  Systemic approaches to the study of a biological cell or tissue rely
increasingly on the use of context-specific metabolic network models. The
reconstruction of such a model from high-throughput data can routinely involve
large numbers of tests under different conditions and extensive parameter
tuning, which calls for fast algorithms. We present FASTCORE, a generic
algorithm for reconstructing context-specific metabolic network models from
global genome-wide metabolic network models such as Recon X. FASTCORE takes as
input a core set of reactions that are known to be active in the context of
interest (e.g., cell or tissue), and it searches for a flux consistent
subnetwork of the global network that contains all reactions from the core set
and a minimal set of additional reactions. Our key observation is that a
minimal consistent reconstruction can be defined via a set of sparse modes of
the global network, and FASTCORE iteratively computes such a set via a series
of linear programs. Experiments on liver data demonstrate speedups of several
orders of magnitude, and significantly more compact reconstructions, over a
chief rival method. Given its simplicity and its excellent performance,
FASTCORE can form the backbone of many future metabolic network reconstruction
algorithms.
