TY - JOUR
T1 - The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation
T2 - A scaffold to query lipid metabolism
AU - Nookaew, Intawat
AU - Jewett, Michael C.
AU - Meechai, Asawin
AU - Thammarongtham, Chinae
AU - Laoteng, Kobkul
AU - Cheevadhanarak, Supapon
AU - Nielsen, Jens
AU - Bhumiratana, Sakarindr
N1 - Funding Information:
The authors gratefully thank Mikael Rørdam Andersen and Kiran Raosaheb Patil for providing the ReMapper and the Reporter software, respectively. This work is supported by a grant from the National Center for Genetic Engineering and Biotechnology (BIOTEC) (grant number BT-B-06-NG-B5-4602). Intawat Nookaew gratefully acknowledges financial support by Thai Graduate Student Institute Science and Technology (TGIST). Michael C. Jewett is grateful to the NSF International Research Fellowship Program for supporting his work.
PY - 2008/8/7
Y1 - 2008/8/7
N2 - Background: 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. Results: 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. Conclusion: 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.
AB - Background: 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. Results: 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. Conclusion: 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.
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U2 - 10.1186/1752-0509-2-71
DO - 10.1186/1752-0509-2-71
M3 - Article
C2 - 18687109
AN - SCOPUS:52649105455
VL - 2
JO - BMC Systems Biology
JF - BMC Systems Biology
SN - 1752-0509
M1 - 71
ER -