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WIREs Syst Biol Med
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Reverse‐engineering human regulatory networks

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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. WIREs Syst Biol Med 2012 doi: 10.1002/wsbm.1159 This article is categorized under: Analytical and Computational Methods > Analytical Methods Biological Mechanisms > Regulatory Biology

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The number of PubMed publications including the term ‘systems biology’ in their title or abstract, since 1999 (2011 data extrapolated from publications from January to September).

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Network of miRNA program‐mediated RNA–RNA interactions in glioblastoma. Each node is an RNA with a color and a size describing its connectivity. Nodes near the center of the graph are contained within more tightly regulated, dense subgraphs, with the densest subgraph shown in red.

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Overlap of mir‐15a/16‐1 target predictions using three distinct algorithms.

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(a) Canonical non‐Smad pathway tumor growth factor (TGF)‐β signaling.46 (b) Transcriptional regulatory module controlling the mesenchymal signature of high‐grade glioma, computationally inferred and validated by biochemical and functional assays.19

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Experimental validation of ARACNe‐inferred targets of BCL6 by (a) shRNA‐mediated silencing, followed by gene expression profiling and (b) ChIP‐chip promoter analysis. Targets are sorted left to right from the most statistically significant in terms of (a) differential expression and (b) probability of binding Bcl6 in the region surrounding the gene transcription start site.36 Red bars represent ARACNe‐inferred targets. Enrichment is computed by gene set enrichment analysis.37

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Analytical and Computational Methods > Analytical Methods
Biological Mechanisms > Regulatory Biology

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