Center for Quantitative Biology View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2004-2015

FUNDING AMOUNT

30635291 USD

ABSTRACT

The overarching goal of the proposed Center for Quantitative Biology remains to instantiate at Princeton a research and teaching environment that fully meets the challenge and opportunity, presented by advances in computation and genomics, to practice a usefully quantitative biological science, sometimes referred to as systems biology. The programs and infrastructure of the Center increase the bandwidth of communication among researchers from different disciplines and departments (including Molecular Biology, Ecology and Evolutionary Biology, Computer Science, Chemistry and Physics). One in four (54/217) papers published with Center support in the last four years is a joint publication between two or more Center faculty. For both undergraduate and graduate students, the Center provides a focus for multidisciplinary teaching and learning; quantitative and biological ideas are integrated from the beginning, with the result that students acquire nearly equal facility in biological and quantitative thinking. The Center's specific aims are: (1) to develop realistic and quantitative models of biological processes; (2) to collect large-scale data sets that comprehensively describe biological processes; (3) to devise new and improved methods for computational analysis and display of complex models, structures and data and to make, upon publication, all underlying data, algorithms and analytical systems publicly accessible; (4) to devise and support new curricula and courses of quantitative biology education for undergraduates, graduate students, and the larger scientific community; and (5) to reduce these ideas to practice in several collaborative and multi-disciplinary projects, each aimed at specific system-level questions in the subject areas of (i) intracellular signaling, (ii) pattern and cell signaling in multicellular organisms, (iii) host-pathogen interactions and (iv) bioinformatics and data visualization. These projects share common quantitative goals, require a common infrastructure (i.e. computation, microarray, imaging and metabolomics core facilities) and all of them benefit from the intellectual synergy and multi-disciplinary cooperation that the Center provides. More... »

URL

http://projectreporter.nih.gov/project_info_description.cfm?aid=8534148

Related SciGraph Publications

  • 2018-12. Evidence for Regulation of Hemoglobin Metabolism and Intracellular Ionic Flux by the Plasmodium falciparum Chloroquine Resistance Transporter in SCIENTIFIC REPORTS
  • 2018-12. Alternative polyadenylation factors link cell cycle to migration in GENOME BIOLOGY
  • 2018. Live Imaging of mRNA Synthesis in Drosophila in RNA DETECTION
  • 2017-12. Widespread changes in mRNA stability contribute to quiescence-specific gene expression patterns in a fibroblast model of quiescence in BMC GENOMICS
  • 2016-11. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder in NATURE NEUROSCIENCE
  • 2016-04. Proteome-wide analysis reveals widespread lysine acetylation of major protein complexes in the malaria parasite in SCIENTIFIC REPORTS
  • 2016-02-04. Probabilistic modelling of chromatin code landscape reveals functional diversity of enhancer-like chromatin states in NATURE COMMUNICATIONS
  • 2016. Biophysical Measurements of Bacterial Cell Shape in BACTERIAL CELL WALL HOMEOSTASIS
  • 2015-12. Genome-wide transcriptome profiling reveals functional networks involving the Plasmodium falciparum drug resistance transporters PfCRT and PfMDR1 in BMC GENOMICS
  • 2015-10. Predicting effects of noncoding variants with deep learning-based sequence model in NATURE METHODS
  • 2015-06. Understanding multicellular function and disease with human tissue-specific networks in NATURE GENETICS
  • 2015-04. Inhibition facilitates direction selectivity in a noisy cortical environment in JOURNAL OF COMPUTATIONAL NEUROSCIENCE
  • 2015-03. Targeted exploration and analysis of large cross-platform human transcriptomic compendia in NATURE METHODS
  • 2014-12. Characterizing a collective and dynamic component of chromatin immunoprecipitation enrichment profiles in yeast in BMC GENOMICS
  • 2014-12. Quantitative analysis of acetyl-CoA production in hypoxic cancer cells reveals substantial contribution from acetate in CANCER & METABOLISM
  • 2014-12. Simultaneous cellular-resolution optical perturbation and imaging of place cell firing fields in NATURE NEUROSCIENCE
  • 2014-06. Quantitative flux analysis reveals folate-dependent NADPH production in NATURE
  • 2014-05. LC-MS and GC-MS based metabolomics platform for cancer research in CANCER & METABOLISM
  • 2014-04. Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection in GENOME BIOLOGY
  • 2014-04. Apical constriction drives tissue-scale hydrodynamic flow to mediate cell elongation in NATURE
  • 2014-02-23. A transcriptional switch underlies commitment to sexual development in malaria parasites in NATURE
  • 2014-02-23. A cascade of DNA-binding proteins for sexual commitment and development in Plasmodium in NATURE
  • 2014. SELEX-seq: A Method for Characterizing the Complete Repertoire of Binding Site Preferences for Transcription Factor Complexes in HOX GENES
  • 2013-12. Comparative gene expression between two yeast species in BMC GENOMICS
  • 2013-12. Disentangling function from topology to infer the network properties of disease genes in BMC SYSTEMS BIOLOGY
  • 2013-08. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations in NATURE
  • 2013-02-03. Finding the sources of missing heritability in a yeast cross in NATURE
  • 2012-12. A microRNA network regulates proliferative timing and extracellular matrix synthesis during cellular quiescence in fibroblasts in GENOME BIOLOGY
  • 2012-12. New Agilent platform DNA microarrays for transcriptome analysis of Plasmodium falciparum and Plasmodium berghei for the malaria research community in MALARIA JOURNAL
  • 2012-12. Poly-acetylated chromatin signatures are preferred epitopes for site-specific histone H4 acetyl antibodies in SCIENTIFIC REPORTS
  • 2012-12. A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis in GENOME MEDICINE
  • 2012-12. Multiparameter behavioral profiling reveals distinct thermal response regimes in Caenorhabditis elegans in BMC BIOLOGY
  • 2012-10. Site-specific genome editing in Plasmodium falciparum using engineered zinc-finger nucleases in NATURE METHODS
  • 2012-08. Proteogenomic characterization and mapping of nucleosomes decoded by Brd and HP1 proteins in GENOME BIOLOGY
  • 2012-06. Mutability and mutational spectrum of chromosome transmission fidelity genes in CHROMOSOMA
  • 2012-06. Ultrasensitive regulation of anapleurosis via allosteric activation of PEP carboxylase in NATURE CHEMICAL BIOLOGY
  • 2012-05. Drosophila Src regulates anisotropic apical surface growth to control epithelial tube size in NATURE CELL BIOLOGY
  • 2012-03-28. Differential positioning of adherens junctions is associated with initiation of epithelial folding in NATURE
  • 2012-03. Chromosome-scale selective sweeps shape Caenorhabditis elegans genomic diversity in NATURE GENETICS
  • 2012. Integrative Approaches for Microarray Data Analysis in NEXT GENERATION MICROARRAY BIOINFORMATICS
  • 2012. Two Flavors of Bulk Segregant Analysis in Yeast in QUANTITATIVE TRAIT LOCI (QTL)
  • 2012. Extraction of Hydrophilic Metabolites from Plasmodium falciparum-Infected Erythrocytes for Metabolomic Analysis in MALARIA
  • 2012. Whole-Genome Analysis of Plasmodium spp. Utilizing a New Agilent Technologies DNA Microarray Platform in MALARIA
  • 2011-12. α-ketoglutarate coordinates carbon and nitrogen utilization via enzyme I inhibition in NATURE CHEMICAL BIOLOGY
  • 2011-12. Nucleosome-coupled expression differences in closely-related species in BMC GENOMICS
  • 2011-12. Accurate proteome-wide protein quantification from high-resolution 15N mass spectra in GENOME BIOLOGY
  • 2011-12. Plasmodium falciparum glutamate dehydrogenase a is dispensable and not a drug target during erythrocytic development in MALARIA JOURNAL
  • 2011-12. Using context to improve protein domain identification in BMC BIOINFORMATICS
  • 2011-09. Active sampling and decision making in Drosophila chemotaxis in NATURE COMMUNICATIONS
  • 2011-06-29. Site-Specific Self-Catalyzed DNA Depurination, the Basis of a Spontaneous Mutagenic Mechanism of Wide Evolutionary Significance in EVOLUTIONARY BIOLOGY – CONCEPTS, BIODIVERSITY, MACROEVOLUTION AND GENOME EVOLUTION
  • 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/2008/2208", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }
        ], 
        "amount": {
          "currency": "USD", 
          "type": "MonetaryAmount", 
          "value": "30635291"
        }, 
        "description": "The overarching goal of the proposed Center for Quantitative Biology remains to instantiate at Princeton a research and teaching environment that fully meets the challenge and opportunity, presented by advances in computation and genomics, to practice a usefully quantitative biological science, sometimes referred to as systems biology. The programs and infrastructure of the Center increase the bandwidth of communication among researchers from different disciplines and departments (including Molecular Biology, Ecology and Evolutionary Biology, Computer Science, Chemistry and Physics). One in four (54/217) papers published with Center support in the last four years is a joint publication between two or more Center faculty. For both undergraduate and graduate students, the Center provides a focus for multidisciplinary teaching and learning; quantitative and biological ideas are integrated from the beginning, with the result that students acquire nearly equal facility in biological and quantitative thinking. The Center's specific aims are: (1) to develop realistic and quantitative models of biological processes; (2) to collect large-scale data sets that comprehensively describe biological processes; (3) to devise new and improved methods for computational analysis and display of complex models, structures and data and to make, upon publication, all underlying data, algorithms and analytical systems publicly accessible; (4) to devise and support new curricula and courses of quantitative biology education for undergraduates, graduate students, and the larger scientific community; and (5) to reduce these ideas to practice in several collaborative and multi-disciplinary projects, each aimed at specific system-level questions in the subject areas of (i) intracellular signaling, (ii) pattern and cell signaling in multicellular organisms, (iii) host-pathogen interactions and (iv) bioinformatics and data visualization. These projects share common quantitative goals, require a common infrastructure (i.e. computation, microarray, imaging and metabolomics core facilities) and all of them benefit from the intellectual synergy and multi-disciplinary cooperation that the Center provides.", 
        "endDate": "2015-08-31T00:00:00Z", 
        "funder": {
          "id": "https://www.grid.ac/institutes/grid.280785.0", 
          "type": "Organization"
        }, 
        "id": "sg:grant.2440526", 
        "identifier": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "2440526"
            ]
          }, 
          {
            "name": "nih_id", 
            "type": "PropertyValue", 
            "value": [
              "P50GM071508"
            ]
          }
        ], 
        "inLanguage": [
          "en"
        ], 
        "keywords": [
          "researchers", 
          "quantitative biology education", 
          "common quantitative goals", 
          "undergraduates", 
          "cells", 
          "computation", 
          "METHODS", 
          "beginning", 
          "biological ideas", 
          "metabolomics", 
          "opportunity", 
          "algorithms", 
          "data visualization", 
          "host-pathogen interactions", 
          "intellectual synergy", 
          "joint publication", 
          "microarray", 
          "research", 
          "molecular biology", 
          "bioinformatics", 
          "years", 
          "focus", 
          "multicellular organisms", 
          "equal facility", 
          "larger scientific community", 
          "overarching goal", 
          "core facilities", 
          "center support", 
          "genomics", 
          "data", 
          "quantitative model", 
          "physics", 
          "computer science", 
          "project", 
          "analytical system", 
          "multi-disciplinary cooperation", 
          "intracellular signaling", 
          "imaging", 
          "paper", 
          "center", 
          "idea", 
          "Princeton", 
          "students", 
          "Center's specific aims", 
          "graduate students", 
          "advances", 
          "teaching environment", 
          "structure", 
          "common infrastructure", 
          "subject area", 
          "communication", 
          "more Center faculty", 
          "systems biology", 
          "large-scale data sets", 
          "display", 
          "evolutionary biology", 
          "infrastructure", 
          "multidisciplinary teaching", 
          "pattern", 
          "complex models", 
          "multi-disciplinary project", 
          "quantitative thinking", 
          "program", 
          "course", 
          "chemistry", 
          "specific system-level questions", 
          "results", 
          "Department", 
          "ecology", 
          "quantitative biological science", 
          "challenges", 
          "bandwidth", 
          "quantitative biology", 
          "new curricula", 
          "different disciplines", 
          "biological processes", 
          "computational analysis", 
          "publication", 
          "learning"
        ], 
        "name": "Center for Quantitative Biology", 
        "recipient": [
          {
            "id": "https://www.grid.ac/institutes/grid.16750.35", 
            "type": "Organization"
          }, 
          {
            "affiliation": {
              "id": "https://www.grid.ac/institutes/grid.16750.35", 
              "name": "PRINCETON UNIVERSITY", 
              "type": "Organization"
            }, 
            "familyName": "BOTSTEIN", 
            "givenName": "DAVID", 
            "id": "sg:person.01021225500.13", 
            "type": "Person"
          }, 
          {
            "member": "sg:person.01021225500.13", 
            "roleName": "PI", 
            "type": "Role"
          }
        ], 
        "sameAs": [
          "https://app.dimensions.ai/details/grant/grant.2440526"
        ], 
        "sdDataset": "grants", 
        "sdDatePublished": "2019-03-07T12:11", 
        "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_4.xml.gz", 
        "startDate": "2004-09-01T00:00:00Z", 
        "type": "MonetaryGrant", 
        "url": "http://projectreporter.nih.gov/project_info_description.cfm?aid=8534148"
      }
    ]
     

    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.2440526'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.2440526'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.2440526'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.2440526'


     

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

    123 TRIPLES      19 PREDICATES      101 URIs      93 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:grant.2440526 schema:about anzsrc-for:2208
    2 schema:amount N854b932843604fd692550f88a7d8459e
    3 schema:description The overarching goal of the proposed Center for Quantitative Biology remains to instantiate at Princeton a research and teaching environment that fully meets the challenge and opportunity, presented by advances in computation and genomics, to practice a usefully quantitative biological science, sometimes referred to as systems biology. The programs and infrastructure of the Center increase the bandwidth of communication among researchers from different disciplines and departments (including Molecular Biology, Ecology and Evolutionary Biology, Computer Science, Chemistry and Physics). One in four (54/217) papers published with Center support in the last four years is a joint publication between two or more Center faculty. For both undergraduate and graduate students, the Center provides a focus for multidisciplinary teaching and learning; quantitative and biological ideas are integrated from the beginning, with the result that students acquire nearly equal facility in biological and quantitative thinking. The Center's specific aims are: (1) to develop realistic and quantitative models of biological processes; (2) to collect large-scale data sets that comprehensively describe biological processes; (3) to devise new and improved methods for computational analysis and display of complex models, structures and data and to make, upon publication, all underlying data, algorithms and analytical systems publicly accessible; (4) to devise and support new curricula and courses of quantitative biology education for undergraduates, graduate students, and the larger scientific community; and (5) to reduce these ideas to practice in several collaborative and multi-disciplinary projects, each aimed at specific system-level questions in the subject areas of (i) intracellular signaling, (ii) pattern and cell signaling in multicellular organisms, (iii) host-pathogen interactions and (iv) bioinformatics and data visualization. These projects share common quantitative goals, require a common infrastructure (i.e. computation, microarray, imaging and metabolomics core facilities) and all of them benefit from the intellectual synergy and multi-disciplinary cooperation that the Center provides.
    4 schema:endDate 2015-08-31T00:00:00Z
    5 schema:funder https://www.grid.ac/institutes/grid.280785.0
    6 schema:identifier N2fe2735df60f46c9a6d3b4072e2f507e
    7 N5f834d97a085417e906a4d39c884f3a8
    8 schema:inLanguage en
    9 schema:keywords Center's specific aims
    10 Department
    11 METHODS
    12 Princeton
    13 advances
    14 algorithms
    15 analytical system
    16 bandwidth
    17 beginning
    18 bioinformatics
    19 biological ideas
    20 biological processes
    21 cells
    22 center
    23 center support
    24 challenges
    25 chemistry
    26 common infrastructure
    27 common quantitative goals
    28 communication
    29 complex models
    30 computation
    31 computational analysis
    32 computer science
    33 core facilities
    34 course
    35 data
    36 data visualization
    37 different disciplines
    38 display
    39 ecology
    40 equal facility
    41 evolutionary biology
    42 focus
    43 genomics
    44 graduate students
    45 host-pathogen interactions
    46 idea
    47 imaging
    48 infrastructure
    49 intellectual synergy
    50 intracellular signaling
    51 joint publication
    52 large-scale data sets
    53 larger scientific community
    54 learning
    55 metabolomics
    56 microarray
    57 molecular biology
    58 more Center faculty
    59 multi-disciplinary cooperation
    60 multi-disciplinary project
    61 multicellular organisms
    62 multidisciplinary teaching
    63 new curricula
    64 opportunity
    65 overarching goal
    66 paper
    67 pattern
    68 physics
    69 program
    70 project
    71 publication
    72 quantitative biological science
    73 quantitative biology
    74 quantitative biology education
    75 quantitative model
    76 quantitative thinking
    77 research
    78 researchers
    79 results
    80 specific system-level questions
    81 structure
    82 students
    83 subject area
    84 systems biology
    85 teaching environment
    86 undergraduates
    87 years
    88 schema:name Center for Quantitative Biology
    89 schema:recipient N116cd1fb86844b8495d2d5154a7d092c
    90 sg:person.01021225500.13
    91 https://www.grid.ac/institutes/grid.16750.35
    92 schema:sameAs https://app.dimensions.ai/details/grant/grant.2440526
    93 schema:sdDatePublished 2019-03-07T12:11
    94 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    95 schema:sdPublisher N824b171ae7604fdfa17e627ae88989fa
    96 schema:startDate 2004-09-01T00:00:00Z
    97 schema:url http://projectreporter.nih.gov/project_info_description.cfm?aid=8534148
    98 sgo:license sg:explorer/license/
    99 sgo:sdDataset grants
    100 rdf:type schema:MonetaryGrant
    101 N116cd1fb86844b8495d2d5154a7d092c schema:member sg:person.01021225500.13
    102 schema:roleName PI
    103 rdf:type schema:Role
    104 N2fe2735df60f46c9a6d3b4072e2f507e schema:name dimensions_id
    105 schema:value 2440526
    106 rdf:type schema:PropertyValue
    107 N5f834d97a085417e906a4d39c884f3a8 schema:name nih_id
    108 schema:value P50GM071508
    109 rdf:type schema:PropertyValue
    110 N824b171ae7604fdfa17e627ae88989fa schema:name Springer Nature - SN SciGraph project
    111 rdf:type schema:Organization
    112 N854b932843604fd692550f88a7d8459e schema:currency USD
    113 schema:value 30635291
    114 rdf:type schema:MonetaryAmount
    115 anzsrc-for:2208 schema:inDefinedTermSet anzsrc-for:
    116 rdf:type schema:DefinedTerm
    117 sg:person.01021225500.13 schema:affiliation https://www.grid.ac/institutes/grid.16750.35
    118 schema:familyName BOTSTEIN
    119 schema:givenName DAVID
    120 rdf:type schema:Person
    121 https://www.grid.ac/institutes/grid.16750.35 schema:name PRINCETON UNIVERSITY
    122 rdf:type schema:Organization
    123 https://www.grid.ac/institutes/grid.280785.0 schema:Organization
     




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


    ...