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In this paper, we aim at modeling and analyzing the behavior of a spatial population of agents through an exploration of their state space. Agents are localized on a dynamic graph and they have internal states. They interact with an environment. The evolu- tion of the agents and of the environment is specified by a set of rules. The framework is carefully designed to enable the construction of a global state space that can be automatically build and analyzed. The formalism, called IRNs for integrated regula- tory networks, may be seen as a spatial extension of logical regulatory networks (`a la Thomas) developed in systems biology. This paper sketches the modeling framework and its semantics. We show how IRN may be used for the modeling of a population of simple agents, the automatic analysis of various reachability properties and the use of symmetries to reduce the size of the state space.