Systems Biology of Ageing by Perturbation and Connectivity Analysis of Gene Expression Time Series

We applied a method for characterizing patterns arising from gene expression time series data. This method has been succesfully applied to two datasets coming from different experimental designs and cell types. The common trait of these datasets was the range of the time series in the order of hours (short time interval with respect to cell time scale), and a conditional activation of transcription factor activity (by c-Myc and DFOXO respectively). We show preliminary results that confirm that such method can be applied to a time series spanning the lifetime of a defined species, in which the progression of ageing is considered as the main perturbation. We observe strong analogies between the global features of the short-time datasets and the life-spanning dataset, with groups of genes showing correlated profiling in both cases. Such correlation is increased whenever genes are correctly grouped by a priori biological knowledge (biological pahways or ontology).