Optimizing sterilization conditions and growth regulator effects on  in vitro  shoot regeneration through direct organogenesis in  Chenopodium quinoa

Hesami, Mohsen; Naderi, Roohangiz; Yoosefzadeh-Najafabadi, Mohsen

doi:10.5114/bta.2018.73561

Cited by 27 publications

(29 citation statements)

References 38 publications

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“…In another study, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was employed to optimize different types and concentrations of disinfectants as well as immersion time for maximizing explant viability and minimizing in vitro contamination in chrysanthemum [33]. However, most studies have found the optimized solution by trials and error [23,34,35]. NSGA-II is the search algorithms inspired by natural selection and genetics concepts.…”

Section: Introductionmentioning

confidence: 99%

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Jafari

Shahsavar

2020

PLoS ONE

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Drought stress as one of the most devastating abiotic stresses affects agricultural and horticultural productivity in many parts of the world. The application of melatonin can be considered as a promising approach for alleviating the negative impact of drought stress. Modeling of morphological responses to drought stress can be helpful to predict the optimal condition for improving plant productivity. The objective of the current study is modeling and predicting morphological responses (leaf length, number of leaves/plants, crown diameter, plant height, and internode length) of citrus to drought stress, based on four input variables including melatonin concentrations, days after applying treatments, citrus species, and level of drought stress, using different Artificial Neural Networks (ANNs) including Generalized Regression Neural Network (GRNN), Radial basis function (RBF), and Multilayer Perceptron (MLP). The results indicated a higher accuracy of GRNN as compared to RBF and MLP. The great accordance between the experimental and predicted data of morphological responses for both training and testing processes support the excellent efficiency of developed GRNN models. Also, GRNN was connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize input variables for obtaining the best morphological responses. Generally, the validation experiment showed that ANN-NSGA-II can be considered as a promising and reliable computational tool for studying and predicting plant morphological and physiological responses to drought stress.

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Section: Introductionmentioning

confidence: 99%

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Jafari

Shahsavar

2020

PLoS ONE

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“…Contamination in plant tissue cultures can be produced by various micro-arthropods (mites, trips, and their vectors), microorganisms (filamentous fungi, yeasts, bacteria), viruses, and viroids (Altan et al, 2010; Da Silva et al, 2016a; Hesami et al, 2018c). Therefore, sterilization step is of high paramount during establishing and maintaining plants in in vitro cultures.…”

Section: Introductionmentioning

confidence: 99%

“…Sterilization of equipment should not be a serious deal in a modern and well-equipped laboratory due to the frequent use of novel microwave-based autoclaves (Da Silva et al, 2016a). However, serious problems are made during the disinfection of biological material (e.g., initial explant) that need more attention and time (Hesami et al, 2018c). In addition, tissues can be a significant potential of hosting different microorganisms thus adequate and proper sterilization treatments require prior to in vitro culture initiation (Mihaljević et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The efficiency of disinfection can be influenced by numerous factors such as the size, age, and type of the explant, the conditions of cultivation and physiological state of the stock plant, time and temperature of exposure, and the type of disinfectant and its concentration (Da Silva et al, 2016b). Besides, these mentioned factors can exert a negative impact on the survival and regeneration potential of candidate explant which is imperative for maximizing the efficient transformation system of plant tissue cultures (Da Silva et al, 2016a; Hesami et al, 2018c). There are several types of disinfectants including hydrogen peroxide (H 2 O 2 ), mercury II chloride (HgCl 2 ), Nano-silver (NS), calcium hypochlorite [Ca(ClO) 2 ], silver nitrate (AgNO 3 ), sodium hypochlorite (NaOCl), and chemotherapeutics (fungicides, antibiotics) that can be used in different contamination levels and conditions (Mihaljević et al, 2013; Nongalleima et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Optimizing in vitro Sterilization of Chrysanthemum via Multilayer Perceptron-Non-dominated Sorting Genetic Algorithm-II (MLP-NSGAII)

2019

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In vitro sterilization is a primary step of plant tissue culture which the ultimate results of in vitro culture are directly depended on the efficiency of the sterilization. Artificial intelligence models in a combination of optimization algorithms could be beneficial computational approaches for modeling and optimizing in vitro culture. The aim of this study was modeling and optimizing in vitro sterilization of chrysanthemum, as a case study, through Multilayer Perceptron- Non-dominated Sorting Genetic Algorithm-II (MLP-NSGAII). MLP was used for modeling two outputs including contamination frequency (CF), and explant viability (EV) based on seven variables including HgCl2, Ca(ClO)2, Nano-silver, H2O2, NaOCl, AgNO3, and immersion times. Subsequently, models were linked to NSGAII for optimizing the process, and the importance of each input was evaluated by sensitivity analysis. Results showed all of the R2 of training and testing data were over 94%. According to MLP-NSGAII, optimal CF (0%), and EV (99.98%) can be obtained from 1.62% NaOCl at 13.96 min immersion time. The results of sensitivity analysis showed that CF and EV were more sensitive to immersion time and less sensitive to AgNO3. Subsequently, the performance of predicted and optimized sterilants × immersion times combination were tested, and results indicated that the differences between the MLP predicted and validation data were negligible. Generally, MLP-NSGAII as a powerful methodology may pave the way for establishing new computational strategies in plant tissue culture.

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“…Therefore, novel biotechnological methods can be employed in order to satisfy the demands of consumers [1,3]. Nowadays, in vitro culture methods as biotechnological tools are applied to the rapid multiplication of rare plant genotypes, micropropagation of disease-free plants, production of plantderived metabolites, and gene transformation [1,[3][4][5].…”

mentioning

confidence: 99%

Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

et al. 2020

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Background: Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy. Results: The results showed that SVR (R 2 > 0.92) had better performance accuracy than MLP (R 2 > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 μM kinetin (KIN), and 18.73 μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum's somatic embryogenesis accurately. Conclusions: SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

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Optimizing sterilization conditions and growth regulator effects on in vitro shoot regeneration through direct organogenesis in Chenopodium quinoa

Cited by 27 publications

References 38 publications

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Modeling and Optimizing in vitro Sterilization of Chrysanthemum via Multilayer Perceptron-Non-dominated Sorting Genetic Algorithm-II (MLP-NSGAII)

Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

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