Customizable visualization in the context of metabolic networks

Systems biology has the ambitious goal to obtain a holistic understanding of the biochemical processes in living organisms. In order to reach this goal experimental studies coupled with computer simulations are applied in an iterative process. Commonly, biologic processes are considered as networks describing various substances in the cell as well as substance transfer. These so-called metabolic networks are subject of many studies that are made to gain insights into the cellular metabolism.
Nowadays, modern high-throughput methods as well as high-performance computer simulation lead to a huge amount of data concerning metabolic networks. Post-processing these data is a big challenge for today's sience. In this situation, visualization becomes more and more important. This thesis deals with the visualization of data in metabolic networks. Therefore, new approaches are developed in four different topics:

Network drawing:
A drawing editor for biochemical network is developed bringing high usability and supporting special requirements of metabolic networks.

Network layout:
In combination with metabolic networks, de facto standards concerning the arrangement of network components have arisen in the last decades. These conventions cannot be met by available automatic layout algorithms. Semi-automatic network layout approaches are developed that support drawing networks according to established standards.

Data visualization:
For the visualization of data in networks, a new approach is introduced in this thesis: the script-based visualization. A new scripting language is developed that allows the users to visualize data according to individual requirements and preferences.

Extensibility:
In order to allow adaptability to future application fields and requirements, a plug-in interface is indispensable. The conceptual design of this interface as well as existing modeling and 3D visualization plug-ins are introduced.
A large amount of example applications brought by this thesis reveals the high benefit of the here introduced approaches for systems biology.