Modular bond-graph modelling and analysis of biomolecular systems

Area of Science:

• Systems Biology
• Biophysics
• Control Theory

Background:

• Bond graphs are established tools for engineering system modeling and analysis.
• Biomolecular systems present complex hierarchical and thermodynamic challenges for modeling.

Purpose of the Study:

• To adapt bond graph methodology for thermodynamically-compliant hierarchical modeling of biomolecular systems.
• To explore the application of bond graphs in analyzing the dynamics, modularity, and feedback structures within biological networks.

Main Methods:

• Developing a specific bond graph structure for biomolecular systems.
• Utilizing block diagrams to represent dynamics and control-theoretical methods for analysis.
• Examining computational and behavioral modularity, including the concept of retroactivity.

Main Results:

• Established a bond graph framework for biomolecular system modeling, elucidating open and closed system thermodynamics.
• Revealed implicit feedback structures and enabled control-theoretical analysis through linearization.
• Demonstrated bond graphs' capability in achieving behavioral modularity and identifying retroactivity sources.

Conclusions:

• Bond graphs provide a powerful, unified framework for modeling, analyzing, and synthesizing biomolecular systems.
• The approach facilitates understanding of modularity, feedback, and retroactivity, with implications for system design.
• A power supply, like ATP hydrolysis, is necessary for bond graph-based retroactivity reduction in biomolecular systems.