Novel experimental design for steady-state processes: a systematic Bayesian approach for enzymes, drug transport, receptor binding, continuous culture and cell transport kinetics.

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.

Efficient and cost-effective experimental determination of kinetic constants and data: the success of a Bayesian systematic approach to drug transport, receptor binding, continuous culture and cell transport kinetics.

Details about the parameters of kinetic systems are crucial for progress in both medical and industrial research, including drug development, clinical diagnosis and biotechnology applications. Such details must be collected by a series of kinetic experiments and investigations. The correct design of the experiment is essential to collecting data suitable for analysis, modelling and deriving the correct information.

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

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.

Effective experimental design: enzyme kinetics in the bioinformatics era.

Acquiring details about the kinetic parameters of enzymes is crucial to both drug development and clinical diagnosis. The correct design of an experiment is crucial to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics now frequently studied, further work is required to estimate parameters of such models with low variance.