Identifying Submodules of Cellular Regulatory Networks

  • Guido Sanguinetti
  • Magnus Rattray
  • Neil D. Lawrence
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4210)


Recent high throughput techniques in molecular biology have brought about the possibility of directly identifying the architecture of regulatory networks on a genome-wide scale. However, the computational task of estimating fine-grained models on a genome-wide scale is daunting. Therefore, it is of great importance to be able to reliably identify submodules of the network that can be effectively modelled as independent subunits. In this paper we present a procedure to obtain submodules of a cellular network by using information from gene-expression measurements. We integrate network architecture data with genome-wide gene expression measurements in order to determine which regulatory relations are actually confirmed by the expression data. We then use this information to obtain non-trivial submodules of the regulatory network using two distinct algorithms, a naive exhaustive algorithm and a spectral algorithm based on the eigendecomposition of an affinity matrix. We test our method on two yeast biological data sets, using regulatory information obtained from chromatin immunoprecipitation.


Regulatory Network Regulatory Intensity Regulatory Relation ChIP Data Spectral Cluster Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guido Sanguinetti
    • 1
  • Magnus Rattray
    • 2
  • Neil D. Lawrence
    • 1
  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUK
  2. 2.School of Computer ScienceUniversity of ManchesterManchesterUK

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