High throughput cultivation systems are an essential step to increase the speed during the development of bioprocesses, e.g., the production of drugs in microorganisms (Neubauer et al., 2013). High numbers of possible combinations of strains and cultivations conditions arise during screening for optimal producers, and miniaturization is a key factor in making these large number of experiments economically and practically feasible (Long et al., 2014). High throughput cultivation systems have evolved over the years with many options for monitoring and manipulation of the cultivation conditions (Hemmerich et al., 2018; Teworte et al., 2022).

However, further challenges must be tackled to mimic the production conditions more closely and thus minimize scale-up risks. Industrial bioprocesses are typically highly dynamic and stress factors such as heterogeneities inside the reactor may lead to metabolic changes (Lin & Neubauer, 2000). Further difficulties lie in limitations in real-time monitoring and the need to handle high numbers of parallel cultivations at very small volumes.

My group tackles those difficulties applying model-based methods. Mathematical models allow us to extract more information from our experimental data, and to optimize the production of the desired active ingredients. Optimal growth conditions for the microbial host, usually Escherichia coli, can be explored in silico, and critical process variables such as the biomass concentration are monitored, e.g., by state estimation using the scarce available online measurements. Based on the detected dynamics, the feed rate is iteratively adapted to avoid violation of cultivation constraints, e.g., overflow metabolism or oxygen limitation. Furthermore, we study the impact of heterogeneities, e.g., oscillations in the glucose concentration, on growth and production by applying different cultivations modes. Altogether, our work aims to study large scale phenomena in small-scale systems, and thus aids in the development of robust strains.