The molecular evolution in a gene regulatory network is classically modeled by Markov jump processes. However, the direct simulation of such models is extremely time consuming. Indeed, even the simplest Markovian model, such as the production module of a single protein involves tens of variables and biochemical reactions and an equivalent number of parameters. We study the asymptotic behavior of multiscale sto- chastic gene networks using weak limits of Markov jump processes. The results allow us to propose new models with reduced execution times. In a new article, we have shown that, depending on the time and concentration scales of the system, the Markov jump processes could be approximated by piecewise deterministic processes. We give some applications of our results for simple gene networks (Cook's model and Lambda-phage model).