two sets of line graphs for H. salinarum and P. aeruginosa

Predicting microbial growth


The goal of this Data+ project was to apply and extend custom analytics solutions to understand and predict microbial population growth. An explosion of data has resulted from tracking the growth of bacteria in high throughput devices. These data were generated to understand how microbes grow. Better models that fit and predict these growth data are needed for better treatment of pathogenic bacterial infections, food safety, beer and bread fermentation, and understanding stress resilience of the microbiome. Using nonparametric statistical models to analyze how microbes grow under stress, the Schmid research lab at Duke has made important discoveries in these areas. These studies generated large data sets and developed statistical models to track and predict how microbes grow and change their gene expression when faced with extreme stress. We built a web application called phenom to make these models accessible to the broader community. In this Data+ project, students beta tested the web app and made improvements, including data visualization, extending the underlying statistical model, and analyzing data using the app.

Project Lead:  Amy Schmid

Project Manager: Christopher Kilner

View the team’s project poster here: Team 11 Data+ Final Poster

View the team’s project video here:



Related People

Data+ 2023: Predicting microbial growth to understand pathogenesis

Data+ 2023: Predicting microbial growth to understand pathogenesis