Modeling mutations, abnormal processes, and disease phenotypes using a workflow/Petri Net model

Mor Peleg, Irene S. Gabashvili, and Russ B. Altman

Abstract

Predicting the molecular- and cellular-level effects of genetic mutations is a challenging task. It calls for models that integrate different data sets, and represent the interactions of mutated gene products with other cellular components, in order to understand their effects on molecular, cellular, and organism-level processes. We have developed a graphical knowledge model for representing molecular functional information as a first step towards modeling the relationship between molecular structure and disease phenotypes. Our model is based on a Workflow model that can be mapped to Petri Nets, and is implemented as a frame-based knowledge base using the Protégé-2000 tool. We use TAMBIS and the UMLS as to describe biological and medical concepts that can be mapped to the participants, roles, and processes in the workflow model. The formal nature of our model allows us to write queries about structural and functional aspects of biological systems –such as relationships between defective processes and the clinical phenotype of the mutation that is causing it. To illustrate the power of this model, we have used it to represent mutations in tRNA and their affects on the process of translation. Mapping the workflow model to Petri Nets enabled us to verify the soundness of some dynamic aspects of tRNA biology and to simulate system behavior in the presence of different mutations. Our model is available at http://www.smi.stanford.edu/people/peleg/Process_Model.htm