Stability of Metabolic Networks via Linear-In-Flux-Expressions

TL;DR

This paper introduces LIFE, a methodology for analyzing large metabolic networks by reducing parameters and linking system stability to graph structure, with applications in perturbation analysis and equilibrium characterization.

Contribution

The work develops a novel framework connecting metabolic network stability to graph properties, providing analytical tools for equilibrium and stability analysis.

Findings

LIFE reduces parameter complexity in metabolic models.

Perturbation analysis yields more accurate empirical data.

Graph structure influences system stability.

Abstract

The methodology named LIFE (Linear-in-Flux-Expressions) was developed with the purpose of simulating and analyzing large metabolic systems. With LIFE, the number of model parameters is reduced by accounting for correlations among the parameters of the system. Perturbation analysis on LIFE systems results in less overall variability of the system, leading to results that more closely resemble empirical data. These systems can be associated to graphs, and characteristics of the graph give insight into the dynamics of the system. This work addresses two main problems: 1. for fixed metabolite levels, find all fluxes for which the metabolite levels are an equilibrium, and 2. for fixed fluxes, find all metabolite levels which are equilibria for the system. We characterize the set of solutions for both problems, and show general results relating stability of systems to the structure of the associated graph. We show that there is a structure of the graph necessary for stable dynamics. Along with these general results, we show how stability analysis from the fields of network flows, compartmental systems, control theory and Markov chains apply to LIFE systems.