Statistical inference for a multiscale stochastic model of enzyme kinetics via propagation of chaos

TL;DR

This paper develops a new statistical inference method for complex enzyme kinetics models using stochastic averaging and particle systems, enabling parameter estimation from limited data.

Contribution

It introduces a reduced stochastic model under QSSA and a novel particle system approach for inference without direct system state observations.

Findings

Established a stochastic averaging principle for enzyme kinetics.

Constructed a particle system that approximates the reduced model.

Proved consistency and non-asymptotic bounds for the estimator.

Abstract

We study a class of Stochastic Differential Equations (SDEs) with jumps modeling multistage Michaelis--Menten enzyme kinetics, in which a substrate is sequentially transformed into a product via a cascade of intermediate complexes. These networks are typically high-dimensional and exhibit multiscale behavior with a strong coupling between different components, posing substantial analytical and computational challenges. In particular, the problem of statistical inference of reaction rates is significantly difficult and becomes even more intricate when direct observations of system states are unavailable and only a random sample of product formation times is observed. We address this problem in two stages. First, in a suitable scaling regime consistent with the Quasi-Steady State Approximation (QSSA), we rigorously establish a stochastic averaging principle yielding a reduced model for the product-substrate dynamics. Guided by the reduced-order dynamics, we next construct a novel Interacting Particle System (IPS) that approximates the product-substrate process at the particle level. This IPS plays a pivotal role in the inference methodology, and we prove several non-asymptotic bounds and limiting results for this system. These results facilitate the construction of an estimator based on a product-form approximate likelihood requiring only a random sample of product formation times. This approach does not need access to the system states, and we rigorously prove consistency of the estimator.