Modeling Signal Transduction of Neural System by Hybrid Petri Net Representation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2005

AUTHORS

Shih Chi Peng , Hsu-Ming Chang , D. Frank Hsu , Chuan Yi Tang

ABSTRACT

Biological neural system can be considered as a series of biochemical reactions and signal transmission. It is important to provide an intuitive representation of the neural system to biologists while keeping its computational consistency. In this paper, we propose a method to exploit Hybrid Petri Net (HPN) for intuitive representation and quantitative modeling. The HPN is an extension of Petri Nets and represented by a directed, bipartite graph in which nodes are either discrete/continuous places (such as ion channels) or discrete/continuous transitions (such as phosphorylation), where places represent conditions and transitions represent activities. It can easily model the interactions among receptors, ionic flows (such as calcium), G-proteins, protein kinases and transcription factors that are very complicate in terms of the dynamics of all participants and their correlations. We demonstrate that, in the biological neural system, it is possible to translate and map these complex phenomena into HPNs in a natural manner. In our model, the dynamic properties of the neural signal processing can be examined, especially the interactions among neural modulators and signal transduction pathways. With such a mechanism model in hand, our ability to collaborate with neural scientists is greatly enhanced so as to simulate and examine the robustness of the neural transmission under the local biochemical perturbations. More... »

PAGES

271-279

Book

TITLE

Operations Research Proceedings 2004

ISBN

978-3-540-24274-1
978-3-540-27679-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-27679-3_34

DOI

http://dx.doi.org/10.1007/3-540-27679-3_34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048695931


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/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National Tsing Hua University", 
          "id": "https://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Dept. of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan, ROC"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Peng", 
        "givenName": "Shih Chi", 
        "id": "sg:person.01366337452.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01366337452.24"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Tsing Hua University", 
          "id": "https://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Dept. of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan, ROC"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chang", 
        "givenName": "Hsu-Ming", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Fordham University", 
          "id": "https://www.grid.ac/institutes/grid.256023.0", 
          "name": [
            "Dept. of Computer and Information Science, Fordham University, New York, NY\u00a010023, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hsu", 
        "givenName": "D. Frank", 
        "id": "sg:person.012372547527.89", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012372547527.89"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Tsing Hua University", 
          "id": "https://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Dept. of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan, ROC"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tang", 
        "givenName": "Chuan Yi", 
        "id": "sg:person.01312526135.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312526135.27"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0022-5193(05)80472-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008318662"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.6.825", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026895472"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bst0311513", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046059009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bst0311513", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046059009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.294.5544.1024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062575413"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2005", 
    "datePublishedReg": "2005-01-01", 
    "description": "Biological neural system can be considered as a series of biochemical reactions and signal transmission. It is important to provide an intuitive representation of the neural system to biologists while keeping its computational consistency. In this paper, we propose a method to exploit Hybrid Petri Net (HPN) for intuitive representation and quantitative modeling. The HPN is an extension of Petri Nets and represented by a directed, bipartite graph in which nodes are either discrete/continuous places (such as ion channels) or discrete/continuous transitions (such as phosphorylation), where places represent conditions and transitions represent activities. It can easily model the interactions among receptors, ionic flows (such as calcium), G-proteins, protein kinases and transcription factors that are very complicate in terms of the dynamics of all participants and their correlations. We demonstrate that, in the biological neural system, it is possible to translate and map these complex phenomena into HPNs in a natural manner. In our model, the dynamic properties of the neural signal processing can be examined, especially the interactions among neural modulators and signal transduction pathways. With such a mechanism model in hand, our ability to collaborate with neural scientists is greatly enhanced so as to simulate and examine the robustness of the neural transmission under the local biochemical perturbations.", 
    "editor": [
      {
        "familyName": "Fleuren", 
        "givenName": "Hein", 
        "type": "Person"
      }, 
      {
        "familyName": "den Hertog", 
        "givenName": "Dick", 
        "type": "Person"
      }, 
      {
        "familyName": "Kort", 
        "givenName": "Peter", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/3-540-27679-3_34", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-24274-1", 
        "978-3-540-27679-1"
      ], 
      "name": "Operations Research Proceedings 2004", 
      "type": "Book"
    }, 
    "name": "Modeling Signal Transduction of Neural System by Hybrid Petri Net Representation", 
    "pagination": "271-279", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/3-540-27679-3_34"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "e7fa7ede54c2f50fc3c4831a2fc4585281638e295c330ddbf3dfc3f4e001821f"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1048695931"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/3-540-27679-3_34", 
      "https://app.dimensions.ai/details/publication/pub.1048695931"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T13:31", 
    "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_8664_00000273.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/3-540-27679-3_34"
  }
]
 

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.10.1007/3-540-27679-3_34'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/3-540-27679-3_34'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/3-540-27679-3_34'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/3-540-27679-3_34'


 

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

110 TRIPLES      23 PREDICATES      31 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/3-540-27679-3_34 schema:about anzsrc-for:06
2 anzsrc-for:0601
3 schema:author Nf47e1712bfc14fe8a32899925c500752
4 schema:citation https://doi.org/10.1016/s0022-5193(05)80472-2
5 https://doi.org/10.1042/bst0311513
6 https://doi.org/10.1093/bioinformatics/18.6.825
7 https://doi.org/10.1126/science.294.5544.1024
8 schema:datePublished 2005
9 schema:datePublishedReg 2005-01-01
10 schema:description Biological neural system can be considered as a series of biochemical reactions and signal transmission. It is important to provide an intuitive representation of the neural system to biologists while keeping its computational consistency. In this paper, we propose a method to exploit Hybrid Petri Net (HPN) for intuitive representation and quantitative modeling. The HPN is an extension of Petri Nets and represented by a directed, bipartite graph in which nodes are either discrete/continuous places (such as ion channels) or discrete/continuous transitions (such as phosphorylation), where places represent conditions and transitions represent activities. It can easily model the interactions among receptors, ionic flows (such as calcium), G-proteins, protein kinases and transcription factors that are very complicate in terms of the dynamics of all participants and their correlations. We demonstrate that, in the biological neural system, it is possible to translate and map these complex phenomena into HPNs in a natural manner. In our model, the dynamic properties of the neural signal processing can be examined, especially the interactions among neural modulators and signal transduction pathways. With such a mechanism model in hand, our ability to collaborate with neural scientists is greatly enhanced so as to simulate and examine the robustness of the neural transmission under the local biochemical perturbations.
11 schema:editor N0e08c23ec4834c7080b54145a6246f22
12 schema:genre chapter
13 schema:inLanguage en
14 schema:isAccessibleForFree false
15 schema:isPartOf N7fea6f5b291b465a8d07c2ae876ec9e6
16 schema:name Modeling Signal Transduction of Neural System by Hybrid Petri Net Representation
17 schema:pagination 271-279
18 schema:productId N02cff75c87dd4a0188b6926345469fed
19 N279b6d5ee65c415fadac61fd324efe4a
20 Nbdfc786b2b664a678880cb67fc683526
21 schema:publisher Nd43f3500d9fa448fa454605edf2d964a
22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048695931
23 https://doi.org/10.1007/3-540-27679-3_34
24 schema:sdDatePublished 2019-04-15T13:31
25 schema:sdLicense https://scigraph.springernature.com/explorer/license/
26 schema:sdPublisher N54192284027c4a7a9e07f3dc87bfb9a7
27 schema:url http://link.springer.com/10.1007/3-540-27679-3_34
28 sgo:license sg:explorer/license/
29 sgo:sdDataset chapters
30 rdf:type schema:Chapter
31 N02cff75c87dd4a0188b6926345469fed schema:name readcube_id
32 schema:value e7fa7ede54c2f50fc3c4831a2fc4585281638e295c330ddbf3dfc3f4e001821f
33 rdf:type schema:PropertyValue
34 N0e08c23ec4834c7080b54145a6246f22 rdf:first N945ffd0c7c154c78a2df5c54fa42768b
35 rdf:rest Nf5cb02dacf1445f4b88af9207a59ad97
36 N0e8fa8bff09c415ebced27f13e0b463f rdf:first N0febca9b452a466c82e4844798921302
37 rdf:rest Nbc4dea0d6d8a46ef9813ae40288b7eba
38 N0febca9b452a466c82e4844798921302 schema:affiliation https://www.grid.ac/institutes/grid.38348.34
39 schema:familyName Chang
40 schema:givenName Hsu-Ming
41 rdf:type schema:Person
42 N16e319572cad4010a9e51b0da26ef914 schema:familyName Kort
43 schema:givenName Peter
44 rdf:type schema:Person
45 N279b6d5ee65c415fadac61fd324efe4a schema:name doi
46 schema:value 10.1007/3-540-27679-3_34
47 rdf:type schema:PropertyValue
48 N34d9fa6a0e2546ea9d18593bdf860aa5 rdf:first sg:person.01312526135.27
49 rdf:rest rdf:nil
50 N54192284027c4a7a9e07f3dc87bfb9a7 schema:name Springer Nature - SN SciGraph project
51 rdf:type schema:Organization
52 N7e55c958640249229617590c1c18bdfb rdf:first N16e319572cad4010a9e51b0da26ef914
53 rdf:rest rdf:nil
54 N7fea6f5b291b465a8d07c2ae876ec9e6 schema:isbn 978-3-540-24274-1
55 978-3-540-27679-1
56 schema:name Operations Research Proceedings 2004
57 rdf:type schema:Book
58 N945ffd0c7c154c78a2df5c54fa42768b schema:familyName Fleuren
59 schema:givenName Hein
60 rdf:type schema:Person
61 Nbc4dea0d6d8a46ef9813ae40288b7eba rdf:first sg:person.012372547527.89
62 rdf:rest N34d9fa6a0e2546ea9d18593bdf860aa5
63 Nbdfc786b2b664a678880cb67fc683526 schema:name dimensions_id
64 schema:value pub.1048695931
65 rdf:type schema:PropertyValue
66 Nd43f3500d9fa448fa454605edf2d964a schema:location Berlin, Heidelberg
67 schema:name Springer Berlin Heidelberg
68 rdf:type schema:Organisation
69 Nd6a0482e2eab4676a78ff61efbaaff7d schema:familyName den Hertog
70 schema:givenName Dick
71 rdf:type schema:Person
72 Nf47e1712bfc14fe8a32899925c500752 rdf:first sg:person.01366337452.24
73 rdf:rest N0e8fa8bff09c415ebced27f13e0b463f
74 Nf5cb02dacf1445f4b88af9207a59ad97 rdf:first Nd6a0482e2eab4676a78ff61efbaaff7d
75 rdf:rest N7e55c958640249229617590c1c18bdfb
76 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
77 schema:name Biological Sciences
78 rdf:type schema:DefinedTerm
79 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
80 schema:name Biochemistry and Cell Biology
81 rdf:type schema:DefinedTerm
82 sg:person.012372547527.89 schema:affiliation https://www.grid.ac/institutes/grid.256023.0
83 schema:familyName Hsu
84 schema:givenName D. Frank
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012372547527.89
86 rdf:type schema:Person
87 sg:person.01312526135.27 schema:affiliation https://www.grid.ac/institutes/grid.38348.34
88 schema:familyName Tang
89 schema:givenName Chuan Yi
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312526135.27
91 rdf:type schema:Person
92 sg:person.01366337452.24 schema:affiliation https://www.grid.ac/institutes/grid.38348.34
93 schema:familyName Peng
94 schema:givenName Shih Chi
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01366337452.24
96 rdf:type schema:Person
97 https://doi.org/10.1016/s0022-5193(05)80472-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008318662
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1042/bst0311513 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046059009
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1093/bioinformatics/18.6.825 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026895472
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1126/science.294.5544.1024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062575413
104 rdf:type schema:CreativeWork
105 https://www.grid.ac/institutes/grid.256023.0 schema:alternateName Fordham University
106 schema:name Dept. of Computer and Information Science, Fordham University, New York, NY 10023, USA
107 rdf:type schema:Organization
108 https://www.grid.ac/institutes/grid.38348.34 schema:alternateName National Tsing Hua University
109 schema:name Dept. of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan, ROC
110 rdf:type schema:Organization
 




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


...