The main goal of this project is to develop computational methods and computer software for integrated analysis and mathematical modeling of genomic, proteomic and metabolic data. The tools will aid to uncover and visualize the regulatory patterns and networks hidden in the data. Various computational methods - such as pattern matching, machine learning, data mining, knowledge representation and sequence analysis - will be applied and further developed in this project. The experimental data will generated by state of the art methods such as DNA arrays, NMR and 2D gel electrophoresis combined with time-of-flight mass spectrometry.
Another goal is to further develop the combined gas/liquid chromatography and mass spectrometry (GC/LC-MS) measurement techniques. The high sensitivity of these techniques provides a new perspective on metabolic research. The understanding and further utilization of the derived information, however, requires the development of new computational methods.
The tools and methods expected from this project will enhance our computational and analytical abilities in carrying out basic research on gene regulation and cellular metabolism, as well as try to maximize the utility and integration of information derived from various sources, i.e. at the gene, protein or metabolite levels. Furthermore, such advances will be of immense importance for the applied biosciences, whose success hinges on the understanding and ability to redirect metabolic fluxes towards a desired product, such as bioethanol or pharmaceuticals.