Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

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Bibliography K.J. Versyck, J.E. Claes, and J.F. Van Impe. Practical identification of unstructured growth-kinetics by application of optimal experimental-design. Biotechnology Progress, 13(5):524–531, 1997. E. Walter and L. Pronzato. Qualitative and quantitativae experiment design for phenomenological models- a survey. Automatica, 26(2):195–213, 1990. V.F. Wendisch. Genome-wide expression analysis in Corynebacterium glutamicum using DNA microarrays. Journal of Biotechnology, 104(1-3):273–285, 2003. D. WeusterBotz, R. Kelle, M. Frantzen, and C. Wandrey. Substrate controlled fed-batch production of L-lysine with Corynebacterium glutamicum. Biotechnology Progress, 13 (4):387–393, 1997. W. Wiechert. Interactive Datenanalyse bei biotechnischen Prozessdaten. PhD thesis, Universität Bonn, 1990. J.A. Williams. Keys to bioreactor selections. Chemical Engineering Progress, 98(3): 34–41, 2002. K.W. Wirtz. A generic model for changes in microbial kinetic coefficients. Journal of Biotechnology, 97(2):147–162, 2002. C. Wittmann and E. Heinzle. Application of MALDI-TOF MS to lysine-producing Corynebacterium glutamicum - a novel approach for metabolic flux analysis. European Journal of Biochemistry, 268(8):2441–2455, 2001. C. Wittmann, H.M. Kim, G. John, and E. Heinzle. Characterization and application of an optical sensor for quantification of dissolved O2 in shake-flasks. Biotechnology Letters, 25(5):377–380, 2003. C. Wittmann, T. Yang, I. Kochems, and E. Heinzle. Dynamic respiratory measurements of Corynebacterium glutamicum using membrane mass spectrometry. Journal of Microbiology and Biotechnology, 11(1):40–49, 2001. W.S. Yip and T.E. Marlin. The effect of model fidelity on real-time optimization performance. Computers & Chemical Engineering, 28(1-2):267–280, 2004. J.Q.Yuan,S.R.Guo,K.Schügerl, and K.H. Bellgardt. Profit optimization for mycelia fed-batch cultivation. Journal of Biotechnology, 54(3):175–193, 1997. Y.H. Zhu, T. Rajalahti, and S. Linko. Application of neural networks to lysine production. Chemical Engineering Journal, 62:207–214, 1996. 197
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Lebenswissenschaften Life Sciences
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Forschungszentrum Jülich GmbH Inst
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’Therefore, mathematical modeling
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Contents 3.4. Application: L-Valine
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Contents 4
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1. Introduction need for improved m
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2. Theory In a batch process, all s
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2. Theory 1997; Goncalves et al., 2
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2. Theory For batch fermentations,
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2. Theory with Yps being the yield
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2. Theory Inhibition Some catalytic
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2. Theory The method of least squar
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2. Theory evaluation can be made by
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2. Theory We define the measured or
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2. Theory build in the model. An ob
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2. Theory nations, the researcher i
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2. Theory W = J · Covθ · J T (2.
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2. Theory arg max ξ � m� m�
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2. Theory with all measured values
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2. Theory which can be estimated us
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2. Theory One option to account for
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2. Theory θ 2 $θ 2 0 area ≅ det
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2. Theory designed using a paramete
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2. Theory proteome14 (Hermann et al
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2. Theory 44
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3. Dynamic Modeling Framework itera
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3. Dynamic Modeling Framework 3.3.
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3. Dynamic Modeling Framework 3.3.3
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3. Dynamic Modeling Framework 52 du
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3. Dynamic Modeling Framework the c
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3. Dynamic Modeling Framework For d
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3. Dynamic Modeling Framework subse
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3. Dynamic Modeling Framework 60
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4. Simulative Comparison of Model D
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5. L-Valine Production Process Deve
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A. Nomenclature Table A.1.: Symbols
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Table A.1.: Symbols and Abbreviatio
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Table A.1.: Symbols and Abbreviatio
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B. Ratio between OD600 and Dry Weig
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C. The Influence of the Oxygen Supp
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The measurements of amino acids and
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kiv [mM] lac [mM] 15 10 5 0 0 10 20
Page 154 and 155: OTR, CTR 0.06 0.04 0.02 0 0 10 20 3
Page 156 and 157: DO properly at a low value in dynam
Page 158 and 159: D. Dilution for Measurement of Amin
Page 160 and 161: E. Models used in the First Model D
Page 162 and 163: Model 1.6 Model 1.6 is similar to m
Page 164 and 165: Table E.1: Model Parameters of the
Page 166 and 167: Table E.2: Model Parameters of the
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Page 170 and 171: Model 2.2 Model 2.2 is similar to m
Page 172 and 173: Table F.1.: Model Parameters of the
Page 174 and 175: Table F.2.: Estimated Lag-Times of
Page 176 and 177: Table F.3: Model Parameters of the
Page 178 and 179: Table F.4.: Estimated Lag-Times of
Page 180 and 181: G. Alternative Off-line Optimized P
Page 182 and 183: G.2. Overall Yield of Product on Su
Page 184 and 185: Summary Fast development of product
Page 186 and 187: Zusammenfassung Es ist wichtig, die
Page 188 and 189: Acknowledgements This thesis result
Page 190 and 191: Bibliography K. Akashi, H. Shibai,
Page 192 and 193: Bibliography A. Bodalo-Santoyo, J.L
Page 194 and 195: Bibliography A.J. Cooper, J.Z. Gino
Page 196 and 197: Bibliography A.G. Fredrickson, R.D.
Page 198 and 199: Bibliography W.G. Hunter and A.M. R
Page 200 and 201: Bibliography A.C. McHardy, A. Tauch
Page 202 and 203: Bibliography E. Radmacher, A. Vaits
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