Modelling biological processes using workflow and Petri Net models

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, TAMBIS, 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. AVAILABILITY The model is available at http://smi.stanford.edu/projects/helix/pubs/process-model/.

[1]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[2]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[3]  Wil M. P. van der Aalst,et al.  The Application of Petri Nets to Workflow Management , 1998, J. Circuits Syst. Comput..

[4]  Michael Grüninger,et al.  The Process Interchange Format and Framework , 1998, The Knowledge Engineering Review.

[5]  H Matsuno,et al.  Hybrid Petri net representation of gene regulatory network. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[6]  Craig Schlenoff,et al.  The Process Specification Language (PSL) Overview and Version 1.0 Specification , 2000 .

[7]  V. N. Reddy,et al.  Qualitative analysis of biochemical reaction systems , 1996, Comput. Biol. Medicine.

[8]  James Lyle Peterson,et al.  Petri net theory and the modeling of systems , 1981 .

[9]  Dov Dori,et al.  The Model Multiplicity Problem: Experimenting with Real-Time Specification Methods , 2000, IEEE Trans. Software Eng..

[10]  Amnon Naamad,et al.  Statemate: a working environment for the development of complex reactive systems , 1988, ICSE '88.

[11]  Thomas Lengauer,et al.  Pathway analysis in metabolic databases via differetial metabolic display (DMD) , 2000, German Conference on Bioinformatics.

[12]  M N Liebman,et al.  Qualitative modeling of normal blood coagulation and its pathological states using stochastic activity networks. , 1997, International journal of biological macromolecules.

[13]  Mark A. Musen,et al.  The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility , 2000, EKAW.

[14]  강문설 [서평]「The Unified Modeling Language User Guide」 , 1999 .

[15]  M. Riley,et al.  MultiFun, a multifunctional classification scheme for Escherichia coli K-12 gene products. , 2000, Microbial & comparative genomics.

[16]  R Hofestädt,et al.  Quantitative modeling of biochemical networks , 1998, Silico Biol..

[17]  Alexander Borgida,et al.  Description Logics in Data Management , 1995, IEEE Trans. Knowl. Data Eng..

[18]  Steffen Schulze-Kremer,et al.  Ontologies for Molecular Biology , 2001, Electron. Trans. Artif. Intell..

[19]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[20]  M. Blackman,et al.  Proteases involved in erythrocyte invasion by the malaria parasite: function and potential as chemotherapeutic targets. , 2000, Current drug targets.

[21]  Dov Dori,et al.  Object-Process Analysis: Maintaining the Balance Between System Structure and Behaviour , 1995, J. Log. Comput..

[22]  M C Kohn,et al.  Identification of regulatory properties of metabolic networks by graph theoretical modeling. , 1991, Journal of theoretical biology.

[23]  Michael Krauthammer,et al.  A knowledge model for analysis and simulation of regulatory networks , 2000, Bioinform..

[24]  Peter D. Karp,et al.  An ontology for biological function based on molecular interactions , 2000, Bioinform..

[25]  Carole A. Goble,et al.  An ontology for bioinformatics applications , 1999, Bioinform..

[26]  M. Diaz,et al.  Modeling and Verification of Time Dependent Systems Using Time Petri Nets , 1991, IEEE Trans. Software Eng..

[27]  Dmitrij Frishman,et al.  Functional and structural genomics using PEDANT , 2001, Bioinform..

[28]  James E. Rumbaugh,et al.  Object-Oriented Modelling and Design , 1991 .

[29]  James L. Peterson,et al.  Petri net theory and the modeling of systems , 1981 .

[30]  David Harel,et al.  Executable object modeling with statecharts , 1996, Proceedings of IEEE 18th International Conference on Software Engineering.