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Bioinformatics Advance Access published online on July 5, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti567
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received January 31, 2005
Revised June 13, 2005
Accepted June 28, 2005

Article

Local modeling of global interactome networks

Denise Scholtens 1*, Marc Vidal 2, and Robert Gentleman 3

1 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA; Current address: Northwestern University Medical School, Department of Preventive Medicine, 680 North Lake Shore Drive Suite 1102, Chicago, IL 60611 USA
2 Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA; Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
3 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA

* To whom correspondence should be addressed.
Denise Scholtens, E-mail: dscholtens{at}northwestern.edu


  Abstract

Motivation: Systems biology requires accurate models of protein complexes, including physical interactions that assemble and regulate these molecular machines. Yeast two-hybrid (Y2H) and affinity-purification/mass-spectrometry (AP-MS) technologies measure different protein-protein relationships, and issues of completeness, sensitivity, and specificity fuel debate over which is best for high-throughput (HT) "interactome" data collection. Static graphs currently used to model Y2H and AP-MS data neglect dynamic and spatial aspects of macromolecular complexes and pleiotropic protein function.

Results: We apply the local modeling methodology proposed by Scholtens and Gentleman (2004) to two publicly available data sets and demonstrate its uses, interpretation, and limitations. Specifically, we use this technology to address four major issues pertaining to protein-protein networks. 1) We motivate the need to move from static global interactome graphs to local protein complex models. 2) We formally show that accurate local interactome models require both Y2H and AP-MS data, even in idealized situations. 3) We briefly discuss experimental design issues and how bait selection affects interpretability of results. 4) We point to the implications of local modeling for systems biology including functional annotation, new complex prediction, pathway interactivity, and coordination with gene expression data.

Availability: The local modeling algorithm and all protein complex estimates reported here can be found in the R package apComplex, available at http://www.bioconductor.org.

Supplementary Information: Included at the end of this manuscript for review.


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