Phenom: A Graphical User Interface for Bayesian Hierarchical Growth Modeling

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: Duke Biology
: Biology
: 2021

The goal of capstone project is to apply and extend custom analytics solutions to discover how life remains resilient in extreme environments. An explosion of data has resulted from recent discoveries of tiny single-celled life hiding out in the most extreme places on Earth. These single-celled creatures, or microbes, thrive in hot springs at boiling temperatures, in brine lakes saturated with salt, and in deserts once thought to be sterile. Because these microbes live in adverse conditions like those on other planets -cold, dry, and bombarded with radiation –experts can use what we learn about microbes in extreme places on Earth to predict what extraterrestrial life might be like. Using nonparametric statistical models to analyze how microbes grow under extreme stress, the Schmid research lab at Duke has discovered genes and mechanisms underlying resilience. These studies generated large data sets tracking how microbes grow and change their gene expression when faced with extreme stress. However, the majority of these data remain to be analyzed. In this project, MIDS students will apply and extend the statistical models to ask how extreme microbes grow and change during exposure to nutrient deprivation, extreme stress, and genetic mutations.