This chapter focuses on modeling the response of logical signaling networks by means of automated reasoning using answer set programming (ASP). In this context, the problem consisting of learning logical networks is crucial in order to achieve unbiased and robust discoveries. Furthermore, it shows that many networks can be compatible with a given set of experimental observations. The chapter discusses how ASP can be used to exhaustively enumerate all these logical networks. Next, in order to gain control over the system, it look for intervention strategies that force a set of target species into a desired steady state. Altogether, this constitutes a pipeline for reasoning on logical signaling networks providing robust insights to system biologists. The chapter illustrates the usage of ASP for solving the aforementioned problems and discusses the novelty of our approach with respect to existing methods.