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- W4240525475 abstract "Abstract The present study aims at bioengineering of medium components using – data and process centric approaches for enhanced production of L-asparaginase, an important biological molecule, by halotolerant Bacillus licheniformis PPD37 strain. To achieve this, first significant medium components were screened followed by optimization of a combination of media components and culture conditions such as L-asparagine, MgSO 4 , NaCl, pH and temperature. Optimization study was carried out using statistical models such as response surface methodology (RSM) – process centric and artificial neural network (ANN) – data centric approaches. The production improved from 2.86 U/mL to 17.089 U/mL, an increase of approximately 6-times of the unoptimized L-asparaginase production. On comparing RSM and ANN models for optimised L-asparaginase production based on R 2 value, mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute deviation (MAD) values, the ANN model emerged as the superior one. As this is the first report to the authors best knowledge on development of inference system using RSM and ANN models for enhanced L-asparaginase production using a halotolerant bacteria, this study could lead to more in-depth and large-scale L-asparaginase production." @default.
- W4240525475 created "2022-05-12" @default.
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- W4240525475 date "2021-06-29" @default.
- W4240525475 modified "2023-10-16" @default.
- W4240525475 title "Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase From Halotolerant Bacillus Licheniformis PPD37" @default.
- W4240525475 doi "https://doi.org/10.21203/rs.3.rs-651645/v1" @default.
- W4240525475 hasPublicationYear "2021" @default.
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