Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach.
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.
Zou
Cunlu
C
Department of Computer Science, University of Warwick, Coventry, UK.
Ladroue
Christophe
C
Guo
Shuixia
S
Feng
Jianfeng
J
D016208:Databases, Factual; D053263:Gene Regulatory Networks; D011506:Proteins / Q000737:chemistry
D011506:Proteins