SIMBA-GNN: Simulation-augmented Microbiome Abundance Graph Neural Network

Mohammad S. Parsa, Javad Aminian Dehkordi, Mohammad Mofrad

Keywords: GNN, Gut microbiome, Cross-feeding, Metabolic Modeling, Machine Learning, Heterogeneous Graph Transformer

TL;DR: We introduce SIMBA, a graph neural network enhanced with metabolic simulations, accurately predicting gut microbial abundances and uncovering cross-feeding interactions (Spearman ρ = 0.85).

Abstract: Understanding gut microbiome dynamics gut requires deciphering complex, metabolically driven interactions beyond taxonomic profiles. We present SIMBA, a novel framework that integrates mechanistic metabolic simulations with a graph neural network (GNN) to predict microbial abundances and uncover cross-feeding relationships. By simulating pairwise interactions among gut microbes using metabolic networks, we generate biologically grounded graphs that capture metabolite cross-feeding and functional relationships. Our custom GNN, enhanced with edge-aware attention, is trained through a multi-stage pipeline combining self-supervised learning, simulation-based pretraining, and fine-tuning on real microbial abundance data. SIMBA achieves state-of-the-art performance (Spearman ρ = 0.85) and enables interpretable insights into keystone taxa and metabolic bottlenecks. This work demonstrates the power of combining metabolic networks with deep learning for precision microbiome analysis.

Submission Number: 120