This work introduces the Genomic Complexity Index (GCI), a multiscale computational framework designed to quantify the structural organization of genes within regulatory and network contexts. Moving beyond traditional gene-centric approaches, the GCI integrates regulatory density (derived from Ensembl annotations) and network connectivity (based on STRING interaction data) to capture how genes are embedded within a multidimensional biological system.Applied to autism-associated genes (including CHD8, SHANK3, BDNF, and SCN2A), the model reveals a consistent hierarchical organization, with CHD8 occupying a high-complexity regime across parameterizations. This observation supports the hypothesis that certain genes may function as structural hubs, integrating chromatin accessibility, regulatory architecture, and network-level interactions.To extend the framework beyond population-level analysis, an exploratory personalized implementation is presented using individual genomic data. This application illustrates how multiscale structural metrics can bridge general genomic models with individual biological profiles, while maintaining appropriate methodological caution and interpretative scope.In addition, an expanded formulation of the GCI is proposed, incorporating intrinsic sequence architecture (e.g., fractal properties, long-range correlations, and periodicities) alongside functional-contextual embedding and a prospective interaction term related to three-dimensional genome organization. This extension positions the GCI as a candidate framework for integrating sequence-level structure, chromatin organization, and system-level network dynamics.Overall, this work supports a shift toward structural and systems-level approaches in genomics, offering a quantitative and extensible framework for investigating complex neurodevelopmental conditions such as autism.
Eduardo Parra (Fri,) studied this question.