Date of Award:


Document Type:


Degree Name:

Doctor of Philosophy (PhD)


Computer Science


Nicholas S. Flann


There have been several techniques developed in recent years to develop computer models of a variety of disease behaviors. Agent-based modeling is a discrete-based modeling approach used agents to represent individual cells that mechanically interact and secrete, consume or react to soluble products. It has become a powerful modeling approach, widely used by computational researchers. In this research, we utilized agent-based modeling to study and explore disease development, particularly in two applications, breast cancer and bioengineering experiments. We further proposed an error-minimization search approach and used it to estimate cellular parameters from multicellular in vitro data.

In this dissertation, in the first study, we developed a 2D agent-based model that attempted to emulate the in vivo structure of breast cancer. The model was applied to describe the progression from DCIS into DCI. This model confirms that the interaction between tumor cells and the surrounding stroma in the duct plays a critical role in tumor growth and metastasis. This interaction depends on many mechanical and chemical factors that work with each other to produce tumor invasion of the surrounding tissue. In the second study, an in silico model was developed and applied to understanding the underlying mechanism of vascular-endothelial growth factor (VEGF) auto-regulation in REP and emulate the in vitro experiments as part of bioengineering research. This model may provide a system with robust predictive modeling and visualization that could enable discovery of the molecular mechanisms involved in age-related macular degeneration (AMD) progression and provide routers to the development of effective treatments. In the third and final study, a searching approach was applied to estimate cellular parameters from spatiotemporal data produced from bioengineered multicellular in vitro experiments. We applied a search method to an integrated cellular and multicellular model of retinal pigment epithelial cells to estimate the auto-regulation parameters of VEGF.