Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

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iomass [OD 600 ] 100 80 60 40 20 0 0 10 20 30 time [h] 40 50 300 240 180 120 60 0 glc, val [mM] (a) Concentrations. Biomass as × in OD600, glucose as ∗ in mM and L-valine as + in mM 5.2. Modeling Based Process Development µ [h −1 −1 −1 ], π [mM.OD .h ] val 600 0.5 0.4 0.3 0.2 0.1 0 0 10 20 30 time [h] 40 50 2.5 2 1.5 1 0.5 0 −1 −1 σ [mM.OD .h ] glc 600 (b) Specific Rates. µ as × in h −1 , σglc as ∗ in mM.OD −1 .h −1 and πval as + in mM.OD −1 .h −1 Figure 5.9.: Results of model discriminating experiment I. Lines are only a visual aid. the discrimination between these models improved. However, many of the models have reached a visually comparable fit, due to the partly strongly changed parameter sets. This is a general problem of the suggested procedure, which is likely to occur in the initial phase of the process development. When the inaccuracy of the model parameters are accounted for in the design criterion, as is the case here, it is only a slight adjustment of the parameter values which is regarded, but not the possibility of using a very distinct parameter set, possibly describing another local optimum in the parameter estimation problem. When a few of such distinct sets are known, which provide acceptable fits to the prior data, they could, however, be used as separate models in the model discriminating design criterion. The amount of models taken into account for the model discriminating design should be limited, in order to keep calculation times at an acceptable level and to avoid the designed experiment to be more designed to keep the predictions of all models within the allowed bounds, than to find differences between the models. This, together with the fact that partly large changes in the parameter values occur upon re-estimation after a new experiment, makes it necessary to also reconsider ’rejected’ models, as was also suggested by several others (Buzzi-Ferraris et al., 1990; Chen and Asprey, 2003). The fit of the best model at this stage might be regarded satisfactory based on the direct visual image in figure 5.10, although it clearly fails to describe some effects properly. Purely based on the ˜χ 2 values, this may not always be very clear. The current situation provides a representative example: since we know that the rate of addition of L-isoleucine and pantothenic acid was increased after 26 hours 1 , the clear increase in the growth rate after this point is qualified as important. The best fit, however, does not show this effect properly, but with a more or less constant growth rate, the simulated 1 This corresponds more or less to 24 hours in the planned experiment, considering a delayed fermentation due to a reduced inoculum concentration and a lag-time. 107
5. L-Valine Production Process Development G[−] 0.2 0.15 0.1 0.05 0 1 2 3 4 5 Model 6 7 8 (a) Relative goodness of fit of eight of the competing models before the model discriminating experiment as solid black bars and after the experiment as grey bars. The models were given arbitrary numbers. biomass [OD 600 ],val [mM] 80 60 40 20 0 0 5 10 time [h] 15 0 20 320 240 160 80 glc [mM] biomass [OD 600 ] 80 60 40 20 0 0 10 20 30 40 0 50 time [h] (b) Fit of the best model after model discriminating experiment I. The left plot shows the fit to the data of the first batch experiment and the right plot to the model discriminating experiment. Markers indicate measured values and lines simulated values. Biomass is shown as solid lines and × as OD600, glucose in mM as dashed lines and ∗ and L-valine in mM as dotted lines and +. 108 Figure 5.10.: Results of fitted models after model discriminating experiment I. 320 240 160 80 glc,val [mM]
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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
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 113: 5. L-Valine Production Process Deve
Page 140 and 141: A. Nomenclature Table A.1.: Symbols
Page 142 and 143: Table A.1.: Symbols and Abbreviatio
Page 144 and 145: Table A.1.: Symbols and Abbreviatio
Page 146 and 147: B. Ratio between OD600 and Dry Weig
Page 148 and 149: C. The Influence of the Oxygen Supp
Page 150 and 151: The measurements of amino acids and
Page 152 and 153: 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
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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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