Inferring gene expression networks through static and dynamic data integration

This paper presents a novel approach for the extraction of gene regulatory networks from DNA microarray data. The approach is characterized by the integration of data coming from static and dynamic experiments, exploiting also prior knowledge on the biological process under analysis. A strategy to learn a gene network from mutant static data has been integrated with a genetic algorithm approach to take into account the information coming from time series experiments. The method has been applied to the reconstruction of an interaction network of genes involved into the Saccharomyces Cerevisiae cell cycle. The proposed approach was able to reconstruct known relationships among genes and to provide meaningful biological results.