Patel Payal, Gosai Haren, Panseriya Haresh, Dave Bharti
Department of Bioscience, School of Science, Indrashil University, Dist. Mehsana, Rajpur-Kadi, Gujarat, India, 382740.
Gujarat Ecology Society, Synergy house, Subhanpura, Vadodara, Gujarat, India, 390003.
Appl Biochem Biotechnol. 2022 Apr;194(4):1659-1681. doi: 10.1007/s12010-021-03707-5. Epub 2021 Nov 30.
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, MgSO, 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 R 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.
本研究旨在利用以数据和过程为中心的方法对培养基成分进行生物工程改造，以提高耐盐地衣芽孢杆菌PPD37菌株生产重要生物分子L-天冬酰胺酶的产量。为此，首先筛选出重要的培养基成分，然后对培养基成分和培养条件（如L-天冬酰胺、MgSO、NaCl、pH值和温度）的组合进行优化。使用响应面法（RSM）（以过程为中心）和人工神经网络（ANN）（以数据为中心）等统计模型进行优化研究。产量从2.86 U/mL提高到17.089 U/mL，未优化的L-天冬酰胺酶产量提高了约6倍。基于R值、平均绝对百分比误差（MAPE）、均方根误差（RMSE）和平均绝对偏差（MAD）值，比较RSM和ANN模型对优化L-天冬酰胺酶生产的效果，ANN模型表现更优。据作者所知，这是首次使用RSM和ANN模型开发推理系统以提高耐盐细菌生产L-天冬酰胺酶产量的报告，该研究可能会带来更深入、大规模的L-天冬酰胺酶生产。
