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. Pathways are biologically meaningful subgraphs, e.g. signaling cascades or metabolic pathways that account for supply and consumption of any intermediate metabolites. Each of the methods described above can provide valuable clues pointing to target proteins. But the crux lies in their clever combination, interconnecting data from different sources. In real life situations, clustering alone may not be able to reconstruct pathways from gene expression data.