Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, Iran.
Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran.
PLoS One. 2024 Jan 24;19(1):e0292359. doi: 10.1371/journal.pone.0292359. eCollection 2024.
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.
愈伤组织发生是在体外进行次生代谢产物生产和间接器官发生的最强大的生物技术方法之一，在百香果（Passiflora caerulea）中也是如此。通过应用机器学习（ML）和优化算法的组合，可以获得对愈伤组织发生的综合了解和优化方案。在本研究中，基于不同类型和浓度的植物生长调节剂（PGRs）（即 2,4-二氯苯氧乙酸（2,4-D）、6-苄基氨基嘌呤（BAP）、1-萘乙酸（NAA）和吲哚-3-丁酸（IBA））以及外植体类型（即叶片、节点和节间），利用多层感知器（MLP）对百香果的愈伤组织发生反应（即愈伤组织发生率和愈伤组织鲜重）进行了预测。此外，将开发的模型集成到遗传算法（GA）中，以优化 PGR 浓度和外植体类型，从而最大限度地提高愈伤组织发生反应。此外，还进行了敏感性分析，以评估每个输入变量对愈伤组织发生反应的重要性。结果表明，MLP 在训练和测试集上对所有研究参数的建模均具有较高的预测准确性（R2＞0.81）。根据优化过程的结果，从叶片外植体在补充有 0.52 mg/L IBA、0.43 mg/L NAA、1.4 mg/L 2,4-D 和 0.2 mg/L BAP 的培养基中培养的培养基中获得的愈伤组织发生率最高（100%）。敏感性分析的结果表明，外植体对外源 PGR 应用的依赖性对愈伤组织发生有影响。一般来说，结果表明，MLP 和 GA 的组合可以提供前瞻性的辅助，以优化和预测体外培养系统，并应对当前百香果组织培养中面临的一些挑战。
