Synthetic biology, a field that sits between Biology and Engineering disciplines, has come into its own in the last decade. The decreasing cost of DNA synthesis has lead to the creation of larger and more complex synthetic gene networks, engineered with functional goals rather than simple demonstration. While many methods have been developed to reduce the time required to produce complex networks, none focus upon the considerable tuning needed to turn structurally correct networks into functional gene networks. To this end, we created a Plug-and-Play synthetic gene network assembly that emphasizes character-driven iteration for producing functional synthetic gene networks. This platform enables post-construction modification and easy tuning of networks through its ability to swap individual parts. To demonstrate this system, we constructed a functional bistable genetic toggle and transformed it into two functionally distinct synthetic networks. Once these networks have been created and tuned at the bench, they next must be delivered to bacteria in their target environment. While this is easy for industrial applications, delivering synthetic networks as medical therapeutics has a host of problems, such as competing microbes, the host immune system, and harsh microenvironments. Therefore, we employed bacteriophage technologies to deliver functional synthetic gene networks to specific bacterial strains in various microenvironments. We first sought to deliver functional genetic networks to bacteria present in the gut microbiome. This allows for functionalization of these bacteria to eventually sense disease states and secrete therapeutics. As a proof of concept a simple circuit was created using the Plug-and-Play platform and tested before being moved into the replicative form plasmid of the M13 bacteriophage. Bacteriophage particles carrying this network were used to infect gut bacteria of mice. Infection and functionality of the synthetic network was monitored from screening fecal samples. Next, we employed phagemid technologies to deliver high copy plasmids expressing antibacterial networks to target bacteria. This allows for sustained expression of antibacterial genes that cause non-lytic bacterial death without reliance upon traditional small molecule antibiotics. Phagemid particles carrying our antibacterial networks were then tested against wild type and antibiotic-resistant bacteria in an in vitro and in vivo environment.

abstract: Synthetic gene networks have evolved from simple proof-of-concept circuits to complex therapy-oriented networks over the past fifteen years. This advancement has greatly facilitated expansion of the emerging field of synthetic biology. Multistability is a mechanism that cells use to achieve a discrete number of mutually exclusive states in response to environmental inputs. However, complex contextual connections of gene regulatory networks in natural settings often impede the experimental establishment of the function and dynamics of each specific gene network. In this work, diverse synthetic gene networks are rationally designed and constructed using well-characterized biological components to approach the cell fate determination and state transition dynamics in multistable systems. Results show that unimodality and bimodality and trimodality can be achieved through manipulation of the signal and promoter crosstalk in quorum-sensing systems, which enables bacterial cells to communicate with each other. Moreover, a synthetic quadrastable circuit is also built and experimentally demonstrated to have four stable steady states. Experiments, guided by mathematical modeling predictions, reveal that sequential inductions generate distinct cell fates by changing the landscape in sequence and hence navigating cells to different final states. Circuit function depends on the specific protein expression levels in the circuit. We then establish a protein expression predictor taking into account adjacent transcriptional regions’ features through construction of ~120 synthetic gene circuits (operons) in Escherichia coli. The predictor’s utility is further demonstrated in evaluating genes’ relative expression levels in construction of logic gates and tuning gene expressions and nonlinear dynamics of bistable gene networks. These combined results illustrate applications of synthetic gene networks to understand the cell fate determination and state transition dynamics in multistable systems. A protein-expression predictor is also developed to evaluate and tune circuit dynamics. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2017