Probabilistic Integrative Modeling of Genome-Scale Metabolic and Regulatory Networks

Prediction of metabolic changes that result from genetic or environmental perturbations has several important applications including diagnosing metabolic disorders and discovering novel drug targets. A cardinal challenge in obtaining accurate predictions is the integration of transcriptional regulatory networks with the corresponding metabolic network. We propose a new method called Probabilistic Regulation of Metabolism (PROM) which achieves this synthesis and enables straightforward, automated and quantitative integration of high-throughput data into constraint-based modeling (PNAS, accepted, 2010), making it an ideal tool for constructing genome-scale regulatory-metabolic network models for less-studied organisms. PROM's novelty lies in the introduction of probabilities to represent gene states and gene–transcription factor interactions. Using PROM, we construct an integrated regulatory-metabolic network for the model organism, Escherichia coli, and demonstrate that our method based on automated inference is more accurate and comprehensive than the current state-of-the-art, which is based on manual curation of literature. After validating the approach, we used PROM to build the first genome-scale integrated metabolic–regulatory model for Mycobacterium tuberculosis, a critically important human pathogen. This work incorporated data from over 1300 microarrays, 2000 transcription factor–target interactions regulating 3300 metabolic reactions, and 1905 knockout phenotypes for E. coli and M. tuberculosis. PROM can detect drug targets, identify knockout phenotypes with accuracies as high as 95% and predict growth-rates quantitatively with correlation of 0.95. Importantly, PROM represents the successful integration of a top-down reconstructed, statistically inferred regulatory network with a bottom-up reconstructed, biochemically detailed metabolic network, bridging two important classes of systems biology models that are rarely combined quantitatively.