A computational framework linking molecular regulation, synaptic plasticity, and brain disorders

Abstract

Neuroplasticity supports learning, development, and adaptive behavior, but also underlies maladaptive circuit remodeling in neuropsychiatric disorders. Although genetic disruptions of synaptic signaling are widely implicated in these conditions, the causal chain from molecular perturbation to systems-level dysfunction remains unclear. Here we introduce a multiscale computational framework--a parameter-free Boolean regulatory model of glutamatergic signaling--that captures the dynamic control of synaptic plasticity. The model generates emergent attractor states corresponding to long-term potentiation and depression and reveals how these states arise from underlying Hebbian principles. In silico gene-knockout simulations show that specific molecular perturbations destabilize these attractors, impairing synaptic plasticity and producing circuit-level alterations characteristic of diverse brain disorders. Remarkably, the model recapitulates the opposing developmental trajectories of autism spectrum disorder and schizophrenia, offering a mechanistic account of their divergent cortical phenotypes. This computationally tractable and generalizable framework quantitatively links genetic variation to synaptic instability and clinical severity, aligning with experimental and clinical observations. By moving beyond correlative associations, it establishes a causal scaffold for tracing how molecular disruptions propagate through synaptic regulatory networks to impair brain function, informing integrative diagnostics and mechanism-based therapeutic strategies.