Synopsis of Undergraduate Dissertation project

The gene regulatory networks play a central role in systems biology. Many cell processes are organized as a network of interacting units. These units represent sets of genes that are coregulated in response to different conditions. The function of most genes of these sets is the production of transcription factors, proteins with a key role in regulating the expression of other genes and therefore the different physiological, biochemical and molecular processes.

On the basis that they play an important role in gene regulation, transcription factors have been associated with various diseases. In this work, we performed functional analysis of gene regulatory networks. We worked initially with raw data, that is the genes that appeared in certain cell lines of human origin we chose. These genes were derived from abnormal conditions and one of our tasks was to compare them with the normal ones by observing the percentage of their expression. Using the RNEA (Regulatory Network Enrichment Analysis) and additional data concerning molecular pathways from the KEGG and GO databases, we created gene regulatory networks from these genes, for each cell line, in order to extract the functional properties of these networks and create gene expression profiles.

The construction of gene regulatory networks can be defined as the process of identifying the interactions of genes from experimental data using computational analysis. Next, we performed visualization and processing of the inferred gene regulatory networks with the help of Cytoscape, a special platform for the visualization of such networks and their integration with gene expression profile. Then, by receiving all nodes (genes) from the respective networks we made heat maps in order to observe what paths, in which the genes belonged, were enriched in total. Finally, we performed pairwise comparison of the networks, using the genes that showed high expression rate (master regulators), so as to create a tree of distances in which every cell line is represented.

The objective was to observe, taking into consideration gene expression profiles from the literature, the degree of similarity between the various cell lines, hence the similarity between the gene regulatory networks. Overall, we tried to extract useful biological information out of these networks and draw conclusions about the impact of several gene regulators on the pathogenesis of several diseases.