Synthetic biology in Saccharomyces cerevisiae or budding yeast has proved indispensable in metabolic engineering, gene circuit engineering, and molecular and cell biology. These applications require robust “plug-and-play” inducible promoter systems for tight and tunable control of gene expression, but current inducible systems have limitations. We have incorporated the parts of the Tet-On system into the MoClo YTK system. With these parts we have built and optimized the Tet-On system in S. cerevisiae through multiple design iterations. Through different combinations of the rtTA transactivators and constitutive promoters, we have created strains with varying responses to doxycycline induction that will be useful for different applications. These systems boast extremely tight control over basal activity indistinguishable from the background strain, nanomolar sensitivities to doxycycline, and maximum output surpassing the strong pTDH3 promoter. Not only are these systems well-characterized and ready-to-use but the incorporation of the parts into the MoClo YTK allows them to be easily customized for specific applications.

Genome-scale technologies have transformed our understanding of the biomolecular signaling networks that underpin cellular function and adaptation. Omics-level analysis has cemented the view that biological signal processing is not the result of linear pathways but an emergent property of complex networks whose functions and dynamics we now seek to understand. In model organisms such as E. coli, biomolecular networks are often elucidated by observing how gene expression patterns change in reaction to experimentally-induced perturbations. However, the high-throughput experimental techniques traditionally used for this purpose are inherently destructive and only offer snapshots of a cell's state. As such, these technologies do not fully capture the information encoded in the dynamics of biomolecular networks, which are complex, time-dependent signals.

In the past twenty years, microfluidic technology combined with fluorescence microscopy has established itself as a powerful tool to study time-dependent biological processes while precisely controlling the cellular environment. This thesis focuses on bridging the gap between genome-wide assays and microfluidics-based dynamic perturbation experiments. Here I report the development of a high-throughput microfluidic platform capable of culturing 2176 unique microbial microcolonies in parallel and monitoring the changes in expression of fluorescent proteins in each strain. By loading the platform with some of the readily available libraries of fluorescent transcriptional reporters and dynamically tuning the growth media, I show that we can measure microbial gene expression dynamics in response to environmental inputs in vivo and genome-wide.

Chapter 1 provides an overview of the role of high-throughput microfluidics in systems and synthetic biology research. Chapter 2 describes the design of a highly multiplexed microfluidic platform for monitoring gene expression in GFP-tagged E. coli with both industrial and research applications. Chapter 3 illustrates the platform's applicability as an environmental biosensor that uses the dynamics of 2000 E. coli GFP-promoter strains coupled with machine learning algorithms to detect the presence of heavy metals in drinking water in real-time. Chapter 4 further demonstrates the potential of microfluidics-based biosensing by reporting the use of devices loaded with diverse engineered microbes to detect pollutants in seawater. Finally, in Chapter 5, I use the platform to probe the dynamics of the S. cerevisiae proteome in response to the drug metformin and lay the foundations for a new type of dynamics-based chemogenetic screen. The overarching aim of this research is the capture of microbial gene expression dynamics in response to environmental stimuli on a genome-wide scale with applications in biosensing and the characterization of drug targets.

Technological advances in molecular biology over the last several decades have enabled researchers to probe the inner workings of living systems at unprecedented scale and resolution. These new capabilities have resulted in the emergence of the field of quantitative biology, which seeks to utilize mathematical modeling along with techniques from systems biology and synthetic biology in order to ascertain the design principles that underlie the structure and function of biological regulatory networks. In pursuit of this goal, it has become clear that heterogeneity at the single-cell level and the dynamics, or time-dependent behavior, of these networks are critical features of a multitude of biological processes. Much of the foundational work establishing the importance of these themes began from the study of microbes such as the budding yeast Saccharomyces cerevisiae. However, even in unicellular organisms such as S. cerevisiae, significant challenges remain in tracking and analyzing single cells over long periods of time, as well as monitoring gene expression dynamics at scale. Development of these capabilities is critical, not only for understanding the cell biology of S. cerevisiae, but also for being able to apply the mechanistic insights obtained from yeast to evolutionarily conserved pathways in higher eukaryotes. In this thesis, I describe the design and application of microfluidic technologies for S. cerevisiae that address these limitations. In Chapter 1, I detail the utility of microfluidic devices and time-lapse fluorescence microscopy for analyzing single cells and recording biological dynamics. In Chapter 2, I describe the development of novel microfluidic devices that enable long-term isolation and tracking of single yeast cells, culminating in a design that can monitor cells over the course of their entire replicative lifespans. In Chapters 3 and 4, I discuss applications of the microfluidic technologies described in Chapter 2. Chapter 3 concerns the discovery of metabolic cycles in flavin fluorescence at the single-cell level, which can oscillate along with and independently of the cell division cycle, persisting even when cellular respiration is blocked. Chapter 4 describes the uncovering of two divergent paths taken by single yeast cells during replicative aging, in which distinct dynamics of heterochromatin silencing at the rDNA region that modulate cellular lifespan can be detected in each group. In Chapter 5, I apply the design principles validated in the construction of microfluidic devices for tracking single cells in order to develop a high-throughput microfluidic platform for recording gene expression dynamics of thousands of yeast strains simultaneously. I demonstrate a proof-of-principle of this technology by monitoring changes in gene expression across more than 4000 yeast strains in real-time during the diauxic shift. The microfluidic devices developed and described herein underscore the importance of single-cell analysis and dynamics-based regulation by elucidating novel sources of heterogeneity at the single-cell level and demonstrating how metabolic, chromatin silencing and gene expression dynamics contribute to the regulation of complex processes such as cell division, replicative aging and growth in changing environments. Further, these technologies establish a foundation upon which future studies can continue to pursue quantitative biology work in yeast, from the single-cell level to the genome scale.

A recent application for synthetic biological gene circuits has been in the realm of cancer diagnostics and therapeutics. Improvement and characterization of these circuits was conducted using three different methods. Firstly, using the synchronized lysis circuit (SLC), delivers cancer therapeutics in waves following oscillating lysis event, the spatiotemporal growth studied in motile bacteria[1,2]. The resulting patterns showed that the SLC strain was rendered immotile due to the stress of the circuit while the wild type showed chemotactic behavior, avoiding the SLC colony. It was discovered that bacteria enter a state of stress caused by overflow metabolism due to high glucose concentrations, resulting in lowered cAMP levels and loss of flagellar development and forcing fimbriae-based movement. This combination of wild type fimbriae motility with high glucose levels and non-motility of SLC strains was used to further observe dual-strain motility which creates flower shaped patterns. Additionally, improvements were made to a biosensor, which utilized horizontal gene transfer (HGT) to sense genetic material indicating presence of colorectal cancer (CRC) tumors[3]. Replacement of the tetracycline repression mechanism with a lacI based repression system was attempted. Further stability improvements were made in the sensor by deleting a prophage sequence and several insertion sequence copies, which increased sensitivity to tumor DNA. Furthermore, bacteria evolved using adaptive laboratory evolution (ALE) were characterized for growth and circuit behavior non-traditional media. Lastly, an inducible GFP lysis circuit was developed to better compare the lysis response between two strains of E. coli, MG1655 and Nissle 1917.

The rapid growth of synthetic and systems biology has resulted in engineered microbes with impactful applications in industries such as environmental biosensing and fermentation. The coupling of these fields with microfluidic technology has enabled the imaging of microbial colonies under continuous growth conditions with precise environmental control to better understand the dynamics involved in complex molecular networks. The emergence of genome-scale microfluidic devices has bridged the gap between the multiplexing of -omics technology and the dynamics of microfluidics; however, existing high-throughput microfluidic devices are cumbersome and often unable to be applied outside of a laboratory. Towards this end, we have developed an elegantly simple microfluidic platform capable of monitoring the temporal gene expression of 2,176 unique microbes with both research and industrial application. In Chapter 1, I introduce the role of microfluidics in synthetic and systems biology. In Chapter 2, I describe the high-throughput microfluidic platform we have engineered and the protocol for building these devices. In Chapter 3, I show the platform's application as an environmental biosensor where the dynamics of 1,807 E. coli GFP-promoter strains coupled with machine learning algorithms are used to detect the presence of six heavy metals in real-time in both laboratory and real-world settings. Finally in Chapter 4, I show the device's application as a fermentation process monitoring system where the dynamics of 4,156 S. cerevisiae strains of a GFP-fusion library show the gene expression profile of a batch culture undergoing a diauxic shift, in real-time, depicting when the batch culture enters different growth states and changes its metabolic profile.

The last decade has seen bacteria at the forefront of biotechnological innovation, with applications including biomolecular computing, living therapeutics, microbiome engineering and microbial factories. These emerging applications are all united by the need to precisely control complex microbial dynamics in spatially extended environments, requiring tools that can bridge the gap between intracellular and population-level coordination. Therefore, in this thesis we propose genetic circuits for predictable control of microbial communities with potential applications in both healthcare and industry, ranging from therapeutics and drug delivery, to bioproduction and bioremediation. In Chapter 1 we focus on intra-population dynamics control by designing a genetic circuit which merges the benefits of the synchronization capabilities of quorum sensing to the advantages of external control of chemical inducers. This circuit design can provide more fine-tuned control of cargo release from bacterial carriers by enabling time and space regulation. Subsequently, in Chapter 2 we engineer and characterize inter-species dynamics of a three-strain bacterial community which interacts through cyclic competition, demonstrating the emergence of non-trivial dynamics. In particular, we found that intrinsic differences in the bacterial competitive strength lead to an unbalanced community that was, counterintuitively, dominated by the weakest strain. We used computational tools to model the three strains dynamics in 2D, providing a tool which accurately simulates the competition outcome of the community for any chosen parameter set. Finally, in Chapter 3 we focus on single cell dynamics and developed a bioinformatics pipeline to analyze dynamic transcriptomics data obtained using an innovative high throughput microfluidic device developed in our lab.

One of the pursuits of synthetic biology is to harness gene expression by reprogramming cells to perform useful functions. As one of the most thoroughly characterized model organisms, Escherichia coli presents significant potential for engineering synthetic circuits. In this work I present both simple circuits designed for biosensing applications, as well as novel strategies that may allow combining such simple circuits into more complex networks. Specifically, I describe methods for both negative and positive dynamic modulation of plasmids in E. coli, allowing for control of gene expression without requiring specially engineered promoters. Combining individual synthetic modules will be necessary for encoding more complex circuit logic and function.

The diversity and impact of the human microbiome has catalyzed interest in how microbial communities can be engineered for applications ranging from human health, to bioproduction and bioremediation. An important aspect of such a forward engineering approach is a detailed understanding of how inter-species bacterial competition affects community com-position and stability. In order to study this, I design a three-strain bacterial community that exhibits “rock-paper-scissors” competition dynamics. Using these engineered bacterial strains, I explore genetic stability, evolution in bacterial competition, and ecological stability. In Chapter Two, I discuss the construction and characterization of these “rock-paper-scissors” genetic circuits. I characterize the dynamics of the competition among strain pairs in microfluidic devices. Next, I integrate the “rock-paper-scissors” bacterial strains with the synchronized lysis circuit. When combined, this circuit exhibits both community level “rock-paper-scissors” dynamics, as well as population level synchronized lysis. In Chapter Three, I demonstrate that cyclical population control can be engineered to stabilize the functionality of intracellular gene circuits. The “rock-paper-scissors dynamic demonstrates rapid cycling of strains in microfluidic devices and leads to an increase in the stability of gene circuit functionality in cell culture. In Chapter Four, I use the “rock-paper-scissors” bacteria strains in order to study competition and community stability. I demonstrate that intrinsic differences in three major mechanisms of bacterial warfare lead to an unbalanced community that is dominated by the weakest strain. The engineering of active warfare between microbial species establishes a framework for exploration of the underlying principles that drive com-plex ecological interactions. In Chapter Five, I investigate bacterial competition between a toxin producing and sensitive wild type E. coli strain in a hybrid growth model. I show that environmental perturbations such as stress or predation can drastically improve the fitness advantage that toxin producing strains have over their sensitive wild type counterparts.

Engineering novel, biological functions with synthetic genetic circuits is easier than ever before due to constant discovery of new genetic parts, development of efficient ways to put those parts together, and innovative methods to model and study complex phenotypes. As the field of Synthetic Biology continues to grow, different focus areas and challenges have come to light. In this dissertation, I identify key themes and current challenges in Synthetic Biology and use novel circuit design and screening approaches to build on existing research in these areas. One growing area of focus is controlling and coordinating microbial behavior at the population level. A major challenge in this area is screening dynamic, population-level circuits for desired behavior with the same throughput as single-cell circuit screening methods. Chapter 2 of this dissertation introduces a novel microfluidic device that enables screening of mutant bacterial libraries for complex, population-level phenotypes that change with time. Chapter 3 focuses on genetic circuit screening as well, but from a more application-based standpoint. Specifically, a workflow is developed for screening bacterial expressed toxins for their ability to inhibit cancer cell growth when released via engineered bacterial cell lysis. Novel candidates are uncovered for bacterial-based cell therapies. While noise and heterogeneity are typically avoided in synthetic biology, Chapter 4 presents a novel strategy for exploiting engineered heterogeneity in bacterial populations to enable the populations to quickly adapt to varying environments. Chapter 5 addresses the need for new, quantitatively-characterized inducible promoter systems in yeast. The systems developed in this chapter are designed to be compatible with existing modular cloning toolkits in yeast, making their implementation simple and standardized. Together, the results presented in this dissertation demonstrate novel strategies for controlling population-level behavior in bacteria, screening genetic circuits for complex phenotypes, and creating new genetic parts for circuit regulation.

The rise of DNA sequencing and synthesis technologies over the past two decades has ushered in a new wave of forward engineering genetic circuits, synthetic biology. Synthetic biology has since been deployed for applications spanning therapeutics, industrial chemical biosynthesis, and environmental sensing. Coupled with advances in genomics and systems biology, synthetic biology has revolutionized our ability to investigate biological networks. Further utilized with the fine-tuned experimental control of microfluidics, synthetic biology has enabled the precise interrogation of single nodes in these biological networks. The emergence of genome-scale microfluidic devices has bridged the gap between the multiplexing of -omics technology and the dynamics of microfluidics. Towards this end, we have developed an elegant, simple microfluidic platform capable of monitoring the temporal gene expression of 2,176 unique microbes with both research and industrial application. In Chapter 1, I provide a brief overview of synthetic biology and microfluidics in the context of biological research. In Chapter 2, I describe the high-throughput microfluidic platform we have engineered and the protocol for building these devices. In Chapter 3, I demonstrate the platform’s utility as an environmental biosensor and research tool, where the dynamics of 1,807 E. coli GFP-promoter strains coupled with machine learning algorithms are used to detect the presence of six heavy metals in real-time in both laboratory and real-world settings. Finally in Chapter 4, I show the device’s application for the dynamical screening of synthetic gene circuit libraries. In all, I highlight the need for the further development of such multiplexed microfluidic platforms and demonstrate their utility for biological research, synthetic gene circuit engineering, and environmental biosensing.