Bioreactors

Carl-Fredrik Mandenius is professor of Engineering Biology at Linköping University (Sweden) since 1999 and head of the Division of Biotechnology. He holds a master and PhD degree in Engineering from Lund University. His main research interests are bioprocess engineering, biosensor technology and biotechnology design.

• Preface xvList of Contributors xvii1 Challenges for Bioreactor Design and Operation 1Carl-Fredrik Mandenius1.1 Introduction 11.2 Biotechnology Milestones with Implications on Bioreactor Design 21.3 General Features of Bioreactor Design 81.4 Recent Trends in Designing and Operating Bioreactors 121.5 The Systems Biology Approach 171.6 Using Conceptual Design Methodology 201.7 An Outlook on Challenges for Bioreactor Design and Operation 29References 322 Design and Operation of Microbioreactor Systems for Screening and Process Development 35Clemens Lattermann and Jochen Büchs2.1 Introduction 352.2 Key Engineering Parameters and Properties in Microbioreactor Design and Operation 362.2.1 Specific Power Input 372.2.2 Out-of-Phase Phenomena 402.2.3 Mixing in Microbioreactors 422.2.4 Gas–Liquid Mass Transfer 442.2.4.1 Influence of the Reactor Material 472.2.4.2 Influence of the Viscosity 492.2.5 Influence of Shear Rates 502.2.6 Ventilation in Shaken Microbioreactors 512.2.7 Hydromechanical Stress 522.3 Design of Novel Stirred and Bubble Aerated Microbioreactors 532.4 Robotics for Microbioreactors 542.5 Fed-Batch and Continuous Operation of Microbioreactors 562.5.1 Diffusion-Controlled Feeding of the Microbioreactor 562.5.2 Enzyme Controlled Feeding of the Microbioreactor 582.5.3 Feeding of Continuous Microbioreactors by Pumps 592.6 Monitoring and Control of Microbioreactors 602.6.1 DOT and pH Measurement 622.6.2 Respiratory Activity 632.7 Conclusion 66Terms 67Greek Letters 68Dimensionless Numbers 69List of Abbreviations 69References 693 Bioreactors on a Chip 77Danny van Noort3.1 Introduction 773.2 Advantages of Microsystems 793.2.1 Concentration Gradients 813.3 Scaling Down the Bioreactor to the Microfluidic Format 823.4 Microfabrication Methods for Bioreactors-On-A-Chip 823.4.1 Etching of Silicon/Glass 833.4.2 Soft Lithography 833.4.3 Hot Embossing 843.4.4 Mechanical Fabrication Technique (Or Poor Man’s Microfluidics) 843.4.5 Laser Machining 853.4.6 Thin Metal Layers 863.5 Fabrication Materials 863.5.1 Inorganic Materials 863.5.2 Elastomers and Plastics 873.5.2.1 Elastomers 873.5.2.2 Thermosets 873.5.2.3 Thermoplastics 873.5.3 Hydrogels 883.5.4 Paper 883.6 Integrated Sensors for Key Bioreactor Parameters 893.6.1 Temperature 893.6.2 pH 903.6.3 O2 903.6.4 Co2 903.6.5 Cell Concentration (OD) 903.6.6 Humidity and Environment Stability 913.6.7 Oxygenation 913.7 Model Organisms Applied to BRoCs 913.8 Applications of Microfluidic Bioreactor Chip 923.8.1 A Chemostat BRoC 923.8.2 Using a BRoC as a Single-Cell Chemostat 953.8.3 Mammalian Cells in the Bioreactor on a Chip 963.8.4 Body-on-a-Chip Bioreactors 983.8.5 Organ-on-a-Chip Bioreactor-Like Applications 993.9 Scale Up 1003.10 Conclusion 101Abbreviations 102References 1034 Scalable Manufacture for Cell Therapy Needs 113Qasim A. Rafiq, Thomas R.J. Heathman, Karen Coopman, Alvin W. Nienow, and Christopher J. Hewitt4.1 Introduction 1134.2 Requirements for Cell Therapy 1154.2.1 Quality 1154.2.2 Number of Cells Required 1174.2.3 Anchorage-Dependent Cells 1184.3 Stem Cell Types and Products 1194.4 Paradigms in Cell Therapy Manufacture 1204.4.1 Haplobank 1214.4.2 Autologous Products 1214.4.3 Allogeneic Products 1234.5 Cell Therapy Manufacturing Platforms 1244.5.1 Scale-Out Technology 1254.5.2 Scale-Up Technology 1274.6 Microcarriers and Stirred-Tank Bioreactors 1284.6.1 Overview of Studies Using a Stirred-Tank Bioreactor and Microcarrier System 1304.7 Future Trends for Microcarrier Culture 1364.8 Preservation of Cell Therapy Products 1384.9 Conclusions 139References 1405 Artificial Liver Bioreactor Design 147Katrin Zeilinger and Jörg C. Gerlach5.1 Need for Innovative Liver Therapies 1475.2 Requirements to Liver Support Systems 1475.3 Bioreactor Technologies Used in Clinical Trials 1485.3.1 Artificial Liver Support Systems 1485.3.2 Bioartificial Liver Support Systems 1495.4 Optimization of Bioartificial Liver Bioreactor Designs 1525.5 Improvement of Cell Biology in Bioartificial Livers 1555.6 Bioreactors Enabling Cell Production for Transplantation 1575.7 Cell Sources for Bioartificial Liver Bioreactors 1585.7.1 Primary Liver Cells 1585.7.2 Hepatic Cell Lines 1615.7.3 Stem Cells 1615.8 Outlook 163References 1646 Bioreactors for Expansion of Pluripotent Stem Cells and Their Differentiation to Cardiac Cells 175Robert Zweigerdt, Birgit Andree, Christina Kropp, and Henning Kempf6.1 Introduction 1756.1.1 Requirement for Advanced Cell Therapies for Heart Repair 1756.1.2 Pluripotent Stem Cell–Based Strategies for Heart Repair 1766.2 Culture Technologies for Pluripotent Stem Cell Expansion 1796.2.1 Matrix-Dependent Cultivation in 2D 1796.2.2 Outscaling hPSC Production in 2D 1796.2.3 Hydrogel-Supported Transition to 3D 1826.3 3D Suspension Culture 1826.3.1 Advantages of Using Instrumented Stirred Tank Bioreactors 1826.3.2 Process Inoculation and Passaging Strategies: Cell Clumps Versus Single Cells 1866.3.3 Microcarriers or Matrix-Free Suspension Culture: Pro and Contra 1876.3.4 Optimization and Current Limitations of hPSC Processing in Stirred Bioreactors 1886.4 Autologous Versus Allogeneic Cell Therapies: Practical and Economic Considerations for hPSC Processing 1896.5 Upscaling hPSC Cardiomyogenic Differentiation in Bioreactors 1906.6 Conclusion 192List of Abbreviations 193References 1937 Culturing Entrapped Stem Cells in Continuous Bioreactors 201Rui Tostoes and Paula M. Alves7.1 Introduction 2017.2 Materials Used in Stem Cell Entrapment 2027.3 Synthetic Materials 2037.3.1 Polymers 2037.3.2 Peptides 2077.3.3 Ceramic 2087.4 Natural Materials 2087.4.1 Proteins 2087.4.2 Polysaccharides 2097.4.3 Complex 2117.5 Manufacturing and Regulatory Constraints 2127.6 Mass Transfer in the Entrapment Material 2147.7 Continuous Bioreactors for Entrapped Stem Cell Culture 2167.8 Future Perspectives 220References 2218 Coping with Physiological Stress During Recombinant Protein Production by Bioreactor Design and Operation 227Pau Ferrer and Francisco Valero8.1 Major Physiological Stress Factors in Recombinant Protein Production Processes 2278.1.1 Physiological Constraints Imposed by High-Cell-Density Cultivation Conditions 2278.1.2 Metabolic and Physiologic Constraints Imposed by High-Level Expression of Recombinant Proteins 2298.1.3 Physiological Constraints in Large-Scale Cultures 2308.2 Monitoring Physiological Stress and Metabolic Load as a Tool for Bioprocess Design and Optimization 2308.2.1 Monitoring of Physiological Responses to Recombinant Gene Expression Using Flow Cytometry 2318.2.2 Monitoring of Reporter Metabolites 2338.2.3 Omics Analytical Tools to Assess the Impact of Recombinant Protein Production on Cell Physiology 2338.3 Design and Operation Strategies to Minimize/Overcome Problems Associated with Physiological Stress and Metabolic Load 2418.3.1 Overcoming Overflow Metabolism and Substrate Toxicity 2418.3.2 Improving the Energy and Building Block Supply 2448.3.3 Expression Strategies and Recombinant Gene Transcriptional Tuning for Stress Minimization 2458.4 Bioreactor Design Considerations to Minimize Shear Stress 246Acknowledgments 247References 2489 Design, Applications, and Development of Single-Use Bioreactors 261Nico M.G. Oosterhuis and Stefan Junne9.1 Introduction 2619.2 Design Challenges of Single-Use Bioreactors 2639.2.1 Material Choice and Testing 2639.2.2 Sterilization 2679.2.3 Sensors and Sampling 2679.2.4 Challenges for Scale-Up and Scale-Down of Single-Use Bioreactors 2689.2.4.1 Scalability of Stirred Single-Use Bioreactors 2709.2.4.2 Scalability of Orbital-Shaken Single-Use Bioreactors 2739.2.4.3 Scalability of Wave-Mixed Single-Use Bioreactors 2759.2.4.4 Recent Advances in the Description of the Mass Transfer in SUBs 2769.3 Cell Culture Application 2779.3.1 Wave-Mixed Bioreactors 2779.3.2 Stirred Single-Use Bioreactors 2789.3.3 Orbital-Shaken Single-Use Bioreactors 2809.3.4 Mass Transfer Requirements for Cell Culture 2809.3.5 Perfusion Processes in Single-Use Equipment 2829.3.6 Plant, Phototrophic Algae and Hairy Root Cell Cultivation in Single-Use Bioreactors 2849.4 Microbial Application of Single-Use Bioreactors 2859.5 Outlook 288List of Abbreviations 289References 29010 Computational Fluid Dynamics for Bioreactor Design 295Anurag S. Rathore, Lalita Kanwar Shekhawat, and Varun Loomba10.1 Introduction 29510.2 Multiphase Flows 29810.2.1 Eulerian–Lagrangian Approach 29810.2.2 Euler–Euler Approach 30310.2.3 Volume of Fluid Approach (VOF) 30410.3 Turbulent Flow 30510.3.1 Reynolds Stress Model 30510.3.2 k–ε Model 30610.3.3 Population Balance Model 30610.4 CFD Simulations 30810.4.1 Creation of Bioreactor Geometry 30810.4.2 Meshing of Solution Domain 30810.4.3 Solver 31010.5 Case Studies for Application of CFD in Modeling of Bioreactors 31010.5.1 CaseStudy1:UseofCFDasaToolforEstablishingProcessDesign Space for Mixing in a Bioreactor 31110.5.2 Case Study 2: Prediction of Two-Phase Mass Transfer Coefficient in Stirred Vessel 31310.5.3 Case Study 3: Numerical Modeling of Gas–Liquid Flow in Stirred Tanks 315Summary 318References 31911 Scale-Up and Scale-Down Methodologies for Bioreactors 323Peter Neubauer and Stefan Junne11.1 Introduction 32311.2 Bioprocess Scale-Down Approaches 32411.2.1 A Historical View on the Development of Scale-Down Systems 32411.2.1.1 Phase 1: Initial Studies of Mixing Behavior and Spatial Distribution Phenomena 32511.2.1.2 Phase 2: Evolvement of Scale-Down Systems Based on Computational Fluid Dynamics 32711.2.1.3 Phase 3: Recent Approaches Considering Hybrid Models 32811.2.2 Scale-Up of Bioreactors 33011.2.2.1 Dissolved Oxygen Concentration 33111.2.2.2 Consideration of Similarities and Dimensionless Numbers 33211.2.2.3 Shear Rate 33311.2.2.4 Cell Physiology 33311.2.3 Most Severe Challenges During Scale-Up 33311.3 Characterization of the Large Scale 33411.4 Computational Methods to Describe the Large Scale 33711.5 Scale-Down Experiments and Physiological Responses 34011.5.1 Scale-Down Experiments with Escherichia coli Cultures 34011.5.2 Scale-Down Experiments with Corynebacterium glutamicum Cultures 34311.5.3 Scale-Down Experiments with Bacillus subtilis Cultures 34411.5.4 Scale-Down Experiments with Yeast Cultures 34511.5.5 Scale-Down Experiments with Cell Line Cultures 34611.6 Outlook 346Nomenclature 347References 34712 Integration of Bioreactors with Downstream Steps 355Ajoy Velayudhan and Nigel Titchener-Hooker12.1 Introduction 35512.2 Improvements in Cell-Culture 35812.3 Interactions with Centrifugation Steps 35912.4 Interactions with Filtration Steps 36012.5 Interactions with Chromatographic Steps 36112.6 Integrated Processes 36412.7 Integrated Models 36612.8 Conclusions 367References 36813 Multivariate Modeling for Bioreactor Monitoring and Control 369Jarka Glassey13.1 Introduction 36913.2 Analytical Measurement Methods for Bioreactor Monitoring 37013.2.1 Traditional Measurement Methods 37113.2.2 Advanced Measurement Methods 37213.2.2.1 Spectral Methods 37213.2.2.2 Other Fingerprinting Methods 37413.2.3 Data Characteristics and Challenges for Modeling 37413.3 Multivariate Modeling Approaches 37613.3.1 Feature Extraction and Classification 37613.3.2 Regression Models 37813.4 Case Studies 37913.4.1 Feature Extraction Using PCA 37913.4.2 Prediction of CQAs 38313.5 Conclusions 386Acknowledgments 387References 38714 Soft Sensor Design for Bioreactor Monitoring and Control 391Carl-Fredrik Mandenius and Robert Gustavsson14.1 Introduction 39114.2 The Process Analytical Technology Perspective on Soft Sensors 39214.3 Conceptual Design of Soft Sensors for Bioreactors 39414.4 "Hardware Sensor" Alternatives 39514.5 The Modeling Part of Soft Sensors 40014.6 Strategy for Using Soft Sensors 40214.7 Applications of Soft Sensors in Bioreactors 40314.7.1 Online Fluorescence Spectrometry for Estimating Media Components in a Bioreactor 40414.7.2 Temperature Sensors for Growth Rate Estimation of a Fed-Batch Bioreactor 40514.7.3 Base Titration for Estimating the Growth Rate in a Batch Bioreactor 40714.7.4 Online HPLC for the Estimation of Mixed-Acid Fermentation By-Products 40914.7.5 Electronic Nose and NIR Spectroscopy for Controlling Cholera Toxin Production 41114.8 Concluding Remarks and Outlook 413References 41415 Design-of-Experiments for Development and Optimization of Bioreactor Media 421Carl-Fredrik Mandenius15.1 Introduction 42115.2 Fundamentals of Design-of-Experiments Methodology 42215.2.1 Screening of Factors 42315.2.2 Evaluation of the Experimental Design 42515.2.3 Specific Design-of-Experiments Methods 42915.3 Optimization of Culture Media by Design-of-Experiments 43115.3.1 Media for Production of Metabolites and Proteins in Microbial Cultures 43215.3.2 Media for the Production of Monoclonal Antibodies and Other Proteins in Mammalian Cell Cultures 43815.3.3 Media for Differentiation and Production of Cells 44115.3.4 Other Applications to Media Design 44315.4 Conclusions and Outlook 447References 44816 Operator Training Simulators for Bioreactors 453Volker C. Hass16.1 Introduction 45316.2 Simulators in the Process Industry 45516.3 Training Simulators 45616.3.1 Training Simulator Types 45716.3.1.1 Simulators for "Standard" Processes 45716.3.1.2 Company-Specific Simulators (Taylor-Made Simulators) 45716.3.1.3 Process Automation and Control 45816.3.1.4 Training Simulators in Academic Education 45816.3.2 Training Simulator Purposes 45916.3.2.1 Training of Process Handling 45916.3.2.2 Training Simulators Supporting Engineering Tasks 46116.4 Requirements on Training Simulators 46116.4.1 Precise Simulation of the Chemical, Biological and Physical Events 46216.4.2 Realistic Simulation of Automation and Control Actions 46216.4.3 Real-Time and Accelerated Simulation 46316.4.4 Realistic User Interfaces 46316.4.5 Multipurpose Usage 46316.4.6 Maintainability for User-Friendly Model Updates 46416.4.7 Adaptability to Modified or Different Processes 46416.5 Architecture of Training Simulators 46416.6 Tools and Development Strategies 46616.7 Process Models and Simulation Technology 46816.7.1 Process Models 46816.7.2 Modeling Strategy 47116.7.3 Software Systems for Model Development 47316.7.4 Multiple Use of Models 47316.8 Training Simulator Examples 47416.8.1 Bioreactor Training Simulator 47416.8.2 Anaerobic Digestion Training Simulator 47716.8.3 Bioethanol Plant Simulator 47916.9 Concluding Remarks 482References 484Index 487