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.