  Gene regulatory networks are collections of genes that interact with one
other and with other substances in the cell. By measuring gene expression over
time using high-throughput technologies, it may be possible to reverse
engineer, or infer, the structure of the gene network involved in a particular
cellular process. These gene expression data typically have a high
dimensionality and a limited number of biological replicates and time points.
Due to these issues and the complexity of biological systems, the problem of
reverse engineering networks from gene expression data demands a specialized
suite of statistical tools and methodologies. We propose a non-standard
adaptation of a simulation-based approach known as Approximate Bayesian
Computing based on Markov chain Monte Carlo sampling. This approach is
particularly well suited for the inference of gene regulatory networks from
longitudinal data. The performance of this approach is investigated via
simulations and using longitudinal expression data from a genetic repair system
in Escherichia coli.
