Network reconstruction is the art of formalizing biological data into qualitative models. These qualitative network models aim to summarize the state-of-the-art knowledge and to provide a starting point for qualitative or quantitative modeling. The network reconstruction process is highly developed for metabolic mass transfer networks, and a number of genome-scale metabolic models are available. The picture looks very different for signal transduction networks: Despite years of dedicated work by the scientific community, we still do not have any comprehensive network models of these information transfer networks. In this chapter, we discuss the reasons for this lag in development. We focus on the specific challenges with information transfer networks in the light of genome-scale mechanistic models, and evaluate the different strategies the scientific community has developed to address these challenges. We conclude that the methods that have been so successful for modeling small signaling modules or metabolic networks are ill-suited to describe the empirical knowledge we have about information transfer due to the resolution difference they introduce. This resolution difference is relatively subtle in small networks, but becomes dramatic in genome-scale models. Hence, to be able to build mechanistic genome-scale models of signal transduction, it is imperative that we use reconstruction approaches that are adapted to this empirical knowledge.