Events Calendar

Title: "Utilizing Light as an Input and Output for Synthetic Biology and Metabolic Engineering"

Advisory Committee: Mo Khalil, PhD – BME (Chair) Mary Dunlop, PhD – BME (Advisor) Wilson Wong, PhD – BME Ji-Xin Cheng, PhD – BME, ECE Joe Larkin, PhD – Biology, Physics

Abstract: Advances in synthetic biology are driven by the synergistic relationship between molecular tools and corresponding experimental readout technologies. Molecular tools allow scientists to perturb or control cells with user-defined inputs, while readout technologies produce measurable outputs from cellular processes. A valuable use case of synthetic biology is in metabolic engineering, where microbes are designed to produce commodity chemicals, fuels, or therapeutics from renewable feedstocks. However, current molecular tools and readout technologies are limited for metabolic engineering. In this thesis, I first expand the molecular toolkit with systems that are designed to maximize function in bioproduction settings. Second, I advance a powerful readout technology, stimulated Raman scattering, to track production in metabolically engineered cells. The through line between both aspects of this work is the medium used to interact with cells: light.

Optogenetic regulation of metabolism is an attractive approach because it enables dynamic control, which can mitigate factors that limit production such as product toxicity and metabolic flux imbalances. Yet, most optogenetic tools in E. coli, a common chassis for bioproduction, operate through transcriptional mechanisms that do not function well in high-cell density bioproduction settings. In the first part of this thesis, I introduce novel mechanisms of post-translational optogenetic control through a modular light-inducible protein degradation tag, called LOVtag, as well as a method of creating and screening split-protein libraries.

In the second part of my thesis, we utilize light as a readout to extract metabolic information. Specifically, we use stimulated Raman scattering (SRS) imaging to measure free fatty acid production in E. coli. We demonstrate that SRS is a promising technique to study metabolically engineered strains with single-cell resolution, longitudinal tracking, and chemical specificity. In sum, the work in this thesis uses light to both control and measure metabolism.