Significance of Tumor Growth Modeling in the Behavior of Homogeneous Cancer Cell Populations: Are Tumor Growth Models Applicable to Both Heterogeneous and Homogeneous Populations?

The ability to predict and slow the spread of cancer in the human body is a task that medical professionals have been trying to accomplish for many years. Being able to give factual basis to the use of certain growth models for application in not just heterogeneous, but also homogeneous cancer cell populations is imperative to treatment research as using mathematical analysis to predict the dynamics of tumor growth allows professionals to simulate how tumors might behave in the human body. This study follows the process of single-cloning and the growth of a homogeneous cell population in a superficial environment over the course of six weeks with the end goal of showing which of five tumor growth models commonly used to predict heterogeneous cancer cell population growth (Exponential, Logistic, Gompertz, Linear, and Bertalanffy) would also best exemplify that of homogeneous cell populations. We hypothesized that the Gompertz, Linear, and Bertalanffy models would provide the best fit to the homogeneous cancer (clonal) cell population growth data while models such as the exponential and logistic model, which are most commonly associated with the growth of heterogeneous cancer cell populations in natural environments (i.e. malignant tumors), would veer off the growth data. It was shown that Gompertz and Linear functions provided the best fit for this population, while exponential and Logistic functions fell slightly behind. The data collection and analysis for this research was performed through the University of Michigan Research Labs and Solver by Frontline Systems.