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Start of funding 01.01.2021
Next Generation Tools for Rational Metabolic Engineering
Prof. Andreas Kremling
Technische Universität München
Department of Mechanical Engineering
Prof. Michael A. Savageau
University of California, Davis
Department of Microbiology & Molecular Genetics
Mathematical models are fundamental tools to understand the non-trivial functioning of biological systems. They enable and facilitate several relevant applications. For instance, altering the bacterial metabolism allows the biotechnological production of medicines and other valuable molecules in an environmentally friendly way. Mechanistic modeling of biochemical networks captures intricate interactions between different levels of cellular organization, which need to be rigorously integrated to understand and successfully optimize biological systems. Mechanistic models are truly predictive because they provide a rigorous link between metabolite concentration, enzyme availability, and intracellular flux distributions.
So far, the application of mechanistic models to identify Metabolic Engineering strategies has been limited, mainly due to the large number of associated parameter values that are uncertain or even unknown. The Savageau laboratory at UC Davis has developed a novel phenotype-centric modeling approach that allows kinetics-based (mechanistic) modeling of metabolic systems without requiring sampling or a priori knowledge of parameter values and does not involve numerical integration of the underlying system of differential equations. The planned cooperation aims to explore the application potential of the phenotype-centric modeling approach to develop cellular factories for applications in biotechnology. The cooperation will combine experimental expertise with computational analysis. The Savageau laboratory at UC Davis will provide extensive knowledge in the phenotype-centric modeling strategy, while the Kremling laboratory at the TU Munich will contribute experimental expertise and access to the industrially relevant bacterial platforms Pseudomonas putida and Escherichia coli.