Metabolic Pathways

Abstract

In this thesis a platform for the analysis of metabolic pathways (or networks) was created.The main goal was to provide a single easy to use user-interface to a variety of different analysis tools, using extensively available standards. While adequate existing state-of-the- art tools in the area of modeling (SBMW, SBW), model checking (BioCHAM, NuSMV), and the simulation of metabolic pathway (Jarnac) were found and thus integrated, new approaches for the visualization and analysis needed to be developed. For the visualization of metabolic pathways and networks, we implemented and tested existing graph layout algorithms, and presented a new approach to lay-out medium size metabolic pathways (500-20,000 vertices) by implementing and combining three well known graph lay-out algorithms (high dimension embedding, spring-embedder preprocessing, spring-embedder), through 3D space density analysis facilitated by the Octree technique. For the analysis of the results of metabolic pathways simulations we present two new techniques: firstly, a powerful technique to visualize pathways simulation data was created to unveil and understand concentration ows through metabolic pathways. This was achieved by mapping the color encoded concentration value of every substance from each time step of the simulation to its graphical representation in the layout. By combining all resulting images (from each time step) and displaying them as a movie, many characteristics such as sub-networks, alternative routes through the network, and differences between a modified pathway and its unmodified version can be revealed. Secondly, a new method to detect co-regulated substances in metabolic pathways and to recognize differences between two versions of a pathway, was established. To do this, we transformed the simulation data into a row-based representation, color-coded these rows, and reordered them with respect to similarity by using a Genetic Algorithm variant. From the arising discrete 2-dimensional matrix consisting of concentration values, a continuous 2-dimensional fourier row function was computed. This function can be used to measure properties, such as similarities in a pathway between time steps, or substances, or to detect and evaluate differences between modified versions of the same pathway.