Accelerating the reconstruction of genome-scale metabolic networks

We have evaluated the method using Lactococcus lactis IL1403, for which a genome-scale metabolic network was published recently. We recovered most of the gene-reaction associations (i.e. 74 – 85%) which are incorporated in the published network. Moreover, we predicted over 200 additional genes to be associated to reactions, including genes with unknown function, genes for transporters and genes with specific metabolic reactions, which are good candidates for an extension to the previously published network. In a comparison of our developed method with the well-established approach Pathologic, we predicted 186 additional genes to be associated to reactions. We also predicted a relatively high number of complete conserved protein complexes, which are derived from curated metabolic networks, illustrating the potential predictive power of our method for protein complexes.We show that our methodology can be applied to accelerate the reconstruction of genome-scale metabolic networks by taking optimal advantage of existing, manually curated networks. As orthology detection is the first step in the method, only the translated open reading frames (ORFs) of a newly sequenced genome are necessary to reconstruct a metabolic network. When more manually curated metabolic networks will become available in the near future, the usefulness of our method in network prediction is likely to increase.In recent years, genome sequencing projects have enormously increased our molecular understanding of biological capabilities of organisms. For many research areas, such as biotechnology and biomedical research, the metabolic capacities of cells are highly relevant. On the basis of the functional annotation of predicted genes, genome-scale metabolic networks can be reconstructed [1-3]. An increasing collection of methods is available to analyze the properties of these networks, both from a graph-theoretical point of view [4-7] as well as from a metabolic engineering point of view (for reviews