Systems Biology of Yeast Metabolism
LEGACY
2018
5th Conference on Constraint-Based Reconstruction and Analysis (COBRA 2018)
General Submissions
Applications in metabolic engineering
Tuesday, October 16, 2018 - 9:15am to 9:45am
1 Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
2 Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
Metabolic Engineering relies on the Design-Build-Test cycle. This cycle includes technologies like mathematical modeling of metabolism, genome editing and advanced tools for phenotypic characterization. In recent years there have been advances in several of these technologies, which has enabled faster development of metabolically engineered strains that can be used for production of fuels and chemicals, but it is still challenging to perform efficient design. There is therefore in particular a need for advancing our ability to model metabolism, and this can be achieved through integration of systems biology tools. The yeast Saccharomyces cerevisiae is widely used for production of fuels, chemicals, pharmaceuticals and materials. Through metabolic engineering of this yeast a number of novel industrial processes have been developed over the last 10 years. Besides its wide industrial use, S. cerevisiae also serves as an eukaryal model organism, and many systems biology tools have therefore been developed for this organism. These tools can be used for detailed phenotypic characterization as well as for metabolic design. In this lecture, advances in metabolic modeling of yeast will be presented. It will be shown how integration of kinetic information into genome-scale metabolic models can significantly improve their predictive strength. Furthermore, through expanding these models to describe protein synthesis and translocation, it is possible to get new insight into what is constraining flux through different metabolic pathways. Finally, it will be shown how advances in quantitative omics allows us to provide a global map of flux control in the metabolic network.