Dipl.-Phys.

König, Niels

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  • Publication
    Fully Automated Cultivation of Adipose-Derived Stem Cells in the StemCellDiscovery - A Robotic Laboratory for Small-Scale, High-Throughput Cell Production Including Deep Learning-Based Confluence Estimation
    ( 2021)
    Ochs, Jelena
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    Biermann, Ferdinand
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    Piotrowski, Tobias
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    Erkens, Frederik
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    Nießing, Bastian
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    Herbst, Laura
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    König, Niels orcid-logo
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    Schmitt, Robert H.
    Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.
  • Publication
    Automated Stem Cell Production by Bio-Inspired Control
    ( 2021)
    Monostori, László
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    Csáji, Balázs C.
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    Egri, Péter
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    Kis, Krisztián B.
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    Váncza, József
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    Ochs, Jelena
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    Jung, Sven
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    König, Niels orcid-logo
    ;
    Pieske, Simon
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    Wein, Stephan
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    Schmitt, Robert
    ;
    Brecher, Christian
    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.
  • Publication
    Reprint of: Automated stem cell production by bio-inspired control
    ( 2021)
    Monostori, László
    ;
    Csáji, Balázs C.
    ;
    Egri, Péter
    ;
    Kis, Krisztián B.
    ;
    Váncza, József
    ;
    Ochs, Jelena
    ;
    Jung, Sven
    ;
    König, Niels orcid-logo
    ;
    Pieske, Simon
    ;
    Wein, Stephan
    ;
    Schmitt, Robert
    ;
    Brecher, Christian
    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.
  • Publication
    Bio-inspired control of automated stem cell production
    ( 2020)
    Egri, Péter
    ;
    Csáji, Balázs C.
    ;
    Kis, Krisztián B.
    ;
    Monostori, László
    ;
    Váncza, József
    ;
    Ochs, Jelena
    ;
    Jung, Sven
    ;
    König, Niels orcid-logo
    ;
    Schmitt, Robert
    ;
    Brecher, Christian
    ;
    Pieske, Simon
    ;
    Wein, Stephan
    The possible role of stem cells in medical treatments can hardly be overestimated. Today they are produced â almost without exemption â with significant human involvement using adaptive protocols that take the growth behavior of the biological material into account. Automated production platforms are being developed and tested in a number of research laboratories with the main goals of improving reproducibility, as well as increasing quality and throughput. However, automated stem cell production differs from the traditional manufacturing processes in (1) the inherent diversity of the products (stem cells), (2) their varying growth rates and process times, (3) the need for their regular observation and process adaptation, and, therefore, (4) for mixed-initiative production control. A distinctive feature of the domain is the symbiotic coexistence and co-evolution of the technical, ICT and biological ingredients in production structures. A challenging way to overcome these issues is the use of biologically-inspired control algorithms. In the paper the application of reinforcement learning is proposed for this purpose. As a first step, a digital simulation of the stem cell production was performed in order to generate patterns for the training process and to test the approach. In addition to the description of the concept, the paper also presents initial research results.
  • Publication
    Highly modular and generic control software for adaptive cell processing on automated production platforms
    ( 2018)
    Jung, Sven
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    Ochs, Jelena
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    Kulik, Michael
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    König, Niels orcid-logo
    ;
    Schmitt, Robert H.
    The expansion of patient derived stem cells requires adaptive processing protocols that consider the growth behavior. In order to minimize human errors and enhance reproducibility, the industry moves towards automated platforms. This bears several challenges for a control software, such as coping with non-deterministic processes and the prevalent heterogeneity of device interfaces. We have developed a service-oriented approach to meet the demand for flexibility while at the same time giving maximum control over data and devices. Hardware modules are integrated via agents into the control software following a plug-and-produce approach. This generic software is also easily adaptable for other applications.
  • Publication
    Das Stammzelllabor der Zukunft. Forschen in einer automatisierten Testumgebung
    ( 2018)
    Ochs, Jelena
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    Jung, Sven
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    Piotrowski, Tobias
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    Niessing, Bastian
    ;
    König, Niels orcid-logo
    ;
    Schmitt, Robert
    Stammzellen sind heiß begehrte Forschungsobjekte, doch die Herstellung und Erforschung der Zellen ist aufwendig. Hier wird eine vollautomatisierte Plattform vorgestellt, die Forschern Arbeit abnehmen soll. Im robotergestützten Labor sollen nicht nur die bisher manuell durchgeführten Zellkulturprozesse automatisiert, sondern auch an neuen Konzepten für die Laborautomatisierung geforscht werden.
  • Publication
    The AUTOSTEM platform for closed manufacture of bone marrow-derived mesenchymal stromal cells using a closed, scalable and automated robotic system
    ( 2017)
    Murphy, Mary
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    Barry, Frank
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    Leschke, Christian
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    Vaughan, B.
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    Gentili, Chiara
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    O'Dea, John
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    Ogourtsov, Vladimir
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    Rafiq, Qasim A.
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    Ochs, Jelena
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    Kulik, Michael
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    Koenig, Niels orcid-logo
  • Publication
    Automation in the context of stem cell production - where are we heading with Industry 4.0?
    ( 2016)
    Kulik, Michael
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    Ochs, Jelena
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    König, Niels orcid-logo
    ;
    Schmitt, Robert
    As the field of cell and gene therapy continues to progress, the need for cell products for therapeutic application is increasing. In order to meet the demands, novel challenges particularly within the manufacturing of these products need to be overcome. By translating and applying automation solutions from the production industries into cell culture applications, standardized processing procedures can be introduced in order to reduce the variability, which is a critical issue within the manufacturing workflow. Additionally, the Industry 4.0 philosophy offers a variety of concepts for the design of adaptive processes that address the specific challenges in stem cell production. Novel tools such as data tracking, data analysis and machine learning are enabling technologies that will help support the safe manufacturing of effective products for future cell and gene therapies.