Inria, CNRS, Ecole Centrale de Lyon, INSA Lyon, Universite Claude Bernard Lyon 1, Université Jean Monnet, ICJ UMR5208, 69603, Villeurbanne, France.
"Tumor Cell Plasticity in Melanoma", Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, INSERM U1052-CNRS UMR5286, Centre Léon Bérard, Université de Lyon, Université Claude Bernard Lyon1, 69008, Lyon, France.
BMC Bioinformatics. 2024 Aug 20;25(1):270. doi: 10.1186/s12859-024-05816-4.
In the present work, we aimed at modeling a relaxation experiment which consists in selecting a subfraction of a cell population and observing the speed at which the entire initial distribution for a given marker is reconstituted.
For this we first proposed a modification of a previously published mechanistic two-state model of gene expression to which we added a state-dependent proliferation term. This results in a system of two partial differential equations. Under the assumption of a linear dependence of the proliferation rate with respect to the marker level, we could derive the asymptotic profile of the solutions of this model.
In order to confront our model with experimental data, we generated a relaxation experiment of the CD34 antigen on the surface of TF1-BA cells, starting either from the highest or the lowest CD34 expression levels. We observed in both cases that after approximately 25 days the distribution of CD34 returns to its initial stationary state. Numerical simulations, based on parameter values estimated from the dataset, have shown that the model solutions closely align with the experimental data from the relaxation experiments.
Altogether our results strongly support the notion that cells should be seen and modeled as probabilistic dynamical systems.
在本工作中，我们旨在构建一个松弛实验模型，该模型包括选择细胞群体的一个子分数，并观察给定标记的整个初始分布重新构成的速度。
为此，我们首先对之前发表的基因表达的两态机械模型进行了修改，在该模型中添加了一个与状态相关的增殖项。这导致了一个具有两个偏微分方程的系统。在增殖率与标记水平呈线性关系的假设下，我们可以推导出该模型解的渐近轮廓。
为了使我们的模型与实验数据相吻合，我们从 TF1-BA 细胞表面的 CD34 抗原的最高或最低表达水平开始，生成了一个松弛实验。在这两种情况下，我们都观察到大约 25 天后，CD34 的分布恢复到其初始的稳定状态。基于从数据集估计的参数值的数值模拟表明，模型解与松弛实验的实验数据非常吻合。
总之，我们的结果强烈支持这样一种观点，即细胞应该被视为概率动力学系统，并对其进行建模。
