Modeling and Optimizing Culture Medium Mineral Composition for in vitro Propagation of Actinidia arguta

Abstract

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 have demonstrated great advantages when used in large dataset analysis. In this study, design of experiments (DOE) and three machine learning (ML) algorithms, 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 hardy kiwi (Actinidia arguta) in vitro micropropagation. 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 modeled and analyzed simultaneously. Neurofuzzy logic models demonstrated that just 11 ions (five macronutrients (N, K, P, Mg, and S) and six micronutrients (Cl, Fe, B, Mo, Na, and I)) out of the 18 tested explained the results obtained. The rules “IF – THEN” allow for easy deduction of 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 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 in vitro micropropagation: (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 other key media components, such as vitamins, PGRs, or organic compounds, particularly glycine, which could modulate the effect of the ions and needs further research for confirmation.

Comparative Analysis of Machine Learning and Evolutionary Optimization Algorithms for Precision Micropropagation of Cannabis sativa: Prediction and Validation of in vitro Shoot Growth and Development Based on the Optimization of Light and Carbohydrate Sources

“…Historically, micropropagation systems were refined using traditional statistical models to sequentially manipulate and optimize single factors. This approach often requires hundreds or even thousands of treatments to be tested, and even then sequential optimization does not account for interactions and can miss the best combinations ( García-Pérez et al, 2020 ; Hameg et al, 2020 ). Due to the cost and time requirements, most species are cultured in conditions optimized for other species with minor modifications and are not fully optimized for any given application.…”

Effect of medium composition, genotype and age of explant on the regeneration of hexaploid plants from endosperm culture of tetraploid kiwiberry (Actinidia arguta)

“…The continuation of the shoots growth from the meristems could be the crucial problem during plant regeneration. The impact of the combination of many factors, like the media composition, light intensity, and CO 2 concentration on the shoots quality is extensively analyzed for Actinidia (Infante et al 1986;Hameg et al 2020).…”

Computer-Based Tools Unmask Critical Mineral Nutrient Interactions in Hoagland Solution for Healthy Kiwiberry Plant Acclimatization

“…cv. "Issai" were obtained from the Department of Plant Biology and Soil Sciences (University of Vigo) as described in detail elsewhere (Hameg, 2019;Hameg et al, 2020). Briefly, micro-shoots were proliferated in Cheng medium (Cheng, 1975) containing 1 mg/L N6benzyladenine (BAP), 1 mg/L gibberellic acid (GA3), 30 g/L sucrose, and 8 g/L agar.…”