State-space Modelling of Replicated Dynamic Genetic Networks

The genomic reality is highly complex and dynamic. Recent developments of high-throughput technologies have enabled researchers to measure the RNA abundance of thousands of genes simultaneously. The challenge is to unravel from such measurements genomic interactions and key biological features of cellular systems. Two common problems are the high-dimensionality of the system and the spurious correlations induced by unmeasured intermediate substrates. Furthermore most currently available models cannot deal with biological replicates. Our goal is to devise a method for inferring large transcriptional or gene regulatory networks from high- throughput data sources such as gene expression microarrays with potentially hidden states, such as unmeasured transcription factors (TFs).

Methodology: We propose a dynamic state space representation to account for the effects of such hidden states. Our inference method is based on a Kalman smoothing algorithm incorporated in the E-step of an EM algorithm. We employ bootstrap confidence intervals for inferring sparse networks, combined with an AIC criterion for determining the size of the latent space. The proposed method is applied to time course microarray data obtained from a well established T-cell experiment.