A model for mechanosensitive cell migration in dynamically morphing soft tissues. (January 2023)

• Record Type: Journal Article
• Authors: Kim, Jaemin; Mailand, Erik; Sakar, Mahmut Selman; Bouklas, Nikolaos
• Is Part Of: Extreme mechanics letters. Volume 58(2023)
• Publication Date: 2023-01
• Subjects: Morphogenesis -- Mechanosensitivity -- Microtissue -- Contractility -- Cell migration -- Wound healing; Mechanics -- Periodicals; Mechanics, Applied -- Periodicals; Mechanics; Electronic journals; Periodicals; 531.05
• DOI: 10.1016/j.eml.2022.101926
• Languages: English
• ISSNs: 2352-4316

Abstract

Abstract: Processes such as embryonic development and wound healing involve a complex coupled interplay between cellular contractility and migration, as well as extracellular matrix (ECM) mechanics and remodeling. Despite intense efforts to describe biomechanical interactions between cells and the surrounding extracellular matrix (ECM), the regulatory principles of the coupled cell migration and contractility within dynamically morphing tissues remain poorly understood. Existing theories dealing with cell/ECM contractility do not account for the evolution of cell concentration that is a result of cell migration, which in turn inadvertently influences contractility and vice-versa. Here, we propose a mechanosensitive transient nonlinear theory to connect cell migration and contractility inside 3D ECM, while accounting for "active" surface effects. Moreover, we introduce a finite element implementation to account for coupling cell migration and contractility capturing the transient large deformation response in complex tissue geometries. Motivated by microtissue experimental setups, simulation results presented in this work, capture changes in the bulk shape of morphing tissues as well as the evolution of the cell concentration for wounded and intact microtissues. The presented theoretical model and computational framework enable us to interpret experimental data, isolate factors that dominate the mechanical behavior, and conduct hypothesis driven research.