Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach
AffiliationUniv Arizona, Dept Math
MetadataShow full item record
PublisherPUBLIC LIBRARY SCIENCE
CitationStepien TL, Lynch HE, Yancey SX, Dempsey L, Davidson LA (2019 ) Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach. PLoS ONE 14(6): e0218021. https://doi.org/10.1371/journal.pone.0218021
RightsCopyright © 2019 Stepien et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium,provided the original author and source are credited.
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AbstractAdvanced imaging techniques generate large datasets capable of describing the structure and kinematics of tissue spreading in embryonic development, wound healing, and the progression of many diseases. These datasets can be integrated with mathematical models to infer biomechanical properties of the system, typically identifying an optimal set of parameters for an individual experiment. However, these methods offer little information on the robustness of the fit and are generally ill-suited for statistical tests of multiple experiments. To overcome this limitation and enable efficient use of large datasets in a rigorous experimental design, we use the approximate Bayesian computation rejection algorithm to construct probability density distributions that estimate model parameters for a defined theoretical model and set of experimental data. Here, we demonstrate this method with a 2D Eulerian continuum mechanical model of spreading embryonic tissue. The model is tightly integrated with quantitative image analysis of different sized embryonic tissue explants spreading on extracellular matrix (ECM) and is regulated by a small set of parameters including forces on the free edge, tissue stiffness, strength of cell-ECM adhesions, and active cell shape changes. We find statistically significant trends in key parameters that vary with initial size of the explant, e.g., for larger explants cell-ECM adhesion forces are weaker and free edge forces are stronger. Furthermore, we demonstrate that estimated parameters for one explant can be used to predict the behavior of other similarly sized explants. These predictive methods can be used to guide further experiments to better understand how collective cell migration is regulated during development.
NoteOpen access journal
VersionFinal published version
SponsorsNational Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 HD044750, R21 ES019259]; National Science FoundationNational Science Foundation (NSF) [CAREER IOS-0845775, CMMI-1100515]