Identifying submodules of cellular regulatory networks

Guido Sanguinetti; Magnus Rattray; Neil D. Lawrence

doi:10.1007/11885191_11

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Identifying submodules of cellular regulatory networks

Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence

International Conference on Computational Methods in Systems Biology, Springer-Verlag , 2006.

Abstract

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.

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BibTeX


@InProceedings{Sanguinetti:trento06,
  title = 	 {Identifying submodules of cellular regulatory networks},
  author = 	 {Sanguinetti, Guido and Rattray, Magnus and Lawrence, Neil D.},
  booktitle = 	 {International Conference on Computational Methods in Systems Biology},
  year = 	 {2006},
  series = 	 {LNCS},
  publisher =    {Springer-Verlag},
  doi = 	 {10.1007/11885191_11},
  url = 	 {/publications/sanguinetti-trento06.html},
  abstract = 	 {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.}
}

Endnote

%0 Conference Paper
%T Identifying submodules of cellular regulatory networks
%A Guido Sanguinetti
%A Magnus Rattray
%A Neil D. Lawrence
%B International Conference on Computational Methods in Systems Biology
%C LNCS
%D 2006	
%F Sanguinetti:trento06
%I Springer-Verlag
%R 10.1007/11885191_11
%U /publications/sanguinetti-trento06.html
%X 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.

RIS


TY  - CPAPER
TI  - Identifying submodules of cellular regulatory networks
AU  - Guido Sanguinetti
AU  - Magnus Rattray
AU  - Neil D. Lawrence
BT  - International Conference on Computational Methods in Systems Biology
DA  - 2006/01/01	
ID  - Sanguinetti:trento06
PB  - Springer-Verlag
DP  - LNCS
DO  - 10.1007/11885191_11
UR  - /publications/sanguinetti-trento06.html
AB  - 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.
ER  -

APA


Sanguinetti, G., Rattray, M. & Lawrence, N.D.. (2006). Identifying submodules of cellular regulatory networks. International Conference on Computational Methods in Systems Biology, in LNCS doi:10.1007/11885191_11 Available from /publications/sanguinetti-trento06.html.