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深度学习辅助的媒介优化改善了Streptococcus zooepidemicus的氨酸产量

Kazuki Watanabe¹, Yoshizumi Kawai², Tomoko Kagenishi²

¹Department of Biotechnology and Environmental Chemistry, Graduate School of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido 090-8507, Japan.

Journal of bioscience and bioengineering

|April 7, 2025

概括

深度学习优化了Streptococcus zooepidemicus中的氨酸 (HA) 生产,在优化介质中达到1.66 g/L,在料批和重复批培养中显著提高产量,达到21.4 g的总产量.

科学领域:

生物技术是生物技术.
微生物发酵微生物发酵
人工智能在生物学中的应用

背景情况:

氨酸 (HA) 是一种有价值的生物聚合物,具有多种应用.
优化微生物发酵过程对于高效的HA生产至关重要.
传统的媒介优化方法可能是耗时和劳动密集型的.

研究的目的:

为了提高氨酸 (HA) 生产效率,使用Streptococcus zooepidemicus.
开发和应用深度学习 (DL) 算法来优化微生物生长介质.
扩大HA生产规模,从小规模培养到生物反应器.

主要方法:

使用深度学习 (DL) 算法来优化生长媒介.
采用L18直角阵列用于初始培训数据集设计 (OA01-18).
进行了小规模的深井板培养,随后进行水箱反应堆和料批次/重复批次培养.

主要成果:

最初的HA产量在0.09-1.39g/L之间.
该DL系统提出了54个优化媒体 (OM01-54).
优化介质OM30产生1.66g/LHA;食批量培养达到9.96g/L;重复批量培养在46小时内产生21.4gHA,生产率为0.465g/L/h.

结论:

基于深度学习的媒介优化显著提高了HA生产效率.
优化的过程是可扩展的,克服了重复批次培养中高粘度的局限性.
这种方法为增强微生物系统中的生物聚合物生产提供了一个强大的策略.

关键词:

人工智能的人工智能是人工智能.贝叶斯的优化是贝叶斯的优化.深度神经网络是一个神经网络.实验的设计实验的设计遗传算法遗传算法遗传算法氨酸是什么? 氨酸是什么?Streptococcus zooepidemicus 这种细菌是动物性流行病.

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