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Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem...

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Autores principales: Arab, Mohammad M., Yadollahi, Abbas, Shojaeiyan, Abdolali, Ahmadi, Hamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069296/
https://www.ncbi.nlm.nih.gov/pubmed/27807436
http://dx.doi.org/10.3389/fpls.2016.01526
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author Arab, Mohammad M.
Yadollahi, Abbas
Shojaeiyan, Abdolali
Ahmadi, Hamed
author_facet Arab, Mohammad M.
Yadollahi, Abbas
Shojaeiyan, Abdolali
Ahmadi, Hamed
author_sort Arab, Mohammad M.
collection PubMed
description One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts ([Formula: see text] , [Formula: see text] , [Formula: see text] , Ca(2+), K(+), [Formula: see text] , Mg(2+), and Cl(−)) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R(2) correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, [Formula: see text] and [Formula: see text] had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 [Formula: see text] , 14 [Formula: see text] , 5 Ca(2+), 25.9 K(+), 0.7 Mg(2+), 1.1 [Formula: see text] , 4.7 [Formula: see text] , and 0.96 Cl(−). The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity.
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spelling pubmed-50692962016-11-02 Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock Arab, Mohammad M. Yadollahi, Abbas Shojaeiyan, Abdolali Ahmadi, Hamed Front Plant Sci Plant Science One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts ([Formula: see text] , [Formula: see text] , [Formula: see text] , Ca(2+), K(+), [Formula: see text] , Mg(2+), and Cl(−)) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R(2) correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, [Formula: see text] and [Formula: see text] had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 [Formula: see text] , 14 [Formula: see text] , 5 Ca(2+), 25.9 K(+), 0.7 Mg(2+), 1.1 [Formula: see text] , 4.7 [Formula: see text] , and 0.96 Cl(−). The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity. Frontiers Media S.A. 2016-10-19 /pmc/articles/PMC5069296/ /pubmed/27807436 http://dx.doi.org/10.3389/fpls.2016.01526 Text en Copyright © 2016 Arab, Yadollahi, Shojaeiyan and Ahmadi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Arab, Mohammad M.
Yadollahi, Abbas
Shojaeiyan, Abdolali
Ahmadi, Hamed
Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock
title Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock
title_full Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock
title_fullStr Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock
title_full_unstemmed Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock
title_short Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock
title_sort artificial neural network genetic algorithm as powerful tool to predict and optimize in vitro proliferation mineral medium for g × n15 rootstock
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069296/
https://www.ncbi.nlm.nih.gov/pubmed/27807436
http://dx.doi.org/10.3389/fpls.2016.01526
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