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

TY - JOUR

T1 - Novel experimental design for steady-state processes

T2 - a systematic Bayesian approach for enzymes, drug transport, receptor binding, continuous culture and cell transport kinetics.

AU - Crabbe, M. James C

AU - Murphy, Emma F.

AU - Gilmour, Steven G.

PY - 2005/1

Y1 - 2005/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33846114805&partnerID=8YFLogxK

U2 - 10.2174/1573406053402550

DO - 10.2174/1573406053402550

M3 - Article

C2 - 16789882

AN - SCOPUS:33846114805

SN - 1573-4064

VL - 1

SP - 21

EP - 29

JO - Medicinal Chemistry

JF - Medicinal Chemistry

IS - 1

ER -