Skip to main content

Multi-agent Simulations of Population Behavior: A Promising Tool for Systems Biology

  • Protocol
  • First Online:
Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1702))

Abstract

This contribution reports on the simulation of some dynamical events observed in the collective behavior of different kinds of populations, ranging from shape-changing cells in a Petri dish to functionally correlated brain areas in vivo. The unifying methodological approach, based upon a Multi-Agent Simulation (MAS) paradigm as incorporated in the NetLogo™ interpreter, is a direct consequence of the cornerstone that simple, individual actions within a population of interacting agents often give rise to complex, collective behavior.

The discussion will mainly focus on the emergence and spreading of synchronous activities within the population, as well as on the modulation of the collective behavior exerted by environmental force-fields. A relevant section of this contribution is dedicated to the extension of the MAS paradigm to Brain Network models. In such a general framework some recent applications taken from the direct experience of the author, and exploring the activation patterns characteristic of specific brain functional states, are described, and their impact on the Systems-Biology universe underlined.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Holland J (1996) Hidden order: how adaptation builds complexity. Addison-Wesley, Reading

    Google Scholar 

  2. Laughlin R (2005) Un universo diverso: reinventare la Fisica da cima a fondo. Codice, Torino

    Google Scholar 

  3. Rodgers J, Nicewander W (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42(1):59–66

    Article  Google Scholar 

  4. Bar-Yam Y (1998) Dynamics of complex systems. Addison-Wesley, Boston, MA

    Google Scholar 

  5. Borschev A (2016) The big book of simulation modeling. www.anylogic.com

  6. DeToni F, Bernardi E (2009) “Il pianeta degli agenti” UTET-Torino. UTET-Torino, Turin

    Google Scholar 

  7. Holland J (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. The MIT Press, Cambridge MA

    Google Scholar 

  8. Wolfram S (2002) A new kind of science. Wolfram Media, Inc, Champaign, IL

    Google Scholar 

  9. Wilenski U (1999) http://ccl.northwestern.edu/netlogo/

  10. Wilensky U, Rand W (2015) Introduction to agent-based modeling: modeling natural, social and engineered complex systems. The MIT Press, Cambridge MA

    Google Scholar 

  11. Colosimo A (2011) Multi Agent Simulators: flexible tools to reproduce collective behaviors, Biophys Bioeng Lett 4(2):34–41

    Google Scholar 

  12. Institute for Systems Biology. https://www.systemsbiology.org

  13. Wyman J, Gill S (1990) Binding and linkage: functional chemistry of biological macromolecules. University Science Books, Sausalito, CA

    Google Scholar 

  14. Bizzarri M, Cucina A, Biava P, Proietti S, D’Anselmi F, Dinicola S, Pasqualato A, Lisi E (2011) Embriyonic morphogenetic field induces phenotypic reversion in cancer cells: review article. Curr Pharm Biotechnol 12:243–253

    Article  CAS  PubMed  Google Scholar 

  15. Leao A (1944) Spreading depression of activity in the cerebral cortex. J Neurophysiol 7:359–390

    Google Scholar 

  16. Lauritzen M (1994) Pathophysiology of the migraineaura. the spreading depression theory. Brain 117:199–210

    Article  PubMed  Google Scholar 

  17. Colosimo A (2008) Biological simulations by autonomous agents: two examples using the netlogo environment. Biophys Bioeng Lett 1(3):40–50

    Google Scholar 

  18. Hines M, Carnevale N (2001) Neuron: A tool for neuroscientists. Neuroscientist 7:123–135

    Article  CAS  PubMed  Google Scholar 

  19. Prinz A (2008) Understanding epilepsy through network modeling. Proc Natl Acad Sci U S A 105(16):5953–5954

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Friston K (2011) functional and effective connectivity: a review. Brain Connect 1(1):13–36

    Article  PubMed  Google Scholar 

  21. Fox M, Snyder A, Vincent J, Corbetta M, Essen DV, Raichle M (2005) The human brain is intrinsicallyorganized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 102:9673–9678

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Schwarz AJ, McGonigle J (2011) Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data. NeuroImage 55(3):113246

    Article  Google Scholar 

  23. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198

    Article  CAS  PubMed  Google Scholar 

  24. Rubinov M, Sporns O (2009) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 53(3):1059–1069

    Google Scholar 

  25. Deco G, Jirsa VK, McIntosh AR (2013) Resting brains never rest: computational insights into potential cognitive architectures. Trends Neurosci 36(268–274):3

    Google Scholar 

  26. Deco G, Jirsa VK (2012) Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. J Neurosci 23:3366–3375

    Article  Google Scholar 

  27. Nakagawa TT, Jirsa VK, Spiegler A, McIntosh AR, Deco G (2013) Bottom up modeling of the connectome: linking structure and function in the resting brain and their changes in aging. NeuroImage 80:318–329

    Article  PubMed  Google Scholar 

  28. Caviness V, Meyer J, Makris N, Kennedy D (1996) Mri-based topographicparcellation of human neocortex: ananatomically specifiedmethod with estimate of reliability. J Cogn Neurosci 8:566–587

    Article  PubMed  Google Scholar 

  29. Binder K (2001) Ising model. In: Michiel H (ed) Encyclopedia of mathematics. Springer, New York, NY

    Google Scholar 

  30. Marinazzo D, Pellicoro M, Wu G, Angelini L, Cortes JM, Stramaglia S (2014) Information transfer and criticality in the Ising model on the human connecrtome. PLoS One 9:1–7

    Article  Google Scholar 

  31. Wilensky U (2003) NetLogo ising model. Evanston, IL, Center for Connected Learning and Computer-Based Modeling, Northwestern University. http://ccl.northwestern.edu/netlogo/models/Ising

    Google Scholar 

  32. Parente F, Colosimo A (2016) The role of negative links in brain networks. Biophys Bioeng Lett 9(1):1–13

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alfredo Colosimo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Colosimo, A. (2018). Multi-agent Simulations of Population Behavior: A Promising Tool for Systems Biology. In: Bizzarri, M. (eds) Systems Biology. Methods in Molecular Biology, vol 1702. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7456-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7456-6_15

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7455-9

  • Online ISBN: 978-1-4939-7456-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics