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Model-assisted design and optimization of biotechnological processes
Citation Link: https://doi.org/10.15480/882.13739
Publikationstyp
Doctoral Thesis
Date Issued
2024
Sprache
English
Author(s)
Arndt, Lukas
Advisor
Pörtner, Ralf
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2024-10-08
Institute
TORE-DOI
Citation
Technische Universität Hamburg (2024)
Today, new developments in the field of biocatalysis enable the replacement of a wide range of chemical synthesis processes with biotransformations. The biocatalysts used in these processes are non-toxic and have a low environmental impact. To fully exploit the biocatalytic potential already in the early stages of process development, knowledge-based optimization of reaction conditions is crucial, but time-consuming. Design of Experiments, process modeling and regression analysis are effective tools for systematically planning experiments, describing parameter interactions, and determining appropriate process conditions. In the model-assisted Design of Experiments approach, these tools are combined. Initially, a process model is set-up, model parameters are estimated and suggested experimental combinations are simulated. After evaluation of the predicted responses only a reduced number of factor combinations consistent with a pre-defined optimization objective are performed experimentally. In this work, the model-assisted Design of Experiments methodology is discussed for the design and optimization of a continuously operated cytidine-5'-triphosphate regeneration reaction by polyphosphate kinase under high hydrostatic pressure. The process development was particularly complex since the packed-bed bioreactor system used and the enzymatic reaction were only partially described in the literature. An adequate process model was formulated to describe the time- and location-dependent changes of substrates and product as well as the influence of process conditions on the enzyme kinetics. The formulation of the process model proved to be extensive due to the use of immobilized enzyme and the consideration of a disperse flow within the reactor. In addition, a suitable modeling approach was elaborated to describe the influence of the process parameter pressure on the enzymatic reaction. To reduce the number of time-consuming individual optimization studies, the process parameters and their interactions were to be optimized and described simultaneously using the model-assisted Design of Experiments methodology. The proposed methodology, integrated into a user-friendly iterative workflow, accompanied the interdisciplinary development process throughout and the process understanding of the reaction system was enhanced. Furthermore, the experimental effort was significantly reduced and process conditions were optimized in terms of maximal cytidine-5'-triphosphate concentration after three iteration rounds. Overall, the applicability of the knowledge-based model-assisted Design of Experiments methodology was shown and proven for a complex biocatalytic reaction system with a limited amount of prior knowledge. The transferability to other reaction systems and a further application of the methodology are discussed at the end of the work.
DDC Class
660.6: Biotechnology
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