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Processes 2017, 5(4), 63; https://doi.org/10.3390/pr5040063

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

1
Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zurich, Switzerland
2
Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
3
Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore
*
Author to whom correspondence should be addressed.
Current address: Bayer AG, 65926 Frankfurt am Main, Germany.
Received: 14 September 2017 / Revised: 20 October 2017 / Accepted: 23 October 2017 / Published: 1 November 2017
(This article belongs to the Special Issue Biological Networks)
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Abstract

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. View Full-Text
Keywords: design of experiments; multi-objective optimization; Fisher information matrix; curvature; biological processes; mathematical modeling design of experiments; multi-objective optimization; Fisher information matrix; curvature; biological processes; mathematical modeling
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Manesso, E.; Sridharan, S.; Gunawan, R. Multi-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix. Processes 2017, 5, 63.

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