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MiS Preprint

Randomizing genome-scale metabolic networks

Areejit Samal and Olivier Martin


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.

Dec 7, 2010
Dec 8, 2010
complex networks, Flux balance analysis, MCMC

Related publications

2011 Journal Open Access
Areejit Samal and Olivier Martin

Randomizing genome-scale metabolic networks

In: PLOS ONE, 6 (2011) 7, p. 22295