Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2458
Publisher DOI: 10.1002/bit.27125
Title: Predicting industrial-scale cell culture seed trains–A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method
Language: English
Authors: Hernández Rodríguez, Tanja 
Posch, Christoph 
Schmutzhard, Julia 
Stettner, Josef 
Weihs, Claus 
Pörtner, Ralf 
Frahm, Björn 
Keywords: Bayes;CHO cell culture;Markov chain Monte Carlo (MCMC);seed train prediction;uncertainty
Issue Date: 26-Jul-2019
Publisher: Wiley
Source: Biotechnology and Bioengineering 11 (116): 2944-2959 (2019-11-01)
Journal or Series Name: Biotechnology and bioengineering 
Abstract (english): Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. For production of biopharmaceuticals in suspension cell culture, seed trains are required to increase cell number from cell thawing up to production scale. Because cultivation conditions during the seed train have a significant impact on cell performance in production scale, seed train design, monitoring, and development of optimization strategies is important. This can be facilitated by model-assisted prediction methods, whereby the performance depends on the prediction accuracy, which can be improved by inclusion of prior process knowledge, especially when only few high-quality data is available, and description of inference uncertainty, providing, apart from a “best fit”-prediction, information about the probable deviation in form of a prediction interval. This contribution illustrates the application of Bayesian parameter estimation and Bayesian updating for seed train prediction to an industrial Chinese hamster ovarian cell culture process, coppled with a mechanistic model. It is shown in which way prior knowledge as well as input uncertainty (e.g., concerning measurements) can be included and be propagated to predictive uncertainty. The impact of available information on prediction accuracy was investigated. It has been shown that through integration of new data by the Bayesian updating method, process variability (i.e., batch-to-batch) could be considered. The implementation was realized using a Markov chain Monte Carlo method.
URI: http://hdl.handle.net/11420/3704
DOI: 10.15480/882.2458
ISSN: 1097-0290
Institute: Bioprozess- und Biosystemtechnik V-1 
Type: (wissenschaftlicher) Artikel
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