Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do...

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Bibliographic Details
Published in:Bioengineering (Basel) 2017-02, Vol.4 (1), p.17
Main Authors: Dewasme, Laurent, Côte, François, Filee, Patrice, Hantson, Anne-Lise, Vande Wouwer, Alain
Format: Article
Language:English
Subjects:
Bioengineering
bioprocess optimization
Bioreactors
Cell culture
Confidence intervals
Dynamic models
E coli
Economic models
Glucose
Mathematical modelling
maximum likelihood principal component analysis
Metabolism
Metabolites
Model testing
Monoclonal antibodies
monoclonal antibody
Nonlinear analysis
Principal components analysis
Process controls
Proteins
Robust control
Sensitivity analysis
Software
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Summary:Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. The simplest macroscopic reaction scheme translating the data is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to determine the kinetic laws. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct and cross-validation tests. Furthermore, model predictions can also be used to predict optimal medium renewal time and composition.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering4010017