This program, called EST123D, is useful for proteins that are not yet characterized other than by ESTs. These technologies are designed for high throughput and are already capable of monitoring the complete gene inventory of small organisms. We have devised a procedure to normalize the resulting data in a way that allows for meaningful comparisons between cell states. Subsequently, we apply statistics and machine learning techniques in order to identify differentially regulated genes, clusters of cell states etc. Pathway modeling While a large part of the current effort is focused on inferring high-level structures from gene expression data, much is already known on the underlying genetic networks.
Several databases exist that document metabolic relations in machine-readable form.
Consequently, we have implemented methods that extract additional protein relations from article abstracts and model them as Petri nets. The resulting graphs can be restricted to species, tissues, diseases or other areas of interest. The generated networks can provide overviews that cross the boundaries of competence fields of human experts. Further means are necessary to allow for more detailed analyses. We can automatically extract pathways from networks that are far too large and complicated to lend themselves to easy interpretation.