Computational strategies, that integrate genomic-level information and metabolic flux data, have improved both prediction of metabolic fluxes and metabolic network identification. Due to the tight interplay between hierarchical (transcriptional) and metabolic control it is not clear how changes in gene expression drive changes in cellular phenotypes manifested through changes in metabolic fluxes. This raises the questions to what extent a change in a metabolic flux should be attributed to changes in gene expression and/or changes in metabolite concentrations and what kind of conclusions can be drawn by comparing gene expression profiles with the associated metabolic fluxes. This review addresses issues related to modeling approaches that attempt to integrate gene expression and metabolic flux data.