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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Harbison, C.T., et al.: Nature 431, 99 (2004)
Lee, T.I., et al.: Science 298, 799 (2002)
Schlitt, T., Brazma, A.: FEBS letts. 579, 1859 (2005)
Luscombe, N.M., et al.: Nature 431, 308 (2004)
Monk, N.A.: Biochemical Society Transactions 31, 1457 (2003)
Xie, X., et al.: Nature 434, 338 (2005)
Martone, R., et al.: Proceedings of the National Academy of Sciences USA 100, 12247 (2003)
Sanguinetti, G., Rattray, M., Lawrence, N.D.: A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. Bioinformatics (to appear, 2006)
Sanguinetti, G., Lawrence, N.D., Rattray, M.: Probabilistic inference of transcription factors concentrations and gene-specific regulatory activities, Technical Report CS-06-06, University of Sheffield (2006)
Sanguinetti, G., Laidler, J., Lawrence, N.D.: Automatic determination of the number of clusters using spectral algorithms. In: Proceedings of MLSP 2005, pp. 55–60 (2005)
Spellman, P.T., et al.: Molecular Biology of the Cell 9, 3273 (1998)
Tu, B.P., Kudlicki, A., Rowicka, M., McKnight, S.L.: Science 310, 1152 (2005)
Liao, J.C., et al.: Proceedings of the National Academy of Sciences USA 100, 15522 (2003)
Alter, O., Brown, P.O., Botstein, D.: Proc. Natl. Acad. Sci. USA 97, 10101 (2000)
Boulesteix, A.-L., Strimmer, K.: Theor. Biol. Med. Model. 2, 1471 (2005)
Enright, A.J., van Dongen, S., Ouzounis, C.: Nucleic Acids Research 30, 1575 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sanguinetti, G., Rattray, M., Lawrence, N.D. (2006). Identifying Submodules of Cellular Regulatory Networks. In: Priami, C. (eds) Computational Methods in Systems Biology. CMSB 2006. Lecture Notes in Computer Science(), vol 4210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11885191_11
Download citation
DOI: https://doi.org/10.1007/11885191_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46166-1
Online ISBN: 978-3-540-46167-8
eBook Packages: Computer ScienceComputer Science (R0)