title: Discerning static and causal interactions in genome-wide reverse engineering problems.
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.
  
  
  BACKGROUND
  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.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy.
authors.forename: Mattia
authors.initials: M
authors.lastname: Zampieri
authors.forename: Nicola
authors.initials: N
authors.lastname: Soranzo
authors.forename: Claudio
authors.initials: C
authors.lastname: Altafini
mesh_terms: D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D005818:Genetic Engineering / Q000379:methods*; D008957:Models, Genetic; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics*
chemical_list: D020543:Proteome