Model-order reduction of biochemical reaction networks

TL;DR

This paper introduces a novel model-order reduction technique for biochemical reaction networks using Kron reduction of the Laplacian matrix, effectively simplifying complex systems while preserving key dynamic behaviors.

Contribution

The paper presents a new reduction method applicable to enzyme kinetics-based networks, demonstrated on yeast glycolysis, improving computational efficiency without losing accuracy.

Findings

Reduced models accurately replicate key metabolite dynamics

Method effectively simplifies complex biochemical networks

Simulation results show good agreement with full models

Abstract

In this paper we propose a model-order reduction method for chemical reaction networks governed by general enzyme kinetics, including the mass-action and Michaelis-Menten kinetics. The model-order reduction method is based on the Kron reduction of the weighted Laplacian matrix which describes the graph structure of complexes in the chemical reaction network. We apply our method to a yeast glycolysis model, where the simulation result shows that the transient behaviour of a number of key metabolites of the reduced-order model is in good agreement with those of the full-order model.