Research Article A Genome-Scale Modeling Approach to Study Inborn Errors of Liver Metabolism: Toward an In Silico Patient

Inborn errors of metabolism (IEM) are genetic diseases caused by mutations in enzymes or transporters affecting specific metabolic reactions that cause a block in the physiological met-abolic fluxes. Therapeutic treatment can be achieved either by decreasing the metabolic flux upstream of the block or by increasing the flux downstream of the block. The identification of upstream and downstream fluxes however is not trivial, since metabolic reactions are inter-twined in a complex network. To overcome this problem, we propose an innovative computa-tional workflow to model the alteration of metabolism caused by IEM and predict the metabolites and reactions that are affected by the mutation. Our workflow exploits a recent genome-scale metabolic network model of hepatocyte metabolism to identify metabolites ac-cumulating in hepatocytes due to single gene mutations in IEM via an innovative ‘‘differential flux analysis.’ ’ We simulated 38 IEMs in the liver, and in about half of the cases, our workflow correctly identified the metabolites known to accumulate in the blood and urine of IEM patients. Key words: differential flux analysis, flux balance analysis, hepatocyte metabolism, inborn errors of metabolism, mathematical modeling. 1