Abstract: Insulin resistance in skeletal muscle plays a key role in the development of Type 2 diabetes. To define the molecular mechanisms underlying insulin-induced changes in gene expression, recent studies, performed using microarrays techniques, identified genes involved in insulin resistance in control vs diabetic subjects, before vs after insulin treatment, i.e. exploiting only steady state information. Although extremely useful in order to identify candidate genes involved in analyzed processes and to develop new physiological hypothesis, these data can tell little about the interactions among genes. To infer genes regulation, it is of paramount importance to monitor dynamic expression profiles, i.e. time-series of expression data collected during the transition from one physiological state to another. A first necessary step, in order to limit the analysis to those genes that actually change expression over time, is to select differentially expressed genes. Methods proposed in the literature usually deal with comparison of static conditions rather than time-course experiment data, and are based on application of modified t-test and ANOVA test which assume Gaussian distribution of analyzed variables. These methods test the significance of the differential expression gene by gene, and their application requires at least two replicated experiments per each condition. In time course experiments, a number of samples is monitored across time and complete replicates of the experiment are seldom available, mainly for cost reasons. Therefore, differentially expressed genes are often selected using an empirical fold change (FC) threshold. This is a far-from-ideal situation, since it is based on an arbitrary choice (e.g. FC=2). In the case of Affymetrix chips, this choice is even more questionable since a constant threshold does not take in account the intensity dependence of the measurement errors, which is a wellknown feature of this technology.. Here, we propose a novel method for gene selection, to be applied on dynamic gene expression profiles, which explicitly accounts for the properties of the measurement errors and addresses the situation where a relative small number of replicates is available.