ArticleMolecular systems biology2010
Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism.
Article in Molecular systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 195 papers.
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Who cites it
195 citing papers in PubMed.
- Article
- Modelling reliable metabolic phenotypes by analysing the context-specific transcriptomics data.NPJ systems biology and applications · 2025Article
- Longitudinal big biological data in the AI era.Molecular systems biology · 2025Review
- ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions.iScience · 2025Article
- Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025Review
- Integration of metatranscriptomics data improves the predictive capacity of microbial community metabolic models.The ISME journal · 2025Article
- Article
- Integration of proteomic data with genome-scale metabolic models: A methodological overview.Protein science : a publication of the Protein Society · 2024Review
- Systems biology of plant metabolic interactions.Journal of biosciences · 2024Review
- State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs.Microorganisms · 2023Review
- Genome-scale metabolic models applied for human health and biopharmaceutical engineering.Quantitative biology (Beijing, China) · 2023Review
- Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux.Nature communications · 2023Article
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
- Extracting functionally accurate context-specific models of Atlantic salmon metabolism.NPJ systems biology and applications · 2023Article
- Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.NPJ systems biology and applications · 2023Article
- Loss of the Kidney Urate Transporter, Urat1, Leads to Disrupted Redox Homeostasis in Mice.Antioxidants (Basel, Switzerland) · 2023Article
- Article
- Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.Metabolites · 2023Review
- Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.Metabolic engineering · 2023Article
135 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.
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