Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism

By Livnat Jerby, Tomer Shlomi, Eytan Ruppin. Molecular Systems Biology, 2010

380 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/30e6754bc75f78708e275f3b830ad08bdf281ec6

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Citations in the corpus

Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “…constraints relating to gene product or metabolite presence/absence; that is, if a metabolite is present, a reaction must be active that produces it (Shlomi et al., 2008), and if a gene product is absent, its catalyzed reactions cannot carry ﬂux (Jerby et al., 2010; Schmidt et al., 2013).”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “| Dynamic multispecies metabolic modelling (DMMM) 105 and model-building algorithm (MBA) 107 use high-throughput data and model simulations to identify which pathways were likely to be expressed and active in the cells when they were sampled.”
Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT (2012, methodology): “The second one [11] was developed by the same authors in order to obtain a functional model for human hepatocytes and is able to integrate also metabolomic and proteomic data.”
Predicting Network Activity from High Throughput Metabolomics (2013, background): “Intense manual curation, however, was required in the building of two high-quality human models [15,16], which were followed by a number of derivatives [17–20].”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, methodology): “Unlike similar methods such as the Model Building Algorithm (MBA) [37] that only uses transcript and protein data as evidence for metabolic functionality, mCADRE uses the gene expression levels and the network topology to calculate connectivity-based evidence scores for all reactions in a model.”
Constraint-based models predict metabolic and associated cellular functions (2014, methodology): “The first study 77 used a model-building algorithm 79 that uses cues from transcriptomic data to prune metabolic reactions from Recon 1 (REF.”