Stochastic enzyme kinetics and the quasi-steady-state reductions: Application of the slow scale linear noise approximation \`a la Fenichel

TL;DR

This paper derives the slow scale linear noise approximation (ssLNA) for stochastic enzyme kinetics using geometric singular perturbation theory, clarifies differences in existing formulations, and challenges accepted validity criteria for Michaelis--Menten models.

Contribution

It provides a geometric singular perturbation theory derivation of ssLNA, explains discrepancies in literature, and extends the approximation to non-classical singular perturbations.

Findings

Derived ssLNA directly from geometric singular perturbation theory.

Clarified the origin of differences in ssLNA formulations.

Disproved a common validity criterion for Michaelis--Menten stochastic models.

Abstract

The linear noise approximation models the random fluctuations from the mean-field model of a chemical reaction that unfolds near the thermodynamic limit. Specifically, the fluctuations obey a linear Langevin equation up to order $\Omega^{-1/2}$, where $\Omega$ is the size of the chemical system (usually the volume). In the presence of disparate timescales, the linear noise approximation admits a quasi-steady-state reduction referred to as the \textit{slow scale} linear noise approximation (ssLNA). Curiously, the ssLNAs reported in the literature are slightly different. The differences in the reported ssLNAs lie at the mathematical heart of the derivation. In this work, we derive the ssLNA directly from geometric singular perturbation theory and explain the origin of the different ssLNAs in the literature. Moreover, we discuss the loss of normal hyperbolicity and we extend the ssLNA derived from geometric singular perturbation theory to a non-classical singularly perturbed problem. In so doing, we disprove a commonly-accepted qualifier for the validity of stochastic quasi-steady-state approximation of the Michaelis--Menten reaction mechanism.