NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). Howe...

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Autores principales:	Matthew C Schmidt, Andrea M Rocha, Kanchana Padmanabhan, Yekaterina Shpanskaya, Jill Banfield, Kathleen Scott, James R Mihelcic, Nagiza F Samatova
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c74b6094af2146be8ac1c4100b299b7e
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spelling	oai:doaj.org-article:c74b6094af2146be8ac1c4100b299b7e2021-11-18T05:51:21ZNIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.1553-734X1553-735810.1371/journal.pcbi.1002490https://doaj.org/article/c74b6094af2146be8ac1c4100b299b7e2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22589706/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.Matthew C SchmidtAndrea M RochaKanchana PadmanabhanYekaterina ShpanskayaJill BanfieldKathleen ScottJames R MihelcicNagiza F SamatovaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 5, p e1002490 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Matthew C Schmidt Andrea M Rocha Kanchana Padmanabhan Yekaterina Shpanskaya Jill Banfield Kathleen Scott James R Mihelcic Nagiza F Samatova NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.
description	Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.
format	article
author	Matthew C Schmidt Andrea M Rocha Kanchana Padmanabhan Yekaterina Shpanskaya Jill Banfield Kathleen Scott James R Mihelcic Nagiza F Samatova
author_facet	Matthew C Schmidt Andrea M Rocha Kanchana Padmanabhan Yekaterina Shpanskaya Jill Banfield Kathleen Scott James R Mihelcic Nagiza F Samatova
author_sort	Matthew C Schmidt
title	NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.
title_short	NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.
title_full	NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.
title_fullStr	NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.
title_full_unstemmed	NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.
title_sort	nibbs-search for fast and accurate prediction of phenotype-biased metabolic systems.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/c74b6094af2146be8ac1c4100b299b7e
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NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.

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