Fast reconstruction of compact context-specific metabolic network models.
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 rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg City, Luxembourg.
Nikos
N
Vlassis
Life Sciences Research Unit, University of Luxembourg, Luxembourg City, Luxembourg.
Maria Pires
MP
Pacheco
Life Sciences Research Unit, University of Luxembourg, Luxembourg City, Luxembourg.
Thomas
T
Sauter
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016678:Genome; D006801:Humans; D016014:Linear Models; D053858:Metabolic Networks and Pathways; D012984:Software