从单细胞基因表达来同时估计基因调节网络结构和RNA动力学
Christopher A Jackson1,2, Maggie Beheler-Amass1,2, Andreas Tjärnberg1,2
1Center For Genomics and Systems Biology, New York University, New York, NY, USA.
bioRxiv : the preprint server for biology
|October 4, 2023
概括
这项研究引入了一种深度学习模型,通过从单细胞RNA测序数据中估计RNA生产和衰变率来预测基因表达,从而推进我们对细胞调节的理解.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 细胞通过mRNA转录和衰变来动态调节基因表达,以应对刺激.
- 识别调控关系和构建预测基因表达模型是关键的生物学挑战.
- 目前的方法通常依赖于转录组测量和因果推断,通过实验估计RNA动态参数.
研究的目的:
- 开发一个深度学习模型来推断因果调节关系和估计RNA动态参数.
- 预测未来的基因表达状态,并模拟转录因子扰动的影响.
- 构建基因表达调节的预测性,生物物理模型.
主要方法:
- 训练了一个深度学习模型,使用来自175,000个Saccharomyces cerevisiae细胞的单细胞RNA测序数据.
- 计算每个细胞的RNA速度,以估计依赖时间的mRNA生成和衰变速率.
- 通过模拟转录因子扰动来验证模型的预测能力.
主要成果:
- 该模型准确地估计了RNA动态参数,并推断出因果调节关系.
- 拉帕米辛治疗被证明可以迅速破坏现有的核糖体蛋白转录的稳定性,同时减缓新的转录生成.
- 与现有模型相比,深度学习框架在恢复已知的监管关系方面表现优异.
结论:
- 开发的深度学习模型为剖析基因表达动态提供了强大的工具.
- 这种方法提高了预测细胞对刺激和遗传干扰的反应的能力.
- 这项工作是迈向基因表达调节综合性预测模型的重要一步.
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