Efficient and accurate experimental design for enzyme kinetics: Bayesian studies reveal a systematic approach

TY - JOUR

T1 - Efficient and accurate experimental design for enzyme kinetics

T2 - Bayesian studies reveal a systematic approach

AU - Murphy, Emma F.

AU - Gilmour, Steven G.

AU - Crabbe, M. James C

PY - 2003/2/28

Y1 - 2003/2/28

N2 - In areas such as drug development, clinical diagnosis and biotechnology research, acquiring details about the kinetic parameters of enzymes is crucial. The correct design of an experiment is critical to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics being frequently studied, attention is needed to estimate parameters of such models with low variance. We demonstrate that a Bayesian approach (the use of prior knowledge) can produce major gains quantifiable in terms of information, productivity and accuracy of each experiment. Developing the use of Bayesian Utility functions, we have used a systematic method to identify the optimum experimental designs for a number of kinetic model data sets. This has enabled the identification of trends between kinetic model types, sets of design rules and the key conclusion that such designs should be based on some prior knowledge of KM and/or the kinetic model. We suggest an optimal and iterative method for selecting features of the design such as the substrate range, number of measurements and choice of intermediate points. The final design collects data suitable for accurate modelling and analysis and minimises the error in the parameters estimated.

AB - In areas such as drug development, clinical diagnosis and biotechnology research, acquiring details about the kinetic parameters of enzymes is crucial. The correct design of an experiment is critical to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics being frequently studied, attention is needed to estimate parameters of such models with low variance. We demonstrate that a Bayesian approach (the use of prior knowledge) can produce major gains quantifiable in terms of information, productivity and accuracy of each experiment. Developing the use of Bayesian Utility functions, we have used a systematic method to identify the optimum experimental designs for a number of kinetic model data sets. This has enabled the identification of trends between kinetic model types, sets of design rules and the key conclusion that such designs should be based on some prior knowledge of KM and/or the kinetic model. We suggest an optimal and iterative method for selecting features of the design such as the substrate range, number of measurements and choice of intermediate points. The final design collects data suitable for accurate modelling and analysis and minimises the error in the parameters estimated.

KW - Bayesian design

KW - Enzyme kinetics

KW - Experimental design

KW - Kinetic parameters

KW - Parameter variance

KW - Prior knowledge

UR - http://www.scopus.com/inward/record.url?scp=0037469541&partnerID=8YFLogxK

U2 - 10.1016/S0165-022X(02)00183-5

DO - 10.1016/S0165-022X(02)00183-5

M3 - Article

C2 - 12628698

AN - SCOPUS:0037469541

SN - 0165-022X

VL - 55

SP - 155

EP - 178

JO - Journal of Biochemical and Biophysical Methods

JF - Journal of Biochemical and Biophysical Methods

IS - 2

ER -