RNA-binding proteins (RBPs) are critical regulators of gene expression. To understand and predict the outcome of RBP-mediated regulation a comprehensive analysis of their interaction with RNA is necessary. The signal transduction and activation of RNA (STAR) family of RBPs includes developmental regulators and tumour suppressors such as Caenorhabditis elegans GLD-1, which is a key regulator of germ cell development. To obtain a comprehensive picture of GLD-1 interactions with the transcriptome, we identified GLD-1-associated mRNAs by RNA immunoprecipitation followed by microarray detection. Based on the computational analysis of these mRNAs we generated a predictive model, where GLD-1 association with mRNA is determined by the strength and number of 7-mer GLD-1-binding motifs (GBMs) within UTRs. We verified this quantitative model both in vitro, by competition GLD-1/GBM-binding experiments to determine relative affinity, and in vivo, by 'transplantation' experiments, where 'weak' and 'strong' GBMs imposed translational repression of increasing strength on a non-target mRNA. This study demonstrates that transcriptome-wide identification of RBP mRNA targets combined with quantitative computational analysis can generate highly predictive models of post-transcriptional regulatory networks.