Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

Abstract

The main aim of the present investigation is modeling and optimization of a new culture medium for in vitro rooting of G×N15 rootstock using an artificial neural network-genetic algorithm (ANN-GA). Six experiments for assessing different media culture, various concentrations of Indole – 3- butyric acid, different concentrations of Thiamine and Fe-EDDHA were designed. The effects of five ionic macronutrients (NH4+, NO3−, Ca2+, K+ and Cl−) on five growth parameters [root number (RN), root length (RL), root percentage (R%), fresh (FW) and dry weight (DW)] were evaluated using the ANN-GA method. The R2 correlation values of 0.88, 0.88, 0.98, 0.94 and 0.87 between observed and predicted values were acquired for all five growth parameters, respectively. The ANN-GA results indicated that among the input variables, K+ (7.6) and NH4+ (4.4), K+ (7.7) and Ca2+ (2.8), K+ (36.7) and NH4+ (4.3), K+ (14.7) and NH4+ (4.4) and K+ (7.6) and NH4+ (4.3) had the highest values of variable sensitivity ratio (VSR) in the data set, for RN, RL, R%, FW and DW, respectively. ANN-GA optimized LS medium for G×N15 rooting contained optimized amounts of 1 mg L−1 IBA, 100, 150, or 200 mg L−1 Fe-EDDHA and 1.6 mg L−1 Thiamine. The efficiency of the optimized culture media was compared to other standard media for Prunus rooting and the results indicated that the optimized medium is more efficient than the others.

Mathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic Algorithm

“…Paclitaxel biosynthesis is the complex biological process that requires the accurate techniques for modeling and optimization. MLP-GA has been efficiently used to solve problems with extremely difficult and unknown solution in various fields (Jamshidi et al, 2016;Arab et al, 2018;Eftekhari et al, 2018;Sheikhi et al, 2020). A growing interest in ANN has mostly been because of its power in solving the problems in a broad range of fields, their ability for modeling nonlinear and complex relationships, prediction ability of the unseen relationships on the unseen data, and having no need of a specification of data statistical distribution (Mahanta, 2017).…”

“…It processes information and makes decision in systems involving vagueness and uncertainty (Patnaik, 1999;Gago et al, 2010). This technology has been widely used as a predictive instrument in a broad range of fields including ecology, food science, agriculture, environmental sciences, plant biology, pharmaceutical research, and biotechnology (Hilbert and Ostendorf, 2001;Daniel et al, 2008;Huang, 2009;Arab et al, 2018;Hesami et al, 2019a;Hesami et al, 2019b;Hesami et al, 2019c;Sheikhi et al, 2020). Multilayer perceptron (MLP), one of the most popular types of ANN, exhibits superior predictive ability as compared to traditional statistical methods to approximate the mathematical functions for analyzing and interpreting different unforeseeable data sets (Ahmadi and Golian, 2011;Jamshidi et al, 2016).…”

“…GA is the evolutionary algorithm making superb solutions to problems and has been applied for bioprocess optimization in plant biology (Osama et al, 2015;Jamshidi et al, 2016;Arab et al, 2018). Indeed, GA is a search algorithm inspired by natural selection and genetics concepts (Holland, 1992).…”

“…Multilayer perceptron-genetic algorithm (MLP-GA), integrating MLP with GA ( Figure 1), causes achieving an accurate model for prediction and optimization of biological process (Jamshidi et al, 2016;Arab et al, 2018;Eftekhari et al, 2018).…”

Choosing the best embryogenesis medium in carrot by data mining technology

“…Recently Multilayer Perceptron (MLP) and neuro-fuzzy logic were used for modeling and predicting in vitro culture process such as shoot proliferation of Prunus rootstocks (1,6), in vitro rooting of Prunus rootstocks (7), in vitro sterilization of chrysanthemum (8), predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf) of pear (9), prediction and optimization of the plant hormones concentration and combinations for in vitro proliferation of Garnem (G × N15) rootstock of Vegetative (1), in vitro rooting and acclimatization of Vitis vinifera L. (10). Different arti cial neural networks (ANNs) such as MLP, Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Radial basis function (RBF) can be used to interpret and process different data.…”