Background: With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the analysis of such data often there is the need to further explore the similarity of genes not only with respect to their expression, but also with respect to their functional annotation which can be obtained from Gene Ontology (GO).

SummaryEpithelial cells express genes whose products signal the presence of pathogenic microorganisms to the immune system. Pathogenicity factors of enteric bacteria modulate host cell gene expression. Using microarray technology we have profiled epithelial cell gene expression upon interaction with Yersinia enterocolitica . Yersinia enterocolitica wild-type and isogenic mutant strains were used to identify host genes modulated by invasin protein (Inv), which is involved in enteroinvasion, and Yersinia outer protein P (YopP) which inhibits innate immune responses. b receptor signalling. These genes were also repressed by pYVencoded factors. Only a few host genes were exclusively induced by pYV-encoded factors. We hypothesize that some of these genes may contribute to pYVmediated silencing of host cells. In conclusion, the data demonstrates that epithelial cells express a limited number of genes upon interaction with enteric Yersinia . Both Inv and YopP appear to modulate gene expression in order to subvert epithelial cell functions involved in innate immunity.

Abstract-With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data the need for a functional grouping of genes arises. In this paper, we propose a new method based on spectral clustering for the partitioning of genes according to their biological function. The functional information is based on Gene Ontology annotation, a mechanism to capture functional knowledge in a shareable and computer processable form. Our functional cluster method promises to automatize, speed up and therefore improve biological data analysis.

Abstract-With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data the need of a functional grouping of genes arises. In this paper, we propose a new clustering algorithm for the partition of genes or gene products according to their known biological function based on Gene Ontology terms. Ontologies offer a mechanism to capture knowledge in a shareable form that is also processable by computers. Our functional cluster algorithm promises to automatize, speed up and therefore improve biological data analysis.

Abstract. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. Different approaches to infer the dependencies of gene regulatory networks by identifying parameters of mathematical models like complex S-systems or simple Random Boolean Networks can be found in literature. Due to the complexity of the inference problem some researchers suggested Evolutionary Algorithms for this purpose. We introduce enhancements to the Evolutionary Algorithm optimization process to infer the parameters of the non-linear system given by the observed data more reliably and precisely. Due to the limited number of available data the inferring problem is under-determined and ambiguous. Further on, the problem often is multi-modal and therefore appropriate optimization strategies become necessary. We propose a new method, which evolves the topology as well as the parameters of the mathematical model to find the correct network.