A linear programming approach to weak reversibility and linear conjugacy of chemical reaction networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-01

AUTHORS

Matthew D. Johnston, David Siegel, Gábor Szederkényi

ABSTRACT

A numerically effective procedure for determining weakly reversible chemical reaction networks that are linearly conjugate to a known reaction network is proposed in this paper. The method is based on translating the structural and algebraic characteristics of weak reversibility to logical statements and solving the obtained set of linear (in)equalities in the framework of mixed integer linear programming. The unknowns in the problem are the reaction rate coefficients and the parameters of the linear conjugacy transformation. The efficacy of the approach is shown through numerical examples. More... »

PAGES

274-288

References to SciGraph publications

  • 2011-06. Finding complex balanced and detailed balanced realizations of chemical reaction networks in JOURNAL OF MATHEMATICAL CHEMISTRY
  • 1972-01. Necessary and sufficient conditions for complex balancing in chemical kinetics in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 1972-01. Complex balancing in general kinetic systems in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 2010-02. Computing sparse and dense realizations of reaction kinetic systems in JOURNAL OF MATHEMATICAL CHEMISTRY
  • 1970-01. The mathematical structure of chemical kinetics in homogeneous single-phase systems in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 2008-07. Identifiability of chemical reaction networks in JOURNAL OF MATHEMATICAL CHEMISTRY
  • 2009-04. Comment on “identifiability of chemical reaction networks” by G. Craciun and C. Pantea in JOURNAL OF MATHEMATICAL CHEMISTRY
  • 1972-01. General mass action kinetics in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 1990. Introduction to Applied Nonlinear Dynamical Systems and Chaos in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10910-011-9911-7

    DOI

    http://dx.doi.org/10.1007/s10910-011-9911-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1030049136


    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/2008/0306", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Physical Chemistry (incl. Structural)", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Chemical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "Department of Applied Mathematics, University of Waterloo, N2L 3G1, Waterloo, ON, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Johnston", 
            "givenName": "Matthew D.", 
            "id": "sg:person.01174250571.19", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01174250571.19"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "Department of Applied Mathematics, University of Waterloo, N2L 3G1, Waterloo, ON, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Siegel", 
            "givenName": "David", 
            "id": "sg:person.01242363771.96", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01242363771.96"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "MTA Institute for Computer Science and Control", 
              "id": "https://www.grid.ac/institutes/grid.4836.9", 
              "name": [
                "(Bio)Process Engineering Group, IIM-CSIC, Spanish National Research Council, C/Eduardo Cabello, 6, 36208, Vigo, Spain", 
                "Computer and Automation Research Institute, Hungarian Academy of Sciences, P.O. Box 63, 1518, Budapest, Hungary"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Szederk\u00e9nyi", 
            "givenName": "G\u00e1bor", 
            "id": "sg:person.012760224621.12", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012760224621.12"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-1-4757-4067-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000039730", 
              "https://doi.org/10.1007/978-1-4757-4067-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4757-4067-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000039730", 
              "https://doi.org/10.1007/978-1-4757-4067-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00255664", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001728211", 
              "https://doi.org/10.1007/bf00255664"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00255664", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001728211", 
              "https://doi.org/10.1007/bf00255664"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0602767103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002189124"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00251527", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009390404", 
              "https://doi.org/10.1007/bf00251527"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00251527", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009390404", 
              "https://doi.org/10.1007/bf00251527"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/14689360802243813", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015258538"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.mbs.2007.07.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015550957"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10910-007-9307-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021306492", 
              "https://doi.org/10.1007/s10910-007-9307-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0020-0190(94)90047-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022741203"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0009-2509(94)80061-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027977071"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jsc.2008.08.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031364908"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00251225", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032254027", 
              "https://doi.org/10.1007/bf00251225"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00251225", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032254027", 
              "https://doi.org/10.1007/bf00251225"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/14689367.2010.545812", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032672253"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10910-011-9804-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039596199", 
              "https://doi.org/10.1007/s10910-011-9804-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0098-1354(93)e0010-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041293982"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00255665", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041541756", 
              "https://doi.org/10.1007/bf00255665"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00255665", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041541756", 
              "https://doi.org/10.1007/bf00255665"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jsco.2001.0512", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044710308"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10910-009-9525-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045140371", 
              "https://doi.org/10.1007/s10910-009-9525-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10910-008-9499-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045720341", 
              "https://doi.org/10.1007/s10910-008-9499-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/9.935056", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061246754"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/050634177", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062846335"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/070698282", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062851414"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/090760751", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062856381"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/090764098", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062856479"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/100812355", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062859575"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/s0036139904440278", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062874995"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iscas.2010.5537543", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095186194"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2012-01", 
        "datePublishedReg": "2012-01-01", 
        "description": "A numerically effective procedure for determining weakly reversible chemical reaction networks that are linearly conjugate to a known reaction network is proposed in this paper. The method is based on translating the structural and algebraic characteristics of weak reversibility to logical statements and solving the obtained set of linear (in)equalities in the framework of mixed integer linear programming. The unknowns in the problem are the reaction rate coefficients and the parameters of the linear conjugacy transformation. The efficacy of the approach is shown through numerical examples.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10910-011-9911-7", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1026076", 
            "issn": [
              "0259-9791", 
              "1572-8897"
            ], 
            "name": "Journal of Mathematical Chemistry", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "50"
          }
        ], 
        "name": "A linear programming approach to weak reversibility and linear conjugacy of chemical reaction networks", 
        "pagination": "274-288", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "8f26cbf5482f9583c804c07fe4845a8de31137d4f62fb21c7090f6b0aa86d47c"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10910-011-9911-7"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1030049136"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10910-011-9911-7", 
          "https://app.dimensions.ai/details/publication/pub.1030049136"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T20:47", 
        "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/v90/0000000001_0000000264/records_8684_00000513.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs10910-011-9911-7"
      }
    ]
     

    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.10.1007/s10910-011-9911-7'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10910-011-9911-7'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10910-011-9911-7'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10910-011-9911-7'


     

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

    166 TRIPLES      21 PREDICATES      53 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10910-011-9911-7 schema:about anzsrc-for:03
    2 anzsrc-for:0306
    3 schema:author N50a03a68b8354d13a2642ce9396b1661
    4 schema:citation sg:pub.10.1007/978-1-4757-4067-7
    5 sg:pub.10.1007/bf00251225
    6 sg:pub.10.1007/bf00251527
    7 sg:pub.10.1007/bf00255664
    8 sg:pub.10.1007/bf00255665
    9 sg:pub.10.1007/s10910-007-9307-x
    10 sg:pub.10.1007/s10910-008-9499-8
    11 sg:pub.10.1007/s10910-009-9525-5
    12 sg:pub.10.1007/s10910-011-9804-9
    13 https://doi.org/10.1006/jsco.2001.0512
    14 https://doi.org/10.1016/0009-2509(94)80061-8
    15 https://doi.org/10.1016/0020-0190(94)90047-7
    16 https://doi.org/10.1016/0098-1354(93)e0010-7
    17 https://doi.org/10.1016/j.jsc.2008.08.006
    18 https://doi.org/10.1016/j.mbs.2007.07.003
    19 https://doi.org/10.1073/pnas.0602767103
    20 https://doi.org/10.1080/14689360802243813
    21 https://doi.org/10.1080/14689367.2010.545812
    22 https://doi.org/10.1109/9.935056
    23 https://doi.org/10.1109/iscas.2010.5537543
    24 https://doi.org/10.1137/050634177
    25 https://doi.org/10.1137/070698282
    26 https://doi.org/10.1137/090760751
    27 https://doi.org/10.1137/090764098
    28 https://doi.org/10.1137/100812355
    29 https://doi.org/10.1137/s0036139904440278
    30 schema:datePublished 2012-01
    31 schema:datePublishedReg 2012-01-01
    32 schema:description A numerically effective procedure for determining weakly reversible chemical reaction networks that are linearly conjugate to a known reaction network is proposed in this paper. The method is based on translating the structural and algebraic characteristics of weak reversibility to logical statements and solving the obtained set of linear (in)equalities in the framework of mixed integer linear programming. The unknowns in the problem are the reaction rate coefficients and the parameters of the linear conjugacy transformation. The efficacy of the approach is shown through numerical examples.
    33 schema:genre research_article
    34 schema:inLanguage en
    35 schema:isAccessibleForFree true
    36 schema:isPartOf N2f68209baf294a549ba0bb2e13dc1507
    37 Na37ad6fdab6a45a1912a8f7aa4a1c2ca
    38 sg:journal.1026076
    39 schema:name A linear programming approach to weak reversibility and linear conjugacy of chemical reaction networks
    40 schema:pagination 274-288
    41 schema:productId N2efb8f3efd1e4e57917e1030fc13d043
    42 Nbfa16cd37fae414e9aeafffb6cb40524
    43 Nedcf6fb6e94d4c0d81ebb28b8e0239bc
    44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030049136
    45 https://doi.org/10.1007/s10910-011-9911-7
    46 schema:sdDatePublished 2019-04-10T20:47
    47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    48 schema:sdPublisher Nedc01b2c1b14482b9b38a78230d887fe
    49 schema:url http://link.springer.com/10.1007%2Fs10910-011-9911-7
    50 sgo:license sg:explorer/license/
    51 sgo:sdDataset articles
    52 rdf:type schema:ScholarlyArticle
    53 N2efb8f3efd1e4e57917e1030fc13d043 schema:name doi
    54 schema:value 10.1007/s10910-011-9911-7
    55 rdf:type schema:PropertyValue
    56 N2f68209baf294a549ba0bb2e13dc1507 schema:volumeNumber 50
    57 rdf:type schema:PublicationVolume
    58 N50a03a68b8354d13a2642ce9396b1661 rdf:first sg:person.01174250571.19
    59 rdf:rest Na736ac77c13c480084a4b900b99cac15
    60 Na37ad6fdab6a45a1912a8f7aa4a1c2ca schema:issueNumber 1
    61 rdf:type schema:PublicationIssue
    62 Na736ac77c13c480084a4b900b99cac15 rdf:first sg:person.01242363771.96
    63 rdf:rest Ne877215b2ed3422e98a7184a09c1de6e
    64 Nbfa16cd37fae414e9aeafffb6cb40524 schema:name readcube_id
    65 schema:value 8f26cbf5482f9583c804c07fe4845a8de31137d4f62fb21c7090f6b0aa86d47c
    66 rdf:type schema:PropertyValue
    67 Ne877215b2ed3422e98a7184a09c1de6e rdf:first sg:person.012760224621.12
    68 rdf:rest rdf:nil
    69 Nedc01b2c1b14482b9b38a78230d887fe schema:name Springer Nature - SN SciGraph project
    70 rdf:type schema:Organization
    71 Nedcf6fb6e94d4c0d81ebb28b8e0239bc schema:name dimensions_id
    72 schema:value pub.1030049136
    73 rdf:type schema:PropertyValue
    74 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
    75 schema:name Chemical Sciences
    76 rdf:type schema:DefinedTerm
    77 anzsrc-for:0306 schema:inDefinedTermSet anzsrc-for:
    78 schema:name Physical Chemistry (incl. Structural)
    79 rdf:type schema:DefinedTerm
    80 sg:journal.1026076 schema:issn 0259-9791
    81 1572-8897
    82 schema:name Journal of Mathematical Chemistry
    83 rdf:type schema:Periodical
    84 sg:person.01174250571.19 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    85 schema:familyName Johnston
    86 schema:givenName Matthew D.
    87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01174250571.19
    88 rdf:type schema:Person
    89 sg:person.01242363771.96 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    90 schema:familyName Siegel
    91 schema:givenName David
    92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01242363771.96
    93 rdf:type schema:Person
    94 sg:person.012760224621.12 schema:affiliation https://www.grid.ac/institutes/grid.4836.9
    95 schema:familyName Szederkényi
    96 schema:givenName Gábor
    97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012760224621.12
    98 rdf:type schema:Person
    99 sg:pub.10.1007/978-1-4757-4067-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000039730
    100 https://doi.org/10.1007/978-1-4757-4067-7
    101 rdf:type schema:CreativeWork
    102 sg:pub.10.1007/bf00251225 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032254027
    103 https://doi.org/10.1007/bf00251225
    104 rdf:type schema:CreativeWork
    105 sg:pub.10.1007/bf00251527 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009390404
    106 https://doi.org/10.1007/bf00251527
    107 rdf:type schema:CreativeWork
    108 sg:pub.10.1007/bf00255664 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001728211
    109 https://doi.org/10.1007/bf00255664
    110 rdf:type schema:CreativeWork
    111 sg:pub.10.1007/bf00255665 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041541756
    112 https://doi.org/10.1007/bf00255665
    113 rdf:type schema:CreativeWork
    114 sg:pub.10.1007/s10910-007-9307-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1021306492
    115 https://doi.org/10.1007/s10910-007-9307-x
    116 rdf:type schema:CreativeWork
    117 sg:pub.10.1007/s10910-008-9499-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045720341
    118 https://doi.org/10.1007/s10910-008-9499-8
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/s10910-009-9525-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045140371
    121 https://doi.org/10.1007/s10910-009-9525-5
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/s10910-011-9804-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039596199
    124 https://doi.org/10.1007/s10910-011-9804-9
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1006/jsco.2001.0512 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044710308
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1016/0009-2509(94)80061-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027977071
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1016/0020-0190(94)90047-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022741203
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1016/0098-1354(93)e0010-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041293982
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1016/j.jsc.2008.08.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031364908
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1016/j.mbs.2007.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015550957
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1073/pnas.0602767103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002189124
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1080/14689360802243813 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015258538
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1080/14689367.2010.545812 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032672253
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1109/9.935056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061246754
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1109/iscas.2010.5537543 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095186194
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1137/050634177 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062846335
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1137/070698282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062851414
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1137/090760751 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062856381
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1137/090764098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062856479
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1137/100812355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062859575
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1137/s0036139904440278 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062874995
    159 rdf:type schema:CreativeWork
    160 https://www.grid.ac/institutes/grid.46078.3d schema:alternateName University of Waterloo
    161 schema:name Department of Applied Mathematics, University of Waterloo, N2L 3G1, Waterloo, ON, Canada
    162 rdf:type schema:Organization
    163 https://www.grid.ac/institutes/grid.4836.9 schema:alternateName MTA Institute for Computer Science and Control
    164 schema:name (Bio)Process Engineering Group, IIM-CSIC, Spanish National Research Council, C/Eduardo Cabello, 6, 36208, Vigo, Spain
    165 Computer and Automation Research Institute, Hungarian Academy of Sciences, P.O. Box 63, 1518, Budapest, Hungary
    166 rdf:type schema:Organization
     




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


    ...