A simple approach to target identification through gene expression studies has been to cluster the expression profiles and look for coregulated genes within clusters. Within systems biology mechanistic models of gene expression are typically constructed through differential equations. mRNA’s production is taken to be proportional to transcription factor activity (with the proportionality given by the sensitivity) and the mRNA is assumed to decay at a particular rate. The assumption that coregulated genes have similar profiles is equivalent to assuming both the decay and the sensitivity are high. In this talk we advocate model based target identification. We develop a simple probabilistic models of transcription (and translation) which encode mRNA (or Transcription Factor) production and decay. Our models are simple enough to allow genome wide target identification, but rich enough to encode dynamical behavior that, allowing us to identify putative targets even when decay rates are low.