Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

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2.7. L-valine Production in Corynebacterium glutamicum the optimal settings of the control parameters can be calculated off-line without further experiments. In some relatively simple cases, this may be possible analytically. For more complex systems with many state variables and control parameters, this becomes complicated or even impossible, so numerical techniques have to be used. The calculated optimal trajectories can then be applied in the process directly (feedforward control), as is also done in the example by Lee et al. (1997) and in the current work in paragraph 5.2.6. Since the process model, the initial conditions and the controlled variables all include inaccuracies, it is, however much, better to include feedback control based on online measured values. This measured variable can be simply the variable which was intended to be controlled, such as the pH or temperature, or an online measurable substrate concentration. Or, if this variable is not measurable online, it may be possible to control a derived variable instead. Biomass growth can, for instance, be monitored by measurement of the oxygen consumption rate and carbon dioxide production rate (Golobic et al., 2000). The availability of a model which can be used for model based optimization may also allow for the use of model based predictive control. Here, the prediction of the model is calculated regularly online and the optimal control is calculated, using this prediction, for a limited time. The basic concepts and an overview of model predictive control can be found in Garcia et al. (1989); Henson (1998). Preuss et al. (2000) applied model predictive control for the glucose feed for the growth of yeast in a fed-batch culture. Another example is the optimization of ethanol production by Zymomonas mobilis by Hodge and Karim (2002). 2.7. L-valine Production in Corynebacterium glutamicum In the current project, the aim is to produce L-valine using a genetic modified strain of C. glutamicum. The gram positive, nonmotile, non spore forming bacterium C. glutamicum is generally regarded as safe (GRAS). It has already been isolated in 1957 in a screening for L-glutamate producing bacteria in soil by Kinoshita and co-workers (Kinoshita et al., 1957) and it is nowadays widely used for the fermentative production of natural amino acids such as L-glutamic acid and L-lysine(Burkovski, 2003a; Hermann, 2003). Due to its industrial importance and its GRAS status, it has been investigated very intensively, which resulted amongst other things in the complete sequencing of its genome in several strains (Degussa, Kyowa Hakko Kogyo, BASF (Ikeda and Nakagawa, 2003; Kalinowski et al., 2003)). Besides the investigation of the genome, also the transcriptome 13 (Hayashi et al., 2002; Krömer et al., 2004; Muffler et al., 2002; Wendisch, 2003), 13 The investigation of the transcriptome indicates the investigation of the gene expression. 41
2. Theory proteome14 (Hermann et al., 2001) and the metabolome15 (Chassagnole et al., 2003; Drysch et al., 2003; Park et al., 1997; Sahm et al., 2000; Vallino and Stephanopoulos, 1993; Varela et al., 2003; Wittmann and Heinzle, 2001) are thoroughly investigated. This also resulted in the availability of many tools for genetic modifications of C. glutamicum (Nampoothiri and Pandey, 1998). C. glutamicum needs biotin for growth, which was an important characteristic because the glutamate production was triggered by biotin limitation in wild-type strains (Shiio et al., 1962). Nowadays, with modern biotechnological techniques, also other growth limitations and other mechanisms are available to achieve overproduction of amino acids (Kimura, 2002), as is also the case in the organism which is used in the current study. Another characteristic of C. glutamicum, which may be of importance for its use, is the complex cell envelope containing mycolic acids (Bayan et al., 2003; Christensen et al., 1999), which are typical for Corynebacteriaceae, Mycobacteriaceae, Nocardiaceae and a few small other families (Glazer and Nikaido, 1995). These mycolic acids form an extra hydrophobic barrier which probably causes the low permeability of the cell wall for hydrophilic compounds (Jarlier and Nikaido, 1990). Hydrophilic compounds therefore have to pass the cell wall through a channel (Costa-Riu et al., 2003a,b). CH3 COO H3C - L-Valine is a natural proteinogenic amino acid and it is one of the essential amino acids for humans. Together with leucine and alanine, it forms the pyruvate family of amino acids. Furthermore, it is a branched chain amino acid just like leucine and isoleucine. It is a fairly hydrophobic (logp =1.64), neutral amino acid (pI=5.96), which makes it NH + 3 likely that it may be able to pass the cell membranes relatively good through passive diffusion (Krämer, 1994). The structure of Figure 2.5.: L-Valine L-valine is shown in figure 2.5. The annual production of L-valine is about 1000 tons, which is among the smaller amounts for natural proteinogenic amino acids (Drauz et al., 2003; Eggeling et al., 2001; Leyval et al., 2003). It is mainly being used in infusion fluids and special dietary nutrition. However, also beneficial effects of addition of L-valine to pig- and chicken feed were reported (Boyd et al., 2000; Harms and Russell, 2001; Kidd et al., 2000). L-Valine is currently being produced by isolation from protein hydrolysates, in fermentative processes or by enzymatic resolution of N-acetyl-D,L-valine 14 The proteome indicates the proteins which are present in the cells. 15 The intracellular concentration of metabolites: intermediates and products of metabolic pathways. 42
Page 1 and 2: Lebenswissenschaften Life Sciences
Page 4 and 5: Forschungszentrum Jülich GmbH Inst
Page 6: ’Therefore, mathematical modeling
Page 9 and 10: Contents 3.4. Application: L-Valine
Page 11 and 12: Contents 4
Page 13 and 14: 1. Introduction need for improved m
Page 15 and 16: 2. Theory In a batch process, all s
Page 17 and 18: 2. Theory 1997; Goncalves et al., 2
Page 19 and 20: 2. Theory For batch fermentations,
Page 21 and 22: 2. Theory with Yps being the yield
Page 23 and 24: 2. Theory Inhibition Some catalytic
Page 25 and 26: 2. Theory The method of least squar
Page 27 and 28: 2. Theory evaluation can be made by
Page 29 and 30: 2. Theory We define the measured or
Page 31 and 32: 2. Theory build in the model. An ob
Page 33 and 34: 2. Theory nations, the researcher i
Page 35 and 36: 2. Theory W = J · Covθ · J T (2.
Page 37 and 38: 2. Theory arg max ξ � m� m�
Page 39 and 40: 2. Theory with all measured values
Page 41 and 42: 2. Theory which can be estimated us
Page 43 and 44: 2. Theory One option to account for
Page 45 and 46: 2. Theory θ 2 $θ 2 0 area ≅ det
Page 47: 2. Theory designed using a paramete
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Page 53 and 54: 3. Dynamic Modeling Framework itera
Page 55 and 56: 3. Dynamic Modeling Framework 3.3.
Page 57 and 58: 3. Dynamic Modeling Framework 3.3.3
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Page 61 and 62: 3. Dynamic Modeling Framework the c
Page 63 and 64: 3. Dynamic Modeling Framework For d
Page 65 and 66: 3. Dynamic Modeling Framework subse
Page 67 and 68: 3. Dynamic Modeling Framework 60
Page 69 and 70: 4. Simulative Comparison of Model D
Page 95 and 96: 5. L-Valine Production Process Deve
Page 97 and 98: 5. L-Valine Production Process Deve
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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
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OTR, CTR 0.06 0.04 0.02 0 0 10 20 3
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DO properly at a low value in dynam
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D. Dilution for Measurement of Amin
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E. Models used in the First Model D
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Model 1.6 Model 1.6 is similar to m
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Table E.1: Model Parameters of the
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Table E.2: Model Parameters of the
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161
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Model 2.2 Model 2.2 is similar to m
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Table F.1.: Model Parameters of the
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Table F.2.: Estimated Lag-Times of
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Table F.3: Model Parameters of the
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Table F.4.: Estimated Lag-Times of
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G. Alternative Off-line Optimized P
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G.2. Overall Yield of Product on Su
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Summary Fast development of product
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Zusammenfassung Es ist wichtig, die
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Acknowledgements This thesis result
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Bibliography K. Akashi, H. Shibai,
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Bibliography A. Bodalo-Santoyo, J.L
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Bibliography A.J. Cooper, J.Z. Gino
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Bibliography A.G. Fredrickson, R.D.
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Bibliography W.G. Hunter and A.M. R
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Bibliography A.C. McHardy, A. Tauch
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Bibliography E. Radmacher, A. Vaits
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Bibliography K.J. Versyck, J.E. Cla
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Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

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