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Systemic approaches to the study of a biological cell or tissue relyincreasingly on the use of context-specific metabolic network models. Thereconstruction of such a model from high-throughput data can routinely involvelarge numbers of tests under different conditions and extensive parametertuning, which calls for fast algorithms. We present FASTCORE, a genericalgorithm for reconstructing context-specific metabolic network models fromglobal genome-wide metabolic network models such as Recon X. FASTCORE takes asinput a core set of reactions that are known to be active in the context ofinterest (e.g., cell or tissue), and it searches for a flux consistentsubnetwork of the global network that contains all reactions from the core setand a minimal set of additional reactions. Our key observation is that aminimal consistent reconstruction can be defined via a set of sparse modes ofthe global network, and FASTCORE iteratively computes such a set via a seriesof linear programs. Experiments on liver data demonstrate speedups of severalorders of magnitude, and significantly more compact reconstructions, over achief rival method. Given its simplicity and its excellent performance,FASTCORE can form the backbone of many future metabolic network reconstructionalgorithms.

Fast Reconstruction of Compact Context-Specific Metabolic Network Models