Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase from Halotolerant...

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 optimisation of a combination of media components and culture conditions such as L-asparagine, MgSO₄, NaCl, pH, and temperature. Optimisation 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 unoptimised L-asparaginase production. On comparing RSM and ANN models for optimised L-asparaginase production based on R² 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.