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TY - THES AU - Helleckes, Laura Marie TI - Automated experimentation, Bayesian statistics and machine learning for high-throughput bioprocess development PB - RWTH Aachen University VL - Dissertation CY - Aachen M1 - RWTH-2024-12118 SP - 1 Online-Ressource : Illustrationen PY - 2024 N1 - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2025 N1 - Dissertation, RWTH Aachen University, 2024 AB - The transition to a sustainable, circular bioeconomy is essential to tackle the socioecological crises of the 21st century. Industrial biotechnology, a cornerstone of this bioeconomy, leverages modern biofoundries that integrate automation and high-throughput experimentation with the Design-Build-Test-Learn (DBTL) cycle to streamline bioprocess development. While advances in automated cloning and genome editing have increased the availability of large strain libraries for early-stage screening, several limitations remain in the Test and Learn phases of DBTL. This work combines automated experimentation, Bayesian statistical modelling and machine learning to bridge the remaining gaps towards autonomous bioprocess development. This requires an <i>experiment-in-the-loop</i>-approach, where simulations are closely coupled with experiments on automated microbioreactor platforms. Consequently, the toolboxes for experimental workflow development and decision making based on process models were extended in this thesis. These improved tools were then applied to biotechnological case studies, focusing on model-driven experimental design and iterative screening. First, manual steps in microbial screening, such as precultures in shake flasks, were replaced by automated solutions. Existing automated microbioreactor platforms were thus extended to enable consecutive screening experiments without human intervention. For example, an automated deep freezer was seamlessly integrated, including the connection to the existing process control infrastructure. Furthermore, automated precultures and microtiter plate recycling were achieved for the microbioreactor, leading to the demonstration of a fully automated, iterative screening with cutinase-secreting <i>Corynebacterium glutamicum</i> strains. With the gaps in automated experimentation closed, the focus was shifted to high-throughput data analysis and process modelling. A need was identified for the evaluation of analytical calibration data, for example from high-throughput enzymatic assays. This led to the development of Bayesian calibration models for biotechnological applications, which describe the relationship between tested quantities and measured values, including uncertainty. The open-source Python package calibr8 was developed to help practitioners with little programming experience to easily implement complex, non-linear calibration models. It serves as a toolbox for high-throughput analytical calibration, as well as a starting point for advanced process models that account for bias in measurement systems. Using calibration models as likelihoods, Bayesian statistical models were developed to represent the technical and biological parameters of a screening process. For example, batch effects between screening experiments were modelled to avoid a bias in the final ranking of strains and conditions. The process models were also used to derive key performance indicators with uncertainties for decision making. In two application studies, Bayesian hierarchical process models were combined with Bayesian optimisation to efficiently design iterative screening experiments. For example, the number of experiments required to screen a strain library of catalytically active <i>inclusion bodies</i> (CatIBs) could be reduced by 25 LB - PUB:(DE-HGF)11 DO - DOI:10.18154/RWTH-2024-12118 UR - https://publications.rwth-aachen.de/record/999462 ER -