Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803.

Journal: BMC biotechnology
PMID: 40089695

Abstract

BACKGROUND: Natural colorants produced by the cyanobacterium include carotenoids, chlorophyll a and phycocyanin. The current study used the Synechocystis sp. PCC 6803 to examine how abiotic stress conditions, such as low temperature as well as high light intensity, affect the pigment accumulations in comparison to the control conditions. Additionally, using the response surface methodology (RSM) and artificial neural network - multi-objective genetic algorithm (ANN-MOGA), the impact of several nitrogen sources such as urea, ammonium chloride, and sodium nitrate as nutritional stress on the pigment accumulations in the Synechocystis sp. PCC 6803 was examined.

Authors

  • Namrata Bhagat
    Enzyme Technology and Protein Bioinformatics Laboratory, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, 221005, India.
  • Guddu Kumar Gupta
    Enzyme Technology and Protein Bioinformatics Laboratory, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, 221005, India.
  • Amritpreet Kaur Minhas
    TERI Deakin Nanobiotechnology Centre, Sustainable Agriculture Division, The Energy and Resources Institute, New Delhi, India.
  • Deepak Chhabra
    Department of Mechanical Engineering, University Institute of Engineering and Technology (UIET), Maharshi Dayanand University, Rohtak, Haryana, 124001, India.
  • Pratyoosh Shukla
    Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana, 124001, India. pratyoosh.shukla@gmail.com.

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