This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2006 .

Research

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

Nicola Neretti^1,2* , Daniel Remondini^2,7* , Marc Tatar³ , John M Sedivy⁴ , Michela Pierini² , Dawn Mazzatti⁵ , Jonathan Powell⁵ , Claudio Franceschi^2,6 and Gastrone C Castellani^1,2,7

¹  Institute for Brain and Neural Systems, Brown University, Providence RI, USA

²  Centro Interdipartimentale "L. Galvani", Università di Bologna, Bologna, Italy

³  Department of Ecology and Evolutionary Biology, Brown University, Providence RI, USA

⁴  Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence RI, USA

⁵  Unilever Corporate Research Center, Colworth, UK

⁶  I.N.R.C.A., Department of Gerontological Sciences, via Birarelli 8, 60121 Ancona, Italy

⁷  DIMORFIPA, Università di Bologna, Bologna, Italy

author email corresponding author email* Contributed equally

BMC Bioinformatics 2007, 8(Suppl 1):S16doi:10.1186/1471-2105-8-S1-S16

Published:	8 March 2007

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.