Spatial and Structural Metrics for Living Cells Inspired by Statistical Mechanics

Area of Science:

• Cell Biology
• Statistical Physics
• Computational Biology

Background:

• Experimental cell biology has advanced significantly.
• A lack of a common theoretical framework hinders interdisciplinary collaboration between experimentalists, computational scientists, and theorists.
• Bridging this gap could accelerate progress in both fields.

Purpose of the Study:

• To demonstrate the application of statistical mechanics tools and concepts to describe intracellular processes using experimental data.
• To propose a foundation for future theoretical and computational models in cell biology based on statistical mechanics.
• To illustrate these concepts using examples of subcellular organization and intracellular transport.

Main Methods:

• Utilized concepts from statistical mechanics, specifically density pair correlation functions.
• Applied these concepts to analyze the organization of subcellular structures.
• Tracked the transport of nano-sized objects within the cell using these methods.

Main Results:

• Successfully described subcellular structures using pair correlation functions.
• Tracked nano-sized object transport within the cell with statistical mechanics.
• Quantified a novel subcellular re-organization previously undetected by molecular biology methods.

Conclusions:

• Statistical mechanics offers a powerful framework for analyzing and modeling cellular processes.
• The proposed approach can reveal new insights into subcellular organization and dynamics.
• This interdisciplinary approach has the potential to advance both theoretical and experimental cell biology.