Computational Modeling of Multi-Mechanism Herbal Strategies Targeting Persistent Infection Dynamics in Lyme disease

This study presents a Python-based computational model of persistent infection dynamics in Lyme disease. The simulation incorporates bacterial growth, tissue sequestration, persister cells, and biofilm formation, along with multi-mechanism interventions modeled after Cryptolepis sanguinolenta, Polygonum cuspidatum, and Andrographis paniculata. Results demonstrate threshold-dependent behavior, where effective outcomes require sufficient biofilm disruption and tissue penetration in addition to antimicrobial activity. Under optimized conditions, the model shows rapid, nonlinear reduction of bacterial populations, while sub-threshold conditions lead to persistence. This work is entirely computational and not based on laboratory or clinical experimentation, but provides a framework for hypothesis generation and further investigation into multi-target treatment strategies.