Additionally I would like thank the other members of my committee. In particular, I would like to thank Professor Tolley for having me as a reader for a course he taught on robotics which both helped to support my education as well as further my understanding of the subject. I would also like to thank Professor Yip, whose reinforcement learning course has inspired me to consider new research possibilities for the future.Further, I would like to thank Dr. Cedric Girerd for his help and advice with regards to this work, as well as his perspective on research in general. Finally, I would also like to thank the other members of the Moromoto Lab for their encouragement and support along the way. This thesis, in part, has been submitted for publication of the material as it may appear in

Many chronic airway diseases result in mucus plugging of the airways. Lungs of an individual with cystic fibrosis are an exemplary case where their mucus-plugged bronchioles create a favorable habitat for microbial colonization. Various pathogens thrive in this environment interacting with each other and driving many of the symptoms associated with CF disease. Like any microbial community, the chemical conditions of their habitat have a significant impact on the community structure and dynamics. For example, different microorganisms thrive in differing levels of oxygen or other solute concentrations. This is also true in the CF lung, where oxygen concentrations are believed to drive community physiology and structure. The methods described here are designed to mimic the lung environment and grow pathogens in a manner more similar to that from which they cause disease. Manipulation of the chemical surroundings of these microbes is then used to study how the chemistry of lung infections governs its microbial ecology. The method, called the WinCF system, is based on artificial sputum medium and narrow capillary tubes meant to provide an oxygen gradient similar to that which exists in mucus-plugged bronchioles. Manipulating chemical conditions, such as the media pH of the sputum or antibiotics pressure, allows for visualization of the microbiological differences in those samples using colored indicators, watching for gas or biofilm production, or extracting and sequencing the nucleic acid contents of each sample.

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