The first genome-scale model was named iFF708. The model consists of 708 metabolic genes associated with 1175 reactions and 733 metabolites. Three cellular compartments, namely cytosol, mitochondria and the extra cellular space were included. The physiological validations and predictions by the model using flux balance analysis (FBA) were shown to have good agreements with many experimental data-sets. Furthermore, large-scale in silico gene deletion analysis of the model showed high accuracy in predicting gene essentiality when comparing to in vivo knock-out phenotypes.
After its release other genome-scale metabolic models of S. cerevisiae, which all differs in scope and objective, were developed originating from the iFF708 model.
One of the most complex parts of metabolism, lipid metabolism, was poorly described in the original genome scale metabolic models. Therefore the model iIN800(1) was constructed by expanding the iFF708 model. It included 143 new reactions associated with 65 ORFs involved in lipid metabolism. It also had an improved biomass equation, involving details in the formation of fatty acids and lipids for carbon- and nitrogen limited growth conditions. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. It has been demonstrated that IN800 can be used as a scaffold to reveal the regulatory importance (using transcriptome data-sets) of lipid metabolism precursors and intermediates that would have been missed in previous models.
We present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap-filling methods and by introducing new reactions and pathways based on studies of the literature and databases.