Skip to main content

Advertisement

Log in

Cell modeling with reusable agent-based formalisms

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Biologists are building increasingly complex models and simulations of cells and other biological entities, and are looking at alternatives to traditional representations. Making use of the object-oriented (OO) paradigm, the Unified Modeling Language (UML) and Real-time Object-Oriented Modeling (ROOM) visual formalisms, and the Rational Rose RealTime (RRT) visual modeling tool, we summarize a previously-described multi-step process for constructing top-down models of cells. We first construct a simple model of a cell using an architecture in which all objects are containers, agents, or passive objects. We then reuse these architectural principles and components to extend our simple cell model into a more complex cell, the goal being to demonstrate that encapsulation familiar to artificial intelligence researchers can be employed by systems biologists in their models. A red blood cell is embedded in a straight-forward manner within a larger system, which is in turn iteratively embedded within still larger systems, including a blood vessel, a circulatory system, a human being, and a simple ecology. Each complexity increment reuses the same architectural principles, including the use of agents, each of which continuously either moves passive small molecules between containers, or transforms these passive objects from one type into another. We show how it is possible to start with a direct diagrammatic representation of a biological structure such as a cell, using terminology familiar to biologists, and by following a process of gradually adding more and more detail, arrive at a system with structure and behavior of arbitrary complexity that can run and be observed on a computer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. M. Hucka et al., “Systems Biology Markup Language (SBML) Level 1: structures and facilities for basic model definitions,” http://sbml.org/documents/, 2005.

  2. M. Hucka et al., “The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models,” Bioinformatics, vol. 19, pp. 524–531, 2003.

    Article  Google Scholar 

  3. W. Hedley et al., “A short introduction to CellML,” Philosophical TransactionsMathematical Physical and Engineering Sciences, vol. 359, pp. 1073–1089, 2001.

    MATH  Google Scholar 

  4. System Biology Workbench, http://www.sbw-sbml.org 2005.

  5. M. Tomita et al., “E-Cell: software environment for whole-cell simulation,” Bioinformatics, vol. 15, pp. 72–84, 1999.

    Article  Google Scholar 

  6. P. Mendes “GEPASI: a software package for modelling the dynamics, steady states and control of biochemical and other systems,” Comput. Appl. Biosci., vol. 9, pp. 563–571, 1993.

    Google Scholar 

  7. P. Mendes “Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3,” Trends. Biochem. Sci., vol. 22, pp. 361–363, 1997.

    Article  Google Scholar 

  8. H. Sauro “JARNAC: a system for interactive metabolic analysis,” http://www.sys-bio.org 2000.

  9. C. Morton-Firth and D. Bray “Predicting Temporal Fluctuations in an Intracellular Signalling Pathway,” Journal of Theoretical Biology, vol. 192, pp. 117–128, 1998.

    Article  Google Scholar 

  10. J. Schaff et al., “Physiological Modeling with Virtual Cell Framework,” Methods in Enzymology, vol. 321, pp. 1–22, 2000.

    Google Scholar 

  11. L. Loew and J. Schaff “The Virtual Cell: A software environment for computational cell biology,” TRENDS in Biotechnology, vol. 19, pp. 401–406, 2000.

    Google Scholar 

  12. B. Slepchenko et al., “Computational Cell Biology: Spatiotemporal Simulation of Cellular Events,” Annual Review of Biophysics and Biomolecular Structure, vol. 31, pp. 423–442, 2002.

    Article  Google Scholar 

  13. S. Khan et al., “A Multi-Agent System for the Quantitative Simulation of Biological Networks,” in Proceedings of the 2nd International Conference on Autonomous Agents and Multi-agent Systems (AAMAS'03), pp. 385–392, 2003.

  14. P. Gonzalez et al., “Cellulat: An agent-based intracellular signalling model,” BioSystems, vol. 68, pp. 171–185, 2003.

    Article  Google Scholar 

  15. D. Harel “Statecharts: A Visual Formalism for Complex Systems,” Science of Computer Programming, vol. 8, pp. 231–274, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  16. D. Harel “On Visual Formalisms,” Communications of the ACM, vol. 31, pp. 514–530, 1998.

    MathSciNet  Google Scholar 

  17. D. Harel “A Grand Challenge for Computing: Full Reactive Modeling of a Multi-Cellular Animal,” in Workshop on Grand Challenges for Computing Research, Edinburgh, Scotland, 2002. http://www.wisdom.weizmann.ac.il/~dharel/papers/GrandChallenge.doc

  18. N. Kam and D. Harel et al., “Formal Modeling of C. elegans Development: A Scenario-Based Approach,” in Proceedings of Computational Methods in Systems Biology: First International Workshop (CMSB 2003), LNCS 2602, Rovereto, Italy, 2003, pp. 4–20.

  19. I-Logix, I-Logix Rhapsody and Statemate. http://www.ilogix.com 2005.

  20. IBM, IBM Rational Rose RealTime. http://www-306.ibm.com/software/rational/orhttp://www-306.ibm.com/software/awd-tools/developer/technical/, 2005.

  21. J. Rumbaugh and I. Jacobsonand G. Booch, The (2nd edition) Unified Modeling Language Reference Manual, Addison-Wesley: Reading, MA, 2005.

    Google Scholar 

  22. B. Selic and G. Gulleksonand P. Ward, Real-Time Object-Oriented Modeling, John Wiley & Sons: New York, 1994.

    Google Scholar 

  23. K. Webb and T. White “UML as a cell and biochemistry modeling language,” BioSystems, vol. 80, pp. 283–302, 2005.

    Article  Google Scholar 

  24. T. Quatrani Visual Modeling with Rational Rose and UML, Addison-Wesley: Reading, MA, 1998.

    Google Scholar 

  25. P. Kruchten The Rational Unified Process: An Introduction (2nd Edition), Addison-Wesley: Reading, MA, 2000.

    Google Scholar 

  26. W. Becker and J. Reeceand M. Poenie, The World of the Cell, 3rd ed, Benjamin/Cummings: Menlo Park, CA, 1996.

    Google Scholar 

  27. P. Mendes Gepasi 3.30. http://www.gepasi.org, 2003.

  28. K. Webb and T. WhiteCombining Analysis and Synthesis in a Model of a Biological Cell, Symposium on Applied Computing (SAC 2004), Nicosia, Cyprus, 2004, pp. 185–190.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tony White.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Webb, K., White, T. Cell modeling with reusable agent-based formalisms. Appl Intell 24, 169–181 (2006). https://doi.org/10.1007/s10489-006-6937-9

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-006-6937-9

Keywords

Navigation