A coarse-grained^M modelling approach for applications in Metabolic Engineering

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To improve biotechnological processes, mathematical models come into play for a better understanding of the inner workings of cells. Although various modelling approaches are available and a high number of models are published, the demand for a quantitative and a dynamic description of cellular processes is still high and not met by all approaches. Also, quantitative data is now available for different cellular levels like proteome, transcriptome, and metabolome that should be integrated into these models. The proposal's overall aim is to develop coarse-grainedM, that is, a framework for dynamic coarse-grained models with a modular structure in a very general form, such that applications to different cellular systems and process designs can be made quickly. Modularity in the context of the proposal means that, starting from a simple structure of a coarse-grained model, functional modules are defined that specify parts of the overall network depending on the problem formulation. To set up these models, a combined approach with modelling based on first principles and a data driven approach with classical elements of machine learning tools will be used. Applications in Metabolic Engineering are addressed such as by-product formation, and heterologous protein production. In addition, also defined mixed cultures are considered where two strain variants are mutual linked for an efficient production of an interesting product.