Metabolic network visualization

Microarrays have created a revolution in biological research by simultaniously extracting gene expression related to a specific specie. This vast extracted data needs to be analyzed. Information visualization using graphs and animation techniques helps scientists understand data relations and interactions. In this research, several graph visulaization techniques for analyzing gene expression data have been developed. The first technique proposes a new approach that focuses on the reactions that transform molecules, using higher-level macro-glyphs that summarize a large number of molecules in a compact unit, thus forming very natural and automatic clusters. We also employ natural clustering approaches to other areas of typical metabolic networks. The result is a graph with about 50-60% of the original node and 20-30% of the original edge count, which simplifies efficient layout and interaction significantly. The second technique focuses on the visualization of gene regulatory networks. This technique is based on drawing the shortest paths between genes of interest using breadth-first-search algorithm. By drawing genes of interest and showing their regulatory networks, we were able to clearly understand the interaction between these genes. Visualization of Gene Regulatory Networks considerably reduces the number of displayed nodes and edges. This reduction in nodes and edges depends on the number of genes of interest as well as to the number of interactions between these genes. A new clustering technique adapted to the gene regulatory network is introduced. This technique further reduces gene regulatory network complexity while preserving the amount of informtion it presents. The last technique focuses on displaying gene expression data without adding additional graph elements. This technique also allows us to compare gene expression of a specific gene relative to the other gene expression. A new tool for graph visualization and interaction called Metabolic Network Visualization or MNV has been developed in order to implement and validates this new techniques. In MNV, users have access to a powerful search, flexible data to graph mapper and intuitive navigation system.