M. James C. Crabbe, Emma F. Murphy and Steven G. Gilmour Pages 21 - 29 ( 9 )
We demonstrate that a Bayesian approach (the use of prior knowledge) to the design of steady-state experiments 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 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. It is equally applicable to enzymes, drug transport, receptor binding, microbial culture and cell transport kinetics.
kinetics, experimental design, bayesian design, kinetic parameters, prior knowledge, parameter variance, drug discovery
School of Animal and Microbial Sciences, University of Reading, Whiteknights, Reading, Berkshire, RG6 6AJ, UK.