PathSys data visualization is done through the Client Side Application, BiologicalNetworks, which implements all business logic and a significant part of the user interface.
Sound Data Model together with strong Ontology capturing a big variety of biological data types is crucial for modeling capabilities of the system. But networks of molecular interactions derived from current biological data are incomplete and complicated. Complete network is clearly beyond human perception. Therefore different levels of abstractions are necessary to make effective analysis of cellular processes and dealing with complexity better.
One of such abstractions presented in PathSys&BiologicalNetworks is Meta-Node (or Graph Node).
Representing a cellular pathway as a single process or grouping related processes under a certain cellular mechanism enhance the comprehensibility of the networks of events. Grouping examples presented inPathSys&BiologicalNetworks are: domain structure of a protein, complex of multidomain proteins, chromosomal regions (promoters, binding sites, etc.) bound to a gene or group of genes, protein with its phosphorylation sites, gene transcription and mRNA translation or degradation mechanisms, whole metabolic pathway etc. Remember that grouping could be done over any data type object from Node Typeshierarchy. Since the data on cellular processes is not complete, different levels of information may be available for certain events. In case where it is not identified which state among a set of states constitutes the substrate, product or effector of a process, or where target process of an effector is obscure, we may need to abstract these states or/and processes in a single state/process to represent the available information despite its incomplete nature (see Figure1 below).
Figure1. Abstraction helps better handling of complex information. For instance, part of a pathway may be collapsed to simplify relatively more complex graph (a->c, b->d). Two types of abstractions for representing information of incomplete nature: State and process abstraction. In addition (b, d) there are regular abstractions, “protein degradation”, “gene expression”, etc.