High-throughput experiments have produced large amounts of heterogeneous data in the life sciences. The integration of data in the life sciences is a key component in the analysis of biological processes. These data may contain errors, but the curation of the vast amount of data generated in the «omic» era cannot be done by individual researchers. To address this problem, community-driven tools could be used to assist with data analysis. In this paper, we focus on a tool with social networking capabilities built on top of the SBMM (Systems Biology Metabolic Modelling) Assistant to enable the collaborative improvement of metabolic pathway models (the application is freely available at http://sbmm.uma.es/SPA).