Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

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Table A.1.: Symbols and Abbreviations (continued) Symbol Description Unit u set of control variables ubθ upper bound of the limited parameter space V volume of suspension in the bioreactor L V weighting matrix containing the expected measurement (co)variances and model variances of two competing models (BF criterion) vmax maximal specific reaction rate val L-valine Wi covariance matrix of the expectation of the measurements according to model i (model variances) xi state variable i y (expected) measured value ˆyi model prediction of variable yi YPS yield of product on substrate YPS1 yield factor for production of L-valine from glucose mM.mM −1 YPX ratio between production of L-valine and biomass mM.OD600 −1 YXS yield of biomass on substrate OD600.mM −1 Y ov XS overall yield of biomass on substrate OD600.mM −1 YXS1 yield factor for biomass growth on glucose OD600.mM −1 YXS2 yield factor for biomass growth on isoleucine OD600.mM −1 YXS3 yield factor for biomass growth on pantothenic acid OD600.µM −1 ˜χ 2 reduced chi-squared measure of goodness of fit - δFglc density of the glucose feed g.L −1 δr density of the suspension in the reactor g.L −1 µ specific growth rate h −1 µmax maximal specific growth rate h −1 µmax,S1 maximal specific growth rate limited by glucose h−1 µmax,S2ext maximal specific growth rate limited by extracellular isoleucine h−1 µmax,S2Int maximal specific growth rate limited by isoleucine which has already been taken up by the cells h−1 µmax,S3ext maximal specific growth rate limited by extracellular pantothenic acid h−1 µmax,S3Int maximal specific growth rate limited by pantothenic acid which has already been taken up by the cells h−1 µS specific growth rate, limited by substrate S h−1 πmax maximal specific production rate mM.OD600 −1 .h −1 137
A. Nomenclature Table A.1.: Symbols and Abbreviations (continued) Symbol Description Unit πmax,S1 maximal specific L-valine production rate, limited by glucose πmax,S3 maximal specific L-valine production rate, limited by pantothenic acid which has already been taken up σ standard deviation Σ matrix of measurement variances σ2 σ measurement variance 2∗ i variance of the expectation of the values according to model i (i.e. ’model variance’) σ2 i measurement variance of the expected values accordingtomodeli Σi,m matrix of measurement variances according to model i σi,t standard deviation of the expected values according to model i due to variance in the timing of the measurement Σi,t matrix of (co)variances caused by variance in the timing of the measurement, according to model i Σij weighting matrix containing the expected measurement (co)variance of the model predictions of model i and j σy,rel.min minimal relative standard deviation of measurement y σrel relative standard deviation σy standard deviation measurement y θ vector of model parameters ˆθ vector of estimated model parameters θlim parameter value in the limited parameter space θunlim parameter value in the unlimited parameter space ξ experiment (i.e. the experiment) set of control variables describing ξ∗ optimized experiment 138 mM.OD600 −1 .h −1 mM.OD600 −1 .h −1
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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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Page 93 and 94: 4. Simulative Comparison of Model D
Page 95 and 96: 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 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
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
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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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