Experimental validation of a nonlinear model predictive controller regulating the acetate concentration in fed-batch Escherichia coli BL21(DE3) cultures
Dewasme, Laurent ; Université de Mons > Faculté Polytechnique > Service Systèmes, Estimation, Commande et Optimisation
Tebbani, Sihem
Dumur, Didier
Vande Wouwer, Alain ; Université de Mons > Faculté Polytechnique > Service Systèmes, Estimation, Commande et Optimisation
Language :
English
Title :
Experimental validation of a nonlinear model predictive controller regulating the acetate concentration in fed-batch Escherichia coli BL21(DE3) cultures
Publication date :
20 January 2022
Journal title :
Advanced Control for Applications
ISSN :
2578-0727
Volume :
4
Issue :
1
Pages :
e95
Peer reviewed :
Peer reviewed
Research unit :
F107 - Systèmes, Estimation, Commande et Optimisation
Luli GW, Strohl WR. Comparison of growth, acetate production, and acetate inhibition of Escherichia coli strains in batch and fed-batch fermentations. Appl Environ Microbiol. 1990;56(4):1004-1011.
van de Walle M, Shiloach J. Proposed mechanism of acetate accumulation in two recombinant Escherichia coli strains during high density fermentation. Biotechnol Bioeng. 1998;57(1):71-78.
Riesenberg D, Schulz V, Knorre WA, et al. High cell density cultivation of Escherichia coli at controlled specific growth rate. J Biotechnol. 1991;20(1):17-27. http://www.sciencedirect.com/science/article/pii/016816569190032Q
Rothen SA, Sauer M, Sonnleitner B, Witholt B. Growth characteristics of Escherichia coli HB101[pGEc47] on defined medium. Biotechnol Bioeng. 1998;58(1):92-100. https://onlinelibrary.wiley.com/doi/abs/10.1002/28SICI291097-0290281998040529583A13C923A3AAID-BIT103E3.0.CO3B2-7
Jana S, Deb JK. Strategies for Efficient Production of Heterologous Proteins in Escherichia Coli. Springer Verlag; 2005.
Abadli M, Dewasme L, Tebbani S, Dumur D, Vande WA. Generic model control applied to E. Coli BL21(DE3) fed-batch cultures. Processes. 2020;8(7):772.
Lee J, Lee SY, Park S, Middelberg APJ. Control of fed-batch fermentations. Biotechnol Adv. 1999;17(1):29-48. Cited By 195.
Qin SJ, Badgwell TA. An overview of nonlinear model predictive control applications. In: Allgöwer F, Zheng A, eds. Nonlinear Model Predictive Control. Progress in Systems and Control Theory, vol 26. Birkhäuser, Basel; 2000.
Forbes MG, Patwardhan RS, Hamadah H, Gopaluni RB. Model predictive controlin industry: challenges and opportunities. IFAC-PapersOnLine. 2015;28(8):531-538. Cited By 44.
Qin SJ, Badgwell TA. A survey of industrial model predictive control technology. Control Eng Pract. 2003;11(7):733-764. Cited By 3000.
Cochrane JA, Alamir M. Model predictive control and parameter estimation applied to gene expression in yeast. IFAC-PapersOnLine. 2018;51(25):282-489.
Preuß K, Le Lann MV, Proth J, Pingaud H. Modelling and predictive control of fed-batch yeast growth on industrial pilot plant scale. Chem Eng J. 2000;78(1):53-59.
Santos LO, Dewasme L, Coutinho D, Vande WA. Nonlinear model predictive control of fed-batch cultures of micro-organisms exhibiting overflow metabolism: Assessment and robustness. Comput Chem Eng. 2012;39:143-151. Cited By 34.
Tebbani S, Dumur D, Hafidi G, Vande WA. Nonlinear predictive control of fed-batch cultures of escherichia coli. Chem Eng Technol. 2010;33(7):1112-1124. https://onlinelibrary.wiley.com/doi/abs/10.1002/ceat.201000029
del Rio-Chanona EA, Zhang D, Vassiliadis VS. Model-based real-time optimisation of a fed-batch cyanobacterial hydrogen production process using economic model predictive control strategy. Chem Eng Sci. 2016;142:289–298.
Ulonska S, Waldschitz D, Kager J, Herwig C. Model predictive control in comparison to elemental balance control in an E. Coli fed-batch. Chem Eng Sci. 2018;191:459–467.
Ashoori A, Moshiri B, Khaki-Sedigh A, Bakhtiari MR. Optimal control of a nonlinear fed-batch fermentation process using model predictive approach. J Process Control. 2009;19(7):1162–1173.
Becerra-Celis G, Tebbani S, Joannis-Cassan C, Isambert A, Boucher P. Estimation of microalgal photobioreactor production based on total inorganic carbon in the medium. IFAC Proc Vol (IFAC-PapersOnline). 2008;41(2):14582–14587.
Tebbani S, Lopes F, Filali R, Dumur D, Pareau D. Nonlinear predictive control for maximization of CO2 bio-fixation by microalgae in a photobioreactor. Bioprocess Biosyst Eng. 2014;37:83–97.
Del Rio-Chanona EA, Dechatiwongse P, Zhang D, et al. Optimal operation strategy for biohydrogen production. Ind Eng Chem Res. 2015;54(24):6334–6343.
Craven S, Whelan J, Glennon B. Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller. J Process Control. 2014;24(4):344-357. Cited By 57.
Aehle M, Bork K, Schaepe S, et al. Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach. Cytotechnology. 2012;64(623):634.
Amribt Z, Dewasme L, Vande Wouwer A, Bogaerts P. Optimization and robustness analysis of hybridoma cell fed-batch cultures using the overflow metabolism model. Bioprocess Biosyst Eng. 2014;37:1637–1652.
Logist F, Houska B, Diehl M, Van Impe JF. Robust multi-objective optimal control of uncertain (bio)chemical processes. Chem Eng Sci. 2011;66(20):4670–4682.
Dewasme L, Richelle A, Dehottay P, et al. Linear robust control of S. Cerevisiae fed-batch cultures at different scales. Biochem Eng J. 2010;53(1):26-37. http://www.sciencedirect.com/science/article/pii/S1369703X09002988 Special Section: CHEMPOR2008 - Integration of Life Sciences and Engineering.
Valentinotti S, Cannizzaro C, Srinivasan B, Bonvin D. An optimal operating strategy for fed-batch fermentations by feeding the overflow metabolite. IFAC Proc Vol. 2004;37(1):365-371. https://www.sciencedirect.com/science/article/pii/S1474667017387591 7th International Symposium on Advanced Control of Chemical Processes (ADCHEM 2003), Hong-Kong, 11-14 January 2004.
Müller M, Meusel W, Husemann U, Greller G, Kraume M. Application of heat compensation calorimetry to an E. coli fed-batch process. J Biotechnol. 2018 jan;266:133-143.
Rocha I Model-based strategies for computer-aided operation of a recombinant E. Coli fermentation. Model-Based Strategies Comput Aided Operat Recomb E Coli Ferment. 2003; Cited By 30.
DeLisa M, Chae H, Weigand W, Valdes J, Rao G, Bentley W. Generic model control of induced protein expression in high cell density cultivation of Escherichia coli using on-line GFP-fusion monitoring. Bioprocess Biosyst Eng. 2001;24(2):83-91. doi:10.1007/s004490100229
Dewasme L, Goffaux G, Hantson AL, Vande WA. Experimental validation of an extended Kalman filter estimating acetate concentration in E. Coli cultures. J Process Control. 2013;23:148-157.
Retamal C, Dewasme L, Hantson AL, Vande WA. Parameter estimation of a dynamic model of Escherichia coli fed-batch cultures. Biochem Eng J. 2018 jul;135:22-35.
Sonnleitner B, Käppeli O. Growth of saccharomyces cerevisiae is controlled by its limited respiratory capacity: formulation and verification of a hypothesis. Biotechnol Bioeng. 1986;28(6):927-937. https://onlinelibrary.wiley.com/doi/abs/10.1002/bit.260280620
Kleman GL, Strohl WR. Acetate metabolism by Escherichia coli in high-cell-density fermentation. Appl Environ Microbiol. 1994;60(11):3952–3958.
Banga JR, Balsa-Canto E, Moles CG, Alonso AA. Dynamic optimization of bioprocesses: efficient and robust numerical strategies. J Biotechnol. 2005;117(4):407-419. doi:10.1016/j.jbiotec.2005.02.013
Gauthier JP, Kupka IAK. Observability and observers for nonlinear systems. SIAM J Control Optim. 1994;32(4):975-994. Cited By 315.
Zeitz M. Observability canonical (phase-variable) form for non-linear time-variable systems. Int J Syst Sci. 1984;15(9):949-958. Cited By 50.
Bogaerts P, Vande WA. Parameter identification for state estimation-Application to bioprocess software sensors. Chem Eng Sci. 2004;59(12):2465–2476.
Julier SJ, Uhlmann JK. New extension of the Kalman filter to nonlinear systems. Signal Processing, Sensor Fusion, and Target Recognition VI; 1997;3068:182–193.
Merwe R, Wan E. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. Proceedings of the Workshop on Advances in Machine Learning; 2003:06.
