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WIREs Syst Biol Med
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Genome‐scale metabolic models applied to human health and disease

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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
Building and solving metabolic models. (a) Metabolic maps show metabolism as a series of metabolites (nodes) connected by specific reactions (directed edges). (b) Metabolic reaction networks can be represented mathematically using a stoichiometry (S) matrix, a flux (v) vector, and a series of differential expressions for each metabolite. (c) Solving for fluxes in a metabolic network often requires the imposition of multiple constraints.
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Scales of metabolic models appropriate for simulating human metabolism. Generally, simulating metabolism in humans involves a tradeoff of resolution for utility. Lower resolution models often are able to answer questions about the broad system, but may be inadequate to suggest specific interventions to alter metabolism. As model resolution increases, there can be more confidence that the model matches biology, but relating the model to human physiology becomes difficult.
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Manual curation/postprocessing increases the number of reactions contained in multiple GEMs. Manual curation following automatic reaction network building increases the size of the metabolic network. This was true across species, across tissue types, and across network building tools.
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Timescales of biological regulation. The characteristic timescale of a system is the time frame during which changes occur. Transcription typically takes hours to minutes, translation takes typically tens of minutes to minutes, and metabolic reactions take seconds to complete. Information on characteristic timescales can be found in bionumbers (http://bionumbers.hms.harvard.edu) using bionumber IDs 111027 (human representative transcription rate), 105336 (human average gene size), 104598 (human representative translation rate), 101652 (human mean protein size), and 111415 (representative kcat values).
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Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models
Analytical and Computational Methods > Computational Methods
Biological Mechanisms > Metabolism

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