A network dynamical simulation model for the study of antibiotic resistance in microbial communities

Antibiotic resistance emerges from ecological and evolutionary processes occurring within complex microbial communities. Interactions among microorganisms can shape the pathways through which resistance traits spread and persist, yet many theoretical approaches treat microbial populations as homogeneous compartments. Here we present a simulation-based network dynamical model that represents microbial communities as ecological association networks. In this formulation, nodes correspond to bacterial populations, metapopulations, or taxon-level ecological units, while resistant counterparts represent state-expanded subpopulations associated with the same ecological unit. Edges represent co-occurrence-based ecological proximity rather than direct physical contacts or confirmed horizontal gene transfer events. Using stochastic simulations across multiple network topologies, we explore how structural properties of microbial communities influence the emergence and persistence of resistance. Parameter sweeps across transmission probability, initial resistance fraction, and antibiotic intervention timing allow us to characterize regimes in which resistance either remains localized or spreads through the community. The model produces time series of resistant and susceptible states and snapshots of evolving network configurations, enabling qualitative comparison across simulation scenarios. Our results show that network structure strongly modulates resistance dynamics. Highly clustered networks tend to trap resistance within local neighborhoods, whereas heterogeneous networks with hub nodes facilitate rapid dissemination. Antibiotic perturbations can either suppress resistance or paradoxically accelerate its expansion depending on network topology and intervention timing. These findings should be interpreted as qualitative results from a minimal proof-of-concept model, not as a direct reconstruction of plasmid transfer, species replacement, or patient-specific microbiome responses.