Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting

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. Comput. 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.