Graph theory and stability analysis of protein complex interaction networks. Issue 2 (1st April 2016)

• Record Type: Journal Article
• Authors: Huang, Chien‐Hung; Chen, Teng‐Hung; Ng, Ka‐Lok
• Is Part Of: IET systems biology. Volume 10:Issue 2(2016)
• Page Start: 64
• Page End: 75
• Publication Date: 2016-04-01
• Subjects: graph theory -- perturbation theory -- proteins -- molecular configurations -- molecular biophysics -- pattern clustering -- pattern classification -- cellular biophysics -- biology computing; stability analysis -- protein complex interaction networks -- biological processes -- protein complex interaction network -- cellular processes -- interaction topology -- graph theoretical approach -- local network topological properties -- topological parameter classification -- Kolmogorov‐Smirnov test -- network structures -- data normalisation tests -- topological‐based perturbations -- highly clustered protein complexes -- brokering‐coefficient‐based perturbations -- betweenness‐based perturbations; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2015.0007
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations. We employed graph theoretical approach to reveal hidden properties and features of four species PCINs. Two main issues are addressed, (i) the global and local network topological properties, and (ii) the stability of the networks under 12 types of perturbations. According to the topological parameter classification, we identified some critical protein complexes and validated that the topological analysis approach could provide meaningful biological interpretations of the protein complex systems. Through the Kolmogorov–Smimov test, we showed that local topological parameters are good indicators to characterise the structure of PCINs. We further demonstrated the effectiveness of the current approach by performing the scalability and data normalization tests. To measure the robustness of PCINs, we proposed to consider eight topological‐based perturbations, which are specifically applicable in scenarios of targeted, sustained attacks. We found that the degree‐based, betweenness‐based and brokering‐coefficient‐based perturbations have the largest effect on network stability.