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

• DOI: 10.1093/bioinformatics/btn220
• Publication Date: 2008-05-09T00:34:00Z
• Authors: Mattia Zampieri; Nicola Soranzo; Claudio Altafini
• Keywords: Reverse engineering

In the past years devising methods for discovering gene regulatory mechanisms at a genome-wide level has become fundamental topic in field of systems biology. The aim is to infer gene-gene interactions an increasingly sophisticated and reliable way through continuous improvement reverse engineering algorithms exploiting microarray data.This work inspired by several studies suggesting that coexpression mostly related 'static' stable binding relationships, like belonging same protein complex, rather than other types more 'causal' transient nature (e.g. transcription factor-binding site interactions). this verify if direct or conditional network inference Pearson correlation former, partial latter) are indeed useful discerning static from causal dependencies artificial real networks (derived Escherichia coli Saccharomyces cerevisiae).Even regime weak power we have in, our analysis confirms differences performances algorithms: robust detecting interactions, ones better especially presence combinatorial transcriptional regulation.Supplementary data available Bioinformatics online.