Date of Award

Spring 1-1-2019

Document Type


Degree Name

Doctor of Philosophy (PhD)

First Advisor

Ryan T. Gill

Second Advisor

Stephanie Bryant

Third Advisor

Virginia Ferguson

Fourth Advisor

Manuel Lladser

Fifth Advisor

Wil Srubar


One of the challenges of the 21st century is creating a sustainable economy that is able to serve the needs of a growing population with the decreasing supply of natural resources. In addition to alternative energy sources, renewable economy concerns production of materials and chemicals. The chemical and polymer industries rely heavily on petroleum for manufacturing consumer goods. Many of these chemicals can be produced from renewable resources using microorganisms. However, the amounts produced are often not enough to make the process commercially viable. By rewiring cellular metabolism and regulation, it is possible to redirect metabolic flux to increase the production of the desired chemical. The complex nature of bacterial metabolic and regulatory networks places challenges on traditional metabolic engineering methods aimed at engineering strains with optimal performance and high product yields. The foundation of this thesis is based on the recent advances in molecular biology that open up new avenues for development of strains with improved production of bio-based materials and chemicals. The work described in this dissertation aims to develop new and improved methods for controlling gene expression in microorganisms on a whole-genome scale to facilitate the study and engineering of complex industrially-relevant phenotypes. Chapter I reviews the recent advances and challenges of engineering microbial phenotypes, focusing on CRISPR-based approaches to metabolic engineering. Chapter II outlines a novel approach for high-throughput discovery of improved production phenotypes using combinatorial gene repression, demonstrating its effectiveness for improving production of 3-hydroxypropionic acid in Escherichia coli. Chapter III focuses on developing a new method for precisely controlling bacterial gene expression to further facilitate metabolic engineering of production phenotypes. This Chapter describes the development and testing of a tunable CRISPRi-based gene repression method using tailored gRNA design, the formulation of a predictive mathematical model for gRNA binding and the development of a high-throughput data generation technique to train this model. Finally, Chapter IV provides directions for potential implementation of the technologies developed in this work, as well as outlines the current challenges and strategies for further improving the process of strain engineering by developing new CRISPR-based tools.