A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition
A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition
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DOI：
10.1016/j.bpj.2022.07.014
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发表时间：
2022-08-16
影响因子：
3.4
通讯作者：
Weinberg，Seth H.
中科院分区：
文献类型：
--
作者：
Mendez，Mario J.;Hoffman，Matthew J.;Weinberg，Seth H.
上皮-间质转化（EMT）是一个生物学过程，在胚胎发育、组织再生和癌症转移中起着核心作用。转化生长因子-β（TGFβ）是这种细胞转变的有效诱导剂，包括从上皮状态到部分或混合EMT状态，再到间充质状态的转变。最近的实验研究表明，在上皮细胞群体中，异质性表型谱响应于不同的时间和TGFβ剂量依赖性刺激而出现。这对计算模型提出了挑战，因为通常获得大多数模型参数来表示典型的细胞反应，而不一定是特异性反应，也不一定是捕获群体变异性。在这项研究中，我们应用了一种数据同化方法，该方法将有限的噪声观测与计算模型的预测相结合，并与参数估计相结合。合成实验通过生成大量模型参数集来模拟在上皮细胞群体中观察到的细胞状态的生物异质性。分析具有生物学显著特征的虚拟上皮细胞的参数（例如，EMT倾向性或抗性）说明这些亚群具有可识别的关键模型参数。我们进行了一系列计算机模拟实验，其中预测系统重建了暴露于时间依赖性外源性TGFβ剂量和EMT抑制或EMT促进扰动的异质群体内每个虚拟细胞的EMT动力学。我们发现，估计群体特异性的关键参数显着提高了细胞反应的预测精度。因此，通过适当的协议设计，我们证明了数据同化方法成功地重建和预测了存在生理模型误差和参数不确定性的异质虚拟上皮细胞群的动态。
Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.