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构建一个机器学习模型,以预测最佳的氨酸通路基因表达水平,以有效地生产酵母中的胡卜素
Shun Shimazaki1, Ryosuke Yamada1, Yoshiki Yamamoto1
1Department of Chemical Engineering, Osaka Metropolitan University, Sakai, Osaka, Japan.
Biotechnology journal
|November 13, 2023
概括
机器学习准确地预测了在酵母中增强胡卜素生产的最佳基因表达. 这种方法可以通过澄清代谢工程中的基因型-表型相关性来提高有价值化合物的产量.
科学领域:
- 代谢工程是代谢工程.
- 合成生物学 合成生物学
- 生物技术是生物技术.
背景情况:
- 了解基因表达和化学生产率之间的关系对于优化代谢途径至关重要.
- 同时修改多个代谢酶基因导致不同的表达比率,使直接与产品产量的相关性复杂化.
研究的目的:
- 开发一种机器学习模型,用于预测最佳的基因表达水平,以增强Saccharomyces cerevisiae中的胡卜素生产.
- 在代谢工程中解决基因型-表型相关性的挑战.
主要方法:
- 收集了关于梅瓦酸途径酶基因表达和酵母酵母中的胡卜素生产的数据.
- 开发和训练了一个监督的机器学习模型,以根据基因表达数据预测胡卜素的生产力.
主要成果:
- 在测试数据上实现了0.6292的预测准确性 (确定系数).
- 预测,与父母菌株相比,工程菌株的胡卜素生产率增加了4.3倍.
- 确定了tHMG1和ERG8基因表达水平对胡卜素生产力产生重大影响.
结论:
- 监督机器学习是有效的澄清基因表达和化学生产力之间的关系.
- 这项研究表明了通过基因表达的预测建模来改善有价值化学品的生产的潜在策略.
- 这些发现有助于克服在代谢工程中的基因型-表型相关性的不确定性.
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