Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

Abstract

Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks coupled to fuzzy logic, known as neurofuzzy logic, to determine the critical factors affecting the mineral nutrition of medicinal plants belonging to Bryophyllum subgenus cultured in vitro. The application of neurofuzzy logic algorithms facilitate the interpretation of the results, as the technology is able to generate useful and understandable “IF-THEN” rules, that provide information about the factor(s) involved in a certain response. In this sense, ammonium, sulfate, molybdenum, copper and sodium were the most important nutrients that explain the variation in the in vitro culture establishment of the medicinal plants in a species-dependent manner. Thus, our results indicate that Bryophyllum spp. display a fine-tuning regulation of mineral nutrition, that was reported for the first time under in vitro conditions. Overall, neurofuzzy model was able to predict and identify masked interactions among such factors, providing a source of knowledge (helpful information) from the experimental data (non-informative per se), in order to make the exploitation and valorization of medicinal plants with high phytochemical potential easier.

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.…”

From Ethnomedicine to Plant Biotechnology and Machine Learning: The Valorization of the Medicinal Plant Bryophyllum sp.

“…Nevertheless, the micropropagation of Bryophyllum is not exempt from difficulties due to its particular metabolism and poor nutritional requirements [151]. In this sense, it was recently reported that ammonium, sulfur, molybdenum, copper, and sodium play a crucial role on growth and plantlet formation on in vitro-cultured Bryophyllum in a species-dependent manner [153]. Therefore, multiple nutritional modifications may be required to achieve genotype-specific optimization, since mineral imbalances and interactions could directly influence the success of PTC protocols, by affecting micropropagation performance [154], and causing undesirable physiological disorders [148].…”

“…Concerning Bryophyllum sp., the application of NFL was already applied to the identification of critical factors involved in plant in vitro nutrition [153] and organogenesis [137], as well as the production of phenolic compounds [62].…”

“…cultured in vitro. Among the 18 different mineral nutrients used on MS formulation, ML detected that only five nutrients were critical on Bryophyllum in vitro culture, in a genotype-dependent manner [153]. Specifically, ammonium, sulfate, sodium, molybdenum, and copper were selected by NFL as the critical factors guiding several growth-related parameters, and the interaction between sulfate and molybdenum was widely reported as responsible for most parameters: root length, plantlet formation, and aerial parts fresh weight [153].…”

“…Furthermore, these experimental conditions for maximizing the antioxidant activity of B. × houghtonii were also validated in terms of preventing lipid oxidation [64] and plant in vitro growth [153]; thus, assessing the effectiveness of ML on the valorization of Bryophyllum.…”