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Open Access Highly Accessed Methodology article

Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE

Yuliang Wang12, James A Eddy13 and Nathan D Price12*

Author affiliations

1 Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA

2 Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, IL, 61801, USA

3 Department of Bioengineering, University of Illinois, Urbana, IL, 61801, USA

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Citation and License

BMC Systems Biology 2012, 6:153  doi:10.1186/1752-0509-6-153

Published: 13 December 2012

Abstract

Background

Human tissues perform diverse metabolic functions. Mapping out these tissue-specific functions in genome-scale models will advance our understanding of the metabolic basis of various physiological and pathological processes. The global knowledgebase of metabolic functions categorized for the human genome (Human Recon 1) coupled with abundant high-throughput data now makes possible the reconstruction of tissue-specific metabolic models. However, the number of available tissue-specific models remains incomplete compared with the large diversity of human tissues.

Results

We developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. Using our method, we reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. Among these, there are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues.

Conclusions

This large collection of 126 genome-scale draft metabolic models provides a useful resource for studying the metabolic basis for a variety of human diseases across many tissues. The functionality of the resulting models and the fast computational speed of the mCADRE algorithm make it a useful tool to build and update tissue-specific metabolic models.

Keywords:
Automated metabolic network reconstruction; Brain; Cancer metabolism; Tissue-specific metabolic model; Constraint-based modeling