Bioreaction Engineering

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