Machine learning deciphers genotype and ammonium as key factors for the micropropagation of Bryophyllum sp. medicinal plants

Abstract

Bryophyllum constitutes a subgenus of succulent plants that have been widely employed worldwide in traditional medicine. Micropropagation is required to optimize their growth and reproduction for biotechnological purposes. The mineral composition of culture media is usually an underestimated factor in the design of the in vitro culture protocols of medicinal plants. Universal and highly cited media mineral formulations, such as the Murashige and Skoog (MS) medium, are generally employed in plant tissue culture studies, although they cause physiological disorders due to their imbalanced mineral composition. In this work, neurofuzzy logic is proposed as a machine-learning-based tool to decipher the key factors (genotype, number of subcultures, and macronutrients) that are involved in the establishment of the Bryophyllum sp. in vitro culture. The results show that genotype played a key role, as it impacts both vegetative growth and asexual reproduction in all of the species that were studied. In addition, ammonium was identified as a significant factor, as concentrations above 15 mM promote a negative effect on vegetative growth and reproduction. These findings should be considered as the starting point for optimizing the establishment of the in vitro culture of Bryophyllum species, with large-scale applications as biofactories of health-promoting compounds, such as polyphenols and bufadienolides.