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Automated Stem Cell Production by Bio-Inspired Control

: Monostori, László; Csáji, Balázs C.; Egri, Péter; Kis, Krisztián B.; Váncza, József; Ochs, Jelena; Jung, Sven; König, Niels; Pieskie, Simon; Wein, Stephan; Schmitt, Robert; Brecher, Christian


CIRP Journal of Manufacturing Science and Technology 33 (2021), S.369-379
ISSN: 1755-5817
Fraunhofer IPT ()
stem cell production; digital simulation; machine learning; reinforcement learning; Policy Gradient; Biological transformation

The potential in treating chronic and life-threatening diseases by stem cell therapies can greatly be exploited via the efficient automation of stem cell production. Working with living material though poses severe challenges to automation. Recently, production platforms has been developed and tested worldwide with the aim to increase the reproducibility, quality and throughput of the process, to minimize human errors, and to reduce costs of production. A distinctive feature of this domain is the symbiotic co-existence and co-evolution of the technical, information and communication, as well as biological ingredients in production structures. A challenging way to overcome the issues of automated production is the use of biologically inspired control algorithms. In the paper an approach is described which combines digital, agent-based simulation and reinforcement learning for this purpose. The modelling of the cell growth behaviour, which is an important prerequisite of the simulation, is also introduced, together with an appropriate model fitting procedure. The applicability of the proposed approach is demonstrated by the results of a comprehensive investigation.