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Hierarchical graphs for rule-based modeling of biochemical systems
Nathan W Lemons1, Bin Hu, William S Hlavacek
1Department of Mathematics and its Applications, Central European University, H-1051 Budapest, Hungary.
BMC Bioinformatics
|February 4, 2011
Summary
Hierarchical graphs offer a more intuitive way to represent complex molecules in rule-based modeling. This approach enhances clarity and understanding of biochemical system dynamics.
Area of Science:
- Computational Biology
- Systems Biology
- Graph Theory
Background:
- Rule-based modeling uses graphs to represent molecules and their interactions.
- Vertex attributes and edges denote molecular components, states, and bonds.
- Graph-rewriting rules model molecular associations, dissociations, and state changes.
Purpose of the Study:
- To introduce hierarchical graphs for representing molecular structures and relationships.
- To demonstrate their application in annotating protein complexes like Lck and TCR.
- To adapt existing graph algorithms for use with hierarchical graphs.
Main Methods:
- Proposing hierarchical graphs for molecular representation.
- Illustrating with examples of protein tyrosine kinase Lck and T cell receptor (TCR).
- Generalizing the Nauty algorithm to HNauty for hierarchical graph isomorphism and canonical labeling.
Main Results:
- Hierarchical graphs effectively represent structural organization of molecular components.
- Computational methods for regular graphs are applicable to hierarchical graphs.
- HNauty algorithm provides canonical labeling for hierarchical graphs and multi-edge graphs.
Conclusions:
- Hierarchical graphs offer more intuitive representations than regular graphs for structured molecules.
- This improves clarity and understanding in rule-based modeling.
- Facilitates better comprehension of complex biochemical systems.
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