Next Article in Journal
How to Generate Economic and Sustainability Reports from Big Data? Qualifications of Process Industry
Next Article in Special Issue
Mathematical Modeling of Tuberculosis Granuloma Activation
Previous Article in Journal
Optimization through Response Surface Methodology of a Reactor Producing Methanol by the Hydrogenation of Carbon Dioxide
Previous Article in Special Issue
Improving Bioenergy Crops through Dynamic Metabolic Modeling
Open AccessFeature PaperArticle

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

Figure 1

MDPI and ACS Style

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

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop