使用人工智能和机器学习技术,对菌阿斯巴拉金酶生产的发酵过程条件的建模和优化进行比较研究
Gurunathan Baskar1,2, Rajendran Sivakumar3,4, Seifedine Kadry5
1Department of Biotechnology, St. Joseph's College of Engineering, Chennai, India.
Preparative biochemistry & biotechnology
|January 6, 2025
概括
人工智能和机器学习优化了使用Aspergillus terreus的L-asparaginase发酵过程. 这增强了用于癌症治疗和食品工业的酶生产.
科学领域:
- 生物技术是生物技术.
- 酶技术 酶技术是一种
- 工业微生物学 工业微生物学
背景情况:
- L-阿斯巴拉金酶是癌症化疗和食品加工中使用的关键酶.
- 优化其生产对于满足工业和医疗需求至关重要.
- 阿斯伯吉勒斯土壤的浸泡发酵是L-阿斯巴拉金酶生物合成的关键方法.
研究的目的:
- 模拟和优化发酵条件,以提高L-阿斯巴拉金酶的生产.
- 应用人工智能和机器学习技术来优化流程.
- 为了确定最大限度地提高L-阿斯巴拉基因酶产量的最佳参数,从阿斯伯吉路斯土壤.
主要方法:
- 利用人工智能和机器学习,特别是随机森林算法.
- 采用中央复合设计用于实验数据生成.
- 优化发酵参数,包括温度,pH值,注射剂大小,和时间.
主要成果:
- 随机森林算法被确定为表现最好的机器学习技术.
- 达到最大的实验L-阿斯巴拉金酶活性为41.58 IU/mL.
- 确定最佳发酵条件:31°C,pH 6.3,2%的注射剂,每分钟150转的和66小时.
结论:
- 人工智能和ML技术有效地优化了潜水发酵过程,以生产L-酸酶.
- 该研究成功地通过优化发酵参数来提高L-阿斯巴拉金酶产量.
- 这些发现为可扩展和高效的L-asparaginase工业生产提供了基础.
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