Biomedical subjects

Steven G Gilmour

Publications and source records attributed to Steven G Gilmour.

7 recordsLinked to original sources

PubMed2006

Design of microarray experiments for genetical genomics studies.

Microarray experiments have been used recently in genetical genomics studies, as an additional tool to understand the genetic mechanisms governing variation in complex traits, such as for estimating heritabilities of mRNA transcript abundances, for mapping expression quantitative trait loci, and for inferring regulatory networks controlling gene expression. Several articles on the design of microarray experiments discuss situations in which treatment effects are assumed fixed and without any structure. In the case of two-color microarray platforms, several authors have studied reference and circular designs. Here, we discuss the optimal design of microarray experiments whose goals refer to specific genetic questions. Some examples are used to illustrate the choice of a design for comparing fixed, structured treatments, such as genotypic groups. Experiments targeting single genes or chromosomic regions (such as with transgene research) or multiple epistatic loci (such as within a selective phenotyping context) are discussed. In addition, microarray experiments in which treatments refer to families or to subjects (within family structures or complex pedigrees) are presented. In these cases treatments are more appropriately considered to be random effects, with specific covariance structures, in which the genetic goals relate to the estimation of genetic variances and the heritability of transcriptional abundances.

Animals↗

PubMed2006

Response surface designs for experiments in bioprocessing.

Many processes in the biological industries are studied using response surface methodology. The use of biological materials, however, means that run-to-run variation is typically much greater than that in many experiments in mechanical or chemical engineering and so the designs used require greater replication. The data analysis which is performed may involve some variable selection, as well as fitting polynomial response surface models. This implies that designs should allow the parameters of the model to be estimated nearly orthogonally. A class of three-level response surface designs is introduced which allows all except the quadratic parameters to be estimated orthogonally, as well as having a number of other useful properties. These subset designs are obtained by using two-level factorial designs in subsets of the factors, with the other factors being held at their middle level. This allows their properties to be easily explored. Replacing some of the two-level designs with fractional replicates broadens the class of useful designs, especially with five or more factors, and sometimes incomplete subsets can be used. It is very simple to include a few two- and four-level factors in these designs by excluding subsets with these factors at the middle level. Subset designs can be easily modified to include factors with five or more levels by allowing a different pair of levels to be used in different subsets.

Biocompatible Materials↗

PubMed2005

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. 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.

Bayes Theorem↗

PubMed2004

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.

Bayes Theorem↗

PubMed2003

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. 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 K(M) 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.

Bayes Theorem↗

PubMed2003

Planning incomplete block experiments when treatments are genetically related.

Selection trials in plant and animal breeding, in incomplete blocks, are described by linear models with random effect parameters associated with treatments with known genetic covariance structure. It is now well known that the information on relatives can improve the analysis and many extensions of this model have been proposed, but no studies have been done on the consequences of this genetical relatedness among treatments for the optimality of block designs. Using a suitable optimality criterion, we show that the knowledge on relatedness may imply that the optimal design is not in the class of designs which are optimal for unrelated treatments. Implications for practical applications are discussed.

Animals↗

PubMed2002

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. This review examines the different options available to produce major gains in information, productivity and the accuracy of each experiment.

Animals↗