Metabolome Analysis

"Overall, this book provides a great introduction to metabolomics and at a retail price of approximately $75, it has great value. The intended audience for this book includes new metabolomics practitioners and undergraduate/graduate students." (J Am Soc Mass Spectrom, 2007)

SILAS G. VILLAS-BAS, PHD, is a Research Scientist at AgResearch Limited in New Zealand. UTE ROESSNER, PHD, is a Post-Doctoral Fellow at the Australian Centre for Plant Functional Genomics at the University of Melbourne, Australia.? MICHAEL A. E. HANSEN, PHD, is a Post-Doctoral Fellow at BioCentrum-DTU, Technical University of Denmark. JRN SMEDSGAARD, PHD, is an Associate Professor at the Center for Microbial Biotechnology, BioCentrum-DTU?at the Technical University of Denmark. JENS NIELSEN, PROFESSOR, Dr. techn., PHD, is the Director of the Center for Microbial Biotechnology at the Technical University of Denmark.

PREFACE. LIST OF CONTRIBUTORS. PART I: CONCEPTS AND METHODOLOGY. 1 Metabolomics in Functional Genomics and Systems Biology. 1.1 From genomic sequencing to functional genomics. 1.2 Systems biology and metabolic models. 1.3 Metabolomics. 1.4 Future perspectives. 2 The Chemical Challenge of the Metabolome. 2.1 Metabolites and metabolism. 2.2 The structural diversity of metabolites. 2.2.1 The chemical and physical properties. 2.2.2 Metabolite abundance. 2.2.3 Primary and secondary metabolism. 2.3 The number of metabolites in a biological system. 2.4 Controlling rates and levels. 2.4.1 Control by substrate level. 2.4.2 Feedback and feedforward control. 2.4.3 Control by pathway independent regulatory molecules. 2.4.4 Allosteric control. 2.4.5 Control by compartmentalization. 2.4.6 The dynamics of the metabolismthe mass fl ow. 2.4.7 Control by hormones. 2.5 Metabolic channeling or metabolons. 2.6 Metabolites are arranged in networks that are part of a cellular interactome. 3 Sampling and Sample Preparation. 3.1 Introduction. 3.2 Quenchingthe fi rst step. 3.2.1 Overview on metabolite turnover. 3.2.2 Different methods for quenching. 3.2.3 Quenching microbial and cell cultures. 3.2.4 Quenching plant and animal tissues. 3.3 Obtaining metabolites from biological samples. 3.3.1 Release of intracellular metabolites. 3.3.2 Structure of the cell envelopesthe main barrier to be broken. 3.3.3 Cell disruption methods. 3.3.4 Nonmechanical disruption of cell envelopes. 3.3.5 Mechanical disruption of cell envelopes. 3.4 Metabolites in the extracellular medium. 3.4.1 Metabolites in solution. 3.4.2 Metabolites in the gas phase. 3.5 Improving detection via sample concentration. 4 Analytical Tools. 4.1 Introduction. 4.2 Choosing a methodology. 4.3 Starting pointsamples. 4.4 Principles of chromatography. 4.4.1 Basics of chromatography. 4.4.2 The chromatogram and terms in chromatography. 4.5 Chromatographic systems. 4.5.1 Gas chromatography. 4.5.2 HPLC systems. 4.6 Mass spectrometry. 4.6.1 The mass spectrometeran overview. 4.6.2 GC-MSthe EI ion source. 4.6.3 LC-MSthe ESI ion source. 4.6.4 Mass analyzerthe quadrupole. 4.6.5 Mass analyzerthe ion-trap. 4.6.6 Mass analyzerthe time-of-fl ight. 4.6.7 Detection and computing in MS. 4.7 The analytical work-fl ow. 4.7.1 Separation by chromatography. 4.7.2 Mass spectrometry. 4.7.3 General analytical considerations. 4.8 Data evaluation. 4.8.1 Structure of data. 4.8.2 The chromatographic separation. 4.8.3 Mass spectral data. 4.8.4 Exporting data for processing. 4.9 Beyond the core methods. 4.9.1 Developments in chromatography. 4.9.2 Capillary electrophoresis. 4.9.3 Tandem MS and advanced scanning techniques. 4.9.4 NMR spectrometry. 4.10 Further reading. 5 Data Analysis. 5.1 Organizing the data. 5.2 Scales of measurement. 5.2.1 Qualitative data. 5.2.2 Quantitative data. 5.3 Data structures. 5.4 Preprocessing of data. 5.4.1 Calibration of data. 5.4.2 Combining profi le scans. 5.4.3 Filtering. 5.4.4 Centroid calculation. 5.4.5 Internal mass scale correction. 5.4.6 Binning. 5.4.7 Baseline correction. 5.4.8 Chromatographic profi le matching. 5.5 Deconvolution of spectroscopic data. 5.6 Data standardization (normalization). 5.7 Data transformations. 5.7.1 Principal component analysis. 5.7.2 Fisher discriminant analysis. 5.8 Similarities and distances between data. 5.8.1 Continuous functions. 5.8.2 Binary functions. 5.9 Clustering techniques. 5.9.1 Hierarchical clustering. 5.9.2 k-means clustering. 5.10 Classifi cation techniques. 5.10.1 Decision theory. 5.10.2 k-nearest neighbor. 5.10.3 Tree-based classifi cation. 5.11 Integrated tools for automation, libraries, and data evaluation. PART II: CASE STUDIES AND REVIEWS. 6 Yeast Metabolomics: The Discovery of New Metabolic Pathways