Correlation analysis reveals the emergence of coherence in the gene expression dynamics following system perturbation

Nicola Neretti, Daniel Remondini, Marc Tatar, John M. Sedivy, Michela Pierini, Dawn Mazzatti, Jonathan Powell, Claudio Franceschi, Gastrone C. Castellani

Research output: Contribution to journalArticlepeer-review

Abstract

Time course gene expression experiments are a popular means to infer co-expression. Many methods have been proposed to cluster genes or to build networks based on similarity measures of their expression dynamics. In this paper we apply a correlation based approach to network reconstruction to three datasets of time series gene expression following system perturbation: 1) Conditional, Tamoxifen dependent, activation of the cMyc proto-oncogene in rat fibroblast; 2) Genomic response to nutrition changes in D. melanogaster; 3) Patterns of gene activity as a consequence of ageing occurring over a life-span time series (25y-90y) sampled from T-cells of human donors. We show that the three datasets undergo similar transitions from an "uncorrelated" regime to a positively or negatively correlated one that is symptomatic of a shift from a "ground" or "basal" state to a "polarized" state. In addition, we show that a similar transition is conserved at the pathway level, and that this information can be used for the construction of "meta-networks" where it is possible to assess new relations among functionally distant sets of molecular functions.

Original languageEnglish
Article numberS16
JournalBMC Bioinformatics
Volume8
Issue numberSUPPL. 1
DOIs
Publication statusPublished - Mar 8 2007

ASJC Scopus subject areas

  • Medicine(all)
  • Structural Biology
  • Applied Mathematics

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