PROGRAMMING BACTERIAL CONSORTIA FOR AUTONOMOUS REGULATION AND COORDINATED ACTIVITY

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dc.contributor.advisorBentley, William E.en_US
dc.contributor.authorStephens, Kristinaen_US
dc.contributor.departmentBioengineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-10-10T05:32:14Z
dc.date.available2020-10-10T05:32:14Z
dc.date.issued2020en_US
dc.description.abstractThe potential of genetically engineered microbes seems nearly infinite with applications ranging from human health to bioprocessing. However, metabolic burden and unbalanced use of cell resources are frequent challenges when engineering cells to carry out synthetic functions. To work around this challenge, engineers are attempting to use co-cultures or synthetic consortia wherein labor is divided amongst subpopulations that work together. This emerging strategy requires new tools to regulate the composition of subpopulations and to enable robust coordination between subpopulations. Here, we investigated and rewired a native cell-cell communication process, quorum sensing, in order to develop tools to regulate co-cultures. We developed modules for signal regulated cell growth rate and cell-cell communication in bacteria, and we used these modules to construct co-cultures with autonomous composition control. Specifically, we developed a “controller” strain for signal modulated cell growth rate by using quorum sensing signals to regulate levels of HPr, a protein involved in sugar transport. We developed a second “translator” strain that detects the universal quorum sensing signal AI-2 and translates it into a species-specific AI-1 signal. The composition of the resulting co-culture adjusts autonomously in response to AI-2. Importantly, we developed a simple mathematical model based on individual monocultures that predicts behavior of the co-culture. Then, we used our model to explore in silico alternate construct designs operating in varied environments. To complement the co-culture model, which explores behavior due to interactions between strains but does not encompass information about the genetic circuits underlying the quorum sensing process, we then developed a gene circuit model of a dual-input synthetic AI-2 quorum sensing system. Finally, we demonstrate that the strategies developed in our co-culture platform can be used to engineer co-cultures where the culture composition is controlled electrically. We also show that these strategies can be used to change the culture composition of a synthetic co-culture where each population is working together to produce pyocyanin, thereby changing the rate of pyocyanin production in the co-culture. The techniques developed here may enable further use of co-cultures or synthetic consortia by synthetic biologists and metabolic engineers for varied applications.en_US
dc.identifierhttps://doi.org/10.13016/q3j2-g562
dc.identifier.urihttp://hdl.handle.net/1903/26582
dc.language.isoenen_US
dc.subject.pqcontrolledBioengineeringen_US
dc.subject.pquncontrolledquorum sensingen_US
dc.subject.pquncontrolledsynthetic biologyen_US
dc.titlePROGRAMMING BACTERIAL CONSORTIA FOR AUTONOMOUS REGULATION AND COORDINATED ACTIVITYen_US
dc.typeDissertationen_US

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