Skip to main content
SpringerLink
Log in

Rules of Engagement: A Guide to Developing Agent-Based Models

  • Protocol
  • First Online:
Microbial Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2349))

Abstract

Agent-based models (ABM), also called individual-based models, first appeared several decades ago with the promise of nearly real-time simulations of active, autonomous individuals such as animals or objects. The goal of ABMs is to represent a population of individuals (agents) interacting with one another and their environment. Because of their flexible framework, ABMs have been widely applied to study systems in engineering, economics, ecology, and biology. This chapter is intended to guide the users in the development of an agent-based model by discussing conceptual issues, implementation, and pitfalls of ABMs from first principles. As a case study, we consider an ABM of the multi-scale dynamics of cellular interactions in a microbial community. We develop a lattice-free agent-based model of individual cells whose actions of growth, movement, and division are influenced by both their individual processes (cell cycle) and their contact with other cells (adhesion and contact inhibition).

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

Access this chapter

Log in via an institution
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 249.99
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. Brehm-Stecher BF, Johnson EA (2004) Single-cell microbiology: tools, technologies, and applications. Microbiol Mol Biol Rev 68:538–559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. SIMULATION 82(9):609–623

    Article  Google Scholar 

  3. Song H-S et al (2014) Mathematical Modeling of microbial community dynamics: a methodological review. PRO 2(4):711–752

    Google Scholar 

  4. Kolmogorov A, Petrovsky L, Piscounov N (1937) Study of the diffusion equation with growth of the quantity of matter and its application to a biological problem. Moscow Univ Bull Math 1:1–25

    Google Scholar 

  5. Murray JD (1988) How the leopard gets its spots. Sci Am 258(3):80–87

    Article  Google Scholar 

  6. Holmes EE et al (1994) Partial differential equations in ecology: spatial interactions and population dynamics. Ecology 75(1):17–29

    Article  Google Scholar 

  7. Baker RE, Gaffney E, Maini P (2008) Partial differential equations for self-organization in cellular and developmental biology. Nonlinearity 21(11):R251

    Article  Google Scholar 

  8. Ward JP, King J (1997) Mathematical modelling of avascular-tumour growth. Math Med Biol 14(1):39–69

    Article  CAS  Google Scholar 

  9. Cantrell RS, Cosner C (2004) Spatial ecology via reaction-diffusion equations. John Wiley & Sons, Hoboken, NJ

    Book  Google Scholar 

  10. Britton NF (1986) Reaction-diffusion equations and their applications to biology. Academic, New York, NY

    Google Scholar 

  11. Alber MS et al (2003) On cellular automaton approaches to modeling biological cells. In: Rosenthal J, Gilliam DS (eds) Mathematical systems theory in biology, communications, computation, and finance, vol 1-39. Springer, New York, NY

    Google Scholar 

  12. Lee Y et al (1995) A cellular automaton model for the proliferation of migrating contact-inhibited cells. Biophys J 69:1284

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Conway J (1970) The game of life. Sci Am 223(4):4

    Google Scholar 

  14. Zeigler BP, Praehofer H, Kim TG (2000) Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems, 2nd edn. Academic, New York, NY

    Google Scholar 

  15. North MJ (2014) A theoretical formalism for analyzing agent-based models. Complex Adapt Syst Model 2(1):1–34

    Article  Google Scholar 

  16. Railsback SE (2001) Concepts from complex adaptive systems as a framework for individual-based modeling. Ecol Model 139:47–62

    Article  Google Scholar 

  17. Zhang L, Athale CA, Deisboeck TS (2007) 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 244:96–107

    Article  CAS  PubMed  Google Scholar 

  18. Mansury Y et al (2002) Emerging patterns in tumor systems: simulating the dynamics of multicellular clusters with an agent-based spatial agglomeration model. J Theor Biol 219(3):343–370

    Article  PubMed  Google Scholar 

  19. Galle J et al (2006) Individual cell-based models of tumor-environment interactions. Am J Pathol 169(5):1802–1811

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Drasdo D, Höhme S (2003) Individual-based approaches to birth and death in avascular tumors. Math Comput Model 37:1163–1175

    Article  Google Scholar 

  21. Drasdo D, Hohme S (2005) A single-cell-based model of tumor growth in vitro: monolayers and spheroids. Phys Biol 2:133–147

    Article  CAS  PubMed  Google Scholar 

  22. Xavier JB et al (2007) Multi-scale individual based model of microbial and bioconversion dynamics in aerobic granular sludge. Environ Sci Technol 41(18):6410–6417

    Article  CAS  PubMed  Google Scholar 

  23. Picioreanu C, Kreft JU, Loosdrecht MCMV (2004) Particle-based multidimensional multispecies biofilm model. Appl Environ Microbiol 70:3024–3040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Modwal A, Rao S (2015) Agent-based modelling of biofilm formation and inhibition in Escherichia coli. Curr Sci 109(5):930–937

    Article  CAS  Google Scholar 

  25. Tisue S, Wilensky U (2004) Netlogo: a simple environment for modeling complexity. in International conference on complex systems. ICCS, Boston, MA

    Google Scholar 

  26. Smaldino PE, Calanchini J, Pickett CL (2015) Theory development with agent-based models. Organ Psychol Rev 5(4):300–317

    Google Scholar 

  27. Bernard RN (1999) Using adaptive agent-based simulation models to assist planners in policy development: the case of rent control, Santa Fe Institute working paper

    Google Scholar 

  28. Emonet T et al (2005) AgentCell: a digital single-cell assay for bacterial chemotaxis. Bioinformatics 21(11):2714–2721

    Article  CAS  PubMed  Google Scholar 

  29. Kreft J-U, Booth G, Wimpenny JWT (1998) BacSim: a simulator for individual-based modelling of bacterial colony growth. Microbiology 144:3275–3287

    Article  CAS  PubMed  Google Scholar 

  30. Kang S et al (2014) Biocellion: accelerating computer simulation of multicellular biological models. Bioinformatics 30(21):3101–3108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Osborne JM et al (2017) Comparing individual-based approaches to modeling the self-organization of multi-cellular tissues. PLoS Comput Biol 13(2):e1005387

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Harvey DG et al (2015) A parallel implementation of an off-lattice individual-based model of multicellular populations. Comput Phys Commun 192:130–137

    Article  CAS  Google Scholar 

  33. Swat MH et al (2012) Multi-scale modeling of tissues using CompuCell3D. Methods Cell Biol 110:325

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kiran M et al (2010) FLAME: simulating large populations of agents on parallel hardware architectures. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems: volume 1. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC

    Google Scholar 

  35. Lardon LA et al (2011) iDynoMiCS: next-generation individual-based modeling of biofilms. Environ Microbiol 13(9):2416–2434

    Article  CAS  PubMed  Google Scholar 

  36. Ginovart M, Lopez D, Valls J (2002) INDSIM: an individual-based discrete simulation model to study bacterial cultures. J Theor Biol 214:305–319

    Article  PubMed  Google Scholar 

  37. Pérez-Rodríguez G et al (2015) Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework. Biomed Res Int 2015:1–12

    Article  Google Scholar 

  38. Collier NT, North M (2012) Parallel agent-based programming with repast for high performance computing. SIMULATION 2012:1–21

    Google Scholar 

  39. Minar N et al (1996) The swarm simulation system: a toolkit for building multi-agent simulations. Santa Fe Institute, Santa Fe, NM

    Google Scholar 

  40. Walker DC, Southgate J (2009) The virtual cell—a candidate co-ordinator for ‘middle-out’modelling of biological systems. Brief Bioinform 10(4):450–461

    Article  CAS  PubMed  Google Scholar 

  41. Resasco DC et al (2012) Virtual Cell: computational tools for modeling in cell biology. Wiley Interdiscip Rev Syst Biol Med 4(2):129–140

    Article  CAS  PubMed  Google Scholar 

  42. Polhill JG et al (2008) Using the ODD protocol for describing three agent-based social simulation models of land-use change. J Artif Soc Soc Simul 11(2):3

    Google Scholar 

  43. Friedman SH et al (2016) MultiCellDS: a standard and a community for sharing multicellular data. bioRxiv 2016:090696

    Google Scholar 

  44. Donachie WD (1968) Relationship between cell size and time of initiation of DNA replication. Nature 219(5158):1077

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Controls and Data Systems Division, SLAC National Accelerator Laboratory, Menlo Park, CA, USA

    Marc Griesemer

  2. Department of Applied Mathematics, University of California, Merced, Merced, CA, USA

    Suzanne S. Sindi

Authors
  1. Marc Griesemer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suzanne S. Sindi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suzanne S. Sindi .

Editor information

Editors and Affiliations

  1. Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, USA

    Ali Navid

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Griesemer, M., Sindi, S.S. (2022). Rules of Engagement: A Guide to Developing Agent-Based Models. In: Navid, A. (eds) Microbial Systems Biology. Methods in Molecular Biology, vol 2349. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1585-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1585-0_16

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1584-3

  • Online ISBN: 978-1-0716-1585-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation