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.

Electronic downloads

• http://www.biomedcentral.com/1471-2105/11/413

• 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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