349335 An Improved Genome-Scale Metabolic Network Model of Scheffersomyces Stipitis

Monday, November 4, 2013
Grand Ballroom B (Hilton)
Jeffrey Liu1, Pranjal Gupta2, Andrew Damiani1 and Jin Wang3, (1)Chemical Engineering, Auburn University, Auburn, AL, (2)Chemical Engineering, Vanderbilt University, Nashville, TN, (3)Auburn University, Auburn, AL

Lignocellulosic ethanol has been identified as one of the most promising long-term renewable energy sources. Although fermentation of glucose derived from biomass using Saccharomyces cerevisiae is well established on a large scale, the conversion of the xylose, one major sugar component of hemicelluloses, to ethanol is still one of the major barriers to industrializing lignocellulosic ethanol processes. Scheffersomyces stipitis has been shown to be the most promising wild strain for direct high-yield fermentation of xylose, and has been a source of genes for genetic engineering in the xylose metabolism of S. cerevisiae. Although the fermentation mechanism for S. stipitis is not well known, various genome-scale computational models have been created with the hope of mapping its metabolic network. However, the two published genome scale models in the existing literature fail to accurately predict the production of xylitol and acetic acid, key byproducts seen in the xylose metabolism of S. stipitis, while a third, unpublished model lacks key reactions in its reaction structure. In this poster, we present a refined model with a complete reaction structure that accurately predicts the production of xylitol and acetic acid.  By allowing genetic engineers to more accurately identify significant pathways and target genes, this new model can assist future efforts in genetic engineering and upregulation of ethanol production in S. cerevisiae.

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