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

Mohammad M Arab; Abbas Yadollahi; Abdolali Shojaeiyan; Hamed Ahmadi

doi:10.3389/fpls.2016.01526

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

Front Plant Sci. 2016 Oct 19:7:1526. doi: 10.3389/fpls.2016.01526. eCollection 2016.

Authors

Mohammad M Arab¹, Abbas Yadollahi², Abdolali Shojaeiyan², Hamed Ahmadi³

Affiliations

¹ Department of Horticultural Science, Faculty of Agriculture, Tarbiat Modares UniversityTehran, Iran; Department of Horticultural Sciences, College of Abooraihan, University of TehranTehran, Iran.
² Department of Horticultural Science, Faculty of Agriculture, Tarbiat Modares University Tehran, Iran.
³ Department of Poultry Sciences, Faculty of Agriculture, Tarbiat Modares University Tehran, Iran.

Abstract

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], Ca²⁺, K⁺, [Formula: see text], Mg²⁺, 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 R² 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 Ca²⁺, 25.9 K⁺, 0.7 Mg²⁺, 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.

Keywords: G × N15 rootstock; Prunus micropropagation; artificial neural network (ANN); genetic algorithm (GA); ion macronutrients; proliferation.