Crohn’s disease is a prevalent inflammatory bowel disease characterized by chronic inflammation of the gastrointestinal tract due to a hyperactive immune system. The disease is predominantly caused by a mutation in the nucleotide-binding oligomerization domain-containing protein 2 (NOD2) gene which detects a constituent of bacteria, muramyl dipeptide (MDP), and triggers protective inflammatory immune promoting bacterial clearance. However, the cellular mechanisms underlying how these mutations lead to chronic inflammation remain incompletely understood. Previous studies have shown that the guanine nucleotide-binding (G) protein ⍺-subunit (G⍺)-interacting vesicle-associated protein (GIV, also known as “Girdin”) is essential in modulating the innate immune sensors including Toll-like receptor 4 (TLR4) and NOD2. In this thesis, we aim to understand the cellular and molecular mechanisms underlying the interactions between GIV and NOD2 in macrophage, which are critical in bacteria sensing and clearance. Using biochemical and functional immunology studies, our research shows that GIV binds to NOD2 and is required for effective MDP/NOD2-mediated protective signaling and bacterial clearance. We further found that GIV and NOD2 mutually regulate each other’s functions for effective intracellular bacteria sensing by NOD2 and cAMP/PKA dependent phagolysosome maturation and bacterial clearance by GIV. In conclusion, this crosstalk is essential for gut immune homeostasis, provides valuable insight into the cellular mechanisms underlying Crohn’s disease, and can be exogenously manipulated for therapeutic purposes to enhance infection resolution and restore gut homeostasis.

Survival analysis, i.e. the collection of statistical methods for analyzing data on the time to an event, is widely used in the applied sciences for studying outcomes including disease onset, device failure, and death. The results of these analyses are used to verify product safety and evaluate the viability of medical therapies, guiding decisions with ramificationsthat can extend around the globe and over decades. Commensurate with the importance of drawing accurate conclusions in these settings, many theoretical and methodological advancements have been developed to improve the reliability and efficiency of survival analyses. Modern causal inference frameworks allow questions about medical treatment efficacy to be formalized with mathematical rigor, and enable a detailed understanding of the necessary conditions for practitioners to be able to estimate causal effects from observed data. Robust and efficient semi-parametric estimators have been developed, capable of incorporating the flexible machine-learning algorithms which have been made practical by the increasing avail- ability of high performance computing. This dissertation is focused on a vision of modern survival analysis, guided by the Causal Roadmap and employing state-of-the-art estimators. In Chapter 2 we review the shortcomings of traditional survival analyses and demonstrate a Targeted Learning primary analysis of the LEADER cardiovascular outcome trial. In Chapter 3 we describe a new R package, concrete, which implements a recently developed continuous-time one-step targeted maximum likelihood estimator (TMLE) for time-to-event estimands with or without competing risks. Lastly in Chapter 4 we apply concrete to three analyses of the data from the SUSTAIN-6 cardiovascular outcome trial, demonstrating the potential of this package and estimator for answering commonly asked causal questions in time-to-event trial analyses.

The goal of synthetic biology is to allow the rapid design of organisms that can find diverse uses in environmental remediation, chemical production, or human health. Genetic engineering has traditionally been done by trial and error, but synthetic biology seeks to apply engineering principles and build complex circuits by rationally composing genetic parts together. We envision engineering microbes to form spatial communities for applications such as programmable tissues. We present novel circuit designs for memory and communication, which are basic building blocks for programming these behaviors. Our memory device uses molecular sequestration instead of cooperativity that was used in almost all previously built synthetic switches. In addition, our design allows predictable tuning of the switching boundaries and enables the rapid design of custom bistable switches that can function as a set-reset latch.

We also present designs for contact-based communication by utilizing the recently discovered contact-dependent inhibition (CDI) system. Such a communication channel could allow programmed spatial features with micron-scale resolution, which can be advantageous compared to existing communication methods that rely on diffusible molecules. We present two strategies for harnessing the CDI system. In the first method, we fuse a small transcriptional activator to the protein that is delivered during CDI. In the second method, we exploit the known biology that the delivered domain can co-localize two other proteins. We use this co-localization effect to trigger an increase in activity from a split enzyme, and design an ultrasensitive response to the small number of molecules delivered during the CDI process. While we were not able to show control of gene expression in touching E. coli cells, we believe that our circuit designs can guide future engineering efforts.