Multi-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix

• Published in: Processes Vol. 5; no. 4; p. 63
• Main Authors: Manesso, Erica, Sridharan, Srinath, Gunawan, Rudiyanto
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2017
• Subjects: Batch culture; Biological activity; Curvature; Data processing; Design of experiments; Design parameters; Dynamic models; Fermentation; Fisher information; Multiple objective analysis; Nonlinear systems; Nonlinearity; Optimization; Parameter estimation; Parameter identification; Yeast
• DOI: 10.3390/pr5040063
• ISSN: 2227-9717

The bottleneck in creating dynamic models of biological networks and processes often lies in estimating unknown kinetic model parameters from experimental data. In this regard, experimental conditions have a strong influence on parameter identifiability and should therefore be optimized to give the maximum information for parameter estimation. Existing model-based design of experiment (MBDOE) methods commonly rely on the Fisher information matrix (FIM) for defining a metric of data informativeness. When the model behavior is highly nonlinear, FIM-based criteria may lead to suboptimal designs, as the FIM only accounts for the linear variation in the model outputs with respect to the parameters. In this work, we developed a multi-objective optimization (MOO) MBDOE, for which the model nonlinearity was taken into consideration through the use of curvature. The proposed MOO MBDOE involved maximizing data informativeness using a FIM-based metric and at the same time minimizing the model curvature. We demonstrated the advantages of the MOO MBDOE over existing FIM-based and other curvature-based MBDOEs in an application to the kinetic modeling of fed-batch fermentation of baker’s yeast.