MicroNet 2.0: Teaching Computers to Predict How Bacteria Behave

Description

Ever wonder how brewers create the perfect beer, or how doctors figure out which antibiotics will work against a nasty infection? It all comes down to understanding how bacteria grow and respond to stress. The Schmid Lab has been collecting massive amounts of data on bacterial behavior, and they've built MicroNet—a web application that uses statistical models to predict how microbes will act under different conditions. This summer, your team will help launch MicroNet 2.0 by testing it with real-world datasets from Duke researchers and the broader microbiology community. 

You'll work on making the tool user-friendly, analyze growth patterns of both harmless and pathogenic bacteria, and contribute to a research paper. If things go well, you might even help design MicroNet 2.0 by brainstorming ways to integrate gene expression data—essentially teaching the app to understand not just how bacteria grow, but why they grow that way at the molecular level. This project sits at the intersection of data science, biology, and web development, making it perfect for anyone who wants to see their code make a real impact on research ranging from food safety to fighting infections.