Cancer involves the dysregulation of multiple signaling pathways in which computational modeling can be applied to understand complex network responses. A computational modeling approach can be used to determine the development of drug resistance in cancers, predict combination therapies, and determine individualized treatment for cancer patients. To this end we have employed mechanistic modeling to a variety of cancer networks. In Chapter 1, we review current methods and progress toward using computational methods for cancer biology. Cancer is no longer considered one gene one disease and computational modeling is an important tool in understanding the development of many cancer types. In Chapter 2, we constructed a mechanistic model of the development of castration resistant prostate cancer (CRPC). Analysis of the model suggested that simultaneously targeting the PI3K and MAPK pathways in addition to anti-androgen therapies could be an effective treatment for CRPC. We experimentally tested this hypothesis in both androgen dependent prostate cancer (ADPC) LNCaP cell lines and LNCaP derived CRPC C4-2 cells using three inhibitors: the androgen receptor inhibitor MDV3100 (enzalutamide), the Raf kinase inhibitor sorafenib, and the PI3K inhibitor LY294002. Consistent with model predictions, cell viability decreased at 72 hrs in the dual and triple inhibition cases in both the LNCaP and C4-2 cell lines. In Chapter 3, we look at the importance of network identification in mechanistic modeling of cancer networks. Cancer is a complex disease and complete biological knowledge of the system is often unknown. Using a small three node protein example we were able to obtain a correct model structure with no a priorii knowledge of the system. We then applied this method to determine transcription factor network structures for six leukemia cell lines: K562, HL60, NB4, U937, HL60 R38+ and HL60 R38-. Starting with an initial best guess model structure we were able to determine additional network modifications for each cell line to improve model fit of experimental data. Potential future directions and closing remarks are offered in Chapter 4. Taken together, the results of these studies demonstrated that computational modeling can aid in identifying therapeutic targets and combination treatments for cancer. Also, the use of computational modeling can improve cancer network identification in the absence of complete biological knowledge.