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iDynoMiCS: next-generation individual-based modelling of biofilms
Laurent A Lardon1, Brian V Merkey, Sónia Martins
1Department of Environmental Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark.
Environmental Microbiology
|March 18, 2011
Summary
A new computational model for biofilms enhances individual-based modeling. Fluctuating oxygen levels reveal that denitrification strategies depend on the cost of rapid metabolic switching, impacting bacterial community diversity.
Area of Science:
- Microbial Ecology
- Computational Biology
- Biofilm Dynamics
Background:
- Individual-based modeling (IBM) of biofilms acknowledges physiological heterogeneity within microbial populations.
- Previous IBM development was hindered by fragmented codebases and lack of standardized models.
- Single-cell microbiology highlights the importance of individual cell states in complex environments.
Purpose of the Study:
- To develop a unified and improved individual-based model for biofilms.
- To investigate the impact of fluctuating oxygen availability on denitrifying bacterial communities.
- To test the hypothesis that costs associated with rapid metabolic switching explain diverse denitrification strategies.
Main Methods:
- Merged features from previous biofilm models and introduced four key improvements: biofilm pressure field, continuous extracellular polymeric substances excretion, stochastic chemostat mode, and separation of growth kinetics.
- Utilized the model to simulate denitrifying bacterial communities under varying oxygen conditions.
- Analyzed the trade-offs between response speed and metabolic switching costs in different environmental fluctuation frequencies.
Main Results:
- The model successfully simulates biofilm dynamics, including shrinking/consolidating biofilms and realistic extracellular matrix fluid behavior.
- Without costs, faster metabolic switching is always advantageous; however, with a cost-benefit trade-off, optimal switching strategies emerge for different fluctuation frequencies.
- Biodiversity of denitrifiers is higher in biofilms than chemostats, increases with costs, and is maximized at intermediate environmental change frequencies.
Conclusions:
- The developed computational model provides a robust and flexible platform for studying biofilm communities.
- Environmental fluctuations and metabolic switching costs are critical drivers of denitrifier diversity and strategy selection.
- The findings suggest that diverse microbial strategies are shaped by the interplay of environmental dynamics and inherent biological costs.
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