Artificial neural network modelling for seedling regeneration in Gracilaria dura (Rhodophyta) under different physiochemical conditions

Vignesh, M.; Kazi, Mudassar Anisoddin; Rathore, Mangal S.; Kavale, Monica Gajanan; Dineshkumar, Ramalingam; Mantri, Vaibhav A.

doi:10.1007/s11240-020-01943-x

Cited by 9 publications

(7 citation statements)

References 28 publications

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“…Also yield improvement up to 3.48-fold increase could be achieved through ANN method (Venkateswarulu et al 2017). Similar conclusions were derived in terms of the superior accuracy of ANN prediction over the RSM method by many researchers (Ebrahimpour et al 2008;Singh et al 2013;Vignesh et al 2020). The accuracy in the predictive ability of ANN has been substantiated in a study of seedling regeneration in G. dura.…”

Section: Discussionsupporting

confidence: 83%

“…The accuracy in the predictive ability of ANN has been substantiated in a study of seedling regeneration in G. dura. This study reports that the average error percentage of ANN prediction as 0.28% and established an optimum method for the seaweed regeneration through ANN (Vignesh et al 2020). 1.58-fold enhancement was reported in Lglutaminase production by Bacillus cereus through this model.…”

Section: Discussionmentioning

confidence: 99%

“…It shall be noted that very few neurons would be inefficient in determining the predictive ability, whereas a greater number of neurons can learn the noise present in the data accounted to train the network. So, the optimal number of neurons in the hidden layer would be determined usually through trial and error method, where in a maximum R-value and minimum MSE-value, is expected (Dineshkumar et al 2015;Vignesh et al 2020). Hence, the ANN topology that results in highest R-value and lowest MSE value will be treated as ideal for predicting the output based on the given inputs.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Neural Network modelling for yield enhancement of L-Glutaminase free L-Asparaginase from Bacillus sp. associated with marine red alga Gracilaria dura

Mathew

Vala

Dineshkumar

et al. 2022

Preprint

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The bacterial flora associated with Gracilaria dura, the sole producer of natural agarose was obtained. Screening for the presence of various enzymes and bioactive compounds resulted in discerning different clinically and industrially important enzymes. Three isolates were identified to be producing L-asparaginase (LA), a potent anticancer drug. The strain 4T1-C26E registered highest LA activity (25.2 IU ml− 1) in the enzyme assay, was selected for yield improvement by optimizing process parameters through Response Surface Methodology (RSM). Various parameters like glucose concentration, L-asparagine concentration, pH of the media, and incubation period were optimized through Central Composite Design (CCD) for enhanced yield of LA by 4T1-C26E, identified as Bacillus licheniformis. The CCD table was exploited for application in artificial neural networking (ANN) for the development of optimum method of cultivation. In this study, we obtained 2.5-fold (88.4 IU ml− 1) increase in LA production by this strain through RSM optimization. Further, Artificial Neural Networking Particle Swarm Optimization (ANN-PSO) predicted the input combinations. The experimental validation of LA production was found to be 4-fold (126.4 IU ml− 1) higher with respect to the unoptimized conditions. This illustrates the effectiveness of ANN-PSO based optimization technique, the error percent between the predicted and experimental output was 4.03%. The most important benefit of LA from 4T1-C26E is that it possesses no L-glutaminase activity. Further, it is extracellularly produced which is suitable for easier downstream processing thereby reducing chances of immunogenic responses by endotoxins. However, characterization of kinetic parameters and cytotoxic studies on cell cultures are mandatory to confirm its pharmaceutical application.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Artificial Neural Network modelling for yield enhancement of L-Glutaminase free L-Asparaginase from Bacillus sp. associated with marine red alga Gracilaria dura

Mathew

Vala

Dineshkumar

et al. 2022

Preprint

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“…9 The commercial cultivation of this seaweed has recently begun in Gujarat, India. The optimal physicochemical factors responsible for the production of plantlets 10 and technical feasibility for rapid and clonal production of viable seedlings for large-scale cultivation have been demonstrated. 11 Metabolomics is one of the emerging areas among several "omic" techniques that have been used to decipher the direct manifestation of biochemical pathways of different primary and secondary metabolisms.…”

Section: Introductionmentioning

confidence: 99%

“…The commercial cultivation of this seaweed has recently begun in Gujarat, India. The optimal physicochemical factors responsible for the production of plantlets and technical feasibility for rapid and clonal production of viable seedlings for large-scale cultivation have been demonstrated …”

Section: Introductionmentioning

confidence: 99%

Profiling of Peak Intensity and Standard Deviation of Metabolites in Industrially Important Red Seaweed Gracilaria dura (Rhodophyta) by ¹H NMR Spectra for Benchmarking

Jaiswar

Thakur

Dawange

et al. 2021

ACS Food Sci. Technol.

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Mapping of regulatory components of cellular functioning by 1H NMR metabolomics has recently paved the way to study biochemical pathways in plants. Despite the enormous complexity and heterogeneity of the metabolites, the estimation of molar concentration allows for their direct quantification without calibration for individual compounds. 1H NMR spectra of near-native extracts were recorded for a large number (55 each) of wild and cultured samples of the industrially important red seaweed Gracilaria dura to obtain quantitative data of all the metabolites. Average and standard deviation of the respective classes were calculated from 1H NMR spectra wherein significant differences between wild and cultured groups were observed in the 1.56–3.50 ppm region. A significant region in the wild samples having a low average (0–0.98) and standard deviation (0.2–0.81) was 1.56–2.96 ppm representing nitrogen-containing compounds. The same region was found to have a much higher standard deviation (0.13–1.73) with average (0–2.03) in cultured samples. In comparison, sugar metabolites (triethanolamine, galactose, lactate, threonine) have a high average peak intensity and similar standard deviation across both groups of samples. This observation reveals that sugar compounds are critical in central metabolic pathways while nitrogen compounds change due to the environment. PCA analysis revealed an interpretable overview of main information enclosed in a multidimensional data set; the differences were correlated to environmental determinants, developmental, and growth states. A signature metabolite profile is presented with minor standard deviation differences between significant parts of the 1H NMR spectra, confirming its use as a rapid chemo-taxonomic tool supplementary to the conventional approach.

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Application of Artificial Neural Network and Genetic Algorithm Modeling for In Vitro Regeneration of Seaweed Seedling Production

Dineshkumar,

Kazi,

Mantri

2024

Plant Cell Culture Protocols

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Artificial neural network modelling for seedling regeneration in Gracilaria dura (Rhodophyta) under different physiochemical conditions

Cited by 9 publications

References 28 publications

Artificial Neural Network modelling for yield enhancement of L-Glutaminase free L-Asparaginase from Bacillus sp. associated with marine red alga Gracilaria dura

Artificial Neural Network modelling for yield enhancement of L-Glutaminase free L-Asparaginase from Bacillus sp. associated with marine red alga Gracilaria dura

Profiling of Peak Intensity and Standard Deviation of Metabolites in Industrially Important Red Seaweed Gracilaria dura (Rhodophyta) by ¹H NMR Spectra for Benchmarking

Application of Artificial Neural Network and Genetic Algorithm Modeling for In Vitro Regeneration of Seaweed Seedling Production

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Artificial neural network modelling for seedling regeneration in Gracilaria dura (Rhodophyta) under different physiochemical conditions

Cited by 9 publications

References 28 publications

Artificial Neural Network modelling for yield enhancement of L-Glutaminase free L-Asparaginase from Bacillus sp. associated with marine red alga Gracilaria dura

Artificial Neural Network modelling for yield enhancement of L-Glutaminase free L-Asparaginase from Bacillus sp. associated with marine red alga Gracilaria dura

Profiling of Peak Intensity and Standard Deviation of Metabolites in Industrially Important Red Seaweed Gracilaria dura (Rhodophyta) by 1H NMR Spectra for Benchmarking

Application of Artificial Neural Network and Genetic Algorithm Modeling for In Vitro Regeneration of Seaweed Seedling Production

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Profiling of Peak Intensity and Standard Deviation of Metabolites in Industrially Important Red Seaweed Gracilaria dura (Rhodophyta) by ¹H NMR Spectra for Benchmarking