8-38: Comprehensive Evaluation of Two Genome-Wide Metabolic Network Models on Scheffersomyces stipitis

In the past few decades, significant progress has been made, specifically in the development of computational and high-throughput experimental approaches that provides scientists with genome-scale metabolic network models that are capable of predicting cellular metabolic behavior and designing recombinant strains for overproduction of target biochemicals1. Genome-scale metabolic network models represent the link between the genotype and phenotype of the organism, where they are usually reconstructed based on the genome sequence annotation and relevant biochemical and physiological information2. These models provide a holistic view of the organism’s metabolism, and constraint-based metabolic flux analysis methods, such as flux balance analysis (FBA), have been used extensively to study genome-wide cellular metabolic networks as they provides quantitative descriptions of the cellular metabolism without the knowledge of kinetic information3,4. Scheffersomyces stipitis is a yeast strain that has the highest native capacity for xylose fermentation, and the potential to significantly impact the production of biomass-derived biofuels and other value-added chemicals5. In this work, two recently published genome-scale metabolic network models6, 7 have been evaluated comprehensively, where KEGG database was used to compare the reaction framework. Overall model7 is more in agreement with KEGG, but there is substantially more gaps that contribute to system error. In silico experiments showed that ethanol yield from xylose is underestimated in both models and PCA determined that model6 had stringent group of metabolic switches for regulation. Hence, modifications are proposed to improve the model performance, and validation experiments will be carried out.