Experimental and In Silico Fermentation of Glucose and Xylose with Scheffersomyces stipitis
Type of Degreedissertation
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Fossil fuel reserves are running out, global warming is becoming a reality, waste recycling is becoming ever more costly and problematic, and unrelenting population growth will require more and more energy and consumer products. There is now an alternative to the 100% oil economy; it is a renewable resource based on biomass. Production and development of these new products are based on biorefinery concept. The substitution of oil products by bio-based products will develop a new bio-economy and industrial processes respecting the sustainable development concept. The carbohydrate fraction of biomass feedstock (i.e. cellulose and hemicellulose in lignocellulosic biomass) is expected to play the biggest role as a renewable carbon source for biochemical products. Scheffersomyces stipitis, a novel yeast for lignocellulosic bioconversion, accepts various substrates and shows good overall performance in hydrolysate. As one of the best xylose-fermenting yeast, it has worked long as the gene provider and now it has the potential to be host for further genetic modification. With the genome sequenced, it is very necessary now to study S. stipitis in a systematic way. In this study, the fermentation of glucose and xylose with S. stipitis has been studied both experimentally and computationally. First, the fermentation of glucose and xylose were studied via experiment. To solve the washout caused by low growth rate with limited oxygen supply, a ``pseudo-continuous'' fermentation was used. The system proved its efficiency and also provided a better approach for improving ethanol tolerance, which was evaluated by the significant improvements of five different definitions on ethanol tolerance. Following the experimental results, a constraint-based core carbon metabolic network model has been constructed based on literatures, databases, and genome data. Flux balance analysis (FBA) was used to investigate the properties of the model under various conditions. To evaluate the performance of the constructed model, bioethanol production was chosen as the study system. The model was verified qualitatively and quantitatively with experimental observations and reported literature data. Different phenotypes in glucose or xylose metabolism with S. stipitis have been identified via phenotype analysis and thus studied via flux distribution. To further extract the underlying biological knowledge under the phenotype shifts, we proposed a new system identification based framework, FBA-PCA, and showed its power on analyzing metabolic network model through the identification of key reactions when oxygen supply rate or the ratio through NADPH- and NADH-linked reactions catalyzed by xylose reductase changes. The methodologies proposed in this dissertation can be applied to other biological system and therefore can broaden the application of the metabolic network models.