ADAPTIVE IMMUNITY AND THE TUMOR IMMUNE MICROENVIRONMENT

The adaptive immune system is essential for production of anti-tumor immune responses, with the majority of current immunotherapeutics designed to modulate the interaction between adaptive immunity and tumor cells within the tumor-immune microenvironment. This dissertation addresses three translational goals regarding our understanding and modulation of anti-tumor adaptive immunity: 1) Improvement of understanding for existing immunotherapies such as checkpoint inhibitor therapy (Chapter 2.1); 2) Improvement of efficacy for novel immunotherapeutics currently in development including tumor neoantigen vaccines (Chapter 4); and 3) Development of next-generation immunotherapies through identification of novel anti-tumor vaccine targets (Chapter 3), as well as development of diagnostic tools including biomarkers of immunotherapy response (Chapter 3) and immune-imaging modalities (Chapter 2.1).Doctor of Philosoph

Complexity in Developmental Systems: Toward an Integrated Understanding of Organ Formation

During animal development, embryonic cells assemble into intricately structured organs by working together in organized groups capable of implementing tightly coordinated collective behaviors, including patterning, morphogenesis and migration. Although many of the molecular components and basic mechanisms underlying such collective phenomena are known, the complexity emerging from their interplay still represents a major challenge for developmental biology. Here, we first clarify the nature of this challenge and outline three key strategies for addressing it: precision perturbation, synthetic developmental biology, and data-driven inference. We then present the results of our effort to develop a set of tools rooted in two of these strategies and to apply them to uncover new mechanisms and principles underlying the coordination of collective cell behaviors during organogenesis, using the zebrafish posterior lateral line primordium as a model system. To enable precision perturbation of migration and morphogenesis, we sought to adapt optogenetic tools to control chemokine and actin signaling. This endeavor proved far from trivial and we were ultimately unable to derive functional optogenetic constructs. However, our work toward this goal led to a useful new way of perturbing cortical contractility, which in turn revealed a potential role for cell surface tension in lateral line organogenesis. Independently, we hypothesized that the lateral line primordium might employ plithotaxis to coordinate organ formation with collective migration. We tested this hypothesis using a novel optical tool that allows targeted arrest of cell migration, finding that contrary to previous assumptions plithotaxis does not substantially contribute to primordium guidance. Finally, we developed a computational framework for automated single-cell segmentation, latent feature extraction and quantitative analysis of cellular architecture. We identified the key factors defining shape heterogeneity across primordium cells and went on to use this shape space as a reference for mapping the results of multiple experiments into a quantitative atlas of primordium cell architecture. We also propose a number of data-driven approaches to help bridge the gap from big data to mechanistic models. Overall, this study presents several conceptual and methodological advances toward an integrated understanding of complex multi-cellular systems

Analyses of the MEC1 DNA Damage Pathway in Saccharomyces cerevisiae

Eukaryotic organisms implement conserved surveillance machinery to sense and respond to DNA damage. Fundamental to the repair process is coordinated regulation of repair genes and initiation of cell cycle arrest protocols. Failure to preserve these checkpoints results in accumulation of mutated DNA and aberrant cell phenotypes that are characteristic of human disease. The yeast, Saccharomyces cerevisiae, utilizes the MEC1 checkpoint pathway to regulate the DNA damage response. This study addressed two overarching themes of transcriptional and post-translational regulation within the MEC1 pathway. We first applied our understanding of the MEC1 DNA damage transcriptional response to develop advanced luciferase whole cell biosensors that could detect a broad range of carcinogens using the promoter sequence of the MEC1 DNA repair gene, HUG1. The enhanced whole cell yeast biosensor exhibited improved sensitivity and dynamic range when compared to fluorescent-based biosensors while reducing reporter read-out processing time through a one-step, in vivo measurement regime. Previous global transcription studies performed in our lab identified a dose-dependent biphasic response of MEC1 repair genes to alkylating agents. The origin of this unique profile, however, remained unknown. Using a GFP promoter-reporter construct placed under MEC1 pathway genes, we found that the biphasic response persists through the MEC1 pathway, and that neither reactive oxygen species accumulation nor pro-apoptotic genes contributed to the expression profile. Cell cycle analysis revealed that cells immediately enter a senescent state after experiencing high alkylating concentrations which we proposed was the root cause of the MEC1 pathway gene repression. The role of a functionally uncharacterized MEC1 DNA repair protein, HUG1, in the DNA damage response was also explored. Using overexpression phenotype and subcellular localization assays, we demonstrated that HUG1 is a negative regulator of the MEC1 pathway and that its co-localization with the positive MEC1 effector, Rnr2p, was likely the source of its regulation. Protein affinity assays confirmed the Hug1p-Rnr2p interaction while mutagenesis analysis probed domains within Hug1p to determine regions necessary to its inhibitory action. Finally, we discovered that Hug1p also interacts with human ribonucleotide reductase homologs, p53R2 and hRRM2, demonstrating that Hug1p uses a conserved interaction motif for its inhibition

The characterization of genetic risk factors associated with autism

Autism is a severe neurodevelopmental disorder. Development of a molecular diagnostic screen is an imperative step towards personalized treatments. Gene expression profiling using buccal samples was employed to identify susceptibility genes and dysregulated signaling pathways. Analyses of differentially regulated genes revealed numerous genes that were associated with development and function of the nervous and immune systems, circadian rhythm, and ERBB signaling. Amongst the affected participants there was a patient with a 3p14.1-p13 deletion, where FOXP1 is located. FOXP2 mutations are responsible for human speech and language disorders. Since FOXP1, FOXP2, and FOXP4 require dimerization for transcriptional activity, investigating the FOXP1/2/4 molecular network provides insight into the neural mechanisms behind language impairments in autism. HEK293 cells were transfected with FOXP1/2/4 constructs. QRT-PCR was used to evaluate mRNA expression of FOXP2 target genes. Results suggest that specific combinations of FOXP1/2/4 dimers may influence the transcription of target genes involved in language acquisition

2018 - The Twenty-third Annual Symposium of Student Scholars

The full program book from the Twenty-third Annual Symposium of Student Scholars, held on April 19, 2018. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1020/thumbnail.jp

2012 IMSAloquium, Student Investigation Showcase

Through SIR and its partnerships, IMSA students engage in rich opportunities to pursue compelling questions of interest, conduct investigations, engage with extraordinary advisors, communicate findings, and ultimately impact society.https://digitalcommons.imsa.edu/archives_sir/1004/thumbnail.jp

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science

Statistical Inference for Propagation Processes on Complex Networks

Die Methoden der Netzwerktheorie erfreuen sich wachsender Beliebtheit, da sie die Darstellung von komplexen Systemen durch Netzwerke erlauben. Diese werden nur mit einer Menge von Knoten erfasst, die durch Kanten verbunden werden. Derzeit verfügbare Methoden beschränken sich hauptsächlich auf die deskriptive Analyse der Netzwerkstruktur. In der hier vorliegenden Arbeit werden verschiedene Ansätze für die Inferenz über Prozessen in komplexen Netzwerken vorgestellt. Diese Prozesse beeinflussen messbare Größen in Netzwerkknoten und werden durch eine Menge von Zufallszahlen beschrieben. Alle vorgestellten Methoden sind durch praktische Anwendungen motiviert, wie die Übertragung von Lebensmittelinfektionen, die Verbreitung von Zugverspätungen, oder auch die Regulierung von genetischen Effekten. Zunächst wird ein allgemeines dynamisches Metapopulationsmodell für die Verbreitung von Lebensmittelinfektionen vorgestellt, welches die lokalen Infektionsdynamiken mit den netzwerkbasierten Transportwegen von kontaminierten Lebensmitteln zusammenführt. Dieses Modell ermöglicht die effiziente Simulationen verschiedener realistischer Lebensmittelinfektionsepidemien. Zweitens wird ein explorativer Ansatz zur Ursprungsbestimmung von Verbreitungsprozessen entwickelt. Auf Grundlage einer netzwerkbasierten Redefinition der geodätischen Distanz können komplexe Verbreitungsmuster in ein systematisches, kreisrundes Ausbreitungsschema projiziert werden. Dies gilt genau dann, wenn der Ursprungsnetzwerkknoten als Bezugspunkt gewählt wird. Die Methode wird erfolgreich auf den EHEC/HUS Epidemie 2011 in Deutschland angewandt. Die Ergebnisse legen nahe, dass die Methode die aufwändigen Standarduntersuchungen bei Lebensmittelinfektionsepidemien sinnvoll ergänzen kann. Zudem kann dieser explorative Ansatz zur Identifikation von Ursprungsverspätungen in Transportnetzwerken angewandt werden. Die Ergebnisse von umfangreichen Simulationsstudien mit verschiedenstensten Übertragungsmechanismen lassen auf eine allgemeine Anwendbarkeit des Ansatzes bei der Ursprungsbestimmung von Verbreitungsprozessen in vielfältigen Bereichen hoffen. Schließlich wird gezeigt, dass kernelbasierte Methoden eine Alternative für die statistische Analyse von Prozessen in Netzwerken darstellen können. Es wurde ein netzwerkbasierter Kern für den logistischen Kernel Machine Test entwickelt, welcher die nahtlose Integration von biologischem Wissen in die Analyse von Daten aus genomweiten Assoziationsstudien erlaubt. Die Methode wird erfolgreich bei der Analyse genetischer Ursachen für rheumatische Arthritis und Lungenkrebs getestet. Zusammenfassend machen die Ergebnisse der vorgestellten Methoden deutlich, dass die Netzwerk-theoretische Analyse von Verbreitungsprozessen einen wesentlichen Beitrag zur Beantwortung verschiedenster Fragestellungen in unterschiedlichen Anwendungen liefern kann

Undergraduate and Graduate Course Descriptions, 2006 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2006