Recent technological advances have made it possible to simultaneously measure multiple protein activities at the single cell level. In contrast to measurements based on aggregated cells, e.g. gene expression analysis from microarrays, single cell-based measurements provide much richer information on the cell states and signaling networks. In this talk, I will propose a Bayesian hierarchical modeling framework for signaling network reconstruction based on single cell interventional data. Using Bayesian hierarchical structure, we model the existence of an association between two proteins both at the overall level across all experiments and at each individual experimental level, from which we infer the pairs of proteins that are associated and their causal relations. This approach can effectively pool information from different interventional experiments and the network sparsity is also explicitly modeled. Both intrinsic noise and measurement error are included in the model. We implement a Markov chain Monte Carlo method for model inference. Simulation results demonstrate the superiority of the hierarchical approach. The usefulness of our model is illustrated through its application to the intracellular signaling networks of human primary naive CD4+ T cells, downstream of CD3, CD28, and LFA-1 activation.