Mathematical models based upon the biochemical reactions that effectively define these networks have much promise as a tool for studying cell-signaling networks. Such models could be studied computationally to generate hypotheses that can be tested experimentally. A major obstacle for developing such models is “combinatorial complexity”, i.e. the number of potential states for the network becomes combinatorially large when post-translational modifications and protein-protein complexes are considered. Explicitly stating all potential states and their potential interactions is not feasible for very large networks. Modelers often deal with this problem by limiting the scope of the network considered, limiting the biochemical resolution considered, or imposing non-physiological reaction specifications that dramatically reduce the number of states. These simplifications are generally unsatisfactory. The cost of these common simplifications on predictive ability is not well understood. We have rather developed a rule-based approach for efficiently specifying and simulating reaction networks. This “rule-based” approach enables simulations of mechanistic models of cell signaling networks with resolution and scope far larger than traditional modeling methods. We have built a comprehensive model of IGF1R phosphorylation and SH2/PTB signaling that can account for over 1014 possible non-isomorphic complexes. We have also built a model of ErbB family signaling that spans from the four ErbB receptors through ERK and Akt activation and that can account for over 10150 possible non-isomorphic complexes. We have used these models to investigate how protein promiscuity may modulate signaling and also to investigate how feedback loops expand the range of signals a network may generate. Now that we have demonstrated the ability to develop and simulate such large models, we turn our attention to how oncogenic mutants disrupt these signaling networks.