Breast cancer is one of the prevailing cancers diagnosed among women today and the second leading cause of cancer death in women. Modeling breast cancer cell growth would be a useful tool in identifying therapeutically relevant targets while reducing the amount of spent resources. We have compiled a detailed signal transduction network incorporating epidermal growth factor receptor (EGFR) signaling and downstream components, such as PLC-[gamma], MAPK, PI3K/Akt, cell cycle signaling, transcription, and translation. Using mass-action kinetics, the model was formulated as a set of ordinary differential equations (ODEs). This resulted in more than 8,000 unknown parameters and more than 3,000 ODEs. Partitioning the original model into smaller sub-models and solving them individually may reduce run-time, while maintaining qualitatively similar results as the unpartitioned model. Experiments were performed on the MDA-MB-231 cell line to observe the effects of growth factor treatment on targets such as transcription factors and post-translationally modified proteins. Combination treatments of different growth factors resulted in negative synergy with respect to the chosen targets, which suggests interference between the different pathways involved in growth. This experimental data serves as a starting point to estimate an initial parameter set that can be used to obtain ensembles of parameters that emulate experimental results. In conclusion we have identified an approach to solving large-scale systems that can be used in conjunction with experimental data to predict novel therapeutic targets.