Accepted for/Published in: JMIR Bioinformatics and Biotechnology
Date Submitted: Nov 29, 2025
Date Accepted: Aug 14, 2026
Application of aiAtlas v1.2 to Simulate Variant Gene-Function Restoration and Rescue Thresholds in Rare Diseases: Validation Case Study for Xeroderma Pigmentosum Group D ERCC2 Variants
ABSTRACT
Background:
Xeroderma pigmentosum group D (XPD), caused by ERCC2 dysfunction, leads to defective nucleotide-excision repair and hypersensitivity to UV radiation. Robust experimental models for variant-level functional assessment remain limited.
Objective:
This study aimed to use aiAtlas v1.2, a mechanistic large-concept simulation model, to evaluate the functional consequences of ERCC2/XPD variants and identify quantitative thresholds for functional rescue under graded gene-function restoration.
Methods:
We simulated 136 virtual aiPSC-derived cell lines spanning wild-type, single-mutation, multi-mutation, human tumor–derived, and gene-fusion ERCC2 states. Twenty-five features encompassing DNA damage and repair, replication stress, pluripotency, and epigenetic remodeling were analyzed using nonparametric statistics with Bonferroni correction, Hodges–Lehmann estimates, and Cliff’s delta with bootstrap confidence intervals. Graded ERCC2 restoration (0–100%) was simulated to evaluate rescue thresholds. Cross-validation, bagging, and bootstrap resampling tested robustness.
Results:
Simulations identified three nonlinear ERCC2/XPD rescue thresholds: ~20–30% (initial stabilization of repair), ~50% (near-normalization), and >80% (full convergence). Reduced-function variants required substantially less restoration than strict loss-of-function variants to cross each threshold. Group comparisons across variant classes showed consistent differences in DNA repair, replication stress, and epigenetic remodeling features, supported by effect-size metrics and confidence intervals.
Conclusions:
aiAtlas v1.2 provides a mechanistic framework for variant-level functional modeling in ERCC2/XPD and identifies quantitative thresholds that support computational dose-window estimation and variant-aware therapeutic stratification. All analyzed values are reported in the main text and summary tables. The aiAtlas simulation engine is proprietary; original Excel/XLSTAT workbooks are not provided. Clinical Trial: N/A
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