The goal of this project has been to create and develop a computational and mathematical model of a lung with Cystic Fibrosis (CF) that is clinically useful in diagnosing and treating CF patients. This research is the first attempt to parameterize the distributions of mucus in a CF lung as a function of time. By default the model makes arbitrary choices at each stage of the construction process, whereby the simplest choice is made. The model is sophisticated enough to fit the average CF patients' spirometric data over time and to identify several interesting parameters: probability of colonization, mucus volume growth rate and scarring rate. Furthermore, modeling some of the spirometric measures requires a full understanding of the flow characteristics through the laminar, transitional, and turbulent regions of human lung. In this thesis we also describe the airfow dynamics mathematically by considering the resistance from both laminar and turbulent airflow throughout the entire respiratory tract. Assuming a rigid airway structure, we implement a model that measures airway clogging in CF lung and recalculate the rate of air flow after these obstructions. In addition, we use the MRI and spirometric data of a patient to spatially track the presence, growth, and clearing of infections. Finally, to complement our developed models and to provide a clinically useful tool, we implement a Graphical User Interface for the models that enables medical doctors to interact with the simulation and tailor their treatment based on contrasts between predicted and observed scenarios.
