Publication detail

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

VERMA, P. ANJUM, S. KHAN, S. ROY, S. ODSTRČILÍK, J. MATHUR, A.

Original Title

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

English Title

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

Type

journal article

Language

en

Original 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.

English 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.

Keywords

Vinca minor, artificial neural network, MATLAB, generalized regression neural network

Released

01.03.2016

Publisher

Springer Science+Business Media

Location

New York, USA

Pages from

1154

Pages to

1166

Pages count

13

URL

BibTex


@article{BUT121513,
  author="Priyanka {Verma} and Shahin {Anjum} and Shamshad Ahmad {Khan} and Sudeep {Roy} and Jan {Odstrčilík} and Ajay Kumar {Mathur}",
  title="Envisaging the Regulation of Alkaloid Biosynthesis and Associated Growth Kinetics in Hairy Roots of Vinca minor Through the Function of Artificial Neural Network",
  annote="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.",
  address="Springer Science+Business Media",
  chapter="121513",
  doi="10.1007/s12010-015-1935-1",
  howpublished="print",
  institution="Springer Science+Business Media",
  number="6",
  volume="2016",
  year="2016",
  month="march",
  pages="1154--1166",
  publisher="Springer Science+Business Media",
  type="journal article"
}