title: Identification of synthetic lethal pairs in biological systems through network information centrality.
abstract: The immense availability of protein interaction data, provided with an abstract network approach is valuable for the improved interpretation of biological processes and protein functions globally. The connectivity of a protein and its structure are related to its functional properties. Highly connected proteins are often functionally cardinal and the knockout of such proteins leads to lethality. In this paper, we propose a new approach based on graph information centrality measures to identify the synthetic lethal pairs in biological systems. To illustrate the efficacy of our approach, we have applied it to a human cancer protein interaction network. It is found that the lethal pairs obtained were analogous to the experimental and computational inferences, implying that our approach can serve as a surrogate for predicting the synthetic lethality.
authors.affiliation: C R Rao Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus, GachiBowli, Hyderabad - 500046, India. maran@crraoaimscs.res.in
authors.forename: T
authors.initials: T
authors.lastname: Kranthi
authors.forename: S B
authors.initials: SB
authors.lastname: Rao
authors.forename: P
authors.initials: P
authors.lastname: Manimaran
mesh_terms: D000465:Algorithms; D063646:Carcinogenesis / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D017353:Gene Deletion; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics* / Q000378:metabolism; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps
chemical_list: D009363:Neoplasm Proteins