Skip to main content
SpringerLink
Log in

Stigmergy for Biological Spatial Modeling

  • Chapter
  • First Online:
Complex Adaptive Systems

Part of the book series: Understanding Complex Systems ((UCS))

  • 1477 Accesses

Abstract

Complex adaptive systems can be characterized as systems that are comprised of groups of agents following simple rules that, collectively, produce emergent, complex behavior. The key to these emergent properties are the interactions—the exchanges of information—between the agents. Many biological systems can be studied using a complex adaptive systems approach, such as colonies of bees or ants. In some of these biological systems, the communication between individual agents is indirect. This type of communication is termed stimergy: a relatively small amount of information being shared through the environment, rather than directly from agent to agent. This information is nonetheless crucial to the self-organizing properties of the system, and is used by the agents to inform decision making, such as when ants follow a trail of pheromones left by other ants. In this chapter we describe computer simulations of two such systems, created and used to conduct experiments on various types of stimergy: collaboration within a predator-prey system, and angiogenesis in cancer growth. The first utilizes a cellular automata model, and the second a multiscale agent-based model. Further, this paper defines various options of communications for these simulations, and examines the use of similar communication paradigms in these two different types of models. Results support that stigmergy can be adapted to a variety of situations. Also, that awareness of the speed of algorithmic decisions can increase its usefulness in biological modeling. These ideas can be adapted to many other modeling situations other than the classic examples of self-organization like bees or ants.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abbott, R.: Cancersim: a computer-based simulation of hanahan and weinberg’s hallmarks of cancer. Master’s thesis, University of New Mexico (2002)

    Google Scholar 

  2. Allsopp, R.C., Vaziri, H., Patterson, C., Goldstein, S., Younglai, E.V., Futcher, A.B., Greider, C.W., Harley, C.B.: Telomere length predicts replicative capacity of human fibroblasts. Proc. Natl. Acad. Sci. U.S.A. 89(21), 10114–10118 (1992)

    Article  Google Scholar 

  3. Alexander, R.A. Anderson, A.M. Weaver, P.T.: Cummings, and Vito Quaranta. Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127(5), 905–915 (2006)

    Article  Google Scholar 

  4. Anderson, P.W.: More is different. Science 177(4047) (1972)

    Article  Google Scholar 

  5. Barkai, N., Shilo, B.-Z.: Variability and robustness in biomolecular systems. Mol. Cell 28(5), 755–760 (2007)

    Article  Google Scholar 

  6. Bauer, A.L., Jackson, T.L., Jiang, Y.: A cell-based model exhibiting branching and anastomosis during tumor-induced angiogenesis. Biophys. J. 92(9), 3105–3121 (2007)

    Article  Google Scholar 

  7. Blanchard, D.C., Griebel, G., Blanchard, R.J.: Conditioning and residual emotionality effects of predator stimuli: some reflections on stress and emotion. Prog. Neuro-Psychopharmacol. Biol. Psychiatr. 27, 1177–1185 (2003)

    Article  Google Scholar 

  8. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems and. Oxford University Press, Oxford (1999)

    MATH  Google Scholar 

  9. Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406(6791), 39–42 (2000)

    Article  Google Scholar 

  10. Bo Shim, E., Kwon, Y.-G., Jong Ko, H.: Computational analysis of tumor angiogenesis patterns using a two-dimensional model. Yonsei Med. J. 46(2), 275–283 (2005)

    Article  Google Scholar 

  11. Coffey, D.S.: Self-organization, complexity and chaos: the new biology for medicine. Nat. Med. 4(8), 882–885 (1998). August

    Article  Google Scholar 

  12. Couzin, I.: Collective minds. Nature 445, (2007)

    Article  Google Scholar 

  13. Couzin, I.D.: Collective cognition in animal groups. Trends Cognit. Sci. 13, 36–43 (2009)

    Article  Google Scholar 

  14. Couzin, I.D., Krause, J., Franks, N.R., Levin, S.A.: Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005)

    Article  Google Scholar 

  15. de Carvalho, K.C., Tome, T.: Self-organized patterns of coexistence out of a predator-prey cellular automaton. Int. J. Mod. Phys. C 17, 1647–1662 (2006)

    Article  MathSciNet  Google Scholar 

  16. Dewdney, A.K.: Sharks and fish wage an ecological war on the toroidal planet wa-tor. Sci. Am. (1984)

    Google Scholar 

  17. Dréau, D., Stanimirov, D., Carmichael, T., Hadzikadic, M.: An agent-based model of solid tumor progression. In: Proceedings of the 1st International Conference on Bioinformatics and Computational Biology, BICoB ’09, pp. 187–198. Springer, Berlin, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Ekman, P.: Basic Emotions. Wiley, New York (1999)

    Book  Google Scholar 

  19. Farina, F., Dennunzio, A.: A predator-prey cellular automaton with parasitic interactions and environmental effects. Fundam. Inf. 83, 337–353 (2008)

    MathSciNet  MATH  Google Scholar 

  20. Frank, S.A., Iwasa, Y., Nowak, M.A.: Patterns of cell divisions and the risk of cancer. Genetics 163, 1527–1532 (2003). April

    Google Scholar 

  21. Gardner, M.: The fantastic combinations of john conway’s new solitaire game ’life’. Sci. Am. 223, 120–123 (1970)

    Article  Google Scholar 

  22. Hanahan, D., Weinberg, R.A.: The hallmarks of cancer. Cell 100, 57–70 (2000). January

    Article  Google Scholar 

  23. Harrington, K., Olsen, M., Siegelmann, H.: Communicated somatic markers benefit both the individual and the species. In: Proceedings of the International Joint Conference on Neural Networks (2011)

    Google Scholar 

  24. Harrington, K., Olsen, M., Siegelmann, H.: Computational neuroecology of communicated somatic markers. (2012)

    Google Scholar 

  25. Hawick, K.A., Scogings, C.J.: A minimal spatial cellular automata for hierarchical predator-prey simulation of food chains (technical report cstn-040). Technical report, Computer Science, Massey University (2009)

    Google Scholar 

  26. Hogeweg, P.: Cellular automata as a paradigm for ecological modeling. Appl. Math. Comput. 27, 81–100 (1988)

    MathSciNet  MATH  Google Scholar 

  27. Hyung Don Ryoo, T.G., Steller, H.: Apoptotic cells can induce compensatory cell proliferation through the jnk and the wingless signaling pathways. Dev. Cell 7(4), 491–501 (2004)

    Google Scholar 

  28. Kitano, H.: Systems biology: a brief overview. Science 295(5560), 1662–1664 (2002)

    Article  Google Scholar 

  29. Lehman, C.L., Tilman, D.: Competition in Spatial Habitats, pp. 185–203. Princeton University Press, Princeton (1997)

    Google Scholar 

  30. Lindahl, T., Wood, R.D.: Quality control by dna repair. Science 286(3) (1999)

    Article  Google Scholar 

  31. Lotka, A.J.: Elements of Physical Biology. Williams and Wilkins (1925)

    Google Scholar 

  32. Low, A., Lang, P.J., Smith, J.C., Bradley, M.M.: Both predator and prey: emotional arousal in threat and reward. Psychol. Sci. 19, 865–873 (2008)

    Article  Google Scholar 

  33. Mark A.J.C.: Mathematical modelling of angiogenesis. J. Neuro-Oncol. 50, 37–51 (2000). https://doi.org/10.1023/A:1006446020377

    Article  Google Scholar 

  34. Markus, M., Böhm, D., Schmick, M.: Simulation of vessel morphogenesis using cellular automata. Math. Biosci. 156(1–2), 191–206 (1999)

    Article  MathSciNet  Google Scholar 

  35. McDougall, S.R., Anderson, A.R.A., Chaplain, M.A.J.: Mathematical modelling of dynamic adaptive tumour-induced angiogenesis: Clinical implications and therapeutic targeting strategies. J. Theor. Biol. 241(3), 564–589 (2006)

    Article  MathSciNet  Google Scholar 

  36. Michor, F., Iwasa, Y., Nowak, M.A.: Dynamics of cancer progression. Nat. Rev. Cancer 4, 197–206 (2004)

    Article  Google Scholar 

  37. Millonas, M.M.: Swarms, phase transitions, and collective intelligence. SFI Stud. Sci. Complex. 17, 417–417 (1994)

    Google Scholar 

  38. Olsen, M.M., Siegelmann, H.T.: Multiscale agent-based model of tumor angiogenesis. Proc. Comput. Sci. 18, 1016–1025 (2013)

    Article  Google Scholar 

  39. Olsen, M., Harrington, K., Siegelmann, H.: Conspecific emotional cooperation biases population dynamics: a cellular automata approach. Int. J. Nat. Comput. Res. 1(3), 51–65 (2010)

    Article  Google Scholar 

  40. Olsen, M.M., Siegelmann-Danieli, N., Siegelmann, H.T.: Dynamic computational model suggests that cellular citizenship is fundamental for selective tumor apoptosis. PLoS One 5(5) (2010)

    Article  Google Scholar 

  41. Olsen, M., Harrington, K., Siegelmann, H.: Computational emotions in a population dynamics cellular automata encourage collective behavior. In: International Conference on Complex Systems (2011)

    Google Scholar 

  42. Olsen, M.M., Fraczkowski, R.: Co-evolution in predator prey through reinforcement. J Comput Sci. 9, 118–124 (2015). https://doi.org/10.1016/j.jocs.2015.04.044

  43. Owen, M.R., Alarcon, T., Maini, P.K., Byrne, H.M.: Angiogenesis and vascular remodelling in normal and cancerous tissues. J. Math. Biol. 58(4–5), 689–721 (2009)

    Article  MathSciNet  Google Scholar 

  44. Park, K.: The internet as a complex system. In: Park, K., Willinger, W. (eds.), The Internet as a Large-Scale Complex System, pp. 1–89. Oxford University Press, Oxford (2005)

    Google Scholar 

  45. PawełTopa. Dynamically reorganising vascular networks modelled using cellular automata approach. In: Proceedings of the 8th International Conference on Cellular Automata for Reseach and Industry, ACRI ’08, pp. 494–49. Springer, Berlin (2008)

    Google Scholar 

  46. Peirce, S.M., Van Gieson, E.J., Skalak, T.C.: Multicellular simulation predicts microvascular patterning and in silico tissue assembly. FASEB J. 18(6), 731–733 (2004)

    Article  Google Scholar 

  47. Pettet, G.J., Please, C.P., Tindall, M.J., McElwain, D.L.S.: The migration of cells in multicell tumor spheroids. Bull. Math. Bio. 63, 231–257 (2001)

    Article  Google Scholar 

  48. Plotkin, J., Nowak, M.A.: Different effects of apoptosis and dna repair on tumorigenesis. J. Theor. Biol. 214, 453–467 (2002)

    Article  Google Scholar 

  49. Plutchik, R.: The nature of emotions. Am. Sci. 89, 344–350 (2001)

    Article  Google Scholar 

  50. Rolls, E.: What are emotions, Why do We Have Emotions, and What is Their Computational Basis in the Brain?. Oxford University Press, Oxford (2005)

    Chapter  Google Scholar 

  51. Rohilla Shalizi, C.: Methods and techniques of complex systems science: An overview. In: Micheli-Tzanakou, E., Deisboeck, T.S., Yasha Kresh, J. (eds.), Complex Systems Science in Biomedicine. Topics in Biomedical Engineering International Book Series, pp. 33–114. Springer, Berlin, (2006)

    Google Scholar 

  52. Shirinifard, A., Scott Gens, J., Zaitlen.: 3d multi-cell simulation of tumor growth and angiogenesis. PLoS ONE 4(10), e7190 (2009)

    Article  Google Scholar 

  53. Simon, H.: The Organization of Complex Systems. George Braziller (1973)

    Google Scholar 

  54. Sirot, E., Touzalin, F.: Coordination and synchronization of vigilance in groups of prey: the role of collective detection and predators’ preference for stragglers. Am. Nat. 173, 47–59 (2009)

    Article  Google Scholar 

  55. Sumpter, D.J.T.: The principles of collective animal behaviour. Phil. Trans. R. Soc. B 361, 5–22 (2006)

    Article  Google Scholar 

  56. Sumpter, D.J.T., Beekman, M.: From nonlinearity to optimality: pheromone trail foraging by ants. Anim. Behav. 66, 273–280 (2003)

    Article  Google Scholar 

  57. Wolkenhauer, O., Auffray, C., Baltrusch, S., Blthgen, N., Byrne, H., Cascante, M., Ciliberto, A., Dale, T., Drasdo, D., Fell, D., Ferrell, J.E., Gallahan, D., Gatenby, R., Gnther, U., Harms, B.D., Herzel, H., Junghanss, C., Kunz, M., van Leeuwen, I., Lenormand, P., Levi, F., Linnebacher, M., Lowengrub, J., Maini, P.K., Malik, A., Rateitschak, K., Sansom, O., Schfer, R., Schrrle, K., Sers, C., Schnell, S., Shibata, D., Tyson, J., Vera, J., White, M., Zhivotovsky, B., Jaster, R.: Systems biologists seek fuller integration of systems biology approaches in new cancer research programs. Cancer Res. 70(1), 12–13 (2010)

    Article  Google Scholar 

  58. Yamada, K.M., Cukierman, E.: Modeling tissue morphogenesis and cancer in 3d. Cell 130 (2007)

    Article  Google Scholar 

  59. Zhang, L., Athale, C.A., Deisboeck, T.S.: Development of a three-dimensional multiscale agent-based tumor model: Simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer. J. Theor. Biol. (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Loyola University Maryland, Baltimore, MD, USA

    Megan Olsen

Authors
  1. Megan Olsen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Megan Olsen .

Editor information

Editors and Affiliations

  1. Complex Systems Institute, Department of Software and Information Systems, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, USA

    Ted Carmichael

  2. Department of Engineering Management and Systems Engineering, College of Engineering and Technology, Old Dominion University, Norfolk, VA, USA

    Andrew J. Collins

  3. Complex Systems Institute, Department of Software and Information Systems, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, USA

    Mirsad Hadžikadić

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Olsen, M. (2019). Stigmergy for Biological Spatial Modeling. In: Carmichael, T., Collins, A., Hadžikadić, M. (eds) Complex Adaptive Systems. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-20309-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20309-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20307-8

  • Online ISBN: 978-3-030-20309-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation