Computational approaches to the integration of gene expression, ChIP-chip and sequence data in the inference of gene regulatory networks.

A major challenge in systems biology is the ability to model complex regulatory interactions, such as gene regulatory networks, and a number of computational approaches have been developed over recent years to address this challenge. This paper reviews a number of these approaches, with a focus on probabilistic graphical models and the integration of diverse data sets, such as gene expression and transcription factor binding site location and activity.