A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition.

Autor:	Mendez MJ; Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio; Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia., Hoffman MJ; School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York., Cherry EM; School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York; School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia., Lemmon CA; Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia., Weinberg SH; Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio; Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia; The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, Ohio. Electronic address: weinberg.147@osu.edu.
Jazyk:	angličtina
Zdroj:	Biophysical journal [Biophys J] 2022 Aug 16; Vol. 121 (16), pp. 3061-3080. Date of Electronic Publication: 2022 Jul 14.
DOI:	10.1016/j.bpj.2022.07.014
Abstrakt:	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. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2022 Biophysical Society. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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