The Many Faces of Process Interaction Graphs: A Data Management Perspective
Our Research Interests We use the expression process interaction graph(PIG) as a general purpose term to cover instances of network-like structures with specific semantics, including signal transduction networks, gene regulatory networks, metabolic pathways and so forth, because we believe that despite the differences in biological significance, they can be treated in a uniform data management framework. Aside from the fact that they are all graph-like entities, PIGs share a few common
... few common characteristics, such as follows: • Regardless of what the nodes and edges represent, in many cases there is an inherent temporal order (perhaps of branching time) that can partition the network into phases. Often, as in the case of gene regulatory networks, a mutation produces an alternate graph which is structurally isomorphic to the normal-case, except that the temporal properties have changed. Other gene mutations produce regulatory graphs whose subgraphs corresponding to certain phases have been altered. • The graphs very often represents objects and how they interact; while the interaction has a generic structure like reactants: A and B products: C and D occurs at: some location L catalyzed by: E precondition: first-order formula φ(· · ·) equation(T): some equation of type T where T can be a chemical reaction formula, an ordinary differential equation with rate constants etc. there is very often a wide heterogeneity in the kind of information represented in this general structure. For example, the location L can have could be "at -300 to -340 bp upstream of gene G" in gene regulatory networks, but "at the lipid sublayer of the cell membrane of cell C" for cell signaling. In the first case, the semantic type of location is a subsequence in a DNA sequence, while in the second, it is a member of an ontology. • Very often the objects in the primary graph are members of one or more DAG-structured (sometimes tree-structured) taxonomies such as the Gene Ontology, the Enzyme classification tree or the Yeast Functional Categories. In these cases, there is often a need to query the taxonomy graph and the PIG together, as though they are a compound graph, where the "join terms" between them are the node (i.e., gene or enzyme) names.