Bioreaction Engineering

The Need for Modeling and Control in Biotechnical Processes.- Some Modeling Basics.- Structure and Operation of Biotechnical Plant.- Types and Structure Elements of the Bioreactor.- The Stirred Tank Reactor as an Example for Reactors with Mechanical Energy Input; Reactors with Energy Input by Compressed Air; Membrane Reactors for Bubble Free Aeration; Liquid-Phase; Gas-Phase; Solid-Phase; Biotic Phase; Modes of Operation of a Bioreactor; Batch Cultivation; Fed-Batch Cultivation; Continuous Cultivation; Cultivation with Cell Retention; Repeated or Cyclic Batch or Fed-Batch Cultivation; Aerobic Processes; Anaerobic Processes; Micro-Aerobic Processes;.- References.- A General Principles and Techniques.- 1 Bioreactor Models.- 1.1 Introduction.- 1.2 Interrelations Between the Cells and Their Physical/ Chemical Environment.- 1.3 Stirred Tank (ST) Reactors.- 1.3.1 Description of the Physical Processes in the Reactors.- 1.3.2 Reactor Models.- 1.3.2.1 Model for the Ideal Stirred Tank Reactor.- 1.4 Bubble Column (BC) and Airlift Tower Loop (ATL) Reactors.- 1.4.1 Description of the Physical Processes in the Reactors.- 1.4.2 Flow Models.- 1.4.3 Reactor Models.- 1.5 Conclusions.- References.- 2 Bioprocess Models.- 2.1 Introduction.- 2.1.1 Intracellular Structure Elements.- 2.1.2 Regulation of the Metabolism.- 2.1.2.1 Bottle-Neck Principle.- 2.1.2.2 Optimality Principle.- 2.1.3 Kinetics of Growth and Product Formation.- 2.1.4 General Model Structure for Biotechnical Processes.- 2.1.5 Transport in Microbial Aggregates-.- 2.2 Unstructured Models.- 2.2.1 Kinetics of Growth and Substrate Uptake.- 2.2.2 Endogenous and Maintenance Metabolism.- 2.2.3 Product Formation.- 2.2.4 Other Parameters Influencing Growth.- 2.3 Structured Models.- 2.3.1 The Constitutive Equations.- 2.3.2 Some Applications of Structured Models.- 2.3.3 Cybernetic Models of the Compartment Type.- 2.3.4 Cybernetic Models of the Metabolic Regulator Type.- 2.4 Segregated Models.- 2.4.1 Simple Segregated Models.- 2.4.2 Segregated Models for Physiological Properties.- 2.4.3 A Model for Spatial Segregation by Wall Attachment.- 2.4.4 Segregated Models for Morphological Differentiation, Morphologically Structured Models.- 2.4.5 Segregated Models for Recombinant Organisms.- 2.4.6 Population Balance Models.- References.- 3 Metabolic Flux Analysis.- 3.1 Introduction.- 3.2 Flux Quantification Methods.- 3.2.1 Metabolite Balancing.- 3.2.2 Isotopic-Tracer Techniques.- 3.3 Applications of Metabolic Flux Analysis in the Elucidation of Metabolic Networks.- 3.4 Conclusions.- References.- 4 Accuracy and Reliability of Measured Data.- 4.1 Accuracy and Reliability of Measured Data.- 4.1.1 Accuracy and Precision of Measurements.- 4.1.2 Accuracy.- 4.1.3 Precision.- 4.2 Measurement Reliability.- 4.2.1 Assessment of Measured Data Reliability by Means of a Knowledge-Based System.- 4.2.2 Numerical and Statistical Tests Performed by the Knowledge-Based System.- 4.2.3 Knowledge-Based Module.- 4.2.4 Methodology of the Knowledge-Based System.- 4.3 Conclusions.- References.- 5 Bioprocess Control.- 5.1 Introduction.- 5.2 Bioprocess Control: Basic Concepts.- 5.2.1 Disturbances.- 5.2.2 Stability.- 5.2.2.1 Equilibrium Points.- 5.2.2.2 Stability Analysis.- 5.2.3 Regulation vs Tracking.- 5.3 Bioprocess Control: Basic Ingredients.- 5.3.1 Dynamical Model.- 5.3.2 Feedback.- 5.3.3 Proportional Action.- 5.3.4 Integral Action.- 5.3.5 Feedforward Action.- 5.3.6 Linear Control vs Nonlinear Control.- 5.3.6.1 Linear Control.- 5.3.6.2 Nonlinear Control.- 5.3.7 Adaptive Control vs Non-Adaptive Control.- 5.3.8 Other Approaches.- 5.4 Adaptive Linearizing Control of Bioprocesses.- 5.4.1 General Dynamical Model.- 5.4.1.1 Example 1: Anaerobic Digestion.- 5.4.1.2 Example 2: Animal Cell Culture.- 5.4.2 Model Reduction.- 5.4.2.1 Singular Perturbation Technique for Low Solubility Products.- 5.4.2.2 A General Rule for Order Reduction.- 5.4.2.3 Example 1: Anaerobic Digestion.- 5.4.3 Control Design.- 5.4.3.1 The Monitoring Tool 1: An Asymptotic Observer.- 5.4.3.2 The Monitoring Tool 2: The Parameter Estimation.- 5.4.3.3 The Control Tool: The Adaptive Linearizing Controller.- 5.4.4 Experimental Results.- References.- 6 On-Line Simulation Techniques for Bioreactor Control Development.- 6.1 Introduction.- 6.2 Application.- 6.2.1 Application in the Biochemical Industry.- 6.2.1.1 Plant Set Up.- 6.2.1.2 Economy.- 6.2.1.3 Quality.- 6.2.1.4 Validation.- 6.2.1.5 Complexity.- 6.2.1.6 Training.- 6.2.2 Application in Education.- 6.3 General Architecture of On-Line Simulation Systems.- 6.3.1 Components of Simulation Systems.- 6.3.1.1 Models.- 6.3.1.2 Numerical Methods.- 6.3.1.3 User Interface.- 6.4 Full Scope Model of the Fermentation Process.- 6.5 Submodels of the Bioreactor Process.- 6.5.1 Engineering Components.- 6.5.1.1 Temperature Control System.- 6.5.1.2 Pressure Behavior.- 6.5.1.3 Aeration Behavior.- 6.6 Mass Balances of the Complete Aerobic Growth Process.- 6.6.1 Gas Phase Balances.- 6.6.2 The O2- and CO2-Transfer Equations.- 6.6.3 The kLa Correlation.- 6.6.4 The Liquid Phase Balances.- 6.6.5 The Feed and Titration Vessels System.- 6.7 The pH Model.- 6.8 The Reaction Model.- 6.9 Application Examples of On-Line Simulation Techniques.- 6.9.1 Training with Virtual Reaction Processes.- 6.9.2 Development of a High Cell Density Cultivation.- 6.9.2.1 The µ-Stat Problem.- 6.9.2.2 Observation of Cell-Specific Growth Rate.- 6.9.2.3 Course and Testing of Processing Strategies.- 6.10 Summary.- References.- B Application of General Principles for Reactor Models.- 7 Application of Computational Fluiddynamics (CFD) to Modeling Stirred Tank Bioreactors.- 7.1 Introduction.- 7.2 Modeling and Simulation of Gas/Liquid Flow in Stirred Tank Reactors.- 7.3 Single Phase Flow.- 7.3.1 Transport Equations.- 7.3.2 Simulations and Comparison with Experimental Observations.- 7.4 Multiple Impellers.- 7.5 Gas-Liquid Flow.- 7.5.1 Interfacial Forces.- 7.5.1.1 Drag Force.- 7.5.1.2 Virtual Mass Force.- 7.5.2 Turbulence Model.- 7.5.3 Impeller Model.- 7.5.4 Simulation Results.- 7.6 Application of CFD to Simulations of Mixing and Biotechnical Processes.- 7.6.1 Methodology.- 7.6.2 Simulation of Tracer Experiments.- 7.6.3 Simulation of Substrate Distributions in Fed Batch Fermentations.- 7.6.4 Production of Acetoin/Butanediol with Bacillus subtilis.- References.- 8 Bubble Column Bioreactors.- 8.1 Introduction.- 8.2 Phenomenology.- 8.3 Basic Equations of Motion.- 8.3.1 Fundamental Laws of Fluid Motion.- 8.3.1.1 Mass Conservation.- 8.3.1.2 Conservation of Momentum.- 8.3.1.3 Navier-Stokes Equation System.- 8.3.1.4 Problems with Solving the Equations of Motion.- 8.3.1.5 Numerical Aspects.- 8.3.2 Two-Fluid Model.- 8.3.3 Euler-Lagrange Approach.- 8.3.3.1 Dynamics of the Dispersed Gas-Phase.- 8.3.3.2 Effective Viscosity.- 8.3.3.3 Mass Transfer and Chemical Reaction.- 8.3.3.4 Mixing Due to the Bubble Rise.- 8.3.3.5 Problem of Bubble Coalescence and Redispersion.- 8.3.3.6 Rating of the Euler-Lagrange Representation.- 8.4 Modeling of Particular Aspects of Bubble Column Reactors.- 8.4.1 Velocity Patterns in Bubble Column Reactors.- 8.4.2 Fate of Individual Cells in the Bubble Column Bioreactor.- 8.4.3 Influence of Tilted Columns.- 8.4.4 Oxygen Distribution in a Yeast Fermenter.- 8.5 Conclusions.- References.- C Application of General Principles for Process Models Including Control.- 9 Baker's Yeast Production.- 9.1 Introduction.- 9.1.1 Metabolic Types of Yeast Growth and Regulatory Effects.- 9.1.2 The Asymmetric Propagation of Yeast.- 9.2 Growth Modeling.- 9.2.1 Stoichiometric Model.- 9.2.2 Cybernetic Modeling of Metabolic Regulation.- 9.2.3 Application of the Model for Simulation of Batch, Fed-Batch, and Continuous Cultivations.- 9.3 Growth in Airlift Tower-Loop Reactors.- 9.4 Population Balance Models for the Asymmetric Cell Cycle of Yeast.- 9.4.1 Age Distribution Model of Yeast for Batch and Fed-Batch Processes.- 9.4.2 Age Distribution Model for Data Analysis of Stable Synchronous Oscillations in a Chemostat.- 9.5 Considerations for Process Optimization.- 9.5.1 Optimization of Product Quality.- 9.5.2 Economic Optimization.- 9.6 Automatic Control of Fed-Batch Processes.- 9.6.1 General Remarks.- 9.6.2 Examples for Applied Control Systems.- References.- 10 Modeling of the Beer Fermentation Process.- 10.1 Introduction.- 10.2 Process Optimization.- 10.2.1 Different Knowledge Representation Techniques.- 10.2.1.1 Classical Approach.- 10.2.1.2 Heuristic Approach.- 10.2.1.3 Alternative Methods to Describe the Kinetics.- 10.2.2 State Prediction for Process Optimization.- 10.2.3 Remarks on Hybrid Models.- 10.3 Process Supervision.- 10.3.1 On-Line Measurement are Difficult to Perform.- 10.3.2 Estimation of the Extract Degradation.- 10.3.2.1 Simple Mathematical Model.- 10.3.2.2 Estimation of the Extract Degradation by Artificial Neural Networks.- 10.3.2.3 Hybrid Modeling.- 10.3.3 Kalman Filters, and an Advanced Method for State Estimation.- 10.4 Process Control.- 10.4.1 Controllers that Consider the Dynamics of the Fermenters.- 10.4.2 Reduction of Energy Costs by Temperature Profile Optimization and Control in a Production-Scale Brewery.- 10.5 Conclusion.- 10.5.1 Summary of the Application of the Techniques to Beer Fermentation.- References.- 11 Lactic Acid Production.- 11.1 Introduction.- 11.2 Classification of Lactic Bacteria.- 11.3 Sugar Metabolism of LAB.- 11.3.1 An Example Showing the Functioning of PTS Systems.- 11.3.2 Sugar Uptake by LAB in General.- 11.3.3 Homolactic vs Heterolactic Fermentation.- 11.4 Nitrogen Uptake and Metabolism.- 11.5 Growth Kinetics and Product Formation Kinetics.- 11.6 Lactic Acid Production on the Industrial Scale.- 11.7 Process Technology in Lactic Acid Fermentation.- References.- 12 Control Strategies for High-Cell Density Cultivation ofEscherichia coli.- 12.1 Introduction.- 12.2 Basic Modeling of a Fed-Batch Strategy.- 12.2.1 The Physiological Model.- 12.2.2 The Reactor Model.- 12.3 Growth Rate Control via Substrate Feeding.- 12.4 Growth Rate Control via Oxygen Supply.- 12.5 Considerations for Improved Observation and Control.- 12.6 A Case Study: Kinetics of Acetate Formation and Recombinant Protein Synthesis in HCDC.- References.- 13 ?-Lactam Antibiotics Production withPenicillium chrysogenum and Acremonium chrysogenum.- 13.1 Introduction.- 13.2 Modeling of Penicillin Production.- 13.2.1 Unstructured and Simple Segregated Models.- 13.2.2 Biosynthesis Model of Penicillin V.- 13.2.3 Morphologically Structured Models for Growth of Hyphae.- 13.2.4 Models for Growth of Fungal Pellets.- 13.2.5 Models for Growth of Pellet Populations.- 13.3 Modeling of Cephalosporin C Production.- 13.3.1 Biosynthesis of Cephalosporin.- 13.3.2 Simple Cybernetic Model for Growth and Production on Sugar and Soy-Oil.- 13.3.3 Segregated Models Describing Morphological Differentiation.- 13.4 Process Control and Optimization.- 13.4.1 Problems and Possibilities.- 13.4.2 Example for Dynamic Optimal Control of Fed-Batch Antibiotics Production.- 13.4.3 Economic Optimization for Mycelia Fed-Batch Cultivation.- References.- D Metabolite Flux Analysis, Metabolic Design.- 14 Quantitative Analysis of Metabolic and Signaling Pathways inSaccharomyces cerevisiae.- 14.1 Introduction.- 14.2 Metabolic Flux Analysis.- 14.2.1 Metabolite Balancing in Compartmented Systems.- 14.2.2 Stoichiometric Model.- 14.2.3 Computational Aspects.- 14.2.4 Results.- 14.3 Measurement of Intracellular Compounds.- 14.3.1 Measurement of Intracellular Metabolites and Signals - General Tools.- 14.3.2 Dynamic Response of Metabolite Pools of Glycolysis.- 14.3.3 Dynamics of the Pentose Phosphate Pathway - an Example for in vivo Diagnosis of Intracellular Enzyme Kinetics.- 14.4 Quantitative Analysis of Glucose Induced Signal Transduction.- 14.4.1 Measurement of Intracellular cAMP.- 14.4.2 Measurement of the PFK2 Activity.- 14.4.3 Measurement of F2,6bP.- 14.5 Comparison Between in vitro and in vivo Kinetics - Illustrated for the Enzyme PFK1 (Phosphofructokinase 1).- References.- 15 Metabolic Analysis ofZymomonas mobilis.- 15.1 Introduction.- 15.1.1 Zymomonas mobilis.- 15.1.2 Substrate Spectrum Engineering.- 15.1.3 Purpose.- 15.2 Methods for Metabolic Analysis.- 15.2.1 Introduction.- 15.2.2 Metabolite Pool Determination.- 15.2.2.1 Invasive Approaches.- 15.2.2.2 In vivo Techniques.- 15.2.2.3 Rapid Sampling.- 15.2.3 Metabolic Flux Analysis.- 15.2.3.1 Basic Carbon Balancing.- 15.2.3.2 Metabolite Balancing.- 15.2.3.3 Stable Isotope Labeling.- 15.2.3.4 NMR Magnetization Transfer.- 15.2.4 Metabolic Modeling.- 15.3 Metabolic Analysis of Zymomonas mobilis.- 15.3.1 Introduction.- 15.3.2 Enzymatic Studies.- 15.3.3 Metabolite Pool Measurements.- 15.3.3.1 Overview.- 15.3.3.2 Glycolytic Intermediates.- 15.3.3.3 Sugars.- 15.3.3.4 Ethanol.- 15.3.4 Flux Analyses.- 15.3.4.1 Overview.- 15.3.4.2 Metabolite Balancing.- 15.3.4.3 NMR and Stable Isotope Labeling.- 15.3.5 Summary.- 15.4 Concluding Remarks.- References.- 16 Metabolic Flux Analysis ofCorynebacterium glutamicum.- 16.1 Introduction.- 16.2 Fundamentals of Intracellular Metabolic Flux Analysis in Corynebacterium glutamicum.- 16.2.1 Metabolite Balancing.- 16.2.1.1 Biomass Composition.- 16.2.1.2 Condensed Bioreaction Network.- 16.2.1.3 Approaches to Resolve Network Underdeterminacy.- 16.2.1.4 Theoretical Lysine Selectivity.- 16.2.1.5 Limitations.- 16.2.2 Isotopic Labeling Combined with NMR Spectroscopy.- 16.2.2.1 Isotopic Atom Balancing.- 16.2.2.2 Resolving Glycolysis and Pentose Phosphate Pathway.- 16.2.2.3 Resolving the Parallel Lysine Biosynthetic Pathways.- 16.2.2.4 Resolving Anaplerosis, Citric Acid Cycle, and the Glyoxylate Shunt.- 16.2.2.5 Resolving the Principal Ammonium-Assimilatory Pathways.- 16.2.2.6 Influence of Reaction Reversibility.- 16.2.2.7 Isotopomers.- 16.2.2.8 Sources of Isotopic Measurement Data.- 16.2.2.9 A Comprehensive Modeling Framework.- 16.3 Metabolite Balancing Studies.- 16.3.1 Overview.- 16.3.2 Comparison of Fluxes During Growth and Lysine Production.- 16.3.3 The Search for Yield-Limiting Flux Control Architectures.- 16.3.3.1 The Pyruvate Branch Point.- 16.3.3.2 The Glucose-6-Phosphate Branch Point.- 16.3.4 Growth Rate-Dependent Modulation of the Central Metabolic Fluxes.- 16.3.4.1 Growth on Lactate.- 16.3.4.2 Growth on Glucose.- 16.3.5 Summary.- 16.4 Studies Based on Isotopic Labeling and NMR.- 16.4.1 Overview.- 16.4.2 The Dual Pathways of Lysine Biosynthesis.- 16.4.2.1 Correlation with Lysine Production.- 16.4.2.2 Correlation with Culture Parameters.- 16.4.3 Distinct Metabolic Modes: Growth, Glutamate Production, and Lysine Production.- 16.4.3.1 Comparing Isogenic Strains in Continuous Cultures.- 16.4.3.2 Comparing Different Strains in Batch Cultures.- 16.4.4 Perturbations of the Redox Metabolism.- 16.4.5 The Ammonium-Assimilating Fluxes.- 16.4.6 Summary.- 16.5 Concluding Remarks.- References.- 17 Analysis of Metabolic Fluxes in Mammalian Cells.- 17.1 Applications of Metabolic Flux Analysis in Mammalian Cells.- 17.1.1 Optimization of Protein Production.- 17.1.2 Metabolic Regulation in Transformed Cells.- 17.1.3 Metabolic Regulation in Non-Transformed Cells.- 17.2 Experimental Techniques.- 17.2.1 Direct Measurement of Extracellular Production and Consumption Rates.- 17.2.1.1 Continuous Suspension Culture.- 17.2.1.2 Perfused Culture.- 17.2.1.3 Batch Culture.- 17.2.2 Detection of Isotope Distribution by 13C-NMR.- 17.2.2.1 Measuring Fractional Enrichments.- 17.2.2.2 Measuring Isotopomer Fractions.- 17.2.2.3 In Vivo NMR.- 17.2.2.4 Extraction NMR.- 17.2.3 Radio-Isotope Tracer Studies and Enzyme Activity Assays.- 17.3. Mathematical Descriptions to Quantify Fluxes in Metabolic Models.- 17.3.1 Determining Fluxes Using Cometabolite Measurements.- 17.3.1.1 Solution of the Stoichiometric Matrix.- 17.3.1.2 The Objective Function.- 17.3.2 General Principles of Isotope Balancing.- 17.3.2.1 Steady State Flux Analysis.- 17.3.2.2 The Isotope Balance.- 17.3.3 Least Squares Fitting of the Algebraic Form.- 17.3.4 Atom Mapping/Transition Matrices.- 17.3.5 Isotopomer Mapping Matrices.- 17.3.6 Transient NMR Measurement.- 17.3.7 Errors in the Determination of Fluxes.- 17.3.7.1 Errors in Linear Models.- 17.3.7.2 Errors in Non-linear Models.- 17.4 Biochemical Pathway Model Formulation and Reduction.- 17.4.1 Reduction of Comprehensive Models.- 17.4.2 Pathway Inclusion and Reduction Assumptions.- 17.5 Observed Metabolic Flux Patterns in Mammalian Cells.- 17.5.1 Linkage of Glycolysis to the Tricarboxylic Acid Cycle.- 17.5.2 Reducing Equivalents.- 17.5.3 Glutaminolysis.- 17.5.4 Pyruvate Carboxylase.- 17.5.5 Pentose Phosphate Pathway.- 17.5.6 Tumors as Nitrogen Sinks.- 17.5.7 Oxidative Glycolysis in the Rat Brain.- 17.6 Specific Uses of Flux Pattern Information.- References.