基于机器学习的生物过程优化,用于低分子量生产
Yuying Wang1, Zimeng Zhang1, Tiantian Zhang1
1School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China.
International journal of biological macromolecules
|March 18, 2025
概括
这项研究优化了使用Sphingomonas sp.的低分子量 (LMW-WG) 生产. 和先进的建模. 优化的发酵实现了高LMW-WG产量,证明了高效的生物处理潜力.
科学领域:
- 生物技术是生物技术.
- 微生物发酵 微生物发酵
- 生物聚合物生产 生物聚合物生产
背景情况:
- 韦兰是一种微生物多糖,具有多种工业应用.
- 优化低分子量 (LMW-WG) 生产的发酵对于增强功能至关重要.
- 斯芬戈蒙纳斯 (Sphingomonas sp.) 是一种有机植物. ATCC 31555 是一个关键的微生物,用于乳生物合成.
研究的目的:
- 使用Sphingomonas sp.优化LMW-WG生产的发酵过程. 在ATCC 31555.5.上使用.
- 确定和建模影响LMW-WG收益率的关键因素.
- 应用先进的计算方法来优化流程.
主要方法:
- 进行单因素实验以确定关键过程参数.
- 一个反向传播的人工神经网络 (BP-ANN) 结合粒子群优化 (PSO) 用于建模和优化.
- 使用动态建模和代谢途径分析来了解生物合成.
主要成果:
- 确定了影响LMW-WG生产的六个关键因素.
- 在优化条件下,LMW-WG.产生了16.28 ± 2.58 g/L的LMW-WG.
- 在优化这个复杂的非线性系统方面,ANN-PSO方法被证明是有效的.
结论:
- 该研究通过综合方法成功优化了LMW-WG生产.
- 这些发现为高效的生物聚合物生产提供了可靠的方法.
- 这项研究支持具有重大潜力的LMW-WG的工业应用.
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