It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

This work was supported in part by the US National Institutes of Health (National Cancer Institute (NCI) grants U54 CA 112970 (to J.W.G.) and 5R01CA180778 (to J.M.S.), NCI award U54CA143869 to M.F.C. and National Institute of General Medical Sciences award 1R01GM109031 to J.M.S.), the Susan G. Komen Foundation (SAC110012 to J.W.G.), the Prospect Creek Foundation (grant to J.W.G.), the EuroinvesXgacion program of MICINN (Spanish Ministry of Science and InnovaXon), partners of the ERASysBio+ iniXaXve supported under the EU ERA-NET Plus Scheme in FP7 (SHIPREC), MICINN (FEDER BIO2008-0205, FEDER BIO2011-22568 and EUI2009-04018 to B.O.), the Royal Society (Wolfson Research Merit Award to S.M.), the German Federal Ministry of Education and Research GANI_MED Consortium (grant 03IS2061A to T.K.), and the US National Library of Medicine (grants R00LM010822 (to X.J.) and R01LM011663 (to X.J. and R.E.N.))