ArticleProceedings of the National Academy of Sciences of the United States of America2026
Reconstruction of human metabolic models with large language models.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models.Bioinformatics (Oxford, England) · 2026Article
- mFLIP: metabolic flux interval prediction.BMC bioinformatics · 2026Article
- Integrating Genome-Scale Metabolic Modeling with Machine Learning Improves Gene Essentiality Prediction in Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
Genome-scale metabolic models (GEMs) have become essential tools for understanding human metabolism. Here, we introduce Human2, a consensus human GEM with enhanced precision and biological relevance, which leverages large language models (LLMs) and GitHub Action checks to streamline automated, efficient, and collaborative curation. Human2 supports the reconstruction of tissue- and organ-specific models tailored to sex- and age-specific human groups. By integrating transcriptomic, proteomic, and kinetic data, we reveal distinct metabolic features across these groups, such as significant differences in arachidonic acid and leukotriene metabolism. The specific models were integrated into a dynamic whole-body framework, marking an enzyme-constrained dynamic model that simulates interorgan metabolite exchanges under varying nutritional states, from feeding to fasting. Our work highlights the transformative role of LLMs in GEM reconstruction and introduces a whole-body dynamic simulation that integrates kinetic data, offering a powerful resource for multiscale human metabolism modeling.
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Registered trials
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