ArticleNPJ systems biology and applications2021
A dynamic multi-tissue model to study human metabolism.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 21 citations in OpenAlex.
- Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025Review
- Extracting functionally accurate context-specific models of Atlantic salmon metabolism.NPJ systems biology and applications · 2023Article
- Article
- Research advances in the application of metabolomics in exercise science.Frontiers in physiology · 2023Review
- Rapid Assessment of Lipidomics Sample Purity and Quantity Using Fourier-Transform Infrared Spectroscopy.Biomolecules · 2022Article
- Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective.Current opinion in systems biology · 2022Article
- Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis.Frontiers in endocrinology · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions (i.e., during fasting, while consuming meals or during exercise), and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.