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Optimal Experimental Design for Parameter Estimation in Unstructured Growth Models
Author(s) -
Baltes M.,
Schneider R.,
Sturm C.,
Reuss M.
Publication year - 1994
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp00029a005
Subject(s) - computer science , transient (computer programming) , estimation theory , optimal design , function (biology) , biological system , mathematical optimization , algorithm , mathematics , machine learning , biology , evolutionary biology , operating system
A method of optimal experimental design for parameter estimation in unstructured growth models is presented. The approach is based on a method suggested by Munack (1991) for application in fed‐batch processes. In a critical analysis of this method, special emphasis is given to the model validity, because unstructured growth models often are not valid under transient conditions. In consequence, a combined object function has been introduced, which considers model validity and the accuracy of the kinetic parameters to be estimated. The application of this method for fed‐batch processes leads to satisfactory results. Investigations of different fed‐batch strategies regarding model validity and the quality of parameter estimation are presented. In addition, an experimental verification has been performed with fermentations of the yeast Trichosporon cutaneum .