Today, PLoS ONE is pleased to publish a Collection of articles representing the output of the best performing methods and strategies of the DREAM 3 challenges (Dialogue for Reverse Engineering Assessments and Methods).
These challenges were posed to systems biology experts during the months of June to September 2008 and discussed at the third DREAM conference, held in late 2008 in Cambridge, MA.The Collection as a whole summarizes the lessons learned by the community and provides a much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.
DREAM was established to address the question of whether mathematical models can be used to help scientists go beyond experimental insight to better understand biological systems. Every year, DREAM organize a challenge in which scientists from around the world are invited to use donated experimental data to produce quantitative models and make blind predictions of previously unseen benchmark experiments known to the DREAM organizers. These predictions are matched to the benchmarks, allowing for a rigorous evaluation of the usefulness of the models. This community effort aims to catalyze discussion about the design, application, and assessment of systems biology models. DREAM is sponsored by Columbia University Center for Multiscale Analysis Genomic and Cellular Networks (MAGNet) and the IBM Computational Biology Center.
Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets; however, the volume of data being generated is accelerating as molecular profiling technology evolves. We hope that this Collection goes some way to explaining the role that Computational algorithms could play in the interpretation of systems biology data and an assessment of their limitations.