Modeling central metabolism and energy biosynthesis across microbial life View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-08-08

AUTHORS

Janaka N. Edirisinghe, Pamela Weisenhorn, Neal Conrad, Fangfang Xia, Ross Overbeek, Rick L. Stevens, Christopher S. Henry

ABSTRACT

BACKGROUND: Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. RESULTS: To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. CONCLUSIONS: We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes. More... »

PAGES

568

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12864-016-2887-8

DOI

http://dx.doi.org/10.1186/s12864-016-2887-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043963135

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27502787


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/2008/0605", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Microbiology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adenosine Triphosphate", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bacteria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Biomass", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Electron Transport Chain Complex Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Energy Metabolism", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genomics", 
        "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": "Molecular Sequence Annotation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Phylogeny", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA", 
            "Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Edirisinghe", 
        "givenName": "Janaka N.", 
        "id": "sg:person.015470706405.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015470706405.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Weisenhorn", 
        "givenName": "Pamela", 
        "id": "sg:person.013210051641.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013210051641.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Conrad", 
        "givenName": "Neal", 
        "id": "sg:person.01360151361.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01360151361.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA", 
            "Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xia", 
        "givenName": "Fangfang", 
        "id": "sg:person.01023644620.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023644620.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Overbeek", 
        "givenName": "Ross", 
        "id": "sg:person.01064013616.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01064013616.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA", 
            "Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stevens", 
        "givenName": "Rick L.", 
        "id": "sg:person.0707416220.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707416220.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA", 
            "Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Henry", 
        "givenName": "Christopher S.", 
        "id": "sg:person.01134232120.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134232120.92"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nprot.2007.99", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016592716", 
          "https://doi.org/10.1038/nprot.2007.99"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s002030050780", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032368824", 
          "https://doi.org/10.1007/s002030050780"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023395418", 
          "https://doi.org/10.1186/1471-2105-8-212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-3783-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018398741", 
          "https://doi.org/10.1007/978-1-4757-3783-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00018-010-0555-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011581115", 
          "https://doi.org/10.1007/s00018-010-0555-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt.1672", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006770979", 
          "https://doi.org/10.1038/nbt.1672"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-9-75", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013431920", 
          "https://doi.org/10.1186/1471-2164-9-75"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-08-08", 
    "datePublishedReg": "2016-08-08", 
    "description": "BACKGROUND: Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles.\nRESULTS: To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80\u00a0%) of our models were found to have some type of aerobic ETC, whereas 5,100 (62\u00a0%) have an anaerobic ETC, and 1,279 (15\u00a0%) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70\u00a0%) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30\u00a0%) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis.\nCONCLUSIONS: We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12864-016-2887-8", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3133834", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023790", 
        "issn": [
          "1471-2164"
        ], 
        "name": "BMC Genomics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "17"
      }
    ], 
    "keywords": [
      "core metabolic model", 
      "electron transport chain", 
      "energy biosynthesis", 
      "metabolic model", 
      "microbial life", 
      "biosynthesis pathway", 
      "functional electron transport chain", 
      "aerobic electron transport chain", 
      "anaerobic electron transport chain", 
      "genome-scale metabolic model", 
      "core metabolic pathways", 
      "anaerobic growth conditions", 
      "microbial tree", 
      "microbial genomes", 
      "model organisms", 
      "phylogenetic analysis", 
      "central metabolism", 
      "improved annotation", 
      "core pathways", 
      "large-scale analysis", 
      "transport chain", 
      "ATP yield", 
      "biosynthesis", 
      "metabolic pathways", 
      "key pathways", 
      "systematic identification", 
      "pathway", 
      "growth conditions", 
      "inconsistent annotations", 
      "annotation", 
      "ATP", 
      "essential biomass precursors", 
      "diverse set", 
      "biomass reactions", 
      "central pathways", 
      "genome", 
      "gapfilling", 
      "microbes", 
      "organisms", 
      "diversity", 
      "metabolism", 
      "trees", 
      "yield", 
      "poor representation", 
      "scientific community", 
      "biomass precursors", 
      "identification", 
      "analysis", 
      "precursors", 
      "community", 
      "energy yield", 
      "ability", 
      "chain", 
      "variability", 
      "conditions", 
      "profile", 
      "types", 
      "extent", 
      "knowledge", 
      "reaction", 
      "tool", 
      "study", 
      "model", 
      "set", 
      "life", 
      "challenges", 
      "lack accuracy", 
      "gap", 
      "need", 
      "method", 
      "flux profiles", 
      "methodology", 
      "correction", 
      "problem", 
      "representation", 
      "accuracy", 
      "core model", 
      "implementation", 
      "bacterial metabolic models", 
      "complex interlinking pathways", 
      "interlinking pathways", 
      "extensive gapfilling", 
      "complex biomass reactions", 
      "unrealistic yields", 
      "unrealistic physiological flux profiles", 
      "physiological flux profiles", 
      "high quality core metabolic models", 
      "quality core metabolic models", 
      "accurate energy biosynthesis", 
      "energy biosynthesis pathways", 
      "accurate ATP yields", 
      "accurate energy yields", 
      "diverse ETC reactions", 
      "ETC reactions"
    ], 
    "name": "Modeling central metabolism and energy biosynthesis across microbial life", 
    "pagination": "568", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1043963135"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12864-016-2887-8"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "27502787"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12864-016-2887-8", 
      "https://app.dimensions.ai/details/publication/pub.1043963135"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-12-01T19:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/article/article_714.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12864-016-2887-8"
  }
]
 

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/s12864-016-2887-8'

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/s12864-016-2887-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12864-016-2887-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12864-016-2887-8'


 

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

273 TRIPLES      22 PREDICATES      138 URIs      123 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12864-016-2887-8 schema:about N157a2e2007ca45efa14048c4feb60c9d
2 N20bb49a33da44e02a747a8b2ea033032
3 N2839b34f6a734e9387139830cdb506a2
4 N64fd0f24bf2e418d8a6135c91809f192
5 N70dcdf93bffc47289835aa55de6bc8bc
6 N7cc2a151e4ea4b7387da3d42a7575899
7 N9f8810a967064c4998072ced1d8f8935
8 Na37c6ea15dec4696a2daab2b2d491ccb
9 Na83316603d454e0696d223063bb0f197
10 Ne3ea55dbbc1340a2a5904eb6e9f4acf9
11 Nf88aa53bfe294159a906b8450a8dd75a
12 anzsrc-for:06
13 anzsrc-for:0605
14 schema:author N36fff564088a48678c3ab48e40de7f23
15 schema:citation sg:pub.10.1007/978-1-4757-3783-7
16 sg:pub.10.1007/s00018-010-0555-8
17 sg:pub.10.1007/s002030050780
18 sg:pub.10.1038/nbt.1672
19 sg:pub.10.1038/nprot.2007.99
20 sg:pub.10.1186/1471-2105-8-212
21 sg:pub.10.1186/1471-2164-9-75
22 schema:datePublished 2016-08-08
23 schema:datePublishedReg 2016-08-08
24 schema:description BACKGROUND: Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. RESULTS: To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. CONCLUSIONS: We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.
25 schema:genre article
26 schema:inLanguage en
27 schema:isAccessibleForFree true
28 schema:isPartOf N0090eba95a0f4e17b52023d53ae1aa84
29 Nd77230bed1644a6eb5069ebf48de2638
30 sg:journal.1023790
31 schema:keywords ATP
32 ATP yield
33 ETC reactions
34 ability
35 accuracy
36 accurate ATP yields
37 accurate energy biosynthesis
38 accurate energy yields
39 aerobic electron transport chain
40 anaerobic electron transport chain
41 anaerobic growth conditions
42 analysis
43 annotation
44 bacterial metabolic models
45 biomass precursors
46 biomass reactions
47 biosynthesis
48 biosynthesis pathway
49 central metabolism
50 central pathways
51 chain
52 challenges
53 community
54 complex biomass reactions
55 complex interlinking pathways
56 conditions
57 core metabolic model
58 core metabolic pathways
59 core model
60 core pathways
61 correction
62 diverse ETC reactions
63 diverse set
64 diversity
65 electron transport chain
66 energy biosynthesis
67 energy biosynthesis pathways
68 energy yield
69 essential biomass precursors
70 extensive gapfilling
71 extent
72 flux profiles
73 functional electron transport chain
74 gap
75 gapfilling
76 genome
77 genome-scale metabolic model
78 growth conditions
79 high quality core metabolic models
80 identification
81 implementation
82 improved annotation
83 inconsistent annotations
84 interlinking pathways
85 key pathways
86 knowledge
87 lack accuracy
88 large-scale analysis
89 life
90 metabolic model
91 metabolic pathways
92 metabolism
93 method
94 methodology
95 microbes
96 microbial genomes
97 microbial life
98 microbial tree
99 model
100 model organisms
101 need
102 organisms
103 pathway
104 phylogenetic analysis
105 physiological flux profiles
106 poor representation
107 precursors
108 problem
109 profile
110 quality core metabolic models
111 reaction
112 representation
113 scientific community
114 set
115 study
116 systematic identification
117 tool
118 transport chain
119 trees
120 types
121 unrealistic physiological flux profiles
122 unrealistic yields
123 variability
124 yield
125 schema:name Modeling central metabolism and energy biosynthesis across microbial life
126 schema:pagination 568
127 schema:productId N4b5c8cc573004dcf9f6282b12f183492
128 N6db1de2c6a4d48d28ee17440e3c5fbf7
129 Ne30d6e1e81314969aa2d2c45706df627
130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043963135
131 https://doi.org/10.1186/s12864-016-2887-8
132 schema:sdDatePublished 2021-12-01T19:38
133 schema:sdLicense https://scigraph.springernature.com/explorer/license/
134 schema:sdPublisher N9ba69a4ca81a47fe9444ca7496990aea
135 schema:url https://doi.org/10.1186/s12864-016-2887-8
136 sgo:license sg:explorer/license/
137 sgo:sdDataset articles
138 rdf:type schema:ScholarlyArticle
139 N0090eba95a0f4e17b52023d53ae1aa84 schema:issueNumber 1
140 rdf:type schema:PublicationIssue
141 N157a2e2007ca45efa14048c4feb60c9d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
142 schema:name Energy Metabolism
143 rdf:type schema:DefinedTerm
144 N20bb49a33da44e02a747a8b2ea033032 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
145 schema:name Bacteria
146 rdf:type schema:DefinedTerm
147 N2839b34f6a734e9387139830cdb506a2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
148 schema:name Phylogeny
149 rdf:type schema:DefinedTerm
150 N36fff564088a48678c3ab48e40de7f23 rdf:first sg:person.015470706405.76
151 rdf:rest Na2e9bd6d58424277a93f93e7880292a0
152 N4b5c8cc573004dcf9f6282b12f183492 schema:name dimensions_id
153 schema:value pub.1043963135
154 rdf:type schema:PropertyValue
155 N53127ae568924102a273f444cf993743 rdf:first sg:person.01134232120.92
156 rdf:rest rdf:nil
157 N64fd0f24bf2e418d8a6135c91809f192 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Genomics
159 rdf:type schema:DefinedTerm
160 N6db1de2c6a4d48d28ee17440e3c5fbf7 schema:name pubmed_id
161 schema:value 27502787
162 rdf:type schema:PropertyValue
163 N70dcdf93bffc47289835aa55de6bc8bc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
164 schema:name Metabolic Networks and Pathways
165 rdf:type schema:DefinedTerm
166 N7cc2a151e4ea4b7387da3d42a7575899 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Adenosine Triphosphate
168 rdf:type schema:DefinedTerm
169 N905745ecb7864a10aadf941299964b42 rdf:first sg:person.01023644620.00
170 rdf:rest Na637c74756d14efc95188b384955a75b
171 N9ba69a4ca81a47fe9444ca7496990aea schema:name Springer Nature - SN SciGraph project
172 rdf:type schema:Organization
173 N9f8810a967064c4998072ced1d8f8935 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
174 schema:name Electron Transport Chain Complex Proteins
175 rdf:type schema:DefinedTerm
176 Na2e9bd6d58424277a93f93e7880292a0 rdf:first sg:person.013210051641.44
177 rdf:rest Nf16bfb36903c45dd8130378d7cfdf6cc
178 Na37c6ea15dec4696a2daab2b2d491ccb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
179 schema:name Biomass
180 rdf:type schema:DefinedTerm
181 Na637c74756d14efc95188b384955a75b rdf:first sg:person.01064013616.82
182 rdf:rest Na97b56623cb745aba726b4ef41071755
183 Na83316603d454e0696d223063bb0f197 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
184 schema:name Models, Biological
185 rdf:type schema:DefinedTerm
186 Na97b56623cb745aba726b4ef41071755 rdf:first sg:person.0707416220.12
187 rdf:rest N53127ae568924102a273f444cf993743
188 Nd77230bed1644a6eb5069ebf48de2638 schema:volumeNumber 17
189 rdf:type schema:PublicationVolume
190 Ne30d6e1e81314969aa2d2c45706df627 schema:name doi
191 schema:value 10.1186/s12864-016-2887-8
192 rdf:type schema:PropertyValue
193 Ne3ea55dbbc1340a2a5904eb6e9f4acf9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
194 schema:name Computational Biology
195 rdf:type schema:DefinedTerm
196 Nf16bfb36903c45dd8130378d7cfdf6cc rdf:first sg:person.01360151361.19
197 rdf:rest N905745ecb7864a10aadf941299964b42
198 Nf88aa53bfe294159a906b8450a8dd75a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
199 schema:name Molecular Sequence Annotation
200 rdf:type schema:DefinedTerm
201 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
202 schema:name Biological Sciences
203 rdf:type schema:DefinedTerm
204 anzsrc-for:0605 schema:inDefinedTermSet anzsrc-for:
205 schema:name Microbiology
206 rdf:type schema:DefinedTerm
207 sg:grant.3133834 http://pending.schema.org/fundedItem sg:pub.10.1186/s12864-016-2887-8
208 rdf:type schema:MonetaryGrant
209 sg:journal.1023790 schema:issn 1471-2164
210 schema:name BMC Genomics
211 schema:publisher Springer Nature
212 rdf:type schema:Periodical
213 sg:person.01023644620.00 schema:affiliation grid-institutes:None
214 schema:familyName Xia
215 schema:givenName Fangfang
216 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023644620.00
217 rdf:type schema:Person
218 sg:person.01064013616.82 schema:affiliation grid-institutes:None
219 schema:familyName Overbeek
220 schema:givenName Ross
221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01064013616.82
222 rdf:type schema:Person
223 sg:person.01134232120.92 schema:affiliation grid-institutes:None
224 schema:familyName Henry
225 schema:givenName Christopher S.
226 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134232120.92
227 rdf:type schema:Person
228 sg:person.013210051641.44 schema:affiliation grid-institutes:None
229 schema:familyName Weisenhorn
230 schema:givenName Pamela
231 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013210051641.44
232 rdf:type schema:Person
233 sg:person.01360151361.19 schema:affiliation grid-institutes:None
234 schema:familyName Conrad
235 schema:givenName Neal
236 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01360151361.19
237 rdf:type schema:Person
238 sg:person.015470706405.76 schema:affiliation grid-institutes:None
239 schema:familyName Edirisinghe
240 schema:givenName Janaka N.
241 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015470706405.76
242 rdf:type schema:Person
243 sg:person.0707416220.12 schema:affiliation grid-institutes:None
244 schema:familyName Stevens
245 schema:givenName Rick L.
246 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707416220.12
247 rdf:type schema:Person
248 sg:pub.10.1007/978-1-4757-3783-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018398741
249 https://doi.org/10.1007/978-1-4757-3783-7
250 rdf:type schema:CreativeWork
251 sg:pub.10.1007/s00018-010-0555-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011581115
252 https://doi.org/10.1007/s00018-010-0555-8
253 rdf:type schema:CreativeWork
254 sg:pub.10.1007/s002030050780 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032368824
255 https://doi.org/10.1007/s002030050780
256 rdf:type schema:CreativeWork
257 sg:pub.10.1038/nbt.1672 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006770979
258 https://doi.org/10.1038/nbt.1672
259 rdf:type schema:CreativeWork
260 sg:pub.10.1038/nprot.2007.99 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016592716
261 https://doi.org/10.1038/nprot.2007.99
262 rdf:type schema:CreativeWork
263 sg:pub.10.1186/1471-2105-8-212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023395418
264 https://doi.org/10.1186/1471-2105-8-212
265 rdf:type schema:CreativeWork
266 sg:pub.10.1186/1471-2164-9-75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013431920
267 https://doi.org/10.1186/1471-2164-9-75
268 rdf:type schema:CreativeWork
269 grid-institutes:None schema:alternateName Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA
270 Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA
271 schema:name Computer Science Department and Computation Institute, University of Chicago, 5640, South Ellis Avenue, Chicago, IL 60637 USA
272 Mathematics and Computer Science Department, Argonne National Laboratory, S. Cass Avenue, Argonne, IL 60439 USA
273 rdf:type schema:Organization
 




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


...