CytoGem-XAI: A Hypergraph Neural Network Framework for Genome-Scale Metabolic Modeling and Interpretable Analysis

Genome-scale metabolic models are essential for understanding cellular metabolism, yet existing deep learning approaches remain black boxes, and traditional flux balance analysis (FBA) cannot provide sample-specific predictions. To our knowledge, CytoGem-XAI is the first framework to combine hypergraph neural network representation with interpretable, FBA-parallel analysis and sample-specific metabolic characterization. Built upon hypergraph representations where reactions are encoded as hyperedges connecting their participating metabolites, CytoGem-XAI introduces three analysis modules: perturbation-based carbon source importance ranking, hard intervention reaction bottleneck identification, and pathway-level topological attribution. Beyond prediction, CytoGem-XAI uniquely enables condition-dependent carbon source essentiality and reaction bottlenecks that vary with genetic background—capabilities absent from both traditional FBA and existing deep learning methods. Trained on 17,400 E. coli growth conditions using 10-fold cross-validation, our framework achieves R ² = 0.862, substantially outperforming AMN ( R ² = 0.81, +6.4%), FBA ( R ² = 0.62, +39%), and gradient boosting baselines ( R ² = 0.71, +21%). Biological validation confirms that CytoGem-XAI identifies known essential carbon sources (e.g., alanine, malate) and rate-limiting enzymes (e.g., TCA cycle), while also revealing N-acetylmuramate—a peptidoglycan precursor—as a previously underappreciated essential nutrient.