从低细胞计数实验和随机模型中识别表型适应的可识别性
Alexander P Browning1,2, Rebecca M Crossley2, Chiara Villa3,4
1School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia.
PLoS computational biology
|June 24, 2025
概括
癌症中的表型可塑性很难量化. 这项研究开发了一个模型,表明人口数据无法准确区分离体和连续抵抗或测量细胞异质性.
科学领域:
- 定量生物学的定量生物学.
- 癌症研究 癌症研究
- 数学建模的数学建模
背景情况:
- 现型可塑性是癌症治疗失败的一个关键因素.
- 现有的定量工具很难描述塑料适应性和区分耐药性表型.
- 低细胞计数增殖试验是常见的实验模型.
研究的目的:
- 开发一个定量框架来分析癌症中的表型可塑性.
- 区分耐药性表型的离散分布和连续分布.
- 评估在扩散分析中模型参数的可识别性.
主要方法:
- 开发了一个基于塑料表型适应的随机个体模型.
- 在低细胞计数试验中模拟了一个连续结构化的表型空间.
- 制定了一个捕获实验噪声的概率函数.
- 应用框架来评估使用人口级数据的参数可识别性.
主要成果:
- 细胞对细胞的异质性几乎无法从常见的实验数据中识别.
- 人口层面的数据不足以区分离散的和持续的耐药性表型.
- 一致的普通微分方程模型可以充分描述人口行为.
结论:
- 目前的人口级数据和分析方法在表征癌症表型可塑性方面有限.
- 未来的实验设计和定量分析需要改进,以探测异质性和抵抗机制.
- 开发的模型为更强大的癌症适应性抵抗分析提供了一个框架.
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