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 (TGF β ) 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 TGF β -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 TGF β 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 TGF β 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.

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conformation. Vesicles where fluorescence photobleached in a single-step were identified as containing single protomers. These traces were either dynamic (fluorescence fluctuated between two states) or nondynamic (no fluctuations). The fraction of protomers that showed dynamic behavior increased saturably with ATP from $40% at 0 ATP to $70% at 1 mM (saturating) ATP. This titration curve could not be fitted with a single binding isotherm. A fit to a model that assumed a heterogeneous mixture of pump molecules with high-and low-affinity ATP binding showed: (a) high-and low-affinity apparent dissociation constants of 0.1 and 40 mM; and (b) that the preparation had equal amounts of high-and low-affinity forms of the pump. Our data suggest that binding heterogeneity is an intrinsic property of the protomer and is not attributable to aggregation.