A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction.

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Genome-scale metabolic models (GEMs) are crucial for understanding cellular metabolism but often contain incomplete reaction networks due to knowledge gaps.
• Inaccurate annotations and insufficient experimental validation further limit the predictive power of existing GEMs.
• Addressing these limitations is essential for advancing fields like strain optimization and biomedical research.

Purpose of the Study:

• To introduce MuSHIN (Multi-way SMILES-based Hypergraph Interface Network), a novel deep hypergraph learning method.
• To predict missing reactions in GEMs by integrating network topology with biochemical domain knowledge.
• To improve the accuracy and predictive capacity of GEMs for various biological applications.

Main Methods:

• MuSHIN utilizes a deep hypergraph learning approach.
• The method integrates network topology information with biochemical domain knowledge.
• It was evaluated on 926 high- and intermediate-quality GEMs with artificially removed reactions.

Main Results:

• MuSHIN demonstrated up to a 17% improvement over state-of-the-art methods in predicting missing reactions.
• The method significantly enhanced phenotypic predictions in 24 draft GEMs related to fermentation.
• These improvements were validated against experimental measurements, confirming the resolution of critical metabolic gaps.

Conclusions:

• MuSHIN offers a powerful new approach for enhancing GEM reconstruction by accurately predicting missing metabolic reactions.
• The method has the potential to accelerate discoveries in systems biology, metabolic engineering, and precision medicine.
• By resolving metabolic gaps, MuSHIN improves the reliability of GEMs for guiding experimental research and biotechnological applications.