Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Software

Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

Christian Bender1*, Silvia vd Heyde2, Frauke Henjes1, Stefan Wiemann1, Ulrike Korf1 and Tim Beißbarth2

Author Affiliations

1 German Cancer Research Center (DKFZ), Division of Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany

2 University of Göttingen, Department of Medical Statistics, Humboldtallee 32, 37073 Göttingen, Germany

For all author emails, please log on.

BMC Bioinformatics 2011, 12:291  doi:10.1186/1471-2105-12-291

Published: 19 July 2011

Abstract

Background

Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks.

Results

We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition) and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property.

Conclusions

The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org webcite, http://cran.r-project.org webcite. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.