Single-cell omics analysis with genome-scale metabolic modeling
Reviewartikel, 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.
Författare
Yu Chen
Shenzhen Institute of Advanced Technology
Johan Gustafsson
Chalmers, Biologi och bioteknik
Göteborgs universitet
Broad Institute
Jingyu Yang
Shenzhen Institute of Advanced Technology
Jens B Nielsen
Chalmers, Life sciences, Systembiologi
BioInnovation Institute
Eduard Kerkhoven
Novo Nordisk Fonden
Chalmers, Life sciences, Systembiologi
Current Opinion in Biotechnology
0958-1669 (ISSN) 1879-0429 (eISSN)
Vol. 86 103078Uppvaknande av kryptiska biosyntetiska genkluster med fet röd jäst
Vetenskapsrådet (VR) (2019-04624), 2019-12-01 -- 2023-11-30.
Ämneskategorier
Bioinformatik (beräkningsbiologi)
Bioinformatik och systembiologi
DOI
10.1016/j.copbio.2024.103078
PubMed
38359604