Qualitative knowledge models in Functional Genomics and Proteomics
Mor Peleg, Irene S. Gabashvili, and Russ B. Altman
Handbook of Neural Engineering. Metin Akay (ed.), John Wiley and Sons (Inc), 2006
The wealth of data generated by genomic and proteomic experiments adding to the growth in the volume of biological information makes it difficult for researchers to assemble all available details into coherent model. Although an accurate model is ideal, full details are gained only incrementally. Therefore, as a first step towards integration of information, we propose a knowledge model for the qualitative representation of the relationships between mutations in genomes and their effects at molecular, cellular, and clinical phenotypic levels. Our framework combines and extends two components: (1) a Workflow model that allows hierarchical process and participant specifications, (2) TAMBIS and the Unified Medical Language System, which serve as controlled biological and medical terminologies. By mapping our framework to Petri Nets, we can perform qualitative simulations to validate models, and aid in predicting system behavior in the presence of dysfunctional components. This can be a step towards accurate quantitative models.
Our application domain is the role of tRNA molecules in protein translation-related disease. As an initial evaluation, we show that Petri Nets derived from the historic and current views of the translation process yield different dynamic behavior. Our model is available at http://mis.hevra.haifa.ac.il/~morpeleg/NewProcessModel/Malaria_PN_Example_Files.html
Keywords: knowledge model, ontology, Workflow, Petri Net, mutation, biological process