Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms

BMC Biotechnology

Research

Marziyeh Jafari¹  Mohammad Hosein Daneshvar² 

[1] Department of Horticultural Science, College of Agriculture, Shiraz University, 7144113131, Shiraz, Iran;Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, 6341773637, Mollasani, Iran;Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, 6341773637, Mollasani, Iran;

关键词: Artificial intelligence;    Callus type;    In vitro culture;    Micropropagation;    Modeling;    Passion fruit;    Plant growth regulator;   

DOI  :  10.1186/s12896-023-00796-4

 received in 2023-02-22, accepted in 2023-07-21,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundOptimization of indirect shoot regeneration protocols is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and/or genome editing in Passiflora caerulea. Comprehensive knowledge of indirect shoot regeneration and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms.Materials and methodsIn the present investigation, the indirect shoot regeneration responses (i.e., de novo shoot regeneration rate, the number of de novo shoots, and length of de novo shoots) of P. caerulea were predicted based on different types and concentrations of PGRs (i.e., TDZ, BAP, PUT, KIN, and IBA) as well as callus types (i.e., callus derived from different explants including leaf, node, and internode) using generalized regression neural network (GRNN) and random forest (RF). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and callus types for maximizing indirect shoot regeneration responses. Moreover, sensitivity analysis was conducted to assess the importance of each input variable on the studied parameters.ResultsThe results showed that both algorithms (RF and GRNN) had high predictive accuracy (R2 > 0.86) in both training and testing sets for modeling all studied parameters. Based on the results of optimization process, the highest de novo shoot regeneration rate (100%) would be obtained from callus derived from nodal segments cultured in the medium supplemented with 0.77 mg/L BAP plus 2.41 mg/L PUT plus 0.06 mg/L IBA. The results of the sensitivity analysis showed the explant-dependent impact of exogenous application of PGRs on indirect de novo shoot regeneration.ConclusionsA combination of ML (GRNN and RF) 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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