BioE PhD Proposal- Jacob Davis

Committee:
Eberhard Voit, Ph.D. (Advisor) (Department of Biomedical Engineering, Georgia Institute of Technology and Emory University)
Sam Brown, Ph.D. (Advisor)  (School of Biological Sciences, Georgia Institute of Technology)
Melissa Kemp, Ph.D  (Department of Biomedical Engineering, Georgia Institute of Technology and Emory University)
Arlene Stecenko, M.D. (Department of Pediatrics, Emory University School of Medicine)
Mark Styczynski, Ph.D (School of Chemical and Biomolecular Engineering, Georgia Institute of Technology)
Denis Tsygankov, Ph.D (Department of Biomedical Engineering, Georgia Institute of Technology)

Experimental and Computational Analysis of Pathogen Emergence and Antibiotic Resistance in a Cystic Fibrosis Airway Infection Model

The human body harbors at least twice as many bacteria cells as human cells. Most of these bacteria are harmless, but the emergence of pathogens is common in many human body systems. Treatment of these infections are often with antibiotics can have non-target effects and remove protective flora from the body. This dissertation project was designed to create a model system of airway bacterial communities that is amenable to the development of effective experimental and computational investigations that shed light on pathogen emergence and antibiotic resistance. For the experimental analysis, I will transform three common bacterial species in human airways with the goal of making them easily quantifiable with available microscopic and spectrophotometric techniques. The bacteria will be grown in a minimal medium and their dynamics will be studied. To quantify the interactions among the different species and predict the dynamics of the community under different settings, mathematical models within the Lotka-Volterra framework will be developed and parameterized. Validation will be performed with a synthetic sputum medium in a porcine lung model. Select metabolites in the model community will be tracked over time, using mass spectrometry and enzymatic assays. Community resistance to a common beta-lactam antibiotic will be studied by tracking how the antibiotic is hydrolyzed by beta-lactamase enzymes of non-targeted species. The existing modeling framework will be expanded to incorporate this antibiotic and metabolic data in the community model. Although the community size of the model system will be small - to allow for comprehensive data generation - this experimental and mathematical system will constitute a prototype for investigating larger models that can be used to predict how pathogens survive in different communities and under altered environmental conditions and antibiotic treatments.