title: The topology of the growing human interactome data.
abstract: We have long moved past the one-gene&ndash;one-function concept originally proposed by Beadle and Tatum back in 1941; but the full understanding of genotype&ndash;phenotype relations still largely relies on the analysis of static, snapshot-like, interaction data sets. Here, we look at what global patterns can be uncovered if we simply trace back the human interactome network over the last decade of protein- protein interaction (PPI) screening. We take a purely topological approach and find that as the human interactome is getting denser, it is not only gaining in structure (in terms of now being better fit by structured network models than before), but also there are patterns in the way in which it is growing: (a) newly added proteins tend to get linked to existing proteins in the interactome that are not know to interact; and (b) new proteins tend to link to already well connected proteins. Moreover, the alignment between human and yeast interactomes spanning over 40% of yeast&rsquo;s proteins &mdash; that are involved in regulation of transcription, RNA splicing and other cellcycle-related processes&mdash;suggests the existence of a part of the interactome which remains topologically and functionally unaffected through evolution. Furthermore, we find a small sub-network, specific to the &ldquo;core&rdquo; of the human interactome and involved in regulation of transcription and cancer development, whose wiring has not changed within the human interactome over the last 10 years of interacome data acquisition. Finally, we introduce a generalisation of the clustering coefficient of a network as a new measure called the cycle coefficient, and use it to show that PPI networks of human and model organisms are wired in a tight way which forbids the occurrence large cycles.
authors: Department of Computing, Imperial College London, London, SW7 2RH, United Kingdom.
authors: Vuk
authors: V
authors: Janjić
authors: Department of Computing, Imperial College London, London, SW7 2RH, United Kingdom.
authors: Nataša
authors: N
authors: Pržulj
mesh_terms: D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000378:metabolism; D017173:Caenorhabditis elegans / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004331:Drosophila melanogaster; D005786:Gene Expression Regulation; D006801:Humans; D009369:Neoplasms / Q000378:metabolism; D025941:Protein Interaction Mapping; D040901:Proteomics / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D013045:Species Specificity; D020798:Two-Hybrid System Techniques