Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics

Yuanzhi Huang, Steven G. Gilmour (), Kalliopi Mylona and Peter Goos ()
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Yuanzhi Huang: Newcastle University
Steven G. Gilmour: King’s College London, Strand
Kalliopi Mylona: King’s College London, Strand

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 4, No 8, 616 pages

Abstract: Abstract Biochemical mechanism studies often assume statistical models derived from Michaelis–Menten kinetics, which are used to approximate initial reaction rate data given the concentration level of a single substrate. In experiments dealing with industrial applications, however, there are typically a wide range of kinetic profiles where more than one factor is controlled. We focus on optimal design of such experiments requiring the use of multifactor hybrid nonlinear models, which presents a considerable computational challenge. We examine three different candidate models and search for tailor-made D- or weighted-A-optimal designs that can ensure the efficiency of nonlinear least squares estimation. We also study a compound design criterion for discriminating between two candidate models, which we recommend for design of advanced kinetic studies. Supplementary materials accompanying this paper appear on-line

Keywords: Biochemistry; Compound criterion; D-optimality; Exchange algorithm; Weighted-A-optimality (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s13253-020-00405-3

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