Peeking into the future: inferring mechanics in dynamical tissues

Area of Science:

• Biophysics
• Computational Biology
• Cellular Mechanics

Background:

• Cells generate forces that shape tissues, creating mechanical stresses.
• Mechanical stress inference offers a computational approach to estimate these forces.
• This method is non-invasive, low-cost, and valuable for understanding tissue mechanics.

Purpose of the Study:

• To review and compare static and dynamic mechanical stress inference modalities.
• To discuss the advantages and limitations of each approach.
• To explore future directions for computational inference of tissue mechanical states.

Main Methods:

• Review of static stress inference methods using single microscopy images.
• Review of dynamic stress inference methods using time-series microscopy images.
• Discussion of physical, mathematical, and computational foundations.

Main Results:

• Static inference is suitable for quasi-equilibrium states but ignores cell rearrangements.
• Dynamic inference accounts for temporal changes by using time-series data.
• Both methods have distinct advantages and limitations depending on the biological context.

Conclusions:

• Mechanical stress inference is a powerful tool for studying tissue mechanics.
• Dynamic inference offers a more comprehensive approach by capturing time-dependent cellular rearrangements.
• Further development in in silico methods promises deeper insights into tissue mechanical states.