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Metabonomics can be viewed as the process of defining multivariate metabolic trajectories that describe the systemic response of organisms to physiological perturbations through time. We have explored the hypothesis that the homothetic geometry of a metabolic trajectory, i.e., the metabolic response irrespective of baseline values and overall magnitude, defines the mode of response of the organism to treatment and is hence the key property when considering the similarity between two sets of measurements. A modeling strategy to test for homothetic geometry, called scaled-to-maximum, aligned, and reduced trajectories (SMART) analysis, is presented that together with principal components analysis (PCA) facilitates the visualization of multivariate response similarity and hence the interpretation of metabonomic data. Several examples of the utility of this approach from toxicological studies are presented as follows: interlaboratory variation in hydrazine response, CCl4 dose−response relationships, and interspecies comparison of bromobenzene toxicity. In each case, the homothetic trajectories hypothesis is shown to be an important concept for the successful multivariate modeling and interpretation of systemic metabolic change. Overall, geometric trajectory analysis based on a homothetic modeling strategy like SMART facilitates the amalgamation and comparison of metabonomic data sets and can improve the accuracy and precision of classification models based on metabolic profile data. Because interlaboratory variation, normal physiological variation, dose−response relationships, and interspecies differences are also key areas of concern in genomic and proteomic as well as metabonomic studies, the methods presented here may also have an impact on many other multilaboratory efforts to produce screenable “-omics” databases useful for gauging toxicity in safety assessment and drug discovery.

@article{keun_geometric_2004, title = {Geometric {Trajectory} {Analysis} of {Metabolic} {Responses} {To} {Toxicity} {Can} {Define} {Treatment} {Specific} {Profiles}}, volume = {17}, issn = {0893-228X}, url = {https://doi.org/10.1021/tx034212w}, doi = {10/cm5x4j}, abstract = {Metabonomics can be viewed as the process of defining multivariate metabolic trajectories that describe the systemic response of organisms to physiological perturbations through time. We have explored the hypothesis that the homothetic geometry of a metabolic trajectory, i.e., the metabolic response irrespective of baseline values and overall magnitude, defines the mode of response of the organism to treatment and is hence the key property when considering the similarity between two sets of measurements. A modeling strategy to test for homothetic geometry, called scaled-to-maximum, aligned, and reduced trajectories (SMART) analysis, is presented that together with principal components analysis (PCA) facilitates the visualization of multivariate response similarity and hence the interpretation of metabonomic data. Several examples of the utility of this approach from toxicological studies are presented as follows: interlaboratory variation in hydrazine response, CCl4 dose−response relationships, and interspecies comparison of bromobenzene toxicity. In each case, the homothetic trajectories hypothesis is shown to be an important concept for the successful multivariate modeling and interpretation of systemic metabolic change. Overall, geometric trajectory analysis based on a homothetic modeling strategy like SMART facilitates the amalgamation and comparison of metabonomic data sets and can improve the accuracy and precision of classification models based on metabolic profile data. Because interlaboratory variation, normal physiological variation, dose−response relationships, and interspecies differences are also key areas of concern in genomic and proteomic as well as metabonomic studies, the methods presented here may also have an impact on many other multilaboratory efforts to produce screenable “-omics” databases useful for gauging toxicity in safety assessment and drug discovery.}, number = {5}, urldate = {2021-06-30}, journal = {Chemical Research in Toxicology}, author = {Keun, Hector C. and Ebbels, Timothy M. D. and Bollard, Mary E. and Beckonert, Olaf and Antti, Henrik and Holmes, Elaine and Lindon, John C. and Nicholson, Jeremy K.}, month = may, year = {2004}, note = {Publisher: American Chemical Society}, pages = {579--587}, }

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