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

Abstract

Background Optimization 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 methods In 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. Results The 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. Conclusions A 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.

Improvement of Culture Conditions and Plant Growth Regulators for In Vitro Callus Induction and Plant Regeneration in Paeonia lactiflora Pall.

“…Plants can be regenerated in vitro through direct and indirect methods. The establishment of an indirect regeneration system is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and genome editing [13]. The indirect shoot regeneration scheme based on callus, or indirect somatic organogenesis, can be divided into three basic stages-callus induction, callus proliferation, and callus regeneration into shoots followed by multiplication [14].…”

Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis

“…Therefore, the application of machine learning techniques offers a promising avenue for uncovering the underlying mechanisms and facilitating the optimization of callus formation protocols in Petunia. The reliability and accuracy of the machine learning approach in modeling and predicting various in vitro culture systems have been previously approved in different species such as cannabis [29,32,[44][45][46][47][48], chickpea [49], ajowan [38], Prunus rootstock [50][51][52], chrysanthemum [27,[53][54][55][56][57], pear rootstock [58][59][60], Passiflora caerulea [28,61], wheat [62], wallflower [63], walnut [64], and tomato [31].…”

Machine learning-mediated Passiflora caerulea callogenesis optimization

“…GA is a powerful optimization technique inspired by the principles of natural selection and evolution, while ANNs are versatile machine learning models that can capture intricate patterns in data [ 37 , 41 , 42 ]. By combining these two approaches, researchers can create a powerful optimization framework to identify optimal combinations of PGRs, nutrient compositions, and other critical factors that influence in vitro culture efficiency [ 18 , 57 ]. The ANN-GA hybrid approach allows for a more systematic and automated exploration of the solution space, leading to improved tissue culture protocols and potentially accelerating the development of desirable plant traits with broader implications for agriculture, horticulture, and biotechnology [ 18 , 53 , 57 , 58 ].…”