In this paper, we propose a stochastic simulation to model and analyze cellular signal transduction. The high number of objects in a simulation requires advanced visualization techniques: first to handle the large data sets, second to support the human perception in the crowded environment, and third to provide an interactive exploration tool. To adjust the state of the cell to an external signal, a specific set of signaling molecules transports the information to the nucleus deep inside the cell. There, key molecules regulate gene expression. In contrast to continuous ODE models we model all signaling molecules individually in a more realistic crowded and disordered environment. Beyond spatiotemporal concentration profiles our data describes the process on a mesoscopic, molecular level, allowing a detailed view of intracellular events. In our proposed schematic visualization individual molecules, their tracks, or reactions can be selected and brought into focus to highlight the signal transduction pathway. Segmentation, depth cues and depth of field are applied to reduce the visual complexity. We also provide a virtual microscope to display images for comparison with wet lab experiments. The method is applied to distinguish different transport modes of MAPK (mitogen-activated protein kinase) signaling molecules in a cell. In addition, we simulate the diffusion of drug molecules through the extracellular space of a solid tumor and visualize the challenges in cancer related therapeutic drug delivery.