Autonomous Control of Antibiotic Resistance via Metabolic Engineering and Non-Markovian Memory Dynamics

Antimicrobial resistance (AMR) poses a critical global health threat, exacerbated by the failure of traditional monotherapies and the emergence of dormant "persister cells" that cause relapse. In this study, we present an in silico spatiotemporal model (V9 Protocol) that integrates Metabolic Engineering with Dynamical Systems Control to eradicate resistant bacteria (R) while preserving beneficial strains (S). The model uses coupled Reaction-Diffusion Partial Differential Equations (PDEs) to simulate a spatial environment where S is granted a competitive advantage through a "tagged nutrient" (C2), while R relies solely on a general nutrient (C1). Furthermore, we introduce a Non-Markovian memory field (M_R) to simulate antibiotic persistence and cellular dormancy. To prevent relapse, a Critical Slowing Down (CSD) metric is employed as an Early Warning Signal to predict system transitions, triggering autonomous and spatially targeted interventions. Our results demonstrate that this multi-layered strategy achieved complete dominance of S (biomass = 10000) and near-extinction of R (biomass = 48.91). The CSD-driven controller optimized the treatment, requiring only 8 targeted interventions, effectively preventing the relapse of dormant cells even after the cessation of the tagged nutrient supply. Three-dimensional spatiotemporal visualizations reveal the formation of a "memory wave" and nutrient-trapping dynamics. This study provides a robust mathematical framework for designing smart, low-dosage therapeutic protocols against AMR.