通过积极学习优化调控DNA
Yuxin Shen1, Grzegorz Kudla2, Diego A Oyarzún1,3
1School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
Computational and structural biotechnology journal
|October 27, 2025
概括
积极学习通过代优化蛋白质表达来增强生物技术的DNA序列设计. 这种机器学习方法在复杂的生物环境中优于传统方法,提高了工程微生物菌株的产量.
科学领域:
- 生物技术是生物技术.
- 合成生物学 合成生物学
- 机器学习 机器学习
背景情况:
- 生物技术应用需要为高异质蛋白表达而设计的微生物菌株.
- 优化调节性DNA元素对于提高蛋白质产量至关重要.
- 机器学习 (ML) 与高通量实验相结合,有助于找到改进的调节序列.
研究的目的:
- 探索积极学习 (AL) 作为优化DNA序列以提高蛋白质表达水平的策略.
- 评估AL循环在复杂的基因型-表型景观中的性能和融合.
主要方法:
- 测量的代周期,ML模型训练和序列采样/选择.
- 利用合成数据和经过实验验证的酵母促进剂序列景观.
- 将AL与一次性优化方法进行比较.
主要成果:
- 积极学习在复杂的,富有经验的景观中,与一次性优化相比,表现出更高的性能.
- 在不同的实验条件下,AL有效地优化了序列.
- 该框架显示了数据集成在各种环境 (实验室,菌株,条件) 中的潜力.
结论:
- 积极学习为生物技术中的DNA序列设计和表型优化提供了有效的框架.
- 在工程微生物系统中,AL为最大限度地提高蛋白质产量提供了一个强大的策略.
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