Probabilistic Methods for the Analysis of Biological Networks

We developed a probabilistic modeling method that uses input–output hidden Markov models to reconstruct dynamic regulatory networks that explain how temporal gene expression is jointly regulated by miRNAs and TFs. We measured miRNA and mRNA expression for postnatal lung development in mice and used studied the regulation of this process. The reconstructed dynamic network correctly identified known miRNAs and TFs. The method has also provided predictions about additional miRNAs regulating this process and the specific developmental phases they regulate, several of which were experimentally validated. Our analysis uncovered links between miRNAs involved in lung development and differentially expressed miRNAs in idiopathic pulmonary fibrosis patients, some of which we have experimentally validated using proliferation assays. Our results show how probabilistic models can be used to integrate diverse data sets and lead to new scientific discoveries.