The inference of gene regulatory networks from expression data is an important area of research that provides insight to the inner workings of a biological system. In this paper we present a new reverse-engineering framework for gene regulatory network reconstruction. It exploits some interesting characteristics of genetic programming, like the ability of solving complex regression problems with little or no information about the underlying data and the ability of performing an automatic selection of features at learning time. Our framework not only reconstructs the topology of the network, but it is also able to simulate its dynamic behaviour, forecasting the gene expression values in the timepoints subsequent to the initial one. In this paper, we test the proposed framework on the well-known IRMA gene regulatory network, and we show that its performances are promising.