Reconstruction of regulatory networks and prediction of biological interactions are important aims of network biology. Advances in high-throughput genomics technologies have accelerated the building of improved network models. However, delineation of the overall network topology requires further effort because many of current network models are based on correlation, rather than causation. In order to overcome this limitation, and to generate a comprehensive network based on causality, we depleted 483 transcription factors (TFs) expressed in S2 cells, and profiled gene expression changes upon knockdown by sequencing a total of 1,920 RNA-Seq libraries. Our knockdown reduced transcript level in approximately 98% of target genes and significantly induced changes in gene expression in more than 40,000 non-target genes. Based on these observations, we described 25 different modules of genes affected by different combinations of TFs. We then integrated chromatin Immunoprecipitation data for 137 TFs and histone marks from the same cell line as well as 1,264 TF binding motifs and implemented a mathematical modeling approach. Our network model predicted more than 70,000 gene interactions that a few of which were previously known, but primarily these were novel predictions. The known interactions include regulatory pathways that govern sex determination. The novel ones largely describe cell cycle progression of the proliferative cells. Our result modeled a regulatory network based on causality and provided a testable catalog of genetic interactions.