At present there is a growing interest in the development of systems in which scientific analysis with high computing or data storage and processing requirements can be performed. The cluster and Grid computing technologies have emerged has the best support infrastructures for this type of systems. Biological sciences are among those who have been benefiting more from the advancement of these technologies, namely in the study of gene expression mechanisms. In that sense, the discovery of transcription factor binding sites and the analysis of gene expression data are particularly relevant. In the first case, we usually search for short segments of DNA, known as motifs, that are well conserved. In the second case, we usually analyze microarray data using data mining techniques like biclustering. In the context of this thesis, efficient algorithms for motif inference in gene promoter regions and for the analysis of gene expression data were made available in the hermes cluster of Instituto Gulbenkian de Ciência. The algorithms were developed in the context of the BioGrid - Parallel Algorithms for Gene Annotation project. During this work, the necessary tasks of implementing, installing and testing were performed, as well as the development of Web interfaces and documentation for every program. In addition to that, a study was conducted in which the model-based testing technique was used to evaluate the software. The algorithms created in the context of the BioGrid project are now available in a reliable, integrated and user-friendly system for a large community of Bioinformatics users.