Participants: Cassandra Azeredo-Tseng (Biochemistry and Applied Math, New College of Florida); Michael Luo (Applied Mathematics, College of New Jersey); Natalie Randall (Math and Computer Science, Austin College)
Project Description. Epithelial-to-mesenchymal transition (EMT) is a form of cell differentiation that is essential to normal human development, but also play a role in cancer pathogenesis. Though this process has been view as a discrete transformation of structural epithelial cells to mobile mesenchymal cells, there is a growing body of evidence which suggests that diverse outcomes can result from EMT. The aim of this project will be to integrate single-cell expression, cap-analysis of gene expression (CAGE) data, and a number of online cancer databases to characterize different cell states following chemically induced EMT. To do this, we will be employing a combination of machine learning and ODE models of gene expression with the aim of understanding how different EMT regulators lead to different outcomes and influence the prognosis of cancer of derived from these cells.
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