Randomizing genome-scale metabolic networks

TL;DR

This paper introduces methods for randomizing genome-scale metabolic networks using Markov Chain Monte Carlo to generate biologically valid ensembles, helping to determine if observed network properties are due to underlying biochemical constraints.

Contribution

It develops new randomization techniques for metabolic networks that preserve biochemical validity and functional constraints, enabling meaningful statistical comparisons.

Findings

Randomized networks resemble real metabolic networks in key properties.

Biochemical and functional constraints largely explain observed network structures.

Proposed methods avoid biologically meaningless reactions during randomization.

Abstract

Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have "unusual" properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints.