This paper deals with the reconstruction of the interaction structure of a gene regulation network from qualitative data in a Boolean framework. The problem in this setup is to find update functions which are in agreement with the data. As the search space grows exponentially with the system size but data are rare, large uncertainties remain in the reconstructed networks. In order to attenuate this problem, we propose to restrict the search space to the biologically meaningful class of unate functions. Using sign-representations, the problem of exploring this reduced search space is transformed into a linear feasibility problem. The sign-representation furthermore allows to incorporate robustness considerations and gives rise to a new measure which can be used to further reduce the uncertainties. The proposed methodology is demonstrated with a Boolean apoptosis signaling model.