Diagnostics for assessing the linear noise and moment closure approximations

TL;DR

This paper introduces a framework with diagnostic tools to evaluate the accuracy of linear noise and moment closure approximations in chemical master equation modeling, aiding modelers in assessing approximation suitability.

Contribution

It presents a general framework and efficient diagnostic methods for assessing the accuracy of common approximation techniques in stochastic chemical kinetics.

Findings

Diagnostic tools effectively evaluate approximation accuracy.

Normality assumptions underpin the proposed diagnostics.

Framework aids in choosing suitable approximation methods.

Abstract

Solving the chemical master equation exactly is typically not possible, so instead we must rely on simulation based methods. Unfortunately, drawing exact realisations, results in simulating every reaction that occurs. This will preclude the use of exact simulators for models of any realistic size and so approximate algorithms become important. In this paper we describe a general framework for assessing the accuracy of the linear noise and two moment approximations. By constructing an efficient space filling design over the parameter region of interest, we present a number of useful diagnostic tools that aids modellers in assessing whether the approximation is suitable. In particular, we leverage the normality assumption of the linear noise and moment closure approximations.