Single-cell omics analysis with genome-scale metabolic modeling

Yu Chen; Johan Gustafsson; Jingyu Yang; Jens Nielsen; Eduard J Kerkhoven

doi:10.1016/j.copbio.2024.103078

Single-cell omics analysis with genome-scale metabolic modeling

Curr Opin Biotechnol. 2024 Apr:86:103078. doi: 10.1016/j.copbio.2024.103078. Epub 2024 Feb 15.

Authors

Yu Chen¹, Johan Gustafsson², Jingyu Yang³, Jens Nielsen⁴, Eduard J Kerkhoven⁵

Affiliations

¹ Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. Electronic address: y.chen3@siat.ac.cn.
² Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
³ Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
⁴ Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; BioInnovation Institute, DK-2200 Copenhagen, Denmark.
⁵ Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden. Electronic address: eduardk@chalmers.se.

PMID: 38359604
DOI: 10.1016/j.copbio.2024.103078

Abstract

Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.

Publication types

Review

MeSH terms

Genome* / genetics
Metabolic Networks and Pathways
Models, Biological*