From time course gene expression to gene regulatory networks

The accelerated development of high-throughput technologies has enabled understanding of how biological systems function at a molecular level, for instance by unraveling the interaction structure of genes responsible for carrying out a given process.   Systems biology has the potential to enhance knowledge acquisition and facilitate the reverse engineering of global regulatory networks using gene expression time course experiments.  

In this talk I will present some models we have developed for estimating a gene interaction network from time course experimental data.  The basic structure of these models is governed by a dynamic Bayesian network, which allows us to include expert biological information as well.  Given the complexity of model fit, we resort to numerical methods for model estimation.  

I will exemplify gene network inference using experimental data from the metabolic change in Streotomyces coelicolor and the circadian clock in Arabidopsis thaliana.