Systems models of key signaling pathways in cancer have been extensively used to under- stand and explore the mechanisms of action of drugs and growth factors on cancer cell signaling. In general, such models predict the effect of environmental stimuli (both chemical such as for e.g., growth factor and drugs as well as mechanical such as matrix stiffness) in terms of activities of proteins such as ERK or AKT which are important regulators of cell fate decisions. Although such models have helped uncover important emergent properties of signaling networks such as ultrasensitivity, bistability, and oscillations, they miss many key features that would make them useful in a clinical setting. 1) The predictions of activity of proteins such as ERK or AKT cannot be directly translated into a clinically useful parameter such as cell kill rate. 2) They don’t work as well when there are multiple biological processes operating under different time and length scales such as receptor-based signaling (4-6 hours) and cell cycle (24-48 hours). 3) The parameter space of such models often exhibits sloppy/stiff character which affects the accuracy of predictions and the robustness of these models. Apart from single-cell systems models of signaling, pharmacokinetic and cell population-based pharmacodynamic models are also extensively used to predict the efficacy of a particular therapy in a clinical setting. However, there are no direct or consistent ways of incorporating patient-specific gene/protein expression data in these models. This thesis describes the development and applications of a multiscale and multiparadigm framework for signaling and pharmacodynamic models that helps us address some of the above short- comings. First two single scale systems models are described which introduces methods of exploration of parameter space and their effect on model predictions. Then the multiscale framework is described and it is applied to two different cancers - Prostate Adenocarcinoma and Nephroblastoma (Wilm’s Tumor). Special mathematical techniques were used to de- velop algorithms that can integrate models of disparate time scales and time resolutions (continuous vs. discrete-time). Such multiscale modeling frameworks have great potential in the field of personalized medicine and in understanding the physics of cancer taking into account the biology of the cells.