Qualitative models of molecular function: Linking genetic polymorphisms of tRNA to their functional sequelae

Mor Peleg, Irene S. Gabashvili, Russ Biagio Altman

Research output: Contribution to journalArticlepeer-review


The exponential growth in the volume of biological Information available makes it difficult for researchers to assemble the details into coherent models. Although an accurate model is ideal, full details are not generally available and are gained only incrementally. Therefore, as a first step toward integration of information, we propose a knowledge model for the qualitative representation of the relationships between mutations in genes 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) Transparent Access to Multiple Bioinformatics Information Sources 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 toward accurate quantitative models. Our application domain is the role of transfer ribonucleic acid 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://smi.stanford.edu/projects/helix/pubs/ process-model/.

Original languageEnglish
Pages (from-to)1875-1886
Number of pages12
JournalProceedings of the IEEE
Issue number12
StatePublished - 2002
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received April 30, 2002; revised September 27, 2002. This work was supported in part by the Burroughs-Wellcome Fund and in part by the NIH under Grant LM-05652 and Grant LM-06422. The authors are with Stanford Medical Informatics, Stanford University, Stanford, CA 94305 USA (e-mail: peleg@smi.stanford.edu; isg@smi. stanford.edu; russ.altman@stanford.edu). Digital Object Identifier 10.1109/JPROC.2002.805304


  • Biological process
  • Knowledge model
  • Mutation
  • Ontology
  • Petri net
  • Workflow

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Computer Science


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