Transcriptional regulation plays a key role in controlling metabolism and a forefront challenge in modeling organisms today is to build integrated models of regulation and metabolism. Predicting the effect of transcriptional perturbations on the metabolic network can lead to accurate predictions on how genetic mutations and perturbations are translated into flux responses at the metabolic level. The Probabilistic Regulation of Metabolism (PROM) algorithm that we developed represents the successful integration of a genome scale transcriptional regulatory network with a biochemically detailed metabolic network, bridging two important classes of systems biology models that have rarely been combined quantitatively.
The construction of an integrated metabolic-regulatory network using PROM requires the following: 1) the reconstructed genome scale metabolic network 2) regulatory network structure, consisting of transcription factors (TF) and their targets 3) gene expression data. We used PROM to build genome-scale models for various model organisms and showed that PROM can detect drug targets, identify gene knockout phenotypes with accuracies as high as 95% and predict microbial growth-rates of transcription factor knockout strains quantitatively with correlation of 0.95.