A systems approach to regulatory networks controlling N-assimilation View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

1983-2015

FUNDING AMOUNT

4784274 USD

ABSTRACT

DESCRIPTION (provided by applicant): The long-term goal of this project is to dynamically model the regulatory networks controlling nitrogen (N) uptake/assimilation in plants. The systems biology approaches that integrate genomic data have generated testable hypotheses for regulatory motifs controlling N-uptake/assimilation in response to nitrogen sensing. The overriding hypothesis being tested is that inorganic-N signals (nitrate) activate motifs involved in regulating nitrate uptake, reduction and assimilation into Glu/Gln, used for biosynthetic reactions. The organic-N products (Glu/Gln) in turn activate motifs controlling Asn synthesized for N-storage, and repress ones controlling N- uptake/assimilation. As the key genes for N-assimilation utilize ATP and NADH, we posit that the associated regulatory motif components constitute an energy conservation mechanism, activating N-assimilation when inorganic-N is available, and repressing/storing it when organic-N levels are high. Using mutants, transgenics, and chromatin-IP, roles for transcription factor (TF) hubs, TF-TF motifs, and miRNA-TFs motifs regulating genes in N-assimilation were validated. This renewal proposes to use these validated regulatory components, denoted sentinels, to fuel a new round of genome-scale testing and gene-specific experimentation to seed the growth of the network and to create a time-dependent dynamic visual presentation model that will detail the flow of N-signal propagation through the N-assimilatory regulatory network in four aims: 1. Test hypotheses for the function of validated TFs and TF-motifs in regulating N-uptake/assimilation in response to inorganic-N or organic-N sensing. 2. Use validated TFs as sentinels to fuel genome-wide discovery of interacting partners. Generate time-course transcriptome data and use an inducible transgenic system to identify both direct and indirect targets system-wide. 3. Test hypotheses for post-transcriptional and post-translational mechanisms predicted by the current network models and generate metabolomic data for incorporation into these models. 4. Analyze and visualize the genomic datasets from time-course and transgenic studies, to generate a time- varying (dynamic) combinatorial view of the core regulatory mechanisms, whether transcriptional, post- transcriptional or post-translational, and their effect on propagating the N-signal through the N-assimilation regulatory network. The growth of this first validated metabolic regulatory network in plants will uncover: i) the topology of regulatory networks in plants including the role of network motifs for comparison to other organisms, ii) mechanisms that control N-use efficiency. The synthesis of these aims should allow for modeling, predicting and testing how perturbations of the system may be used to enhance N-use efficiency, which impacts energy-use (fertilizers/biofuels), nitrate contamination of the environment and human nutrition. The systems approach, identification of sentinel genes, related neighbors, conditional expression analysis, and circuit formation can be applied to any species with available genome data and will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with applications to medicine. PUBLIC HEALTH RELEVANCE: Our long-term goal is to combine systems biology, genomic and genetic approaches to model the regulatory networks controlling nitrogen-uptake/assimilation in response to nitrogen signals and interactions. Our proposal aims to allow us to model, predict and test how perturbations of these regulatory networks may be used to enhance N-use efficiency in plants, which will have a significant impact on energy-use, reduce nitrate contamination of the environment and improve human nutrition. Moreover, as the systems biology approaches and tools we have and will continue to develop can be applied to any species for which genome data is available, these studies will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with applications to medicine. More... »

URL

http://projectreporter.nih.gov/project_info_description.cfm?aid=8258262

Related SciGraph Publications

  • 2019-12. Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions. in NATURE COMMUNICATIONS
  • 2018-12. Fungi stabilize connectivity in the lung and skin microbial ecosystems in MICROBIOME
  • 2018-03. Fast analytical methods for finding significant labeled graph motifs in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2018. μChIP-Seq for Genome-Wide Mapping of In Vivo TF-DNA Interactions in Arabidopsis Root Protoplasts in ROOT DEVELOPMENT
  • 2016-12. “Hit-and-Run” transcription: de novo transcription initiated by a transient bZIP1 “hit” persists after the “run” in BMC GENOMICS
  • 2015-12. AtNIGT1/HRS1 integrates nitrate and phosphate signals at the Arabidopsis root tip in NATURE COMMUNICATIONS
  • 2013-12. Integrated RNA-seq and sRNA-seq analysis identifies novel nitrate-responsive genes in Arabidopsis thaliana roots in BMC GENOMICS
  • 2013-06. Gene regulatory networks in plants: learning causality from time and perturbation in GENOME BIOLOGY
  • 2010-12. Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate in GENOME BIOLOGY
  • 2010-12. Modeling the global effect of the basic-leucine zipper transcription factor 1 (bZIP1) on nitrogen and light regulation in Arabidopsis in BMC SYSTEMS BIOLOGY
  • 2009-12. A system biology approach highlights a hormonal enhancer effect on regulation of genes in a nitrate responsive "biomodule" in BMC SYSTEMS BIOLOGY
  • 2009-12. In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana in BMC BIOINFORMATICS
  • 2009-07. A mutation in the ProteosomalRegulatory Particle AAA-ATPase-3 in Arabidopsis impairs the light-specific hypocotyl elongation response elicited by a glutamate receptor agonist, BMAA in PLANT MOLECULAR BIOLOGY
  • 2008-12. An integrated genetic, genomic and systems approach defines gene networks regulated by the interaction of light and carbon signaling pathways in Arabidopsis in BMC SYSTEMS BIOLOGY
  • 2007-01. Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis in GENOME BIOLOGY
  • 2004-10. Genome-wide patterns of carbon and nitrogen regulation of gene expression validate the combined carbon and nitrogen (CN)-signaling hypothesis in plants in GENOME BIOLOGY
  • 2004-01. Genome-wide investigation of light and carbon signaling interactions in Arabidopsis in GENOME BIOLOGY
  • 2003-12. Expressed sequence tag analysis in Cycas, the most primitive living seed plant in GENOME BIOLOGY
  • 1995-12. Molecular evolution of duplicate copies of genes encoding cytosolic glutamine synthetase in Pisum sativum in PLANT MOLECULAR BIOLOGY
  • 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/2008/2206", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }
        ], 
        "amount": {
          "currency": "USD", 
          "type": "MonetaryAmount", 
          "value": "4784274"
        }, 
        "description": "DESCRIPTION (provided by applicant): The long-term goal of this project is to dynamically model the regulatory networks controlling nitrogen (N) uptake/assimilation in plants. The systems biology approaches that integrate genomic data have generated testable hypotheses for regulatory motifs controlling N-uptake/assimilation in response to nitrogen sensing. The overriding hypothesis being tested is that inorganic-N signals (nitrate) activate motifs involved in regulating nitrate uptake, reduction and assimilation into Glu/Gln, used for biosynthetic reactions. The organic-N products (Glu/Gln) in turn activate motifs controlling Asn synthesized for N-storage, and repress ones controlling N- uptake/assimilation. As the key genes for N-assimilation utilize ATP and NADH, we posit that the associated regulatory motif components constitute an energy conservation mechanism, activating N-assimilation when inorganic-N is available, and repressing/storing it when organic-N levels are high. Using mutants, transgenics, and chromatin-IP, roles for transcription factor (TF) hubs, TF-TF motifs, and miRNA-TFs motifs regulating genes in N-assimilation were validated. This renewal proposes to use these validated regulatory components, denoted sentinels, to fuel a new round of genome-scale testing and gene-specific experimentation to seed the growth of the network and to create a time-dependent dynamic visual presentation model that will detail the flow of N-signal propagation through the N-assimilatory regulatory network in four aims: 1. Test hypotheses for the function of validated TFs and TF-motifs in regulating N-uptake/assimilation in response to inorganic-N or organic-N sensing. 2. Use validated TFs as sentinels to fuel genome-wide discovery of interacting partners. Generate time-course transcriptome data and use an inducible transgenic system to identify both direct and indirect targets system-wide. 3. Test hypotheses for post-transcriptional and post-translational mechanisms predicted by the current network models and generate metabolomic data for incorporation into these models. 4. Analyze and visualize the genomic datasets from time-course and transgenic studies, to generate a time- varying (dynamic) combinatorial view of the core regulatory mechanisms, whether transcriptional, post- transcriptional or post-translational, and their effect on propagating the N-signal through the N-assimilation regulatory network. The growth of this first validated metabolic regulatory network in plants will uncover: i) the topology of regulatory networks in plants including the role of network motifs for comparison to other organisms, ii) mechanisms that control N-use efficiency. The synthesis of these aims should allow for modeling, predicting and testing how perturbations of the system may be used to enhance N-use efficiency, which impacts energy-use (fertilizers/biofuels), nitrate contamination of the environment and human nutrition. The systems approach, identification of sentinel genes, related neighbors, conditional expression analysis, and circuit formation can be applied to any species with available genome data and will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with applications to medicine. PUBLIC HEALTH RELEVANCE: Our long-term goal is to combine systems biology, genomic and genetic approaches to model the regulatory networks controlling nitrogen-uptake/assimilation in response to nitrogen signals and interactions. Our proposal aims to allow us to model, predict and test how perturbations of these regulatory networks may be used to enhance N-use efficiency in plants, which will have a significant impact on energy-use, reduce nitrate contamination of the environment and improve human nutrition. Moreover, as the systems biology approaches and tools we have and will continue to develop can be applied to any species for which genome data is available, these studies will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with applications to medicine.", 
        "endDate": "2015-04-30T00:00:00Z", 
        "funder": {
          "id": "https://www.grid.ac/institutes/grid.280785.0", 
          "type": "Organization"
        }, 
        "id": "sg:grant.2510586", 
        "identifier": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "2510586"
            ]
          }, 
          {
            "name": "nih_id", 
            "type": "PropertyValue", 
            "value": [
              "R01GM032877"
            ]
          }
        ], 
        "inLanguage": [
          "en"
        ], 
        "keywords": [
          "inducible transgenic system", 
          "hub", 
          "species", 
          "NADH", 
          "post-translational", 
          "use efficiency", 
          "function", 
          "one", 
          "miRNA-TFs motifs", 
          "comparison", 
          "renewal", 
          "system", 
          "TF-motifs", 
          "uptake", 
          "levels", 
          "metabolic regulatory networks", 
          "identification", 
          "long-term goal", 
          "regulatory motif", 
          "storage", 
          "mutants", 
          "Analyze", 
          "signal propagation", 
          "sentinel gene", 
          "assimilatory regulatory network", 
          "growth", 
          "public health relevance", 
          "genome-wide discovery", 
          "genes", 
          "nitrate uptake", 
          "transcription factors", 
          "plants", 
          "testable hypotheses", 
          "post", 
          "transgenic", 
          "time-dependent dynamic visual presentation model", 
          "description", 
          "turn", 
          "applicants", 
          "chromatin-IP", 
          "ATP", 
          "key genes", 
          "mechanism", 
          "N-use efficiency", 
          "genome data", 
          "time- varying", 
          "N- uptake/assimilation", 
          "proposal", 
          "interaction", 
          "environment", 
          "nitrogen sensing", 
          "biosynthetic reactions", 
          "activate motifs", 
          "Asn", 
          "nitrogen", 
          "post-translational mechanisms", 
          "response", 
          "network", 
          "metabolomics data", 
          "time-course", 
          "systems approach", 
          "perturbations", 
          "nitrate contamination", 
          "model", 
          "circuit formation", 
          "systems biology", 
          "synthesis", 
          "associated regulatory motif components", 
          "role", 
          "project", 
          "energy conservation mechanism", 
          "assimilation", 
          "biology", 
          "regulatory circuits", 
          "nitrogen signal", 
          "incorporation", 
          "aim", 
          "energy-use", 
          "genome-scale testing", 
          "systems biology approach", 
          "signal", 
          "broad spectrum", 
          "significant impact", 
          "effect", 
          "new round", 
          "flow", 
          "tool", 
          "gene-specific experimentation", 
          "hypothesis", 
          "sentinel", 
          "transgenic studies", 
          "sensing", 
          "regulatory networks", 
          "genomic data", 
          "current network models", 
          "study", 
          "topology", 
          "core", 
          "nitrate", 
          "related neighbors", 
          "genetic approaches", 
          "post- transcriptional", 
          "test hypotheses", 
          "application", 
          "indirect targets system", 
          "overriding hypothesis", 
          "researchers", 
          "reduction", 
          "modeling", 
          "nitrogen-uptake", 
          "TF-TF motifs", 
          "combinatorial view", 
          "other organisms", 
          "conditional expression analysis", 
          "network motifs", 
          "products", 
          "Glu/Gln", 
          "available genome data", 
          "human nutrition", 
          "fertilizers/biofuels", 
          "partners", 
          "genomic datasets", 
          "medicine", 
          "regulatory components", 
          "regulatory mechanisms", 
          "Generate time-course transcriptome data", 
          "uptake/assimilation", 
          "motifs"
        ], 
        "name": "A systems approach to regulatory networks controlling N-assimilation", 
        "recipient": [
          {
            "id": "https://www.grid.ac/institutes/grid.137628.9", 
            "type": "Organization"
          }, 
          {
            "affiliation": {
              "id": "https://www.grid.ac/institutes/grid.137628.9", 
              "name": "NEW YORK UNIVERSITY", 
              "type": "Organization"
            }, 
            "familyName": "CORUZZI", 
            "givenName": "GLORIA M", 
            "id": "sg:person.012675371174.46", 
            "type": "Person"
          }, 
          {
            "member": "sg:person.012675371174.46", 
            "roleName": "PI", 
            "type": "Role"
          }
        ], 
        "sameAs": [
          "https://app.dimensions.ai/details/grant/grant.2510586"
        ], 
        "sdDataset": "grants", 
        "sdDatePublished": "2021-01-19T03:12", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com.uberresearch.data.processor/core_data/20181219_192338/projects/base/nih_projects_9.xml.gz", 
        "startDate": "1983-12-01T00:00:00Z", 
        "type": "MonetaryGrant", 
        "url": "http://projectreporter.nih.gov/project_info_description.cfm?aid=8258262"
      }
    ]
     

    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/grant.2510586'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.2510586'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.2510586'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.2510586'


     

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

    172 TRIPLES      19 PREDICATES      150 URIs      142 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:grant.2510586 schema:about anzsrc-for:2206
    2 schema:amount Nc643ae05492545869eb2a771173730da
    3 schema:description DESCRIPTION (provided by applicant): The long-term goal of this project is to dynamically model the regulatory networks controlling nitrogen (N) uptake/assimilation in plants. The systems biology approaches that integrate genomic data have generated testable hypotheses for regulatory motifs controlling N-uptake/assimilation in response to nitrogen sensing. The overriding hypothesis being tested is that inorganic-N signals (nitrate) activate motifs involved in regulating nitrate uptake, reduction and assimilation into Glu/Gln, used for biosynthetic reactions. The organic-N products (Glu/Gln) in turn activate motifs controlling Asn synthesized for N-storage, and repress ones controlling N- uptake/assimilation. As the key genes for N-assimilation utilize ATP and NADH, we posit that the associated regulatory motif components constitute an energy conservation mechanism, activating N-assimilation when inorganic-N is available, and repressing/storing it when organic-N levels are high. Using mutants, transgenics, and chromatin-IP, roles for transcription factor (TF) hubs, TF-TF motifs, and miRNA-TFs motifs regulating genes in N-assimilation were validated. This renewal proposes to use these validated regulatory components, denoted sentinels, to fuel a new round of genome-scale testing and gene-specific experimentation to seed the growth of the network and to create a time-dependent dynamic visual presentation model that will detail the flow of N-signal propagation through the N-assimilatory regulatory network in four aims: 1. Test hypotheses for the function of validated TFs and TF-motifs in regulating N-uptake/assimilation in response to inorganic-N or organic-N sensing. 2. Use validated TFs as sentinels to fuel genome-wide discovery of interacting partners. Generate time-course transcriptome data and use an inducible transgenic system to identify both direct and indirect targets system-wide. 3. Test hypotheses for post-transcriptional and post-translational mechanisms predicted by the current network models and generate metabolomic data for incorporation into these models. 4. Analyze and visualize the genomic datasets from time-course and transgenic studies, to generate a time- varying (dynamic) combinatorial view of the core regulatory mechanisms, whether transcriptional, post- transcriptional or post-translational, and their effect on propagating the N-signal through the N-assimilation regulatory network. The growth of this first validated metabolic regulatory network in plants will uncover: i) the topology of regulatory networks in plants including the role of network motifs for comparison to other organisms, ii) mechanisms that control N-use efficiency. The synthesis of these aims should allow for modeling, predicting and testing how perturbations of the system may be used to enhance N-use efficiency, which impacts energy-use (fertilizers/biofuels), nitrate contamination of the environment and human nutrition. The systems approach, identification of sentinel genes, related neighbors, conditional expression analysis, and circuit formation can be applied to any species with available genome data and will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with applications to medicine. PUBLIC HEALTH RELEVANCE: Our long-term goal is to combine systems biology, genomic and genetic approaches to model the regulatory networks controlling nitrogen-uptake/assimilation in response to nitrogen signals and interactions. Our proposal aims to allow us to model, predict and test how perturbations of these regulatory networks may be used to enhance N-use efficiency in plants, which will have a significant impact on energy-use, reduce nitrate contamination of the environment and improve human nutrition. Moreover, as the systems biology approaches and tools we have and will continue to develop can be applied to any species for which genome data is available, these studies will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with applications to medicine.
    4 schema:endDate 2015-04-30T00:00:00Z
    5 schema:funder https://www.grid.ac/institutes/grid.280785.0
    6 schema:identifier N14a80f70057f4a1c85089ce9cfa6a25e
    7 N8060736266104960b14768795c09464b
    8 schema:inLanguage en
    9 schema:keywords ATP
    10 Analyze
    11 Asn
    12 Generate time-course transcriptome data
    13 Glu/Gln
    14 N- uptake/assimilation
    15 N-use efficiency
    16 NADH
    17 TF-TF motifs
    18 TF-motifs
    19 activate motifs
    20 aim
    21 applicants
    22 application
    23 assimilation
    24 assimilatory regulatory network
    25 associated regulatory motif components
    26 available genome data
    27 biology
    28 biosynthetic reactions
    29 broad spectrum
    30 chromatin-IP
    31 circuit formation
    32 combinatorial view
    33 comparison
    34 conditional expression analysis
    35 core
    36 current network models
    37 description
    38 effect
    39 energy conservation mechanism
    40 energy-use
    41 environment
    42 fertilizers/biofuels
    43 flow
    44 function
    45 gene-specific experimentation
    46 genes
    47 genetic approaches
    48 genome data
    49 genome-scale testing
    50 genome-wide discovery
    51 genomic data
    52 genomic datasets
    53 growth
    54 hub
    55 human nutrition
    56 hypothesis
    57 identification
    58 incorporation
    59 indirect targets system
    60 inducible transgenic system
    61 interaction
    62 key genes
    63 levels
    64 long-term goal
    65 mechanism
    66 medicine
    67 metabolic regulatory networks
    68 metabolomics data
    69 miRNA-TFs motifs
    70 model
    71 modeling
    72 motifs
    73 mutants
    74 network
    75 network motifs
    76 new round
    77 nitrate
    78 nitrate contamination
    79 nitrate uptake
    80 nitrogen
    81 nitrogen sensing
    82 nitrogen signal
    83 nitrogen-uptake
    84 one
    85 other organisms
    86 overriding hypothesis
    87 partners
    88 perturbations
    89 plants
    90 post
    91 post- transcriptional
    92 post-translational
    93 post-translational mechanisms
    94 products
    95 project
    96 proposal
    97 public health relevance
    98 reduction
    99 regulatory circuits
    100 regulatory components
    101 regulatory mechanisms
    102 regulatory motif
    103 regulatory networks
    104 related neighbors
    105 renewal
    106 researchers
    107 response
    108 role
    109 sensing
    110 sentinel
    111 sentinel gene
    112 signal
    113 signal propagation
    114 significant impact
    115 species
    116 storage
    117 study
    118 synthesis
    119 system
    120 systems approach
    121 systems biology
    122 systems biology approach
    123 test hypotheses
    124 testable hypotheses
    125 time- varying
    126 time-course
    127 time-dependent dynamic visual presentation model
    128 tool
    129 topology
    130 transcription factors
    131 transgenic
    132 transgenic studies
    133 turn
    134 uptake
    135 uptake/assimilation
    136 use efficiency
    137 schema:name A systems approach to regulatory networks controlling N-assimilation
    138 schema:recipient Nbe42d6c7b0b2445a8003e0f7b2c2d335
    139 sg:person.012675371174.46
    140 https://www.grid.ac/institutes/grid.137628.9
    141 schema:sameAs https://app.dimensions.ai/details/grant/grant.2510586
    142 schema:sdDatePublished 2021-01-19T03:12
    143 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    144 schema:sdPublisher Nc538340a9d7348e096a261060970a91c
    145 schema:startDate 1983-12-01T00:00:00Z
    146 schema:url http://projectreporter.nih.gov/project_info_description.cfm?aid=8258262
    147 sgo:license sg:explorer/license/
    148 sgo:sdDataset grants
    149 rdf:type schema:MonetaryGrant
    150 N14a80f70057f4a1c85089ce9cfa6a25e schema:name nih_id
    151 schema:value R01GM032877
    152 rdf:type schema:PropertyValue
    153 N8060736266104960b14768795c09464b schema:name dimensions_id
    154 schema:value 2510586
    155 rdf:type schema:PropertyValue
    156 Nbe42d6c7b0b2445a8003e0f7b2c2d335 schema:member sg:person.012675371174.46
    157 schema:roleName PI
    158 rdf:type schema:Role
    159 Nc538340a9d7348e096a261060970a91c schema:name Springer Nature - SN SciGraph project
    160 rdf:type schema:Organization
    161 Nc643ae05492545869eb2a771173730da schema:currency USD
    162 schema:value 4784274
    163 rdf:type schema:MonetaryAmount
    164 anzsrc-for:2206 schema:inDefinedTermSet anzsrc-for:
    165 rdf:type schema:DefinedTerm
    166 sg:person.012675371174.46 schema:affiliation https://www.grid.ac/institutes/grid.137628.9
    167 schema:familyName CORUZZI
    168 schema:givenName GLORIA M
    169 rdf:type schema:Person
    170 https://www.grid.ac/institutes/grid.137628.9 schema:name NEW YORK UNIVERSITY
    171 rdf:type schema:Organization
    172 https://www.grid.ac/institutes/grid.280785.0 schema:Organization
     




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


    ...