Modeling and optimizing In vitro sterilization of chrysanthemum via multilayer perceptron-non-dominated sorting genetic algorithm-II (MLP-NSGAII)

Frontiers in Plant Science

Volumn 10, Issue , 2019, Pages

  Hesami, Mohsen ^a   Naderi, Roohangiz ^a   Tohidfar, Masoud ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b SHAHID BEHESHTI UNIVERSITY   (Iran)

Author keywords

Artificial intelligence; Data driven model; In vitro culture; Optimization algorithm; Sensitivity analysis

Indexed keywords

EID: 85064208943     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2019.00282     Document Type: Article

References (63)

1. Alanagh, E. N., Garoosi, G-A., Haddad, R., Maleki, S., Landín, M., Gallego, P. P., (2014). Design of tissue culture media for efficient Prunus rootstock micropropagation using artificial intelligence models. Plant Cell Tissue Organ Cult., 117, 349–359. 10.1007/s11240-014-0444-1.
2. Altan, F., Bürün, B., Sahin, N., (2010). Fungal contaminants observed during micropropagation of Lilium candidum L. and the effect of chemotherapeutic substances applied after sterilization. Afr. J. Biotechnol., 9, 991–995. 10.5897/AJB08.090.
3. Arab, M. M., Yadollahi, A., Eftekhari, M., Ahmadi, H., Akbari, M., Khorami, S. S., (2018). Modeling and optimizing a new culture medium for in vitro rooting of G × N15 Prunus rootstock using artificial neural network-genetic algorithm. Sci. Rep., 8:e9977. 10.1038/s41598-018-27858-4.
4. Arab, M. M., Yadollahi, A., Hosseini-Mazinani, M., Bagheri, S., (2014). Effects of antimicrobial activity of silver nanoparticles on in vitro establishment of G × N15 (hybrid of almond × peach) rootstock. J. Genet. Eng. Biotechnol., 12, 103–110. 10.1016/j.jgeb.2014.10.002.
5. Arab, M. M., Yadollahi, A., Shojaeiyan, A., Ahmadi, H., (2016). Artificial neural network genetic algorithm as powerful tool to predict and optimize in vitro proliferation mineral medium for G × N15 rootstock. Front. Plant Sci., 7:e1526. 10.3389/fpls.2016.01526, 27807436.
6. Araghinejad, S., Hosseini-Moghari, S -M., Eslamian, S., (2017). Application of data-driven models in drought forecasting in Handbook of Drought and Water Scarcity, ed Eslamian, S., (NewYork, NY: CRC Press), 423–440.
7. Arora, A., Sairam, R., Srivastava, G., (2002). Oxidative stress and antioxidative system in plants. Curr. Sci., 82, 1227–1238.
8. Assareh, M. H., Sardabi, H., (2005). Macropropagation and micropropagation of Ziziphus spina-christi. Pesquisa Agropecuária Brasileira, 40, 459–465. 10.1590/S0100-204X2005000500006.
9. Bais, H. P., Sudha, G. S., Ravishankar, G. A., (2000). Putrescine and silver nitrate influences shoot multiplication, in vitro flowering and endogenous titers of polyamines in Cichorium intybus L. cv. Lucknow local. J. Plant Growth Regul., 19, 238–248. 10.1007/s003440000012, 11038231.
10. Boyette, M., Ritchie, D., Carballo, S., Blankenship, S., Sanders, D., (1993). Chlorination and postharvest disease control. Horttechnology, 3, 395–400. 10.21273/HORTTECH.3.4.395.
11. Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S-M., Loáiciga, H. A., (2016a). Development of a comparative multiple criteria framework for ranking pareto optimal solutions of a multiobjective reservoir operation problem. J. Irrigation Drainage Eng., 142:e04016019. 10.1061/(ASCE)IR.1943-4774.0001028.
12. Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S-M., Loáiciga, H. A., (2016b). WASPAS application and evolutionary algorithm benchmarking in optimal reservoir optimization problems. J. Water Resour. Plann. Manag., 143:04016070. 10.1061/(ASCE)WR.1943-5452.0000716.
13. Campbell, C. T., Tomes, D. T., (1984). Establishment and multiplication of red clover plants by in vitro shoot tip culture. Plant Cell Tissue Organ Cult., 3, 49–57. 10.1007/BF00035920.
14. Curvetto, N., Marinangeli, P., Mockel, G., (2006). Hydrogen peroxide in micropropagation of Lilium: a comparison with a traditional methodology. Biocell, 30, 497–500. 17375470.
15. Da Silva, J. A. T., (2003). Chrysanthemum: advances in tissue culture, cryopreservation, postharvest technology, genetics and transgenic biotechnology. Biotechnol. Adv., 21, 715–766. 10.1016/S0734-9750(03)00117-4.
16. Da Silva, J. A. T., Kulus, D., (2014). Chrysanthemum biotechnology: discoveries from the recent literature. Folia Horticult., 26, 67–77. 10.2478/fhort-2014-0007.
17. Da Silva, J. A. T., Kulus, D., Zhang, X., Zeng, S., Ma, G., Piqueras, A., (2016a). Disinfection of explants for saffron (Crocus sativus) tissue culture. Environ.Exp. Biol., 14, 183–198. 10.22364/eeb.14.25.
18. Da Silva, J. A. T., Winarto, B., Dobránszki, J., Cardoso, J. C., Zeng, S., (2016b). Tissue disinfection for preparation of Dendrobium in vitro culture. Folia Horticult., 28, 57–75. 10.1515/fhort-2016-0008.
19. Eslamian, S., Abedi-Koupai, J., Amiri, M., Gohari, S., (2009). Estimation of daily reference evapotranspiration using support vector. Res. J. Environ. Sci., 3, 439–447. 10.2166/wcc.2018.003.
20. Farooq, S., Farooq, T., Rao, T., (2002). Micropropagation of Annona squamosa L. using nodal explants. Pak. J. Biol. Sci., 5, 43–46. 10.3923/pjbs.2002.43.46.
21. Gago, J., Landín, M., Gallego, P. P., (2010a). A neurofuzzy logic approach for modeling plant processes: a practical case of in vitro direct rooting and acclimatization of Vitis vinifera L. Plant Sci., 179, 241–249. 10.1016/j.plantsci.2010.05.009.
22. Gago, J., Martínez-Núñez, L., Landín, M., Flexas, J., Gallego, P. P., (2014). Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology. PLoS ONE, 9:e85989. 10.1371/journal.pone.0085989, 24465829.
23. Gago, J., Martínez-Núñez, L., Landín, M., Gallego, P., (2010b). Artificial neural networks as an alternative to the traditional statistical methodology in plant research. J. Plant Physiol., 167, 23–27. 10.1016/j.jplph.2009.07.007, 19716625.
24. Gago, J., Pérez-Tornero, O., Landín, M., Burgos, L., Gallego, P. P., (2011). Improving knowledge of plant tissue culture and media formulation by neurofuzzy logic: a practical case of data mining using apricot databases. J. Plant Physiol., 168, 1858–1865. 10.1016/j.jplph.2011.04.008, 21676490.
25. Hesami, M., Daneshvar, M. H., (2016). Development of a regeneration protocol through indirect organogenesis in Chenopodium quinoa willd. Indo. Am. J. Agric. Vet. Sci., 4, 25–32. 10.1007/ijlbpr_56e24bb54f0a9.pdf.
26. Hesami, M., Daneshvar, M. H., (2018). In vitro adventitious shoot regeneration through direct and indirect organogenesis from seedling-derived hypocotyl segments of Ficus religiosa L.: an important medicinal plant. HortScience, 53, 55–61. 10.21273/HORTSCI12637-17.
27. Hesami, M., Daneshvar, M. H., Lotfi-Jalalabadi, A., (2017a). Effect of sodium hypochlorite on control of in vitro contamination and seed germination of Ficus religiosa. Iran. J. Plant Physiol., 7, 2157–2162. 10.22034/ijpp.2017.537980.
28. Hesami, M., Daneshvar, M. H., Yoosefzadeh-Najafabadi, M., (2018a). Establishment of a protocol for in vitro seed germination and callus formation of Ficus religiosa L., an important medicinal plant. Jundishapur J. Nat. Pharm. Prod., 13:e62682. 10.5812/jjnpp.62682.
29. Hesami, M., Daneshvar, M. H., Yoosefzadeh-Najafabadi, M., Alizadeh, M., (2018b). Effect of plant growth regulators on indirect shoot organogenesis of Ficus religiosa through seedling derived petiole segments. J. Genet. Eng. Biotechnol., 16, 175–180. 10.1016/j.jgeb.2017.11.001, 30647720.
30. Hesami, M., Naderi, R., Yoosefzadeh-Najafabadi, M., (2018c). Optimizing sterilization conditions and growth regulator effects on in vitro shoot regeneration through direct organogenesis in Chenopodium quinoa. BioTechnologia, 99, 49–57. 10.5114/bta.2018.73561.
31. Hesami, M., Naderi, R., Yoosefzadeh-Najafabadi, M., Rahmati, M., (2017b). Data-driven modeling in plant tissue culture. J. Appl. Environ. Biol. Sci., 7, 37–44.
32. Hornik, K., Stinchcombe, M., White, H., (1989). Multilayer feedforward networks are universal approximators. Neural Netw., 2, 359–366. 10.1016/0893-6080(89)90020-8.
33. Hosseini-Moghari, S-M., Araghinejad, S., Azarnivand, A., (2017). Drought forecasting using data-driven methods and an evolutionary algorithm. Model. Earth Syst. Environ., 3, 1675–1689. 10.1007/s40808-017-0385-x.
34. Jamshidi, S., Yadollahi, A., Ahmadi, H., Arab, M., Eftekhari, M., (2016). Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models. Front. Plant Sci., 7:e274. 10.3389/fpls.2016.00274, 27066013.
35. Khorsandi, Z., Mahdavi, M., Salajeghe, A., Eslamian, S., (2011). Neural network application for monthly precipitation data reconstruction. J. Environ. Hydrol., 19, 1–12.
36. Kim, D. H., Gopal, J., Sivanesan, I., (2017). Nanomaterials in plant tissue culture: the disclosed and undisclosed. RSC Adv., 7, 36492–36505. 10.1039/C7RA07025J.
37. Li, X., Wong, K -C., (2017). Elucidating genome-wide protein-RNA interactions using differential evolution. IEEE/ACM Trans. Comput. Biol. Bioinform., 16, 272–282. 10.1109/TCBB.2017.2776224, 29990254.
38. Li, X., Wong, K-C., (2018a). Evolutionary multiobjective clustering and its applications to patient stratification. IEEE Trans. Cybern., 99, 1–14. 10.1109/TCYB.2018.2817480.
39. Li, X., Wong, K-C., (2018b). Multiobjective patient stratification using evolutionary multiobjective optimization. IEEE J. Biomed. Health Inform., 22, 1619–1629. 10.1109/JBHI.2017.2769711, 29990162.
40. Li, X., Zhang, S., Wong, K-C., (2018a). Nature-inspired multiobjective epistasis elucidation from genome-wide association studies. IEEE/ACM Trans. Comput. Biol. Bioinform. [Epub ahead of print]. 10.1109/TCBB.2018.2849759, 29994485.
41. Li, X., Zhang, S., Wong, K-C., (2018b). Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning. Bioinformatics, e1056. [Epub ahead of print]. 10.1093/bioinformatics/bty1056, 30596898.
42. Mandal, A., Datta, S., (2005). Direct somatic embryogenesis and plant regeneration from ray florets of chrysanthemum. Biol. Plant., 49, 29–33. 10.1007/s10535-005-0033-6.
43. Marinescu, M., Teodorescu, A., Sutan, N., (2013). Preliminary results on the in vitro propagation by leaf explants and axillary buds of Iris aphylla L. J. Horticult. For. Biotechnol., 17, 279–282.
44. May, R., Trigiano, R., (1991). Somatic embryogenesis and plant regeneration from leaves of Dendranthema grandiflora. J. Am. Soc. Horticult. Sci., 116, 366–371. 10.21273/JASHS.116.2.366.
45. Mihaljević, I., Dugalić, K., Tomaš, V., Viljevac, M., Pranjić, A., Cmelik, Z.,. (2013). In vitro sterilization procedures for micropropagation of 'Oblačinska' sour cherry. J. Agric. Sci., 58, 117–126. 10.2298/JAS1302117M.
46. Murashige, T., Skoog, F., (1962). A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol. Plant., 15, 473–497. 10.1111/j.1399-3054.1962.tb08052.x.
47. Naing, A. H., Kim, C. K., Yun, B. J., Jin, J. Y., Lim, K. B., (2013). Primary and secondary somatic embryogenesis in Chrysanthemum cv. Euro. Plant Cell Tissue Organ Cult., 112, 361–368. 10.1007/s11240-012-0243-5.
48. Newton, W., Hastings, R., Bosher, J., (1933). Sterilization of narcissus bulbs by immersion in silver nitrate—potassium cyanide solution in vacuo. Can. J. Res., 9, 31–36. 10.1139/cjr33-062.
49. Nezami-Alanagh, E., Garoosi, G-A., Landin, M., Gallego, P. P., (2018). Combining DOE with neurofuzzy logic for healthy mineral nutrition of pistachio rootstocks in vitro culture. Front. Plant Sci., 9:e1474. 10.3389/fpls.2018.01474, 30374362.
50. Nezami-Alanagh, E., Garoosi, G-A., Maleki, S., Landín, M., Gallego, P. P., (2017). Predicting optimal in vitro culture medium for Pistacia vera micropropagation using neural networks models. Plant Cell Tissue Organ Cult., 129, 19–33. 10.1007/s11240-016-1152-9.
51. Niazian, M., Sadat-Noori, S. A., Abdipour, M., Tohidfar, M., Mortazavian, S. M. M., (2018). Image processing and artificial neural network-based models to measure and predict physical properties of embryogenic callus and number of somatic embryos in ajowan (Trachyspermum ammi (L.) Sprague). In Vitro Cell. Dev. Biol. Plant, 54, 54–68. 10.1007/s11627-017-9877-7.
52. Noda, N., Yoshioka, S., Kishimoto, S., Nakayama, M., Douzono, M., Tanaka, Y.,. (2017). Generation of blue chrysanthemums by anthocyanin B-ring hydroxylation and glucosylation and its coloration mechanism. Sci. Adv., 3:e1602785. 10.1126/sciadv.1602785, 28782017.
53. Nongalleima, K., Dikash Singh, T., Amitabha, D., Deb, L., Sunitibala Devi, H., (2014). Optimization of surface sterilization protocol, induction of axillary shoots regeneration in Zingiber zerumbet (L.) Sm. as affected by season. Biol. Rhythm Res., 45, 317–324. 10.1080/09291016.2013.818196.
54. Pavingerová, D., Dostál, J., Bískov,á, R., Benetka, V., (1994). Somatic embryogenesis and Agrobacterium-mediated transformation of chrysanthemum. Plant Sci., 97, 95–101. 10.1016/0168-9452(94)90111-2.
55. Purohit, S. D., Teixeira Da Silva, J., Habibi, N., (2011). Current approaches for cheaper and better micropropagation technologies. Int. J. Plant Dev. Biol., 5, 1–36.
56. Shinoyama, H., Nomura, Y., Tsuchiya, T., Kazuma, T., (2004). A simple and efficient method for somatic embryogenesis and plant regeneration from leaves of chrysanthemum [Dendranthema× grandiflorum (Ramat.) Kitamura]. Plant Biotechnol., 21, 25–33. 10.5511/plantbiotechnology.21.25.
57. Tanaka, K., Kanno, Y., Kudo, S., Suzuki, M., (2000). Somatic embryogenesis and plant regeneration in chrysanthemum (Dendranthema grandiflorum (Ramat.) Kitamura). Plant Cell Rep., 19, 946–953. 10.1007/s002990000225, 30754837.
58. Tang, Z., Fishwick, P. A., (1993). Feedforward neural nets as models for time series forecasting. ORSA J. Comput., 5, 374–385. 10.1287/ijoc.5.4.374.
59. Wanas, N., Auda, G., Kamel, M. S., Karray, F., (1998). On the optimal number of hidden nodes in a neural network, in IEEE Canadian Conference on Electrical and Computer Engineering (Waterloo, ON), 918–921. 10.1109/CCECE.1998.685648.
60. Wang, Y., Shen, Y., Zhang, X., Cui, G., Sun, J., (2018). An Improved Non-Dominated Sorting Genetic Algorithm-II (INSGA-II) applied to the design of DNA codewords. Math. Comput. Simul., 151, 131–139. 10.1016/j.matcom.2018.03.011.
61. Wong, F. S., (1991). Time series forecasting using backpropagation neural networks. Neurocomputing, 2, 147–159. 10.1016/0925-2312(91)90045-D.
62. Xu, P., Zhang, Z., Wang, B., Xia, X., Jia, J., (2012). Somatic embryogenesis and plant regeneration in chrysanthemum (Yuukou). Plant Cell Tissue Organ Cult., 111, 393–397. 10.1007/s11240-012-0201-2.
63. Xu, T., Zhang, L., Sun, X., Tang, K., (2005). Efficient in vitro plant regeneration of Pinellia ternata (Thunb) Breit. Acta Biol. Crac. Ser. Bot., 2, 27–32.