Your browser doesn't support javascript.
loading
NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.
da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu.
Affiliation
  • da Rocha EL; Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
  • Ung CY; Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
  • McGehee CD; Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
  • Correia C; Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
  • Li H; Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic College of Medicine, Rochester, MN 55905, USA li.hu@mayo.edu.
Nucleic Acids Res ; 44(10): e100, 2016 06 02.
Article in En | MEDLINE | ID: mdl-26975659
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Computational Biology / Transcriptome / Protein Interaction Maps Type of study: Prognostic_studies Limits: Female / Humans Language: En Journal: Nucleic Acids Res Year: 2016 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Computational Biology / Transcriptome / Protein Interaction Maps Type of study: Prognostic_studies Limits: Female / Humans Language: En Journal: Nucleic Acids Res Year: 2016 Type: Article Affiliation country: United States