Protocol for inferring mechanical stresses in tissues using ForSys, an open-source Python tool.

Area of Science:

• Biomechanical Engineering
• Computational Biology
• Tissue Mechanics

Background:

• Estimating mechanical stress in tissues is crucial for understanding tissue function and disease.
• Traditional methods often involve invasive procedures, limiting their applicability.
• Computational approaches offer a non-invasive alternative for stress inference.

Purpose of the Study:

• To present a detailed protocol for inferring mechanical stresses in biological tissues.
• To introduce ForSys, an open-source Python tool designed for this purpose.
• To guide users through the installation, usage, and data analysis steps.

Main Methods:

• Utilizing ForSys, an open-source Python tool for computational stress inference.
• Describing the setup of the Python environment and the use of command-line and graphical user interfaces (GUI).
• Detailing data import, correction of time-point connections, scale parameter determination, and result visualization.

Main Results:

• Successful implementation of a protocol for non-invasive stress inference in tissues.
• Demonstration of ForSys's capabilities in preparing data, performing calculations, and quantifying results.
• Provision of a reproducible workflow for researchers in biomechanics and related fields.

Conclusions:

• ForSys provides a rapid and accessible computational method for tissue stress inference.
• The presented protocol enables researchers to estimate mechanical tissue states without invasive experiments.
• This work facilitates advancements in understanding tissue mechanics and developing computational models.