The dynamics of epigenetic persistence in nicotine dependence: An adaptive network model

This paper develops and analyses a multi-level, adaptive computational model of nicotine addiction using the network-oriented modelling paradigm. The model formalizes the contemporary theory of addiction as a failure of self-regulation, where a deep adaptive pathway, initiated by epigenetic modifications of the CHRNA5 gene, progressively dismantles the prefrontal cortex's (PFC) top-down control over behaviour. This process creates a stable, self-sustaining addicted state characterized by impaired executive function and heightened cue-reactivity. A continuous simulation was performed to test the model's dynamics across three scenarios: addiction onset following an environmental trigger, standard symptom-focused therapy, and a hypothetical epigenetic therapy. The results demonstrate that the model successfully replicates the transition to a stable addicted state. It further shows that standard therapy, by only suppressing craving, leads to immediate relapse upon cessation because the underlying regulatory pathway remains impaired. In contrast, the epigenetic therapy, by targeting the root biological cause and restoring the PFC's control pathway, induces a lasting recovery. This work presents an integrative framework conceptualizing addiction as a chronic disease of impaired self-regulation, suggesting that effective long-term interventions must target these root biological mechanisms rather than surface-level symptoms.