CorGi4ME: A coarse-grained^M modelling approach for applications in Metabolic Engineering

The development of meaningful models that could be used for systems biotechnology or Metabolic Engineering problems is still a challenging task. Although various modelling approaches are available and a high number of models, especially for E. coli, have been published in recent years, the demand for a quantitative and dynamic description is 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.

This project aims at developing coarse-grained^M which is a framework for dynamic coarse-grained models with a modular structure in a very general form, such that Metabolic Engineering applications to different cellular systems and process designs can be made quickly. Modularity, which is a crucial concept during the modelling, means that starting from a simple structure of a coarse-grained model, functional modules are defined that specify and detail parts of the overall network depending on the problem formulation.

The goal will be achieved by combining bottom-up (modelling based on first principles) and top-down approaches (data-driven, applying classical machine learning tools). The project is structured with three objectives: A rough plan of the project develops first a coarse-grained model with basic features and requirements on model structure and parameterization; then the model will be calibrated based on experimental data. Later, this basic model is extended for applications in Metabolic Engineering.

Project supervisor: M.Sc. Jiahui (Garfield) Qin

Project start date: 01.02.2024