IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v247y2012icp1-10.html
   My bibliography  Save this article

A combination of cellular automata and agent-based models for simulating the root surface colonization by bacteria

Author

Listed:
  • Muci, Adrialy L.
  • Jorquera, Milko A.
  • Ávila, Ándres I.
  • Rengel, Zed
  • Crowley, David E.
  • de la Luz Mora, María

Abstract

Models of root colonization by bacteria facilitate conceptual and practical understanding of fundamental processes that influence plant health, mineral nutrition, and stress tolerance. In this study, we explored the use of cellular automata and agent-based models to simulate the primary colonization of roots by bacteria as determined by selected parameters related to bacterial growth (nutrient uptake, fitness, reproduction and starvation) and environmental constraints (space, nutrient depletion, and pH). The results were then compared with observations from experiments examining the colonization of plant roots by a GFP – strain of Escherichia coli. The latter experiments were conducted with plants grown in agar medium containing calcium phosphate that allowed visualization of bacterial distribution (aggregates and abundance) and phosphate solubilization at root microsites. The numerical models revealed outcomes for diverse numerical scenarios, which agreed with the in vivo data and provided a basic framework for describing bacterial colonization of plant roots. Further efforts will be required to evaluate factors affecting the competence and ecology of bacterial communities at rhizosphere microsites, but offer promise for the development of precise predictive models with practical applications for agriculture.

Suggested Citation

  • Muci, Adrialy L. & Jorquera, Milko A. & Ávila, Ándres I. & Rengel, Zed & Crowley, David E. & de la Luz Mora, María, 2012. "A combination of cellular automata and agent-based models for simulating the root surface colonization by bacteria," Ecological Modelling, Elsevier, vol. 247(C), pages 1-10.
  • Handle: RePEc:eee:ecomod:v:247:y:2012:i:c:p:1-10
    DOI: 10.1016/j.ecolmodel.2012.07.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380012003845
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2012.07.035?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gignoux, Jacques & Davies, Ian D. & Flint, Shayne R., 2022. "3Worlds, a simulation platform for ecosystem modelling," Ecological Modelling, Elsevier, vol. 473(C).
    2. Kai Wang & Haiqing Hao & Shuguang Jiang & Zhengyan Wu & Chuanbo Cui & Hao Shao & Weiqing Zhang, 2019. "Escape route optimization by cellular automata based on the multiple factors during the coal mine disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(1), pages 91-115, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Smith, Peter, 2008. "New perspectives on realism, tractability, and complexity in economics," MPRA Paper 10899, University Library of Munich, Germany.
    2. Hommes, Cars & Huang, Hai & Wang, Duo, 2005. "A robust rational route to randomness in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 29(6), pages 1043-1072, June.
    3. Youwei Li & Xue-Zhong He, 2005. "Long Memory, Heterogeneity, and Trend Chasing," Computing in Economics and Finance 2005 113, Society for Computational Economics.
    4. Brock, William A. & Hommes, Cars H. & Wagener, Florian O. O., 2005. "Evolutionary dynamics in markets with many trader types," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 7-42, February.
    5. Scott C. Linn & Nicholas S. P. Tay, 2007. "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning," Management Science, INFORMS, vol. 53(7), pages 1165-1180, July.
    6. Nicolas Audet & Toni Gravelle & Jing Yang, 2002. "Alternative Trading Systems: Does One Shoe Fit All?," Staff Working Papers 02-33, Bank of Canada.
    7. Ivan Jericevich & Murray McKechnie & Tim Gebbie, 2021. "Calibrating an adaptive Farmer-Joshi agent-based model for financial markets," Papers 2104.09863, arXiv.org.
    8. Christian Peretti, 2007. "Long Memory and Hysteresis," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 363-389, Springer.
    9. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    10. Mikhail Anufriev & Giulio Bottazzi, 2005. "Price and Wealth Dynamics in a Speculative Market with an Arbitrary Number of Generic Technical Traders," LEM Papers Series 2005/06, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    11. Marco Casari, 2002. "Can genetic algorithms explain experimental anomalies? An application to common property resources," UFAE and IAE Working Papers 542.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    12. Alexander Ludwig & Alexander Zimper, 2013. "A decision-theoretic model of asset-price underreaction and overreaction to dividend news," Annals of Finance, Springer, vol. 9(4), pages 625-665, November.
    13. Sornette, Didier & Zhou, Wei-Xing, 2006. "Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 704-726.
    14. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    15. Marco Raberto & Silvano Cincotti & Sergio Focardi & Michele Marchesi, 2003. "Traders' Long-Run Wealth in an Artificial Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 255-272, October.
    16. Großklags, Jens & Schmidt, Carsten & Siegel, Jonathan, 2000. "Dumb software agents on an experimental asset market," SFB 373 Discussion Papers 2000,96, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    17. J.-H. Steffi Yang & Satchell, S.E., 2002. "The Impact of Technical Analysis on Asset Price Dynamics," Cambridge Working Papers in Economics 0219, Faculty of Economics, University of Cambridge.
    18. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    19. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    20. C. Lawrenz & F. Westerhoff, 2003. "Modeling Exchange Rate Behavior with a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 209-229, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:247:y:2012:i:c:p:1-10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.