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Steve O'Hagan

Publications and source records attributed to Steve O'Hagan.

4 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

Direct infusion electrospray ionization mass spectra of crude cell extracts for microbial characterizations: influence of solvent conditions on the detection of proteins.

Direct infusion electrospray ionization mass spectrometry (DIES-MS) of crude bacterial extracts is a rapid method that can be used to characterize microbial cells. Phospholipids, metabolites, and proteins can be detected rapidly with minimal sample preparation. However, several factors influence the detection of signals in such high-throughput analyses. We studied the influence of solvent conditions, including the organic content and pH of the solvent, on the extraction and subsequent detection of signals in DIES-MS, with a view to improving the detection of protein signals. Unfractionated cell extracts from three strains of the Gram-negative Escherichia coli (including one encoding a recombinant green fluorescence protein), and the Gram-positive Bacillus sphaericus and B. subtilis were investigated. Both pH and the organic content of the solvent were found to influence the spectral information as observed from principal component analysis of the spectral data. A polar solvent with higher organic content resulted in the extraction of phospholipids that overtly dominate the spectral information. Decreasing the organic content of the extraction solvent resulted in the improved detection of protein peaks. Altering the pH of the extraction solvent resulted in different protein profiles from the same bacterium, as observed after spectral deconvolution. In addition, the protein profiles were also different when using different organic solvents. Spectral deconvolution showed several protein peaks that had mass-based homology with those in protein databases for the (sequenced) organisms studied. These results suggest that a combination of solvent conditions can be used to generate protein profiles rapidly that when combined can provide additional valuable proteomic information.

Bacillus↗
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↗