Accurate modelling of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. In practice many of them are difficult to measure in vivo. For example, it is very hard to measure the active concentration levels of the transcription factor proteins that drive the process.
In this talk we will show how, by making use of structural information about the interaction network (e.g. arising form ChIP-chip data), transcription factor activities can estimated using probabilistic inference. We propose two different probabilistic models: a simple linear model with Kalman filter based dynamics for genome/transcriptome wide studies and a differential equation based Gaussian process model with a more physically realistic parameterisation for smaller interaction networks.