Understanding gene expression at a systems level is crucial for gaining correct insights in development and disease of complex organisms. Transcription regulatory networks describe gene expression as a function of inputs specified by physical and funcitonal interactions between transcription factors, miRNAs, cofactors and DNA. We investigate reverse-engineering algorithms to more correctly infer transcription regulatory networks from different genome-wide datasets.