Quo Vadis, enzymology data? Introductory remarks

The post-genomic era is characterized by a gold-rush mood, because many previously separate disciplines, ranging from biology and biochemistry to physics, mathematics and computer sciences, have grown together and contribute to the generation of enormous amounts of experimental and theoretical data. These data are published in journals and often collected in electronic data repositories. Such resources provide, as a challenge for intelligent data mining, many potential chances to create new knowledge and to gain insights into complex biological systems. One approach of, for example, systems biologists, is not only to depict the cellular metabolic pathways such as those drawn in the well-known Boehringer poster or the KEGG pathway map but to enter in the third dimension with a higher level of information such as the e-cell project (Tomita et al., 1999; Takahashi et al., 2004). Apart to the basic scientific understanding of metabolic networks the application of these digitized maps can also be useful for the simulation of the treatment of diseases such as diabetes which could lead to the development of new “intelligent” drugs (Werner, 2002). However, the way to this scientific goldmine is paved with serious problems. Have you also been faced with the difficulty for comparing your kinetic data obtained from your experimental results with those published in the literature? Have you been interested in the effect of directed mutations within the catalytic domain or within structure determining sections of the protein on structure– function relationships regarding the catalytic properties? Or did you just want to understand the experimental results in the literature and to draw the conclusion in reference to the materials and methods described? Or have you tried to construct a computer model on the basis of published data?