Machine learning-mediated Passiflora caerulea callogenesis optimization

Abstract

Callogenesis is one of the most powerful biotechnological approaches for in vitro secondary metabolite production and indirect organogenesis in Passiflora caerulea. Comprehensive knowledge of callogenesis and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. In the present investigation, the callogenesis responses (i.e., callogenesis rate and callus fresh weight) of P. caerulea were predicted based on different types and concentrations of plant growth regulators (PGRs) (i.e., 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), 1-naphthaleneacetic acid (NAA), and indole-3-Butyric Acid (IBA)) as well as explant types (i.e., leaf, node, and internode) using multilayer perceptron (MLP). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and explant types for maximizing callogenesis responses. Furthermore, sensitivity analysis was conducted to assess the importance of each input variable on the callogenesis responses. The results showed that MLP had high predictive accuracy (R2 > 0.81) in both training and testing sets for modeling all studied parameters. Based on the results of the optimization process, the highest callogenesis rate (100%) would be obtained from the leaf explant cultured in the medium supplemented with 0.52 mg/L IBA plus 0.43 mg/L NAA plus 1.4 mg/L 2,4-D plus 0.2 mg/L BAP. The results of the sensitivity analysis showed the explant-dependent impact of the exogenous application of PGRs on callogenesis. Generally, the results showed that a combination of MLP and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.

Assessing Cadmium Stress Resilience in Myrtle Genotypes Using Machine Learning Predictive Models: A Comparative In Vitro Analysis

“…Nonetheless, various machine learning (ML) models have recently been utilized to effectively achieve precision, forecasting, and enhancement tasks in plant tissue culture procedures. ML, a subset of artificial intelligence (AI), is extensively utilized in plant-related disciplines, such as plant breeding [19], drought stress [20][21][22], in vitro micropropagation [23][24][25][26], in vitro germination [27,28], and various other domains within plant science. By leveraging AI technologies, scholars can analyze and clarify extensive datasets acquired from multiple sources.…”

Untitled

“…In contrast, ML algorithms, such as artificial neural networks (ANNs), support vector machines (SVMs), and genetic algorithms (GAs), offer higher accuracy and efficiency. For instance, ML has been successfully applied to optimize sterilization protocols [38,39], seed germination conditions [31,40], and callus induction processes [41][42][43][44]. Additionally, ML has optimized somatic embryogenesis [45,46] and haploid production [47,48].…”