Systems metabolic engineering

Biotechnology shows great potential for meeting UN Sustainable Development Goals, yet optimizing cell cultivation processes remains challenging due to biological complexity. Industrial-scale production often uses fed-batch operation, necessitating testing under these conditions during cell factory development. While miniaturized fed-batch systems exist, data analysis is complex. Additionally, flux distribution data, key for cell factory engineering, is rarely included in characterization experiments due to high costs and complex processing. This thesis develops a method for multiomics characterization of cell factories under fed-batch conditions. We created two Python packages: one for analyzing fed-batch bioprocess data and another for high-throughput fluxomics analysis. Additionally, we also developed a small-scale data management system for diverse datasets.

We applied these tools in a case study of four yeast strains producing increasing amounts of pcoumaric acid. Despite suboptimal strains and experimental design, our analysis yielded insights into the metabolic phenotypes of these cell factories. The tools developed here can facilitate future multiomics characterization of cell factories under fed-batch conditions, potentially accelerating the development of more efficient and sustainable biotechnological processes.