Loading…
Efficient Reconstruction of Predictive Consensus Metabolic Network Models
Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGE...
Saved in:
Published in: | PLoS computational biology 2016-08, Vol.12 (8), p.e1005085-e1005085 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c745t-7a1ffdff3a738f55940ef705879212911836b9fd6d260f68d539ba29315a19c73 |
---|---|
cites | cdi_FETCH-LOGICAL-c745t-7a1ffdff3a738f55940ef705879212911836b9fd6d260f68d539ba29315a19c73 |
container_end_page | e1005085 |
container_issue | 8 |
container_start_page | e1005085 |
container_title | PLoS computational biology |
container_volume | 12 |
creator | van Heck, Ruben G A Ganter, Mathias Martins Dos Santos, Vitor A P Stelling, Joerg |
description | Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. |
doi_str_mv | 10.1371/journal.pcbi.1005085 |
format | article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1820282112</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A479404693</galeid><doaj_id>oai_doaj_org_article_3da064f53d804f92982dfb5521566ccb</doaj_id><sourcerecordid>A479404693</sourcerecordid><originalsourceid>FETCH-LOGICAL-c745t-7a1ffdff3a738f55940ef705879212911836b9fd6d260f68d539ba29315a19c73</originalsourceid><addsrcrecordid>eNqVk11v0zAUhiMEYmPwDxBE4gYuWvwRf-0CaaoGVNoGGnBtOf4oHqld7GSFf4_TdtOKdgGKFDv28745Psenqp5DMIWYwbdXcUhBddOVbv0UAkAAJw-qQ0gInjBM-MM784PqSc5XAJSpoI-rA8QIxQyBw2p-6pzX3oa-vrQ6htynQfc-hjq6-nOyxpeva1vPypYNecj1ue1VGzuv6wvbr2P6UZ9HY7v8tHrkVJfts914VH17f_p19nFy9unDfHZyNtGsIf2EKeiccQ4rhrkjRDTAOgYIZwJBJCDkmLbCGWoQBY5yQ7BoFRIYEgWFZvioern1XXUxy10WsoQcAcQRhKgQ8y1horqSq-SXKv2WUXm5WYhpIVXqve6sxEYB2jiCDQeNE0hwZFxLCIKEUq3b4nW89VqrhQ0-lJcMKmmfN4adb9Novh6SDN04rIY2y1ILSMdQ3-1CHdqlNbpkOaluL6L9neC_y0W8LnoAGaTF4PXOIMWfg829XPqsbdepYOOwOTTjAhGM_wGFBJeiE17QV3-h92dxRy1USZQPLpYQ9WgqTxpWytZQMf52eg9VHmOXvtwn63xZ3xO82RMUpre_-oUacpbzL5f_wV7ss82W1SnmnKy7TTMEcmyYm0PKsWHkrmGK7MXdEt2KbjoE_wGy2BDI</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1820282112</pqid></control><display><type>article</type><title>Efficient Reconstruction of Predictive Consensus Metabolic Network Models</title><source>Open Access: PubMed Central</source><source>ProQuest Publicly Available Content database</source><creator>van Heck, Ruben G A ; Ganter, Mathias ; Martins Dos Santos, Vitor A P ; Stelling, Joerg</creator><contributor>Reed, Jennifer L.</contributor><creatorcontrib>van Heck, Ruben G A ; Ganter, Mathias ; Martins Dos Santos, Vitor A P ; Stelling, Joerg ; Reed, Jennifer L.</creatorcontrib><description>Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1005085</identifier><identifier>PMID: 27563720</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Bacteria - metabolism ; Biology and Life Sciences ; Computational Biology - methods ; Computer and Information Sciences ; Databases, Genetic ; Enzymes ; Gene expression ; Genetic aspects ; Genome-wide association studies ; Genomes ; Knowledge ; Linear programming ; Medicine and Health Sciences ; Metabolic Networks and Pathways - physiology ; Metabolism ; Metabolites ; Models, Biological ; Observations ; Organisms ; Pathogens ; Physical Sciences ; Software ; Systeem en Synthetische Biologie ; Systems and Synthetic Biology ; VLAG ; Yeasts - metabolism</subject><ispartof>PLoS computational biology, 2016-08, Vol.12 (8), p.e1005085-e1005085</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Public Library of Science. 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: van Heck RGA, Ganter M, Martins dos Santos VAP, Stelling J (2016) Efficient Reconstruction of Predictive Consensus Metabolic Network Models. PLoS Comput Biol 12(8): e1005085. doi:10.1371/journal.pcbi.1005085</rights><rights>2016 van Heck et al 2016 van Heck et al</rights><rights>Wageningen University & Research</rights><rights>2016 Public Library of Science. 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: van Heck RGA, Ganter M, Martins dos Santos VAP, Stelling J (2016) Efficient Reconstruction of Predictive Consensus Metabolic Network Models. PLoS Comput Biol 12(8): e1005085. doi:10.1371/journal.pcbi.1005085</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c745t-7a1ffdff3a738f55940ef705879212911836b9fd6d260f68d539ba29315a19c73</citedby><cites>FETCH-LOGICAL-c745t-7a1ffdff3a738f55940ef705879212911836b9fd6d260f68d539ba29315a19c73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1820282112/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1820282112?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27563720$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Reed, Jennifer L.</contributor><creatorcontrib>van Heck, Ruben G A</creatorcontrib><creatorcontrib>Ganter, Mathias</creatorcontrib><creatorcontrib>Martins Dos Santos, Vitor A P</creatorcontrib><creatorcontrib>Stelling, Joerg</creatorcontrib><title>Efficient Reconstruction of Predictive Consensus Metabolic Network Models</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.</description><subject>Algorithms</subject><subject>Bacteria - metabolism</subject><subject>Biology and Life Sciences</subject><subject>Computational Biology - methods</subject><subject>Computer and Information Sciences</subject><subject>Databases, Genetic</subject><subject>Enzymes</subject><subject>Gene expression</subject><subject>Genetic aspects</subject><subject>Genome-wide association studies</subject><subject>Genomes</subject><subject>Knowledge</subject><subject>Linear programming</subject><subject>Medicine and Health Sciences</subject><subject>Metabolic Networks and Pathways - physiology</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Models, Biological</subject><subject>Observations</subject><subject>Organisms</subject><subject>Pathogens</subject><subject>Physical Sciences</subject><subject>Software</subject><subject>Systeem en Synthetische Biologie</subject><subject>Systems and Synthetic Biology</subject><subject>VLAG</subject><subject>Yeasts - metabolism</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqVk11v0zAUhiMEYmPwDxBE4gYuWvwRf-0CaaoGVNoGGnBtOf4oHqld7GSFf4_TdtOKdgGKFDv28745Psenqp5DMIWYwbdXcUhBddOVbv0UAkAAJw-qQ0gInjBM-MM784PqSc5XAJSpoI-rA8QIxQyBw2p-6pzX3oa-vrQ6htynQfc-hjq6-nOyxpeva1vPypYNecj1ue1VGzuv6wvbr2P6UZ9HY7v8tHrkVJfts914VH17f_p19nFy9unDfHZyNtGsIf2EKeiccQ4rhrkjRDTAOgYIZwJBJCDkmLbCGWoQBY5yQ7BoFRIYEgWFZvioern1XXUxy10WsoQcAcQRhKgQ8y1horqSq-SXKv2WUXm5WYhpIVXqve6sxEYB2jiCDQeNE0hwZFxLCIKEUq3b4nW89VqrhQ0-lJcMKmmfN4adb9Novh6SDN04rIY2y1ILSMdQ3-1CHdqlNbpkOaluL6L9neC_y0W8LnoAGaTF4PXOIMWfg829XPqsbdepYOOwOTTjAhGM_wGFBJeiE17QV3-h92dxRy1USZQPLpYQ9WgqTxpWytZQMf52eg9VHmOXvtwn63xZ3xO82RMUpre_-oUacpbzL5f_wV7ss82W1SnmnKy7TTMEcmyYm0PKsWHkrmGK7MXdEt2KbjoE_wGy2BDI</recordid><startdate>20160801</startdate><enddate>20160801</enddate><creator>van Heck, Ruben G A</creator><creator>Ganter, Mathias</creator><creator>Martins Dos Santos, Vitor A P</creator><creator>Stelling, Joerg</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>QVL</scope><scope>DOA</scope></search><sort><creationdate>20160801</creationdate><title>Efficient Reconstruction of Predictive Consensus Metabolic Network Models</title><author>van Heck, Ruben G A ; Ganter, Mathias ; Martins Dos Santos, Vitor A P ; Stelling, Joerg</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c745t-7a1ffdff3a738f55940ef705879212911836b9fd6d260f68d539ba29315a19c73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Bacteria - metabolism</topic><topic>Biology and Life Sciences</topic><topic>Computational Biology - methods</topic><topic>Computer and Information Sciences</topic><topic>Databases, Genetic</topic><topic>Enzymes</topic><topic>Gene expression</topic><topic>Genetic aspects</topic><topic>Genome-wide association studies</topic><topic>Genomes</topic><topic>Knowledge</topic><topic>Linear programming</topic><topic>Medicine and Health Sciences</topic><topic>Metabolic Networks and Pathways - physiology</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Models, Biological</topic><topic>Observations</topic><topic>Organisms</topic><topic>Pathogens</topic><topic>Physical Sciences</topic><topic>Software</topic><topic>Systeem en Synthetische Biologie</topic><topic>Systems and Synthetic Biology</topic><topic>VLAG</topic><topic>Yeasts - metabolism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>van Heck, Ruben G A</creatorcontrib><creatorcontrib>Ganter, Mathias</creatorcontrib><creatorcontrib>Martins Dos Santos, Vitor A P</creatorcontrib><creatorcontrib>Stelling, Joerg</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer science database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest Biological Science Journals</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest Publicly Available Content database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>NARCIS:Publications</collection><collection>Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>van Heck, Ruben G A</au><au>Ganter, Mathias</au><au>Martins Dos Santos, Vitor A P</au><au>Stelling, Joerg</au><au>Reed, Jennifer L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient Reconstruction of Predictive Consensus Metabolic Network Models</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2016-08-01</date><risdate>2016</risdate><volume>12</volume><issue>8</issue><spage>e1005085</spage><epage>e1005085</epage><pages>e1005085-e1005085</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27563720</pmid><doi>10.1371/journal.pcbi.1005085</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1553-7358 |
ispartof | PLoS computational biology, 2016-08, Vol.12 (8), p.e1005085-e1005085 |
issn | 1553-7358 1553-734X 1553-7358 |
language | eng |
recordid | cdi_plos_journals_1820282112 |
source | Open Access: PubMed Central; ProQuest Publicly Available Content database |
subjects | Algorithms Bacteria - metabolism Biology and Life Sciences Computational Biology - methods Computer and Information Sciences Databases, Genetic Enzymes Gene expression Genetic aspects Genome-wide association studies Genomes Knowledge Linear programming Medicine and Health Sciences Metabolic Networks and Pathways - physiology Metabolism Metabolites Models, Biological Observations Organisms Pathogens Physical Sciences Software Systeem en Synthetische Biologie Systems and Synthetic Biology VLAG Yeasts - metabolism |
title | Efficient Reconstruction of Predictive Consensus Metabolic Network Models |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T04%3A37%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Efficient%20Reconstruction%20of%20Predictive%20Consensus%20Metabolic%20Network%20Models&rft.jtitle=PLoS%20computational%20biology&rft.au=van%20Heck,%20Ruben%20G%20A&rft.date=2016-08-01&rft.volume=12&rft.issue=8&rft.spage=e1005085&rft.epage=e1005085&rft.pages=e1005085-e1005085&rft.issn=1553-7358&rft.eissn=1553-7358&rft_id=info:doi/10.1371/journal.pcbi.1005085&rft_dat=%3Cgale_plos_%3EA479404693%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c745t-7a1ffdff3a738f55940ef705879212911836b9fd6d260f68d539ba29315a19c73%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1820282112&rft_id=info:pmid/27563720&rft_galeid=A479404693&rfr_iscdi=true |