A schematic drawing of the approach used in this work for model-based analysis of growth phenotyping and gene expression data to identify new network components and interactions. The approach combines in silico modeling of genome-scale metabolic and regulatory networks with analysis of in vivo data obtained by gene expression and growth phenotyping experiments. Specific mispredictions of either gene expression changes or growth phenotypes are identified and used as inputs for systematic model expansion. The primary data types used for model expansion are ChIP-chip data on protein–DNA interactions and the presence of known TF-binding motifs on promoters. The result of the expansion is a model that includes new regulatory interactions that allow improved prediction of expression changes and growth phenotypes of knockout and overexpression strains.