SDREM: Signaling and Dynamic Regulatory Events Miner
Accurate models of the cross talk between signaling pathways and transcriptional regulatory networks within cells are essential to understand complex response programs. We present a new computational method that combines condition-specific time series expression data with general protein interaction data to reconstruct dynamic and causal stress response networks. These networks characterize the pathways involved in the response, their time of activation, and the affected genes. The signaling and regulatory components of our networks are linked via a set of common transcription factors that serve as targets in the signaling network and as regulators of the transcriptional response network. SDREM has been applied to infer the pathways involved in yeast stress responses and the human immune response to viral infection.
The figure above shows the regulatory paths (left) and signaling network (right) that SDREM learns in the yeast osmotic stress response.
SDREM has been described in
MT-SDREM is a SDREM extension that jointly models multiple conditions using a multi-task learning framework. It can be downloaded at the MT-SDREM site and is described in