Reconstruction of human metabolic models with large language models
Journal article, 2026
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.
large language model
genome-scale metabolic model
whole-body model
organ-specific model
Author
Jiahao Luo
Tsinghua University
Hao Wang
Chalmers, Life Sciences, Systems and Synthetic Biology
Devlin Moyer
Boston University
Zhetao Guo
Tsinghua University
Jonathan Robinson
BioInnovation Institute
Johan Gustafsson
Broad Institute
University of Gothenburg
Petre Mihail Anton
Chalmers, Life Sciences, Systems and Synthetic Biology
ELIXIR Hub
Yu Chen
Shenzhen Institute of Advanced Technology
Eduard Kerkhoven
Chalmers, Life Sciences, Systems and Synthetic Biology
Novo Nordisk Foundation
Jens B Nielsen
BioInnovation Institute
Chalmers, Life Sciences, Systems and Synthetic Biology
Feiran Li
Tsinghua University
Proceedings of the National Academy of Sciences of the United States of America
0027-8424 (ISSN) 1091-6490 (eISSN)
Vol. 123 15 e2516511123Subject Categories (SSIF 2025)
Cell and Molecular Biology
Bioinformatics (Computational Biology)
Bioinformatics and Computational Biology
DOI
10.1073/pnas.2516511123