Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10525
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Main Title: Monitoring Parallel Robotic Cultivations with Online Multivariate Analysis
Author(s): Hans, Sebastian
Ulmer, Christian
Narayanan, Harini
Brautaset, Trygve
Krausch, Niels
Neubauer, Peter
Schäffl, Irmgard
Sokolov, Michael
Cruz-Bournazou, Mariano Nicolas
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/11638
http://dx.doi.org/10.14279/depositonce-10525
License: https://creativecommons.org/licenses/by/4.0/
Abstract: In 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.
Subject(s): high throughput bioprocess development
online data analysis
multivariate analysis
principal component analysis
laboratory automation
SiLA
design of experiments
bioprocess monitoring
Issue Date: 14-May-2020
Date Available: 3-Sep-2020
Is Part Of: 10.14279/depositonce-12144
Language Code: en
DDC Class: 660 Chemische Verfahrenstechnik
Sponsor/Funder: BMBF, 031L0018A, ERASysApp2 - Verbundprojekt: LEANPROT - Entwicklung einer Systembiologie-Plattform für die Entwicklung von lean-proteome-Escherichia coli-Stämmen - Deutsches Teilprojekt A
DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlin
Journal Title: Processes
Publisher: MDPI
Volume: 8
Issue: 5
Article Number: 582
Publisher DOI: 10.3390/pr8050582
EISSN: 2227-9717
TU Affiliation(s): Fak. 3 Prozesswissenschaften » Inst. Biotechnologie » FG Bioverfahrenstechnik
Appears in Collections:Technische Universität Berlin » Publications

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