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. We have developed a systematic and iterative Bayesian method and sets of rules for the design of enzyme kinetic experiments. Our method selects the optimum design to collect data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. The rules select features of the design such as the substrate range and the number of measurements. We show here that this method can be directly applied to the study of other important kinetic systems, including drug transport, receptor binding, microbial culture and cell transport kinetics. It is possible to reduce the errors in the estimated parameters and, most importantly, increase the efficiency and cost-effectiveness by reducing the necessary amount of experiments and data points measured. (C) 2003 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Kinetic modeling of the generation of 2-and 3-methylbutanal in a heated extract of beef liver

Quantitative control of aroma generation during the Maillard reaction presents great scientific and industrial interest. Although there have been many studies conducted in simplified model systems, the results are difficult to apply to complex food systems, where the presence of other components can have a significant impact. In this work, an aqueous extract of defatted beef liver was chosen as a simplified food matrix for studying the kinetics of the Mallard reaction. Aliquots of the extract were heated under different time and temperature conditions and analyzed for sugars, amino acids, and methylbutanals, which are important Maillard-derived aroma compounds formed in cooked meat. Multiresponse kinetic modeling, based on a simplified mechanistic pathway, gave a good fit with the experimental data, but only when additional steps were introduced to take into account the interactions of glucose and glucose-derived intermediates with protein and other amino compounds. This emphasizes the significant role of the food matrix in controlling the Maillard reaction.

Knowledge-elicitation and data-mining: fusing human and industrial plant information

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.