Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics

Area of Science:

• Biophysics
• Computational Biology
• Systems Biology

Background:

• Digital twins are valuable in engineering for prototyping but face integration challenges in biophysics.
• Composing multi-scale biophysical models into digital twins is constrained by physical consistency and data extraction difficulties.
• Existing methods struggle with ensuring conservation laws across composite models and deriving meaningful insights.

Purpose of the Study:

• To address the challenges of physical consistency and complexity in biophysical digital twins.
• To explore novel approaches for extracting clinically and scientifically relevant information from complex biophysical models.
• To demonstrate a practical implementation of these approaches for biophysical modeling.

Main Methods:

• Utilizing bond graphs for ensuring physical consistency of conservation laws across composite models.
• Applying principles of statistical mechanics and maximum entropy to guide multicellular biophysics.
• Defining tissue-specific network motifs based on cellular architecture, metabolism, and communication constraints.
• Approximating probability distributions of tissue network motifs using exponential random graph models.

Main Results:

• Bond graphs effectively address physical consistency challenges in composite models.
• Statistical mechanics and network motif analysis offer a promising framework for managing biophysical model complexity.
• The proposed methods allow for addressing complexity through energy considerations and mean measures of variables.
• A prototype demonstrated practical implementation and the type of extractable information.

Conclusions:

• The integration of statistical mechanics and network motif analysis provides a viable strategy for developing complex biophysical digital twins.
• These approaches can overcome limitations in extracting actionable insights from multi-scale biophysical models.
• The developed framework enables the creation of more informative and computationally tractable biophysical digital twins for research and clinical applications.