Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic netw...

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Autores principales:	Joshua J Hamilton, Jennifer L Reed
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aca8e373699543c89d2aec94fecb824f
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spelling	oai:doaj.org-article:aca8e373699543c89d2aec94fecb824f2021-11-18T07:21:59ZIdentification of functional differences in metabolic networks using comparative genomics and constraint-based models.1932-620310.1371/journal.pone.0034670https://doaj.org/article/aca8e373699543c89d2aec94fecb824f2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22666308/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.Joshua J HamiltonJennifer L ReedPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 4, p e34670 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Joshua J Hamilton Jennifer L Reed Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.
description	Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.
format	article
author	Joshua J Hamilton Jennifer L Reed
author_facet	Joshua J Hamilton Jennifer L Reed
author_sort	Joshua J Hamilton
title	Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.
title_short	Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.
title_full	Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.
title_fullStr	Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.
title_full_unstemmed	Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.
title_sort	identification of functional differences in metabolic networks using comparative genomics and constraint-based models.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/aca8e373699543c89d2aec94fecb824f
work_keys_str_mv	AT joshuajhamilton identificationoffunctionaldifferencesinmetabolicnetworksusingcomparativegenomicsandconstraintbasedmodels AT jenniferlreed identificationoffunctionaldifferencesinmetabolicnetworksusingcomparativegenomicsandconstraintbasedmodels
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Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.

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