Bayesian methods for elucidating genetic regulatory networks
For an extensive discussion of the principles underlying biological networks, we refer the reader to a recent review (Barabasi and Oltvai 2004).Biological networks are usually depicted as nodes connected by edges.Nodes represent proteins, genes, or enzymatic substrates that translate extracellular signals from the environment.Edges often represent direct molecular interactions, regulatory interactions (such as the binding of a TF to the promoter of its target genes), or the sharing of functional properties.One important characteristic of biological networks is their scale-free structure: The number of nodes that make a large number of connections with other nodes (referred to as “hubs”) is much lower than the number of nodes with few connections.
In this review, we illustrate recent developments in the area of genomics and computational biology that have allowed several laboratories to elucidate regulatory networks in organisms as diverse as yeast and mammals.
This presumably facilitates the efficient propagation and integration of signals.
One other notable characteristic of biological networks is the relative paucity of hubs that connect directly to one another.
These approaches should allow us to elucidate complete transcriptional regulatory codes for yeast as well as mammalian cells.
Cells must continually adapt to changing conditions by altering their gene expression patterns.