This chapter presents a declarative approach for analyzing and building genetic regulatory networks (GRNs). First, it proposes an implementation of a model checker for linear temporal logic (LTL) and computational tree logic (CTL) formulas in answer set programming (ASP), a logic programming language based on the stable model semantic. Second, the chapter discusses a specification in ASP of Thomas GRNs that are both a generalization of Boolean networks and an approximation of piecewise linear differential equations. Then, it describes a methodology for analyzing networks with a declarative approach, including consistency repairing and learning of properties from a set of consistent models. The aim is not only to exhibit the importance of logic programming, but also to point up the advantages of ASP, coming from non‐monotonicity, minimality of models, expressiveness and practical performances. Finally, the provided functionalities and the proposed methodology are evaluated over three real biological applications.