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

Author

Listed:
  • Can Chen

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Chen Liao

    (Memorial Sloan Kettering Cancer Center)

  • Yang-Yu Liu

    (Brigham and Women’s Hospital and Harvard Medical School
    University of Illinois at Urbana-Champaign)

Abstract

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.

Suggested Citation

  • Can Chen & Chen Liao & Yang-Yu Liu, 2023. "Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38110-7
    DOI: 10.1038/s41467-023-38110-7
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    1. Alexandre Almeida & Alex L. Mitchell & Miguel Boland & Samuel C. Forster & Gregory B. Gloor & Aleksandra Tarkowska & Trevor D. Lawley & Robert D. Finn, 2019. "A new genomic blueprint of the human gut microbiota," Nature, Nature, vol. 568(7753), pages 499-504, April.
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    1. Mohammad H. Mirhakkak & Xiuqiang Chen & Yueqiong Ni & Thorsten Heinekamp & Tongta Sae-Ong & Lin-Lin Xu & Oliver Kurzai & Amelia E. Barber & Axel A. Brakhage & Sebastien Boutin & Sascha Schäuble & Gian, 2023. "Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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