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Nonparametric identification of regulatory interactions from spatial and temporal gene expression data
Anil Aswani, Keranen Soile VE, James Brown, Charless C Fowlkes, David W Knowles, Mark D Biggin, Peter Bickel, Claire Tomlin

Citation
Anil Aswani, Keranen Soile VE, James Brown, Charless C Fowlkes, David W Knowles, Mark D Biggin, Peter Bickel, Claire Tomlin. "Nonparametric identification of regulatory interactions from spatial and temporal gene expression data". BMC Bioinformatics, 413(11), August 2010.

Abstract
BACKGROUND: The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions. RESULTS: Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for eve mRNA pattern formation in the Drosophila melanogaster blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same cis-regulatory module depending on the factors' concentration, and implies different modes of activation and repression. CONCLUSIONS: Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.

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Citation formats  
  • HTML
    Anil Aswani, Keranen Soile VE, James Brown, Charless C
    Fowlkes, David W Knowles, Mark D Biggin, Peter Bickel,
    Claire Tomlin. <a
    href="http://chess.eecs.berkeley.edu/pubs/783.html"
    >Nonparametric identification of regulatory interactions
    from spatial and temporal gene expression data</a>,
    <i>BMC Bioinformatics</i>, 413(11), August 2010.
  • Plain text
    Anil Aswani, Keranen Soile VE, James Brown, Charless C
    Fowlkes, David W Knowles, Mark D Biggin, Peter Bickel,
    Claire Tomlin. "Nonparametric identification of
    regulatory interactions from spatial and temporal gene
    expression data". <i>BMC
    Bioinformatics</i>, 413(11), August 2010.
  • BibTeX
    @article{AswaniSoileVEBrownFowlkesKnowlesBigginBickelTomlin10_NonparametricIdentificationOfRegulatoryInteractionsFrom,
        author = {Anil Aswani and Keranen Soile VE and James Brown
                  and Charless C Fowlkes and David W Knowles and
                  Mark D Biggin and Peter Bickel and Claire Tomlin},
        title = {Nonparametric identification of regulatory
                  interactions from spatial and temporal gene
                  expression data},
        journal = {BMC Bioinformatics},
        volume = {413},
        number = {11},
        month = {August},
        year = {2010},
        abstract = {BACKGROUND: The correlation between the expression
                  levels of transcription factors and their target
                  genes can be used to infer interactions within
                  animal regulatory networks, but current methods
                  are limited in their ability to make correct
                  predictions. RESULTS: Here we describe a novel
                  approach which uses nonparametric statistics to
                  generate ordinary differential equation (ODE)
                  models from expression data. Compared to other
                  dynamical methods, our approach requires minimal
                  information about the mathematical structure of
                  the ODE; it does not use qualitative descriptions
                  of interactions within the network; and it employs
                  new statistics to protect against over-fitting. It
                  generates spatio-temporal maps of factor activity,
                  highlighting the times and spatial locations at
                  which different regulators might affect target
                  gene expression levels. We identify an ODE model
                  for eve mRNA pattern formation in the Drosophila
                  melanogaster blastoderm and show that this
                  reproduces the experimental patterns well.
                  Compared to a non-dynamic, spatial-correlation
                  model, our ODE gives 59% better agreement to the
                  experimentally measured pattern. Our model
                  suggests that protein factors frequently have the
                  potential to behave as both an activator and
                  inhibitor for the same cis-regulatory module
                  depending on the factors' concentration, and
                  implies different modes of activation and
                  repression. CONCLUSIONS: Our method provides an
                  objective quantification of the regulatory
                  potential of transcription factors in a network,
                  is suitable for both low- and moderate-dimensional
                  gene expression datasets, and includes
                  improvements over existing dynamic and static
                  models.},
        URL = {http://chess.eecs.berkeley.edu/pubs/783.html}
    }
    

Posted by Christopher Brooks on 24 Nov 2010.
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