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Chapter 1 Introduction..............................................................1Chapter 2 Bioprocesses and their modelling...........................7 Contents

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Contents

Chapter 1 Introduction ...1

Chapter 2 Bioprocesses and their modelling ...7

2.1 Bioprocesses ...7

2.1.1 Cells and cell cultures ... 8

2.1.2 Bioreactor... 8

2.2 Bioprocess modelling...10

2.2.1 General characteristics of models ... 10

2.2.2 Characteristics of bioprocess models ... 14

2.2.3 Bioprocess models ... 15

2.2.3.1 First principles models ... 16

2.2.3.1.1 Transport phenomena... 19

2.2.3.1.2 Yield coefficient matrix ... 21

2.2.3.1.3 Kinetics ... 22

2.2.3.2 Black box models ... 26

2.2.3.2.1 Basic neural network architectures... 27

2.2.3.2.2 MultiLayer Perceptron ... 29

2.2.3.2.3 Radial Basis Function Network... 31

2.2.3.2.4 Artificial Neural Networks and bioprocess modelling... 31

2.2.3.3 Hybrid models ... 32

2.3 Parameter estimation ...34

2.3.1 Identifiability... 34

2.3.2 Optimization criteria ... 36

2.3.2.1 Maximum Likelihood Criterion ... 36

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vi

2.3.2.2 Markov criterion... 40

2.3.2.3 Least squares criterion ... 42

2.3.3 Optimization algorithms ... 43

2.3.3.1 First order methods... 43

2.3.3.2 Second order methods ... 44

2.3.3.3 Zero order methods... 45

2.3.3.4 Conclusion... 46

2.3.4 Validation... 47

2.3.5 Particular case: Artificial neural networks ... 47

2.3.5.1 Learning algorithms... 48

2.3.5.2 Optimization criterion and parameter reduction... 49

2.3.5.3 Conclusion... 50

2.4 Conclusion...51

Appendix 2.1 Covariance matrix of the regularized least squares estimator ...52

Chapter 3 First principles models of bioprocesses versus Hybrid neural network models ...55

3.1 Systematic parameter identification methods...57

3.1.1 Systematic identification of the pseudo-stoichiometry ... 58

3.1.1.1 Identification of the pseudo-stoichiometry independently of the kinetics ... 59

3.1.1.2 Systematic generation of C-identifiable reaction schemes .. 61

3.1.1.3 Maximum likelihood estimation of the pseudo-stoichiometry ... 66

3.1.2 First principles kinetic parameter identification... 71

3.1.2.1 Linear first estimation... 72

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3.1.2.2 Markov estimation... 73

3.1.2.3 Covariance matrix and parameter reduction... 76

3.1.3 Neural Network parameter identification... 77

3.1.3.1 Linear identification of the output parameters... 79

3.1.3.2 Nonlinear estimations... 80

3.1.3.3 Remarks... 83

3.1.4 Optimization algorithms ... 84

3.2 Case studies...85

3.2.1 Simulated databases ... 86

3.2.2 Real industrial cell culture ... 88

3.3 Comparison of macroscopic models ...89

3.3.1 Reaction scheme selection ... 90

3.3.1.1 Component degradation and mass balances ... 91

3.3.1.2 Decoupled identification method... 92

3.3.1.3 Lysis rate and pseudo-stoichiometry estimation... 94

3.3.1.4 Industrial bioprocess pseudo-stoichiometry ... 96

3.3.1.5 Conclusion... 101

3.3.2 Hybrid results and discussion... 102

3.3.3 Macroscopic comparison ... 107

3.3.4 Conclusion ... 109

3.4 Conclusion...111

Appendix 3.1 Covariance matrix of the regularized Markov estimator ...113

Appendix 3.2 Macroscopic model comparison: visual results ...117

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viii

Chapter 4 New kinetic model structure ...125

4.1 Generalized kinetic model structure...127

4.2 Systematic identification procedure ...129

4.3 Covariance matrix and parameter reduction ...131

4.4 Case studies...132

4.4.1 Simulated case studies ... 132

4.4.1.1 Simple microbial growth ... 133

4.4.1.2 Simulated animal cell culture ... 134

4.4.2 Pilot yeast cultures ... 136

4.4.2.1 The main phenomena in a yeast culture ... 137

4.4.2.2 Experimental protocol and measurement analysis ... 139

4.4.2.3 Experiments and measurement errors... 140

4.5 Model identification – Results and discussion ...142

4.5.1 Reaction scheme and pseudo-stoichiometry ... 142

4.5.2 Kinetic identification - Results and discussion ... 144

4.6 Conclusion...156

Appendix 4.1 Feeding profiles of the fed-batch yeast cultures ...157

Appendix 4.2 Model of Sonnleitner and Käppeli (1986) ...159

Appendix 4.3 Model comparison: visual results ...167

Chapter 5 Protein mutations and free energy changes...179

5.1 Protein Structure...181

5.2 Native conformation and folding free energy ...187

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5.3 Native conformation prediction and energetic functions...188

5.4 Protein mutations and stability changes...190

5.4.1 Stability prediction methods ... 191

5.4.2 PoPMuSiC... 194

5.4.2.1 Data-derived potentials... 195

5.4.2.2 Selected potentials and Protein representation ... 197

5.4.2.3 Folding free energy changes... 201

5.4.2.4 PoPMuSiC performances and limitations... 203

5.5 PoPMuSiC improvements ...204

5.5.1 Mean force potentials... 206

5.5.1.1 Local potentials ... 206

5.5.1.2 Distance potentials... 211

5.5.1.3 Corrections and selected potentials ... 215

5.5.2 Artificial neural networks and stability changes prediction ... 218

5.5.2.1 Radial basis function network ... 220

5.5.2.2 MultiLayer Perceptron... 225

5.5.2.3 Optimization algorithms ... 228

5.5.3 Results and discussion ... 228

5.5.3.1 RBF versus MLP ... 230

5.5.3.2 Suppression of potentials based on four descriptors... 239

5.5.3.3 Outlier suppression... 248

5.5.3.3.1 RBF based on the whole set of potentials ... 248

5.5.3.3.2 MLP based on the whole set of potentials... 252

5.5.3.3.3 RBF without potentials based on 4 descriptors ... 255

5.5.3.3.4 MLP without potentials based on 4 descriptors ... 258

5.5.3.3.5 Discussion ... 261

5.5.3.4 Constrained MLP model... 263

5.5.3.5 Final solution ... 269

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5.6 Conclusion...275

Appendix 5.1: Database DB DB DB DB

1403

...278

Appendix 5.2 ProTherm and learning sets...280

Appendix 5.3: Outlier suppression...300

Appendix 5.4: Constrained MLP model ...309

Appendix 5.5: Final MLP model ...313

Chapter 6 Conclusion and perspectives ...317

Bibliography...323

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