Email updates

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

Open Access Research article

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach

Cunlu Zou1, Christophe Ladroue1, Shuixia Guo2 and Jianfeng Feng13*

Author Affiliations

1 Department of Computer Science, University of Warwick, Coventry, UK

2 Department of Mathematics, Hunan Normal University, Changsha, China

3 Centre for Computational Systems Biology, Fudan University, Shanghai, China

For all author emails, please log on.

BMC Bioinformatics 2010, 11:337  doi:10.1186/1471-2105-11-337

Published: 21 June 2010

Abstract

Background

Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.

Results

Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.

Conclusions

The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.