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Can we capture an accurate view of tissue metabolism from an expression profile?
Author(s) -
Lewis Nathan
Publication year - 2018
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2018.32.1_supplement.803.8
Subject(s) - computational biology , metabolomics , transcriptome , phenotype , proteomics , systems biology , biology , selection (genetic algorithm) , computer science , genomics , bioinformatics , genome , gene expression , gene , genetics , machine learning
Genome‐scale models of metabolism have been constructed with the aim of illuminating the molecular basis of cell phenotypes. Since some enzymes are only active in specific cell or tissue types, several algorithms use various omics data types (e.g., transcriptomics, proteomics, metabolomics, genomic variant calls) to construct cell line and tissue‐specific metabolic models from genome‐scale models. However, it is unclear how algorithm and parameter selection (e.g., gene expression thresholds, metabolic constraints) affects model content and predictive accuracy. To investigate this, we built hundreds of models of four different cancer cell lines using transcriptomics, metabolomics, various algorithms and different parameter sets. Model content varied substantially across different parameter sets, but all algorithms generally increased accuracy in identifying cancer cell vulnerabilities. However, the choice of algorithm had the largest impact on model accuracy. Finally, we further developed a framework to predict, de novo, the metabolic capabilities of a cell or tissue, based on its gene expression and metabolomic profiles. These insights will guide further development of tissue and cell type specific models, and enable us to predict phenotype from genotype. Support or Funding Information The Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517 and NNF16CC0021858) and from the NIGMS (R35 GM119850).Tissue specific metabolic models can be constructed using various omics data, but careful decisions on algorithms and parameter choice are necessary to predict a tissue's metabolic functions de novo .This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .