Genome‐scale metabolic models applied to human health and disease

Advances in genome sequencing, high throughput measurement of gene and protein expression levels, data accessibility, and computational power have allowed genome‐scale metabolic models ( GEMs ) to become a useful tool for understanding metabolic alterations associated with many different diseases. Despite the proven utility of GEMs , researchers confront multiple challenges in the use of GEMs , their application to human health and disease, and their construction and simulation in an organ‐specific and disease‐specific manner. Several approaches that researchers are taking to address these challenges include using proteomic and transcriptomic‐informed methods to build GEMs for individual organs, diseases, and patients and using constraints on model behavior during simulation to match observed metabolic fluxes. We review the challenges facing researchers in the use of GEMs , review the approaches used to address these challenges, and describe advances that are on the horizon and could lead to a better understanding of human metabolism. WIREs Syst Biol Med 2017, 9:e1393. doi: 10.1002/wsbm.1393 This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Metabolism Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models