title: Some perspectives on network modeling in therapeutic target prediction.
abstract: Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.
authors.affiliation: Department of Physics, Pennsylvania State University, University Park, PA.
authors.forename: Reka
authors.initials: R
authors.lastname: Albert
authors.affiliation: Department of Computer Science, University of Illinois at Chicago, Chicago, IL.
authors.forename: Bhaskar
authors.initials: B
authors.lastname: DasGupta
authors.affiliation: Department of Computer Science, University of Illinois at Chicago, Chicago, IL.
authors.forename: Nasim
authors.initials: N
authors.lastname: Mobasheri
keywords: Boolean models; complex network of interactions; drug target identification; node essentiality; signal transduction networks; transitive reduction