Accurate reconstruction of the regulatory networks that control gene expression 
is one of the key current challenges in molecular biology. Although gene 
expression and chromatin state dynamics are ultimately encoded by constellations 
of binding sites recognized by regulators such as transcriptions factors (TFs) 
and microRNAs (miRNAs), our understanding of this regulatory code and its 
context-dependent read-out remains very limited. Given that there are thousands 
of potential regulators in mammals, it is not practical to use direct 
experimentation to identify which of these play a key role for a particular 
system of interest. We developed a methodology that models gene expression or 
chromatin modifications in terms of genome-wide predictions of regulatory sites 
and completely automated it into a web-based tool called ISMARA (Integrated 
System for Motif Activity Response Analysis). Given only gene expression or 
chromatin state data across a set of samples as input, ISMARA identifies the key 
TFs and miRNAs driving expression/chromatin changes and makes detailed 
predictions regarding their regulatory roles. These include predicted activities 
of the regulators across the samples, their genome-wide targets, enriched gene 
categories among the targets, and direct interactions between the regulators. 
Applying ISMARA to data sets from well-studied systems, we show that it 
consistently identifies known key regulators ab initio. We also present a number 
of novel predictions including regulatory interactions in innate immunity, a 
master regulator of mucociliary differentiation, TFs consistently disregulated 
in cancer, and TFs that mediate specific chromatin modifications.