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Mechanosensing model of fibroblast cells adhered on a substrate with varying stiffness and thickness

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posted on 2024-02-12, 12:14 authored by Wenjian Yang, Ma Luo, Yanfei Gao, Xiqiao Feng, Jinju ChenJinju Chen

Mechanosensing of cells to the surrounding material is crucial for their physiological and pathological processes. However, materials design to guide cellular responses is largely ad hoc due to the lack of comprehensive modelling techniques for quantitative understanding. In this paper, we propose a computational model to study the mechanosensing of fibroblast cells seeded on elastic hydrogel substrates with different stiffness and thickness. We consider the sensing mechanisms of cells to mechanical cues, including the rigidity and deformation of the substrate, and the traction forces of neighboring cells, which regulate the active changes of stress fibers and focal adhesions. This model allows us to predict the coupled effects of substrate stiffness and thickness on stress fiber formation and disassociation, and affinity integrin density. We also examine the combined effect of cell size and substrate thickness on the mechanosensing of fibroblast cells. The results reveal that a cell can sense its neighboring cell by deforming the underlying substrate. Our simulations also provide physical insights in the enhanced mechanosensing capacity of collective cells. The present modelling framework is not only important for profound understanding of cell mechanosensing, but also has the potential to guide the rationale design of biomaterials for tissue engineering and wound healing.

Funding

An New Frontier in Design: The Simulation of Open Engineered Biological Systems

Engineering and Physical Sciences Research Council

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National Natural Science Foundation of China [12032014]

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Materials

Published in

Journal of the Mechanics and Physics of Solids

Volume

171

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Acceptance date

2022-11-12

Publication date

2022-11-19

Copyright date

2022

ISSN

0022-5096

eISSN

1873-4782

Language

  • en

Depositor

Prof Jinju Chen. Deposit date: 11 February 2024

Article number

105137

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