Phylogenetic analysis of modularity in protein interaction networks
- Equal contributors
1 Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, USA
2 Case Center for Proteomics & Bioinformatics, Case Western Reserve University, Cleveland, USA
3 Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, USA
4 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, USA
5 Genomic Medicine Institute, Cleveland Clinic, Cleveland, USA
BMC Bioinformatics 2009, 10:333 doi:10.1186/1471-2105-10-333Published: 14 October 2009
In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity.
In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology.
These results establish modularity and network proximity as useful features in comparative network analysis and motivate detailed studies of the evolutionary histories of network modules.