A general moment expansion method for stochastic kinetic models

TL;DR

This paper introduces a versatile moment expansion method for stochastic kinetic models that can approximate higher moments of chemical systems, providing efficient analysis beyond traditional linear noise approximations.

Contribution

The paper develops a general moment expansion technique applicable to any propensity type and any number of moments, improving stochastic system analysis.

Findings

Higher order moments are crucial for accurate mean estimation in some systems.

The method is computationally efficient compared to stochastic simulations.

Using too few moments can lead to inaccurate parameter estimation.

Abstract

Moment approximation methods are gaining increasing attention for their use in the approximation of the stochastic kinetics of chemical reaction systems. In this paper we derive a general moment expansion method for any type of propensities and which allows expansion up to any number of moments. For some chemical reaction systems, more than two moments are necessary to describe the dynamic properties of the system, which the linear noise approximation (LNA) is unable to provide. Moreover, also for systems for which the mean does not have a strong dependence on higher order moments, moment approximation methods give information about higher order moments of the underlying probability distribution. We demonstrate the method using a dimerisation reaction, Michaelis-Menten kinetics and a model of an oscillating p53 system. We show that for the dimerisation reaction and Michaelis-Menten enzyme kinetics system higher order moments have limited influence on the estimation of the mean, while for the p53 system, the solution for the mean can require several moments to converge to the average obtained from many stochastic simulations. We also find that agreement between lower order moments does not guarantee that higher moments will agree. Compared to stochastic simulations our approach is numerically highly efficient at capturing the behaviour of stochastic systems in terms of the average and higher moments, and we provide expressions for the computational cost for different system sizes and orders of approximation. {We show how the moment expansion method can be employed to efficiently {quantify parameter sensitivity}.} Finally we investigate the effects of using too few moments on parameter estimation, and provide guidance on how to estimate if the distribution can be accurately approximated using only a few moments.