An Overview of Data Models for the Analysis of Biochemical Pathways View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2003-02-28

AUTHORS

Yves Deville , David Gilbert , Jacques van Helden , Shoshana Wodak

ABSTRACT

p ]Various forms of data models can be used for the analysis of biochemical pathways such as metabolic, regulatory, or signal transduction pathways. This paper overviews and classifies the different forms of data models found in the literature, and describes how these models have been used in the analysis of biochemical pathways. The quantity of available information on biochemical pathways for different organisms is increasing very rapidly, and it has now become possible to perform detailed analyses of metabolic pathway structures for entire organisms. However, such analyses face dificulties due to the nature of the databases which are often heterogeneous, incomplete, or inconsistent. This makes pathway analysis a challenging problem in system biology and in bioinformatics.Various forms of data models can be used for the analysis of biochemical pathways such as metabolic, regulatory, or signal transduction pathways. This paper overviews and classifies the different forms of data models found in the literature, and describes how these models have been used in the analysis of biochemical pathways. The quantity of available information on biochemical pathways for different organisms is increasing very rapidly, and it has now become possible to perform detailed analyses of metabolic pathway structures for entire organisms. However, such analyses face dificulties due to the nature of the databases which are often heterogeneous, incomplete, or inconsistent. This makes pathway analysis a challenging problem in system biology and in bioinformatics.In this overview, we concentrate on models of network structure, focusing on the analysis of existing information, collected from experiments and stored in databases. We overview and classify the different forms of data models found in the literature using a unified framework. We describe how these models have been used in the analysis of biochemical pathways. This enables us to underline the strengths and weaknesses of the different approaches, and at the same time highlights some relevant future research directions. More... »

PAGES

174-174

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-36481-1_23

DOI

http://dx.doi.org/10.1007/3-540-36481-1_23

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035403678


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/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Computing Science and Engineering Department, Universit\u00e9 catholique de Louvain, Place Saint-Barbe 2, B-1348, Louvain-la-Neuve, Belgium", 
          "id": "http://www.grid.ac/institutes/grid.7942.8", 
          "name": [
            "Computing Science and Engineering Department, Universit\u00e9 catholique de Louvain, Place Saint-Barbe 2, B-1348, Louvain-la-Neuve, Belgium"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Deville", 
        "givenName": "Yves", 
        "id": "sg:person.013062700451.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013062700451.47"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, G12 8QQ, Glasgow, Scotland, UK", 
          "id": "http://www.grid.ac/institutes/grid.8756.c", 
          "name": [
            "Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, G12 8QQ, Glasgow, Scotland, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gilbert", 
        "givenName": "David", 
        "id": "sg:person.01024373510.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01024373510.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Unit\u00e9 de Conformation des Macromol\u00e9cules Biologiques, Universit\u00e9 Libre de Bruxelles, 50 av. F.D. Roosevelt, B-1050, Bruxelles, Belgium", 
          "id": "http://www.grid.ac/institutes/grid.4989.c", 
          "name": [
            "Unit\u00e9 de Conformation des Macromol\u00e9cules Biologiques, Universit\u00e9 Libre de Bruxelles, 50 av. F.D. Roosevelt, B-1050, Bruxelles, Belgium"
          ], 
          "type": "Organization"
        }, 
        "familyName": "van Helden", 
        "givenName": "Jacques", 
        "id": "sg:person.0626672543.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0626672543.46"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Unit\u00e9 de Conformation des Macromol\u00e9cules Biologiques, Universit\u00e9 Libre de Bruxelles, 50 av. F.D. Roosevelt, B-1050, Bruxelles, Belgium", 
          "id": "http://www.grid.ac/institutes/grid.4989.c", 
          "name": [
            "Unit\u00e9 de Conformation des Macromol\u00e9cules Biologiques, Universit\u00e9 Libre de Bruxelles, 50 av. F.D. Roosevelt, B-1050, Bruxelles, Belgium"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wodak", 
        "givenName": "Shoshana", 
        "id": "sg:person.0664143231.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0664143231.02"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2003-02-28", 
    "datePublishedReg": "2003-02-28", 
    "description": "Abstract p ]Various forms of data models can be used for the analysis of biochemical pathways such as metabolic, regulatory, or signal transduction pathways. This paper overviews and classifies the different forms of data models found in the literature, and describes how these models have been used in the analysis of biochemical pathways. The quantity of available information on biochemical pathways for different organisms is increasing very rapidly, and it has now become possible to perform detailed analyses of metabolic pathway structures for entire organisms. However, such analyses face dificulties due to the nature of the databases which are often heterogeneous, incomplete, or inconsistent. This makes pathway analysis a challenging problem in system biology and in bioinformatics.Various forms of data models can be used for the analysis of biochemical pathways such as metabolic, regulatory, or signal transduction pathways. This paper overviews and classifies the different forms of data models found in the literature, and describes how these models have been used in the analysis of biochemical pathways. The quantity of available information on biochemical pathways for different organisms is increasing very rapidly, and it has now become possible to perform detailed analyses of metabolic pathway structures for entire organisms. However, such analyses face dificulties due to the nature of the databases which are often heterogeneous, incomplete, or inconsistent. This makes pathway analysis a challenging problem in system biology and in bioinformatics.In this overview, we concentrate on models of network structure, focusing on the analysis of existing information, collected from experiments and stored in databases. We overview and classify the different forms of data models found in the literature using a unified framework. We describe how these models have been used in the analysis of biochemical pathways. This enables us to underline the strengths and weaknesses of the different approaches, and at the same time highlights some relevant future research directions.", 
    "editor": [
      {
        "familyName": "Priami", 
        "givenName": "Corrado", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/3-540-36481-1_23", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-00605-3", 
        "978-3-540-36481-8"
      ], 
      "name": "Computational Methods in Systems Biology", 
      "type": "Book"
    }, 
    "keywords": [
      "metabolic pathway structure", 
      "signal transduction pathways", 
      "biochemical pathways", 
      "different organisms", 
      "transduction pathways", 
      "entire organism", 
      "pathway analysis", 
      "systems biology", 
      "pathway structure", 
      "organisms", 
      "pathway", 
      "biology", 
      "bioinformatics", 
      "such analyses", 
      "available information", 
      "different forms", 
      "detailed analysis", 
      "relevant future research directions", 
      "form", 
      "analysis", 
      "structure", 
      "overview", 
      "database", 
      "paper overviews", 
      "quantity", 
      "information", 
      "nature", 
      "experiments", 
      "future research directions", 
      "model", 
      "different approaches", 
      "same time", 
      "research directions", 
      "network structure", 
      "approach", 
      "time", 
      "challenging problem", 
      "direction", 
      "literature", 
      "framework", 
      "strength", 
      "unified framework", 
      "problem", 
      "weakness", 
      "data model", 
      "dificulties"
    ], 
    "name": "An Overview of Data Models for the Analysis of Biochemical Pathways", 
    "pagination": "174-174", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1035403678"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/3-540-36481-1_23"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/3-540-36481-1_23", 
      "https://app.dimensions.ai/details/publication/pub.1035403678"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:12", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_212.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/3-540-36481-1_23"
  }
]
 

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.10.1007/3-540-36481-1_23'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/3-540-36481-1_23'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/3-540-36481-1_23'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/3-540-36481-1_23'


 

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

133 TRIPLES      23 PREDICATES      71 URIs      64 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/3-540-36481-1_23 schema:about anzsrc-for:06
2 anzsrc-for:0601
3 schema:author N60e8f50cf6ae494d82daecfde1bee13a
4 schema:datePublished 2003-02-28
5 schema:datePublishedReg 2003-02-28
6 schema:description Abstract p ]Various forms of data models can be used for the analysis of biochemical pathways such as metabolic, regulatory, or signal transduction pathways. This paper overviews and classifies the different forms of data models found in the literature, and describes how these models have been used in the analysis of biochemical pathways. The quantity of available information on biochemical pathways for different organisms is increasing very rapidly, and it has now become possible to perform detailed analyses of metabolic pathway structures for entire organisms. However, such analyses face dificulties due to the nature of the databases which are often heterogeneous, incomplete, or inconsistent. This makes pathway analysis a challenging problem in system biology and in bioinformatics.Various forms of data models can be used for the analysis of biochemical pathways such as metabolic, regulatory, or signal transduction pathways. This paper overviews and classifies the different forms of data models found in the literature, and describes how these models have been used in the analysis of biochemical pathways. The quantity of available information on biochemical pathways for different organisms is increasing very rapidly, and it has now become possible to perform detailed analyses of metabolic pathway structures for entire organisms. However, such analyses face dificulties due to the nature of the databases which are often heterogeneous, incomplete, or inconsistent. This makes pathway analysis a challenging problem in system biology and in bioinformatics.In this overview, we concentrate on models of network structure, focusing on the analysis of existing information, collected from experiments and stored in databases. We overview and classify the different forms of data models found in the literature using a unified framework. We describe how these models have been used in the analysis of biochemical pathways. This enables us to underline the strengths and weaknesses of the different approaches, and at the same time highlights some relevant future research directions.
7 schema:editor N82c5f3c0d5684e3c88d6ceda47e3a095
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N623c82105fad4022a51f23593a6317c4
12 schema:keywords analysis
13 approach
14 available information
15 biochemical pathways
16 bioinformatics
17 biology
18 challenging problem
19 data model
20 database
21 detailed analysis
22 different approaches
23 different forms
24 different organisms
25 dificulties
26 direction
27 entire organism
28 experiments
29 form
30 framework
31 future research directions
32 information
33 literature
34 metabolic pathway structure
35 model
36 nature
37 network structure
38 organisms
39 overview
40 paper overviews
41 pathway
42 pathway analysis
43 pathway structure
44 problem
45 quantity
46 relevant future research directions
47 research directions
48 same time
49 signal transduction pathways
50 strength
51 structure
52 such analyses
53 systems biology
54 time
55 transduction pathways
56 unified framework
57 weakness
58 schema:name An Overview of Data Models for the Analysis of Biochemical Pathways
59 schema:pagination 174-174
60 schema:productId N7934fa197c1243628719d9213ffcc05b
61 N9deb72f8c0f34450b5ec37981a30ad38
62 schema:publisher N44889406f155417b8b7c0f350c6ecf9d
63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035403678
64 https://doi.org/10.1007/3-540-36481-1_23
65 schema:sdDatePublished 2022-01-01T19:12
66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
67 schema:sdPublisher N883b037644b84e068bad3dca2b58eabd
68 schema:url https://doi.org/10.1007/3-540-36481-1_23
69 sgo:license sg:explorer/license/
70 sgo:sdDataset chapters
71 rdf:type schema:Chapter
72 N08dcc0431bbb4c5db1e7f11640935fd3 rdf:first sg:person.01024373510.92
73 rdf:rest N24a9cd013e2f47e191fb0f4f161f4f0c
74 N24a9cd013e2f47e191fb0f4f161f4f0c rdf:first sg:person.0626672543.46
75 rdf:rest N2e7c6603be2c4be993d716aa54297e25
76 N2e7c6603be2c4be993d716aa54297e25 rdf:first sg:person.0664143231.02
77 rdf:rest rdf:nil
78 N44889406f155417b8b7c0f350c6ecf9d schema:name Springer Nature
79 rdf:type schema:Organisation
80 N60e8f50cf6ae494d82daecfde1bee13a rdf:first sg:person.013062700451.47
81 rdf:rest N08dcc0431bbb4c5db1e7f11640935fd3
82 N623c82105fad4022a51f23593a6317c4 schema:isbn 978-3-540-00605-3
83 978-3-540-36481-8
84 schema:name Computational Methods in Systems Biology
85 rdf:type schema:Book
86 N7934fa197c1243628719d9213ffcc05b schema:name dimensions_id
87 schema:value pub.1035403678
88 rdf:type schema:PropertyValue
89 N82c5f3c0d5684e3c88d6ceda47e3a095 rdf:first Ne676530558f34bcbb650cac3d53f0cc8
90 rdf:rest rdf:nil
91 N883b037644b84e068bad3dca2b58eabd schema:name Springer Nature - SN SciGraph project
92 rdf:type schema:Organization
93 N9deb72f8c0f34450b5ec37981a30ad38 schema:name doi
94 schema:value 10.1007/3-540-36481-1_23
95 rdf:type schema:PropertyValue
96 Ne676530558f34bcbb650cac3d53f0cc8 schema:familyName Priami
97 schema:givenName Corrado
98 rdf:type schema:Person
99 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
100 schema:name Biological Sciences
101 rdf:type schema:DefinedTerm
102 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
103 schema:name Biochemistry and Cell Biology
104 rdf:type schema:DefinedTerm
105 sg:person.01024373510.92 schema:affiliation grid-institutes:grid.8756.c
106 schema:familyName Gilbert
107 schema:givenName David
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01024373510.92
109 rdf:type schema:Person
110 sg:person.013062700451.47 schema:affiliation grid-institutes:grid.7942.8
111 schema:familyName Deville
112 schema:givenName Yves
113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013062700451.47
114 rdf:type schema:Person
115 sg:person.0626672543.46 schema:affiliation grid-institutes:grid.4989.c
116 schema:familyName van Helden
117 schema:givenName Jacques
118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0626672543.46
119 rdf:type schema:Person
120 sg:person.0664143231.02 schema:affiliation grid-institutes:grid.4989.c
121 schema:familyName Wodak
122 schema:givenName Shoshana
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0664143231.02
124 rdf:type schema:Person
125 grid-institutes:grid.4989.c schema:alternateName Unité de Conformation des Macromolécules Biologiques, Université Libre de Bruxelles, 50 av. F.D. Roosevelt, B-1050, Bruxelles, Belgium
126 schema:name Unité de Conformation des Macromolécules Biologiques, Université Libre de Bruxelles, 50 av. F.D. Roosevelt, B-1050, Bruxelles, Belgium
127 rdf:type schema:Organization
128 grid-institutes:grid.7942.8 schema:alternateName Computing Science and Engineering Department, Université catholique de Louvain, Place Saint-Barbe 2, B-1348, Louvain-la-Neuve, Belgium
129 schema:name Computing Science and Engineering Department, Université catholique de Louvain, Place Saint-Barbe 2, B-1348, Louvain-la-Neuve, Belgium
130 rdf:type schema:Organization
131 grid-institutes:grid.8756.c schema:alternateName Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, G12 8QQ, Glasgow, Scotland, UK
132 schema:name Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, G12 8QQ, Glasgow, Scotland, UK
133 rdf:type schema:Organization
 




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


...