Towards a rigorous assessment of systems biology models: the DREAM3 challenges.

TitleTowards a rigorous assessment of systems biology models: the DREAM3 challenges.
Publication TypeJournal Article
Year of Publication2010
AuthorsPrill, RJ, Marbach, D, Saez-Rodriguez, J, Sorger, PK, Alexopoulos, LG, Xue, X, Clarke, ND, Altan-Bonnet, G, Stolovitzky, G
JournalPLoS One
Date Published2010
KeywordsAlgorithms, Animals, Cluster Analysis, Computational Biology, Gene Expression Profiling, Gene Regulatory Networks, Humans, Models, Biological, Protein Interaction Mapping, Reproducibility of Results, Signal Transduction, Software, Systems Biology

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.

Alternate JournalPLoS ONE
PubMed ID20186320
PubMed Central IDPMC2826397
Grant ListAI083408 / AI / NIAID NIH HHS / United States
P50 GM068762-07 / GM / NIGMS NIH HHS / United States
P50-GM68762 / GM / NIGMS NIH HHS / United States
R01 AI083408 / AI / NIAID NIH HHS / United States
R01 AI083408-01 / AI / NIAID NIH HHS / United States
U54-CA112967 / CA / NCI NIH HHS / United States