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根据深度学习的基础上,对Schizochytrium的形态分类和高产油菌株的突变性选择

Yu Qin¹, Ying Ou², Jian Zheng³

¹Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China.

Microbiological research

|February 10, 2026

概括

研究人员开发了一种人工智能系统来监测精神分裂的发酵,从而改善omega-3的产量. 这种深度学习方法增强了微生物菌株的选择,以高效地制造多可胺酸 (DHA).

科学领域:

生物技术是生物技术.
微生物发酵微生物发酵
机器学习机器学习

背景情况:

斯基佐希是欧米茄-3脂肪酸的重要工业生产者,特别是多可沙赫萨酸 (DHA).
目前的发酵过程缺乏实时监测,限制了omega-3产品的优化和商业化.
了解思索体形态与脂质生产之间的联系至关重要,但尚未解决.

研究的目的:

开发一种自动化图像分析系统,用于实时监测精神分裂发酵.
建立细胞形态,生物质动态和脂质生物合成之间的相关性.
利用深度学习来增强菌株选择和优化DHA生产.

主要方法:

开发一个包含细胞分裂和脂质滴滴参数的13类形态型分类系统.
实现一个自定义的对象检测架构 (MLC-YOLO) 用于图像分析.
在发酵过程中对形态型和脂质产量之间的相关性分析,以及随后的突变菌株隔离.

主要成果:

MLC-YOLO框架实现了84.2%的平均平均精度 (mAP),超过了标准YOLOv8s.
脂质和单细胞 (G11) 与脂质产量正相关 (p < 0.001).
使用选择指数分离了一个突变菌株 (S62),其脂质含量增加了1.96% (50.46%).

结论:

基于深度学习的细胞表型化是对精神分裂症菌株选择的可行策略.
开发的系统解决了关于精神分裂体形态学和脂质生产的知识差距.
这项研究推进了用于工业omega-3生产的智能生物制造系统.

关键词:

生物过程优化优化细胞形态细胞形态深度学习是一种深度学习.生产石油的微生物生产石油的微生物精神分裂 sp 的病例.

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