Model-based optimization of biopharmaceutical manufacturing in Pichia pastoris based on dynamic flux balance analysis

Computers and Chemical Engineering - Tập 118 - Trang 1-13 - 2018
Victor N. Emenike1,2,3, René Schenkendorf1,2, Ulrike Krewer1,2
1Institute of Energy and Process Systems Engineering, Technische Universität Braunschweig, Franz-Liszt-Straße 35, Braunschweig 38106, Germany
2Center of Pharmaceutical Engineering (PVZ), Technische Universität Braunschweig, Franz-Liszt-Straße 35a, Braunschweig 38106, Germany
3International Max Planck Research School for Advanced Methods in Process and Systems Engineering, Sandtorstraße 1, Magdeburg 39106, Germany

Tài liệu tham khảo

Aggarwal, 2014, What’s fueling the biotech engine–2012 to 2013, Nature, 32, 32 Anesiadis, 2008, Dynamic metabolic engineering for increasing bioprocess productivity, Metab. Eng., 10, 255, 10.1016/j.ymben.2008.06.004 Baumrucker, 2008, MPEC problem formulations and solution strategies with chemical engineering applications, Comput. Chem. Eng., 32, 2903, 10.1016/j.compchemeng.2008.02.010 Bellman, 1952, On the theory of dynamic programming, Proc. Natl. Acad. Sci., 38, 716, 10.1073/pnas.38.8.716 Biegler, 1984, Solution of dynamic optimization problems by successive quadratic programming and orthogonal collocation, Comput. Chem. Eng., 8, 243, 10.1016/0098-1354(84)87012-X Biegler, 2007, An overview of simultaneous strategies for dynamic optimization, Chem. Eng. Process., 46, 1043, 10.1016/j.cep.2006.06.021 Biegler, 2010 Bock, 1984, A multiple shooting algorithm for direct solution of optimal control problems, IFAC Proc. Vol., 17, 1603, 10.1016/S1474-6670(17)61205-9 Boghigian, 2010, Metabolic flux analysis and pharmaceutical production, Metab. Eng., 12, 81, 10.1016/j.ymben.2009.10.004 Boyd, 2004 Brockman, 2015, Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites, Metab. Eng., 28, 104, 10.1016/j.ymben.2014.12.005 Brockman, 2015, Dynamic metabolic engineering: new strategies for developing responsive cell factories, Biotechnol. J., 10, 1360, 10.1002/biot.201400422 Çalık, 2011, Dynamic flux balance analysis for pharmaceutical protein production by Pichia pastoris: human growth hormone, Enzyme Microb. Technol., 48, 209, 10.1016/j.enzmictec.2010.09.016 Çelik, 2009, Fed-batch methanol feeding strategy for recombinant protein production by Pichia pastoris in the presence of co-substrate sorbitol, Yeast, 26, 473, 10.1002/yea.1679 Çelik, 2009, A structured kinetic model for recombinant protein production by Mut+ strain of Pichia pastoris, Chem. Eng. Sci., 64, 5028, 10.1016/j.ces.2009.08.009 Çelik, 2010, Metabolic flux analysis for recombinant protein production by Pichia pastoris using dual carbon sources: effects of methanol feeding rate, Biotechnol. Bioeng., 105, 317, 10.1002/bit.22543 Cereghino, 2002, Production of recombinant proteins in fermenter cultures of the yeast Pichia pastoris, Curr. Opin. Biotechnol., 13, 329, 10.1016/S0958-1669(02)00330-0 Cereghino, 2000, Heterologous protein expression in the methylotrophic yeast Pichia pastoris, FEMS Microbiol. Rev., 24, 45, 10.1111/j.1574-6976.2000.tb00532.x Colson, 2007, An overview of bilevel optimization, Ann. Oper. Res., 153, 235, 10.1007/s10479-007-0176-2 Cos, 2006, A simple model-based control for Pichia pastoris allows a more efficient heterologous protein production bioprocess, Biotechnol. Bioeng., 95, 145, 10.1002/bit.21005 Cuthrell, 1987, On the optimization of differential-algebraic process systems, AlChE J., 33, 1257, 10.1002/aic.690330804 d’Anjou, 2001, A rational approach to improving productivity in recombinant Pichia pastoris fermentation, Biotechnol. Bioeng., 72, 1, 10.1002/1097-0290(20010105)72:1<1::AID-BIT1>3.0.CO;2-T Driouch, 2012, Integration of in vivo and in silico metabolic fluxes for improvement of recombinant protein production, Metab. Eng., 14, 47, 10.1016/j.ymben.2011.11.002 Drud, 1994, CONOPT–a large-scale GRG code, ORSA J. Comput., 6, 207, 10.1287/ijoc.6.2.207 Emenike, 2016, Model-based optimal design of continuous-flow reactors for the synthesis of active pharmaceutical ingredients, Chem. Ing. Tech., 88, 1215, 10.1002/cite.201650267 Emenike, 2017, Model-based optimization of the recombinant protein production in Pichia pastoris based on dynamic flux balance analysis and elementary process functions, 2815 Emenike, 2018, A systematic reactor design approach for the synthesis of active pharmaceutical ingredients, Eur. J. Pharm. Biopharm., 126, 75, 10.1016/j.ejpb.2017.05.007 Fazenda, 2013, Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris, Microb. Cell Fact., 12, 51, 10.1186/1475-2859-12-51 Fourer, 2003 Freund, 2008, Towards a methodology for the systematic analysis and design of efficient chemical processes: Part 1. From unit operations to elementary process functions, Chem. Eng. Process., 47, 2051, 10.1016/j.cep.2008.07.011 Güneş, 2016, Oxygen transfer as a tool for fine-tuning recombinant protein production by Pichia pastoris under glyceraldehyde-3-phosphate dehydrogenase promoter, Bioprocess Biosyst. Eng., 39, 1061, 10.1007/s00449-016-1584-y Hessel, 2009, Novel process windows–gate to maximizing process intensification via flow chemistry, Chem. Eng. Technol., 32, 1655, 10.1002/ceat.200900474 Heyland, 2010, Quantitative physiology of Pichia pastorisduring glucose-limited high-cell density fed-batch cultivation for recombinant protein production, Biotechnol. Bioeng., 107, 357, 10.1002/bit.22836 Heyland, 2011, Carbon metabolism limits recombinant protein production in Pichia pastoris, Biotechnol. Bioeng., 108, 1942, 10.1002/bit.23114 Hjersted, 2006, Optimization of fed-batch Saccharomyces cerevisiae fermentation using dynamic flux balance models, Biotechnol. Prog., 22, 1239, 10.1002/bp060059v Höffner, 2013, A reliable simulator for dynamic flux balance analysis, Biotechnol. Bioeng., 110, 792, 10.1002/bit.24748 Isidro, 2016, Hybrid metabolic flux analysis and recombinant protein prediction in Pichia pastoris X-33 cultures expressing a single-chain antibody fragment, Bioprocess Biosyst. Eng., 39, 1351, 10.1007/s00449-016-1611-z Jacobs, 1985, Isolation and characterization of genomic and cDNA clones of human erythropoietin, Nature, 313, 806, 10.1038/313806a0 Jahic, 2002, Modeling of growth and energy metabolism of Pichia pastoris producing a fusion protein, Bioprocess Biosyst. Eng., 24, 385, 10.1007/s00449-001-0274-5 Joy, 2010, Study of the growth of Escherichia coli on mixed substrates using dynamic flux balance analysis, IFAC Proc. Vol., 43, 401, 10.3182/20100707-3-BE-2012.0059 Jungo, 2007, A quantitative analysis of the benefits of mixed feeds of sorbitol and methanol for the production of recombinant avidin with Pichia pastoris, J. Biotechnol., 131, 57, 10.1016/j.jbiotec.2007.05.019 Kobayashi, 2000, High level secretion of recombinant human serum albumin by fed-batch fermentation of the methylotrophic yeast, Pichia pastoris, based on optimal methanol feeding strategy, J. Biosci. Bioeng., 90, 280, 10.1016/S1389-1723(00)80082-1 Kumar, 2017, Applications of polynomial chaos expansions in optimization and control of bioreactors based on dynamic metabolic flux balance models, Chem. Eng. Sci., 167, 18, 10.1016/j.ces.2017.03.035 Kyparisis, 1985, On uniqueness of Kuhn-Tucker multipliers in nonlinear programming, Math. Program., 32, 242, 10.1007/BF01586095 Li, 2008, Mitigation of curse of dimensionality in dynamic programming, IFAC Proc. Vol., 41, 7778, 10.3182/20080706-5-KR-1001.01315 Llaneras, 2012, Dynamic metabolic flux analysis for online estimation of recombinant protein productivity in Pichia pastoris cultures, IFAC Proc. Vol., 45, 629, 10.3182/20120215-3-AT-3016.00112 Love, 2013, Enabling global access to high-quality biopharmaceuticals, Curr. Opin. Chem. Eng., 2, 383, 10.1016/j.coche.2013.09.002 Lu, 2015, Control systems technology in the advanced manufacturing of biologic drugs, 1505 Mahadevan, 2002, Dynamic flux balance analysis of diauxic growth in Escherichia coli, Biophys. J., 83, 1331, 10.1016/S0006-3495(02)73903-9 Meadows, 2010, Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation, Metab. Eng., 12, 150, 10.1016/j.ymben.2009.07.006 Morales, 2014, Validation of an FBA model for Pichia pastoris in chemostat cultures, BMC Syst. Biol., 8, 142, 10.1186/s12918-014-0142-y Mozdzierz, 2015, A perfusion-capable microfluidic bioreactor for assessing microbial heterologous protein production, Lab Chip, 15, 2918, 10.1039/C5LC00443H Muñoz, 2008, A simple structured model for recombinant IDShr protein production in Pichia pastoris, Biotechnol. Lett., 30, 1727, 10.1007/s10529-008-9750-1 Nikdel, 2018, A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis, Bioprocess Biosyst. Eng., 41, 641, 10.1007/s00449-018-1899-y Niu, 2013, Dynamic modeling of methylotrophic Pichia pastoris culture with exhaust gas analysis: from cellular metabolism to process simulation, Chem. Eng. Sci., 87, 381, 10.1016/j.ces.2012.11.006 Nocon, 2014, Model based engineering of Pichia pastoriscentral metabolism enhances recombinant protein production, Metab. Eng., 24, 129, 10.1016/j.ymben.2014.05.011 Orth, 2010, What is flux balance analysis?, Nat. Biotechnol., 28, 245, 10.1038/nbt.1614 Park, 1988, Optimal production of secreted protein in fed-batch reactors, AlChE J., 34, 1550, 10.1002/aic.690340917 Peschel, 2012 Peschel, 2010, Methodology for the design of optimal chemical reactors based on the concept of elementary process functions, Ind. Eng. Chem. Res., 49, 10535, 10.1021/ie100476q Pontryagin, 1962 Potvin, 2012, Bioprocess engineering aspects of heterologous protein production in Pichia pastoris: a review, Biochem. Eng. J., 64, 91, 10.1016/j.bej.2010.07.017 Raghunathan, 2003, Data reconciliation and parameter estimation in flux-balance analysis, Biotechnol. Bioeng., 84, 700, 10.1002/bit.10823 Ralph, 2004, Some properties of regularization and penalization schemes for MPECs, Optim. Methods Softw., 19, 527, 10.1080/10556780410001709439 Ren, 2003, Macrokinetic model for methylotrophic Pichia pastoris based on stoichiometric balance, J. Biotechnol., 106, 53, 10.1016/j.jbiotec.2003.08.003 Sager, 2009, Reformulations and algorithms for the optimization of switching decisions in nonlinear optimal control, J. Process Control, 19, 1238, 10.1016/j.jprocont.2009.03.008 Saitua, 2017, Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris, BMC Syst. Biol., 11, 27, 10.1186/s12918-017-0408-2 Sánchez, 2014, Construction of robust dynamic genome-scale metabolic model structures of Saccharomyces cerevisiae through iterative re-parameterization, Metab. Eng., 25, 159, 10.1016/j.ymben.2014.07.004 Schiestl, 2011, Acceptable changes in quality attributes of glycosylated biopharmaceuticals, Nat. Biotechnol., 29, 310, 10.1038/nbt.1839 Schuetz, 2007, Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli, Mol. Syst. Biol., 3, 119, 10.1038/msb4100162 Segre, 2002, Analysis of optimality in natural and perturbed metabolic networks, Proc. Natl. Acad. Sci., 99, 15112, 10.1073/pnas.232349399 Sola, 2007, Metabolic flux profiling of Pichia pastoris grown on glycerol/methanol mixtures in chemostat cultures at low and high dilution rates, Microbiology, 153, 281, 10.1099/mic.0.29263-0 Sreekrishna, 1997, Strategies for optimal synthesis and secretion of heterologous proteins in the methylotrophic yeast Pichia pastoris, Gene, 190, 55, 10.1016/S0378-1119(96)00672-5 St. John, 2017, Efficient estimation of the maximum metabolic productivity of batch systems, Biotechnol. Biofuels, 10, 28, 10.1186/s13068-017-0709-0 Thiele, 2010, A protocol for generating a high-quality genome-scale metabolic reconstruction, Nat. Protoc., 5, 93, 10.1038/nprot.2009.203 Tziampazis, 1994, Modeling of cell culture processes, Cytotechnology, 14, 191, 10.1007/BF00749616 Vassiliadis, 1994, Solution of a class of multistage dynamic optimization problems. 1. Problems without path constraints, Ind. Eng. Chem. Res., 33, 2111, 10.1021/ie00033a014 Vassiliadis, 1994, Solution of a class of multistage dynamic optimization problems. 2. Problems with path constraints, Ind. Eng. Chem. Res., 33 Vercammen, 2014, Dynamic estimation of specific fluxes in metabolic networks using non-linear dynamic optimization, BMC Syst. Biol., 8, 132, 10.1186/s12918-014-0132-0 Vercammen, 2017, Application of a dynamic metabolic flux algorithm during a temperature-induced lag phase, Food Bioprod. Process., 102, 1, 10.1016/j.fbp.2016.10.003 Wachsmuth, 2013, On LICQ and the uniqueness of Lagrange multipliers, Oper. Res. Lett., 41, 78, 10.1016/j.orl.2012.11.009 Waldherr, 2016, State estimation in constraint based models of metabolic-genetic networks, 6683 Walsh, 2014, Biopharmaceutical benchmarks 2014, Nat. Biotechnol., 32, 992, 10.1038/nbt.3040 Wegerhoff, 2016, Control of the production of Saccharomyces cerevisiae on the basis of a reduced metabolic model, IFAC-PapersOnLine, 49, 201, 10.1016/j.ifacol.2016.12.126 Wells, 2017, Cellular engineering for therapeutic protein production: product quality, host modification, and process improvement, Biotechnol. J., 12, 1860, 10.1002/biot.201600105 Xie, 2005, Use of different carbon sources in cultivation of recombinant Pichia pastoris for angiostatin production, Enzyme Microb. Technol., 36, 210, 10.1016/j.enzmictec.2004.06.010 Yang, 2008, A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks, Comput. Chem. Eng., 32, 2072, 10.1016/j.compchemeng.2007.10.015 Zhang, 2000, Modeling Pichia pastoris growth on methanol and optimizing the production of a recombinant protein, the heavy-chain fragment C of botulinum neurotoxin, serotype A, Biotechnol. Bioeng., 70, 1, 10.1002/1097-0290(20001005)70:1<1::AID-BIT1>3.0.CO;2-Y Zhao, 2017, Dynamic flux balance analysis with nonlinear objective function, J. Math. Biol., 75, 1487, 10.1007/s00285-017-1127-4