Caching is an important element to tackle performance issues in largely distributed data management. However, caches are efficient only if they are well configured according to the context of use. As a consequence, they are usually built from scratch. Such an approach appears to be expensive and time consuming in grids where the various characteristics lead to many heterogeneous cache requirements. This paper proposes a framework facilitating the construction of sophisticated and dynamically adaptable caches for heterogeneous applications. Such a framework has enabled the evaluation of several configurations for distributed data querying systems and leads us to propose innovative approaches for semantic and cooperative caching. This paper also reports the results obtained in bioinformatics data management on grids showing the relevance of our proposals.