Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting

TL;DR

This paper introduces a trajectory reweighting method for efficiently computing steady-state parameter sensitivities in stochastic biochemical models without multiple simulations, enabling faster analysis of complex networks.

Contribution

The paper presents a novel trajectory reweighting approach that calculates multiple sensitivity coefficients simultaneously in stochastic simulations, avoiding repeated parameter perturbations.

Findings

Method recovers Girsanov transform results

Efficient computation of steady-state sensitivities

Successful application to signaling networks and genetic switches

Abstract

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.