Control of Dynamically Inherent Biological Processes in Cell Technolog

The cultivation of human cells for diagnostic or biomedical applications is an interesting topic for the automation industry. However, classical automation concepts using an imperative programming paradigm reach their limits because of the inherent dynamical behavior of donor specific cells. In particular sophisticated cell culture processes, such as many stem cell technologies, have still to be executed manually, which renders them labor intensive and causes varying cell quality. In contrast to classical products, which for example are made of metal or plastic, living cells in culture are subject to changes in growth and functional properties depending on internal or external stimuli. Therefore, constant observation of the cells over extended periods of time is necessary. The evaluation of these observations serves as a basis for decision-making and online process adaptation. Finally, process adaptation leads to adjustments in scheduling. This paper presents a novel concept of rule-based modeling of cell culture processes, process adaption and dynamic scheduling. The concept was validated using a general process chain including the most common decision situation within a cell cultivation process.