Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reacti...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Caroline Colijn, Aaron Brandes, Jeremy Zucker, Desmond S Lun, Brian Weiner, Maha R Farhat, Tan-Yun Cheng, D Branch Moody, Megan Murray, James E Galagan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2852ac27df534bb5894c94d50aae109c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2852ac27df534bb5894c94d50aae109c
record_format	dspace
spelling	oai:doaj.org-article:2852ac27df534bb5894c94d50aae109c2021-11-25T05:42:11ZInterpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.1553-734X1553-735810.1371/journal.pcbi.1000489https://doaj.org/article/2852ac27df534bb5894c94d50aae109c2009-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19714220/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.Caroline ColijnAaron BrandesJeremy ZuckerDesmond S LunBrian WeinerMaha R FarhatTan-Yun ChengD Branch MoodyMegan MurrayJames E GalaganPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 8, p e1000489 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Caroline Colijn Aaron Brandes Jeremy Zucker Desmond S Lun Brian Weiner Maha R Farhat Tan-Yun Cheng D Branch Moody Megan Murray James E Galagan Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.
description	Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.
format	article
author	Caroline Colijn Aaron Brandes Jeremy Zucker Desmond S Lun Brian Weiner Maha R Farhat Tan-Yun Cheng D Branch Moody Megan Murray James E Galagan
author_facet	Caroline Colijn Aaron Brandes Jeremy Zucker Desmond S Lun Brian Weiner Maha R Farhat Tan-Yun Cheng D Branch Moody Megan Murray James E Galagan
author_sort	Caroline Colijn
title	Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.
title_short	Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.
title_full	Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.
title_fullStr	Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.
title_full_unstemmed	Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.
title_sort	interpreting expression data with metabolic flux models: predicting mycobacterium tuberculosis mycolic acid production.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/2852ac27df534bb5894c94d50aae109c
work_keys_str_mv	AT carolinecolijn interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT aaronbrandes interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT jeremyzucker interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT desmondslun interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT brianweiner interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT maharfarhat interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT tanyuncheng interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT dbranchmoody interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT meganmurray interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction AT jamesegalagan interpretingexpressiondatawithmetabolicfluxmodelspredictingmycobacteriumtuberculosismycolicacidproduction
_version_	1718414507608375296

Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

Ejemplares similares