Awarded in 2026
Home Department: Applied Physics
Faculty Advisor: Benjamin Good (Applied Physics)

Title: Predicting Phage Therapy from Transient Population Crashes

Abstract:
Antibiotic-resistant infections are a growing public health threat, and bacteriophages, viruses that infect and kill bacteria, are being explored as new treatments. However, phage therapy remains hard to predict because phages and bacteria exhibit complex dynamics during treatment and bacteria may evolve resistance.

This project will use mathematical models and experiments to identify why phage therapy succeeds in some cases but fails in others. The central hypothesis is that rapid initial changes in bacterial and phage populations determine the success of phage therapy, and that analyzing how infection, phage, and environmental parameters influence these dynamics will lead to more effective prediction of treatment outcomes. The work will also examine when resistance is likely to evolve and when combining phages with antibiotics may improve success.

The models will be tested in a microbiome-based laboratory system that can track bacteria, phages, and environmental conditions over time. By revealing the key factors that control phage therapy outcomes, this research could help make phage-based treatments more predictable and guide better strategies for treating antibiotic-resistant infections.