ME: In Silico Analysis of the Escherichia coli Metabolic Genotype and the Construction of Selected Isogenic Strains View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

1999-2002

FUNDING AMOUNT

270758 USD

ABSTRACT

The objective of this project is to develop analytical tools that could be applied to genomic sequence information in order to predict the complete phenotype of an organism. This research is a combined effort between investigators at the University of California, San Diego (UCSD) and Harvard University (HU). The research will involve a dynamic interaction between algorithm development and mathematical representations of phenotypes (in silico analysis) and experimentally determined patterns of gene expression and growth. The in silico work will be performed at UCSD and the experimental work will be performed at HU. The three specific goals outlined are: (1) to develop algorithms that can use bioinformatic data bases to identify genes and their functions, predict the metabolic biochemistry a cell can perform, and represent the physiological behavior of a given strain based on its metabolism, (2) to develop algorithms that will predict the impact of altering the genotype on growth, metabolic flux and pathway utilization, and predict changes in genome-wide patterns of gene expression as a function of changes in growth conditions, and (3) to develop experimental approaches using E. coli K-12 to directly test the predictions made by in silico analysis. The approaches will involve the use of DNA chip technology to monitor genome-wide changes in gene expression in wild type cells under well-defined environmental conditions and the rigorous construction of isogenic strains whose metabolic activities can be experimentally determined and compared to the predictions generated by the in silico analysis. This Award is part of the Interagency Activity in Metabolic Engineering (NSF 98-49). More... »

URL

http://www.nsf.gov/awardsearch/showAward?AWD_ID=9814092&HistoricalAwards=false

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": "270758"
    }, 
    "description": "The objective of this project is to develop analytical tools that could be applied to genomic sequence information in order to predict the complete phenotype of an organism. This research is a combined effort between investigators at the University of California, San Diego (UCSD) and Harvard University (HU). The research will involve a dynamic interaction between algorithm development and mathematical representations of phenotypes (in silico analysis) and experimentally determined patterns of gene expression and growth. The in silico work will be performed at UCSD and the experimental work will be performed at HU. The three specific goals outlined are: (1) to develop algorithms that can use bioinformatic data bases to identify genes and their functions, predict the metabolic biochemistry a cell can perform, and represent the physiological behavior of a given strain based on its metabolism, (2) to develop algorithms that will predict the impact of altering the genotype on growth, metabolic flux and pathway utilization, and predict changes in genome-wide patterns of gene expression as a function of changes in growth conditions, and (3) to develop experimental approaches using E. coli K-12 to directly test the predictions made by in silico analysis. The approaches will involve the use of DNA chip technology to monitor genome-wide changes in gene expression in wild type cells under well-defined environmental conditions and the rigorous construction of isogenic strains whose metabolic activities can be experimentally determined and compared to the predictions generated by the in silico analysis. This Award is part of the Interagency Activity in Metabolic Engineering (NSF 98-49).", 
    "endDate": "2002-02-28T00:00:00Z", 
    "funder": {
      "id": "https://www.grid.ac/institutes/grid.457810.f", 
      "type": "Organization"
    }, 
    "id": "sg:grant.3465017", 
    "identifier": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "3465017"
        ]
      }, 
      {
        "name": "nsf_id", 
        "type": "PropertyValue", 
        "value": [
          "9814092"
        ]
      }
    ], 
    "inLanguage": [
      "en"
    ], 
    "keywords": [
      "NSF 98", 
      "phenotype", 
      "award", 
      "genomic sequence information", 
      "University", 
      "experimental work", 
      "strains", 
      "San Diego", 
      "cells", 
      "wild type cells", 
      "prediction", 
      "impact", 
      "genome-wide patterns", 
      "investigators", 
      "function", 
      "mathematical representation", 
      "silico analysis", 
      "pathway utilization", 
      "environmental conditions", 
      "E.", 
      "OBJECTIVE", 
      "determined patterns", 
      "growth", 
      "gene expression", 
      "California", 
      "part", 
      "metabolism", 
      "rigorous construction", 
      "interagency activities", 
      "dynamic interaction", 
      "research", 
      "Selected Isogenic Strains", 
      "project", 
      "DNA chip technology", 
      "algorithm development", 
      "isogenic strains", 
      "physiological behavior", 
      "growth conditions", 
      "order", 
      "metabolic flux", 
      "Harvard University", 
      "changes", 
      "bioinformatic data bases", 
      "genotypes", 
      "approach", 
      "metabolic engineering", 
      "complete phenotype", 
      "K-12", 
      "organisms", 
      "silico work", 
      "specific goals", 
      "genes", 
      "experimental approach", 
      "Escherichia coli Metabolic Genotype", 
      "construction", 
      "use", 
      "analytical tools", 
      "combined efforts", 
      "metabolic activity", 
      "Me", 
      "genome-wide changes", 
      "algorithms", 
      "UCSD", 
      "metabolic biochemistry"
    ], 
    "name": "ME: In Silico Analysis of the Escherichia coli Metabolic Genotype and the Construction of Selected Isogenic Strains", 
    "recipient": [
      {
        "id": "https://www.grid.ac/institutes/grid.266100.3", 
        "type": "Organization"
      }, 
      {
        "affiliation": {
          "id": "https://www.grid.ac/institutes/grid.266100.3", 
          "name": "University of California-San Diego", 
          "type": "Organization"
        }, 
        "familyName": "Palsson", 
        "givenName": "Bernhard", 
        "id": "sg:person.011260472057.92", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.011260472057.92", 
        "roleName": "PI", 
        "type": "Role"
      }
    ], 
    "sameAs": [
      "https://app.dimensions.ai/details/grant/grant.3465017"
    ], 
    "sdDataset": "grants", 
    "sdDatePublished": "2019-03-07T12:32", 
    "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/nsf_projects_23.xml.gz", 
    "startDate": "1999-03-01T00:00:00Z", 
    "type": "MonetaryGrant", 
    "url": "http://www.nsf.gov/awardsearch/showAward?AWD_ID=9814092&HistoricalAwards=false"
  }
]
 

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.3465017'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.3465017'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.3465017'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.3465017'


 

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

108 TRIPLES      19 PREDICATES      86 URIs      78 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:grant.3465017 schema:about anzsrc-for:2206
2 schema:amount N9a65c3257b824681b25c7fabebb10eec
3 schema:description The objective of this project is to develop analytical tools that could be applied to genomic sequence information in order to predict the complete phenotype of an organism. This research is a combined effort between investigators at the University of California, San Diego (UCSD) and Harvard University (HU). The research will involve a dynamic interaction between algorithm development and mathematical representations of phenotypes (in silico analysis) and experimentally determined patterns of gene expression and growth. The in silico work will be performed at UCSD and the experimental work will be performed at HU. The three specific goals outlined are: (1) to develop algorithms that can use bioinformatic data bases to identify genes and their functions, predict the metabolic biochemistry a cell can perform, and represent the physiological behavior of a given strain based on its metabolism, (2) to develop algorithms that will predict the impact of altering the genotype on growth, metabolic flux and pathway utilization, and predict changes in genome-wide patterns of gene expression as a function of changes in growth conditions, and (3) to develop experimental approaches using E. coli K-12 to directly test the predictions made by in silico analysis. The approaches will involve the use of DNA chip technology to monitor genome-wide changes in gene expression in wild type cells under well-defined environmental conditions and the rigorous construction of isogenic strains whose metabolic activities can be experimentally determined and compared to the predictions generated by the in silico analysis. This Award is part of the Interagency Activity in Metabolic Engineering (NSF 98-49).
4 schema:endDate 2002-02-28T00:00:00Z
5 schema:funder https://www.grid.ac/institutes/grid.457810.f
6 schema:identifier N2733636d34524128b2b0790ca735e03a
7 N2f664dab8f8f41058ddedb25a30a80e4
8 schema:inLanguage en
9 schema:keywords California
10 DNA chip technology
11 E.
12 Escherichia coli Metabolic Genotype
13 Harvard University
14 K-12
15 Me
16 NSF 98
17 OBJECTIVE
18 San Diego
19 Selected Isogenic Strains
20 UCSD
21 University
22 algorithm development
23 algorithms
24 analytical tools
25 approach
26 award
27 bioinformatic data bases
28 cells
29 changes
30 combined efforts
31 complete phenotype
32 construction
33 determined patterns
34 dynamic interaction
35 environmental conditions
36 experimental approach
37 experimental work
38 function
39 gene expression
40 genes
41 genome-wide changes
42 genome-wide patterns
43 genomic sequence information
44 genotypes
45 growth
46 growth conditions
47 impact
48 interagency activities
49 investigators
50 isogenic strains
51 mathematical representation
52 metabolic activity
53 metabolic biochemistry
54 metabolic engineering
55 metabolic flux
56 metabolism
57 order
58 organisms
59 part
60 pathway utilization
61 phenotype
62 physiological behavior
63 prediction
64 project
65 research
66 rigorous construction
67 silico analysis
68 silico work
69 specific goals
70 strains
71 use
72 wild type cells
73 schema:name ME: In Silico Analysis of the Escherichia coli Metabolic Genotype and the Construction of Selected Isogenic Strains
74 schema:recipient N98b861cabc344cac97857811e145feba
75 sg:person.011260472057.92
76 https://www.grid.ac/institutes/grid.266100.3
77 schema:sameAs https://app.dimensions.ai/details/grant/grant.3465017
78 schema:sdDatePublished 2019-03-07T12:32
79 schema:sdLicense https://scigraph.springernature.com/explorer/license/
80 schema:sdPublisher N5db64be79acd44b2aff55cc0a4cc5cb3
81 schema:startDate 1999-03-01T00:00:00Z
82 schema:url http://www.nsf.gov/awardsearch/showAward?AWD_ID=9814092&HistoricalAwards=false
83 sgo:license sg:explorer/license/
84 sgo:sdDataset grants
85 rdf:type schema:MonetaryGrant
86 N2733636d34524128b2b0790ca735e03a schema:name nsf_id
87 schema:value 9814092
88 rdf:type schema:PropertyValue
89 N2f664dab8f8f41058ddedb25a30a80e4 schema:name dimensions_id
90 schema:value 3465017
91 rdf:type schema:PropertyValue
92 N5db64be79acd44b2aff55cc0a4cc5cb3 schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 N98b861cabc344cac97857811e145feba schema:member sg:person.011260472057.92
95 schema:roleName PI
96 rdf:type schema:Role
97 N9a65c3257b824681b25c7fabebb10eec schema:currency USD
98 schema:value 270758
99 rdf:type schema:MonetaryAmount
100 anzsrc-for:2206 schema:inDefinedTermSet anzsrc-for:
101 rdf:type schema:DefinedTerm
102 sg:person.011260472057.92 schema:affiliation https://www.grid.ac/institutes/grid.266100.3
103 schema:familyName Palsson
104 schema:givenName Bernhard
105 rdf:type schema:Person
106 https://www.grid.ac/institutes/grid.266100.3 schema:name University of California-San Diego
107 rdf:type schema:Organization
108 https://www.grid.ac/institutes/grid.457810.f schema:Organization
 




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


...