Gaussian graphical models (GGMs) are undirected network models where the nodes represent the random variables and the edges their partial correlations. GGMs are straightforward to implement, easy to interpret, and have an acceptable computational cost. These advantages have made GGMs popular to reconstruct gene regulatory networks from high throughput data. In this talk, I will discuss the reconstruction of GGMs in the high dimensional case (n « p). In this scenario, the inference becomes challenging and requires regularization/shrinkage. I will present a novel approach to infer GGMs structures based on the Ledoit-Wolf (LW) shrinkage, which accounts for the sample size, the number of variables, and the shrinkage value.