Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study

Hesami, Mohsen; Naderi, Roohangiz; Tohidfar, Masoud

doi:10.1007/s00253-020-10978-1

Cited by 31 publications

(23 citation statements)

References 111 publications

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“…To solve this challenge, machine learning algorithms as a statistical and computational approach have been suggested as a solution [154]. Recently, machine learning algorithms have been used for developing and optimizing medium in multiple species such as Pistacia vera [155], Centella asiatica [156], pear rootstock [157], and chrysanthemum [158]. Therefore, these methods can be used for designing and optimizing Cannabis-specific medium and environmental conditions.…”

Section: Future Directionsmentioning

confidence: 99%

The Past, Present and Future of Cannabis sativa Tissue Culture

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Hesami

et al. 2021

Plants

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The recent legalization of Cannabis sativa L. in many regions has revealed a need for effective propagation and biotechnologies for the species. Micropropagation affords researchers and producers methods to rapidly propagate insect-/disease-/virus-free clonal plants and store germplasm and forms the basis for other biotechnologies. Despite this need, research in the area is limited due to the long history of prohibitions and restrictions. Existing literature has multiple limitations: many publications use hemp as a proxy for drug-type Cannabis when it is well established that there is significant genotype specificity; studies using drug-type cultivars are predominantly optimized using a single cultivar; most protocols have not been replicated by independent groups, and some attempts demonstrate a lack of reproducibility across genotypes. Due to culture decline and other problems, the multiplication phase of micropropagation (Stage 2) has not been fully developed in many reports. This review will provide a brief background on the history and botany of Cannabis as well as a comprehensive and critical summary of Cannabis tissue culture. Special attention will be paid to current challenges faced by researchers, the limitations of existing Cannabis micropropagation studies, and recent developments and future directions of Cannabis tissue culture technologies.

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Section: Future Directionsmentioning

confidence: 99%

The Past, Present and Future of Cannabis sativa Tissue Culture

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Hesami

et al. 2021

Plants

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“…Although a few studies [ 78 , 81 ] have tested newer PGRs and additives such as 6-benzylamino-9-(tetrahydroxypyranyl) purin (BAP9THP) and meta-topolin (mT) for cannabis micropropagation, there are still some promising PGRs and additives, such as polyamines, brassinosteroids, nano-particles, and nitric oxide (NO) that have not been used for developing cannabis micropropagation protocols. NO, a messenger molecule regulating plant development such as flowering, germination, fruit ripening, and organ senescence, has been recently characterized as a phytohormone [ 96 , 97 ]. It was shown that NO can be experimentally applied in the media as sodium nitroprusside (SNP), which eliminates the difficulty in the application of NO in its gaseous form [ 98 ].…”

Section: In Vitro Culture In Cannabismentioning

confidence: 99%

“…It was shown that NO can be experimentally applied in the media as sodium nitroprusside (SNP), which eliminates the difficulty in the application of NO in its gaseous form [ 98 ]. Several studies showed that NO is one of the main signaling pathways in in vitro organogenesis and somatic embryogenesis [ 96 , 99 ]. Therefore, the application of SNP may pave the way for obtaining somatic embryogenesis or improving organogenesis protocols in cannabis.…”

Section: In Vitro Culture In Cannabismentioning

confidence: 99%

Advances and Perspectives in Tissue Culture and Genetic Engineering of Cannabis

Hesami

Baiton

Alizadeh

et al. 2021

IJMS

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For a long time, Cannabis sativa has been used for therapeutic and industrial purposes. Due to its increasing demand in medicine, recreation, and industry, there is a dire need to apply new biotechnological tools to introduce new genotypes with desirable traits and enhanced secondary metabolite production. Micropropagation, conservation, cell suspension culture, hairy root culture, polyploidy manipulation, and Agrobacterium-mediated gene transformation have been studied and used in cannabis. However, some obstacles such as the low rate of transgenic plant regeneration and low efficiency of secondary metabolite production in hairy root culture and cell suspension culture have restricted the application of these approaches in cannabis. In the current review, in vitro culture and genetic engineering methods in cannabis along with other promising techniques such as morphogenic genes, new computational approaches, clustered regularly interspaced short palindromic repeats (CRISPR), CRISPR/Cas9-equipped Agrobacterium-mediated genome editing, and hairy root culture, that can help improve gene transformation and plant regeneration, as well as enhance secondary metabolite production, have been highlighted and discussed.

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“…Artificial intelligence (AI) is applied to address matters that cannot be clarified by traditional computational methods. Artificial neural networks (ANNs) are one of the main parts of AI discovering complex nonlinear relationships amongst input and output data [7,13,[24][25][26][27][28][29][30]. Indeed, ANNs are brain-inspired systems that emulate human brain capability of sensing and thinking, in a simplified way, to processes information and identify patterns [31].…”

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A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

et al. 2021

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Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study

Cited by 31 publications

References 111 publications

The Past, Present and Future of Cannabis sativa Tissue Culture

The Past, Present and Future of Cannabis sativa Tissue Culture

Advances and Perspectives in Tissue Culture and Genetic Engineering of Cannabis

A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

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