Title Details

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