SWIFTCORE: a tool for the context-specific reconstruction of genome-scale metabolic networks

Abstract

Abstract Background 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. Results 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. Conclusions 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/.

Keywords

• Systems biology
• Metabolic network analysis
• Metabolic network reconstruction
• Context-specific models