Higher-order adaptive dynamical system modeling of the role of epigenetics in Rett Syndrome

This paper introduces a higher-order adaptive self-modelling network model to simulate the role of epigenetics in the development and treatment of Rett syndrome (RTT). RTT is a neurodevelopmental disorder caused by mutations in the MECP2 gene. The model is constructed using temporal-causal network modeling principles and integrates multiple levels of biological and emotional adaptation. While MECP2 dysfunction is central to RTT, recent findings emphasize the role of environmental factors, mainly early-life stress. One of the epigenetic consequences of this stress, is the reduced expression of brain-derived neurotrophic factor (BDNF) and widespread dysfunction in emotional and cognitive regulation. In this study, a computational simulation is used to explore both the development of RTT and the potential effectiveness of a hypothetical epigenetic therapy aimed at restoring BDNF expression. The results highlight how targeted intervention could reverse or mitigate the long-term neurological impacts of RTT.