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Perspectives in mathematical modelling for microbial ecology

Author

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  • Wade, M.J.
  • Harmand, J.
  • Benyahia, B.
  • Bouchez, T.
  • Chaillou, S.
  • Cloez, B.
  • Godon, J.-J.
  • Moussa Boudjemaa, B.
  • Rapaport, A.
  • Sari, T.
  • Arditi, R.
  • Lobry, C.

Abstract

Although mathematical modelling has reached a degree of maturity in the last decades, microbial ecology is still developing, albeit at a rapid pace thanks to new insights provided by modern molecular tools. However, whilst microbiologists have long enjoyed the perspectives that particular mathematical frameworks can provide, there remains a reluctance to fully embrace the potential of models, which appear too complex, esoteric or distant from the “real-world”. Nevertheless there is a strong case for pursuing the development of mathematical models to describe microbial behaviour and interactions, dynamically, spatially and across scales. Here we put forward perspectives on the current state of mathematical modelling in microbial ecology, looking back at the developments that have defined the synergies between the disciplines, and outline some of the existing challenges that motivate us to provide practical models in the hope that greater engagement with empiricists and practitioners in the microbiological domain may be achieved. We also indicate recent advances in modelling that have had impact in both the fundamental understanding of microbial ecology and its practical application in engineered biological systems. In this way, it is anticipated that interest can be garnered from across the microbiological spectrum resulting in a broader uptake of mathematical concepts in lecture theatres, laboratories and industrial systems.

Suggested Citation

  • Wade, M.J. & Harmand, J. & Benyahia, B. & Bouchez, T. & Chaillou, S. & Cloez, B. & Godon, J.-J. & Moussa Boudjemaa, B. & Rapaport, A. & Sari, T. & Arditi, R. & Lobry, C., 2016. "Perspectives in mathematical modelling for microbial ecology," Ecological Modelling, Elsevier, vol. 321(C), pages 64-74.
    • Handle: RePEc:eee:ecomod:v:321:y:2016:i:c:p:64-74
    DOI: 10.1016/j.ecolmodel.2015.11.002
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    References listed on IDEAS

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    1. Harmand, J. & Godon, J.J., 2007. "Density-dependent kinetics models for a simple description of complex phenomena in macroscopic mass-balance modeling of bioreactors," Ecological Modelling, Elsevier, vol. 200(3), pages 393-402.
    2. Pan-Jun Kim & Nathan D Price, 2011. "Genetic Co-Occurrence Network across Sequenced Microbes," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-9, December.
    3. Campillo, F. & Lobry, C., 2012. "Effect of population size in a predator–prey model," Ecological Modelling, Elsevier, vol. 246(C), pages 1-10.
    4. Fritsch, Coralie & Harmand, Jérôme & Campillo, Fabien, 2015. "A modeling approach of the chemostat," Ecological Modelling, Elsevier, vol. 299(C), pages 1-13.
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    Cited by:

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    2. Andrey Degermendzhi & Alexander Abakumov, 2023. "Control Factors for the Equilibrium Composition of Microbial Communities in Open Systems: Theory and Experiments," Mathematics, MDPI, vol. 11(14), pages 1-21, July.
    3. Maciej Leszczynski & Przemyslaw Perlikowski & Piotr Brzeski, 2024. "A Unified Approach for the Calculation of Different Sample-Based Measures with the Single Sampling Method," Mathematics, MDPI, vol. 12(7), pages 1-19, March.
    4. Gerrit Ansmann & Tobias Bollenbach, 2021. "Building clone-consistent ecosystem models," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-25, February.
    5. Abdulrahman Ali Alsolami & Miled El Hajji, 2023. "Mathematical Analysis of a Bacterial Competition in a Continuous Reactor in the Presence of a Virus," Mathematics, MDPI, vol. 11(4), pages 1-18, February.

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