Modeling of Solid Tumor Progression Thresholds using a Complex Adaptive System Approach

Simulation techniques used to generate complex biological models are becoming promising research tools in oncology. Using a general Complex Adaptive Systems model that can be tailored to map various phenomena, here, we describe how this model applies to tumor growth. The multi-agent modeling environment is generated using Netlogo. The stochastic model consists of active objects including normal immune and cancer cells. The simulations conducted mimicked the tumor progression success and failure and the status of the tumor mass despite constant variations remained stable for an extended time. Furthermore, increasing the efficiency of the immune cells led to decreases in tumor cell numbers variable in both occurrence time and duration.