title: Graph theory and stability analysis of protein complex interaction networks.
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.
authors.affiliation: Department of Computer Science and Information Engineering, National Formosa University, Yun-Lin 63205, Taiwan.
authors.forename: Chien-Hung
authors.initials: CH
authors.lastname: Huang
authors.affiliation: Department of Computer Science and Information Engineering, National Formosa University, Yun-Lin 63205, Taiwan.
authors.forename: Teng-Hung
authors.initials: TH
authors.lastname: Chen
authors.affiliation: Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan. ppiddi@gmail.com.
authors.forename: Ka-Lok
authors.initials: KL
authors.lastname: Ng
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome