Format

Send to

Choose Destination
Mol Syst Biol. 2006;2:2006.0003. Epub 2006 Jan 17.

Systems-level analyses identify extensive coupling among gene expression machines.

Author information

1
Bauer Center for Genomics Research, Harvard University, Cambridge, MA 02138, USA.

Abstract

Here, we develop computational methods to assess and consolidate large, diverse protein interaction data sets, with the objective of identifying proteins involved in the coupling of multicomponent complexes within the yeast gene expression pathway. From among approximately 43 000 total interactions and 2100 proteins, our methods identify known structural complexes, such as the spliceosome and SAGA, and functional modules, such as the DEAD-box helicases, within the interaction network of proteins involved in gene expression. Our process identifies and ranks instances of three distinct, biologically motivated motifs, or patterns of coupling among distinct machineries involved in different subprocesses of gene expression. Our results confirm known coupling among transcription, RNA processing, and export, and predict further coupling with translation and nonsense-mediated decay. We systematically corroborate our analysis with two independent, comprehensive experimental data sets. The methods presented here may be generalized to other biological processes and organisms to generate principled, systems-level network models that provide experimentally testable hypotheses for coupling among biological machines.

PMID:
16738550
PMCID:
PMC1681477
DOI:
10.1038/msb4100045
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center