Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning

GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps (e.g., missing reactions). Existing gap-filling methods typically require phenotypic data as input to tease out missing reactions. We still lack a computational method for rapid and accurate gap-filling of metabolic networks before experimental data is available. Here we present a deep learning-based method - CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) - to predict missing reactions in GEMs purely from metabolic network topology. We demonstrate that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions over 926 high- and intermediate-quality GEMs. Furthermore, CHESHIRE is able to improve the phenotypic predictions of 49 draft GEMs for fermentation products and amino acids secretions. Both types of validation suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes. A computational method for rapid and accurate gap-filling of metabolic networks without using phenotypic data is unavailable. Here, the authors address this problem by developing a deep learning based method that can predict missing reactions using topological features of the metabolic networks.

Automated summary

In this study, a deep learning-based method (CHESHIRE) is proposed to predict missing reactions in genome-scale metabolic models using topological features of the metabolic networks. Results show that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions and improving phenotypic predictions. This method is of great significance for the curation of genome-scale metabolic models.