title: The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks.
abstract: Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.
authors.affiliation: Levich Institute and Physics Department, City College of New York, New York, New York, United States of America.
authors.forename: Yuliang
authors.initials: Y
authors.lastname: Jin
authors.forename: Dmitrij
authors.initials: D
authors.lastname: Turaev
authors.forename: Thomas
authors.initials: T
authors.lastname: Weinmaier
authors.forename: Thomas
authors.initials: T
authors.lastname: Rattei
authors.forename: Hernán A
authors.initials: HA
authors.lastname: Makse
mesh_terms: D000465:Algorithms; D000818:Animals; D030562:Databases, Protein; D019143:Evolution, Molecular; D020440:Gene Duplication; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic; D008958:Models, Molecular; D054730:Protein Interaction Domains and Motifs / Q000235:genetics; D011506:Proteins / Q000737:chemistry / Q000235:genetics
chemical_list: D011506:Proteins