Deciphering a transcriptional regulatory code: modeling short-range repression in the Drosophila embryo

Systems biology seeks a genomic‐level interpretation of transcriptional regulatory information represented by patterns of protein‐binding sites. Obtaining this information without direct experimentation is challenging; minor alterations in binding sites can have profound effects on gene expression, and underlie important aspects of disease and evolution. Quantitative modeling offers an alternative path to develop a global understanding of the transcriptional regulatory code. Recent studies have focused on endogenous regulatory sequences; however, distinct enhancers differ in many features, making it difficult to generalize to other cis ‐regulatory elements. We applied a systematic approach to simpler elements and present here the first quantitative analysis of short‐range transcriptional repressors, which have central functions in metazoan development. Our fractional occupancy‐based modeling uncovered unexpected features of these proteins’ activity that allow accurate predictions of regulation by the Giant, Knirps, Krüppel, and Snail repressors, including modeling of an endogenous enhancer. This study provides essential elements of a transcriptional regulatory code that will allow extensive analysis of genomic information in Drosophila melanogaster and related organisms. Synopsis Transcriptional regulatory information, represented by patterns of protein‐binding sites on DNA, comprises an important portion of genetic coding. Despite the abundance of genomic sequences now available, identifying and characterizing this information remain a major challenge. Minor changes in protein‐binding sites can have profound effects on gene expression, and such changes have been shown to underlie important aspects of disease and evolution. Thus, an important aim in contemporary systems biology is to develop a global understanding of the transcriptional regulatory code, allowing prediction of gene output based on DNA sequence information. Recent studies have focused on endogenous transcriptional regulatory sequences (Janssens et al , 2006 ; Zinzen et al , 2006 ; Segal et al , 2008 ); however, distinct enhancers differ in many features, including transcription factor activity, spacing, and cooperativity, making it difficult to learn the effects of individual features and generalize them to other cis ‐regulatory elements. We have pursued a bottom up approach to understand the mechanistic processing of regulatory elements by the transcriptional machinery, using a well‐defined