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Structural distance and evolutionary relationship of networks.
1Max Planck Institute for Molecular Genetics, Berlin, Germany. banerjee@molgen.mpg.de
Bio Systems
|December 3, 2011
Summary
This study introduces a novel method to quantify topological distances between networks using graph Laplacian eigenvalues. The approach successfully distinguishes between network classes and traces evolutionary relationships, as demonstrated with metabolic networks.
Area of Science:
- Network science
- Evolutionary biology
- Computational biology
Background:
- Understanding universal network properties is crucial for evolutionary studies.
- Gene duplication and mutation drive changes in biological network structures.
- Structural differences can reveal evolutionary relationships between systems.
Purpose of the Study:
- To develop a method for quantifying topological distance between networks of varying sizes.
- To leverage the spectrum of the normalized graph Laplacian for this quantification.
- To assess the method's ability to identify network class similarities and evolutionary links.
Main Methods:
- Utilizing eigenvalues of the normalized graph Laplacian to capture global and local network properties.
- Proposing a method to quantify topological distance based on spectral differences.
- Analyzing a dataset of 43 metabolic networks from different species.
Main Results:
- Network architectures show greater similarity within the same class than between different classes.
- The proposed method effectively retraces evolutionary relationships through structural differences.
- Metabolic networks clearly separated into three distinct groups: Bacteria, Archaea, and Eukarya.
Conclusions:
- The spectral method provides a robust measure of structural distance between networks.
- This measure aids in elucidating evolutionary relationships, aligning with cladistic findings.
- The approach is valuable for comparative network analysis across different biological domains.
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