Pruning genome-scale metabolic models to consistent ad functionem networks.

Metabolic networks represent a set of reactions and associated metabolites that may occur in a given cell or tissue. They are frequently reconstructed from pure genomic data without thorough biochemical validation. Such genome-scale metabolic networks may thus either lack relevant or contain non-existent reactions and metabolites. Filling gaps and removing falsely predicted reactions can be a cumbersome procedure. On the other hand, using the network to build mathematical models addressing a specific problem (e.g. analyzing changes in the level of cellular ATP at substrate depletion) it may turn out that the network comprises more reactions and metabolites than actually needed or, on the contrary, that essential reactions are missing. Therefore, we propose a method to prune the whole network to a smaller sub-network which contains no dead ends and blocked reactions, i.e reactions that may neither proceed in forward nor backward direction. Inspection of this reduced network reveals its actual functional capabilities in terms of producible metabolites. We apply our method to a genome-scale metabolic network of E. coli. Depending on the choice of the exchangeable metabolites, composition of the external medium, and type of thermodynamic constraints we obtain different reduced network variants that may serve as a basis for flux balance models.