Stochastic Models for Revealing the Dynamics of the Growth of Small Tumor Populations

The widely accepted model of tumor growth assumes tumors grow exponentially at a constant rate during early tumorigenesis when populations are small. The possibility that tumors might exhibit altered slower growth dynamics or even net cell death below a critical tumor size has yet to be fully explored. Deterministic growth models are capable of describing larger populations because population variation becomes small compared with the average, but when the population being modeled is small, the inherent stochasticity of the birth and death process produces significant variation. Recent advances in high throughput data collection allow for precise and sufficiently large data sets needed to capture this variation. Therefore, we present a stochastic modeling framework to describe and test the potential for altered growth dynamics at small tumor populations.