密度物理信息的神经网络揭示了信号传导中细胞异质性的来源
Hyeontae Jo1, Hyukpyo Hong1,2, Hyung Ju Hwang3
1Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea.
Patterns (New York, N.Y.)
|February 19, 2024
概括
我们开发了Density-PINNs以从压力反应推断信号通路转导时间. 结合缓慢和快速的途径减少细胞反应异质性,帮助疾病治疗策略.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 细胞信号转导涉及时间延迟,这对于路径动态和精度至关重要.
- 多模转导时间分布表明通过具有不同速度的多个路径进行调节.
- 了解这些时间延迟对于预测细胞反应和开发向疗法至关重要.
研究的目的:
- 开发一种新的计算方法,密度-PINNs,从可测量的应力反应数据推断转导时间分布.
- 分析单细胞基因表达数据,以了解信号通路速度和异质性如何与细胞反应有关.
- 通过途径调节确定通过途径调节实现一致细胞反应的策略.
主要方法:
- 开发了密度PINN (密度物理信息的神经网络) 来推断转导时间分布.
- 应用密度PINN对抗生素压力下的16种促进者的单细胞基因表达数据.
- 分析了信号启动/传导时间和响应强度异质性之间的关系.
主要成果:
- 信号启动和转导速度较慢的促体在响应强度方面表现出更大的细胞间异质性.
- 通过缓慢和快速路径共同调节显著降低了这种反应异质性.
- 密度-PINNs成功地从应激反应数据中推断了转导时间分布.
结论:
- 细胞反应异质性与信号通路的速度有关.
- 通过不同速度的路径进行协调调节,可以提高响应的一致性.
- 密度PINNs为分析时间延迟系统提供了强大的工具,在疾病治疗和理解传染病方面具有应用.
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