Hybridoma cell culture optimization using nonlinear model predictive control

Dewasme, Laurent; Amribt, Zakaria; Santos, Lino O.; Hantson, Anne-Lise; Bogaerts, Philippe; Wouwer, Alain Vande

doi:10.3182/20131216-3-in-2044.00045

Cited by 12 publications

(21 citation statements)

References 12 publications

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“…To partly avoid optimal policy implementation problems, various alternatives are suggested in the literature. For instance, an observer-based robust control strategy for controlling overflow metabolism cultures operated in fedbatch mode in order to maximize the biomass productivity [31,46], or on-line optimal operation based on the time-varying characteristics of the biomass culture during the batch [18,28,30,46], or during a batch-to-batch operation mode [29], or by adjusting the biomass load in the bioreactor according to the time-varying characteristics of the biomass culture, in a semi-continuous operating mode [30].…”

Section: In-silico Analysis Resultsmentioning

confidence: 99%

“…This in-silico analysis opens the possibility to optimize the bioreactor performances by placing the setpoint (SP) in the most favourable location, by adjusting the substrate and biomass initial load according to the characteristics of the modified / improved biomass with an enhanced evolution (with the below k net ≠ 0). This in-silico analysis is also a valuable tool to determine an optimal operation of the bioreactor based on the timevarying characteristics of the biomass culture during the batch [18,28], or during a batch-to-batch operation mode (not approached here [29] ), or by adjusting the biomass load in the bioreactor according to the time-varying characteristics of the biomass culture, in a semi-continuous operating mode (not approached here [30] ).…”

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confidence: 99%

“…A separate preliminary determination of the biomass kinetic characteristics (see the below net k net ≠ 0) in an incipient stage of the bioprocess can lead to determine the optimal operating policy of a batch (this paper), or of a fed-batch bioreactor [28,31]. Optimization analysis is realized gradually, by considering one control variable (i.e.…”

mentioning

confidence: 99%

“…However, when significant inconsistecies vs. experimental evidence are reported, such checks will lead to the process lumped model updating/completions, and re-estimation of some of their parameters for correcting its adequacy in order to perform futures bioreactor analyses, and to eventually correct its optimal operating policy. To partly avoid such problems various alternatives are suggested in the literature, such as an observer-based robust control strategy for controlling overflow metabolism cultures operated in fed-batch mode in order to maximize the biomass productivity [18,[28][29]31], even if their implementation is difficult and costly.…”

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confidence: 99%

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In-silico Optimization of a Batch Bioreactor for mAbs Production in Relationship to the Net Evolution of the Hybridoma Cell Culture

Maria¹,

Gîjiu²,

Cebanu³

et al. 2019

Rev. Chim.

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Bioreactor optimization is a common engineering problem difficult to be solved due to the large number of influential variables of high variability. Production of monoclonal antibody is a well-known method to synthesize a large number of identical antibodies (that is of uniform characteristics, also called monoclonal antibodies, mAb). Due to such reasons intense efforts have been invested to maximize the production of mAb by using hybridoma cell culture. Based on an adequate kinetic model from literature (experimentally checked) this paper focus on pointing-out the major role of the net evolution of the viable biomass (growth, and decay) in the location of the optimal operating setpoint (SP) of a three-phase mechanically agitated batch bioreactor (TPMAB) with immobilized hybridoma culture. This in-silico analysis opens the possibility I) to optimize the bioreactor performances by placing the SP in the most favourable location, by adjusting the substrate and biomass initial load in the bioreactor according to the preliminary determined characteristics of a modified / improved biomass; ii) to optimize the batch-to-batch operation mode (not approached here) according to the time-varying characteristics of the biomass culture, or iii) to determine the optimal operation of the bioreactor in a fed-batch operating mode (not approached here).

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Section: In-silico Analysis Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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In-silico Optimization of a Batch Bioreactor for mAbs Production in Relationship to the Net Evolution of the Hybridoma Cell Culture

Maria¹,

Gîjiu²,

Cebanu³

et al. 2019

Rev. Chim.

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“…The NMPC algorithm is an adaptive model-based controller [25,26] which has been widely used for control and optimization [27][28][29][30][31][32][33][34][35][36][37][38][39][40], but hardly for process development. The usage of NMPC algorithms in this study is motivated by the success of Open Loop Feedback Optimal (OLFO) strategies [9,41,42].…”

Section: Setup Of the Nmpcmentioning

confidence: 99%

NMPC-Based Workflow for Simultaneous Process and Model Development Applied to a Fed-Batch Process for Recombinant C. glutamicum

et al. 2020

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For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. By using the NMPC algorithm, the process is designed with respect to a target function (product yield, biomass concentration) with a drastically decreased number of experiments. A workflow for the usage of the NMPC algorithm as a process development tool is outlined. The NMPC algorithm is capable of improving various process states, such as product yield and biomass concentration. It uses on-line and at-line data and controls and optimizes the process by model-based process extrapolation. In this study, the algorithm is applied to a Corynebacterium glutamicum process. In conclusion, the potency of the NMPC algorithm as a powerful tool for process development is demonstrated. In particular, the benefits of the system regarding the characterization and optimization of a fed-batch process are outlined. With the NMPC algorithm, process development can be run simultaneously to strain development, resulting in a shortened time to market for novel products.

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