多种模式的细胞环境指令调整为有条件的DNA调节序列生成与大语言模型.
Junhao Liu1, Pengpeng Zhang1, Siwei Xu1
1University of California, Irvine.
概括
设计合成生物学增强剂是一项挑战. 一个新的框架Leonine,使用大语言模型 (LLM) 和蜂数据进行上下文感知增强器设计,改进转录因子的招聘和功能.
科学领域:
- 合成生物学 合成生物学
- 基因组学就是基因组学.
- 计算生物学是一种计算生物学.
背景情况:
- 设计有效的调节性DNA序列,如增强剂,对于合成生物学至关重要.
- 目前的方法与细胞类型特定的转录因子 (TF) 招募相扎,导致次优增强剂设计.
研究的目的:
- 介绍Leonine,一种用于设计细胞类型特定增强剂的新框架.
- 将增强器设计重新定义为一个多模式的问答任务,整合DNA序列和细胞环境.
主要方法:
- 开发了Leonine,一个利用在DNA序列上训练的大型语言模型 (LLM) 的框架.
- 集成的多模式细胞数据,包括促进子序列,基因表达和细胞类型信息.
- 创建了一个大规模的数据集,包含超过150万个特定于细胞类型的促进剂-增强剂对,以及评估的基准.
主要成果:
- 与最先进的LLM相比,Leonin在七种细胞类型中表现出卓越的性能.
- 为特定的监管环境量身定制的生物和功能一致的增强剂序列.
- 在优化增强器序列以实现有效的转录激活方面超越现有方法.
结论:
- 莱奥宁为文本意识的DNA序列设计建立了一个新的范式.
- 通过精确控制基因调节,推动了合成生物学领域的发展.
- 为优化特定细胞环境中的增强器功能提供了强大的工具.
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