title: Duplication-divergence model of protein interaction network.
abstract: We investigate a very simple model describing the evolution of protein-protein interaction networks via duplication and divergence. The model exhibits a remarkably rich behavior depending on a single parameter, the probability to retain a duplicated link during divergence. When this parameter is large, the network growth is not self-averaging and an average node degree increases algebraically. The lack of self-averaging results in a great diversity of networks grown out of the same initial condition. When less than a half of links are (on average) preserved after divergence, the growth is self-averaging, the average degree increases very slowly or tends to a constant, and a degree distribution has a power-law tail. The predicted degree distributions are in a very good agreement with the distributions observed in real protein networks.
authors.affiliation: Ariadne Genomics Inc., Rockville, Maryland 20850, USA. iispolat@lauca.usach.cl
authors.forename: I
authors.initials: I
authors.lastname: Ispolatov
authors.forename: P L
authors.initials: PL
authors.lastname: Krapivsky
authors.forename: A
authors.initials: A
authors.lastname: Yuryev
mesh_terms: D003198:Computer Simulation; D019143:Evolution, Molecular; D020440:Gene Duplication; D005786:Gene Expression Regulation / Q000502:physiology*; D014644:Genetic Variation / Q000235:genetics; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D011506:Proteins