Physics-Informed Predictive Modeling of Bacterial Vaginosis Pathogen Dynamics
Wednesday, July 16, 2025
3:27 PM - 3:38 PM EDT
Location: 119 A
Introduction: Bacterial vaginosis (BV) is a prevalent reproductive condition with frequent recurrences despite initially effective antibiotic treatment [1]. Bacterial competition, nutrients, and pH shifts significantly affect BV progression and treatment outcomes. These factors are costly to test experimentally. While mathematical modeling can explore them, it may overlook complex system dynamics. Machine learning offers predictive power for pathogen-environment interactions [2]. This study implements a novel model to predict the governing relation between bacterial growth and environment characteristics.
Learning Objectives:
At the completion of this activity, participants will know
Characterize key factors contributing to bacterial vaginosis pathogenic dynamics.
Provide a scalable framework integrating clinical data for patient-specific treatment optimization.
Davis Verhoeven, B.S. – Research Technician, Department of Bioengineering, University of Louisville; Hermann Frieboes, Ph.D. – Professor, Department of Bioengineering, University of Louisville
