低频ERK和Akt活动动态可预测随机细胞分裂事件的发生
Jamie J R Bennett1, Alan D Stern2, Xiang Zhang3
1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
NPJ systems biology and applications
|June 4, 2024
概括
机器学习模型通过分析细胞内信号传递动态来预测细胞分裂. 细胞周期中的ERK通路活性,而不仅仅是初始反应,是预测细胞命运和癌症进展的关键.
科学领域:
- 细胞生物学 细胞生物学
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 像ERK1/2 (ERK) 和Akt1/2 (Akt) 这样的细胞内信号通路对于细胞命运决定至关重要.
- 单细胞反应异质性使理解信号动态和细胞分裂之间的联系变得复杂.
- 预测基于信号动态的细胞分裂对于癌症研究至关重要.
研究的目的:
- 开发和验证机器学习模型,以使用单细胞信号动态预测细胞分裂事件.
- 确定哪些信号通路 (ERK,Akt) 和它们的动态特征最能预测细胞分裂.
- 评估预测模型在不同细胞类型中的通用性.
主要方法:
- 在增长因子刺激后,在单个MCF10A细胞中收集了ERK和Akt活动的时间过程数据.
- 应用离散波形变换 (DWT) 来从信号时间过程中提取低频特征.
- 使用 Ensemble 集成框架用于数据集成和构建预测机器学习模型.
主要成果:
- 机器学习模型有效地预测了MCF10A细胞中的细胞分裂 (F-测量=0.524,AUC=0.726).
- ERK信号动态比Akt信号动态更能预测细胞分裂.
- 结合ERK和Akt数据改善了预测性能;模型对RPE细胞进行了概括.
结论:
- 细胞内信号的动态,特别是整个细胞周期的ERK活动,是细胞分裂的强大预测因素.
- 机器学习,结合像DWT这样的特征提取技术,提供了一种强大的方法来破译复杂的细胞命运决定.
- 这种数据驱动的方法在理解和预测细胞行为方面具有潜在的应用,在各种生物环境中,包括疾病.
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