Inferring nonstationary gene networks from temporal gene expression data

Reverse-engineering transcriptional networks from longitudinal expression profiles is a crucial step towards the study of gene regulatory mechanisms. Genes dynamically orchestrate to each other, the stationarity assumption made by existing methods of transcriptional interaction inference is no longer adequate. As such, we need a new approach to handle the nonstationary behavior in gene expression. On the other hand, microarrays for human studies are equipped with a large number of probe sets, leading the inference of dynamic networks to a computationally intensive task. Hence, there is a need to design the inference algorithm in a tractable manner. This paper develops a Bayesian network approach to inferring the nonstationary transcriptional interactions. The applications of our approach to a clinical study of mechanical periodontal therapy demonstrates a significant improvement over stationary models. Our nonstationary network model also explains the anti-inflammatory effect of mechanical pe-riodontal therapy.