Bleris, LeonidasLee, Mark2023-03-272023-03-272021-122021-12-01December 2https://hdl.handle.net/10735.1/9643Genome editing has revolutionized not only the future of biological research, but also holds the promise of being a powerful therapeutic for genetic diseases. When considering the multitude of genetic regulations that contribute to various biological processes and their individual contributions that permit diseased cellular states, especially in instances where more than a single genetic aberration is attributed to the diseased phenotype, it is crucial to consider the interconnectivities of gene regulators and their individual contributions to cell health. Biological network maps that reveal the relation of gene products to one another can provide insight into the biological properties they govern. A biological network map consists of nodes (gene products) connected by edges that are dictated by the nature of the interaction between the two nodes. Nodal ablation (i.e., knocking out a gene to render it non-functional) has been crucial in understanding diseased states. However, this type of mutational analysis essentially disregards the impact that individual edges have on the network as a whole. The goal of my dissertation work was to utilize the genome editing tool Cas9 to disrupt the p53-miR-34a network in an edge-specific manner in order to demonstrate not only the complexity of these networks, but to also underscore the importance that individual edges have on the tumor suppressor phenotype. To this end, I, along with a team of researchers, developed a genetic screen using Cas9-bearing lentiviral vectors to disrupt 93 miR-34a binding sites within the 3’ untranslated region (UTR) of 71 genes impactful to cell survival under apoptotic conditions. I quantified the degree of apoptosis in two colorectal cancer cell lines that differ in functional p53 status, and that each harbored miR-34a binding site mutations within the pro-survival gene Bcl-2 3’UTR, demonstrating the importance of the miR-34a-Bcl-2 edge on apoptotic progression. Concurrently, I investigated the phenomenon of cell cycle desynchronization by tracking the DNA distribution of a population of cells starting from a synchronized state until asynchrony with flow cytometry analysis. In doing so, I utilized statistical tools to quantify the degree of desynchronization that does not rely on individual cell cycle phase labeling. Additionally, with the help of my peers, tested and validated a mathematical model the capitulates experimental observations. I explored the sensitivity of the model to changes in its parameters to reveal that cell cycle variability within the population is a main contributor to cell cycle desynchronization. Furthermore, I tested this model prediction by treating cells with lipopolysaccharide to enhance cellular noise, resulting in a greater variability of cell cycle duration, which was also shown to increase the rate of cell cycle desynchronization. Taken together, my research provides insight into the importance individual edges have to biological networks and their resulting phenotypes, as well as the underlying sources of cell population heterogeneity and its contribution to cell cycle variability.application/pdfenBiology, MolecularBiology, CellThesis2023-03-27