Epithelial-mesenchymal transition (EMT) is a fundamental 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, which is composed of transitions from an epithelial state to intermediate or partial EMT state(s) to a mesenchymal state. Using computational models to predict cell state transitions in a specific experiment is inherently difficult for reasons including model parameter uncertainty and error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGFβ-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept “synthetic” in silico experiments, in which experimental observations were produced from a known computational model with the addition of noise. We mimic parameter uncertainty in in vitro experiments by incorporating model error that shifts the TGFβ doses associated with the state transitions and reproduces experimentally observed variability in cell state by either shifting a single parameter or generating “populations” of model parameters. We performed synthetic experiments for a wide range of TGFβ doses, investigating different cell steady-state conditions, and conducted parameter studies varying properties of the data-assimilation approach including the time interval between observations and incorporating multiplicative inflation, a technique to compensate for underestimation of the model uncertainty and mitigate the influence of model error. We find that cell state can be successfully reconstructed and the future cell state predicted in synthetic experiments, even in the setting of model error, when experimental observations are performed at a sufficiently short time interval and incorporate multiplicative inflation. Our study demonstrates the feasibility and utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT.

中文翻译：

---

细胞命运预测：预测上皮间质转化的数据同化方法

上皮间质转化 (EMT) 是一种基本的生物学过程，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，它由从上皮状态到中间或部分 EMT 状态到间充质状态的转变组成。由于模型参数不确定性和与实验观察相关的误差等原因，使用计算模型来预测特定实验中的细胞状态转变本身就很困难。在这项研究中，我们证明了使用集成卡尔曼滤波器的数据同化方法，该方法将有限的噪声观测与 TGFβ 诱导的 EMT 计算模型的预测相结合，可以重建细胞状态并预测状态转换的时间。我们在硅实验中的概念验证“合成”中使用了我们的方法，其中实验观察是从已知的计算模型中产生的，并添加了噪声。我们通过合并模型误差来模拟体外实验中的参数不确定性，该误差会改变与状态转变相关的 TGFβ 剂量，并通过改变单个参数或生成模型参数的“群体”来重现实验观察到的细胞状态变异性。我们对各种 TGFβ 剂量进行了合成实验，研究了不同的细胞稳态条件，并对数据同化方法的不同特性进行了参数研究，包括观测之间的时间间隔和结合乘法膨胀，这是一种补偿模型不确定性低估并减轻模型误差影响的技术。我们发现，当在足够短的时间间隔内进行实验观察并结合乘法膨胀时，即使在模型错误的情况下，也可以成功地重建细胞状态并在合成实验中预测未来的细胞状态。我们的研究证明了数据同化方法在预测经历 EMT 的细胞命运方面的可行性和实用性。我们发现，当在足够短的时间间隔内进行实验观察并结合乘法膨胀时，即使在模型错误的情况下，也可以成功地重建细胞状态并在合成实验中预测未来的细胞状态。我们的研究证明了数据同化方法在预测经历 EMT 的细胞命运方面的可行性和实用性。我们发现，当在足够短的时间间隔内进行实验观察并结合乘法膨胀时，即使在模型错误的情况下，也可以成功地重建细胞状态并在合成实验中预测未来的细胞状态。我们的研究证明了数据同化方法在预测经历 EMT 的细胞命运方面的可行性和实用性。