Modeling and analysis of gene regulatory networks with a Bayesian-driven approach

Modeling of gene regulatory networks play an important role in the post genomic era. In this work, we propose a Bayesian inference based model to quantitatively analyze the transcriptional regulatory network when the structure of regulatory network is given. In the proposed model, the dynamics of transcription factors are treated as a Markov process. Besides, the sequence features of genes are employed to calculate the binding affinity between transcription factor and its target genes. Experimental results on the real biological datasets show that the present model can effectively identify the activity levels of transcription factors, as well as the regulatory parameters.