Based on a model of subtilin production by Bacillus subtilis, in this paper we discuss the parameter identification of stochastic hybrid dynamics that are typically found in biological regulatory networks. In accordance with the structure of the model, identification is split in two subproblems: estimation of the genetic network regulating subtilin production from gene expression data, and estimation of population dynamics based on nutrient and population profiles. Techniques for parameter estimation from sparse and irregularly sampled observations are developed and applied to simulated data. Numerical results are provided to show the effectiveness of our methods.