Preprint Article Version 1 This version not peer reviewed

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

Version 1 : Received: 14 September 2017 / Approved: 15 September 2017 / Online: 15 September 2017 (11:07:52 CEST)

A peer-reviewed article of this Preprint also exists.

Manesso, E.; Sridharan, S.; Gunawan, R. Multi-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix. Processes 2017, 5, 63. Manesso, E.; Sridharan, S.; Gunawan, R. Multi-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix. Processes 2017, 5, 63.

Journal reference: Processes 2017, 5, 63
DOI: 10.3390/pr5040063

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 since the FIM only accounts for the linear variation of the model outputs with respect to the parameters. In this work, we developed a multi-objective optimization (MOO) MBDOE, where 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.

Subject Areas

design of experiments; multi-objective optimization; Fisher information matrix; curvature; biological processes; mathematical modeling

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