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Predicting optimal in vitro culture medium for Pistacia vera micropropagation using neural networks models

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Abstract

In this study, artificial intelligence techniques—specifically artificial neural networks (ANNs) in combination with fuzzy logic (neurofuzzy logic) or with genetic algorithms (ANNs–GA)—have been employed, as modeling tools, to get insight, to predict and to optimize the effect of several independent factors on four growth parameters during Pistacia vera micropropagation. Twenty-six media ingredients, including mineral ions (or salts), glycine, vitamins and plant growth regulators (PGRs) at different concentrations, were used as inputs and four growth parameters: proliferation rate, shoot length, total and healthy fresh weight as outputs on the models. The IF-THEN rules from neurofuzzy logic models have allowed discovering the positive (BAP, nicotinic-acid and pyridoxine-HCl) and negative (NO3 , Mg2+, Ag+ and gluconate) effects on the growth parameters and the fundamental role of BAP over all of them. Also, ANNs–GA technology has permitted to estimate the best combination of media ingredients to simultaneously maximize the four parameters of growth: 4.4 new shoots per explant; 28.7 mm length; 1.1 and 0.53 g total and healthy fresh weight, respectively, minimizing physiological disorders. In our opinion, the information obtained in this study is extremely useful to improve the massive multiplication of pistachio plant, in particular, but also demonstrate the ability of artificial intelligence technology to design plant tissue culture media with predictable and tailorable characteristics.

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Abbreviations

ANNs:

Artificial neural networks

BAP:

6-Benzylaminopurine

Ca-gluconate:

Calcium gluconate

GA:

Genetic algorithms

HFW:

Healthy fresh weight

IBA:

Indole-3-butyric acid

PGRs:

Plant growth regulators

POM:

Pistachio optimal medium

PR:

Proliferation rate

SL:

Shoot length

STN:

Shoot-tip necrosis

TFW:

Total fresh weight

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Acknowledgements

This work was supported by Department of Biotechnology at Imam Khomeini International University (IKIU), Qazvin, Iran and especial thanks go to faculty members for their professional supporting helps.

Author contributions

The authors have made the following declarations regarding their contributions. Conceived and designed the experiments: ENA, GAG. Performed the experiments: ENA. Analyzed the data: ENA, SM, ML, PPG. Contributed reagents/materials: ENG, GAG. Contributed modeling/analysis tools: ML, PPG. All authors contributed to the writing of the manuscript.

Author information

Correspondence to Ghasem-Ali Garoosi or Pedro Pablo Gallego.

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The authors declare that they have no conflict of interest.

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Key message

Combination of artificial intelligence techniques to get deep understanding of effective culture media ingredients and to optimize culture media to improve plant micropropagation, for first time, have been employed.

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Nezami-Alanagh, E., Garoosi, G., Maleki, S. et al. Predicting optimal in vitro culture medium for Pistacia vera micropropagation using neural networks models. Plant Cell Tiss Organ Cult 129, 19–33 (2017). https://doi.org/10.1007/s11240-016-1152-9

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Keywords

  • 6-Benzylaminopurine
  • Culture media design
  • Formulation and optimization
  • Indol-3-butyric acid
  • Pistacia vera cv. “Ghazvini” rootstock
  • Physiological disorders