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Assembly and Tracking of Microbial Community Development within a Microwell Array Platform
Published on: June 6, 2017
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Ecological modelling approaches for predicting emergent properties in microbial communities.
Naomi Iris van den Berg1, Daniel Machado2, Sophia Santos3
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
Nature Ecology & Evolution
|May 16, 2022
Summary
Emergent properties are key to microbial ecosystems, but their complexity requires mathematical modeling. This review examines various modeling approaches to understand and manage these vital microbial communities.
Area of Science:
- Ecology
- Microbiology
- Mathematical Modeling
Background:
- Emergent properties, non-linear characteristics of microbial communities, are crucial for ecosystem functions like resilience and self-organization.
- Understanding the link between microbial community structure and function necessitates mathematical modeling due to the non-linear nature of emergent properties.
Purpose of the Study:
- To review and analyze various ecosystem modeling approaches for their utility in understanding emergent properties in microbial communities.
- To evaluate the scope, advantages, and limitations of different modeling techniques in the context of emergent properties.
Main Methods:
- Review of established ecosystem modeling approaches: Lotka-Volterra, consumer-resource, trait-based, individual-based, and genome-scale metabolic models.
- Analysis of each model's capacity to capture and predict emergent properties in microbial ecosystems.
Main Results:
- Different models offer varying strengths in representing emergent properties, with no single approach being universally superior.
- Lotka-Volterra models provide a basic framework, while consumer-resource, trait-based, individual-based, and genome-scale metabolic models offer increasing complexity and mechanistic detail.
Conclusions:
- Mathematical modeling is essential for quantitatively linking microbial community structure to emergent functions.
- Future research should focus on integrating complementary modeling approaches to enable the rational modulation of complex microbial ecosystems.
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