Hans, SebastianUlmer, ChristianNarayanan, HariniBrautaset, TrygveKrausch, NielsNeubauer, PeterSchäffl, IrmgardSokolov, MichaelCruz-Bournazou, Mariano Nicolas2020-09-032020-09-032020-05-14https://depositonce.tu-berlin.de/handle/11303/11638http://dx.doi.org/10.14279/depositonce-10525In conditional microbial screening, a limited number of candidate strains are tested at different conditions searching for the optimal operation strategy in production (e.g., temperature and pH shifts, media composition as well as feeding and induction strategies). To achieve this, cultivation volumes of >10 mL and advanced control schemes are required to allow appropriate sampling and analyses. Operations become even more complex when the analytical methods are integrated into the robot facility. Among other multivariate data analysis methods, principal component analysis (PCA) techniques have especially gained popularity in high throughput screening. However, an important issue specific to high throughput bioprocess development is the lack of so-called golden batches that could be used as a basis for multivariate analysis. In this study, we establish and present a program to monitor dynamic parallel cultivations in a high throughput facility. PCA was used for process monitoring and automated fault detection of 24 parallel running experiments using recombinant E. coli cells expressing three different fluorescence proteins as the model organism. This approach allowed for capturing events like stirrer failures and blockage of the aeration system and provided a good signal to noise ratio. The developed application can be easily integrated in existing data- and device-infrastructures, allowing automated and remote monitoring of parallel bioreactor systems.en660 Chemische Verfahrenstechnikhigh throughput bioprocess developmentonline data analysismultivariate analysisprincipal component analysislaboratory automationSiLAdesign of experimentsbioprocess monitoringArticle2020-06-102227-9717