Model-based assessment of mammalian cell metabolic functionalities using omics data

Cell Reports: Methods

Austin W.T. Chiang¹  Edwin F. Juarez²  Jill P. Mesirov²  Jahir M. Gutierrez³  Laurent Heirendt⁴  Joanne K. Liu⁴  Nathan E. Lewis⁴  Benjamin P. Kellman⁴  Chintan Joshi⁵  Tyler Reagan⁶  Tyler Bath⁶  Jooyong Lee⁷  Alexander T. Wenzel⁸  Helen Masson⁸  Shangzhong Li⁹  Kimberly Robasky⁹  David Borland⁹  Zerong Li⁹  Christophe Trefois⁹  Anne Richelle⁹ 

[1] Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA;Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA;Department of Medicine, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA;Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA;Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA;Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA;Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA;Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA;Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA;

关键词: metabolic function;    omic data;    systems biology;    functional analysis;    genome-scale metabolic network;    transcriptomic;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org—CellFie). Motivation: The existence of complex interdependencies between genes, proteins, and metabolites challenge the interpretation of omics experiments. Data-driven approaches have been particularly useful for identifying gene sets of interest. However, it remains difficult to gain a mechanistic understanding of and to quantify a cell's functions from enriched ontology terms. Genome-scale systems biology models can be used to analyze these datasets, but they require specialized training and can take extensive effort to deploy. Here, we developed a framework to directly predict how changes in omics experiments correspond to cell or tissue functions, thereby facilitating phenotype-relevant interpretation of these complex datum types.

【 授权许可】

Unknown