A computational bridge between traction force microscopy and tissue contraction.

Area of Science:

• Biomedical Engineering
• Mechanobiology
• Computational Biology

Background:

• Arterial wall mechanics depend on smooth muscle cell (SMC) contractility.
• Cellular responses to stimuli can differ from tissue-level observations.
• Understanding this scale discrepancy is crucial for disease research.

Purpose of the Study:

• To develop a multiscale model connecting microscale SMC signaling to macroscale arterial mechanics.
• To create a flexible framework applicable to various physiological and pathological conditions.
• To bridge the gap between cellular and tissue-level contractility phenomena.

Main Methods:

• A multiscale computational model integrating biochemical signaling and fiber network mechanics.
• Analysis of microscale (cell) and macroscale (tissue) systems.
• Application to in vitro traction force microscopy and ex vivo isometric contraction experiments.

Main Results:

• The model predicts active contractility is independent of stretch at intermediate strain.
• It accurately simulates both cell-scale and tissue-scale contractility.
• The model replicates experimentally observed behaviors, including hyperglycemia-induced hypercontractility.

Conclusions:

• The multiscale model effectively translates cell-scale mechanics to tissue-scale phenotypes.
• This framework can leverage existing cellular data for understanding vascular diseases.
• It offers potential for developing novel smooth muscle cell-targeting therapeutics.