Imperial College London
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Professor Cleo Kontoravdi

Faculty of Engineering, Department of Chemical Engineering

Professor of Biological Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6655cleo.kontoravdi98 Website

 
 
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Location

 

310ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gopalakrishnan:2022:10.1016/j.ymben.2022.12.003,
author = {Gopalakrishnan, S and Joshi, CJ and Valderrama-Gomez, MA and Icten, E and Rolandi, P and Johnson, W and Kontoravdi, C and Lewis, NE},
doi = {10.1016/j.ymben.2022.12.003},
journal = {Metabolic Engineering},
pages = {181--191},
title = {Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data},
url = {http://dx.doi.org/10.1016/j.ymben.2022.12.003},
volume = {75},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
AU  - Gopalakrishnan,S
AU  - Joshi,CJ
AU  - Valderrama-Gomez,MA
AU  - Icten,E
AU  - Rolandi,P
AU  - Johnson,W
AU  - Kontoravdi,C
AU  - Lewis,NE
DO  - 10.1016/j.ymben.2022.12.003
EP  - 191
PY  - 2022///
SN  - 1096-7176
SP  - 181
TI  - Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data
T2  - Metabolic Engineering
UR  - http://dx.doi.org/10.1016/j.ymben.2022.12.003
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000916944200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://www.sciencedirect.com/science/article/pii/S1096717622001525?via%3Dihub
VL  - 75
ER  -
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