Stochastic Averaging and Sensitivity Analysis for Two Scale Reaction Networks

TL;DR

This paper introduces stochastic averaging techniques for multiscale reaction networks, significantly improving efficiency in estimating steady-state expectations and sensitivities by reducing bias and variance.

Contribution

It develops a two-time-scale framework for bias bounds, proposes a new low-variance estimator, and introduces an adaptive stopping rule for micro-equilibration in multiscale simulations.

Findings

Bias bounds for averaging method established

New low-variance estimator for steady-state sensitivity proposed

Adaptive stopping rule for micro-equilibration developed

Abstract

In the presence of multiscale dynamics in a reaction network, direct simulation methods become inefficient as they can only advance the system on the smallest scale. This work presents stochastic averaging techniques to accelerate computations for obtaining estimates of expected values and sensitivities with respect to the steady state distribution. A two-time-scale formulation is used to establish bounds on the bias induced by the averaging method. Further, this formulation provides a framework to create an accelerated `averaged' version of most single-scale sensitivity estimation method. In particular, we propose a new lower-variance ergodic likelihood ratio type estimator for steady-state estimation and show how one can adapt it to accelerated simulations of multiscale systems.Lastly, we develop an adaptive "batch-means" stopping rule for determining when to terminate the micro-equilibration process.