Reconstruction of human metabolic models with large language models
Clinical Snapshot
PICO Framework
| P — Population | Human metabolic systems across different tissues, organs, sex, and age groups |
| I — Intervention | Human2 consensus genome-scale metabolic model (GEM) enhanced with large language models |
| C — Comparator | Previous human metabolic models and traditional GEM reconstruction methods |
| O — Outcomes | Enhanced precision and biological relevance of metabolic models, tissue/organ-specific metabolic features, whole-body dynamic simulation capabilities |
Bottom Line
This methodological study presents Human2, an enhanced genome-scale metabolic model that incorporates large language models for improved precision in human metabolism research. The model offers sex- and age-specific customisation and whole-body dynamic simulation capabilities, potentially advancing personalised medicine approaches. While the computational innovation is promising, the abstract lacks crucial validation metrics and statistical evidence for claimed improvements. The identified differences in arachidonic acid and leukotriene metabolism warrant further investigation. For Australian clinicians, this tool could eventually support more precise metabolic disease management and drug therapy optimisation, though clinical validation remains necessary. The collaborative framework may accelerate metabolic research, but practitioners should await peer-reviewed validation studies before considering clinical applications. This represents an important step toward more sophisticated metabolic modelling but requires careful evaluation of real-world performance.
Key Findings
P Value: Not reported
Effect Size: Not quantified in abstract
Primary Outcome: Development of Human2 consensus genome-scale metabolic model with enhanced precision
Nnt Or Sensitivity: Model accuracy and biological relevance improvements not quantified
Confidence Interval: Not reported
Clinical Application
High feasibility for research applications, requires computational expertise and infrastructure Highly relevant for Australian precision medicine initiatives, drug development research, and metabolic disease studies. Could support TGA drug evaluation processes and inform RACGP guidelines on metabolic disorders Researchers and clinicians studying human metabolism, drug development, and personalised medicine applications
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.
References
- 1.Luo, J., Wang, H., Moyer, D., Guo, Z., Robinson, J. L., Gustafsson, J., Anton, M., Chen, Y., Kerkhoven, E. J., Nielsen, J., & Li, F. (2026). Reconstruction of human metabolic models with large language models. Proceedings of the National Academy of Sciences, 123, Article 2516511123. https://doi.org/10.1073/pnas.2516511123
Related Research
Briefings in bioinformatics
Literature-informed gene extraction and ranking for multimodal data fusion.
6 July 2026
Journal of chemical information and modeling
scFNSA: A Factorized Node-Set Attentive Framework for Single-Cell Multi-Omics Integration
13 May 2026
Genome research
Incorporating valuable prior knowledge to improve deep learning prediction of genetic perturbation responses
11 May 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service