通过综合单细胞和数学建模方法进行细胞周期停止的动态建模
Javiera Cortés-Ríos1,2, Maria Rodriguez-Fernandez1, Peter K Sorger3
1Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Chile.
bioRxiv : the preprint server for biology
|March 10, 2025
概括
这项研究引入了一个新的框架,用于将多重成像数据与数学模型集成在一起,从而在多种实验条件下实现动态细胞周期分析. 这将从复杂的细胞数据中提升生物学洞察力.
科学领域:
- 细胞和分子生物学 细胞和分子生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 高多重成像分析提供细胞状态的静态快照.
- 伪时间技术可以从静态数据中进行动态轨迹分析.
- 将多个实验条件集成到模型中仍然是一个挑战.
研究的目的:
- 开发数据处理和模型培训方法,将多重复合,多条件免疫光数据与数学建模集成在一起.
- 为了使细胞过程的动态建模,如细胞循环,在各种不同的实验条件.
- 克服技术限制,阻碍当前模型的预测能力.
主要方法:
- 提出了新的数据处理和模型培训策略.
- 开发了用于振荡和停止细胞动态的训练方法.
- 用MCF-10A乳腺上皮细胞数据对细胞周期模型应用方法.
- 综合多重复合,多条件免疫光数据.
主要成果:
- 在复杂的多条件数据上成功训练了细胞循环模型.
- 通过预测生长因子敏感性和抑制剂反应来验证模型.
- 已证明适用于振荡和停止动态数据集的应用.
- 框架集成多重复合的免疫光数据用于数学建模.
结论:
- 拟议的框架使用多重成像数据促进了细胞过程的动态建模.
- 这种方法通过整合多个条件来增强数学模型的预测能力.
- 预计该框架将广泛应用于其他高含量测量技术,如质量细胞计.
- 通过可访问的大型数据集的动态建模,可以获得更深入的生物学见解.
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