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Author ORCID Identifier


Campus-Only Access for Five (5) Years

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Chemical Engineering

Year Degree Awarded


Month Degree Awarded


First Advisor

Lauren Andrews

Subject Categories

Bacteriology | Other Microbiology


Engineered probiotic bacteria have been proposed as a next-generation strategy for noninvasively detecting biomarkers in the gastrointestinal tract and interrogating the gut-brain axis. For these living cells to function as diagnostic or therapeutic devices, we implement synthetic gene regulatory networks to enable cells to sense, record, and dynamically respond to biochemicals, known as genetic circuits. Theory-based approaches to create genetic circuits were previously established for a laboratory strain of Escherichia coli, yet how circuit component behavior varies between non-model and clinically relevant bacterial strains such as the probiotic Escherichia coli Nissle 1917 (EcN) is poorly understood. Furthermore, incorporating new or previously studied sensors into genetic circuits in non-native contexts often requires tuning. Here, we first propose an approach to efficiently engineer transcription-factor based metabolite biosensors using a single DNA design to prototype and modulate expression of the transcription factor. We demonstrate this approach in EcN by creating sensors for two depression-associated metabolites in the gut: the neurotransmitter gamma-aminobutyric acid (GABA) and the short-chain fatty acid propionate. Using this rapid prototyping approach, we engineer highly functional biosensors for specified in vivo metabolite concentrations that achieve a >50-fold dynamic range and improved sensitivity over the wildtype sensor. This strategy may be broadly useful for accelerating the engineering of metabolite biosensors for living diagnostics and therapeutics. We next used a set of DNA binding proteins to build modular genetically-encoded logic gates that can be composed into genetic circuits. A set of genetic logic gates and small molecule sensors were experimentally characterized to determine and quantify differences in the performance of these components between a laboratory strain of E. coli and EcN and further determine how these affect predictions of signal processing via circuit design algorithms. Here, we present a set of genetic circuits in EcN encoding for multiple logic functions and transcriptionally-encoded memory. We show that accounting for the strain-specific behavior of circuit components is crucial for designing genetic circuits and accurately predicting their performance in EcN. Furthermore, using these genetic design algorithms, we design and demonstrate a novel analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal. Finally, we used our newly engineered sensor for GABA to implement rapid sensor-guided metabolic engineering of GABA biosynthesis in EcN and then used genetic circuitry to temporally control biosynthesis. By leveraging our set of characterized logic gates and a mathematical model, we constructed genetic circuits with layered feedback to control the rate of GABA biosynthesis and implement feedback control. This work demonstrates new approaches to predictively design genetic circuitry that can sense and control dynamic bioprocesses in living diagnostics and therapeutics.


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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Available for download on Sunday, September 01, 2024