title: Reverse-engineering human regulatory networks.
abstract: The explosion of genomic, transcriptomic, proteomic, metabolomic, and other omics data is challenging the research community to develop rational models for their organization and interpretation to generate novel biological knowledge. The development and use of gene regulatory networks to mechanistically interpret this data is an important development in molecular biology, usually captured under the banner of systems biology. As a result, the repertoire of methods for the reconstruction of comprehensive and cell-context-specific maps of regulatory interactions, or interactomes, has also exploded in the past few years. In this review, we focus on Network Biology and more specifically on methods for reverse engineering transcriptional, post-transcriptional, and post-translational human interaction networks and show how their interrogation is starting to impact our understanding of cellular pathophysiology and one's ability to predict cellular phenotypes from genome-wide molecular observations.
authors.affiliation: Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA.
authors.forename: Celine
authors.initials: C
authors.lastname: Lefebvre
authors.forename: Gabrielle
authors.initials: G
authors.lastname: Rieckhof
authors.forename: Andrea
authors.initials: A
authors.lastname: Califano
mesh_terms: D053263:Gene Regulatory Networks; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D055432:Metabolomics; D035683:MicroRNAs / Q000378:metabolism; D060066:Protein Interaction Maps; D046188:Protein Modification, Translational; D040901:Proteomics; D049490:Systems Biology; D059467:Transcriptome
chemical_list: D035683:MicroRNAs