Note: Front Cover -- The Handbook of Metabonomics and Metabolomics -- Copyright Page -- Table of Contents -- Foreword -- Preface -- Chapter 1 Metabonomics and Metabolomics Techniques and Their Applications in Mammalian Systems -- 1.1. Metabonomics and metabolomics in relation to other "omics" approaches -- 1.2. The concept of global systems biology -- 1.3. Interactions between host and other genomes -- 1.4. Time-scales of "-omics" events -- 1.5. Samples for metabonomics and metabolomics -- 1.6. Analytical technologies -- 1.7. Chemometric methods -- 1.8. New approaches to biomarker identification using chemometrics -- 1.9. Standardisation of metabolic experiments and their reporting -- 1.10. Overview of applications of metabonomics and metabolomics to mammalian systems -- 1.11. Concluding remarks -- References -- Chapter 2 Cellular Metabolomics: The Quest for Pathway Structure -- 2.1. Introduction -- 2.2. How large is the metabolome? -- 2.3. Metabolite identification -- 2.4. Pathway identification -- 2.5. "Omics" data integration -- 2.6. Metabolic fluxes -- 2.7. Metabolic networks -- Acknowledgments -- References -- Chapter 3 NMR Spectroscopy Techniques for Application to Metabonomics -- 3.1. Introduction -- 3.2. Principles of NMR -- 3.3. Hardware requirements and automation -- 3.4. Sample handling -- 3.5. NMR Experiments and their processing -- 3.6. Data pre-processing -- 3.7. Outlook -- References -- Chapter 4 High-Resolution Magic Angle Spinning NMR Spectroscopy -- 4.1. Introduction -- 4.2. Nuclear spin Hamiltonian -- 4.3. Coherence averaging by magic angle spinning -- 4.4. MAS NMR experiments -- 4.5. Pulse sequences -- 4.6. Applications -- Acknowledgements -- References -- Chapter 5 Chromatographic and Electrophoretic Separations Combined with Mass Spectrometry for Metabonomics -- Abstract -- 5.1. Introduction. , 5.2. Gas Chromatography-Mass Spectrometry -- 5.3. Liquid Chromatography-Mass Spectrometry -- 5.4. HPLC-MS for metabonomics -- 5.5. Capillary LC-MS -- 5.6. Ultra performance Liquid Chromatography-Mass Spectrometry -- 5.7. Capillary zone electrophoresis-Mass Spectrometry -- 5.8. GC-, LC- and CE-MS for metabonomics: A perspective -- 5.9. Conclusions -- References -- Chapter 6 Chemometrics Techniques for Metabonomics -- 6.1. Introduction -- 6.2. Chemometric approaches to metabonomic studies -- 6.3. Summary -- 6.4. Extensions and future outlook -- References -- Chapter 7 Non-linear Methods for the Analysis of Metabolic Profiles -- 7.1. Introduction -- 7.2. Unsupervised methods -- 7.3. Supervised methods -- 7.4. Other methods -- 7.5. Conclusions -- Acknowledgements -- References -- Chapter 8 Databases and Standardisation of Reporting Methods for Metabolic Studies -- 8.1. Introduction: The challenges -- 8.2. Tackling the challenges -- 8.3. Standards in action - a working example -- 8.4. Final remarks -- References -- Chapter 9 Metabonomics in Preclinical Pharmaceutical Discovery and Development -- 9.1. Introduction -- 9.2. Background -- 9.3. Methods -- 9.4. Preclinical efficacy -- 9.5. Preclinical toxicity -- 9.6. Conclusions -- References -- Chapter 10 Applications of Metabonomics in Clinical Pharmaceutical R& -- D -- 10.1. Introduction -- 10.2. Clinical applications of metabonomics in pharmaceutical R& -- D to establish Confidence in Rationale and Confidence in Safety -- 10.3. Summary and future outlook -- References -- Chapter 11 Exploiting the Potential of Metabonomics in Large Population Studies: Three Venues -- 11.1. Introduction -- 11.2. Disease risk and pathogenesis -- 11.3. Metabolic signatures of physical and psychosocial experience -- 11.4. Metabolic distinctions in populations studies -- 11.5. Summary and discussion -- Acknowledgments. , References -- Chapter 12 Metabolite Profiling and Cardiovascular Disease -- 12.1. Introduction -- 12.2. Current approaches to molecular fingerprinting -- 12.3. Profiling diagnostics for coronary heart disease -- 12.4. Metabonomics of coronary heart disease -- 12.5. The next steps -- References -- Chapter 13 The Role of NMR-based Metabolomics in Cancer -- 13.1. In the era of "-omics" -- 13.2. Current metabolomics -- 13.3. Current oncology -- 13.4. From histological to molecular pathology -- 13.5. Chemical detection of cancer -- 13.6. Magnetic resonance spectroscopy and cancer -- 13.7. Development of intact tissue MRS -- 13.8. From tissue MR spectra to cancer metabolomics -- 13.9. Future directions and implications -- References -- Chapter 14 NMR Spectroscopy of Body Fluids as a Metabolomics Approach to Inborn Errors of Metabolism -- 14.1. Introduction -- 14.2. Methods -- 14.3. NMR spectra of body fluids -- 14.4. Future perspectives -- References -- Chapter 15 A Survey of Metabonomics Approaches for Disease Characterisation -- 15.1. Introduction -- 15.2. Healthy individuals -- 15.3. Effects of nutrition supplements and natural remedies -- 15.4. Genetically modified animal models of disease and genetic disorders -- 15.5. Metabonomics in disease studies -- 15.6. Concluding remarks -- References -- Chapter 16 Metabolic Profiling: Applications in Plant Science -- 16.1. Introduction -- 16.2. The plant metabolome -- 16.3. Metabolite profiling technologies -- 16.4. Agrochemical development -- 16.5. Metabolite engineering in plants -- 16.6. Plant secondary metabolism -- 16.7. Complex problems in plant science -- 16.8. Plant functional genomics -- 16.9. Breeding -- 16.10. Nutrition and medicine -- 16.11. Outlook to systems biology -- 16.12. Summary and outlook -- References -- Chapter 17 In vivo NMR Applications of Metabonomics -- 17.1. Introduction. , 17.2. In situ NMR Assignments of body fluid metabolites -- 17.3. In vivo NMR Spectroscopy -- 17.4. Conclusions -- References -- Chapter 18 Applications of Metabonomics Within Environmental Toxicology -- 18.1. Introduction -- 18.2. Examining the impact of environmental toxins on mammalian species using laboratory models -- 18.3. Examining the impact of environmental toxins on wild mammal species -- 18.4. Use of high resolution Magic Angle Spinning NMR spectroscopy in studies on the effects of environmental contaminants on mammals -- 18.5. Examining the impact of environmental toxins on fish -- 18.6. Examining the impact of environmental toxins on invertebrates -- 18.7. Future directions and conclusions -- References -- Chapter 19 Global Systems Biology Through Integration of "Omics" Results -- 19.1. Introduction -- 19.2. Integration and correlation of metabonomic and metabolomic analytical data -- 19.3. Integration of metabolomic and metabonomic data from different types of sample -- 19.4. Integration of data from different omics approaches -- 19.5. Future requirements for modelling hyper-complex biological systems -- References -- Index.

Note: Front Cover -- The Handbook of Metabolic Phenotyping -- Copyright -- Contents -- Contributors -- Foreword -- Preface -- Chapter 1: An Overview of Metabolic Phenotyping and Its Role in Systems Biology -- 1. The History and Evolution of Metabolic Profiling -- 1.1. The Early Years -- 1.2. Modern Metabolic Profiling Using NMR Spectroscopy -- 1.3. Modern Metabolic Profiling Using Mass Spectrometry -- 1.4. Definition of the Field and Efforts in Standardization -- 1.5. Development of Computational Technologies -- 1.5.1. Multivariate Statistics -- 1.5.2. Statistical Spectroscopy -- 1.5.3. Databases -- 2. Application Areas -- 2.1. Drug Metabolism and Toxicology -- 2.2. Plant-Based Metabolomics -- 2.3. Human Disease -- 2.3.1. Hepatology and Gastroenterology -- 2.3.2. Neuropathologies -- 2.3.3. Cancer -- 2.3.4. Cardiometabolic Disease -- 2.3.5. Organ Transplantation -- 2.3.6. Renal Diseases -- 2.3.7. Infectious Diseases -- 2.3.8. Epidemiology & -- MWAS -- 2.3.9. Nutrition -- 2.4. Mechanistic Studies of Disease Models -- 2.4.1. In Vivo Models -- 2.4.2. In Vitro Models -- 2.5. Predictive Metabolic Phenotyping (Pharmacometabonomics) -- 2.6. Emerging Metabolic Profiling Technologies -- 3. Concluding Remarks -- References -- Chapter 2: NMR Spectroscopy Methods in Metabolic Phenotyping -- 1. The Nature of NMR Spectroscopy -- 2. NMR and the Complex Mixture Problem -- 3. NMR Sample Storage and Preparation -- 4. Initial 1H NMR Analysis, High-Throughput Screening, and Basic Editing Techniques -- 4.1. Solvent Signal Suppression -- 4.2. Editing of Basic 1D 1H NMR Spectra -- 5. Higher Dimensional NMR: The Quest for Resolution, Correlation, and Component Identity -- 5.1. ``Go-to´´ Techniques: Basic 2D NMR Methods -- 5.1.1. Heteronuclear Single Quantum Coherence -- 5.1.2. HSQC-TOCSY -- 5.1.3. TOCSY -- 5.1.4. J-Resolved. , 5.2. Diffusion-Ordered Methods for Self-Diffusion Coefficient Measurement -- 5.2.1. 2D Correlations With Diffusion Encoding by 3D NMR -- 5.2.2. Pureshift and Diffusion Encoding: A Resolution Revolution -- 6. Improving 2D Resolution Without Cost: Experimental Methods and Data Treatments -- 6.1. PSYCHE-Based 2D Correlations With Covariance Processing -- 6.2. Pure Shift 2D Heteronuclear Correlations -- 6.3. Nonuniform Sampling -- 7. Substantially Boosting Signal Sensitivity -- 7.1. Dissolution Dynamic Nuclear Polarization -- 7.2. Signal Amplification by Reversible Exchange -- 8. Quantitation -- 8.1. HSQC0 -- 9. Preparation of Data for Chemometric Analysis -- 10. Conclusion, Perspective, and Outlook -- References -- Chapter 3: The Role of Ultra Performance Liquid Chromatography-Mass Spectrometry in Metabolic Phenotyping -- 1. Introduction -- 2. The Emergence and Requirements of Ultra Performance Liquid Chromatography for Metabolic Phenotyping -- 3. The Requirements of Mass Spectrometers for Metabolic Phenotyping -- 4. Types of Data Acquired in UPLC-MS Studies -- 5. Quality Assurance and Quality Control in UPLC-MS-Based Metabolic Phenotyping -- 5.1. System Suitability Test Solution [10, 35] -- 5.2. Isotopically Labeled Internal Standards [10, 17, 80] -- 5.3. Within-Study Pooled Quality Control Sample [10, 17, 82] -- 5.4. Long-Term Reference Material [17, 85] -- 5.5. Extraction Blank Samples -- 6. Analytical Robustness for Small-Scale and Large-Scale Studies -- 7. Data Processing -- 8. Metabolite Annotation and Identification -- 9. Exemplar Applications -- Acknowledgments -- References -- Chapter 4: GC-MS-Based Metabolic Phenotyping -- 1. Introduction -- 2. GC-MS as an Analytical Technique -- 3. GC-MS Metabolic Phenotyping Workflow -- 4. Sample Preparation for GC-MS-Based Metabolic Phenotyping -- 5. Data Processing -- 6. Statistical Evaluation. , 7. Biochemical Interpretation -- 8. GC-MS Troubleshooting -- 9. Clinical Applications -- 10. Conclusion -- References -- Chapter 5: Metabolic Phenotyping Using Capillary Electrophoresis Mass Spectrometry -- 1. Introduction -- 1.1. Instrumentation -- 1.2. Principles of Capillary Zone Electrophoresis -- 1.3. Conditions That Can Be Modified to Optimize the Separation and Limitations When Coupled to MS -- 1.4. Strengths -- 1.5. Weaknesses -- 1.6. Coupling to MS -- 2. Workflow -- 2.1. Design -- 2.2. Sample -- 2.3. Separation and Detection -- 2.3.1. Materials -- 2.3.2. Capillary Preparation -- 2.3.3. CE-MS Method -- 2.4. Data -- 2.5. Statistics -- 2.6. Identification -- 2.7. Meaning -- 3. Applications -- 3.1. Cancer -- 3.2. Metabolic Disorders -- 3.3. Neurodegenerative Diseases and Brain Disorders -- 3.4. Others -- 3.4.1. Infectious Diseases -- 3.4.2. Alcohol Abuse -- 3.4.3. Clinical Approaches -- 4. Conclusions and Future Trends -- References -- Chapter 6: Supercritical Fluid Chromatography for Metabolic Phenotyping: Potential and Applications -- 1. Introduction -- 2. Application of SFC to Nonpolar Metabolite Analysis -- 2.1. Lipids -- 2.2. Bile and Bile Acids -- 2.3. Applications of SFC to Polar Metabolite Analysis -- 3. Conclusion -- References -- Chapter 7: The iKnife: Development and Clinical Applications of Rapid Evaporative Ionization Mass Spectrometry -- Abbreviations -- 1. Introduction -- 2. Basic Principles of Mass Spectrometry -- 3. Nonambient and Ambient Mass Spectrometry -- 4. Development of Rapid Evaporative Ionization Mass Spectrometry (REIMS) (the iKnife) -- 5. Model Generation: The Concept of Profiling -- 6. Clinical Applications -- 6.1. Oncological Surgery: Introduction -- 6.2. Breast Cancer: The iKnife as a Diagnostic and Margin Assessment Tool -- 6.3. Brain Tumors: The iKnife as a Diagnostic and Extent of Resection Assessment Tool. , 6.4. Gastroenterology: The iEndoscope -- 7. Microbiology Applications -- 8. Conclusions -- References -- Chapter 8: Univariate Statistical Modeling, Multiple Testing Correction, and Visualization in Metabolome-Wide Association ... -- 1. Metabolic Phenotyping: Opportunities and Challenges -- 1.1. Application to Public Health Studies -- 1.2. Overview of the Analytical Pipeline in Metabolic Phenotyping -- 2. Metabolome-Wide Association Study: Univariate Approaches -- 2.1. Imputation and Normalization -- 2.2. Statistical Analyses Using Univariate Approaches -- 2.3. Linear and Generalized Linear Models -- 2.4. (Generalized) Linear Mixed Models -- 2.5. Meta-Analyses -- 3. Metabolome-Wide Significance Level -- 3.1. FWER and FDR Control -- 3.2. Defining the Metabolome-Wide Significance Level -- 4. Results Prioritization and Visualization -- 5. Conclusions -- Acknowledgment -- References -- Chapter 9: Multivariate Statistical Methods for Metabolic Phenotyping -- 1. Introduction -- 1.1. Mathematical Notations -- 1.2. Data Transformations -- 1.2.1. Mean Centering -- 1.2.2. Autoscaling -- 1.2.3. Generalized Equation for Scaling -- 1.3. Data Set Partitioning -- 2. Unsupervised Pattern Recognition -- 2.1. Unsupervised Methods for Dimension Reduction and Multivariate Projections -- 2.1.1. Principal Component Analysis -- 2.1.2. Projection Pursuit and Independent Component Analysis -- 2.1.3. Kernel Principal Component Analysis -- 2.1.4. Sparse Principal Component Analysis -- 2.2. Outlier Detection -- 2.2.1. Hotelling's T2 -- 2.2.2. Quality Control Samples -- 2.2.3. Robust Principal Component Analysis -- 2.3. Other Data Exploration Approaches for Specific Experimental Designs and Multiway Analysis -- 2.3.1. Multiway Methods -- 2.3.2. Multivariate Curve Resolution -- 2.3.3. Nonnegative Principal Component Analysis and Nonnegative Matrix Factorization. , 3. Supervised Pattern Recognition and Multivariate Regression -- 3.1. Discriminant and Cluster Analysis -- 3.1.1. Linear Discriminant Analysis -- 3.1.2. Quadratic Discriminant Analysis -- 3.1.3. k-Nearest Neighbors -- 3.1.4. k-Means, k-Medoids, and Fuzzy Alternatives -- 3.2. Multivariate Regression -- 3.2.1. Multiple Linear Regression -- 3.2.2. Principal Component Regression -- 3.2.3. Partial Least Squares -- 3.2.4. Kernel Partial Least Squares -- 3.2.5. Orthogonal Signal Correction-Filtered Partial Least Squares and Orthogonal Projections to Latent Structures -- 3.2.6. Two-Block Orthogonal Projections to Latent Structures -- 3.2.7. Kernel Orthogonal Projections to Latent Structures -- 3.3. Penalized Regression -- 3.3.1. Ridge Regression and Kernel Ridge Regression -- 3.3.2. Sparse Penalized Regression Using the Lasso -- 3.3.3. The Elastic Net, a Weighted Average of Lasso and Ridge Regression -- 3.4. Nonparametric Machine Learning and Recursive Partitioning Methods -- 3.4.1. Support Vector Machines -- 3.4.2. Variable Importance in Linear Support Vector Machines -- 3.4.3. Uncovering Variable Importance for Nonlinear Kernel-Based Models -- 3.4.4. Decision Trees for Classification -- 3.4.5. Random Forests -- 3.5. Prediction Error Rates and Model Fits -- 3.6. Other Supervised Methods and Those for Specific Experimental Designs -- 3.6.1. ANOVA-Based Decompositions -- 3.6.2. Bayesian Approaches -- 3.6.3. Deep Learning Algorithms -- 4. Statistical Approaches for Biomarker Identification -- 4.1. Statistical Total Correlation Spectroscopy -- 4.2. Correlation Networks of Biological Coregulation -- 4.3. Subset Optimization by Reference Matching and the Statistical Use of Second Dimension Data -- 4.4. Statistical Hetero Spectroscopy and Data Fusion -- 5. Conclusion -- References -- Chapter 10: Data-Driven Visualizations in Metabolic Phenotyping. , 1. Visualizing Metabolic Phenotyping Data and Analysis.