title: NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.
abstract: The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.
authors.affiliation: Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
authors.forename: Edroaldo Lummertz
authors.initials: EL
authors.lastname: da Rocha
authors.affiliation: Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
authors.forename: Choong Yong
authors.initials: CY
authors.lastname: Ung
authors.affiliation: Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
authors.forename: Cordelia D
authors.initials: CD
authors.lastname: McGehee
authors.affiliation: Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
authors.forename: Cristina
authors.initials: C
authors.lastname: Correia
authors.affiliation: Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA li.hu@mayo.edu.
authors.forename: Hu
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics / Q000378:metabolism; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D050171:Dyslipidemias / Q000235:genetics / Q000378:metabolism; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D060066:Protein Interaction Maps; D015398:Signal Transduction; D012984:Software; D059467:Transcriptome