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Learning Epithelial Elasticity via Local Tension Remodeling.

Sadjad Arzash1, Shiladitya Banerjee1

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This summary is machine-generated.

Epithelial tissues remember mechanical stress through a novel model, altering their stiffness and responding to distant stimuli. This physical learning allows programmable tissue rheology.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Materials Science

Background:

  • Epithelial tissues dynamically remodel in response to mechanical forces.
  • Understanding how tissues store mechanical memory is a key challenge.

Purpose of the Study:

  • Investigate mechanical memory in epithelial sheets using an active Vertex Model.
  • Identify consequences of history-dependent mechanosensitive remodeling.

Main Methods:

  • Developed an active Vertex Model for epithelial sheets.
  • Incorporated a local, mechanosensitive tension-remodeling rule.
  • Analyzed tissue response to localized and cyclic deformations.

Main Results:

  • Localized stress permanently reprograms global shear modulus, tunable by remodeling rate.
  • Tissues exhibit long-range mechanical memory, with distant stimuli responses modulated by prior events.
  • Cyclic deformation programs tissue properties, including auxetic Poisson ratio.

Conclusions:

  • Epithelial mechanics operates under unsupervised physical learning.
  • Mechanosensitive remodeling rates control programmable tissue-scale rheology.