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Envisaging the Regulation of Alkaloid Biosynthesis and Associated Growth Kinetics in Hairy Roots of Vinca minor Through the Function of Artificial Neural Network

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Abstract

Artificial neural network based modeling is a generic approach to understand and correlate different complex parameters of biological systems for improving the desired output. In addition, some new inferences can also be predicted in a shorter time with less cost and labor. As terpenoid indole alkaloid pathway in Vinca minor is very less investigated or elucidated, a strategy of elicitation with hydroxylase and acetyltransferase along with incorporation of various precursors from primary shikimate and secoiridoid pools via simultaneous employment of cyclooxygenase inhibitor was performed in the hairy roots of V. minor. This led to the increment in biomass accumulation, total alkaloid concentration, and vincamine production in selected treatments. The resultant experimental values were correlated with algorithm approaches of artificial neural network that assisted in finding the yield of vincamine, alkaloids, and growth kinetics using number of elicits. The inputs were the hydroxylase/acetyltransferase elicitors and cyclooxygenase inhibitor along with various precursors from shikimate and secoiridoid pools and the outputs were growth index (GI), alkaloids, and vincamine. The approach incorporates two MATLAB codes; GRNN and FFBPNN. Growth kinetic studies revealed that shikimate and tryptophan supplementation triggers biomass accumulation (GI = 440.2 to 540.5); while maximum alkaloid (3.7 % dry wt.) and vincamine production (0.017 ± 0.001 % dry wt.) was obtained on supplementation of secologanin along with tryptophan, naproxen, hydrogen peroxide, and acetic anhydride. The study shows that experimental and predicted values strongly correlate each other. The correlation coefficient for growth index (GI), alkaloids, and vincamine was found to be 0.9997, 0.9980, 0.9511 in GRNN and 0.9725, 0.9444, 0.9422 in FFBPNN, respectively. GRNN provided greater similarity between the target and predicted dataset in comparison to FFBPNN. The findings can provide future insights to calculate growth index, alkaloids, and vincamine in combination to different elicits.

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Abbreviations

ANN:

Artificial neural network

GRNN:

Generalized regression neural network

FFBPNN:

Feed forward back propagation neural network

MAE:

Mean absolute error

NMSE:

Normalized mean square error

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Acknowledgments

The work presented here has been supported by DST-FAST TRACK SERC/LS-261/2012, grant project “Grantová Agentura České Republiky” (P102/11/1068); “European Regional Development Fund- Project FNUSA-ICRC” (CZ.1.05/1.1.00/02.0123) and “European Social Fund” (CZ.1.07/2.3.00/30.0039).

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    Authors and Affiliations

    1. Department of Plant Biotechnology, Central Institute of Medicinal and Aromatic Plants (CSIR-CIMAP) Council of Scientific and Industrial Research PO-CIMAP, Lucknow, 226015, India

      Priyanka Verma, Shamshad Ahmad Khan & Ajay Kumar Mathur

    2. IIDS Center of Bioinformatics, Nehru Science Center, University of Allahabad, Allahabad, 211001, India

      Shahin Anjum

    3. Department of Biomedical Engineering, FEEC, Brno University of Technology, Brno, Czech Republic

      Sudeep Roy & Jan Odstrcilik

    4. International Clinical Research Center (ICRC), St. Anne’s University Hospital, Brno, Czech Republic

      Jan Odstrcilik

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    1. Priyanka Verma
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    Correspondence to Priyanka Verma.

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    Priyanka Verma and Shahin Anjum contributed equally to this work.

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    Verma, P., Anjum, S., Khan, S.A. et al. Envisaging the Regulation of Alkaloid Biosynthesis and Associated Growth Kinetics in Hairy Roots of Vinca minor Through the Function of Artificial Neural Network. Appl Biochem Biotechnol 178, 1154–1166 (2016). https://doi.org/10.1007/s12010-015-1935-1

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