Model validation of simple-graph representations of metabolism

TL;DR

This study evaluates how well simple graph representations of metabolic networks capture modular structures, using a model with tunable clustering to compare different network types and their ability to reflect biological modularity.

Contribution

The paper introduces a model of reaction systems with controllable modularity and assesses which simple graph representations best encode network modularity.

Findings

Substrate-product and substance networks best capture modular structure.

The model reproduces correlations between molecule degree and mass.

Proposes a general framework for studying reaction-system modularity.

Abstract

The large-scale properties of chemical reaction systems, such as the metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information -- lists of chemical reactions -- available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure -- network modularity (the propensity for edges to be cluster into dense groups that are sparsely connected between each other). To reach this goal, we design a model of reaction-systems where network modularity can be controlled and measure how well the reduction to simple graphs capture the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate-product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction-systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreate correlations between degree and mass of the molecules.