Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-09-19

AUTHORS

Wu-Hsiung Wu, Feng-Sheng Wang, Maw-Shang Chang

ABSTRACT

BACKGROUND: Improving the synthesis rate of desired metabolites in metabolic systems is one of the main tasks in metabolic engineering. In the last decade, metabolic engineering approaches based on the mathematical optimization have been used extensively for the analysis and manipulation of metabolic networks. Experimental evidence shows that mutants reflect resilience phenomena against gene alterations. Although researchers have published many studies on the design of metabolic systems based on kinetic models and optimization strategies, almost no studies discuss the multi-objective optimization problem for enzyme manipulations in metabolic networks considering resilience phenomenon. RESULTS: This study proposes a generalized fuzzy multi-objective optimization approach to formulate the enzyme intervention problem for metabolic networks considering resilience phenomena and cell viability. This approach is a general framework that can be applied to any metabolic networks to investigate the influence of resilience phenomena on gene intervention strategies and maximum target synthesis rates. This study evaluates the performance of the proposed approach by applying it to two metabolic systems: S. cerevisiae and E. coli. Results show that the maximum synthesis rates of target products by genetic interventions are always over-estimated in metabolic networks that do not consider the resilience effects. CONCLUSIONS: Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. The proposed generalized fuzzy multi-objective optimization approach has the potential to be a good and practical framework in the design of metabolic networks. More... »

PAGES

145-145

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1752-0509-5-145

DOI

http://dx.doi.org/10.1186/1752-0509-5-145

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025493971

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/21929795


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health 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"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0803", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Computer Software", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1199", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Other Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Amino Acids", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bioreactors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Enzymes", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Escherichia coli", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ethanol", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fermentation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fuzzy Logic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metabolic Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metabolic Networks and Pathways", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Biological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Saccharomyces cerevisiae", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.412047.4", 
          "name": [
            "Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Wu-Hsiung", 
        "id": "sg:person.0710177757.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0710177757.90"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.412047.4", 
          "name": [
            "Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Feng-Sheng", 
        "id": "sg:person.011660076724.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011660076724.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.412047.4", 
          "name": [
            "Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chang", 
        "givenName": "Maw-Shang", 
        "id": "sg:person.013174232477.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013174232477.45"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-1-4899-1633-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020883739", 
          "https://doi.org/10.1007/978-1-4899-1633-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-10-386", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037962186", 
          "https://doi.org/10.1186/1471-2105-10-386"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011-09-19", 
    "datePublishedReg": "2011-09-19", 
    "description": "BACKGROUND: Improving the synthesis rate of desired metabolites in metabolic systems is one of the main tasks in metabolic engineering. In the last decade, metabolic engineering approaches based on the mathematical optimization have been used extensively for the analysis and manipulation of metabolic networks. Experimental evidence shows that mutants reflect resilience phenomena against gene alterations. Although researchers have published many studies on the design of metabolic systems based on kinetic models and optimization strategies, almost no studies discuss the multi-objective optimization problem for enzyme manipulations in metabolic networks considering resilience phenomenon.\nRESULTS: This study proposes a generalized fuzzy multi-objective optimization approach to formulate the enzyme intervention problem for metabolic networks considering resilience phenomena and cell viability. This approach is a general framework that can be applied to any metabolic networks to investigate the influence of resilience phenomena on gene intervention strategies and maximum target synthesis rates. This study evaluates the performance of the proposed approach by applying it to two metabolic systems: S. cerevisiae and E. coli. Results show that the maximum synthesis rates of target products by genetic interventions are always over-estimated in metabolic networks that do not consider the resilience effects.\nCONCLUSIONS: Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. The proposed generalized fuzzy multi-objective optimization approach has the potential to be a good and practical framework in the design of metabolic networks.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/1752-0509-5-145", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1327442", 
        "issn": [
          "1752-0509"
        ], 
        "name": "BMC Systems Biology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "5"
      }
    ], 
    "keywords": [
      "fuzzy multi-objective optimization approach", 
      "multi-objective optimization approach", 
      "optimization approach", 
      "resilience phenomenon", 
      "multi-objective optimization problem", 
      "multi-objective optimization", 
      "mathematical optimization", 
      "optimization problem", 
      "general framework", 
      "optimization strategy", 
      "metabolic networks", 
      "intervention problem", 
      "optimization", 
      "enzyme manipulation", 
      "problem", 
      "metabolic systems", 
      "gene intervention", 
      "network", 
      "phenomenon", 
      "approach", 
      "system", 
      "resilience effects", 
      "maximum synthesis rate", 
      "framework", 
      "main task", 
      "practical framework", 
      "model", 
      "engineering", 
      "prediction", 
      "design", 
      "kinetic model", 
      "experimental evidence", 
      "performance", 
      "last decade", 
      "engineering approach", 
      "manipulation", 
      "results", 
      "analysis", 
      "effect", 
      "task", 
      "strategies", 
      "researchers", 
      "potential", 
      "rate", 
      "influence", 
      "study", 
      "decades", 
      "products", 
      "metabolic engineering", 
      "target products", 
      "intervention strategies", 
      "evidence", 
      "viability", 
      "synthesis rate", 
      "cerevisiae", 
      "metabolic engineering approaches", 
      "intervention", 
      "genetic interventions", 
      "alterations", 
      "coli", 
      "metabolites", 
      "gene alterations", 
      "mutants", 
      "cell viability", 
      "generalized fuzzy multi-objective optimization approach", 
      "enzyme intervention problem", 
      "gene intervention strategies", 
      "maximum target synthesis rates", 
      "target synthesis rates"
    ], 
    "name": "Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects", 
    "pagination": "145-145", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1025493971"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1752-0509-5-145"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "21929795"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1752-0509-5-145", 
      "https://app.dimensions.ai/details/publication/pub.1025493971"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T18:25", 
    "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/article/article_551.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/1752-0509-5-145"
  }
]
 

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.10.1186/1752-0509-5-145'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-5-145'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-5-145'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-5-145'


 

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

214 TRIPLES      22 PREDICATES      112 URIs      98 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1752-0509-5-145 schema:about N00ac704e2b5f4e40b5985d8f15091d7b
2 N2afd0374fe504f78bd53dde0897050c4
3 N3f77d1cfd9ba49b58c56f19a42a550ad
4 N54c3d773051844159903c2c93d790c59
5 N58f0a975bba04e0083320caee1ba83d9
6 N67e98fb6adeb4f6682367e3c76408fac
7 N83675c05f1434a98ac144cc52198e7a5
8 N903f88e6255b4c6581cdbe99782c7b65
9 Nb31a7672e99e441a9ef1420bc6877fee
10 Nd2ec825536584faea25147cd4102868a
11 Neca80cc9132a4cbbafd63aa41521c1f4
12 anzsrc-for:06
13 anzsrc-for:0601
14 anzsrc-for:08
15 anzsrc-for:0803
16 anzsrc-for:11
17 anzsrc-for:1199
18 schema:author N6b0146f123dc44b29a96d5f429459bd2
19 schema:citation sg:pub.10.1007/978-1-4899-1633-4
20 sg:pub.10.1186/1471-2105-10-386
21 schema:datePublished 2011-09-19
22 schema:datePublishedReg 2011-09-19
23 schema:description BACKGROUND: Improving the synthesis rate of desired metabolites in metabolic systems is one of the main tasks in metabolic engineering. In the last decade, metabolic engineering approaches based on the mathematical optimization have been used extensively for the analysis and manipulation of metabolic networks. Experimental evidence shows that mutants reflect resilience phenomena against gene alterations. Although researchers have published many studies on the design of metabolic systems based on kinetic models and optimization strategies, almost no studies discuss the multi-objective optimization problem for enzyme manipulations in metabolic networks considering resilience phenomenon. RESULTS: This study proposes a generalized fuzzy multi-objective optimization approach to formulate the enzyme intervention problem for metabolic networks considering resilience phenomena and cell viability. This approach is a general framework that can be applied to any metabolic networks to investigate the influence of resilience phenomena on gene intervention strategies and maximum target synthesis rates. This study evaluates the performance of the proposed approach by applying it to two metabolic systems: S. cerevisiae and E. coli. Results show that the maximum synthesis rates of target products by genetic interventions are always over-estimated in metabolic networks that do not consider the resilience effects. CONCLUSIONS: Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. The proposed generalized fuzzy multi-objective optimization approach has the potential to be a good and practical framework in the design of metabolic networks.
24 schema:genre article
25 schema:inLanguage en
26 schema:isAccessibleForFree true
27 schema:isPartOf N424fe4a9ebab4d1c90c5f7e9ad961fe4
28 Ne94caddea82548f8992bbb6ff195125d
29 sg:journal.1327442
30 schema:keywords alterations
31 analysis
32 approach
33 cell viability
34 cerevisiae
35 coli
36 decades
37 design
38 effect
39 engineering
40 engineering approach
41 enzyme intervention problem
42 enzyme manipulation
43 evidence
44 experimental evidence
45 framework
46 fuzzy multi-objective optimization approach
47 gene alterations
48 gene intervention
49 gene intervention strategies
50 general framework
51 generalized fuzzy multi-objective optimization approach
52 genetic interventions
53 influence
54 intervention
55 intervention problem
56 intervention strategies
57 kinetic model
58 last decade
59 main task
60 manipulation
61 mathematical optimization
62 maximum synthesis rate
63 maximum target synthesis rates
64 metabolic engineering
65 metabolic engineering approaches
66 metabolic networks
67 metabolic systems
68 metabolites
69 model
70 multi-objective optimization
71 multi-objective optimization approach
72 multi-objective optimization problem
73 mutants
74 network
75 optimization
76 optimization approach
77 optimization problem
78 optimization strategy
79 performance
80 phenomenon
81 potential
82 practical framework
83 prediction
84 problem
85 products
86 rate
87 researchers
88 resilience effects
89 resilience phenomenon
90 results
91 strategies
92 study
93 synthesis rate
94 system
95 target products
96 target synthesis rates
97 task
98 viability
99 schema:name Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects
100 schema:pagination 145-145
101 schema:productId N137c02c83a2d4e29bc2b81b5bec477c1
102 N241f3fa9951343e8a0f708a4ad7d97d0
103 N801adafc09c14e06918f1f27f44991b9
104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025493971
105 https://doi.org/10.1186/1752-0509-5-145
106 schema:sdDatePublished 2022-01-01T18:25
107 schema:sdLicense https://scigraph.springernature.com/explorer/license/
108 schema:sdPublisher N136be93a5c7f45e2882a9a0d9afa5e30
109 schema:url https://doi.org/10.1186/1752-0509-5-145
110 sgo:license sg:explorer/license/
111 sgo:sdDataset articles
112 rdf:type schema:ScholarlyArticle
113 N00ac704e2b5f4e40b5985d8f15091d7b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Fuzzy Logic
115 rdf:type schema:DefinedTerm
116 N136be93a5c7f45e2882a9a0d9afa5e30 schema:name Springer Nature - SN SciGraph project
117 rdf:type schema:Organization
118 N137c02c83a2d4e29bc2b81b5bec477c1 schema:name pubmed_id
119 schema:value 21929795
120 rdf:type schema:PropertyValue
121 N241f3fa9951343e8a0f708a4ad7d97d0 schema:name dimensions_id
122 schema:value pub.1025493971
123 rdf:type schema:PropertyValue
124 N2afd0374fe504f78bd53dde0897050c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Bioreactors
126 rdf:type schema:DefinedTerm
127 N3f77d1cfd9ba49b58c56f19a42a550ad schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Amino Acids
129 rdf:type schema:DefinedTerm
130 N424fe4a9ebab4d1c90c5f7e9ad961fe4 schema:issueNumber 1
131 rdf:type schema:PublicationIssue
132 N54c3d773051844159903c2c93d790c59 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
133 schema:name Saccharomyces cerevisiae
134 rdf:type schema:DefinedTerm
135 N58f0a975bba04e0083320caee1ba83d9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Fermentation
137 rdf:type schema:DefinedTerm
138 N67e98fb6adeb4f6682367e3c76408fac schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
139 schema:name Escherichia coli
140 rdf:type schema:DefinedTerm
141 N6b0146f123dc44b29a96d5f429459bd2 rdf:first sg:person.0710177757.90
142 rdf:rest Ne1c6b056580447c0bb7bec5df4a84063
143 N7430a3f41a0a4c849fa400e7b172e98d rdf:first sg:person.013174232477.45
144 rdf:rest rdf:nil
145 N801adafc09c14e06918f1f27f44991b9 schema:name doi
146 schema:value 10.1186/1752-0509-5-145
147 rdf:type schema:PropertyValue
148 N83675c05f1434a98ac144cc52198e7a5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
149 schema:name Models, Biological
150 rdf:type schema:DefinedTerm
151 N903f88e6255b4c6581cdbe99782c7b65 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Enzymes
153 rdf:type schema:DefinedTerm
154 Nb31a7672e99e441a9ef1420bc6877fee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Metabolic Networks and Pathways
156 rdf:type schema:DefinedTerm
157 Nd2ec825536584faea25147cd4102868a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Ethanol
159 rdf:type schema:DefinedTerm
160 Ne1c6b056580447c0bb7bec5df4a84063 rdf:first sg:person.011660076724.07
161 rdf:rest N7430a3f41a0a4c849fa400e7b172e98d
162 Ne94caddea82548f8992bbb6ff195125d schema:volumeNumber 5
163 rdf:type schema:PublicationVolume
164 Neca80cc9132a4cbbafd63aa41521c1f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
165 schema:name Metabolic Engineering
166 rdf:type schema:DefinedTerm
167 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
168 schema:name Biological Sciences
169 rdf:type schema:DefinedTerm
170 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
171 schema:name Biochemistry and Cell Biology
172 rdf:type schema:DefinedTerm
173 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
174 schema:name Information and Computing Sciences
175 rdf:type schema:DefinedTerm
176 anzsrc-for:0803 schema:inDefinedTermSet anzsrc-for:
177 schema:name Computer Software
178 rdf:type schema:DefinedTerm
179 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
180 schema:name Medical and Health Sciences
181 rdf:type schema:DefinedTerm
182 anzsrc-for:1199 schema:inDefinedTermSet anzsrc-for:
183 schema:name Other Medical and Health Sciences
184 rdf:type schema:DefinedTerm
185 sg:journal.1327442 schema:issn 1752-0509
186 schema:name BMC Systems Biology
187 schema:publisher Springer Nature
188 rdf:type schema:Periodical
189 sg:person.011660076724.07 schema:affiliation grid-institutes:grid.412047.4
190 schema:familyName Wang
191 schema:givenName Feng-Sheng
192 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011660076724.07
193 rdf:type schema:Person
194 sg:person.013174232477.45 schema:affiliation grid-institutes:grid.412047.4
195 schema:familyName Chang
196 schema:givenName Maw-Shang
197 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013174232477.45
198 rdf:type schema:Person
199 sg:person.0710177757.90 schema:affiliation grid-institutes:grid.412047.4
200 schema:familyName Wu
201 schema:givenName Wu-Hsiung
202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0710177757.90
203 rdf:type schema:Person
204 sg:pub.10.1007/978-1-4899-1633-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020883739
205 https://doi.org/10.1007/978-1-4899-1633-4
206 rdf:type schema:CreativeWork
207 sg:pub.10.1186/1471-2105-10-386 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037962186
208 https://doi.org/10.1186/1471-2105-10-386
209 rdf:type schema:CreativeWork
210 grid-institutes:grid.412047.4 schema:alternateName Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan
211 Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan
212 schema:name Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan
213 Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan
214 rdf:type schema:Organization
 




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


...