to the very high
state space dimension of these equations, numerical simulations are
computationally expensive. This is a particular problem for analysis tasks
requiring repeated simulations for different parameter values. Such tasks are
computationally expensive to the point of infeasibility with the chemical
master equation. Results: In this article, we apply parametric model order
reduction techniques in order to construct accurate low-dimensional parametric
models of the chemical master equation. These surrogate models can be used in
various parametric analysis task such as identifiability analysis, parameter
estimation, or sensitivity analysis. As biological examples, we consider two
models for gene regulation networks, a bistable switch and a network displaying
stochastic oscillations. Conclusions: The results show that the parametric
model reduction yields efficient models of stochastic biochemical reaction
networks, and that these models can be useful for systems biology applications
involving parametric analysis problems such as parameter exploration,
optimization, estimation or sensitivity analysis.
