Transcriptional networks: Dynamic information fluxes

Cells are alive, but which essential features do they have that make them so? Which properties do they possess that allow them to persist in an environment, replicating themselves and producing mRNA and proteins? Cells are self-organized: processes and structures emerge from local interactions of many modules. Particularly, abstract models permit us to study connections and regulation of cell components clustered to two goals, i.e., to accomplish cell survival and to achieve a successful differentiation. Contexts where a cell exists are differentiated by defined groups of expressed genes. Furthermore, a transcriptional network could emerge to complement another or substitute it. In recent years, high-throughput technology has accelerated the sequencing of a large variety of genomes including human. Microarrays and serial analysis of gene expression (SAGE) have allowed a parallel analysis of the expression levels of thousands of genes, creating a strong demand for suitable theoretical and computational tools to analyze such data. This chapter reviews the current knowledge on trends of reconstruction of transcriptional regulatory networks. This information is of interest for understanding reverse engineering in transcriptional networks, how groups of molecules within functional modules carry out cellular functions, and how they might interact within a network.