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Rapid media transition for metabolic control analysis of fed batch fermentation processes [Elektronische Ressource] / Hannes Peter Link

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TECHNISCHE UNIVERSITÄT MÜNCHENLehrstuhl für BioverfahrenstechnikRapid Media Transition for Metabolic ControlAnalysis of Fed-Batch Fermentation ProcessesHannes Peter LinkVollständiger Abdruck der von der Fakultät für Maschinenwesen der TechnischenUniversität München zur Erlangung des akademischen Grades einesDoktor-Ingenieursgenehmigten Dissertation.Vorsitzender: Univ.-Prof. Dr.-Ing. habil. Nikolaus A. AdamsPrüfer der Dissertation: 1.Univ.-Prof. Dirk Weuster-Botz2.Prof. Dr. rer. nat. Uwe SauerEidgenössische Technische Hochschule Zürich/ SchweizDie Dissertation wurde am 01.07.2009 bei der Technischen Universität Müncheneingereicht und durch die Fakultät für Maschinenwesenam 18.09.2009 angenommen.As far as the laws of mathematics refer to reality, they are not certain; and asfar as they are certain, they do not refer to reality.Albert EinsteinPhysicist (1879-1955)5PrefaceThis thesis would not be realized without the help and support of many others.My thank goes to Prof. Dr.-Ing. Dirk Weuster-Botz for an excellent supervision and thepossibility to work under the great conditions at his institute. He always encouraged meto develop my own ideas and gave me substantial help at the right time.I am also thankful to Prof. Dr. Uwe Sauer for his agreement to act as my co-examinerand to Prof. Dr.-Ing. Nikolaus Adams to act as chairman of my thesis.

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Published 01 January 2009
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TECHNISCHE UNIVERSITÄT MÜNCHEN
Lehrstuhl für Bioverfahrenstechnik
Rapid Media Transition for Metabolic Control
Analysis of Fed-Batch Fermentation Processes
Hannes Peter Link
Vollständiger Abdruck der von der Fakultät für Maschinenwesen der Technischen
Universität München zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs
genehmigten Dissertation.
Vorsitzender: Univ.-Prof. Dr.-Ing. habil. Nikolaus A. Adams
Prüfer der Dissertation: 1.Univ.-Prof. Dirk Weuster-Botz
2.Prof. Dr. rer. nat. Uwe Sauer
Eidgenössische Technische Hochschule Zürich/ Schweiz
Die Dissertation wurde am 01.07.2009 bei der Technischen Universität München
eingereicht und durch die Fakultät für Maschinenwesen
am 18.09.2009 angenommen.As far as the laws of mathematics refer to reality, they are not certain; and as
far as they are certain, they do not refer to reality.
Albert Einstein
Physicist (1879-1955)5
Preface
This thesis would not be realized without the help and support of many others.
My thank goes to Prof. Dr.-Ing. Dirk Weuster-Botz for an excellent supervision and the
possibility to work under the great conditions at his institute. He always encouraged me
to develop my own ideas and gave me substantial help at the right time.
I am also thankful to Prof. Dr. Uwe Sauer for his agreement to act as my co-examiner
and to Prof. Dr.-Ing. Nikolaus Adams to act as chairman of my thesis.
I would like to thank the semester and diploma students Christoph Gürtner, Johannes
Weinzierl, Li Sun and Bernd Anselment, as well as my succesor Michael Weiner for their
assistance.
ThankstoEzequielFranco-LaraforallthethingsIlearnedfromhimwhenIwasastudent.
Thanks to my colleagues at the department for an excellent working atmosphere, their
help and the good times we shared outside the laboratory.
I am grateful that I have good friends, they know who they are and I appreciate their
support not only during the writing up part of my thesis.
Finally, I want to thank my parents Christa and Walter Link, they support me in every-
thing I do and furthermore they are a great inspiration.Contents I
Contents
1. Introduction 1
2. Thesis Motivation and Objective 2
3. Theoretical Background 4
3.1. Bioreactor processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1.1. Microbial growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1.2. Operation of bioreactors . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1.3. Estimation of specific rates in batch cultivations . . . . . . . . . . . 6
3.1.4. Elemental balances . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2. Metabolic processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.1. Models of metabolic pathways . . . . . . . . . . . . . . . . . . . . . 9
3.2.2. Structural analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2.3. Metabolic flux analysis . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3. Kinetics of enzyme-catalyzed reactions . . . . . . . . . . . . . . . . . . . . 14
3.3.1. Mechanistic models . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.2. Thermokinetic models . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.3. Linear-logarithmic (lin-log) models . . . . . . . . . . . . . . . . . . 16
3.3.4. The power-law formalism . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4. Metabolic Control Analysis (MCA) . . . . . . . . . . . . . . . . . . . . . . 17
3.4.1. Estimation of elasticity coefficients . . . . . . . . . . . . . . . . . . 18
3.4.2. of control coefficients . . . . . . . . . . . . . . . . . . . 19
3.5. Thermodynamic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.6. Central metabolism of Escherichia coli . . . . . . . . . . . . . . . . . . . . 22
3.7. Metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.7.1. Sampling and inactivation of metabolism . . . . . . . . . . . . . . . 26
3.7.2. Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.7.3. Analytical platforms . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.8. Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4. Material and Methods 31
4.1. Micro-organism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2. Cultivation media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3. Cultivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.3.1. Pre-cultures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32II Contents
4.3.2. Fed-batch cultivation . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4. Rapid Media Transition (RMT) . . . . . . . . . . . . . . . . . . . . . . . . 34
4.4.1. RMT experiments in batch mode . . . . . . . . . . . . . . . . . . . 35
4.4.2. RMT experiments in fed-batch mode . . . . . . . . . . . . . . . . . 35
134.5. Preparation of U- C labeled cell extracts . . . . . . . . . . . . . . . . . . . 37
4.6. Analytical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.6.1. Sampling of biomass and extracellular metabolites . . . . . . . . . . 38
4.6.2. HPLC analysis of culture supernatant . . . . . . . . . . . . . . . . . 38
4.6.3. Sampling of intracellular metabolites . . . . . . . . . . . . . . . . . 38
4.6.4. LC-MS analysis of cell extracts . . . . . . . . . . . . . . . . . . . . 41
4.7. Computational methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.7.1. Estimation of extracellular fluxes . . . . . . . . . . . . . . . . . . . 42
4.7.2. Elemental balances . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.7.3. Stoichiometric metabolite balancing (MFA) . . . . . . . . . . . . . 43
4.7.4. Flux Balance Analysis (FBA) . . . . . . . . . . . . . . . . . . . . . 44
4.7.5. Network-embedded thermodynamic (NET) analysis . . . . . . . . . 44
5. Results and Discussion 45
5.1. Measurements of intracellular metabolites . . . . . . . . . . . . . . . . . . 45
5.1.1. Screening of quenching fluids . . . . . . . . . . . . . . . . . . . . . . 45
5.1.2. Leakage of adenylates in a glycerol based quenching fluid . . . . . . 47
5.1.3. Measurements with different sampling protocols . . . . . . . . . . . 52
5.2. Theoretical aspects of steady state analysis . . . . . . . . . . . . . . . . . . 55
5.2.1. Comparing the lin-log approach and the double modulation method 55
5.2.2. Case study: Kinetic rate law of phosphoglucose isomerase (PGI) . . 57
5.2.3. Case study: Reconstituted pathway of Giersch (1995) . . . . . . . . 61
5.2.4. Conclusions from theoretical considerations . . . . . . . . . . . . . . 66
5.3. Steady state experiments with E. coli during a fed-batch cultivation . . . . 67
5.3.1. Fed-Batch cultivation of E. coli . . . . . . . . . . . . . . . . . . . . 67
5.3.2. Rapid media transition in batch operation mode . . . . . . . . . . . 72
5.3.3. Rapid media in fed-batch mode . . . . . . . . . . . . . . 86
5.4. Proteome analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.5. Kinetic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.5.1. Reaction rate and substrate concentration . . . . . . . . . . . . . . 102
5.5.2. Active regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.5.3. The lin-log approach . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.5.4. Redox and energy cofactors . . . . . . . . . . . . . . . . . . . . . . 111Contents III
5.6. Thermodynamic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.7. Metabolic Control Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.7.1. Metabolic network and stoichiometry . . . . . . . . . . . . . . . . . 117
5.7.2. Elasticity coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.7.3. Flux control coefficients . . . . . . . . . . . . . . . . . . . . . . . . 129
6. Conclusions and Future Perspectives 137
7. References 141
8. Abbreviations 155
A. Appendix 158
A.1. Chemicals and equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
A.2. ESI parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
A.3. Elementary matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
A.4. External calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
A.5. IDMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
A.6. Data set of Giersch (1995) . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
A.7. Stoichiometric models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
A.7.1. FBA and MFA models . . . . . . . . . . . . . . . . . . . . . . . . . 165
A.7.2. MCA model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
A.8. Biomass composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
A.9. Proteome analysis (2-DE gels) . . . . . . . . . . . . . . . . . . . . . . . . . 170
A.10.Fluxes in batch RMT experiments . . . . . . . . . . . . . . . . . . . . . . . 177
A.11.Fluxes in fed-batch RMT experiments . . . . . . . . . . . . . . . . . . . . 179
A.11.1.Glucose (aerobic) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
A.11.2.Pyruvate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
A.11.3.Succinate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
A.11.4.Glucose (anaerobic) . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
List of Tables 187
List of Figures 189