Quantifying RNA-Protein Phase Separation Dynamics in Autistic Brain Tissue via Computational Hypergraph Analysis

**Abstract:** This research investigates the emerging role of RNA-protein phase separation in the pathophysiology of autism spectrum disorder (ASD). Specifically, we focus on the functionally uncharacterized RNA species found in post-mortem autistic brain tissue and their propensity to form aberrant liquid-liquid phase separation (LLPS) events. We propose a novel framework leveraging computational hypergraph analysis to quantify the dynamic phase separation behavior of these RNA-protein complexes *in silico*, predicting their impact on neuronal function and presenting a potential therapeutic target. Our approach surpasses existing computational models by incorporating spatio-temporal dynamics and high-order interactions within a hypergraph representation, revealing previously uncharacterized regulatory hubs and potential therapeutic intervention points.