Network-based scoring system for genome-scale metabolic reconstructions

Here, we propose a complementary network-based scoring system that exploits the statistical regularities of a metabolic network as a bipartite graph. As an illustration, we apply it to the metabolism of Escherichia coli. The model is adjusted to the observations to derive connection probabilities between individual metabolite-reaction pairs and, after validation, to assess the reliability of each reaction in probabilistic terms. This network-based scoring system uncovers very specific reactions that could be functionally or evolutionary important, identifies prominent experimental targets, and enables further confirmation of modeling results.We foresee a wide range of potential applications at different sub-cellular or supra-cellular levels of biological interactions given the natural bipartivity of many biological networks.A crucial milestone to understand and control cellular behavior is the building up of reliable reconstructions of the interactions spanning different functional levels. Such reconstructions find a natural abstraction in the form of complex networks [1-3], where nodes represent cellular components such as genes, proteins, or metabolites, and edges identify the presence of biological interactions between them [4]. These network representations enable to map the large-scale structure of cellular interactions [5,6], to explore the basic principles of transcriptome and proteome organization [6], to identify missing genes encoding for specific metabolic functions [7], or to analyze emergent global phenomena in metabolism like robustness and regulation [8-11].At present, the information for complex network representations of cellular systems comes primarily from web-based databases, oftentimes manually curated with information from multiple sources, like annotations from the literature or new experiments [12]. It is common to take the reliability of these data for granted and to draw from them resolute inferences about the properties or the behavior of