一种基于机器学习的方法,用于改善Escherichia coli食批次发酵中的等离子体DNA生产
Zhixian Xu1, Xiaofeng Zhu1, Ali Mohsin1
1State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China.
Biotechnology journal
|June 19, 2024
概括
人工智能 (AI) 通过优化等离子体产量来增强发酵. 机器学习准确地预测了加热策略,与传统方法相比,大大提高了生产.
科学领域:
- 生物技术和发酵科学 生物技术和发酵科学
- 工业微生物学 工业微生物学
- 人工智能在生物处理中的应用
背景情况:
- 传统的等离子体发酵由于主观的过程控制而遭受不稳定的产量.
- 优化发酵需要了解复杂的代谢动态和环境因素.
研究的目的:
- 使用人工智能开发一种智能,自我优化的发酵过程方法.
- 通过数据驱动的过程控制来提高等离子体产量和生产率.
主要方法:
- 多参数相关性分析以确定影响等离子体产量的关键因素.
- 生物质,等离子体和基质度的运动模型的开发.
- 机器学习 (随机森林) 模型用于预测最佳加热策略.
主要成果:
- 确定加热速度和时间对于平衡的细胞生长和等离子体生产至关重要.
- 使用人工智能预测的策略,实现了1167.74毫克L-1等离子体产量和8.87毫克L-1/OD600的特定生产率.
- 与传统方法相比,塑产量增加了71%和特定生产率增加了21%.
结论:
- 由人工智能驱动的方法将经验性发酵优化转化为一种高效,理性的自我优化方法.
- 该方法适用于其他发酵产品,推进智能自动化.
- 机器学习为发酵过程的动态调节提供了一个强大的工具.
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