LAUSR

High-throughput omics technologies have enabled the comprehensive reconstructions of genome-scale metabolic networks for many organisms. However, only a subset of reactions is active in each cell which differs from tissue to tissue or from patient to patient. Reconstructing a subnetwork of the generic metabolic network from a provided set of context-specific active reactions is a demanding computational task. We propose swiftcc and swiftcore as effective methods for flux consistency checking and the context-specific reconstruction of genome-scale metabolic networks which consistently outperform the previous approaches. We have derived an approximate greedy algorithm which efficiently scales to increasingly large metabolic networks. swiftcore is freely available for non-commercial use in the GitHub repository at https://mtefagh.github.io/swiftcore/.