LAPSE:2023.4785
Published Article
LAPSE:2023.4785
Hybrid Dynamic Models of Bioprocesses Based on Elementary Flux Modes and Multilayer Perceptrons
February 23, 2023
The derivation of minimal bioreaction models is of primary importance to develop monitoring and control strategies of cell/microorganism culture production. These minimal bioreaction models can be obtained based on the selection of a basis of elementary flux modes (EFMs) using an algorithm starting from a relatively large set of EFMs and progressively reducing their numbers based on geometric and least-squares residual criteria. The reaction rates associated with the selected EFMs usually have complex features resulting from the combination of different activation, inhibition and saturation effects from several culture species. Multilayer perceptrons (MLPs) are used in order to undertake the representation of these rates, resulting in a hybrid dynamic model combining the mass-balance equations provided by the EFMs to the rate equations described by the MLPs. To further reduce the number of kinetic parameters of the model, pruning algorithms for the MLPs are also considered. The whole procedure ends up with reduced-order macroscopic models that show promising prediction results, as illustrated with data of perfusion cultures of hybridoma cell line HB-58.
Keywords
biotechnology, dynamic models, elementary flux modes, hybrid modeling, identification, metabolic network, Model Reduction, multilayer perceptron, neural networks, pruning, reaction systems
Suggested Citation
Maton M, Bogaerts P, Vande Wouwer A. Hybrid Dynamic Models of Bioprocesses Based on Elementary Flux Modes and Multilayer Perceptrons. (2023). LAPSE:2023.4785
Author Affiliations
Maton M: Systems, Estimation, Control and Optimization (SECO), Université de Mons, 7000 Mons, Belgium [ORCID]
Bogaerts P: 3BIO-BioControl, Université Libre de Bruxelles, 1050 Brussels, Belgium [ORCID]
Vande Wouwer A: Systems, Estimation, Control and Optimization (SECO), Université de Mons, 7000 Mons, Belgium [ORCID]
Journal Name
Processes
Volume
10
Issue
10
First Page
2084
Year
2022
Publication Date
2022-10-14
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr10102084, Publication Type: Journal Article
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doi:10.3390/pr10102084
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Feb 23, 2023
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