Date of Award

Spring 1-1-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

First Advisor

Ryan T. Gill

Second Advisor

Kenneth Krauter

Third Advisor

Jeffrey C. Cameron

Fourth Advisor

Joel Kralj

Fifth Advisor

Shelley D. Copley

Abstract

Since Mendel’s work established the basis of inheritance in the late 1800s, multiple decades of research characterized monogenic traits across all domains of life. Yet, we still have a fairly limited knowledge on the genotype behind the vast majority of phenotypes. It is now evident that discrete biological functions can rarely be linked to a single gene. Further, these multigenic traits are often interconnected via a sophisticated and robust metabolic and regulatory network, selected by evolution in order to optimally distribute resources. The complexity of these multigenic traits challenges traditional genetic tools, broadly limiting our capability to understand and rewire cellular functions. From a biotechnology, pharmaceutical and microbiology perspective, an architecture that is frequently explored when studying complex phenotypes is metabolic pathways. Fueled by decreasing costs in DNA synthesis, reading and writing, recent advances in synthetic biology are providing the framework for novel technologies that enable facile interrogation and testing of multigenic phenotypes. Therefore, the research described in this dissertation set out to push forward the advances in synthetic biology, developing and demonstrating novel tools to potentially accelerate the study and engineering of phenotypes at the pathway scale. In Chapter I, I review recent advances in synthetic biology that accelerate the study of phenotypes. In Chapter II, I address efforts that require heterologous functions. I demonstrate a new CRISPR-based technology that allows integration of entire metabolic pathways in a single step and at high efficiencies into the Escherichia coli chromosome. In Chapter III, I address efforts that require the investigation and optimization of native biochemical pathways in the context of the native regulatory network. I demonstrate the ability to map genotype-phenotype relationships on the pathway-scale and uncover principles that would be difficult to predict a priori. Finally, I conclude in Chapter IV with an overview of the challenges and future directions of the technologies demonstrated here, discussing future implementations that could further accelerate the study of complex phenotypes.

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