Reverse Engineering of Gene Regulatory Networks: A Comparative Study

Deciphering the complex structure of transcriptional regulation of gene expression by means of computational methods is a challenging task emerged in the last decades. Large-scale experiments, not only gene expression measurements from microarrays but also promoter sequence searches for transcription factor binding sites and investigations of protein-DNA interactions, have spawned various computational approaches to infer the underlying gene regulatory networks (GRNs). Identifying interactions yields to an understanding of the topology of GRNs and, ultimately, of the molecular role, of each gene. On the basis of such networks computer models of cellular systems are set up and in silico experiments can be performed to test hypotheses and generate predictions on different states of these networks. Furthermore, an investigation of the system behavior under different conditions is possible [1]. Therefore reverse engineering can be considered as an intermediate step from bioinformatics to systems biology.The basic assumption of most reverse engineering algorithms is that causality of transcriptional regulation can be inferred from changes in mRNA expression profiles. One is interested in identifying the regulatory components of the expression of each gene. Transcription factors bind to specific parts of DNA in the promoter region of a gene and, thus, effect the transcription of the gene. They can activate, enhance, or inhibit the transcription. Changes of abundances of transcription factors cause changes in the amount of transcripts of their target genes. This process is highly complex and interactions between transcription factors result in a more interwoven regulatory network. Besides the transcription factor level, transcriptional regulation can be affected as well on DNA and mRNA levels, for example, by chemical and structural modifications of DNA or by blocking the translation of mRNAs by microRNAs [2]. Usually these additional regulation levels are neglected or inc