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Title: Development and Validation of a Kinetic Model for NO* Metabolism in Pseudomonas aeruginosa
Authors: Ren, Anna
Advisors: Brynildsen, Mark P.
Department: Chemical and Biological Engineering
Class Year: 2014
Abstract: Antibiotic resistance is becoming a bigger problem everyday as bacteria develop resistance to our existing drugs faster than we can find new ones. As such, it is important that we develop not only new chemicals for treating diseases, but new methods. Reactive nitrogen species (RNS) are an important part of the body's immune response to pathogens. They target several essential mechanisms in pathogens, making them a potentially powerful weapon for fighting disease. However, before RNS and in particular, nitric oxide (NO), can be effectively used in drug therapies, we must better understand how it is metabolized by pathogens. To reach this end, we have developed a kinetic model to predict how doses of nitric oxide will be funneled through the pathogen Pseudomonas aeruginosa during metabolism. This allowed us to see which reactions played the largest parts in the metabolism of NO, and these reactions may become future targets of drugs to enhance the bacteria's susceptibility to NO. The model was built off of a previously developed platform for kinetic modeling of NO metabolism for E. coli. The P. aeruginosa model thus also serves to demonstrate how such a platform can be easily extended to different organisms and can be a valuable tool for drug discovery in many organisms. The model was updated from the original platform through a mixture of literature search for reactions and kinetic parameters and training on experimental data. The model was found to fit the wild-type P. aeruginosa very well, but there were some discrepancies when using it to predict the NO metabolism in the Δfhp mutant strain. The mutant was used as an experimental validation measure for the model, so while the discrepancies seem like they may have arisen from the experimental data rather than inaccuracy on the model's part, further testing must be done before analysis from the model can be trusted.
Extent: 38 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Chemical and Biological Engineering, 1931-2016

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