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Preface |
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xvii | |
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1 | (338) |
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1 Metabolic Engineering Perspectives |
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3 | (20) |
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1.1 History and Overview of Metabolic Engineering |
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3 | (2) |
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1.2 Understanding Cellular Metabolism and Physiology |
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5 | (4) |
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1.2.1 Computational Methods in Understanding Metabolism |
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6 | (1) |
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1.2.2 Experimental Methods in Understanding Metabolism |
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7 | (2) |
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1.3 General Approaches to Metabolic Engineering |
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9 | (6) |
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1.3.1 Rational Metabolic Engineering |
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10 | (2) |
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1.3.2 Combinatorial Metabolic Engineering |
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12 | (2) |
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1.3.3 Systems Metabolic Engineering |
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14 | (1) |
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1.4 Host Organism Selection |
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15 | (1) |
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1.5 Substrate Considerations |
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15 | (1) |
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1.6 Metabolic Engineering and Synthetic Biology |
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16 | (1) |
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1.7 The Future of Metabolic Engineering |
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17 | (6) |
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19 | (4) |
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2 Genome-Scale Models: Two Decades of Progress and a 2020 Vision |
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23 | (50) |
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23 | (1) |
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2.2 Flux Balance Analysis |
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23 | (7) |
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2.2.1 Dynamic Mass Balances |
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23 | (2) |
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2.2.2 Analogy to Deriving Enzymatic Rate Equations |
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25 | (1) |
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2.2.3 Formulating Flux Balances at the Genome-Scale |
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25 | (1) |
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2.2.4 Constrained Optimization |
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26 | (1) |
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26 | (1) |
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2.2.6 Additional Constraints |
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27 | (1) |
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2.2.7 Flux-Concentration Duality |
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28 | (1) |
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28 | (2) |
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2.3 Network Reconstruction |
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30 | (6) |
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2.3.1 Assembling the Reactome |
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30 | (1) |
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2.3.2 Basic Principles of Network Reconstruction |
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30 | (2) |
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32 | (1) |
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2.3.4 GEMs Have a Genomic Basis |
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32 | (1) |
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2.3.5 Computational Queries |
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32 | (1) |
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33 | (2) |
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35 | (1) |
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2.3.8 Availability of GEMs |
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35 | (1) |
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35 | (1) |
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2.4 Brief History of the GEM for E. coli |
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36 | (6) |
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36 | (1) |
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36 | (2) |
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38 | (1) |
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38 | (3) |
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41 | (1) |
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42 | (1) |
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2.5 From Metabolism to the Proteome |
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42 | (8) |
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42 | (1) |
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2.5.2 Capabilities of ME Models |
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43 | (1) |
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2.5.2.1 Growth-Coupled Metabolic Designs Can Be Reproduced in GEMs |
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43 | (1) |
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2.5.2.2 ME Models Can Reflect Properties of the Metalloproteome |
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44 | (1) |
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2.5.2.3 ME Models Can Compute the Biomass Objective Function |
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44 | (2) |
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2.5.2.4 Computing Stresses |
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46 | (3) |
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49 | (1) |
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50 | (6) |
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50 | (1) |
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2.6.2 Transcriptional Regulation |
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51 | (1) |
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52 | (1) |
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52 | (3) |
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55 | (1) |
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56 | (3) |
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56 | (1) |
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2.7.2 Contextualization of GEMs Within Workflows |
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57 | (2) |
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2.8 What Does the Future Look Like for GEMs? |
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59 | (14) |
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62 | (1) |
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63 | (1) |
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63 | (10) |
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3 Quantitative Metabolic Flux Analysis Based on Isotope Labeling |
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73 | (64) |
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73 | (4) |
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3.1.1 What Metabolic Flux Analysis Is About |
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73 | (3) |
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3.1.2 The Variants of 13C-MFA |
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76 | (1) |
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3.2 A Toy Example Illustrates the Basic Principles |
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77 | (6) |
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3.2.1 Fluxomics: More Than Just a Branch of Metabolomics |
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77 | (2) |
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3.2.2 Isotope Labeling: The Key to Metabolic Fluxes |
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79 | (3) |
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3.2.3 From the Data to the Intracellular Fluxes |
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82 | (1) |
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3.2 A INST-13C-MFA: Metabolic Stationary, but Isotopically Nonstationary |
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83 | (14) |
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3.2.5 From Measurements to Flux Estimates: Parameter Fitting |
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84 | (2) |
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3.2.6 Flux Estimates Have Confidence Bounds: Statistical Analysis |
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86 | (4) |
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3.2.7 The Classical Approach at Metabolic and Isotopic Stationary State |
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90 | (1) |
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3.2.8 An Additional Source of Information: Carbon Atom Transitions |
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91 | (2) |
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3.2.9 Input Labeling Design: How Informative Can an Experiment Be Made? |
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93 | (1) |
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3.2.10 The Isotopomers of a Single Metabolite Can Be a Rich Source of Information |
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94 | (1) |
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3.2.11 Bidirectional Reaction Steps: More Than Just Nuisance Factors |
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95 | (1) |
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3.2.12 Isotopomer Fractions Cannot Be Measured Comprehensively |
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96 | (1) |
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3.3 Lessons Learned from the Example |
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97 | (3) |
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3.3.1 Definition of 13C-MFA Revisited |
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97 | (2) |
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3.3.2 Statistical Evaluation and Optimal Experimental Design |
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99 | (1) |
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3.4 How to Configure an Isotope Labeling Experiment |
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100 | (8) |
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3.4.1 Modeling and Simulation of Isotope Labeling Experiments |
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101 | (1) |
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3.4.2 Metabolic Network Specification |
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101 | (2) |
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3.4.3 Atom Transition Network Specification |
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103 | (1) |
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3.4.4 Input Labeling Composition |
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104 | (2) |
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3.4.5 Measurement Specification |
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106 | (1) |
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107 | (1) |
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3.4.7 In Silico Experimental ILE Design |
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108 | (1) |
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3.5 Putting Theory into Practice |
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108 | (16) |
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3.5.1 A Recipe How to Start |
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108 | (2) |
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3.5.2 Metabolic and Isotopic Stationarity |
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110 | (1) |
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3.5.3 Measuring Extracellular Fluxes |
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111 | (1) |
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3.5.4 Administering Labeled Substrate(s) |
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112 | (1) |
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3.5.5 Metabolomics: Sampling, Sample Preparation, and Analytical Procedures |
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113 | (2) |
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3.5.6 Adjusting Labeling Enrichments for Isotopic Steady State Approximation |
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115 | (1) |
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3.5.7 Correcting Labeling Enrichments for Natural Isotope Abundance |
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116 | (1) |
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3.5.8 Simulation of Labeling Data and Flux Estimation |
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117 | (6) |
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3.5.9 Delicacies of INST-13C-MFA |
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123 | (1) |
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3.6 Future Challenges of 13C-MFA |
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124 | (13) |
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125 | (1) |
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125 | (1) |
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126 | (11) |
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4 Proteome Constraints in Genome-Scale Models |
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137 | (16) |
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137 | (1) |
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137 | (2) |
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4.3 Formulation of Proteome Constraints |
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139 | (11) |
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4.3.1 Coarse-Grained Integration of Proteome Constraints |
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139 | (5) |
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4.3.2 Fine-Tuned Integration of Proteome Constraints |
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144 | (6) |
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150 | (3) |
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151 | (2) |
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5 Kinetic Models of Metabolism |
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153 | (18) |
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153 | (1) |
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5.2 Definition of Enzyme Kinetics |
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153 | (2) |
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5.2.1 Michaelis--Menten Formula |
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153 | (2) |
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5.3 Factors Affecting Intracellular Enzyme Kinetics |
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155 | (1) |
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5.4 Kinetic Model: Definition and Scope |
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156 | (2) |
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5.4.1 What Is a Kinetic Model? |
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156 | (1) |
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5.4.2 Scope of Kinetic Models |
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156 | (1) |
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5.4.3 How to Build a Functional Kinetic Model? |
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157 | (1) |
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5.5 Main Mathematical Expressions in Description of Reaction Rates |
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158 | (1) |
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5.5.1 Mechanistic Rate Expressions |
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158 | (1) |
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5.6 Approximative Rate Expressions |
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159 | (1) |
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5.7 Approaches to Assign Parameters in the Rate Expressions |
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160 | (3) |
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5.7.1 Direct Measurements of Kinetic Parameters in Enzyme Assays |
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161 | (1) |
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161 | (1) |
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5.7.3 Inferring from Measured Fluxes |
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162 | (1) |
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5.7 A Parameters Inference Using the Statistical Analysis |
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163 | (3) |
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166 | (1) |
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167 | (4) |
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168 | (3) |
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6 Metabolic Control Analysis |
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171 | (42) |
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6.1 The Metabolic Engineering Context of Metabolic Control Analysis |
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171 | (3) |
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174 | (16) |
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6.2.1 Metabolic Steady State |
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174 | (1) |
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6.2.2 Flux Control Coefficients |
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175 | (1) |
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6.2.3 Examples of the Flux-Enzyme Relationship |
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176 | (2) |
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6.2.4 Flux Summation Theorem |
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178 | (1) |
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6.2.5 Concentration Control Coefficients |
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179 | (2) |
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6.2.6 Linking Control Coefficients to Enzyme Properties |
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181 | (1) |
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6.2.6.1 Enzyme Rate Equations and Elasticity Coefficients |
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181 | (3) |
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6.2.6.2 Elasticities and Control Coefficients |
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184 | (2) |
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6.2.6.3 Block Coefficients and Top-Down Analysis |
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186 | (1) |
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6.2.7 Feedback Inhibition |
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186 | (2) |
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6.2.8 Large Alterations of Enzyme Activity |
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188 | (2) |
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6.3 Implications of MCA for Metabolic Engineering Strategies |
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190 | (6) |
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6.3.1 Abolishing Feedback Inhibition |
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191 | (3) |
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6.3.2 Increasing Demand for Product |
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194 | (1) |
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6.3.3 Inhibition of Competing Pathways |
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195 | (1) |
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6.3 A Designing Large Changes in Metabolic Flux |
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196 | (9) |
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6.3.4.1 Yeast Tryptophan Synthesis |
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197 | (2) |
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6.3.4.2 The Universal Method |
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199 | (1) |
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6.3.4.3 Bacterial Production of Aromatic Amino Acids |
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200 | (2) |
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6.3.4.4 Penicillin and Other Instances |
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202 | (1) |
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6.3.5 Impacts on Yield from a Growing System |
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203 | (2) |
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205 | (8) |
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Appendix 6.A Feedback Inhibition Simulation |
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205 | (2) |
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207 | (6) |
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7 Thermodynamics of Metabolic Pathways |
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213 | (24) |
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7.1 Bioenergetics in Life and in Metabolic Engineering |
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213 | (2) |
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7.2 Thermodynamics-Based Flux Analysis Workflow |
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215 | (13) |
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7.2.1 Thermodynamic Model Curation |
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215 | (1) |
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7.2.1.1 Estimation of the Standard Free Energies of Formation |
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216 | (4) |
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7.2.1.2 Compensating for Compartment-Specific Ionic Strength and pH |
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220 | (1) |
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7.2.1.3 Compensating the Free Energy of Formation for Isomer Distributions |
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221 | (2) |
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7.2.1.4 Computing the Transformed Free Energies of Reaction |
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223 | (4) |
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7.2.2 Mathematical Formulation |
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227 | (1) |
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7.3 Thermodynamics-Based Flux Analysis Applications |
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228 | (3) |
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7.3.1 Constraining the Flux Space with Metabolomics Data |
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228 | (1) |
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7.3.2 Characterizing the Feasible Concentration Space |
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229 | (2) |
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7.4 Conclusion and Future Perspectives |
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231 | (6) |
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233 | (4) |
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237 | (22) |
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237 | (1) |
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8.1 De Novo Design of Metabolic Pathways |
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237 | (1) |
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8.1.1 Manual Versus Computational Design |
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238 | (1) |
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8.2 Pathway Design Workflow |
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238 | (9) |
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8.2.1 Biochemical Search Space |
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238 | (2) |
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8.2.1.1 Reaction Prediction |
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240 | (1) |
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8.2.1.2 Retrobiosynthesis |
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241 | (1) |
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8.2.1.3 Network Data Representation |
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242 | (1) |
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242 | (1) |
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8.2.2.1 Stoichiometric Matrix-Based Search |
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243 | (1) |
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8.2.2.2 Graph-Based Search |
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243 | (1) |
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244 | (1) |
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244 | (1) |
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8.2.3.1 Enzyme Prediction for Orphan and Novel Reactions |
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244 | (2) |
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8.2.3.2 Choice of Protein Sequence |
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246 | (1) |
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8.2.4 Pathway Feasibility |
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246 | (1) |
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8.2.4.1 Chassis Metabolic Model |
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246 | (1) |
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8.2.4.2 Stoichiometric Feasibility |
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246 | (1) |
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8.2.4.3 Thermodynamic Feasibility |
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246 | (1) |
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8.2.4.4 Kinetic Feasibility |
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247 | (1) |
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8.2.4.5 Toxicity of Intermediates |
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247 | (1) |
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247 | (6) |
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8.3.1 Available Tools for Pathway Design |
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247 | (2) |
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8.3.2 Successful Applications of Pathway Design Tools |
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249 | (1) |
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8.3.3 Practical Example of Pathway Design |
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249 | (1) |
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8.3.3.1 Creating a Biochemical Network Around BDO |
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249 | (2) |
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8.3.3.2 Search for Biosynthetic Pathways |
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251 | (1) |
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8.3.3.3 Finding Enzymes for Novel Reactions |
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251 | (1) |
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8.3.3.4 Stoichiometric and Thermodynamic Pathway Evaluation |
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251 | (1) |
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8.3.3.5 Overall Ranking of Pathways |
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251 | (2) |
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8.4 Conclusions and Future Perspectives |
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253 | (6) |
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254 | (5) |
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259 | (42) |
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259 | (1) |
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260 | (2) |
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9.2.1 Experimental Design |
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260 | (1) |
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9.2.2 Targeted and Untargeted Metabolomics |
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260 | (1) |
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9.2.3 Sequences and Standards |
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261 | (1) |
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9.3 Analytical Techniques |
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262 | (10) |
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262 | (2) |
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9.3.2 Separation Techniques |
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264 | (1) |
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9.3.2.1 Liquid Chromatography |
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264 | (2) |
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9.3.2.2 Gas Chromatography |
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266 | (1) |
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9.3.2.3 Alternative Separation Techniques |
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266 | (2) |
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268 | (1) |
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9.3.3.1 Ionization Techniques |
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268 | (1) |
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9.3.3.2 Low-Resolution MS |
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269 | (1) |
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9.3.3.3 High-Resolution MS |
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270 | (1) |
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9.3.3.4 Acquisition Modes for Targeted MS |
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271 | (1) |
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9.3.3.5 Acquisition Modes for Untargeted Metabolomics |
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272 | (1) |
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272 | (7) |
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9.4.1 Data Processing in Untargeted Metabolomics |
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273 | (1) |
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9.4.1.1 Preprocessing of Individual MS Runs |
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273 | (1) |
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273 | (1) |
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9.4.1.3 Peak Alignment and Retention Time Correction |
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274 | (1) |
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274 | (1) |
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274 | (1) |
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274 | (2) |
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276 | (1) |
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9.4.2 Data Analysis and Interpretation |
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277 | (1) |
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9.4.2.1 Univariate Statistics |
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277 | (1) |
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9.4.2.2 Multivariate Statistics |
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278 | (1) |
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278 | (1) |
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9.5 Emerging Trends for Cellular Analyses |
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279 | (2) |
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9.5.1 High-Throughput Metabolomics for Large Scale Screening |
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279 | (1) |
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9.5.2 Single Cell Metabolomics |
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280 | (1) |
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281 | (1) |
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9.6 Applications of Metabolomics in Metabolic Engineering |
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281 | (3) |
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9.6.1 Pathway Design by Thermodynamic Analysis |
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281 | (2) |
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9.6.2 Alleviating Pathway Bottlenecks |
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283 | (1) |
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9.6.3 Reduction of Side Products and Metabolite Damage |
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284 | (1) |
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9.6 A Improving Stress Tolerance |
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284 | (1) |
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9.6.5 Engineer Medium Composition |
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285 | (1) |
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285 | (16) |
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286 | (15) |
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10 Genome Editing of Eukarya |
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301 | (38) |
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Helene Faustrup Kildegaard |
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10.1 Basic Principles of Genome Editing |
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301 | (3) |
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304 | (6) |
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10.2.1 Zinc-Finger Nucleases |
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304 | (2) |
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10.2.2 Transcription Activator-Like Effectors Nucleases |
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306 | (2) |
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308 | (2) |
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10.3 Genome Editing of Industrially Relevant Eukaryotes |
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310 | (10) |
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310 | (3) |
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313 | (3) |
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10.3.3 Chinese Hamster Ovary Cells |
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316 | (4) |
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320 | (19) |
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320 | (19) |
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Preface |
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xvii | |
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339 | (552) |
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11 Metabolic Engineering of Escherichia coli |
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341 | (62) |
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Cindy Pricilia Surya Prabowo |
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12 Metabolic Engineering of Corynebacterium glutamicum |
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403 | (66) |
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13 Metabolic Engineering of Bacillus -- New Tools, Strains, and Concepts |
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469 | (50) |
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14 Metabolic Engineering of Pseudomonas |
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519 | (38) |
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15 Metabolic Engineering of Lactic Acid Bacteria |
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557 | (54) |
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16 Metabolic Engineering and the Synthetic Biology Toolbox for Clostridium |
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611 | (42) |
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17 Metabolic Engineering of Filamentous Actinomycetes |
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653 | (36) |
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18 Metabolic Engineering of Yeast |
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689 | (46) |
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19 Harness Yarrowia lipolytica to Make Small Molecule Products |
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735 | (30) |
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20 Metabolic Engineering of Filamentous Fungi |
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765 | (38) |
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21 Metabolic Engineering of Photosynthetic Cells -- in Collaboration with Nature |
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803 | (56) |
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22 Metabolic Engineering for Large-Scale Environmental Bioremediation |
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859 | (32) |
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Index |
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891 | |