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Bioinformatics Advance Access originally published online on May 8, 2008
Bioinformatics 2008 24(13):1510-1515; doi:10.1093/bioinformatics/btn220
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Discerning static and causal interactions in genome-wide reverse engineering problems

Mattia Zampieri , Nicola Soranzo and Claudio Altafini *

SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy

*To whom correspondence should be addressed.


  Abstract

Background: In the past years devising methods for discovering gene regulatory mechanisms at a genome-wide level has become a fundamental topic in the field of systems biology. The aim is to infer gene-gene interactions in an increasingly sophisticated and reliable way through the continuous improvement of reverse engineering algorithms exploiting microarray data.

Motivation: This work is inspired by the several studies suggesting that coexpression is mostly related to ‘static’ stable binding relationships, like belonging to the same protein complex, rather than other types of interactions more of a ‘causal’ and transient nature (e.g. transcription factor–binding site interactions). The aim of this work is to verify if direct or conditional network inference algorithms (e.g. Pearson correlation for the former, partial Pearson correlation for the latter) are indeed useful in discerning static from causal dependencies in artificial and real gene networks (derived from Escherichia coli and Saccharomyces cerevisiae).

Results: Even in the regime of weak inference power we have to work in, our analysis confirms the differences in the performances of the algorithms: direct methods are more robust in detecting stable interactions, conditional ones are better for causal interactions especially in presence of combinatorial transcriptional regulation.

Contact: altafini{at}sissa.it

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Olga Troyanskaya

Received on January 16, 2008; revised on April 17, 2008; accepted on May 1, 2008

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