Robotic integration enables autonomous operation of laboratory scale stirred tank bioreactors with model‐driven process analysis. Issue 7 (3rd May 2021) - Morschett, Holger; Tenhaef, Niklas; Hemmerich, Johannes; Herbst, Laura; Spiertz, Markus; Dogan, Deniz; Wiechert, Wolfgang; Noack, Stephan; Oldiges, Marco
Abstract: Given its geometric similarity to large‐scale production plants and the excellent possibilities for precise process control and monitoring, the classic stirred tank bioreactor (STR) still represents the gold standard for bioprocess development at a laboratory scale. However, compared to microbioreactor technologies, bioreactors often suffer from a low degree of process automation and deriving key performance indicators (KPIs) such as specific rates or yields often requires manual sampling and sample processing. A widely used parallelized STR setup was automated by connecting it to a liquid handling system and controlling it with a custom‐made process control system. This allowed for the setup of a flexible modular platform enabling autonomous operation of the bioreactors without any operator present. Multiple unit operations like automated inoculation, sampling, sample processing and analysis, and decision making, for example for automated induction of protein production were implemented to achieve such functionality. The data gained during application studies was used for fitting of bioprocess models to derive relevant KPIs being in good agreement with literature. By combining the capabilities of STRs with the flexibility of liquid handling systems, this platform technology can be applied to a multitude of different bioprocess development pipelines at laboratory scale. Abstract : The use of laboratory scale bioreactors is characterized by substantial manual efforts and is thus often limited with respect to throughput and sampling density. Leveraging the concept introduced here, the operation of such bioreactors was fully automated by coupling them to a versatile laboratory robotics platform. The obtained high density, high quality data was profitably employed in the mechanistic process modeling to extract relevant performance indicators in a statistically validated manner.