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We have developed a new simple method for transport, storage, and analysis of genetic material from the corals Agaricia agaricites, Dendrogyra cylindrica, Eusmilia ancora, Meandrina meandrites, Montastrea annularis, Porites astreoides, Porites furcata, Porites porites, and Siderastrea siderea at room temperature. All species yielded sufficient DNA from a single FTA® card (19 μg–43 ng) for subsequent PCR amplification of both coral and zooxanthellar DNA. The D1 and D2 variable region of the large subunit rRNA gene (LSUrDNA) was amplified from the DNA of P. furcata and S. siderea by PCR. Electrophoresis yielded two major DNA bands: an 800-base pair (bp) DNA, which represented the coral ribosomal RNA (rRNA) gene, and a 600-bp DNA, which represented the zooxanthellar srRNA gene. Extraction of DNA from the bands yielded between 290 μg total DNA (S. siderea coral DNA) and 9 μg total DNA (P. furcata zooxanthellar DNA). The ability to transport and store genetic material from scleractinian corals without resort to laboratory facilities in the field allows for the molecular study of a far wider range and variety of coral sites than have been studied to date. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]