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Publisher DOI: 10.1002/biot.202200381
Title: Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths
Language: English
Authors: Bayer, Benjamin 
Duerkop, Mark 
Pörtner, Ralf  
Möller, Johannes 
Keywords: bioprocess characterization; Chinese hamster ovary cells; design of experiments; machine learning; mechanistic modelling; parameter identification; quality by design; upstream bioprocessing
Issue Date: Jan-2023
Publisher: Wiley-VCH
Source: Biotechnology Journal 1 (18): 2200381 (2023-01)
Abstract (english): 
Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.
DOI: 10.15480/882.4864
ISSN: 1860-7314
Journal: Biotechnology journal 
Institute: Bioprozess- und Biosystemtechnik V-1 
Document Type: Article
Project: Projekt DEAL 
Funded by: Bundesministerium für Bildung und Forschung (BMBF) 
More Funding information: Johannes Möller and Ralf Pörtner acknowledge partial funding by the German Federal Ministry of Education and Research (BMBF, Grant 031B0577A-C).
License: CC BY-NC 4.0 (Attribution-NonCommercial) CC BY-NC 4.0 (Attribution-NonCommercial)
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