Molecular Assembly and Computation: From Theory to Experimental Demonstrations View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2002

AUTHORS

John H. Reif

ABSTRACT

While the topic of Molecular Computation would have appeared even a half dozen years ago to be purely conjectural, it now is an emerging subfield of computer science with the development of its theoretical basis and a number of moderate to large-scale experimental demonstrations. This paper focuses on a subarea of Molecular Computation known as DNA self- assembly. Self-assembly is the spontaneous self-ordering of substructures into superstructures driven by the selective affinity of the substructures. DNA provides a molecular scale material for effecting this programmable self-assembly, using the selective affinity of pairs of DNA strands to form DNA nanostructures. DNA self-assembly is the most advanced and versatile system known for programmable construction of patterned systems on the molecular scale. The methodology of DNA self-assembly begins with the synthesis of single-strand DNA molecules that self-assemble into macromolecular building blocks called DNA tiles. These tiles have sticky ends that match the sticky ends of other DNA tiles, facilitating further assembly into large structures known as DNA tiling lattices. In principal you can make the DNA tiling assemblies form any computable two- or three-dimensional pattern, however complex, with the appropriate choice of the tile’s component DNA. This paper overviews the evolution of DNA self-assembly techniques from pure theory to experimental practice. We describe how some theoretical developments have made a major impact on the design of self-assembly experiments, as well as a number of theoretical challenges remaining in the area of DNA self-assembly. We descuss algorithms and software for the design, simulation and optimization of DNA tiling assemblies. We also describe the first experimental demonstrations of DNA self-assemblies that execute molecular computations and the assembly of patterned objects at the molecular scale. Recent experimental results indicate that this technique is scalable. Molecular imaging devices such as atomic force microscopes and transmission electron microscopes allow visualization of self-assembled two-dimensional DNA tiling lattices composed of hundreds of thousands of tiles. These assemblies can be used as scaffolding on which to position molecular electronics and robotics components with precision and specificity. The programmability lets this scaffolding have the patterning required for fabricating complex devices made of these components. More... »

PAGES

1-21

Book

TITLE

Automata, Languages and Programming

ISBN

978-3-540-43864-9
978-3-540-45465-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-45465-9_1

DOI

http://dx.doi.org/10.1007/3-540-45465-9_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039883174


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": "Duke University", 
          "id": "https://www.grid.ac/institutes/grid.26009.3d", 
          "name": [
            "Department of Computer Science, Duke University, Box 90129, Durham, NC, 27708-0129"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Reif", 
        "givenName": "John H.", 
        "id": "sg:person.01117132720.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01117132720.17"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/elps.1150100512", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000149651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/elps.1150100512", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000149651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-5193(82)90002-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002849846"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01418780", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009316877", 
          "https://doi.org/10.1007/bf01418780"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01418780", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009316877", 
          "https://doi.org/10.1007/bf01418780"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.278.5336.252", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009663792"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja993393e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010206052"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja993393e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010206052"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/28998", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015431161", 
          "https://doi.org/10.1038/28998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/28998", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015431161", 
          "https://doi.org/10.1038/28998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.matsci.28.1.153", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019630058"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja960162o", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022248208"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja960162o", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022248208"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-7799(99)01360-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026476591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0303-2647(99)00041-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027748984"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja9817601", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033828999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1521-3757(19981204)110:23<3408::aid-ange3408>3.0.co;2-s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036335965"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1521-3757(19981204)110:23<3408::aid-ange3408>3.0.co;2-s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036335965"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja982824a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036975349"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/415062a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038458584", 
          "https://doi.org/10.1038/415062a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/415062a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038458584", 
          "https://doi.org/10.1038/415062a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.97.3.984", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038764966"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp972635z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038984968"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp972635z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038984968"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0957-4484/9/3/018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041384153"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/07391102.2000.10506629", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043935419"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja9900398", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045743884"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/scientificamerican0600-86", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045998106", 
          "https://doi.org/10.1038/scientificamerican0600-86"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/30193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046607984", 
          "https://doi.org/10.1038/30193"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/30193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046607984", 
          "https://doi.org/10.1038/30193"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35035038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048486242", 
          "https://doi.org/10.1038/35035038"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35035038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048486242", 
          "https://doi.org/10.1038/35035038"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-44992-2_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051261911", 
          "https://doi.org/10.1007/3-540-44992-2_6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-44992-2_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051261911", 
          "https://doi.org/10.1007/3-540-44992-2_6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/bi00064a003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055159372"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/bi00160a003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055163166"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/bi9823479", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055216848"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/bi9823479", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055216848"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.120195", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057684376"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1090/memo/0066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059343115"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1116/1.578979", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062189026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.7973651", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062650775"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icec.1997.592308", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093290568"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2002", 
    "datePublishedReg": "2002-01-01", 
    "description": "While the topic of Molecular Computation would have appeared even a half dozen years ago to be purely conjectural, it now is an emerging subfield of computer science with the development of its theoretical basis and a number of moderate to large-scale experimental demonstrations. This paper focuses on a subarea of Molecular Computation known as DNA self- assembly. Self-assembly is the spontaneous self-ordering of substructures into superstructures driven by the selective affinity of the substructures. DNA provides a molecular scale material for effecting this programmable self-assembly, using the selective affinity of pairs of DNA strands to form DNA nanostructures. DNA self-assembly is the most advanced and versatile system known for programmable construction of patterned systems on the molecular scale. The methodology of DNA self-assembly begins with the synthesis of single-strand DNA molecules that self-assemble into macromolecular building blocks called DNA tiles. These tiles have sticky ends that match the sticky ends of other DNA tiles, facilitating further assembly into large structures known as DNA tiling lattices. In principal you can make the DNA tiling assemblies form any computable two- or three-dimensional pattern, however complex, with the appropriate choice of the tile\u2019s component DNA. This paper overviews the evolution of DNA self-assembly techniques from pure theory to experimental practice. We describe how some theoretical developments have made a major impact on the design of self-assembly experiments, as well as a number of theoretical challenges remaining in the area of DNA self-assembly. We descuss algorithms and software for the design, simulation and optimization of DNA tiling assemblies. We also describe the first experimental demonstrations of DNA self-assemblies that execute molecular computations and the assembly of patterned objects at the molecular scale. Recent experimental results indicate that this technique is scalable. Molecular imaging devices such as atomic force microscopes and transmission electron microscopes allow visualization of self-assembled two-dimensional DNA tiling lattices composed of hundreds of thousands of tiles. These assemblies can be used as scaffolding on which to position molecular electronics and robotics components with precision and specificity. The programmability lets this scaffolding have the patterning required for fabricating complex devices made of these components.", 
    "editor": [
      {
        "familyName": "Widmayer", 
        "givenName": "Peter", 
        "type": "Person"
      }, 
      {
        "familyName": "Eidenbenz", 
        "givenName": "Stephan", 
        "type": "Person"
      }, 
      {
        "familyName": "Triguero", 
        "givenName": "Francisco", 
        "type": "Person"
      }, 
      {
        "familyName": "Morales", 
        "givenName": "Rafael", 
        "type": "Person"
      }, 
      {
        "familyName": "Conejo", 
        "givenName": "Ricardo", 
        "type": "Person"
      }, 
      {
        "familyName": "Hennessy", 
        "givenName": "Matthew", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/3-540-45465-9_1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3012907", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.3455780", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": {
      "isbn": [
        "978-3-540-43864-9", 
        "978-3-540-45465-6"
      ], 
      "name": "Automata, Languages and Programming", 
      "type": "Book"
    }, 
    "name": "Molecular Assembly and Computation: From Theory to Experimental Demonstrations", 
    "pagination": "1-21", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/3-540-45465-9_1"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "bdfc7212d7b4c017668b16da6c1297a6c2c45f80a6fdf30c7300bf460e386a07"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1039883174"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/3-540-45465-9_1", 
      "https://app.dimensions.ai/details/publication/pub.1039883174"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T16:18", 
    "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_8675_00000268.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/3-540-45465-9_1"
  }
]
 

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.10.1007/3-540-45465-9_1'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/3-540-45465-9_1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/3-540-45465-9_1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/3-540-45465-9_1'


 

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

194 TRIPLES      23 PREDICATES      58 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/3-540-45465-9_1 schema:about anzsrc-for:03
2 anzsrc-for:0306
3 schema:author Nae6c2e6ac00e4a55ab813d04519dacf1
4 schema:citation sg:pub.10.1007/3-540-44992-2_6
5 sg:pub.10.1007/bf01418780
6 sg:pub.10.1038/28998
7 sg:pub.10.1038/30193
8 sg:pub.10.1038/35035038
9 sg:pub.10.1038/415062a
10 sg:pub.10.1038/scientificamerican0600-86
11 https://doi.org/10.1002/(sici)1521-3757(19981204)110:23<3408::aid-ange3408>3.0.co;2-s
12 https://doi.org/10.1002/elps.1150100512
13 https://doi.org/10.1016/0022-5193(82)90002-9
14 https://doi.org/10.1016/s0167-7799(99)01360-8
15 https://doi.org/10.1016/s0303-2647(99)00041-6
16 https://doi.org/10.1021/bi00064a003
17 https://doi.org/10.1021/bi00160a003
18 https://doi.org/10.1021/bi9823479
19 https://doi.org/10.1021/ja960162o
20 https://doi.org/10.1021/ja9817601
21 https://doi.org/10.1021/ja982824a
22 https://doi.org/10.1021/ja9900398
23 https://doi.org/10.1021/ja993393e
24 https://doi.org/10.1021/jp972635z
25 https://doi.org/10.1063/1.120195
26 https://doi.org/10.1073/pnas.97.3.984
27 https://doi.org/10.1080/07391102.2000.10506629
28 https://doi.org/10.1088/0957-4484/9/3/018
29 https://doi.org/10.1090/memo/0066
30 https://doi.org/10.1109/icec.1997.592308
31 https://doi.org/10.1116/1.578979
32 https://doi.org/10.1126/science.278.5336.252
33 https://doi.org/10.1126/science.7973651
34 https://doi.org/10.1146/annurev.matsci.28.1.153
35 schema:datePublished 2002
36 schema:datePublishedReg 2002-01-01
37 schema:description While the topic of Molecular Computation would have appeared even a half dozen years ago to be purely conjectural, it now is an emerging subfield of computer science with the development of its theoretical basis and a number of moderate to large-scale experimental demonstrations. This paper focuses on a subarea of Molecular Computation known as DNA self- assembly. Self-assembly is the spontaneous self-ordering of substructures into superstructures driven by the selective affinity of the substructures. DNA provides a molecular scale material for effecting this programmable self-assembly, using the selective affinity of pairs of DNA strands to form DNA nanostructures. DNA self-assembly is the most advanced and versatile system known for programmable construction of patterned systems on the molecular scale. The methodology of DNA self-assembly begins with the synthesis of single-strand DNA molecules that self-assemble into macromolecular building blocks called DNA tiles. These tiles have sticky ends that match the sticky ends of other DNA tiles, facilitating further assembly into large structures known as DNA tiling lattices. In principal you can make the DNA tiling assemblies form any computable two- or three-dimensional pattern, however complex, with the appropriate choice of the tile’s component DNA. This paper overviews the evolution of DNA self-assembly techniques from pure theory to experimental practice. We describe how some theoretical developments have made a major impact on the design of self-assembly experiments, as well as a number of theoretical challenges remaining in the area of DNA self-assembly. We descuss algorithms and software for the design, simulation and optimization of DNA tiling assemblies. We also describe the first experimental demonstrations of DNA self-assemblies that execute molecular computations and the assembly of patterned objects at the molecular scale. Recent experimental results indicate that this technique is scalable. Molecular imaging devices such as atomic force microscopes and transmission electron microscopes allow visualization of self-assembled two-dimensional DNA tiling lattices composed of hundreds of thousands of tiles. These assemblies can be used as scaffolding on which to position molecular electronics and robotics components with precision and specificity. The programmability lets this scaffolding have the patterning required for fabricating complex devices made of these components.
38 schema:editor Nb317e80020ad43838219cd856378c29c
39 schema:genre chapter
40 schema:inLanguage en
41 schema:isAccessibleForFree true
42 schema:isPartOf Nbdac45aa34d848d4a64c82af7c180b05
43 schema:name Molecular Assembly and Computation: From Theory to Experimental Demonstrations
44 schema:pagination 1-21
45 schema:productId N22014b10d1f5406aa2e704c2f03a1c2e
46 N4a46c5a152c7478aad1efa0e4b79acc0
47 Nfae24c18337245c3b5f2dee03352ba99
48 schema:publisher Ncb85180f03754ee990f75775d774afd0
49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039883174
50 https://doi.org/10.1007/3-540-45465-9_1
51 schema:sdDatePublished 2019-04-15T16:18
52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
53 schema:sdPublisher N3df658f0228348a2bdf12c318246a91f
54 schema:url http://link.springer.com/10.1007/3-540-45465-9_1
55 sgo:license sg:explorer/license/
56 sgo:sdDataset chapters
57 rdf:type schema:Chapter
58 N1065166633a546339ce1a7f14a79012b schema:familyName Widmayer
59 schema:givenName Peter
60 rdf:type schema:Person
61 N22014b10d1f5406aa2e704c2f03a1c2e schema:name doi
62 schema:value 10.1007/3-540-45465-9_1
63 rdf:type schema:PropertyValue
64 N3df658f0228348a2bdf12c318246a91f schema:name Springer Nature - SN SciGraph project
65 rdf:type schema:Organization
66 N4a46c5a152c7478aad1efa0e4b79acc0 schema:name dimensions_id
67 schema:value pub.1039883174
68 rdf:type schema:PropertyValue
69 N5e6db48326fe4bdf9c18d4171599ea39 rdf:first Ne2a48a10873c43c1ad1574adc1636225
70 rdf:rest Nfb5889e9370348368246ba92cb6ab685
71 N606b16805e6447af9dec8b4c76d52f8f schema:familyName Eidenbenz
72 schema:givenName Stephan
73 rdf:type schema:Person
74 N6d337f70372543d4991146b634e13cd3 schema:familyName Conejo
75 schema:givenName Ricardo
76 rdf:type schema:Person
77 N89e6aa3e98104208ad1ba2656c69d1d1 rdf:first N6d337f70372543d4991146b634e13cd3
78 rdf:rest Nd4621f487b69446593b707301cc13925
79 N962dcac660454bae98473a1c25492b03 schema:familyName Morales
80 schema:givenName Rafael
81 rdf:type schema:Person
82 N963fcd7e941543bf86ce65de6a58d877 rdf:first N606b16805e6447af9dec8b4c76d52f8f
83 rdf:rest N5e6db48326fe4bdf9c18d4171599ea39
84 Na18608473b7b4dfeb0b2068b6efd49b1 schema:familyName Hennessy
85 schema:givenName Matthew
86 rdf:type schema:Person
87 Nae6c2e6ac00e4a55ab813d04519dacf1 rdf:first sg:person.01117132720.17
88 rdf:rest rdf:nil
89 Nb317e80020ad43838219cd856378c29c rdf:first N1065166633a546339ce1a7f14a79012b
90 rdf:rest N963fcd7e941543bf86ce65de6a58d877
91 Nbdac45aa34d848d4a64c82af7c180b05 schema:isbn 978-3-540-43864-9
92 978-3-540-45465-6
93 schema:name Automata, Languages and Programming
94 rdf:type schema:Book
95 Ncb85180f03754ee990f75775d774afd0 schema:location Berlin, Heidelberg
96 schema:name Springer Berlin Heidelberg
97 rdf:type schema:Organisation
98 Nd4621f487b69446593b707301cc13925 rdf:first Na18608473b7b4dfeb0b2068b6efd49b1
99 rdf:rest rdf:nil
100 Ne2a48a10873c43c1ad1574adc1636225 schema:familyName Triguero
101 schema:givenName Francisco
102 rdf:type schema:Person
103 Nfae24c18337245c3b5f2dee03352ba99 schema:name readcube_id
104 schema:value bdfc7212d7b4c017668b16da6c1297a6c2c45f80a6fdf30c7300bf460e386a07
105 rdf:type schema:PropertyValue
106 Nfb5889e9370348368246ba92cb6ab685 rdf:first N962dcac660454bae98473a1c25492b03
107 rdf:rest N89e6aa3e98104208ad1ba2656c69d1d1
108 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
109 schema:name Chemical Sciences
110 rdf:type schema:DefinedTerm
111 anzsrc-for:0306 schema:inDefinedTermSet anzsrc-for:
112 schema:name Physical Chemistry (incl. Structural)
113 rdf:type schema:DefinedTerm
114 sg:grant.3012907 http://pending.schema.org/fundedItem sg:pub.10.1007/3-540-45465-9_1
115 rdf:type schema:MonetaryGrant
116 sg:grant.3455780 http://pending.schema.org/fundedItem sg:pub.10.1007/3-540-45465-9_1
117 rdf:type schema:MonetaryGrant
118 sg:person.01117132720.17 schema:affiliation https://www.grid.ac/institutes/grid.26009.3d
119 schema:familyName Reif
120 schema:givenName John H.
121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01117132720.17
122 rdf:type schema:Person
123 sg:pub.10.1007/3-540-44992-2_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051261911
124 https://doi.org/10.1007/3-540-44992-2_6
125 rdf:type schema:CreativeWork
126 sg:pub.10.1007/bf01418780 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009316877
127 https://doi.org/10.1007/bf01418780
128 rdf:type schema:CreativeWork
129 sg:pub.10.1038/28998 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015431161
130 https://doi.org/10.1038/28998
131 rdf:type schema:CreativeWork
132 sg:pub.10.1038/30193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046607984
133 https://doi.org/10.1038/30193
134 rdf:type schema:CreativeWork
135 sg:pub.10.1038/35035038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048486242
136 https://doi.org/10.1038/35035038
137 rdf:type schema:CreativeWork
138 sg:pub.10.1038/415062a schema:sameAs https://app.dimensions.ai/details/publication/pub.1038458584
139 https://doi.org/10.1038/415062a
140 rdf:type schema:CreativeWork
141 sg:pub.10.1038/scientificamerican0600-86 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045998106
142 https://doi.org/10.1038/scientificamerican0600-86
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1002/(sici)1521-3757(19981204)110:23<3408::aid-ange3408>3.0.co;2-s schema:sameAs https://app.dimensions.ai/details/publication/pub.1036335965
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1002/elps.1150100512 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000149651
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/0022-5193(82)90002-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002849846
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/s0167-7799(99)01360-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026476591
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/s0303-2647(99)00041-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027748984
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1021/bi00064a003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055159372
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1021/bi00160a003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055163166
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1021/bi9823479 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055216848
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1021/ja960162o schema:sameAs https://app.dimensions.ai/details/publication/pub.1022248208
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1021/ja9817601 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033828999
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1021/ja982824a schema:sameAs https://app.dimensions.ai/details/publication/pub.1036975349
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1021/ja9900398 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045743884
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1021/ja993393e schema:sameAs https://app.dimensions.ai/details/publication/pub.1010206052
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1021/jp972635z schema:sameAs https://app.dimensions.ai/details/publication/pub.1038984968
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1063/1.120195 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057684376
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1073/pnas.97.3.984 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038764966
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1080/07391102.2000.10506629 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043935419
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1088/0957-4484/9/3/018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041384153
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1090/memo/0066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059343115
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1109/icec.1997.592308 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093290568
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1116/1.578979 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062189026
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1126/science.278.5336.252 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009663792
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1126/science.7973651 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062650775
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1146/annurev.matsci.28.1.153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019630058
191 rdf:type schema:CreativeWork
192 https://www.grid.ac/institutes/grid.26009.3d schema:alternateName Duke University
193 schema:name Department of Computer Science, Duke University, Box 90129, Durham, NC, 27708-0129
194 rdf:type schema:Organization
 




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


...