Modular assembly of dynamic models in systems biology

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Large-scale dynamic models in systems biology necessitate breaking down complex problems into manageable, modular components.
• Existing software tools have advanced model reusability, but integrating underlying biophysical principles remains a challenge.
• Modularity and abstraction are key developments in modern systems biology modeling.

Purpose of the Study:

• To demonstrate the compatibility and utility of bond graphs as a framework for modular modeling in systems biology.
• To highlight how bond graphs can integrate biophysical principles into modular modeling approaches.
• To illustrate the application of bond graphs for constructing large-scale dynamic models.

Main Methods:

• Utilizing bond graphs, a physics-based modeling technique that inherently supports modularity.
• Applying bond graphs to a mitogen-activated protein kinase (MAPK) cascade model to showcase module reusability.
• Implementing bond graphs in a glycolysis model to demonstrate adjustable model granularity.

Main Results:

• The bond graph framework effectively supports modularity and abstraction in systems biology.
• The MAPK cascade model demonstrated the reusability of bond graph modules.
• The glycolysis model illustrated the flexibility of bond graphs in adjusting model granularity.

Conclusions:

• Bond graphs provide a desirable framework for constructing large-scale dynamic models in systems biology.
• The integration of bond graphs facilitates both modularity and physics-based modeling.
• This approach addresses the need for incorporating biophysical principles into reusable and adaptable biological models.