  Parameter sensitivity analysis is a powerful tool in the building and
analysis of biochemical network models. For stochastic simulations, parameter
sensitivity analysis can be computationally expensive, requiring multiple
simulations for perturbed values of the parameters. Here, we use trajectory
reweighting to derive a method for computing sensitivity coefficients in
stochastic simulations without explicitly perturbing the parameter values,
avoiding the need for repeated simulations. The method allows the simultaneous
computation of multiple sensitivity coefficients. Our approach recovers results
originally obtained by application of the Girsanov measure transform in the
general theory of stochastic processes [A. Plyasunov and A. P. Arkin, J. Comp.
Phys. 221, 724 (2007)]. We build on these results to show how the method can be
used to compute steady-state sensitivity coefficients from a single simulation
run, and we present various efficiency improvements. For models of biochemical
signaling networks the method has a particularly simple implementation. We
demonstrate its application to a signaling network showing stochastic focussing
and to a bistable genetic switch, and present exact results for models with
linear propensity functions.
