The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-12

AUTHORS

Intawat Nookaew, Michael C Jewett, Asawin Meechai, Chinae Thammarongtham, Kobkul Laoteng, Supapon Cheevadhanarak, Jens Nielsen, Sakarindr Bhumiratana

ABSTRACT

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. More... »

PAGES

71

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1752-0509-2-71

DOI

http://dx.doi.org/10.1186/1752-0509-2-71

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019085908

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18687109


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