Gene regulatory networks consist of interacting species, that influence each other in complex ways. Biological data collection from microarray experiments has proved to be challenging task, given the large number of species that need to be measured. Even the with recent advances and improved tools for genetic analysis, it is impossible to measure the contribution from every species, which makes biological networks harder to infer. These unmeasured species are commonly described as "hidden variables". Thse include: the presence of unknown regulatory molecules, degradation of mRNA and protein levels and influences from other genes that have not been measured in a given experiment. The aim of this project is to evaluate the performance of a subset of selected available in silico methods for biological network reconstruction from data. The analysis will provide the user with an overview of the relative capabilities of some of the current methods available, with specific emphasis to the type of data input they require and the mathematical assumptions they make.