Substance graphs are optimal simple-graph representations of metabolism

TL;DR

This paper evaluates different graph representations of metabolic networks and finds that substance graphs, connecting all participating metabolites, best preserve functional modules and identify key metabolites.

Contribution

The study systematically compares four graph models of metabolism and demonstrates that substance graphs are the most effective for functional analysis.

Findings

Substance graphs outperform other models in functional clustering.

Substance graphs better identify currency metabolites.

Evaluation across multiple organisms confirms robustness.

Abstract

One approach to studying the system-wide organization of biochemistry is to use statistical graph theory. Even in such a heavily simplified method, which disregards most of the dynamic aspects of biochemistry, one is faced with fundamental questions, such as how the chemical reaction systems should be reduced to a graph retaining as much functional information as possible from the original reaction system. In such graph representations, should the edges go between substrates and products, or substrates and substrates, or both? Should vertices represent substances or reactions? Different definitions encode different information about the reaction system. In this paper we evaluate four different graph representations of metabolism, applied to data from different organisms and databases. The graph representations are evaluated by comparing the overlap between clusters (network modules) and annotated functions, and also by comparing the set of identified currency metabolites with those that other authors have identified using qualitative biological arguments. We find that a "substance network," where all metabolites participating in a reaction are connected, is relatively better than others, evaluated both with respect to the functional overlap between modules and functions and to the number and identity of identified currency metabolites.