Uncovering the hidden geometry behind metabolic networks

TL;DR

This paper introduces a geometric embedding of metabolic networks that reveals hidden structural similarities and a backbone-like pathway organization, providing new insights into their systemic functions.

Contribution

It develops a simple geometric model to explain metabolic network topology and uncovers a common underlying structure in E. coli and human metabolisms.

Findings

High congruence between E. coli and human metabolic geometries

Revealed a backbone-like structure of biochemical pathways

Pathways viewed as interconnected rather than isolated units

Abstract

Metabolism is a fascinating cell machinery underlying life and disease and genome-scale reconstructions provide us with a captivating view of its complexity. However, deciphering the relationship between metabolic structure and function remains a major challenge. In particular, turning observed structural regularities into organizing principles underlying systemic functions is a crucial task that can be significantly addressed after endowing complex network representations of metabolism with the notion of geometric distance. Here, we design a cartographic map of metabolic networks by embedding them into a simple geometry that provides a natural explanation for their observed network topology and that codifies node proximity as a measure of hidden structural similarities. We assume a simple and general connectivity law that gives more probability of interaction to metabolite/reaction pairs which are closer in the hidden space. Remarkably, we find an astonishing congruency between the architecture of E. coli and human cell metabolisms and the underlying geometry. In addition, the formalism unveils a backbone-like structure of connected biochemical pathways on the basis of a quantitative cross-talk. Pathways thus acquire a new perspective which challenges their classical view as self-contained functional units.