The problem of gene regulatory network inference is a major concern of systems biology. In recent years, a novel methodology has gained momentum, called community network approach. Community networks integrate predictions from individual methods in a "metapredictor," in order to compose the advantages of different methods and soften individual limitations. This article proposes a novel methodology to integrate prediction ensembles using constraint programming, a declarative modeling and problem solving paradigm. Constraint programming naturally allows the modeling of dependencies among components of the problem as constraints, facilitating the integration and use of different forms of knowledge. The new paradigm, referred to as constrained community network, uses constraints to capture properties of the regulatory networks (e.g., topological properties) and to guide the integration of knowledge derived from different families of network predictions. The article experimentally shows the potential of this approach: The addition of biological constraints can offer significant improvements in prediction accuracy.