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The central goal of systems biology is to gain a predictive, system-level understanding of biological networks. This can be done, for example, byinferring causal networks from observations on a perturbed biological system. An ideal experimental design for causal inference is randomized,multifactorial perturbation. The recognition that the genetic variation in a segregating population represents randomized, multifactorialperturbations (Jansen and Nap (2001), Jansen (2003)) gave rise to Systems Genetics (SG), where a segregating or genetically randomizedpopulation is genotyped for many DNA variants, and profiled for phenotypes of interest (e.g. disease phenotypes), gene expression, andpotentially other ‘omics’ variables (protein expression, metabolomics, DNA methylation, etc.; Figure 1. Figure 1 was taken from Jansen and Nap(2001)). In this challenge we explore the use of Systems Genetics data for elucidating causal network models among genes, i.e. Gene Networks (DREAM5 SYSGEN A) and predicting complex disease phenotypes (DREAM5 SYSGEN B). More info