Heather is a rising senior at the University of California, Irvine. Heather spends her extra time involved in the Society of Hispanic Professional Engineers and Ballet Folklorico de UCI. She is passionate about bringing STEM opportunities to her community, the latinx community. At UCLA this summer, she is researching cytokines which are a type of cell signaling protein that are secreted from one cell and recognized by another to regulate immune response. The IL-2 cytokine family is of particular importance as engineered variants of IL-2 could help treat immune diseases such as cancer. Our objective is to analyze protein abundance data, specifically of the three IL-2 receptor components present on the cell surface of different immune cell lines, to understand how each protein influences the activity of the cell. At different time points, the activity and receptors were quantified using flow cytometry -- the former by looking at pSTAT5 presence in the cell. IL-2 binds to IL-2R and triggers the signaling pathway which leads to the output of pSTAT5. The high-throughput flow cytometry data was taken for visualization and analysis in Python using the package FlowCytometryTools. Data analysis is used to find relations between receptor components across IL-2 variants such as IL-15. Machine learning could be applied to look for patterns such as multivariate predictors of IL-2 response, or how the relationship between IL-2R components and response changes with IL-2 dose or time.