基因调节DNA的进化,可进化性和工程
Eeshit Dhaval Vaishnav1,2, Carl G de Boer3,4, Jennifer Molinet5,6
1Massachusetts Institute of Technology, Cambridge, MA, USA. edv@mit.edu.
Nature
|March 10, 2022
概括
科学家开发了深度神经网络模型,将DNA序列映射到基因表达,揭示了调节进化的原理. 这种方法有助于设计调节序列和理解进化约束.
科学领域:
- 基因组学
- 进化生物学
- 系统生物学
背景情况:
- 非编码DNA调节序列的突变显著影响基因表达,生物特征和健康状况.
- 将DNA序列映射到健身场景中对于理解进化至关重要,但由于序列空间广而具有挑战性.
研究的目的:
- 开发序列表达模型以捕捉监管适应性景观.
- 解读规范演变的原则.
- 为表达工程设计调节序列.
主要方法:
- 利用数百万个随机抽取的酵母促进体DNA序列及其测量表达水平.
- 开发了深度神经网络模型,从DNA序列预测基因表达.
- 应用模型研究不同进化模式下的表达差异 (漂移,选择-突变).
主要成果:
- 通过序列到表达模型实现了优异的预测性能,使得它能够在广的序列空间中进行概括.
- 监管的演变是快速的,受益率下降的限制,并受到相互矛盾的环境目标的限制.
- 稳定选择缓解了监管的复杂性,监管突变效应的大小遵循了一个功率定律.
结论:
- 开发的模型为设计调节序列和工程基因表达提供了一般框架.
- 这项研究提供了监管进化,突变强度和可进化的动态.
- 提出了一种检测选择特征和发现规范演变的方法.
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