Reconstructing gene regulatory networks from time-series microarray data

A gene regulatory network depicts which genes turn on which and at what moment. Knowledge of such gene networks is key to an understanding of the biological process. We propose here to use a statistical method for the reconstruction of gene regulatory networks based on Bayesian networks from microarray data. We describe a nonlinear model for the rate of gene transcription in which levels of gene expression are continuous. The reconstruction becomes an optimization problem where optimization algorithms are employed to search for optimal solutions. We apply the methodology to reconstruct the regulatory network of 41 yeast cell-cycle genes from a real microarray data set. The result obtained is promising: more than 70% (31 out of 43 arcs) of the reconstructed regulations are consistent with experimental findings.