Modelling biological processes using workflow and petri net models

Mor Peleg, Iwei Yeh, Russ B. Altman

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning. Results: We assessed eleven diverse models that were developed in the fields of software engineering, business, and biology, to evaluate their suitability for representing and simulating biological processes. Based on this assessment, we combined the best aspects of two models: Workflow/Petri Net and a biological concept model. The Workflow model can represent nesting and ordering of processes, the structural components that participate in the processes, and the roles that they play. It also maps to Petri Nets, which allow verification of formal properties and qualitative simulation. The biological concept model, TAM-BIS, provides a framework for describing biological entities that can be mapped to the workflow model. We tested our model by representing malaria parasites invading host erythrocytes, and composed queries, in five general classes, to discover relationships among processes and structural components. We used reachability analysis to answer queries about the dynamic aspects of the model.

Original languageEnglish
Pages (from-to)825-837
Number of pages13
JournalBioinformatics
Volume18
Issue number6
DOIs
StatePublished - 2002
Externally publishedYes

Bibliographical note

Funding Information:
The work was funded by the Burroughs Wellcome Fund and grant GM07365-26 from the National Institute of General Medical Sciences. We thank Dr T.Hanekamp for his help in validating the biological correctness of our models and for his insights about representing inhibitors and data from related Plasmodium species. We thank Dr H.Ginsburg for helping us validate the correctness of the Malaria example and for his helpful suggestions.

ASJC Scopus subject areas

  • Computational Mathematics
  • Molecular Biology
  • Biochemistry
  • Statistics and Probability
  • Computer Science Applications
  • Computational Theory and Mathematics

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