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Structured model and parameter estimation in plant cell cultures of Thevetia peruviana

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Abstract

In this work, a mechanistic model for predicting the dynamic behavior of extracellular and intracellular nutrients, biomass production, and the main metabolites involved in the central carbon metabolism in plant cell cultures of Thevetia peruviana is presented. The proposed model is the first mechanistic model implemented for plant cell cultures of this species, and includes 28 metabolites, 33 metabolic reactions, and 61 parameters. Given the over-parametrization of the model, its nonlinear nature and the strong correlation among the effects of the parameters, a parameter estimation routine based on identifiability analysis was implemented. This routine reduces the parameter’s search space by selecting the most sensitive and linearly independent parameters. Results have shown that only 19 parameters are identifiable. Finally, the model was used for analyzing the fluxes distribution in plant cell cultures of T. peruviana. This analysis shows high uptake of phosphates and parallel uptake of glucose and fructose. Furthermore, it has pointed out the main central carbon metabolism routes for promoting biomass production in this cell culture.

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References

  1. Arias M, Angarita MJ, Restrepo JM et al (2009) Elicitation with methyl-jasmonate stimulates peruvoside production in cell suspension cultures of Thevetia peruviana. Vitr Cell Dev Biol Plant 46:233–238. doi:10.1007/s11627-009-9249-z

    Google Scholar 

  2. Siwach P, Grover K, Gill AR (2011) The influence of plant growth regulators, explant nature and sucrose concentration on in vitro callus growth of Thevetia peruviana schum. Asian J Biotechnol 3:280–292

    Article  CAS  Google Scholar 

  3. Kohls S, Scholz-Böttcher BM, Teske J et al (2012) Cardiac glycosides from Yellow Oleander (Thevetia peruviana) seeds. Phytochemistry 75:114–127. doi:10.1016/j.phytochem.2011.11.019

    Article  CAS  Google Scholar 

  4. Tian D-M, Cheng H-Y, Jiang M-M et al (2015) Cardiac Glycosides from the Seeds of Thevetia peruviana. J Nat Prod 79:38–50. doi:10.1021/acs.jnatprod.5b00611

    Article  Google Scholar 

  5. Amaringo FV, Hormaza A, Arias M (2011) Thevetin B: glicósido cardiotónico predominante en Thevetia peruviana. Sci Tech 298–303

  6. Rincón-pérez J, Rodríguez-hernández L, Ruíz-valdiviezo VM et al (2016) Fatty acids profile, phenolic compounds and antioxidant capacity in elicited callus of Thevetia peruviana (Pers.) K. Schum. J Oleo Sci 318:311–318

    Article  Google Scholar 

  7. Cloutier M, Bouchard-Marchand É, Perrier M, Jolicoeur M (2008) A Predictive nutritional model for plant cells and hairy roots. Biotechnol Bioeng 99:189–200. doi:10.1002/bit

    Article  CAS  Google Scholar 

  8. Leduc M, Tikhomiroff C, Cloutier M et al (2006) Development of a kinetic metabolic model: application to Catharanthus roseus hairy root. Bioprocess Biosyst Eng 28:295–313. doi:10.1007/s00449-005-0034-z

    Article  CAS  Google Scholar 

  9. Alberton KPF, Alberton AL, Di Maggio JA et al (2015) Simultaneous parameters identifiability and estimation of an E. coli metabolic network model. Biomed Res Int 2015:1–21. doi:10.1155/2015/454765

    Article  Google Scholar 

  10. Balsa-Canto E, Alonso AA, Banga JR (2010) An iterative identification procedure for dynamic modeling of biochemical networks. BMC Syst Biol 4:11. doi:10.1186/1752-0509-4-11

    Article  Google Scholar 

  11. Brun R, Reichert P, Ku HR (2001) Practical identi ability analysis of large environmental simulation models. Water Resour Res 37:1015–1030

    Article  Google Scholar 

  12. Degenring D, Froemel C, Dikta G, Takors R (2004) Sensitivity analysis for the reduction of complex metabolism models. J Process Control 14:729–745. doi:10.1016/j.jprocont.2003.12.008

    Article  CAS  Google Scholar 

  13. Jiménez-Hornero JE, Santos-Dueñas IM, García-García I (2009) Optimization of biotechnological processes. The acetic acid fermentation. Part II: practical identifiability analysis and parameter estimation. Biochem Eng J 45:7–21. doi:10.1016/j.bej.2009.01.010

    Article  Google Scholar 

  14. Kravaris C, Hahn J, Chu Y (2013) Advances and selected recent developments in state and parameter estimation. Comput Chem Eng 51:111–123. doi:10.1016/j.compchemeng.2012.06.001

    Article  CAS  Google Scholar 

  15. López DC, Barz T, Peñuela M et al (2013) Model-based identifiable parameter determination applied to a simultaneous saccharification and fermentation process model for bio-ethanol production. Biotechnol Prog 29:1064–1082. doi:10.1002/btpr.1753

    Article  Google Scholar 

  16. Mailier J, Delmotte A, Cloutier M et al (2011) Parametric sensitivity analysis and reduction of a detailed nutritional model of plant cell cultures. Biotechnol Bioeng 108:1108–1118. doi:10.1002/bit.23030

    Article  CAS  Google Scholar 

  17. Weijers SR, Vanrolleghem PA (1997) A procedure for selecting best identifiable parameters in calibrating activated sludge model No. 1 to full-scale plant data. Water Sci Technol 36:69–79. doi:10.1016/S0273-1223(97)00463-0

    Article  CAS  Google Scholar 

  18. Yao KZ, Shaw BM, Kou B et al (2003) Modeling ethylene/butene copolymerization with multi-site catalysts: parameter estimability and experimental design. Polym React Eng 11:563–588. doi:10.1081/PRE-120024426

    Article  CAS  Google Scholar 

  19. Almquist J, Cvijovic M, Hatzimanikatis V et al (2014) Kinetic models in industrial biotechnology: improving cell factory performance. Metab Eng 24:38–60. doi:10.1016/j.ymben.2014.03.007

    Article  CAS  Google Scholar 

  20. Bramble JL, Graves DJ, Brodelius P (1991) Calcium and phosphate effects on growth and alkaloid production in Coffea arabica: experimental results and mathematical model. Biotechnol Bioeng 37:859–868. doi:10.1002/bit.260370910

    Article  CAS  Google Scholar 

  21. Hooker BS, Lee JM (1992) Application of a new structured model to tabacco cell cultures. Biotechnology 39:765–774

    CAS  Google Scholar 

  22. van Gulik WM, ten Hoopen HJG, Heijnen JJ (1992) Kinetics and stoichiometry of growth of plant cell cultures of Catharanthus roseus and Nicotiana tabacum in batch and continuous fermentors. Biotechnol Bioeng 40:863–874. doi:10.1002/bit.260400802

    Article  Google Scholar 

  23. Takeda T, Takeuchi T, Seki M et al (1998) Kinetic analysis of cell growth and vitamin E production in plant cell culture of Carthamus tinctorius using a structured model. Biochem Eng J 1:233–242

    Article  CAS  Google Scholar 

  24. Choi J-W, Kim Y-K, Lee WH et al (1999) Kinetic model of cell growth and secondary metabolite synthesis in plant cell culture of Thalictrum rugosum. Biotechnol Bioprocess Eng 4:129–137

    Article  CAS  Google Scholar 

  25. Choi J-W, Kim Y-K, Park H-K et al (1999) Kinetic model for biotransformation of digitoxin in plant cell suspension culture of Digitalis lanata. Biotechnol Bioprocess Eng 4:281–286

    Article  CAS  Google Scholar 

  26. Schlatmann JE, Ten Hoopen HJG, Heijnen JJ (1999) A simple structured model for maintenance, biomass formation, and ajmalicine production by nondividing Catharanthus roseus cells. Biotechnol Bioeng 66:147–157. doi:10.1002/(SICI)1097-0290(1999)66:3<147:AID-BIT2>3.0.CO;2-N

    Article  CAS  Google Scholar 

  27. Pires Cabral PMN, Lima Costa ME, Cabral JMS (2000) A structured growth model for Cynara cardunculus cell suspension. Bioprocess Eng 23:199–203. doi:10.1007/s004499900151

    Article  CAS  Google Scholar 

  28. Zhang J, Su WW (2002) Estimation of intracellular phosphate content in plant cell cultures using an extended Kalman filter. J Biosci Bioeng 94:8–14. doi:10.1263/jbb.94.8

    Article  CAS  Google Scholar 

  29. Li C, Yuan Y-J, Wu J-C, Hu Z-D (2003) A structured kinetic model for suspension cultures of Taxus chinensis var. mairei induced by an oligosaccharide from Fusarium oxysporum. Biotechnol Lett 25:1335–1343. doi:10.1023/A:1024980420790

    Article  CAS  Google Scholar 

  30. Villadsen J, Nielsen J, Lidén G (2011) Bioreaction Engineering principles, Third

  31. Bellgardt K-H (2000) Bioprocess Models. In: Schügerl K, Bellgardt K-H (eds) Bioreact. Eng. Model. Control, 1st ed. Springer-Verlag Berlin Heidelberg, pp 44–105

  32. Wiechert W, Noack S (2011) Mechanistic pathway modeling for industrial biotechnology: challenging but worthwhile. Curr Opin Biotechnol 22:604–610. doi:10.1016/j.copbio.2011.01.001

    Article  CAS  Google Scholar 

  33. Alberton KPF, Alberton AL, Di Maggio JA et al (2013) Accelerating the parameters identifiability procedure: set by set selection. Comput Chem Eng 55:181–197. doi:10.1016/j.compchemeng.2013.04.014

    Article  CAS  Google Scholar 

  34. Cloutier M, Perrier M, Jolicoeur M (2007) Dynamic flux cartography of hairy roots primary metabolism. Phytochemistry 68:2393–2404. doi:10.1016/j.phytochem.2007.04.028

    Article  CAS  Google Scholar 

  35. Cloutier M, Chen J, Tatge F et al (2009) Kinetic metabolic modelling for the control of plant cells cytoplasmic phosphate. J Theor Biol 259:118–131. doi:10.1016/j.jtbi.2009.02.022

    Article  CAS  Google Scholar 

  36. Cloutier M, Chen J, De Dobbeleer C et al (2009) A systems approach to plant bioprocess optimization. Plant Biotechnol J 7:939–951. doi:10.1111/j.1467-7652.2009.00455.x

    Article  CAS  Google Scholar 

  37. Prakash G, Srivastava AK (2006) Modeling of azadirachtin production by Azadirachta indica and its use for feed forward optimization studies. Biochem Eng J 29:62–68. doi:10.1016/j.bej.2005.02.027

    Article  CAS  Google Scholar 

  38. Omar R, Abdullah MA, Hasan MA et al (2006) Kinects and modelling of cell growth and substrate uptake in Centella asiatica cell culture. Biotechnol Bioprocess Eng 11:223–229

    Article  CAS  Google Scholar 

  39. Yang SM, Shao DG, Luo YJ (2005) A novel evolution strategy for multiobjective optimization problem. Appl Math Comput 170:850–873. doi:10.1016/j.amc.2004.12.025

    Google Scholar 

  40. Shibasaki N, Obika R, Yonemoto T, Tadaki T (1995) Kinetic-analysis for effect of initial substrate concentration on growth and secondary metabolite production in cultures of Nicotiana-tabacum. J Chem Technol Biotechnol 63:201–208

    Article  CAS  Google Scholar 

  41. Westgate PJ, Emery AH, Hasegawa PM, Heinstein PF (1991) Microbiology biotechnology growth of Cephalotaxus harrin ltonia plant-cell cultures. Appl Microbiol Biotechnol 34:798–803

    Article  CAS  Google Scholar 

  42. Li C, Yuan Y, Wu J, Hu Z (2003) A structured kinetic model for suspension cultures of Taxus chinensis var. mairei induced by an oligosaccharide from Fusarium oxysporum. Biotechnol Lett 25:1335–1343

    Article  CAS  Google Scholar 

  43. Sun J, Zhang C, Zhang X et al (2012) Extracellular ATP signaling and homeostasis in plant cells. Plant Signal Behav 7:566–569. doi:10.4161/psb.19857

    Article  CAS  Google Scholar 

  44. McLean KAP, McAuley KB (2012) Mathematical modelling of chemical processes-obtaining the best model predictions and parameter estimates using identifiability and estimability procedures. Can J Chem Eng 90:351–366. doi:10.1002/cjce.20660

    Article  CAS  Google Scholar 

  45. Ataíde F, Hitzmann B (2009) When is optimal experimental design advantageous for the analysis of Michaelis–Menten kinetics? Chemom Intell Lab Syst 99:9–18. doi:10.1016/j.chemolab.2009.07.005

    Article  Google Scholar 

  46. Yue H, Brown M, Knowles J et al (2006) Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kappaB signalling pathway. Mol BioSyst 2:640–649. doi:10.1039/b609442b

    Article  CAS  Google Scholar 

  47. Bendtsen AB, Glarborg P, Dam-Johansen K (2001) Visualization methods in analysis of detailed chemical kinetics modelling. Comput Chem 25:161–170. doi:10.1016/S0097-8485(00)00077-2

    Article  CAS  Google Scholar 

  48. Chu Y, Hahn J (2009) Parameter set selection via clustering of parameters into pairwise indistinguishable groups of parameters. Ind Eng Chem Res 48:6000–6009. doi:10.1021/ie800432s

    Article  CAS  Google Scholar 

  49. De Bruyn JW, Van Keulen HA, Ferguson JHA (1968) Rapid method for the simultaneous determination of glucose and fructose using anthrone reagent. JSci Fd Agric 19:597–601

    Article  Google Scholar 

  50. Cataldo DA, Maroon M, Schrader LE, Youngs VL (1975) Rapid colorimetric determination of nitrate in plant tissue by nitration of salicylic acid. Commun Soil Sci Plant Anal 6:71–80. doi:10.1080/00103627509366547

    Article  CAS  Google Scholar 

  51. Eaton A, Clesceri L, Greenberg A, Franson M (1998) Standard methods for the examination of water and wastewater. part 4000 inorganic nonmetallic constituents. Stand. Methods Exam. Water Wastewater

  52. Petersen B, Gernaey K, Vanrolleghem PA (2001) Practical identifiability of model parameters by combined respirometric-titrimetric measurements. Water Sci Technol 43:347–355

    CAS  Google Scholar 

  53. Gadkar KG, Gunawan R, Doyle FJ (2005) Iterative approach to model identification of biological networks. BMC Bioinformatics 6:155. doi:10.1186/1471-2105-6-155

    Article  Google Scholar 

  54. Krook J, Vreugdenhil D, Van Der Plas LHW (2000) Uptake and phosphorylation of glucose and fructose in Daucus carota cell suspensions are differently regulated. Plant Physiol Biochem 38:603–612. doi:10.1016/S0981-9428(00)00776-2

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank the Universidad de Antioquia for the financial support provided to this work through the CODI Grant MDC11-1-08. Furthermore, the support provided by the Universidad Nacional de Colombia, Medellin campus, and the Bioprocesses Research Group of Universidad de Antioquia during the experimental part developed in this work is gratefully acknowledged. Adriana Villegas is grateful to the Universidad Cooperativa de Colombia for the Sustainability Strategy Grant 1336, Universidad de Antioquia, and the LSU Agricultural Center for funding her internship at the Audubon Sugar Institute.

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Correspondence to Adriana Villegas.

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Villegas, A., Arias, J.P., Aragón, D. et al. Structured model and parameter estimation in plant cell cultures of Thevetia peruviana . Bioprocess Biosyst Eng 40, 573–587 (2017). https://doi.org/10.1007/s00449-016-1722-6

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