Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G x 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 x 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 (NH4+ NO3 PO42 Ca2+ K+ SO42 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 x 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, NH4+ and NO3 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 NO3 14 NH4+ 5 Ca2+ 25.9 K+ 0.7 Mg2+ 1.1 PO42 4.7 SO42 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 x length of microshoots) YAS was found to be superior to other media for in vitro multiplication of G x 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.
Front. Plant Sci.