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Response surface methodology (RSM) and artificial neural network (ANN) integrated optimization for lipase production by Bacillus holotolerans

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Abstract

Response surface methodology (RSM) and artificial neural networks (ANN) are considered the most efficient way for optimization and modeling studies to design and develop various biosimilars. The primary objective of this study was to create empirical modeling and optimization of media parameters for producing B. halotolerans VSH 09 lipase using RSM and ANN. One-factor-at-a-time (OFAT) analysis revealed that triacylglycerols hydrolyzed by lipase manifest substantial activity. The subsequent screening for best carbon, nitrogen, and inducer was performed using the Placket–Burman design (PBD). The statistically significant variables were further examined for their optimum level using Box–Behnken design (BBD). The lipase production was optimized (26.04 IU/ml) under the ideal molasses (2.5%), peptone (2%), and salt (0.1% CaCO3, 0.1% (NH4)2SO4, and 0.1% MgSO4.7H2O). Both models revealed impeccable predictions; however, more interestingly, it was evaluated that ANN outperforms the RSM regarding data fitting and estimation capabilities.

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Acknowledgements

The authors would like to thank all the support received from KLE Technological University and Amity University Uttar Pradesh. A special thanks to Dr. Gururaj Pathak, Assistant Professor, Kirloskar Institute of Management, Harihara, for his constant support and coordination during manuscript preparation.

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No source of funding has been received for the current research work.

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Authors and Affiliations

  1. Department of Biotechnology, KLE Technological University, Hubballi, Karnataka, 580031, India

    Veeranna Shivaputrayya Hombalimath

  2. Molecular Genetics Lab, Centre for Cellular and Molecular Biology, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, 201310, India

    Dummi Mahadevan Gurumurthy

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  1. Veeranna Shivaputrayya Hombalimath
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  2. Dummi Mahadevan Gurumurthy
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VH and DMG contributed equally to the experimental design and execution of results. VH was involved in data compiling, and DMG extensively contributed to manuscript writing and formatting.

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Correspondence to Veeranna Shivaputrayya Hombalimath or Dummi Mahadevan Gurumurthy.

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Hombalimath, V.S., Gurumurthy, D.M. Response surface methodology (RSM) and artificial neural network (ANN) integrated optimization for lipase production by Bacillus holotolerans. Syst Microbiol and Biomanuf (2023). https://doi.org/10.1007/s43393-023-00220-0

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