Single-cell omics analysis with genome-scale metabolic modeling
Review article, 2024
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.
Author
Yu Chen
Shenzhen Institute of Advanced Technology
Johan Gustafsson
Chalmers, Biology and Biological Engineering
University of Gothenburg
Broad Institute
Jingyu Yang
Shenzhen Institute of Advanced Technology
Jens B Nielsen
Chalmers, Life Sciences, Systems and Synthetic Biology
BioInnovation Institute
Eduard Kerkhoven
Novo Nordisk Foundation
Chalmers, Life Sciences, Systems and Synthetic Biology
Current Opinion in Biotechnology
0958-1669 (ISSN) 1879-0429 (eISSN)
Vol. 86 103078Awakening of cryptic biosynthetic gene clusters using obese red yeast
Swedish Research Council (VR) (2019-04624), 2019-12-01 -- 2023-11-30.
Subject Categories
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1016/j.copbio.2024.103078
PubMed
38359604