Note: Cover -- Title Page -- Copyright -- Contents -- Editor Biography -- List of Contributors -- Chapter 1 Introduction to Smart Power Systems -- 1.1 Problems in Conventional Power Systems -- 1.2 Distributed Generation (DG) -- 1.3 Wide Area Monitoring and Control -- 1.4 Automatic Metering Infrastructure -- 1.5 Phasor Measurement Unit -- 1.6 Power Quality Conditioners -- 1.7 Energy Storage Systems -- 1.8 Smart Distribution Systems -- 1.9 Electric Vehicle Charging Infrastructure -- 1.10 Cyber Security -- 1.11 Conclusion -- References -- Chapter 2 Modeling and Analysis of Smart Power System -- 2.1 Introduction -- 2.2 Modeling of Equipment's for Steady‐State Analysis -- 2.2.1 Load Flow Analysis -- 2.2.1.1 Gauss Seidel Method -- 2.2.1.2 Newton Raphson Method -- 2.2.1.3 Decoupled Load Flow Method -- 2.2.2 Short Circuit Analysis -- 2.2.2.1 Symmetrical Faults -- 2.2.2.2 Unsymmetrical Faults -- 2.2.3 Harmonic Analysis -- 2.3 Modeling of Equipments for Dynamic and Stability Analysis -- 2.4 Dynamic Analysis -- 2.4.1 Frequency Control -- 2.4.2 Fault Ride Through -- 2.5 Voltage Stability -- 2.6 Case Studies -- 2.6.1 Case Study 1 -- 2.6.2 Case Study 2 -- 2.6.2.1 Existing and Proposed Generation Details in the Vicinity of Wind Farm -- 2.6.2.2 Power Evacuation Study for 50 MW Generation -- 2.6.2.3 Without Interconnection of the Proposed 50 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation -- 2.6.2.4 Observations Made from Table -- 2.6.2.5 With the Interconnection of Proposed 50 MW Generation from Wind Farm on 66 kV level of 220/66 kV Substation -- 2.6.2.6 Normal Condition without Considering Contingency -- 2.6.2.7 Contingency Analysis -- 2.6.2.8 With the Interconnection of Proposed 60 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation -- 2.7 Conclusion -- References. , Chapter 3 Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications -- 3.1 Introduction -- 3.2 Multilevel Cascaded Boost Converter -- 3.3 Modes of Operation of MCBC -- 3.3.1 Mode‐1 Switch SA Is ON -- 3.3.2 Mode‐2 Switch SA Is ON -- 3.3.3 Mode‐3‐Operation - Switch SA Is ON -- 3.3.4 Mode‐4‐Operation - Switch SA Is ON -- 3.3.5 Mode‐5‐Operation - Switch SA Is ON -- 3.3.6 Mode‐6‐Operation - Switch SA Is OFF -- 3.3.7 Mode‐7‐Operation - Switch SA Is OFF -- 3.3.8 Mode‐8‐Operation - Switch SA Is OFF -- 3.3.9 Mode‐9‐Operation - Switch SA Is OFF -- 3.3.10 Mode 10‐Operation - Switch SA is OFF -- 3.4 Simulation and Hardware Results -- 3.5 Prominent Structures of Estimated DC-DC Converter with Prevailing Converter -- 3.5.1 Voltage Gain and Power Handling Capability -- 3.5.2 Voltage Stress -- 3.5.3 Switch Count and Geometric Structure -- 3.5.4 Current Stress -- 3.5.5 Duty Cycle Versus Voltage Gain -- 3.5.6 Number of Levels in the Planned Converter -- 3.6 Power Electronic Converters for Renewable Energy Sources (Applications of MLCB) -- 3.6.1 MCBC Connected with PV Panel -- 3.6.2 Output Response of PV Fed MCBC -- 3.6.3 H‐Bridge Inverter -- 3.7 Modes of Operation -- 3.7.1 Mode 1 -- 3.7.2 Mode 2 -- 3.7.3 Mode 3 -- 3.7.4 Mode 4 -- 3.7.5 Mode 5 -- 3.7.6 Mode 6 -- 3.7.7 Mode 7 -- 3.7.8 Mode 8 -- 3.7.9 Mode 9 -- 3.7.10 Mode 10 -- 3.8 Simulation Results of MCBC Fed Inverter -- 3.9 Power Electronic Converter for E‐Vehicles -- 3.10 Power Electronic Converter for HVDC/Facts -- 3.11 Conclusion -- References -- Chapter 4 Recent Advancements in Power Electronics for Modern Power Systems‐Comprehensive Review on DC‐Link Capacitors Concerning Power Density Maximization in Power Converters -- 4.1 Introduction -- 4.2 Applications of Power Electronic Converters -- 4.2.1 Power Electronic Converters in Electric Vehicle Ecosystem. , 4.2.2 Power Electronic Converters in Renewable Energy Resources -- 4.3 Classification of DC‐Link Topologies -- 4.4 Briefing on DC‐Link Topologies -- 4.4.1 Passive Capacitive DC Link -- 4.4.1.1 Filter Type Passive Capacitive DC Links -- 4.4.1.2 Filter Type Passive Capacitive DC Links with Control -- 4.4.1.3 Interleaved Type Passive Capacitive DC Links -- 4.4.2 Active Balancing in Capacitive DC Link -- 4.4.2.1 Separate Auxiliary Active Capacitive DC Links -- 4.4.2.2 Integrated Auxiliary Active Capacitive DC Links -- 4.5 Comparison on DC‐Link Topologies -- 4.5.1 Comparison of Passive Capacitive DC Links -- 4.5.2 Comparison of Active Capacitive DC Links -- 4.5.3 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation -- 4.6 Future and Research Gaps in DC‐Link Topologies with Balancing Techniques -- 4.7 Conclusion -- References -- Chapter 5 Energy Storage Systems for Smart Power Systems -- 5.1 Introduction -- 5.2 Energy Storage System for Low Voltage Distribution System -- 5.3 Energy Storage System Connected to Medium and High Voltage -- 5.4 Energy Storage System for Renewable Power Plants -- 5.4.1 Renewable Power Evacuation Curtailment -- 5.5 Types of Energy Storage Systems -- 5.5.1 Battery Energy Storage System -- 5.5.2 Thermal Energy Storage System -- 5.5.3 Mechanical Energy Storage System -- 5.5.4 Pumped Hydro -- 5.5.5 Hydrogen Storage -- 5.6 Energy Storage Systems for Other Applications -- 5.6.1 Shift in Energy Time -- 5.6.2 Voltage Support -- 5.6.3 Frequency Regulation (Primary, Secondary, and Tertiary) -- 5.6.4 Congestion Management -- 5.6.5 Black Start -- 5.7 Conclusion -- References -- Chapter 6 Real‐Time Implementation and Performance Analysis of Supercapacitor for Energy Storage -- 6.1 Introduction -- 6.2 Structure of Supercapacitor -- 6.2.1 Mathematical Modeling of Supercapacitor. , 6.3 Bidirectional Buck-Boost Converter -- 6.3.1 FPGA Controller -- 6.4 Experimental Results -- 6.5 Conclusion -- References -- Chapter 7 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator -- 7.1 Introduction -- 7.2 Proposed MPPT Control Algorithm -- 7.3 Wind Energy Conversion System -- 7.3.1 Wind Turbine Characteristics -- 7.3.2 Model of PMSG -- 7.4 Fuzzy Logic Command for the MPPT of the PMSG -- 7.4.1 Fuzzification -- 7.4.2 Fuzzy Logic Rules -- 7.4.3 Defuzzification -- 7.5 Results and Discussions -- 7.6 Conclusion -- References -- Chapter 8 A Novel Nearest Neighbor Searching‐Based Fault Distance Location Method for HVDC Transmission Lines -- 8.1 Introduction -- 8.2 Nearest Neighbor Searching -- 8.3 Proposed Method -- 8.3.1 Power System Network Under Study -- 8.3.2 Proposed Fault Location Method -- 8.4 Results -- 8.4.1 Performance Varying Nearest Neighbor -- 8.4.2 Performance Varying Distance Matrices -- 8.4.3 Near Boundary Faults -- 8.4.4 Far Boundary Faults -- 8.4.5 Performance During High Resistance Faults -- 8.4.6 Single Pole to Ground Faults -- 8.4.7 Performance During Double Pole to Ground Faults -- 8.4.8 Performance During Pole to Pole Faults -- 8.4.9 Error Analysis -- 8.4.10 Comparison with Other Schemes -- 8.4.11 Advantages of the Scheme -- 8.5 Conclusion -- Acknowledgment -- References -- Chapter 9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability -- 9.1 Introduction -- 9.2 Power System Models -- 9.2.1 PSS Integrated Single Machine Infinite Bus Power Network -- 9.2.2 PSS‐UPFC Integrated Single Machine Infinite Bus Power Network -- 9.3 Methods -- 9.3.1 Group Method Data Handling Model -- 9.3.2 Extreme Learning Machine Model -- 9.3.3 Neurogenetic Model -- 9.3.4 Multigene Genetic Programming Model -- 9.4 Data Preparation and Model Development. , 9.4.1 Data Production and Processing -- 9.4.2 Machine Learning Model Development -- 9.5 Results and Discussions -- 9.5.1 Eigenvalues and Minimum Damping Ratio Comparison -- 9.5.2 Time‐Domain Simulation Results Comparison -- 9.5.2.1 Rotor Angle Variation Under Disturbance -- 9.5.2.2 Rotor Angular Frequency Variation Under Disturbance -- 9.5.2.3 DC‐Link Voltage Variation Under Disturbance -- 9.6 Conclusions -- References -- Chapter 10 Augmentation of PV‐Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System -- 10.1 Introduction -- 10.2 PV‐Wind Hybrid Power Generation Configuration -- 10.3 Proposed Systems Topologies -- 10.3.1 Structure of PV System -- 10.3.2 The MPPTs Technique -- 10.3.3 NN Predictive Controller Technique -- 10.3.4 ANFIS Technique -- 10.3.5 Training Data -- 10.4 Wind Power Generation Plant -- 10.5 Pitch Angle Control Techniques -- 10.5.1 PI Controller -- 10.5.2 NARMA‐L2 Controller -- 10.5.3 Fuzzy Logic Controller Technique -- 10.6 Proposed DVRs Topology -- 10.7 Proposed Controlling Technique of DVR -- 10.7.1 ANFIS and PI Controlling Technique -- 10.8 Results of the Proposed Topologies -- 10.8.1 PV System Outputs (MPPT Techniques Results) -- 10.8.2 Main PV System outputs -- 10.8.3 Wind Turbine System Outputs (Pitch Angle Control Technique Result) -- 10.8.4 Proposed PMSG Wind Turbine System Output -- 10.8.5 Performance of DVR (Controlling Technique Results) -- 10.8.6 DVRs Performance -- 10.9 Conclusion -- References -- Chapter 11 Deep Reinforcement Learning and Energy Price Prediction -- 11.1 Introduction -- 11.2 Deep and Reinforcement Learning for Decision‐Making Problems in Smart Power Systems -- 11.2.1 Reinforcement Learning -- 11.2.1.1 Markov Decision Process (MDP) -- 11.2.1.2 Value Function and Optimal Policy -- 11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings. , 11.2.3 Deep Reinforcement Learning Algorithms.

Note: Cover -- Title Page -- Copyright -- Contents -- Foreword: Cyanobacteria Biotechnology -- Acknowledgments -- Part I Core Cyanobacteria Processes -- Chapter 1 Inorganic Carbon Assimilation in Cyanobacteria: Mechanisms, Regulation, and Engineering -- 1.1 Introduction - The Need for a Carbon‐Concentrating Mechanism -- 1.2 The Carbon‐Concentrating Mechanism (CCM) Among Cyanobacteria -- 1.2.1 Ci Uptake Proteins/Mechanisms -- 1.2.2 Carboxysome and RubisCO -- 1.3 Regulation of Ci Assimilation -- 1.3.1 Regulation of the CCM -- 1.3.2 Further Regulation of Carbon Assimilation -- 1.3.3 Metabolic Changes and Regulation During Ci Acclimation -- 1.3.4 Redox Regulation of Ci Assimilation -- 1.4 Engineering the Cyanobacterial CCM -- 1.5 Photorespiration -- 1.5.1 Cyanobacterial Photorespiration -- 1.5.2 Attempts to Engineer Photorespiration -- 1.6 Concluding Remarks -- Acknowledgments -- References -- Chapter 2 Electron Transport in Cyanobacteria and Its Potential in Bioproduction -- 2.1 Introduction -- 2.2 Electron Transport in a Bioenergetic Membrane -- 2.2.1 Linear Electron Transport -- 2.2.2 Cyclic Electron Transport -- 2.2.3 ATP Production from Linear and Cyclic Electron Transport -- 2.3 Respiratory Electron Transport -- 2.4 Role of Electron Sinks in Photoprotection -- 2.4.1 Terminal Oxidases -- 2.4.2 Hydrogenase and Flavodiiron Complexes -- 2.4.3 Carbon Fixation and Photorespiration -- 2.4.4 Extracellular Electron Export -- 2.5 Regulating Electron Flux into Different Pathways -- 2.5.1 Electron Flux Through the Plastoquinone Pool -- 2.5.2 Electron Flux Through Fdx -- 2.6 Spatial Organization of Electron Transport Complexes -- 2.7 Manipulating Electron Transport for Synthetic Biology Applications -- 2.7.1 Improving Growth of Cyanobacteria -- 2.7.2 Production of Electrical Power in BPVs -- 2.7.3 Hydrogen Production -- 2.7.4 Production of Industrial Compounds. , 2.8 Future Challenges in Cyanobacterial Electron Transport -- References -- Chapter 3 Optimizing the Spectral Fit Between Cyanobacteria and Solar Radiation in the Light of Sustainability Applications -- 3.1 Introduction -- 3.2 Molecular Basis and Efficiency of Oxygenic Photosynthesis -- 3.3 Fit Between the Spectrum of Solar Radiation and the Action Spectrum of Photosynthesis -- 3.4 Expansion of the PAR Region of Oxygenic Photosynthesis -- 3.5 Modulation and Optimization of the Transparency of Photobioreactors -- 3.6 Full Control of the Light Regime: LEDs Inside the PBR -- 3.7 Conclusions and Prospects -- References -- Part II Concepts in Metabolic Engineering -- Chapter 4 What We Can Learn from Measuring Metabolic Fluxes in Cyanobacteria -- 4.1 Central Carbon Metabolism in Cyanobacteria: An Overview and Renewed Pathway Knowledge -- 4.1.1 Glycolytic Routes Interwoven with the Calvin Cycle -- 4.1.2 Tricarboxylic Acid Cycling -- 4.2 Methodologies for Predicting and Quantifying Metabolic Fluxes in Cyanobacteria -- 4.2.1 Flux Balance Analysis and Genome‐Scale Reconstruction of Metabolic Network -- 4.2.2 13C‐Metabolic Flux Analysis -- 4.2.3 Thermodynamic Analysis and Kinetics Analysis -- 4.3 Cyanobacteria Fluxome in Response to Altered Nutrient Modes and Environmental Conditions -- 4.3.1 Autotrophic Fluxome -- 4.3.2 Photomixotrophic Fluxome -- 4.3.3 Heterotrophic Fluxome -- 4.3.4 Photoheterotrophic Fluxome -- 4.3.5 Diurnal Metabolite Oscillations -- 4.3.6 Nutrient States' Impact on Metabolic Flux -- 4.4 Metabolic Fluxes Redirected in Cyanobacteria for Biomanufacturing Purposes -- 4.4.1 Restructuring the TCA Cycle for Ethylene Production -- 4.4.2 Maximizing Flux in the Isoprenoid Pathway -- 4.4.2.1 Measuring Precursor Pool Size to Evaluate Potential Driving Forces for Isoprenoid Production -- 4.4.2.2 Balancing Intermediates for Increased Pathway Activity. , 4.4.2.3 Kinetic Flux Profiling to Detect Bottlenecks in the Pathway -- 4.5 Synopsis and Future Directions -- Acknowledgments -- References -- Chapter 5 Synthetic Biology in Cyanobacteria and Applications for Biotechnology -- 5.1 Introduction -- 5.2 Getting Genes into Cyanobacteria -- 5.2.1 Transformation -- 5.2.2 Expression from Episomal Plasmids -- 5.2.3 Delivery of Genes to the Chromosome -- 5.3 Basic Synthetic Control of Gene Expression in Cyanobacteria -- 5.3.1 Quantifying Transcription and Translation in Cyanobacteria -- 5.3.2 Controlling Transcription with Synthetic Promoters -- 5.3.2.1 Constitutive Promoters -- 5.3.2.2 Regulated Promoters that Are Sensitive to Added Compounds (Inducible) -- 5.3.2.3 CRISPR Interference for Transcriptional Repression -- 5.3.3 Controlling Translation -- 5.3.3.1 Ribosome Binding Sites (Cis‐Acting) -- 5.3.3.2 Riboswitches (Cis‐Acting) -- 5.3.3.3 Small RNAs (Trans‐Acting) -- 5.4 Exotic Signals for Controlling Expression -- 5.4.1 Oxygen -- 5.4.2 Light Color -- 5.4.3 Cell Density or Growth Phase -- 5.4.4 Engineering Regulators for Altered Sensing Properties: State of the Art -- 5.5 Advanced Regulation: The Near Future -- 5.5.1 Logic Gates and Timing Circuits -- 5.5.2 Orthogonal Transcription Systems -- 5.5.3 Synthetic Biology Solutions to Increase Stability -- 5.5.4 Synthetic Biology Solutions for Cell Separation and Product Recovery -- 5.6 Conclusions -- Acknowledgments -- References -- Chapter 6 Sink Engineering in Photosynthetic Microbes -- 6.1 Introduction -- 6.2 Source and Sink -- 6.3 Regulation of Sink Energy in Plants -- 6.3.1 Sucrose and Other Signaling Carbohydrates -- 6.3.2 Hexokinases -- 6.3.3 Sucrose Non‐fermenting Related Kinases -- 6.3.4 TOR Kinase -- 6.3.5 Engineered Pathways as Sinks in Photosynthetic Microbes -- 6.3.6 Sucrose -- 6.3.7 2,3‐Butanediol -- 6.3.8 Ethylene -- 6.3.9 Glycerol. , 6.3.10 Isobutanol -- 6.3.11 Isoprene -- 6.3.12 Limonene -- 6.3.13 P450, an Electron Sink -- 6.4 What Are Key Source/Sink Regulatory Hubs in Photosynthetic Microbes? -- 6.5 Concluding Remarks -- Acknowledgment -- References -- Chapter 7 Design Principles for Engineering Metabolic Pathways in Cyanobacteria -- 7.1 Introduction -- 7.2 Cofactor Optimization -- 7.2.1 Recruiting NADPH‐Dependent Enzymes Wherever Possible -- 7.2.2 Engineering NADH‐Specific Enzymes to Utilize NADPH -- 7.2.3 Increasing NADH Pool in Cyanobacteria Through Expression of Transhydrogenase -- 7.3 Incorporation of Thermodynamic Driving Force into Metabolic Pathway Design -- 7.3.1 ATP Driving Force in Metabolic Pathways -- 7.3.2 Increasing Substrate Pool Supports the Carbon Flux Toward Products -- 7.3.3 Product Removal Unblocks the Limitations of Product Titer -- 7.4 Development of Synthetic Pathways for Carbon Conserving Photorespiration and Enhanced Carbon Fixation -- 7.5 Summary and Future Perspective on Cyanobacterial Metabolic Engineering -- References -- Chapter 8 Engineering Cyanobacteria for Efficient Photosynthetic Production: Ethanol Case Study -- 8.1 Introduction -- 8.2 Pathway for Ethanol Synthesis in Cyanobacteria -- 8.2.1 Pyruvate Decarboxylase and Type II Alcohol Dehydrogenase -- 8.2.2 Selection of Better Enzymes in the Pdc-AdhII Pathway -- 8.2.3 Systematic Characterization of the PdcZM-Slr1192 Pathway -- 8.3 Selection of Optimal Cyanobacteria "Chassis," Strain for Ethanol Production -- 8.3.1 Synechococcus PCC 6803 and Synechococcus PCC 7942 -- 8.3.2 Synechococcus PCC 7002 -- 8.3.3 Anabaena PCC 7120 -- 8.3.4 Nonconventional Cyanobacteria Species -- 8.4 Metabolic Engineering Strategies Toward More Efficient and Stable Ethanol Production -- 8.4.1 Enhancing the Carbon Flux via Overexpression of Calvin Cycle Enzymes -- 8.4.2 Blocking Pathways that Are Competitive to Ethanol. , 8.4.3 Arresting Biomass Formation -- 8.4.4 Engineering Cofactor Supply -- 8.4.5 Engineering Strategies Guided by In Silico Simulation -- 8.4.6 Stabilizing Ethanol Synthesis Capacity in Cyanobacterial Cell Factories -- 8.5 Exploring the Response in Cyanobacteria to Ethanol -- 8.5.1 Response of Cyanobacterial Cells Toward Exogenous Added Ethanol -- 8.5.2 Response of Cyanobacteria to Endogenous Synthesized Ethanol -- 8.6 Metabolic Engineering Strategies to Facilitate Robust Cultivation Against Biocontaminants -- 8.6.1 Engineering Cyanobacteria Cell Factories to Adapt for Selective Environmental Stresses -- 8.6.2 Engineering Cyanobacteria Cell Factories to Utilize Uncommon Nutrients -- 8.7 Conclusions and Perspectives -- References -- Chapter 9 Engineering Cyanobacteria as Host Organisms for Production of Terpenes and Terpenoids -- 9.1 Terpenoids and Industrial Applications -- 9.2 Terpenoid Biosynthesis in Cyanobacteria -- 9.2.1 Methylerythritol‐4‐Phosphate Pathway -- 9.2.2 Formation of Terpene Backbones -- 9.3 Natural Occurrence and Physiological Roles of Terpenes and Terpenoids in Cyanobacteria -- 9.4 Engineering Cyanobacteria for Terpenoid Production -- 9.4.1 Metabolic Engineering -- 9.4.1.1 Terpene Synthases -- 9.4.1.2 Increasing Supply of Terpene Backbones -- 9.4.1.3 Engineering the Native MEP Pathway -- 9.4.1.4 Implementing the MVA Pathway -- 9.4.1.5 Enhancing Precursor Supply -- 9.4.2 Optimizing Growth Conditions for Production -- 9.4.3 Product Capture and Harvesting -- 9.5 Summary and Outlook -- Acknowledgments -- References -- Chapter 10 Cyanobacterial Biopolymers -- 10.1 Polyhydroxybutryate -- 10.1.1 Introduction -- 10.1.2 PHB Metabolism in Cyanobacteria -- 10.1.3 Industrial Applications of PHB -- 10.1.3.1 Physical Properties of PHB and Its Derivatives -- 10.1.3.2 Biodegradability -- 10.1.3.3 Application of PHB as a Plastic. , 10.1.3.4 Reactor Types.

Note: Cover -- Title Page -- Copyright -- Contents -- About the Series Editors -- Chapter 1 Platform Technology for Therapeutic Protein Production -- 1.1 Introduction -- 1.2 Overall Trend Analysis -- 1.2.1 Mammalian Cell Lines -- 1.2.2 Brief Introduction of Advances and Techniques -- 1.3 General Guidelines for Recombinant Cell Line Development -- 1.3.1 Host Selection -- 1.3.2 Expression Vector -- 1.3.3 Transfection/Selection -- 1.3.4 Clone Selection -- 1.3.4.1 Primary Parameters During Clone Selection -- 1.3.4.2 Clone Screening Technologies -- 1.4 Process Development -- 1.4.1 Media Development -- 1.4.2 Culture Environment -- 1.4.3 Culture Mode (Operation) -- 1.4.4 Scale‐up and Single‐Use Bioreactor -- 1.4.5 Quality Analysis -- 1.5 Downstream Process Development -- 1.5.1 Purification -- 1.5.2 Quality by Design (QbD) -- 1.6 Trends in Platform Technology Development -- 1.6.1 Rational Strategies for Cell Line and Process Development -- 1.6.2 Hybrid Culture Mode and Continuous System -- 1.6.3 Recombinant Human Cell Line Development for Therapeutic Protein Production -- 1.7 Conclusion -- Acknowledgment -- Conflict of Interest -- References -- Chapter 2 Cell Line Development for Therapeutic Protein Production -- 2.1 Introduction -- 2.2 Mammalian Host Cell Lines for Therapeutic Protein Production -- 2.2.1 CHO Cell Lines -- 2.2.2 Human Cell Lines -- 2.2.3 Other Mammalian Cell Lines -- 2.3 Development of Recombinant CHO Cell Lines -- 2.3.1 Expression Systems for CHO Cells -- 2.3.2 Cell Line Development Process Using CHO Cells Based on Random Integration -- 2.3.2.1 Vector Construction -- 2.3.2.2 Transfection and Selection -- 2.3.2.3 Gene Amplification -- 2.3.2.4 Clone Selection -- 2.3.3 Cell Line Development Process Using CHO Cells Based On Site‐Specific Integration -- 2.4 Development of Recombinant Human Cell Lines -- 2.4.1 Necessity for Human Cell Lines. , 2.4.2 Stable Cell Line Development Process Using Human Cell Lines -- 2.5 Important Consideration for Cell Line Development -- 2.5.1 Clonality -- 2.5.2 Stability -- 2.5.3 Quality of Therapeutic Proteins -- 2.6 Conclusion -- References -- Chapter 3 Transient Gene Expression‐Based Protein Production in Recombinant Mammalian Cells -- 3.1 Introduction -- 3.2 Gene Delivery: Transient Transfection Methods -- 3.2.1 Calcium Phosphate‐Based Transient Transfection -- 3.2.2 Electroporation -- 3.2.3 Polyethylenimine‐Based Transient Transfection -- 3.2.4 Liposome‐Based Transient Transfection -- 3.3 Expression Vectors -- 3.3.1 Expression Vector Composition and Preparation -- 3.3.2 Episomal Replication -- 3.3.3 Coexpression Strategies -- 3.4 Mammalian Cell Lines -- 3.4.1 HEK293 Cell‐Based TGE Platforms -- 3.4.2 CHO Cell‐Based TGE Platforms -- 3.4.3 TGE Platforms Using Other Cell Lines -- 3.5 Cell Culture Strategies -- 3.5.1 Culture Media for TGE -- 3.5.2 Optimization of Cell Culture Processes for TGE -- 3.5.3 qp‐Enhancing Factors in TGE‐Based Culture Processes -- 3.5.4 Culture Longevity‐Enhancing Factors in TGE‐Based Culture Processes -- 3.6 Large‐Scale TGE‐Based Protein Production -- 3.7 Concluding Remarks -- References -- Chapter 4 Enhancing Product and Bioprocess Attributes Using Genome‐Scale Models of CHO Metabolism -- 4.1 Introduction -- 4.1.1 Cell Line Optimization -- 4.1.2 CHO Genome -- 4.1.2.1 Development of Genomic Resources of CHO -- 4.1.2.2 Development of Transcriptomics and Proteomics Resources of CHO -- 4.2 Genome‐Scale Metabolic Model -- 4.2.1 What Is a Genome‐Scale Metabolic Model -- 4.2.2 Reconstruction of GEMs -- 4.2.2.1 Knowledge‐Based Construction -- 4.2.2.2 Draft Reconstruction -- 4.2.2.3 Curation of the Reconstruction -- 4.2.2.4 Conversion to a Computational Format -- 4.2.2.5 Model Validation and Evaluation -- 4.3 GEM Application. , 4.3.1 Common Usage and Prediction Capacities of Genome‐Scale Models -- 4.3.2 GEMs as a Platform for Omics Data Integration, Linking Genotype to Phenotype -- 4.3.3 Predicting Nutrient Consumption and Controlling Phenotype -- 4.3.4 Enhancing Protein Production and Bioprocesses -- 4.3.5 Case Studies -- 4.4 Conclusion -- Acknowledgments -- References -- Chapter 5 Genome Variation, the Epigenome and Cellular Phenotypes -- 5.1 Phenotypic Instability in the Context of Mammalian Production Cell Lines -- 5.2 Genomic Instability -- 5.3 Epigenetics -- 5.3.1 DNA Methylation -- 5.3.2 Histone Modifications -- 5.3.3 Downstream Effectors -- 5.3.4 Noncoding RNAs -- 5.4 Control of CHO Cell Phenotype by the Epigenome -- 5.5 Manipulating the Epigenome -- 5.5.1 Global Epigenetic Modification -- 5.5.1.1 Manipulating Global DNA Methylation -- 5.5.1.2 Manipulating Global Histone Acetylation -- 5.5.2 Targeted Epigenetic Modification -- 5.5.2.1 Targeted Histone Modification -- 5.5.2.2 Targeted DNA Methylation -- 5.6 Conclusion and Outlook -- References -- Chapter 6 Adaption of Generic Metabolic Models to Specific Cell Lines for Improved Modeling of Biopharmaceutical Production and Prediction of Processes -- 6.1 Introduction -- 6.1.1 Constraint‐Based Models -- 6.1.2 Limitations of Flux Balance Analysis -- 6.1.2.1 Thermodynamically Infeasible Cycles -- 6.1.2.2 Genetic Regulation -- 6.1.2.3 Limitation of Intracellular Space -- 6.1.2.4 Multiple States in the Solution -- 6.1.2.5 Biological Objective Function -- 6.1.2.6 Kinetics and Metabolite Concentrations -- 6.2 Main Source of Optimization Issues with Large Genome‐Scale Models: Thermodynamically Infeasible Cycles -- 6.2.1 Definition of Thermodynamically Infeasible Fluxes -- 6.2.2 Loops Involving External Exchange Reactions -- 6.2.2.1 Reversible Passive Transporters from Major Facilitator Superfamily (MFS). , 6.2.2.2 Reversible Passive Antiporters from Amino Acid‐Polyamine‐organoCation (APC) Superfamily -- 6.2.2.3 Na+‐linked Transporters -- 6.2.2.4 Transport via Proton Symport -- 6.2.3 Tools to Identify Thermodynamically Infeasible Cycles -- 6.2.3.1 Visualizing Fluxes on a Network Map -- 6.2.3.2 Algorithms Developed -- 6.2.4 Methods Available to Remove Thermodynamically Infeasible Cycles -- 6.2.4.1 Manual Curation -- 6.2.4.2 Software and Algorithms Developed for the Removal of Thermodynamically Infeasible Loops from Flux Distributions -- 6.3 Consideration of Additional Biological Cellular Constraints -- 6.3.1 Genetic Regulation -- 6.3.1.1 Advantages of Considering Gene Regulation in Genome‐Scale Modeling -- 6.3.1.2 Methods Developed to Take into Account a Feedback of FBA on the Regulatory Network -- 6.3.2 Context Specificity -- 6.3.2.1 What Are Context‐Specific Models (CSMs)? -- 6.3.2.2 Methods and Algorithms Developed to Reconstruct Context‐Specific Models (CSMs) -- 6.3.2.3 Performance of CSMs -- 6.3.2.4 Cautions About CSMs -- 6.3.3 Molecular Crowding -- 6.3.3.1 Consequences on the Predictions -- 6.3.3.2 Methods Developed to Account for a Total Enzymatic Capacity into the FBA Framework -- 6.4 Conclusion -- References -- Chapter 7 Toward Integrated Multi‐omics Analysis for Improving CHO Cell Bioprocessing -- 7.1 Introduction -- 7.2 High‐Throughput Omics Technologies -- 7.2.1 Sequencing‐Based Omics Technologies -- 7.2.1.1 Historical Developments of Nucleotide Sequencing Techniques -- 7.2.1.2 Genome Sequencing of CHO Cells -- 7.2.1.3 Transcriptomics of CHO Cells -- 7.2.1.4 Epigenomics of CHO Cells -- 7.2.2 Mass Spectrometry‐Based Omics Technologies -- 7.2.2.1 Mass Spectrometry Techniques -- 7.2.2.2 Proteomics of CHO Cells -- 7.2.2.3 Metabolomics/Lipidomics of CHO Cells -- 7.2.2.4 Glycomics of CHO Cells -- 7.3 Current CHO Multi‐omics Applications. , 7.3.1 Bioprocess Optimization -- 7.3.2 Cell Line Characterization -- 7.3.3 Engineering Target Identification -- 7.4 Future Prospects -- References -- Chapter 8 CRISPR Toolbox for Mammalian Cell Engineering -- 8.1 Introduction -- 8.2 Mechanism of CRISPR/Cas9 Genome Editing -- 8.3 Variants of CRISPR‐RNA‐guided Endonucleases -- 8.3.1 Diversity of CRISPR/Cas Systems -- 8.3.2 Engineered Cas9 Variants -- 8.4 Experimental Design for CRISPR‐mediated Genome Editing -- 8.4.1 Target Site Selection and Design of gRNAs -- 8.4.2 Delivery of CRISPR/Cas9 Components -- 8.5 Development of CRISPR/Cas9 Tools -- 8.5.1 CRISPR/Cas9‐mediated Gene Editing -- 8.5.1.1 Gene Knockout -- 8.5.1.2 Site‐Specific Gene Integration -- 8.5.2 CRISPR/Cas9‐mediated Genome Modification -- 8.5.2.1 Transcriptional Regulation -- 8.5.2.2 Epigenetic Modification -- 8.5.3 RNA Targeting -- 8.6 Genome‐Scale CRISPR Screening -- 8.7 Applications of CRISPR/Cas9 for CHO Cell Engineering -- 8.8 Conclusion -- Acknowledgment -- References -- Chapter 9 CHO Cell Engineering for Improved Process Performance and Product Quality -- 9.1 CHO Cell Engineering -- 9.2 Methods in Cell Line Engineering -- 9.2.1 Overexpression of Engineering Genes -- 9.2.2 Gene Knockout -- 9.2.3 Noncoding RNA‐mediated Gene Silencing -- 9.3 Applications of Cell Line Engineering Approaches in CHO Cells -- 9.3.1 Enhancing Recombinant Protein Production -- 9.3.2 Repression of Cell Death and Acceleration of Growth -- 9.3.3 Modulation of Posttranslational Modifications to Improve Protein Quality -- 9.4 Conclusions -- References -- Chapter 10 Metabolite Profiling of Mammalian Cells -- 10.1 Value of Metabolic Data for the Enhancement of Recombinant Protein Production -- 10.2 Technologies Used in the Generation of Metabolic Data Sets -- 10.2.1 Targeted and Untargeted Metabolic Analysis. , 10.2.2 Analytical Technologies Used in the Generation of Metabolite Profiles.

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