Stochastic Averaging and Statistical Inference of Glycolytic Pathway

TL;DR

This paper develops a rigorous probabilistic framework to derive reduced deterministic models from stochastic glycolytic pathway networks, enabling accurate parameter estimation from limited observational data.

Contribution

It introduces a mathematically rigorous method to connect stochastic biochemical networks with reduced ODE models and statistically consistent parameter estimation.

Findings

Reduced Othmer-Aldridge model derived from stochastic dynamics

Proved estimators are consistent with limited data

Demonstrated approximation accuracy under specific scaling regimes

Abstract

Many biological processes exhibit oscillatory behavior. Among these, glycolytic oscillations have been extensively studied due to their well-characterized biochemical reaction networks. However, the complexity of these networks necessitates low-dimensional ordinary differential equation (ODE) models to identify core mechanisms and perform stability analysis. While previous studies proposed reduced ODE models, these were typically introduced from deterministic descriptions rather than the underlying stochastic dynamics, which more accurately represent discrete reaction events occurring at random times. In this paper, we develop a rigorous probabilistic framework for deriving a reduced Othmer-Aldridge model of the glycolytic pathway from its stochastic formulation. The full system is modeled as a multiscale continuous-time Markov chain with different time and abundance scales. Under an appropriate scaling regime and specific structural conditions, we prove that the dynamics of the slow components are approximated by a two-dimensional ODE. The proof is technically involved due to the network's complexity and strong coupling between its components. We further consider the problem of parameter estimation when observations are limited to the slow species: fructose-6-phosphate and ADP. The reduced system yields a tractable loss function depending solely on these variables. We prove that the resulting estimators are statistically consistent when the data originate from the full stochastic reaction network. Together, these results provide a mathematically rigorous framework linking stochastic biochemical reaction networks, reduced deterministic dynamics, and statistically reliable parameter estimation.