Defining transcriptional network by combining expression data with multiple sources of prior knowledge

Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. In this paper, a transcriptional regulation model is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity and the activity level of transcription factor (TF) are incorporated into a general learning model. The model relies on a continuous time, differential equation description of transcriptional dynamics where transcription factors are treated as latent on/off variables and are modeled using a switching stochastic process. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than some previous models can do.