Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

TL;DR

This paper introduces a two-step sensitivity analysis method for high-dimensional stochastic reaction networks, combining Fisher Information Matrix-based screening with finite-difference gradient estimation to efficiently identify sensitive parameters and accelerate computations.

Contribution

The paper presents a novel combined sensitivity analysis strategy that leverages Fisher Information and variance reduction techniques for faster parameter screening in complex stochastic systems.

Findings

Efficiently identifies insensitive parameters in high-dimensional networks.

Achieves several-fold speedup over existing methods.

Accurately estimates sensitivities of remaining parameters.

Abstract

In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner based also on the new sensitivity bound. In the second step of the proposed strategy, the finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the number of the sensitive parameters.