Dynamic Bayesian networks offer a powerful modelling tool to unravel cellular mechanisms. In particular, Gaussian Networks have recently been used to model gene expression data, thanks to their capability to avoid information loss associated with discretization and their good computational efficiency. Gaussian Networks typically describe the conditional mean of a node as a linear regression of the parent variables. Such model can be generalized by using a linear regression of nonlinear transformations of the parent values. In this paper we investigate the use of both models and evaluate the performance of Gaussian Networks in learning the complex dynamic interactions among genes and proteins. To this aim, we analyzed simulated data produced by a mathematical model of cell cycle control in budding yeast. The results obtained allowed us to appraise the performance of the different models and confirmed the suitability of dynamic Bayesian networks for a first level, genome-wide analysis of high throughput dynamic data.