Bioprocess optimization under uncertainty using ensemble modeling
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Author / Producer
Date
2017-02-20
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
The performance of model-based bioprocess optimizations depends on the accuracy of the mathematical model. However, models of bioprocesses often have large uncertainty due to the lack of model identifiability. In the presence of such uncertainty, process optimizations that rely on the predictions of a single “best fit” model, e.g. the model resulting from a maximum likelihood parameter estimation using the available process data, may perform poorly in real life. In this study, we employed ensemble modeling to account for model uncertainty in bioprocess optimization. More specifically, we adopted a Bayesian approach to define the posterior distribution of the model parameters, based on which we generated an ensemble of model parameters using a uniformly distributed sampling of the parameter confidence region. The ensemble-based process optimization involved maximizing the lower confidence bound of the desired bioprocess objective (e.g. yield or product titer), using a mean-standard deviation utility function. We demonstrated the performance and robustness of the proposed strategy in an application to a monoclonal antibody batch production by mammalian hybridoma cell culture.
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Publication status
published
External links
Editor
Book title
Journal / series
Volume
244
Pages / Article No.
34 - 44
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Bioprocess; Optimization; Uncertainty; Ensemble modeling; Monoclonal antibody
Organisational unit
03898 - Gunawan, Rudiyanto (ehemalig)
Notes
Funding
157154 - Ensemble Inference of Gene Regulatory Networks (SNF)