RespectM揭示了代谢异质性为重塑DBTL循环的深度学习提供了力量
Xuanlin Meng1, Ping Xu1, Fei Tao1
1State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.
iScience
|July 10, 2023
概括
合成生物学 合成生物学
科学领域:
- 合成生物学和代谢工程.
- 计算生物学和机器学习.
- 代谢学和高通量查.
背景情况:
- 合成生物学中的设计-构建-测试-学习 (DBTL) 周期在"学习"阶段与预测能力作斗争.
- 稀少的数据和复杂的代谢网络限制了对生物系统行为的理解.
- 目前的方法在捕捉单细胞代谢异质性方面缺乏效率.
研究的目的:
- 为高通量单细胞代谢学开发一种新的方法.
- 训练使用代谢异质性的预测模型.
- 增强DBTL循环中的"学习"步骤,以改善合理设计.
主要方法:
- 利用质谱成像用于在单细胞水平上检测代谢物.
- 获取了4,321个单细胞代谢学数据集,以捕捉代谢异质性.
- 应用了一个可优化的深度神经网络,并训练了一种异质性驱动学习 (HPL) 模型.
主要成果:
- 开发了RespectM,一种在单细胞 (500细胞/小时) 中有效检测代谢物的方法.
- 成功训练了一个利用代谢异质性的HPL模型.
- 使用HPL模型识别了高甘油三生产的最小操作.
结论:
- HPL策略有效地从代谢异质性中吸取教训.
- 这种方法显著提高了DBTL周期的预测能力.
- HPL有可能在合成生物学中彻底改变理性设计.
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