The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism
1 Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
2 Biochemical Engineering and Pilot Plant Research and Development Unit, National Center for Genetic Engineering and Biotechnology (BIOTEC) at King Mongkut's University of Technology Thonburi, Bangkhuntien, Bangkok 10150, Thailand
3 School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
4 National Science and Technology Development Agency, Ministry of Science and Technology, Thailand Science Park, Klong Luang, Pathumthani 12120, Thailand
5 Center for Microbial Biotechnology, Biocentrum, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
6 Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
7 Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
BMC Systems Biology 2008, 2:71 doi:10.1186/1752-0509-2-71Published: 7 August 2008
Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism.
The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. 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. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets.
Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.