Biological processes, including those involved in immune response and disease progression, are often dynamic. To model the regulatory and signaling networks that are activated as part of these systems we are developing methods to combine the abundant static regulatory, proteomic and epigenetic data with time series gene and miRNA expression data. The reconstructed networks characterize the pathways involved in the response, their time of activation, and the affected genes. I will present methods based on probabilistic graphical models and on combinatorial search algorithms for reconstructing these networks and will discuss application of the methods to study response to flu, HIV progression and to the analysis of single cell data.