Abstract
The multilayer perceptron (MLP) topology of an artificial neural network (ANN) was applied to create two predictor models in Agrobacterium-mediated gene transformation of tobacco. Agrobacterium-mediated transformation parameters, including Agrobacterium strain, Agrobacterium cell density, acetosyringone concentration, and inoculation duration, were assigned as inputs for ANN–MLP, and their effects on the percentage of putative and PCR-verified transgenic plants were investigated. The best ANN models for predicting the percentage of putative and PCR-verified transgenic plants were selected based on basic network quality statistics. Ex-post error calculations of the relative approximation error (RAE), the mean absolute error (MAE), the root mean square error (RMS), and the mean absolute percentage error (MAPE) demonstrated the prediction quality of the developed models when compared to stepwise multiple regression. Moreover, significant correlations between the ANN-predicted and the actual values of the percentage of putative transgenes (R2 = 0.956) and the percentage of PCR-verified transgenic plants (R2 = 0.671) indicate the superiority of the established ANN models over the classical stepwise multiple regression in predicting the percentage of putative (R2 = 0.313) and PCR-verified (R2= 0.213) transgenic plants. The best combination of the multiple inputs analyzed in this investigation, to achieve maximum actual and predicted transgenic plants, was at OD600 = 0.8 for the LB4404 strain of Agrobacterium × 300 μmol/L acetosyringone × 20 min immersion time. According to the sensitivity analysis of ANN models, the Agrobacterium strain was the most important influential parameter in Agrobacterium-mediated transformation of tobacco. The prediction efficiency of the developed model was confirmed by the data series of Agrobacterium-mediated transformation of an important medicinal plant with low transformation efficiency. The results of this study are pivotal to model and predict the transformation of other important Agrobacterium-recalcitrant plant genotypes and to increase the transformation efficiency by identifying critical parameters. This approach can substantially reduce the time and cost required to optimize multi-factorial Agrobacterium-mediated transformation strategies.
Highlights
A rapid improvement in the important economic traits of plants is needed due to climate change and the steady increase in global population
The four-way interaction of Agrobacterium strain, Agrobacterium cell density, acetosyringone concentration, and inoculation duration was significant for the percentage of putative and polymerase chain reaction (PCR)-verified transgenic tobacco plants at the 1% probability level (Table 1)
This study demonstrates that a novel artificial neural network (ANN) is an accurate approach for assessing the effect of Agrobacterium strains, Agrobacterium cell densities, acetosyringone concentrations, and inoculation durations on the percentage of putative and PCR-verified transgenes in tobacco
Summary
A rapid improvement in the important economic traits of plants is needed due to climate change and the steady increase in global population. The Agrobacterium method is a simple, efficient, and practical protocol for the transfer of foreign DNA and is the first prerequisite to produce genetically modified plants (Abbasi et al, 2020) This is challenging because of the low efficiency in most of the important plants, as many factors may affect this process. Different suspension solutions (OD600 = 0.2, 0.4, 0.6, 0.8, and 1.0), along with the immersion durations (10, 20, 30, and 40 min) and acetosyringone concentrations (50, 100, 150, and 200 μM), were investigated in Agrobacteriummediated gene transformation of Pinus tabuliformis and at 600 nm for Agrobacterium, an optical density of 0.8 × 150 μM acetosyringone × 30 min immersion time were reported as the optimal gene transformation factors (Liu et al, 2020).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
