In this work, we studied a common problem in Systems Biology, which is the inference or reverse engineering of gene regulatory networks from gene expression data. We addressed this problem using the Boolean formalism, where the expression of a gene is represented only by two possible values: 0 (not expressed) or 1 (expressed). Besides that, our methodology is based on a feature selection approach and we used an algorithm named IFFS - improved forward floating selection. We performed experiments to compare two measures of gene interactions used in the criterion function of the algorithm, the coefficient of determination and the mutual information computed via Tsallis entropy. Besides that, we also incorporated a biological prior knowledge source of gene interactions from a database known as STRING. To validate the methodology, we used data from the DREAM challenge and a dataset from a budding yeast cell-cycle study. The results showed that, generally, the mutual information performs slightly better than the coefficient of determination, and that incorporating biological knowledge improves the results.

Models of gene regulatory networks (GRN) have been proposed along with algorithms for inferring their structure. By structure, we mean the relationships among the genes of the biological system under study. Despite the large number of genes found in the genome of an organism, it is believed that a small set of genes is responsible for maintaining a specific core regulatory mechanism (small subnetworks). We propose an algorithm for inference of subnetworks of genes from a small initial set of genes called seed and time series gene expression data. The algorithm has two main steps: First, it grows the seed of genes by adding genes to it, and second, it searches for subnetworks that can be biologically meaningful. The seed growing step is treated as a feature selection problem and we used a thresholded Boolean network with a perturbation model to design the criterion function that is used to select the features (genes). Given that the reverse engineering of GRN is a problem that does not necessarily have one unique solution, the proposed algorithm has as output a set of networks instead of one single network. The algorithm also analyzes the dynamics of the networks which can be time-consuming. Nevertheless, the algorithm is suitable when the number of genes is small. The results showed that the algorithm is capable of recovering an acceptable rate of gene interactions and to generate regulatory hypotheses that can be explored in the wet lab.