Integration of clinical data with a genome-scale metabolic model of the human adipocyte
Journal article, 2013

We evaluated the presence/absence of proteins encoded by 14 077 genes in adipocytes obtained from different tissue samples using immunohistochemistry. By combining this with previously published adipocyte-specific proteome data, we identified proteins associated with 7340 genes in human adipocytes. This information was used to reconstruct a comprehensive and functional genome-scale metabolic model of adipocyte metabolism. The resulting metabolic model, iAdipocytes1809, enables mechanistic insights into adipocyte metabolism on a genome-wide level, and can serve as a scaffold for integration of omics data to understand the genotype-phenotype relationship in obese subjects. By integrating human transcriptome and fluxome data, we found an increase in the metabolic activity around androsterone, ganglioside GM2 and degradation products of heparan sulfate and keratan sulfate, and a decrease in mitochondrial metabolic activities in obese subjects compared with lean subjects. Our study hereby shows a path to identify new therapeutic targets for treating obesity through combination of high throughput patient data and metabolic modeling.


genome-scale metabolic model

flux balance analysis




Adil Mardinoglu

Chalmers, Chemical and Biological Engineering, Life Sciences

Rasmus Ågren

Chalmers, Chemical and Biological Engineering, Life Sciences

C. Kampf

Uppsala University

A. Asplund

Uppsala University

Intawat Nookaew

Chalmers, Chemical and Biological Engineering, Life Sciences

Peter Jacobson

University of Gothenburg

A. J. Walley

Hammersmith Hospital

P. Froguel

Hammersmith Hospital

Centre national de la recherche scientifique (CNRS)

Lena M S Carlsson

University of Gothenburg

M. Uhlen

AlbaNova University Center

Jens B Nielsen

Chalmers, Chemical and Biological Engineering, Life Sciences

Molecular Systems Biology

17444292 (eISSN)

Vol. 9 1 649- 649


C3SE (Chalmers Centre for Computational Science and Engineering)

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Bioinformatics and Systems Biology



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