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A bunch of tiny individuals—Individual-based modeling for microbes

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  • Hellweger, Ferdi L.
  • Bucci, Vanni

Abstract

The individual-based (aka agent-based) approach is now well established in ecological modeling. Traditionally, most applications have been to organisms at higher trophic levels, where the importance of population heterogeneity (intra-population variability), complete life cycles and behavior adapted to internal and external conditions has been recognized for some time. However, advances in molecular biology and biochemistry have brought about an increase in the application of individual-based modeling (IBM) to microbes as well. This literature review summarizes 46 IBM papers for bacteria in wastewater treatment plants, phytoplankton in ocean and inland waters, bacteria in biofilms, bacteria in food and other environs, and “digital organisms” and “domesticated computer viruses”in silico. The use of IBM in these applications was motivated by population heterogeneity (45%), emergence (24%), absence of a continuum (5%), and other unknown reasons (26%). In general, the challenges and concepts of IBM modeling for microbes and higher trophic levels are similar. However, there are differences in the microbe population dynamics and their environment that create somewhat different challenges, which have led to somewhat different modeling concepts. Several topics are discussed, including producing, maintaining and changing population heterogeneity (different life histories, internal variability, positive feedback, inter-generation memory), dealing with very large numbers of individuals (different up-scaling methods, including representative space vs. super-individual, number vs. biomass based, discrete vs. continuous kinetics, various agent accounting methods), handling space, simulating interactions with the extracellular environment (hybrid Eulerian–Lagrangian approach), modeling agent–agent interaction (self-shading, predation, shoving) and passive transport (random walk with spatially variable diffusivity, well-mixed reactors). Overall, the literature indicates that the application of IBM to microbes is developing into a mature field. However, several challenges remain, including simulating various types of agent–agent interactions (formation and function of colonies or filaments, sexual reproduction) and even smaller individuals (viruses, genes). Further increases in intracellular detail and complexity in microbe IBMs may be considered the combination of systems biology and systems ecology, or the new field of systems bioecology.

Suggested Citation

  • Hellweger, Ferdi L. & Bucci, Vanni, 2009. "A bunch of tiny individuals—Individual-based modeling for microbes," Ecological Modelling, Elsevier, vol. 220(1), pages 8-22.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:1:p:8-22
    DOI: 10.1016/j.ecolmodel.2008.09.004
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    References listed on IDEAS

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    1. El Saadi, N. & Bah, A., 2007. "An individual-based model for studying the aggregation behavior in phytoplankton," Ecological Modelling, Elsevier, vol. 204(1), pages 193-212.
    2. Richard E. Lenski & Charles Ofria & Robert T. Pennock & Christoph Adami, 2003. "The evolutionary origin of complex features," Nature, Nature, vol. 423(6936), pages 139-144, May.
    3. Hellweger, Ferdi L., 2008. "The role of inter-generation memory in diel phytoplankton division patterns," Ecological Modelling, Elsevier, vol. 212(3), pages 382-396.
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    1. Deygout, C. & Lesne, A. & Campillo, F. & Rapaport, A., 2013. "Homogenised model linking microscopic and macroscopic dynamics of a biofilm: Application to growth in a plug flow reactor," Ecological Modelling, Elsevier, vol. 250(C), pages 15-24.
    2. Fritsch, Coralie & Harmand, Jérôme & Campillo, Fabien, 2015. "A modeling approach of the chemostat," Ecological Modelling, Elsevier, vol. 299(C), pages 1-13.
    3. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    4. Bouderbala, Ilhem & El Saadi, Nadjia & Bah, Alassane & Auger, Pierre, 2019. "A simulation study on how the resource competition and anti-predator cooperation impact the motile-phytoplankton groups’ formation under predation stress," Ecological Modelling, Elsevier, vol. 391(C), pages 16-28.
    5. Castellani, Marco & Våge, Selina & Strand, Espen & Thingstad, T. Frede & Giske, Jarl, 2013. "The Scaled Subspaces Method: A new trait-based approach to model communities of populations with largely inhomogeneous density," Ecological Modelling, Elsevier, vol. 251(C), pages 173-186.
    6. Gras, Anna & Ginovart, Marta & Valls, Joaquim & Baveye, Philippe C., 2011. "Individual-based modelling of carbon and nitrogen dynamics in soils: Parameterization and sensitivity analysis of microbial components," Ecological Modelling, Elsevier, vol. 222(12), pages 1998-2010.
    7. Clark, James R. & Daines, Stuart J. & Lenton, Timothy M. & Watson, Andrew J. & Williams, Hywel T.P., 2011. "Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters," Ecological Modelling, Elsevier, vol. 222(23), pages 3823-3837.

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