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Identifying Strong Statistical Bias in the Local Structure of Metabolic Networks - The Metabolic Network of Saccharomyces Cerevisiae as a Test Case

The detection of strong statistical bias in metabolic networks is of much interest for highlighting potential selective preferences. However, previous approaches to this problem have relied on ambiguous representations of the coupling among chemical reactions or in physically unrealizable null models, which raise interpretation problems. Here we present an approach that avoids these problems. It relies in a bipartite-graph representation of chemical reactions, and it prompts a near-comprehensive examination of statistical bias in the relative frequencies of topologically related metabolic structures within a predefined scope. It also lends naturally to a comprehensive visualization of such statistical relationships. The approach was applied to the metabolic network of Saccharomyces cerevisiae, where it highlighted a preference for sparse local structures and flagged strong context-dependences of the reversibility of reactions and of the presence/absence of some types of reactions.