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.