Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

TLDR

Systems biology increasingly relies on computational modeling to manage large quantitative data sets, and the DREAM initiative fosters community discussion on model design and assessment. The study evaluates the four DREAM3 challenges—signaling cascade identification, signaling response prediction, gene expression prediction, and an in silico network challenge. Participants were assessed on anonymized datasets for network inference and measurement prediction across these challenges. Forty teams submitted 413 predicted networks and measurement test sets, most predictions were no better than random, yet aggregating multiple teams’ predictions sometimes outperformed any single method, underscoring the value of community benchmarking.

Abstract

Background Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.