Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization
[Display omitted] •Metabolic-hydrodynamic simulation to predict yield loss due to substrate gradient.•Novel design approach for representative scale-down simulators.•Use of simulations to assess effect of reactor design on process yield.•Experimental verification of penicillin production rate in fed...
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| Published in | Chemical engineering science Vol. 175; pp. 12 - 24 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
2018
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| Online Access | Get full text |
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| Abstract | [Display omitted]
•Metabolic-hydrodynamic simulation to predict yield loss due to substrate gradient.•Novel design approach for representative scale-down simulators.•Use of simulations to assess effect of reactor design on process yield.•Experimental verification of penicillin production rate in fed-batch simulation.•Prediction of emerging population heterogeneity in fed-batch simulation.
We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate qp of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization. |
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| AbstractList | [Display omitted]
•Metabolic-hydrodynamic simulation to predict yield loss due to substrate gradient.•Novel design approach for representative scale-down simulators.•Use of simulations to assess effect of reactor design on process yield.•Experimental verification of penicillin production rate in fed-batch simulation.•Prediction of emerging population heterogeneity in fed-batch simulation.
We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate qp of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization. |
| Author | Tang, Wenjun Chu, Ju Wang, Guan van Winden, Wouter A. Noorman, Henk J. Mudde, Robert F. Haringa, Cees Deshmukh, Amit T. Heijnen, Joseph J. van Gulik, Walter M. |
| Author_xml | – sequence: 1 givenname: Cees surname: Haringa fullname: Haringa, Cees organization: Transport Phenomena, Department of Chemical Engineering, Delft University of Technology, Delft, The Netherlands – sequence: 2 givenname: Wenjun surname: Tang fullname: Tang, Wenjun organization: State Key Laboratory of Bioreactor Engineering, East China University of Technology, Shanghai, People’s Republic of China – sequence: 3 givenname: Guan surname: Wang fullname: Wang, Guan organization: State Key Laboratory of Bioreactor Engineering, East China University of Technology, Shanghai, People’s Republic of China – sequence: 4 givenname: Amit T. surname: Deshmukh fullname: Deshmukh, Amit T. organization: DSM Biotechnology Center, Delft, The Netherlands – sequence: 5 givenname: Wouter A. surname: van Winden fullname: van Winden, Wouter A. organization: DSM Biotechnology Center, Delft, The Netherlands – sequence: 6 givenname: Ju surname: Chu fullname: Chu, Ju organization: State Key Laboratory of Bioreactor Engineering, East China University of Technology, Shanghai, People’s Republic of China – sequence: 7 givenname: Walter M. surname: van Gulik fullname: van Gulik, Walter M. organization: Cell Systems Engineering, Department of Biotechnology, Delft University of Technology, Delft, The Netherlands – sequence: 8 givenname: Joseph J. surname: Heijnen fullname: Heijnen, Joseph J. organization: Cell Systems Engineering, Department of Biotechnology, Delft University of Technology, Delft, The Netherlands – sequence: 9 givenname: Robert F. surname: Mudde fullname: Mudde, Robert F. organization: Transport Phenomena, Department of Chemical Engineering, Delft University of Technology, Delft, The Netherlands – sequence: 10 givenname: Henk J. surname: Noorman fullname: Noorman, Henk J. email: henk.noorman@dsm.com organization: DSM Biotechnology Center, Delft, The Netherlands |
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