A neural network approach for the prediction of in vitro culture parameters for maximum biomass yields in hairy root cultures

Prakash, Om; Mehrotra, Shakti; Krishna, Aneesh; Mishra, Bhartendu Nath

doi:10.1016/j.jtbi.2010.05.020

Cited by 30 publications

(17 citation statements)

References 15 publications

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“…ANN technology has been found to be completely applicable for experiments with different numbers of data points, which makes it possible to use more casual experimental designs than is allowed with statistical approaches (Ahmadi and Golian, 2011). Recently, several studies have demonstrated the effectiveness of ANNs in the field of plant tissue culture for different purposes such as predicting the number of shoots per explant and average shoot length (Gago et al, 2010a, 2011; Nezami Alanagh et al, 2014), modeling the weight of root biomass (Mehrotra et al, 2008, 2013; Prakash et al, 2010), and predicting the number of roots per microshoots and survival percentage (Gago et al, 2010a,b). ANN-GA is a hybrid technology that combines the adaptive learning capabilities from ANN with a GA.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

et al. 2016

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One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts (NH4+, NO3-, PO42-, Ca2+, K+, SO42-, Mg2+, and Cl−) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R2 correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, NH4+ and NO3- had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 NO3-, 14 NH4+, 5 Ca2+, 25.9 K+, 0.7 Mg2+, 1.1 PO42-, 4.7 SO42-, and 0.96 Cl−. The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

et al. 2016

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“…With this view, nowadays, bioreactor technology utilizes engineering principles and mathematical formulations for mass production. Various research groups are working on issues like optimization of physical, biological, and chemical culture conditions (Prakash et al 2010;Mehrotra et al 2013a;Stiles and Liu 2013), offline and/or online measurement of growth (Uozumi 2004), mass transfer behavior (Liu et al 2011), synergistic effects of various physical and chemical parameters on growth, downstream processing (intracellular/extracellular), and product recovery (Bhagyalakshmi and Thimmaraju 2009;2012) in the course and/or at the end of scale-up. This may fairly help in filling the gap between capital cost and the benefits of technology at industrial scale (Stiles and Liu 2013).…”

Section: Bioreactor Technologymentioning

confidence: 99%

Hairy root biotechnology—indicative timeline to understand missing links and future outlook

et al. 2015

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Agrobacterium rhizogenes-mediated hairy roots (HR) were developed in the laboratory to mimic the natural phenomenon of bacterial gene transfer and occurrence of disease syndrome. The timeline analysis revealed that during 90 s, the research expanded to the hairy root-based secondary metabolite production and different yield enhancement strategies like media optimization, up-scaling, metabolic engineering etc. An outlook indicates that much emphasis has been given to the strategies that are helpful in making this technology more practical in terms of high productivity at low cost. However, a sequential analysis of literature shows that this technique is upgraded to a biotechnology platform where different intra- and interdisciplinary work areas were established, progressed, and diverged to provide scientific benefits of various hairy root-based applications like phytoremediation, molecular farming, biotransformation, etc. In the present scenario, this biotechnology research platform includes (a) elemental research like hairy root-mediated secondary metabolite production coupled with productivity enhancement strategies and (b) HR-based functional research. The latter comprised of hairy root-based applied aspects such as generation of agro-economical traits in plants, production of high value as well as less hazardous molecules through biotransformation/farming and remediation, respectively. This review presents an indicative timeline portrayal of hairy root research reflected by a chronology of research outputs. The timeline also reveals a progressive trend in the state-of-art global advances in hairy root biotechnology. Furthermore, the review also discusses ideas to explore missing links and to deal with the challenges in future progression and prospects of research in all related fields of this important area of plant biotechnology.

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“…Inoculum density, medium pH, sucrose conc., media volume Prakash et al (2010) ular culture condition efficiently. Later, Prakash et al (2010) developed a regression and feedforward neural network model for prediction of optimal culture conditions for prediction of hairy root maximum biomass yield.…”

Section: Glycyrrhiza Glabramentioning

confidence: 99%

“…Later, Prakash et al (2010) developed a regression and feedforward neural network model for prediction of optimal culture conditions for prediction of hairy root maximum biomass yield. It was found that both networks predicted culture conditions efficiently; however, regression neural network was more accurate.…”

Section: Glycyrrhiza Glabramentioning

confidence: 99%

Machine Learning Techniques in Plant Biology

Osama

Mishra

Somvanshi

2015

PlantOmics: The Omics of Plant Science

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A neural network approach for the prediction of in vitro culture parameters for maximum biomass yields in hairy root cultures

Cited by 30 publications

References 15 publications

Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

Hairy root biotechnology—indicative timeline to understand missing links and future outlook

Machine Learning Techniques in Plant Biology

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