Determining synchronously updated fixed points and attractors in a prototype boolean gene regulation model of diphtheria pathogenesis

This study employs dynamic modeling and simulation to provide theoretical insights into the systemic behaviors underlying diphtheria pathogenesis. A Boolean network model was developed to formalize the hypothesized interactions among eight genes identified in the literature as central to toxin production, immune response, and disease transmission. Computational exploration of the state-space dynamics within this model revealed three distinct attractors, each hypothesized to represent key disease states. Structural analysis of these attractors and their basins of attraction offered insights into network architectures potentially responsible for bistable switches between chronic infection and recovery, endogenous inflammatory oscillations reflective of periodic fever cycles, and modular topologies enabling alternative developmental pathways. These findings demonstrate the utility of Boolean modeling in uncovering organizing principles—such as periodicity, bistability, and evolvability—that govern disease emergence in complex systems. The study highlights testable network signatures that could refine our understanding of diphtheria and similar pathologies, and while preliminary, it underscores the potential of iterative computational and experimental approaches to inform more effective control strategies.