Gene regulatory networks describe how cells control the expression of genes, which, together with some additional regulation
further downstream, determines the production of proteins essential for cellular function. The level of expression of each
gene in the genome is modified by controlling whether and how vigorously it is transcribed to RNA, and subsequently translated
to protein. RNA and protein expression will influence expression rates of other genes, thus giving rise to a complicated network
structure.
An analysis of regulatory processes within the cell will significantly further our understanding of cellular dynamics. It
will shed light on normal and abnormal, diseased cellular events, and may provide information on pathways in dire diseases
such as cancer. These pathways can provide information on how the disease develops, and what processes are involved in progression.
Ultimately, we can hope that this will provide us with new therapeutic approaches and targets for drug design.
It is thus no surprise that many efforts have been undertaken to reconstruct gene regulatory networks from gene expression
measurements. In this chapter, we will provide an introductory overview over the field. In particular, we will present several
different approaches to gene regulatory network inference, discuss their strengths and weaknesses, and provide guidelines
on which models are appropriate under what circumstances. In addition, we sketch future developments and open problems.