Fc[epsilon]RI is a multi-subunit receptor found on the surface of mast cells and basophils and binds immunoglobulin E (IgE) with high affinity. Stimulation of this receptor, typically via antigen-mediated crosslinking of IgE, can lead to release of histamine and other mediators that are involved in the allergic immune response. Thus, understanding the workings of Fc[epsilon]RI-mediated signaling brings us closer to understanding one of the most widespread health conditions in the developed world. Fc[epsilon]RI-mediated signaling processes are inherently complex, involving highly interconnected systems in which quantitative factors can play a decisive role. Such systems can be difficult to decipher using intuition alone, but computational models can extend our reasoning abilities and enable us to develop nontrivial hypotheses that generate experimentally testable predictions. To develop a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated to impact cellular decisionmaking, we developed a computational interaction library. The library consists of executable rules for protein-protein and protein-lipid interactions. The library is visualized to facilitate understanding of network circuitry and identification of network motifs. Using this library, we investigated branching pathways from the adaptor protein Lat, which influence production of the phospholipid phosphatidylinositol (3,4,5)trisphosphate (PIP3) at the plasma membrane and the soluble second messenger inositol trisphosphate (IP3). We found that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. Such robustness has been observed experimentally for some readouts. We also developed a model that proposes an explanation for experimentally observed oscillations in Ca2+ concentration, which is an important outcome of Fc[epsilon]RI stimulation. To investigate Fc[epsilon]RI signaling from another angle, we modeled interactions between the receptor and a structurally defined ligand for IgE. We parameterized the model for consistency with kinetic fluorescence data as well as super-resolution imaging measurements of antigen-induced receptor aggregation. To facilitate this study, we developed a specialized tool for fitting biochemical models to experimental data. Finally, we examined how patterns of exposure to stimulatory and non-stimulatory ligands affect mast cells' secretory responses. Through iterative modeling and experimental tests, we learned that a tug-of-war between positive signals from the tyrosine kinase Syk and negative signals from the lipid phosphatase Ship-1 govern the magnitude of responses, with the adaptor protein Shc1 influencing how the balance of positive and negative signals changes with time.