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Impact of the Error Structure on the Design and Analysis of Enzyme Kinetic Models

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  • Elham Yousefi

    (Johannes Kepler University Linz)

  • Werner G. Müller

    (Johannes Kepler University Linz)

Abstract

The statistical analysis of enzyme kinetic reactions usually involves models of the response functions which are well defined on the basis of Michaelis–Menten type equations. The error structure, however, is often without good reason assumed as additive Gaussian noise. This simple assumption may lead to undesired properties of the analysis, particularly when simulations are involved and consequently negative simulated reaction rates may occur. In this study, we investigate the effect of assuming multiplicative log normal errors instead. While there is typically little impact on the estimates, the experimental designs and their efficiencies are decisively affected, particularly when it comes to model discrimination problems.

Suggested Citation

  • Elham Yousefi & Werner G. Müller, 2023. "Impact of the Error Structure on the Design and Analysis of Enzyme Kinetic Models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 31-56, April.
    • Handle: RePEc:spr:stabio:v:15:y:2023:i:1:d:10.1007_s12561-022-09347-5
    DOI: 10.1007/s12561-022-09347-5
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    References listed on IDEAS

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    1. Yuanzhi Huang & Steven G. Gilmour & Kalliopi Mylona & Peter Goos, 2020. "Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 601-616, December.
    2. Harman, Radoslav & Jurík, Tomás, 2008. "Computing c-optimal experimental designs using the simplex method of linear programming," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 247-254, December.
    3. U. S. Pasaribu, 1999. "Statistical assumptions underlying the fitting of the Michaelis-Menten equation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(3), pages 327-341.
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