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.

通过干细胞生产的有效自动化，可以极大地利用干细胞疗法来治疗慢性和危及生命的疾病。但是，使用活动材料给自动化带来了严峻的挑战。最近，已经在全球范围内开发和测试了生产平台，目的是提高过程的可重复性，质量和吞吐量，以最大程度地减少人为错误并降低生产成本。该领域的一个显着特征是技术，信息和通信以及生产结构中的生物成分共生共存和共同发展。克服自动化生产问题的一种挑战性方法是使用受生物启发的控制算法。本文介绍了一种将数字，为此，基于主体的模拟和强化学习。还介绍了细胞生长行为的建模，这是仿真的重要前提，并附带了适当的模型拟合程序。全面调查的结果证明了该方法的适用性。