Connecting theory and experiment in cell and tissue mechanics

Area of Science:

• Integrative biology and biophysics
• Computational and theoretical biology
• Cellular and molecular mechanics

Background:

• Complex biological systems are governed by physical laws, necessitating integration of experimental data with theoretical models.
• Effective modeling requires collaboration between experimental cell biologists and theoreticians, who often face communication challenges.
• Understanding biological phenomena requires bridging diverse scales, from molecular to tissue levels.

Purpose of the Study:

• To provide a perspective on integrating physical and mathematical models with experimental data for studying complex biological systems.
• To highlight different modeling approaches, including bottom-up and top-down strategies, and their applications.
• To promote effective collaboration between experimentalists and theoreticians in biological research.

Main Methods:

• Discussion of various modeling approaches: bottom-up hypothesis-driven and top-down data-driven models.
• Exploration of integrating physical models with experimental data across multiple biological scales (molecular, cellular, tissue).
• Emphasis on strategies for constraining model complexity and fostering crosstalk between experimental design and model development.

Main Results:

• Physical models offer conceptual insights and can create unifying frameworks for biological phenomena.
• Integration of physical models with experimental data aids in understanding complex systems like cell mechanics.
• Effective collaboration and appropriate model selection are crucial for advancing biological understanding.

Conclusions:

• Fruitful collaborations between experimental biologists and theoreticians, supported by integrated modeling, are key to advancing the understanding of complex biological systems.
• Physical models provide powerful tools for generating insights and unifying diverse biological observations.
• This perspective advocates for a synergistic approach combining experimental data with theoretical modeling to tackle fundamental questions in biology.