Fine-tuning Engineered Gene Regulatory Networks Expressed in Escherichia coli using Hypervariable Simple Sequence Repeats

Abstract

Synthetic biology aims to borrow from the vast diversity of living systems shaped by evolutionary processes to create synthetic biological systems with comparable functional complexity to natural systems that meet pressing needs in health, energy, and the environ- ment. Construction of these systems is both aided by the richness of this evolutionary toolkit and hindered by its complexity. This dissertation presents a methodology to fine-tune en- gineered gene networks in Escherichia coli that accelerates the realization of functionally complex behaviors using focused variation to thoroughly sample gene expression levels for a target network. This tuning approach exploits errors that occur during replication of tandem DNA repeats to predictably vary gene expression. Using this approach, we have generated DNA libraries that vary in repeat length to predictably tune the expression of a fluorescent protein over a large range with high resolution. We have demonstrated the utility of the approach by tuning the expression of two transcription factors to optimize three functional behaviors of a bistable genetic switch. Finally, to extend the reach of the approach, we have investigated methods to control mutation rates of the repeats to rapidly optimize gene networks in vivo via directed evolution. This tuning methodology is extensi- ble to biological mechanisms that affect other gene network parameters, is compatible with computational strategies for tuning networks, and should advance the field of synthetic biology by enabling timely realization of functionally complex behaviors in cells.