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We developed CHESHIRE, a deep learning method to predict missing metabolic reactions in genome-scale metabolic models (GEMs) using only network structure. This approach aids in GEM curation without needing experimental data.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Metabolic Engineering

Background:

  • Genome-scale metabolic models (GEMs) are crucial for understanding cellular metabolism.
  • Knowledge gaps, such as missing reactions, limit the accuracy of existing GEMs.
  • Current gap-filling methods often rely on experimental phenotypic data, which is not always available.

Purpose of the Study:

  • To develop a computational method for predicting missing reactions in GEMs using only network topology.
  • To provide a rapid and accurate gap-filling solution for metabolic networks prior to experimental validation.
  • To enhance the curation process of GEMs by identifying unknown metabolic links.

Main Methods:

  • A deep learning-based method named CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) was developed.
  • CHESHIRE predicts missing reactions based solely on the topology of the metabolic network.
  • The method was validated by predicting artificially removed reactions and improving phenotypic predictions of draft GEMs.

Main Results:

  • CHESHIRE demonstrated superior performance compared to other topology-based methods in predicting missing reactions across 926 GEMs.
  • The method successfully improved phenotypic predictions for fermentation products and amino acid secretions in 49 draft GEMs.
  • Validation confirmed CHESHIRE's capability in uncovering previously unknown reaction-phenotype relationships.

Conclusions:

  • CHESHIRE offers a powerful, data-independent approach for metabolic network gap-filling.
  • This method significantly aids in the curation of genome-scale metabolic models.
  • CHESHIRE facilitates the discovery of novel metabolic pathways and their contribution to cellular phenotypes.