通过综合单细胞和数学建模方法进行细胞循环停止的动态建模
Javiera Cortés-Ríos1,2, Maria Rodriguez-Fernandez1, Peter Karl Sorger3
1Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.
PLoS computational biology
|October 7, 2025
概括
这项研究引入了分析多重成像数据的新框架,使细胞行为在多种实验条件下的动态建模成为可能. 这有助于我们更好地理解复杂的生物过程,如细胞循环.
科学领域:
- 细胞和分子生物学 细胞和分子生物学
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 高复合成像提供了细胞状态的静态快照.
- 伪时间分析将静态数据转换为动态细胞轨迹.
- 将多个实验条件集成到数学模型中是一项挑战.
研究的目的:
- 开发用于将多重复合,多条件免疫光数据与数学建模集成的方法.
- 为了使细胞群体具有振荡和停止状态的动态建模.
- 创建适用于各种高含量生物测量技术的框架.
主要方法:
- 针对多重免疫光数据的数据处理和模型培训策略的建议.
- 开发了数学模型的训练策略,以适应振荡和停止细胞动态.
- 使用MCF-10A乳腺上皮细胞数据训练细胞周期模型的应用方法.
主要成果:
- 在多条件免疫光数据上成功训练了细胞周期模型.
- 通过预测生长因子敏感性和对抑制剂的反应来验证模型.
- 证明了框架对具有不同初始条件和动态的细胞的适用性.
结论:
- 开发的框架有助于使用多重数据进行复杂生物系统的动态建模.
- 这种方法增强了从静态细胞快照中获得的数学模型的预测能力.
- 该框架可将其应用于其他高内容测量技术,从而扩大了对动态建模的访问.
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