We have used state space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation. State space models are a class of dynamic Bayesian networks which assume that the observed measurements depend on some hidden state variables which evolve according to Markovian dynamics. These hidden variables can capture effects which cannot be measured in a gene expression profiling experiment, for example: genes that have not be included in the microarray, levels of regulatory proteins, the effects of mRNA and protein degradation, etc. We have approached the problem of inferring the model structure of these state-space models using both classical and Bayesian methods. A bootstrap procedure is used to derive classical confidence intervals for parameters representing `gene-gene' interactions over time. Variational approximations are used to perform the analogous model selection task in the Bayesian context. Certain interactions are robustly reproduced in both the classical and the Bayesian analysis of these regulatory networks. The resulting models place JunB and JunD at the centre of the mechanisms that control apoptosis and proliferation. These mechanisms are key for clonal expansion and to control the long term behavior (e.g. programmed cell death) of these cells.