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Warwick B Dunn

Publications and source records attributed to Warwick B Dunn.

8 recordsLinked to original sources
PubMed2007

Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics.

Metabolomics seeks to measure potentially all the metabolites in a biological sample, and consequently, we need to develop and optimize methods to increase significantly the number of metabolites we can detect. We extended the closed-loop (iterative, automated) optimization system that we had previously developed for one-dimensional GC-TOF-MS (O'Hagan, S.; Dunn, W. B.; Brown, M.; Knowles, J. D.; Kell, D. B. Anal. Chem. 2005, 77, 290-303) to comprehensive two-dimensional (GCxGC) chromatography. The heuristic approach used was a multiobjective version of the efficient global optimization algorithm. In just 300 automated runs, we improved the number of metabolites observable relative to those in 1D GC by some 3-fold. The optimized conditions allowed for the detection of over 4000 raw peaks, of which some 1800 were considered to be real metabolite peaks and not impurities or peaks with a signal/noise ratio of less than 5. A variety of computational methods served to explain the basis for the improvement. This closed-loop optimization strategy is a generic and powerful approach for the optimization of any analytical instrumentation.

Biomarkers↗
PubMed2006

MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics.

BACKGROUND: The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions. DESCRIPTION: MeMo is a formal model for representing metabolomic data and the associated metadata. Two predominant platforms (SQL and XML) are used to encode the model. MeMo has been implemented as a relational database using a hybrid approach combining the advantages of the two technologies. It represents a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. The MeMo model and the associated software are available at http://dbkgroup.org/memo/. CONCLUSION: The maturity of relational database technology is used to support efficient data processing. The scalability and self-descriptiveness of XML are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the creation of new experimental techniques. Special consideration is given to data integration issues as part of the systems biology agenda. MeMo has been physically integrated and cross-linked to related metabolomic and genomic databases. Semantic integration with other relevant databases has been supported through ontological annotation. Compatibility with other data formats is supported by automatic conversion.

Computer Simulation↗
PubMed2006

Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles.

There has been considerable progress recently towards developing therapeutic strategies for Huntington's disease (HD), with several compounds showing beneficial effects in transgenic mouse models. However, human trials in HD are difficult, costly and time-consuming due to the slow disease course, insidious onset and patient-to-patient variability. Identification of molecular biomarkers associated with disease progression will aid the development of effective therapies by allowing further validation of animal models and by providing hopefully more sensitive measures of disease progression. Here, we apply metabolic profiling by gas chromatography-time-of-flight-mass spectrometry to serum samples from human HD patients and a transgenic mouse model in a hypothesis-generating search for disease biomarkers. We observed clear differences in metabolic profiles between transgenic mice and wild-type littermates, with a trend for similar differences in human patients and control subjects. Thus, the metabolites responsible for distinguishing transgenic mice also comprised a metabolic signature tentatively associated with the human disease. The candidate biomarkers composing this HD-associated metabolic signature in mouse and humans are indicative of a change to a pro-catabolic phenotype in early HD preceding symptom onset, with changes in various markers of fatty acid breakdown (including glycerol and malonate) and also in certain aliphatic amino acids. Our data raise the prospect of a robust molecular definition of progression of HD prior to symptom onset, and if validated in a genuinely prospective fashion these biomarker trajectories could facilitate the development of useful therapies for this disease.

Adult↗
PubMed2005

Measuring the metabolome: current analytical technologies.

The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.

Animals↗
PubMed2005

Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations.

The number of instrumental parameters controlling modern analytical apparatus can be substantial, and varying them systematically to optimize a particular chromatographic separation, for example, is out of the question because of the astronomical number of combinations that are possible (i.e., the "search space" is very large). However, heuristic methods, such as those based on evolutionary computing, can be used to explore such search spaces efficiently. We here describe the implementation of an entirely automated (closed-loop) strategy for doing this and apply it to the optimization of gas chromatographic separations of the metabolomes of human serum and of yeast fermentation broths. Without human intervention, the Robot Chromatographer system (i) initializes the settings on the instrument, (ii) controls the analytical run, (iii) extracts the variables defining the analytical performance (specifically the number of peaks, signal/noise ratio, and run time), (iv) chooses (via the PESA-II multiobjective genetic algorithm), and (v) programs the next series of instrumental settings, the whole continuing in an iterative cycle until suitable sets of optimal conditions have been established. Genetic programming was used to remove noise peaks and to establish the basis for the improvements observed. The system showed that the number of peaks observable depended enormously on the conditions used and served to increase them by as much as 3-fold (e.g., to over 950 in human serum) while in many cases maintaining or reducing the run time and preserving excellent signal/noise ratios. The evolutionary closed-loop machine learning strategy we describe is generic to any type of analytical optimization.

Fermentation↗
PubMed2005

Metabolic footprinting and systems biology: the medium is the message.

One element of classical systems analysis treats a system as a black or grey box, the inner structure and behaviour of which can be analysed and modelled by varying an internal or external condition, probing it from outside and studying the effect of the variation on the external observables. The result is an understanding of the inner make-up and workings of the system. The equivalent of this in biology is to observe what a cell or system excretes under controlled conditions - the 'metabolic footprint' or exometabolome - as this is readily and accurately measurable. Here, we review the principles, experimental approaches and scientific outcomes that have been obtained with this useful and convenient strategy.

Animals↗