In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2001-02

AUTHORS

Jeremy S. Edwards, Rafael U. Ibarra, Bernhard O. Palsson

ABSTRACT

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells. More... »

PAGES

125

Journal

TITLE

Nature Biotechnology

ISSUE

2

VOLUME

19

Related Patents

  • Microorganisms And Methods For The Biosynthesis Of Adipate, Hexamethylenediamine And 6-Aminocaproic Acid
  • Microorganisms For The Production Of Adipic Acid And Other Compounds
  • Methods And Organisms For Utilizing Synthesis Gas Or Other Gaseous Carbon Sources And Methanol
  • Microorganisms And Methods For The Biosynthesis Of Butadiene
  • Methods For Identifying Drug Targets Based On Genomic Sequence Data
  • Process Of Separating Components Of A Fermentation Broth
  • Methods For Synthesis Of Olefins And Derivatives
  • Semi-Synthetic Terephthalic Acid Via Microorganisms That Produce Muconic Acid
  • Microorganisms For The Production Of 1,4-Butanediol
  • Microorganisms And Methods For The Biosynthesis Of Propylene
  • Methods For The Synthesis Of Olefins And Derivatives
  • Compositions And Methods For The Biosynthesis Of 1,4-Butanediol And Its Precursors
  • Microorganisms And Methods For Carbon-Efficient Biosynthesis Of Mek And 2-Butanol
  • Microorganisms For The Production Of 1,4-Butanediol And Related Methods
  • Multicellular Metabolic Models And Methods
  • Methods And Systems To Identify Operational Reaction Pathways
  • Microorganisms And Methods For The Coproduction 1,4-Butanediol And Gamma-Butyrolactone
  • Methods And Organisms For Utilizing Synthesis Gas Or Other Gaseous Carbon Sources And Methanol
  • Microorganisms And Methods For The Coproduction 1,4-Butanediol And Gamma-Butyrolactone
  • Microorganisms And Methods For The Biosynthesis Of Fumarate, Malate, And Acrylate
  • Compositions And Methods For The Biosynthesis Of 1,4-Butanediol And Its Precursors
  • Microorganisms For The Production Of 2-Hydroxyisobutyric Acid
  • Microorganisms For The Production Of Methacrylic Acid
  • Microorganisms And Methods For The Biosynthesis Of Aromatics, 2,4-Pentadienoate And 1,3-Butadiene
  • Microorganisms For Producing Butadiene And Methods Related Thereto
  • Organisms For The Production Of 1,3-Butanediol
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/84379

    DOI

    http://dx.doi.org/10.1038/84379

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1015458337

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/11175725


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Acetates", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Biological Transport", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computer Simulation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Escherichia coli", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genome, Bacterial", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Kinetics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Models, Biological", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Oxygen Consumption", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Succinates", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of California, San Diego", 
              "id": "https://www.grid.ac/institutes/grid.266100.3", 
              "name": [
                "Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Edwards", 
            "givenName": "Jeremy S.", 
            "id": "sg:person.013621313027.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013621313027.86"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of California, San Diego", 
              "id": "https://www.grid.ac/institutes/grid.266100.3", 
              "name": [
                "Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ibarra", 
            "givenName": "Rafael U.", 
            "id": "sg:person.01066771374.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066771374.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of California, San Diego", 
              "id": "https://www.grid.ac/institutes/grid.266100.3", 
              "name": [
                "Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Palsson", 
            "givenName": "Bernhard O.", 
            "id": "sg:person.011260472057.92", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011260472057.92"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1093/nar/27.1.29", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001521131"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/43199", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003059937", 
              "https://doi.org/10.1038/43199"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/43199", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003059937", 
              "https://doi.org/10.1038/43199"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0958-1669(93)90127-i", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003627669"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.97.10.5528", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005244921"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/28.1.123", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007406550"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/(sici)1097-0290(19971120)56:4<398::aid-bit6>3.0.co;2-j", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008226959"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/bit.260280715", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008765230"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/bit.260280715", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008765230"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/elps.1150180807", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009841727"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/elps.1150180807", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009841727"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0167-7799(99)01316-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010759109"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/(sici)1097-0290(19990220)62:4<375::aid-bit1>3.0.co;2-o", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014243964"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1042/bj2380781", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015947652"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1042/bj2380781", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015947652"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1074/jbc.274.25.17410", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016660230"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0167-7799(97)01067-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019774568"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/bp990048k", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020291595"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt1094-994", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021388132", 
              "https://doi.org/10.1038/nbt1094-994"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0168-9525(98)01659-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022962026"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jtbi.1993.1057", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024270097"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/bp9900357", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024459140"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1042/bj3420567", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024982241"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1042/bj3420567", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024982241"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/bp9701269", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025187246"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/bit.260260303", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026431730"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jtbi.1993.1203", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027302274"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/13690", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029022565", 
              "https://doi.org/10.1038/13690"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/13690", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029022565", 
              "https://doi.org/10.1038/13690"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0169-2607(92)90102-d", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029836415"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0169-2607(92)90102-d", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029836415"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/17.3.286", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031768383"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0968-0004(00)01754-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031798241"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/elps.1150180805", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033935772"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/elps.1150180805", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033935772"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/15.9.749", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036653677"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/27.19.3821", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038144095"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jtbi.1999.0956", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038370844"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/15.1.72", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039270933"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/(sici)1097-0290(19980720)59:2<227::aid-bit10>3.0.co;2-b", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041927985"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jtbi.2000.1088", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044018760"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/28.1.56", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046020515"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/26.1.43", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048540931"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1296-441", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051306917", 
              "https://doi.org/10.1038/ng1296-441"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1042/0264-6021:3420567", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056714202"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1042/0264-6021:3420567", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056714202"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.278.5338.680", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062558446"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1074542320", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1079669191", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1082777895", 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2001-02", 
        "datePublishedReg": "2001-02-01", 
        "description": "A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/84379", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.3465017", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2515629", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3468893", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1115214", 
            "issn": [
              "1087-0156", 
              "1546-1696"
            ], 
            "name": "Nature Biotechnology", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "19"
          }
        ], 
        "name": "In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data", 
        "pagination": "125", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "49a7014bd4abf328d480dee83e7851dab9eac3676e35135dda97c609a4c7c5bf"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "11175725"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "9604648"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/84379"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1015458337"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/84379", 
          "https://app.dimensions.ai/details/publication/pub.1015458337"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T12:24", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000362_0000000362/records_87100_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://www.nature.com/articles/nbt0201_125"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/84379'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/84379'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/84379'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/84379'


     

    This table displays all metadata directly associated to this object as RDF triples.

    249 TRIPLES      21 PREDICATES      79 URIs      30 LITERALS      18 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/84379 schema:about N02b293470b75468d9f6014e54bad821c
    2 N03e5f2c65126488687e5d6212dea9c95
    3 N06ba5c896c464f95b119db924623b931
    4 N21cc2091eae4431a83c01b4fbc3e5709
    5 N5666dcd3059a4d7d876327760549c4c4
    6 N5e56fb9c63684ed48e736a379213e17a
    7 N5f4969ae01f5498e9f94a3637bad0321
    8 Nbadbdaad9c094650ac7a09828267c626
    9 Nbbf6a00bcc9e4d55b80aa9ad00382434
    10 anzsrc-for:06
    11 anzsrc-for:0604
    12 schema:author Na668d459ce844fa4bf037d81ec531fc3
    13 schema:citation sg:pub.10.1038/13690
    14 sg:pub.10.1038/43199
    15 sg:pub.10.1038/nbt1094-994
    16 sg:pub.10.1038/ng1296-441
    17 https://app.dimensions.ai/details/publication/pub.1074542320
    18 https://app.dimensions.ai/details/publication/pub.1079669191
    19 https://app.dimensions.ai/details/publication/pub.1082777895
    20 https://doi.org/10.1002/(sici)1097-0290(19971120)56:4<398::aid-bit6>3.0.co;2-j
    21 https://doi.org/10.1002/(sici)1097-0290(19980720)59:2<227::aid-bit10>3.0.co;2-b
    22 https://doi.org/10.1002/(sici)1097-0290(19990220)62:4<375::aid-bit1>3.0.co;2-o
    23 https://doi.org/10.1002/bit.260260303
    24 https://doi.org/10.1002/bit.260280715
    25 https://doi.org/10.1002/elps.1150180805
    26 https://doi.org/10.1002/elps.1150180807
    27 https://doi.org/10.1006/jtbi.1993.1057
    28 https://doi.org/10.1006/jtbi.1993.1203
    29 https://doi.org/10.1006/jtbi.1999.0956
    30 https://doi.org/10.1006/jtbi.2000.1088
    31 https://doi.org/10.1016/0169-2607(92)90102-d
    32 https://doi.org/10.1016/0958-1669(93)90127-i
    33 https://doi.org/10.1016/s0167-7799(97)01067-6
    34 https://doi.org/10.1016/s0167-7799(99)01316-5
    35 https://doi.org/10.1016/s0168-9525(98)01659-x
    36 https://doi.org/10.1016/s0968-0004(00)01754-0
    37 https://doi.org/10.1021/bp9701269
    38 https://doi.org/10.1021/bp9900357
    39 https://doi.org/10.1021/bp990048k
    40 https://doi.org/10.1042/0264-6021:3420567
    41 https://doi.org/10.1042/bj2380781
    42 https://doi.org/10.1042/bj3420567
    43 https://doi.org/10.1073/pnas.97.10.5528
    44 https://doi.org/10.1074/jbc.274.25.17410
    45 https://doi.org/10.1093/bioinformatics/15.1.72
    46 https://doi.org/10.1093/bioinformatics/15.9.749
    47 https://doi.org/10.1093/bioinformatics/17.3.286
    48 https://doi.org/10.1093/nar/26.1.43
    49 https://doi.org/10.1093/nar/27.1.29
    50 https://doi.org/10.1093/nar/27.19.3821
    51 https://doi.org/10.1093/nar/28.1.123
    52 https://doi.org/10.1093/nar/28.1.56
    53 https://doi.org/10.1126/science.278.5338.680
    54 schema:datePublished 2001-02
    55 schema:datePublishedReg 2001-02-01
    56 schema:description A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.
    57 schema:genre research_article
    58 schema:inLanguage en
    59 schema:isAccessibleForFree false
    60 schema:isPartOf N5a5914a006fe4c5f84e87eb790d17884
    61 N7dcce2a5ca464e1caf3925ee4d81001a
    62 sg:journal.1115214
    63 schema:name In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data
    64 schema:pagination 125
    65 schema:productId N06b032d7ea7d46d49e86ab4c9b5419e0
    66 N4c8dfe53999047a3a44e10c4c8e2013a
    67 N74295e7cc6024329b8d4d13245e47081
    68 N7b4dc408e3a54d44bc039c05fc9c83b8
    69 Ndb490c08d4644fcfb61cd53b3fdfd88c
    70 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015458337
    71 https://doi.org/10.1038/84379
    72 schema:sdDatePublished 2019-04-11T12:24
    73 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    74 schema:sdPublisher N37625c7c8c2a425dbf199b6f0c0ba066
    75 schema:url https://www.nature.com/articles/nbt0201_125
    76 sgo:license sg:explorer/license/
    77 sgo:sdDataset articles
    78 rdf:type schema:ScholarlyArticle
    79 N02b293470b75468d9f6014e54bad821c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    80 schema:name Genome, Bacterial
    81 rdf:type schema:DefinedTerm
    82 N03e5f2c65126488687e5d6212dea9c95 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    83 schema:name Kinetics
    84 rdf:type schema:DefinedTerm
    85 N06b032d7ea7d46d49e86ab4c9b5419e0 schema:name doi
    86 schema:value 10.1038/84379
    87 rdf:type schema:PropertyValue
    88 N06ba5c896c464f95b119db924623b931 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    89 schema:name Acetates
    90 rdf:type schema:DefinedTerm
    91 N21cc2091eae4431a83c01b4fbc3e5709 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    92 schema:name Oxygen Consumption
    93 rdf:type schema:DefinedTerm
    94 N37625c7c8c2a425dbf199b6f0c0ba066 schema:name Springer Nature - SN SciGraph project
    95 rdf:type schema:Organization
    96 N4c8dfe53999047a3a44e10c4c8e2013a schema:name readcube_id
    97 schema:value 49a7014bd4abf328d480dee83e7851dab9eac3676e35135dda97c609a4c7c5bf
    98 rdf:type schema:PropertyValue
    99 N5666dcd3059a4d7d876327760549c4c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    100 schema:name Computer Simulation
    101 rdf:type schema:DefinedTerm
    102 N5a5914a006fe4c5f84e87eb790d17884 schema:issueNumber 2
    103 rdf:type schema:PublicationIssue
    104 N5e56fb9c63684ed48e736a379213e17a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    105 schema:name Succinates
    106 rdf:type schema:DefinedTerm
    107 N5f4969ae01f5498e9f94a3637bad0321 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    108 schema:name Models, Biological
    109 rdf:type schema:DefinedTerm
    110 N64b56c9340b040949f8b301efb090211 rdf:first sg:person.01066771374.55
    111 rdf:rest N9a243d07dab34b039054abaab2b15512
    112 N74295e7cc6024329b8d4d13245e47081 schema:name dimensions_id
    113 schema:value pub.1015458337
    114 rdf:type schema:PropertyValue
    115 N7b4dc408e3a54d44bc039c05fc9c83b8 schema:name pubmed_id
    116 schema:value 11175725
    117 rdf:type schema:PropertyValue
    118 N7dcce2a5ca464e1caf3925ee4d81001a schema:volumeNumber 19
    119 rdf:type schema:PublicationVolume
    120 N9a243d07dab34b039054abaab2b15512 rdf:first sg:person.011260472057.92
    121 rdf:rest rdf:nil
    122 Na668d459ce844fa4bf037d81ec531fc3 rdf:first sg:person.013621313027.86
    123 rdf:rest N64b56c9340b040949f8b301efb090211
    124 Nbadbdaad9c094650ac7a09828267c626 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    125 schema:name Biological Transport
    126 rdf:type schema:DefinedTerm
    127 Nbbf6a00bcc9e4d55b80aa9ad00382434 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    128 schema:name Escherichia coli
    129 rdf:type schema:DefinedTerm
    130 Ndb490c08d4644fcfb61cd53b3fdfd88c schema:name nlm_unique_id
    131 schema:value 9604648
    132 rdf:type schema:PropertyValue
    133 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    134 schema:name Biological Sciences
    135 rdf:type schema:DefinedTerm
    136 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    137 schema:name Genetics
    138 rdf:type schema:DefinedTerm
    139 sg:grant.2515629 http://pending.schema.org/fundedItem sg:pub.10.1038/84379
    140 rdf:type schema:MonetaryGrant
    141 sg:grant.3465017 http://pending.schema.org/fundedItem sg:pub.10.1038/84379
    142 rdf:type schema:MonetaryGrant
    143 sg:grant.3468893 http://pending.schema.org/fundedItem sg:pub.10.1038/84379
    144 rdf:type schema:MonetaryGrant
    145 sg:journal.1115214 schema:issn 1087-0156
    146 1546-1696
    147 schema:name Nature Biotechnology
    148 rdf:type schema:Periodical
    149 sg:person.01066771374.55 schema:affiliation https://www.grid.ac/institutes/grid.266100.3
    150 schema:familyName Ibarra
    151 schema:givenName Rafael U.
    152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066771374.55
    153 rdf:type schema:Person
    154 sg:person.011260472057.92 schema:affiliation https://www.grid.ac/institutes/grid.266100.3
    155 schema:familyName Palsson
    156 schema:givenName Bernhard O.
    157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011260472057.92
    158 rdf:type schema:Person
    159 sg:person.013621313027.86 schema:affiliation https://www.grid.ac/institutes/grid.266100.3
    160 schema:familyName Edwards
    161 schema:givenName Jeremy S.
    162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013621313027.86
    163 rdf:type schema:Person
    164 sg:pub.10.1038/13690 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029022565
    165 https://doi.org/10.1038/13690
    166 rdf:type schema:CreativeWork
    167 sg:pub.10.1038/43199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003059937
    168 https://doi.org/10.1038/43199
    169 rdf:type schema:CreativeWork
    170 sg:pub.10.1038/nbt1094-994 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021388132
    171 https://doi.org/10.1038/nbt1094-994
    172 rdf:type schema:CreativeWork
    173 sg:pub.10.1038/ng1296-441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051306917
    174 https://doi.org/10.1038/ng1296-441
    175 rdf:type schema:CreativeWork
    176 https://app.dimensions.ai/details/publication/pub.1074542320 schema:CreativeWork
    177 https://app.dimensions.ai/details/publication/pub.1079669191 schema:CreativeWork
    178 https://app.dimensions.ai/details/publication/pub.1082777895 schema:CreativeWork
    179 https://doi.org/10.1002/(sici)1097-0290(19971120)56:4<398::aid-bit6>3.0.co;2-j schema:sameAs https://app.dimensions.ai/details/publication/pub.1008226959
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1002/(sici)1097-0290(19980720)59:2<227::aid-bit10>3.0.co;2-b schema:sameAs https://app.dimensions.ai/details/publication/pub.1041927985
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.1002/(sici)1097-0290(19990220)62:4<375::aid-bit1>3.0.co;2-o schema:sameAs https://app.dimensions.ai/details/publication/pub.1014243964
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.1002/bit.260260303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026431730
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.1002/bit.260280715 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008765230
    188 rdf:type schema:CreativeWork
    189 https://doi.org/10.1002/elps.1150180805 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033935772
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1002/elps.1150180807 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009841727
    192 rdf:type schema:CreativeWork
    193 https://doi.org/10.1006/jtbi.1993.1057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024270097
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1006/jtbi.1993.1203 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027302274
    196 rdf:type schema:CreativeWork
    197 https://doi.org/10.1006/jtbi.1999.0956 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038370844
    198 rdf:type schema:CreativeWork
    199 https://doi.org/10.1006/jtbi.2000.1088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044018760
    200 rdf:type schema:CreativeWork
    201 https://doi.org/10.1016/0169-2607(92)90102-d schema:sameAs https://app.dimensions.ai/details/publication/pub.1029836415
    202 rdf:type schema:CreativeWork
    203 https://doi.org/10.1016/0958-1669(93)90127-i schema:sameAs https://app.dimensions.ai/details/publication/pub.1003627669
    204 rdf:type schema:CreativeWork
    205 https://doi.org/10.1016/s0167-7799(97)01067-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019774568
    206 rdf:type schema:CreativeWork
    207 https://doi.org/10.1016/s0167-7799(99)01316-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010759109
    208 rdf:type schema:CreativeWork
    209 https://doi.org/10.1016/s0168-9525(98)01659-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1022962026
    210 rdf:type schema:CreativeWork
    211 https://doi.org/10.1016/s0968-0004(00)01754-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031798241
    212 rdf:type schema:CreativeWork
    213 https://doi.org/10.1021/bp9701269 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025187246
    214 rdf:type schema:CreativeWork
    215 https://doi.org/10.1021/bp9900357 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024459140
    216 rdf:type schema:CreativeWork
    217 https://doi.org/10.1021/bp990048k schema:sameAs https://app.dimensions.ai/details/publication/pub.1020291595
    218 rdf:type schema:CreativeWork
    219 https://doi.org/10.1042/0264-6021:3420567 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056714202
    220 rdf:type schema:CreativeWork
    221 https://doi.org/10.1042/bj2380781 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015947652
    222 rdf:type schema:CreativeWork
    223 https://doi.org/10.1042/bj3420567 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024982241
    224 rdf:type schema:CreativeWork
    225 https://doi.org/10.1073/pnas.97.10.5528 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005244921
    226 rdf:type schema:CreativeWork
    227 https://doi.org/10.1074/jbc.274.25.17410 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016660230
    228 rdf:type schema:CreativeWork
    229 https://doi.org/10.1093/bioinformatics/15.1.72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039270933
    230 rdf:type schema:CreativeWork
    231 https://doi.org/10.1093/bioinformatics/15.9.749 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036653677
    232 rdf:type schema:CreativeWork
    233 https://doi.org/10.1093/bioinformatics/17.3.286 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031768383
    234 rdf:type schema:CreativeWork
    235 https://doi.org/10.1093/nar/26.1.43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048540931
    236 rdf:type schema:CreativeWork
    237 https://doi.org/10.1093/nar/27.1.29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001521131
    238 rdf:type schema:CreativeWork
    239 https://doi.org/10.1093/nar/27.19.3821 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038144095
    240 rdf:type schema:CreativeWork
    241 https://doi.org/10.1093/nar/28.1.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007406550
    242 rdf:type schema:CreativeWork
    243 https://doi.org/10.1093/nar/28.1.56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046020515
    244 rdf:type schema:CreativeWork
    245 https://doi.org/10.1126/science.278.5338.680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062558446
    246 rdf:type schema:CreativeWork
    247 https://www.grid.ac/institutes/grid.266100.3 schema:alternateName University of California, San Diego
    248 schema:name Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412.
    249 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...