Abstract: Revealing networks of biological components is one key objective in systems biology. With microarrays, researchers now routinely measure expression profiles at the genome level under various conditions, and, such data may be used to statistically infer gene regulation networks. Gaussian graphical models (GGMs) have proven useful for this purpose by modeling the Markovian dependence among genes. However, a single GGM may not be adequate to describe the potentially differing networks across these conditions, and hence it is more natural to infer multiple GGMs from such data. In the present study, we propose a class of nonconvex penalty functions aiming for the joint estimation of multiple GGMs. Our proposed penalty functions impose a joint sparsity constraint in a flexible way, which becomes effective regularization when all networks are sparse. We illustrate the property of our proposed nonconvex penalty functions by simulation study. We then apply the method to a gene expression data set from the GenCord Project, and show that our method can identify prominent pathways for different conditions.