Unravelling Gene Networks from Steady-State Experimental Perturbation Data

Reverse engineering gene networks exclusively from microarray expression data sets trough computational analysis is a difficult but important task. We present a method (SNI) for deriving gene interactions among genes and reconstruction gene regulatory networks from steady-state experimental perturbation data. The predictive power of our approach is tested and verified on both simulated data generated from artificial scale-free networks and Escherichia coli gene profiling data. Comparing with other inferring approaches, the analyzed results illustrate that SNI is a useful tool and outperform other approaches for predicting regulatory genes especially when the network is very sparse.