Towards quantitative metabolomics and in vivo kinetic modeling in S. cerevisiae

Abstract

The quantitative analysis of enzyme kinetics in vivo, leading to the construction of predictive kinetic models of metabolic reaction networks, is an old ambition in Biochemistry and a central goal within the emerging field of Systems Biology. However, this objective remains largely unfulfilled because of a fundamental problem: the gap between the overwhelming complexity of kinetic models and the limited availability and information content of in vivo data. To close this gap, efforts must be aimed at expanding our ability to generate high-quality quantitative in vivo data and at developing and improving approaches to manage the complexity of kinetic models. This thesis systematically addresses several of the key challenges towards bridging the gap between model and data, with the ultimate goal of enabling network-wide in vivo kinetic modeling, in S. cerevisiae as well as other biological systems.