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. Here we focused on the Granger causality approach in both the time and frequency domains 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 from 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and opened up many experimentally testable predictions. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. Our approach is general and can be easily applied to other types of temporal data.