Modeling and Optimizing Culture Medium Mineral Composition for in vitro Propagation of Actinidia arguta - Hameg, Radhia; Arteta, Tomás A.; Landin, Mariana; Gallego, Pedro P.; Barreal, M. Esther (Frontiers in Plant Science, 2020)
The design of plant tissue culture media remains a complicated task due to the interactions of many factors. The use of computer-based tools is still very scarce, although they demonstrated great advantages in large datasets analysis. In this study, design of experiments (DOE) and three machine learning (ML) approaches, artificial neural networks (ANNs), fuzzy logic and genetic algorithms (GA), were combined to decipher the key minerals and predict the optimal combination of salts for healthy tissue culture. A five-factor experimental design of 33 salt treatments was defined using DOE. Later, the effect of the ionic variations generated by these five factors on three morpho-physiological growth responses: shoot number (SN), shoot length (SL) and leaves area (LA) and on three quality responses: shoots quality (SQ), basal callus (BC) and hyperhydricity (H) were modelled and analyzed simultaneously. Neurofuzzy logic models demonstrated that just 11 ions: 5 macronutrients (N, K, P, Mg & S) and 6 micronutrients (Cl, Fe, B, Mo, Na & I) out of the 18 tested, explained the results obtained. The rules "IF - THEN", allow to easily deduce the concentration range of each ion that causes a positive effect on growth responses and guarantees healthy shoots. Secondly, using a combination of ANNs-GA, a new optimized medium was designed and the desired values for each response parameter were accurately predicted. Finally, the experimental validation of the model showed that the optimized R medium significantly promotes SQ and reduces BC and H compared to standard media generally used in plant tissue culture. This study demonstrated the suitability of computer-based tools for improving plant culture media: i) DOE to design more efficient experiments saving time and cost; ii) ANNs combined with fuzzy logic to understand the cause-effect of several factors on the response parameters, and iii) ANNs-GA to predict new mineral media formulation, which improve growth response, avoiding morpho-physiological abnormalities. The lack of predictability on some response parameters can be due to others key media components, such as vitamins, PGRs or organic compounds, particularly glycine, could modulate the effect of the ions and need further research for confirmation.