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A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks

Wallqvist, Anders, López-Agudelo, Víctor A., Mendum, Tom A., Laing, Emma, Wu, Huihai, Baena, Andres, Barrera, Luis F., Beste, Dany J. V. and Rios-Estepa, Rigoberto (2020) A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks PLOS Computational Biology, 16 (6), e1007533.

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Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine
Authors :
Wallqvist, Anders
López-Agudelo, Víctor A.
Mendum, Tom
Baena, Andres
Barrera, Luis F.
Beste, Dany J.
Rios-Estepa, Rigoberto
Date : 15 June 2020
DOI : 10.1371/journal.pcbi.1007533
Copyright Disclaimer : © 2020 Lopez-Agudelo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Depositing User : Clive Harris
Date Deposited : 16 Jun 2020 16:24
Last Modified : 16 Jun 2020 16:24

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