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|Type:||Artigo de periódico|
|Title:||Iod907, The First Genome-scale Metabolic Model For The Milk Yeast Kluyveromyces Lactis|
|Abstract:||We describe here the first genome-scale metabolic model of Kluyveromyces lactis, iOD907. It is partially compartmentalized (four compartments), composed of 1867 reactions and 1476 metabolites. The iOD907 model performed well when comparing the positive growth of K. lactis to Biolog experiments and to an online catalogue of strains that provides information on carbon sources in which K. lactis is able to grow. Chemostat experiments were used to adjust non-growth-associated energy requirements, and the model proved accurate when predicting the biomass, oxygen and carbon dioxide yields. When compared to published experiments, in silico knockouts accurately predicted in vivo phenotypes. The iOD907 genome-scale metabolic model complies with the MIRIAM (minimum information required for the annotation of biochemical models) standards for the annotation of enzymes, transporters, metabolites and reactions. Moreover, it contains direct links to Kyoto encyclopedia of genes and genomes (KEGG; for enzymes, metabolites and reactions) and to the Transporters Classification Database (TCDB) for transporters, allowing easy comparisons to other models. Furthermore, this model is provided in the well-established systems biology markup language (SBML) format, which means that it can be used in most metabolic engineering platforms, such as OptFlux or Cobra. The model is able to predict the behavior of K. lactis under different environmental conditions and genetic perturbations. Furthermore, by performing simulations and optimizations, it can be important in the design of minimal media and will allow insights on the milk yeast's metabolism, as well as identifying metabolic engineering targets for improving the production of products of interest. This article describes the first genome-scale metabolic model of Kluyveromyces lactis iOD907. It is composed of 1867 reactions and 1476 metabolites. Experimental data were used to iteratively improve the mathematical model up until in vivo phenotypes could be predicted. This model will allow insights on metabolism and targets for metabolic engineering of milk yeast. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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