Impact of the error structure on the design and analysis of enzyme kinetic models

TL;DR

This paper examines how different error assumptions, specifically additive Gaussian versus multiplicative lognormal errors, influence the design and analysis of enzyme kinetic models, especially affecting experimental design and model discrimination.

Contribution

It highlights the importance of error structure assumptions in enzyme kinetics, showing that multiplicative errors significantly impact experimental design and model discrimination.

Findings

Error assumptions affect experimental design efficiency.

Multiplicative errors influence model discrimination.

Little impact on parameter estimates.

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 lognormal 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.