In this expert handbook both the topics and contributors are selected so as to provide an authoritative view of possible applications for this new technology. The result is an up-to-date survey of current challenges and opportunities in the design and operation of bioreactors for high-value products in the biomedical and chemical industries.
Combining theory and practice, the authors explain such leading-edge technologies as single-use bioreactors, bioreactor simulators, and soft sensor monitoring, and discuss novel applications, such as stem cell production, process development, and multi-product reactors, using case studies from academia as well as from industry. A final section addresses the latest trends, including culture media design and systems biotechnology, which are expected to have an increasing impact on bioreactor design.
With its focus on cutting-edge technologies and discussions of future developments, this handbook will remain an invaluable reference for many years to come.
Table of Contents:
Preface XV
List of Contributors XVII
1 Challenges for Bioreactor Design and Operation 1
Carl-Fredrik Mandenius
1.1 Introduction 1
1.2 Biotechnology Milestones with Implications on Bioreactor Design 2
1.3 General Features of Bioreactor Design 8
1.4 Recent Trends in Designing and Operating Bioreactors 12
1.5 The Systems Biology Approach 17
1.6 Using Conceptual Design Methodology 20
1.7 An Outlook on Challenges for Bioreactor Design and Operation 29
References 32
2 Design and Operation of Microbioreactor Systems for Screening and Process Development 35
Clemens Lattermann and Jochen Büchs
2.1 Introduction 35
2.2 Key Engineering Parameters and Properties in Microbioreactor Design and Operation 36
2.2.1 Specific Power Input 37
2.2.2 Out-of-Phase Phenomena 40
2.2.3 Mixing in Microbioreactors 42
2.2.4 Gas–Liquid Mass Transfer 44
2.2.4.1 Influence of the Reactor Material 47
2.2.4.2 Influence of the Viscosity 49
2.2.5 Influence of Shear Rates 50
2.2.6 Ventilation in Shaken Microbioreactors 51
2.2.7 Hydromechanical Stress 52
2.3 Design of Novel Stirred and Bubble Aerated Microbioreactors 53
2.4 Robotics for Microbioreactors 54
2.5 Fed-Batch and Continuous Operation of Microbioreactors 56
2.5.1 Diffusion-Controlled Feeding of the Microbioreactor 56
2.5.2 Enzyme Controlled Feeding of the Microbioreactor 58
2.5.3 Feeding of Continuous Microbioreactors by Pumps 59
2.6 Monitoring and Control of Microbioreactors 60
2.6.1 DOT and pH Measurement 62
2.6.2 Respiratory Activity 63
2.7 Conclusion 66
Terms 67
Greek Letters 68
Dimensionless Numbers 69
List of Abbreviations 69
References 69
3 Bioreactors on a Chip 77
Danny van Noort
3.1 Introduction 77
3.2 Advantages of Microsystems 79
3.2.1 Concentration Gradients 81
3.3 Scaling Down the Bioreactor to the Microfluidic Format 82
3.4 Microfabrication Methods for Bioreactors-On-A-Chip 82
3.4.1 Etching of Silicon/Glass 83
3.4.2 Soft Lithography 83
3.4.3 Hot Embossing 84
3.4.4 Mechanical Fabrication Technique (Or Poor Man’s Microfluidics) 84
3.4.5 Laser Machining 85
3.4.6 Thin Metal Layers 86
3.5 Fabrication Materials 86
3.5.1 Inorganic Materials 86
3.5.2 Elastomers and Plastics 87
3.5.2.1 Elastomers 87
3.5.2.2 Thermosets 87
3.5.2.3 Thermoplastics 87
3.5.3 Hydrogels 88
3.5.4 Paper 88
3.6 Integrated Sensors for Key Bioreactor Parameters 89
3.6.1 Temperature 89
3.6.2 pH 90
3.6.3 O2 90
3.6.4 CO2 90
3.6.5 Cell Concentration (OD) 90
3.6.6 Humidity and Environment Stability 91
3.6.7 Oxygenation 91
3.7 Model Organisms Applied to BRoCs 91
3.8 Applications of Microfluidic Bioreactor Chip 92
3.8.1 A Chemostat BRoC 92
3.8.2 Using a BRoC as a Single-Cell Chemostat 95
3.8.3 Mammalian Cells in the Bioreactor on a Chip 96
3.8.4 Body-on-a-Chip Bioreactors 98
3.8.5 Organ-on-a-Chip Bioreactor-Like Applications 99
3.9 Scale Up 100
3.10 Conclusion 101
Abbreviations 102
References 103
4 Scalable Manufacture for Cell Therapy Needs 113
Qasim A. Rafiq, Thomas R.J. Heathman, Karen Coopman, AlvinW. Nienow, and Christopher J. Hewitt
4.1 Introduction 113
4.2 Requirements for CellTherapy 115
4.2.1 Quality 115
4.2.2 Number of Cells Required 117
4.2.3 Anchorage-Dependent Cells 118
4.3 Stem Cell Types and Products 119
4.4 Paradigms in Cell Therapy Manufacture 120
4.4.1 Haplobank 121
4.4.2 Autologous Products 121
4.4.3 Allogeneic Products 123
4.5 CellTherapy Manufacturing Platforms 124
4.5.1 Scale-Out Technology 125
4.5.2 Scale-Up Technology 127
4.6 Microcarriers and Stirred-Tank Bioreactors 128
4.6.1 Overview of Studies Using a Stirred-Tank Bioreactor and Microcarrier System 130
4.7 Future Trends for Microcarrier Culture 136
4.8 Preservation of CellTherapy Products 138
4.9 Conclusions 139
References 140
5 Artificial Liver Bioreactor Design 147
Katrin Zeilinger and Jörg C. Gerlach
5.1 Need for Innovative LiverTherapies 147
5.2 Requirements to Liver Support Systems 147
5.3 Bioreactor Technologies Used in Clinical Trials 148
5.3.1 Artificial Liver Support Systems 148
5.3.2 Bioartificial Liver Support Systems 149
5.4 Optimization of Bioartificial Liver Bioreactor Designs 152
5.5 Improvement of Cell Biology in Bioartificial Livers 155
5.6 Bioreactors Enabling Cell Production for Transplantation 157
5.7 Cell Sources for Bioartificial Liver Bioreactors 158
5.7.1 Primary Liver Cells 158
5.7.2 Hepatic Cell Lines 161
5.7.3 Stem Cells 161
5.8 Outlook 163
References 164
6 Bioreactors for Expansion of Pluripotent Stem Cells and Their Differentiation to Cardiac Cells 175
Robert Zweigerdt, Birgit Andree, Christina Kropp, and Henning Kempf
6.1 Introduction 175
6.1.1 Requirement for Advanced Cell Therapies for Heart Repair 175
6.1.2 Pluripotent Stem Cell–Based Strategies for Heart Repair 176
6.2 Culture Technologies for Pluripotent Stem Cell Expansion 179
6.2.1 Matrix-Dependent Cultivation in 2D 179
6.2.2 Outscaling hPSC Production in 2D 179
6.2.3 Hydrogel-Supported Transition to 3D 182
6.3 3D Suspension Culture 182
6.3.1 Advantages of Using Instrumented Stirred Tank Bioreactors 182
6.3.2 Process Inoculation and Passaging Strategies: Cell Clumps Versus Single Cells 186
6.3.3 Microcarriers or Matrix-Free Suspension Culture: Pro and Contra 187
6.3.4 Optimization and Current Limitations of hPSC Processing in Stirred Bioreactors 188
6.4 Autologous Versus Allogeneic Cell Therapies: Practical and Economic Considerations for hPSC Processing 189
6.5 Upscaling hPSC Cardiomyogenic Differentiation in Bioreactors 190
6.6 Conclusion 192
List of Abbreviations 193
References 193
7 Culturing Entrapped StemCells in Continuous Bioreactors 201
Rui Tostoes and Paula M. Alves
7.1 Introduction 201
7.2 Materials Used in Stem Cell Entrapment 202
7.3 Synthetic Materials 203
7.3.1 Polymers 203
7.3.2 Peptides 207
7.3.3 Ceramic 208
7.4 Natural Materials 208
7.4.1 Proteins 208
7.4.2 Polysaccharides 209
7.4.3 Complex 211
7.5 Manufacturing and Regulatory Constraints 212
7.6 Mass Transfer in the Entrapment Material 214
7.7 Continuous Bioreactors for Entrapped Stem Cell Culture 216
7.8 Future Perspectives 220
References 221
8 Coping with Physiological Stress During Recombinant Protein Production by Bioreactor Design and Operation 227
Pau Ferrer and Francisco Valero
8.1 Major Physiological Stress Factors in Recombinant Protein Production Processes 227
8.1.1 Physiological Constraints Imposed by High-Cell-Density Cultivation Conditions 227
8.1.2 Metabolic and Physiologic Constraints Imposed by High-Level Expression of Recombinant Proteins 229
8.1.3 Physiological Constraints in Large-Scale Cultures 230
8.2 Monitoring Physiological Stress and Metabolic Load as a Tool for Bioprocess Design and Optimization 230
8.2.1 Monitoring of Physiological Responses to Recombinant Gene Expression Using Flow Cytometry 231
8.2.2 Monitoring of Reporter Metabolites 233
8.2.3 Omics Analytical Tools to Assess the Impact of Recombinant Protein Production on Cell Physiology 233
8.3 Design and Operation Strategies to Minimize/Overcome Problems Associated with Physiological Stress and Metabolic Load 241
8.3.1 Overcoming Overflow Metabolism and Substrate Toxicity 241
8.3.2 Improving the Energy and Building Block Supply 244
8.3.3 Expression Strategies and Recombinant Gene Transcriptional Tuning for Stress Minimization 245
8.4 Bioreactor Design Considerations to Minimize Shear Stress 246
Acknowledgments 247
References 248
9 Design, Applications, and Development of Single-Use Bioreactors 261
Nico M.G. Oosterhuis and Stefan Junne
9.1 Introduction 261
9.2 Design Challenges of Single-Use Bioreactors 263
9.2.1 Material Choice and Testing 263
9.2.2 Sterilization 267
9.2.3 Sensors and Sampling 267
9.2.4 Challenges for Scale-Up and Scale-Down of Single-Use Bioreactors 268
9.2.4.1 Scalability of Stirred Single-Use Bioreactors 270
9.2.4.2 Scalability of Orbital-Shaken Single-Use Bioreactors 273
9.2.4.3 Scalability ofWave-Mixed Single-Use Bioreactors 275
9.2.4.4 Recent Advances in the Description of the Mass Transfer in SUBs 276
9.3 Cell Culture Application 277
9.3.1 Wave-Mixed Bioreactors 277
9.3.2 Stirred Single-Use Bioreactors 278
9.3.3 Orbital-Shaken Single-Use Bioreactors 280
9.3.4 Mass Transfer Requirements for Cell Culture 280
9.3.5 Perfusion Processes in Single-Use Equipment 282
9.3.6 Plant, Phototrophic Algae and Hairy Root Cell Cultivation in Single-Use Bioreactors 284
9.4 Microbial Application of Single-Use Bioreactors 285
9.5 Outlook 288
List of Abbreviations 289
References 290
10 Computational Fluid Dynamics for Bioreactor Design 295
Anurag S. Rathore, Lalita Kanwar Shekhawat, and Varun Loomba
10.1 Introduction 295
10.2 Multiphase Flows 298
10.2.1 Eulerian–Lagrangian Approach 298
10.2.2 Euler–Euler Approach 303
10.2.3 Volume of Fluid Approach (VOF) 304
10.3 Turbulent Flow 305
10.3.1 Reynolds Stress Model 305
10.3.2 k–𝜀 Model 306
10.3.3 Population Balance Model 306
10.4 CFD Simulations 308
10.4.1 Creation of Bioreactor Geometry 308
10.4.2 Meshing of Solution Domain 308
10.4.3 Solver 310
10.5 Case Studies for Application of CFD inModeling of Bioreactors 310
10.5.1 Case Study 1:Use of CFDas a Tool for Establishing Process Design Space for Mixing in a Bioreactor 311
10.5.2 Case Study 2: Prediction of Two-Phase Mass Transfer Coefficient in Stirred Vessel 313
10.5.3 Case Study 3: Numerical Modeling of Gas–Liquid Flow in Stirred Tanks 315
Summary 318
References 319
11 Scale-Up and Scale-Down Methodologies for Bioreactors 323
Peter Neubauer and Stefan Junne
11.1 Introduction 323
11.2 Bioprocess Scale-Down Approaches 324
11.2.1 A Historical View on the Development of Scale-Down Systems 324
11.2.1.1 Phase 1: Initial Studies of Mixing Behavior and Spatial Distribution Phenomena 325
11.2.1.2 Phase 2: Evolvement of Scale-Down Systems Based on Computational Fluid Dynamics 327
11.2.1.3 Phase 3: Recent Approaches Considering Hybrid Models 328
11.2.2 Scale-Up of Bioreactors 330
11.2.2.1 Dissolved Oxygen Concentration 331
11.2.2.2 Consideration of Similarities and Dimensionless Numbers 332
11.2.2.3 Shear Rate 333
11.2.2.4 Cell Physiology 333
11.2.3 Most Severe Challenges During Scale-Up 333
11.3 Characterization of the Large Scale 334
11.4 Computational Methods to Describe the Large Scale 337
11.5 Scale-Down Experiments and Physiological Responses 340
11.5.1 Scale-Down Experiments with Escherichia coli Cultures 340
11.5.2 Scale-Down Experiments with Corynebacterium glutamicum Cultures 343
11.5.3 Scale-Down Experiments with Bacillus subtilis Cultures 344
11.5.4 Scale-Down Experiments with Yeast Cultures 345
11.5.5 Scale-Down Experiments with Cell Line Cultures 346
11.6 Outlook 346
Nomenclature 347
References 347
12 Integration of Bioreactors with Downstream Steps 355
Ajoy Velayudhan and Nigel Titchener-Hooker
12.1 Introduction 355
12.2 Improvements in Cell-Culture 358
12.3 Interactions with Centrifugation Steps 359
12.4 Interactions with Filtration Steps 360
12.5 Interactions with Chromatographic Steps 361
12.6 Integrated Processes 364
12.7 Integrated Models 366
12.8 Conclusions 367
References 368
13 MultivariateModeling for Bioreactor Monitoring and Control 369
Jarka Glassey
13.1 Introduction 369
13.2 Analytical Measurement Methods for Bioreactor Monitoring 370
13.2.1 Traditional Measurement Methods 371
13.2.2 Advanced Measurement Methods 372
13.2.2.1 Spectral Methods 372
13.2.2.2 Other FingerprintingMethods 374
13.2.3 Data Characteristics and Challenges for Modeling 374
13.3 Multivariate Modeling Approaches 376
13.3.1 Feature Extraction and Classification 376
13.3.2 Regression Models 378
13.4 Case Studies 379
13.4.1 Feature Extraction Using PCA 379
13.4.2 Prediction of CQAs 383
13.5 Conclusions 386
Acknowledgments 387
References 387
14 Soft Sensor Design for Bioreactor Monitoring and Control 391
Carl-Fredrik Mandenius and Robert Gustavsson
14.1 Introduction 391
14.2 The Process Analytical Technology Perspective on Soft Sensors 392
14.3 Conceptual Design of Soft Sensors for Bioreactors 394
14.4 "Hardware Sensor" Alternatives 395
14.5 The Modeling Part of Soft Sensors 400
14.6 Strategy for Using Soft Sensors 402
14.7 Applications of Soft Sensors in Bioreactors 403
14.7.1 Online Fluorescence Spectrometry for Estimating Media Components in a Bioreactor 404
14.7.2 Temperature Sensors for Growth Rate Estimation of a Fed-Batch Bioreactor 405
14.7.3 Base Titration for Estimating the Growth Rate in a Batch Bioreactor 407
14.7.4 Online HPLC for the Estimation of Mixed-Acid Fermentation By-Products 409
14.7.5 Electronic Nose and NIR Spectroscopy for Controlling Cholera Toxin Production 411
14.8 Concluding Remarks and Outlook 413
References 414
15 Design-of-Experiments for Development and Optimization of Bioreactor Media 421
Carl-Fredrik Mandenius
15.1 Introduction 421
15.2 Fundamentals of Design-of-Experiments Methodology 422
15.2.1 Screening of Factors 423
15.2.2 Evaluation of the Experimental Design 425
15.2.3 Specific Design-of-Experiments Methods 429
15.3 Optimization of Culture Media by Design-of-Experiments 431
15.3.1 Media for Production of Metabolites and Proteins in Microbial Cultures 432
15.3.2 Media for the Production of Monoclonal Antibodies and Other Proteins in Mammalian Cell Cultures 438
15.3.3 Media for Differentiation and Production of Cells 441
15.3.4 Other Applications to Media Design 443
15.4 Conclusions and Outlook 447
References 448
16 Operator Training Simulators for Bioreactors 453
Volker C. Hass
16.1 Introduction 453
16.2 Simulators in the Process Industry 455
16.3 Training Simulators 456
16.3.1 Training Simulator Types 457
16.3.1.1 Simulators for "Standard" Processes 457
16.3.1.2 Company-Specific Simulators (Taylor-Made Simulators) 457
16.3.1.3 Process Automation and Control 458
16.3.1.4 Training Simulators in Academic Education 458
16.3.2 Training Simulator Purposes 459
16.3.2.1 Training of Process Handling 459
16.3.2.2 Training Simulators Supporting Engineering Tasks 461
16.4 Requirements on Training Simulators 461
16.4.1 Precise Simulation of the Chemical, Biological and Physical Events 462
16.4.2 Realistic Simulation of Automation and Control Actions 462
16.4.3 Real-Time and Accelerated Simulation 463
16.4.4 Realistic User Interfaces 463
16.4.5 Multipurpose Usage 463
16.4.6 Maintainability for User-Friendly Model Updates 464
16.4.7 Adaptability to Modified or Different Processes 464
16.5 Architecture of Training Simulators 464
16.6 Tools and Development Strategies 466
16.7 Process Models and Simulation Technology 468
16.7.1 Process Models 468
16.7.2 Modeling Strategy 471
16.7.3 Software Systems for Model Development 473
16.7.4 Multiple Use of Models 473
16.8 Training Simulator Examples 474
16.8.1 Bioreactor Training Simulator 474
16.8.2 Anaerobic Digestion Training Simulator 477
16.8.3 Bioethanol Plant Simulator 479
16.9 Concluding Remarks 482
References 484
Index 487
About the Author :
Carl-Fredrik Mandenius is professor of Engineering Biology at Linkoping 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.
