Flux networks in metabolic graphs

TL;DR

This paper introduces a method to visualize and analyze flux distributions in metabolic networks by constructing conserved flux networks using linear programming solutions and metabolite properties, aiding interpretation of complex models.

Contribution

It presents a novel approach to assign conserved fluxes to metabolic graph edges and links dual LP solutions to metabolite properties, enhancing network visualization and understanding.

Findings

Conserved flux networks can be constructed from reaction fluxes and metabolite properties.

Strong correlation observed between metabolite shadow prices and conserved properties.

Method successfully applied to models of E. coli, yeast, and M. barkeri.

Abstract

A metabolic model can be represented as bipartite graph comprising linked reaction and metabolite nodes. Here it is shown how a network of conserved fluxes can be assigned to the edges of such a graph by combining the reaction fluxes with a conserved metabolite property such as molecular weight. A similar flux network can be constructed by combining the primal and dual solutions to the linear programming problem that typically arises in constraint-based modelling. Such constructions may help with the visualisation of flux distributions in complex metabolic networks. The analysis also explains the strong correlation observed between metabolite shadow prices (the dual linear programming variables) and conserved metabolite properties. The methods were applied to recent metabolic models for Escherichia coli, Saccharomyces cerevisiae, and Methanosarcina barkeri. Detailed results are reported for E. coli; similar results were found for the other organisms.