A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

Ackerman, Emily E.; Alcorn, John F.; Hase, Takeshi; Shoemaker, Jason E.

Existing influenza virus studies using PPI networks require additional data such as differentially expressed gene information [63] or protein context [30] to construct host response networks. Alternative methods such as DeltaNet [64, 65] and ProTINA [66] utilize gene transcription profiles to infer protein drug targets, but rely on the accurate deduction of gene regulatory networks. More recent PPI studies have used network growing functions such as GeneMANIA, STRING, and IPA [67] to predict IAV host factors and studied infected cell systems through the integration of screening data with network methods [33, 68] . Approaches incorporating time course data into network analysis have also been explored [69] . While these methods (which include basic network metrics such as degree and betweenness of PPI networks) have been successful at identifying disease host factors and in drug target development in the existing body of work, this dual controllability study offers a novel, in-depth analysis of the role of individual proteins in the context of total system function and how possible changes to the system can be interpreted.