Note: Intro -- Title page -- Copyright page -- Contents -- Foreword -- Preface -- Contributors -- 1: Classical Strain Improvement -- 1.0 Introduction -- 1.1 The Approach Defined -- 1.2 Mutagenesis -- 1.2.1 Numerical Considerations in Screen Design -- 1.2.2 Random Genetic Drift -- 1.2.3 Forced Mutagenesis -- 1.2.4 Strain Mating -- 1.3 Genotypic Landscapes -- 1.4 Screening -- 1.4.1 Rational Screens -- 1.4.2 Random Screens -- 1.4.3 Screening Platforms -- 1.5 Conclusions -- References -- 2: Tracer-Based Analysis of Metabolic Flux Networks -- 2.0 Introduction -- 2.1 Setting Up a Stoichiometric Network Model -- 2.2 Small-Scale Models versus Genome Scale Models -- 2.3 Network Analysis: Maximum Theoretical Yield -- 2.4 (Stoichiometric) Metabolic Flux Analysis -- 2.5 Carrying Out a Labeling Experiment -- 2.6 MEASURING ISOTOPE LABELING PATTERNS -- 2.7 Tracer-Based MFA -- 2.8 Validating Metabolic Flux Networks -- 2.9 Conclusions -- Acknowledgments -- References -- 3: Integration of "Omics" Data with Genome-Scale Metabolic Models -- 3.0 Introduction -- 3.1 Genome-Scale Metabolic Networks -- 3.2 Constraint-Based Modeling Theory -- 3.3 Current Analysis of Omics Data -- 3.4 New Approaches to Developing Model Constraints -- 3.5 Use of Gene Expression Data in Metabolic Models -- 3.6 Use of Metabolomics Data in Metabolic Models: TMFA Example -- 3.7 Integration of Multiple Omics Data Sets -- 3.8 Future Directions and Applications to Strain Engineering -- References -- 4: Strain Improvement via Evolutionary Engineering -- 4.0 Introduction -- 4.1 Methodologies for Evolutionary Engineering -- 4.1.1 Adaptive Evolution -- 4.1.2 Genome Shuffling -- 4.1.3 Global Transcriptional Machinery Engineering -- 4.1.4 Transposon Insertion Mutagenesis -- 4.1.5 Multiplex Automated Genome Engineering -- 4.1.6 Tractable Multiplex Recombineering. , 4.1.7 Chemically Induced Chromosomal Evolution -- 4.1.8 Multiscale Analysis of Library Enrichment (SCALE) -- 4.1.9 Screening and Selection -- 4.2 Examples of Evolutionary Engineering -- 4.2.1 Enhancement of Product Yield and Productivity -- 4.2.2 Extension of Substrate Range -- 4.2.3 Improvement of Cellular Properties -- 4.3 Conclusions and Future Prospects -- Acknowledgments -- References -- 5: Rapid Fermentation Process Development and Optimization -- 5.0 Introduction -- 5.1 Overview of Classical Fermentation Process Development Methodology -- 5.1.1 Noninvasive Sensor Technologies -- 5.2 Fermentation Process Development and Optimization -- 5.2.1 Medium Design and Optimization -- 5.2.2 Optimization of Growth Conditions -- 5.3 Rapid Process Development and Optimization Using Conventional Fermentation System -- 5.3.1 Dynamic DO Control to Determine Optimal Feed Rate for Carbon Source-Limited Fermentation -- 5.3.2 Feed Forward Control for Carbon Source Excess Fermentation -- 5.4 Strain Evaluation and Process Optimization under Scale-Down Conditions -- 5.4.1 Identify Scale-Down Parameters -- 5.4.2 Scale-Down of Mixing Related Parameters -- 5.4.3 Oxygen Uptake Rate (OUR) Clipping -- 5.4.4 Dissolved CO2 -- 5.5 Control and Sensor Technologies for Minibioreactor -- 5.5.1 Temperature Sensing and Control -- 5.5.2 Mixing -- 5.5.3 DO -- 5.5.4 pH -- 5.5.5 Cell Concentration -- 5.5.6 Feeding -- 5.6 Commercial High-Throughput Fermentation Systems -- 5.6.1 Shaken Minibioreactors -- 5.6.2 Stirred Minibioreactor -- 5.6.3 Parallel Benchtop Fermentation System -- 5.7 Trends in Development of High the greata-Throughput Minibioreactor System -- 5.8 Case Studies of Fermentation Process Development and Optimization Using High-Throughput Minibioreactors -- 5.8.1 Case Study 1: Protein Production -- 5.8.2 Case Study 2: Antibody Fragment Expression. , 5.9 Conclusions and THE Path Forward -- References -- 6: The Clavulanic Acid Strain Improvement Program at DSM Anti-Infectives -- 6.0 Introduction -- 6.1 The Biosynthetic Pathway to Clavulanic Acid -- 6.2 The Strategy for Improvement of Multiple Complex Phenotypes -- 6.3 Results and Discussion -- 6.3.1 The Panlabs Years-Results from 1991 to 1999 -- 6.3.2 The DSM Years-Results from 1999 to 2006 -- 6.4 Future Perspectives -- Acknowledgments -- References -- 7: Metabolic Engineering of Recombinant E. coli for the Production of 3-Hydroxypropionate -- 7.0 Introduction to Biosynthesis of 3-Hydroxypropionic Acid -- 7.1 Organic Acid Toxicity -- 7.2 Understanding 3-HP Toxicity -- 7.2.1 Choosing an Approach for Evolving Tolerance -- 7.2.2 Selection Design for Evolving 3-HP Tolerance -- 7.2.3 Taking a Closer Look at Selection Design -- 7.2.4 Constructing the 3-HP Toleragenic Complex -- 7.3 Strain Design -- 7.3.1 Evaluation of the 3-HP-TGC -- 7.3.2 Complex Tolerant Phenotype: Metabolism of 3-HP to a Toxic Intermediate -- 7.4 Combining 3-HP Tolerance and 3-HP Production -- 7.5 Summary -- References -- 8: Complex System Engineering: A Case Study for an Unsequenced Microalga -- 8.0 Historical Perspective -- 8.1 Analysis of Algal Biomass Composition -- 8.1.1 Defining the Parameters of an "Ideal" Strain -- 8.1.2 Tool Development for the Analysis of Growth and Lipid Production -- 8.1.3 Selection and Characterization of a Promising C. vulgaris Strain -- 8.2 Development of Hypothesis-Driven Strain Improvement Strategies -- 8.2.1 Systems Biology Analysis in an Unsequenced Microalga -- 8.2.2 Transcriptome-to-Proteome Pipelining -- 8.2.3 Identification of Strain Engineering Targets -- 8.3 Implementation of Biological Tools I-Development of a Transformation System -- 8.3.1 Vector Construction -- 8.3.2 Protoplast Preparation and Transformation of C. vulgaris UTEX395. , 8.3.3 Stability Evaluation of Transformants -- 8.3.4 C. vulgaris Endogenous Promoter Identification and Characterization -- 8.4 Implementation of Biological Tools II-Development of a Self-Lysing, Oil-Producing Alga for Biofuels Production -- 8.4.1 Algal Lipid Extraction -- 8.4.2 Algal Cell Wall Complexity and Enzymatic Treatment Effects -- 8.4.3 High-Resolution Imaging of Enzymatic Treatment Effects -- 8.4.4 Production Strain Development -- 8.5 Concluding Remarks -- Acknowledgments -- References -- 9: Meiotic Recombination-Based Genome Shuffling of Saccharomyces cerevisiae and Schefferomyces stiptis for Increased Inhibitor Tolerance to Lignocellulosic Substrate Toxicity -- 9.0 Introduction -- 9.1 Methodology -- 9.1.1 Meiotic Recombination-Mediated Genome Shuffling -- 9.1.2 Inducing Genome Shuffling through Meiosis versus Protoplast Fusion -- 9.2 Results and Discussion of Strain Development -- 9.2.1 Generation of Mutant Pools -- 9.2.2 Screening and Selection of Mutant and Evolved Populations -- 9.2.3 Increasing HWSSL Tolerance through Genome Shuffling -- 9.2.4 Tolerance to HWSSL Leads to Increased Ethanol Production -- 9.2.5 Tolerance to HWSSL Leads to Cross-Tolerance to Multiple Inhibitors -- 9.2.6 Comparison between the S. stipitis and S. cerevisiae Genome Shuffling Studies -- 9.3 Conclusions and Future Directions -- References -- Index -- Supplemental Images.