Cellular adaptation to changing environments is intricately coordinated through many molecular and mechanical responses. As cells evolve, abnormal growth patterns occur (that cannot be controlled by normal mechanisms) leading to cancer. With mathematical modeling we can integrate different characteristics of tumor growth for a non-experimental study of cancer. Through incorporation of some of the prior clinical, experimental, and mathematical studies, we utilize a cellular automaton model to take into account multiple factors affecting tumor growth in healthy tissue. This in silico model of tumor growth is ultimately based upon molecular and life cycle features that affect the growth rates of cancer. The life cycle parameters used in this model include such factors as replication rates, nutrient concentrations, and drug concentrations/effects. This type of model may be used in conjunction with prototype drug data to inexpensively and rapidly design treatment protocols, by comparing their effectiveness on tumor growth for different conditions and cancer types.