Optimal design of dynamic experiments in the development of cybernetic models for bioreactors

[Display omitted] •Optimal Design of Dynamic Experiments for cybernetic models is addressed.•As design criterion Global Sensitivity Analysis is used.•Bayesian Optimization is used in the resolution of the design problem.•Experimental feedback helps fast reducing parametric uncertainty.•Maximizing th...

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Bibliographic Details
Published in:Chemical engineering research & design 2018-08, Vol.136, p.334-346
Main Authors: Luna, Martin F., Martínez, Ernesto C.
Format: Article
Language:English
Subjects:
Baking yeast
Bayesian analysis
Bayesian Optimization
Bioreactors
Cultivation
Cybernetic model
Cybernetics
Design of Dynamic Experiments
Design of experiments
Design optimization
Global Sensitivity Analysis
Glucose
Mathematical models
Metabolism
Reactors
Regulatory mechanisms (biology)
Sensitivity analysis
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Summary:[Display omitted] •Optimal Design of Dynamic Experiments for cybernetic models is addressed.•As design criterion Global Sensitivity Analysis is used.•Bayesian Optimization is used in the resolution of the design problem.•Experimental feedback helps fast reducing parametric uncertainty.•Maximizing the information content allows converging to a robust parameterization. Cybernetic models of bioreactors are appealing due to their capacity to account for regulatory mechanisms in cell metabolism by modeling the synthesis of enzymes and their activities. For a given objective of interest, experimental data used to fit the cybernetic model parameters should be maximally informative. To excite purposefully the most relevant metabolic pathways, a dynamic experiment is designed by accounting for the sensitivity of the chosen objective to time-varying operating conditions. In this work, the bioreactor feeding profile and sampling times are designed to maximize the information content. A Bayesian optimization approach is proposed to solve the resulting mathematical program. As a case study, biomass production is used as the objective to be maximized in fed-batch cultivation of Saccharomyces cerevisiae growing on glucose as a carbon source. Experimental results demonstrate that the proposed approach helps to iteratively improve a cybernetic model by designing experiments that maximize the information content.
ISSN:0263-8762
1744-3563
DOI:10.1016/j.cherd.2018.05.036