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Biochem Soc Trans. 2018 Apr 17;46(2):403-412. doi: 10.1042/BST20170265. Epub 2018 Mar 14.

Review and perspective on mathematical modeling of microbial ecosystems.

Author information

1
Botanical Institute, University of Cologne, Cologne, Germany a.succurro@uni-koeln.de.
2
Cluster of Excellence on Plant Sciences (CEPLAS), Düsseldorf, Germany.
3
Institute of Quantitative and Theoretical Biology, Heinrich-Heine-University, Düsseldorf, Germany.

Abstract

Understanding microbial ecosystems means unlocking the path toward a deeper knowledge of the fundamental mechanisms of life. Engineered microbial communities are also extremely relevant to tackling some of today's grand societal challenges. Advanced meta-omics experimental techniques provide crucial insights into microbial communities, but have been so far mostly used for descriptive, exploratory approaches to answer the initial 'who is there?'

QUESTION:

An ecosystem is a complex network of dynamic spatio-temporal interactions among organisms as well as between organisms and the environment. Mathematical models with their abstraction capability are essential to capture the underlying phenomena and connect the different scales at which these systems act. Differential equation models and constraint-based stoichiometric models are deterministic approaches that can successfully provide a macroscopic description of the outcome from microscopic behaviors. In this mini-review, we present classical and recent applications of these modeling methods and illustrate the potential of their integration. Indeed, approaches that can capture multiple scales are needed in order to understand emergent patterns in ecosystems and their dynamics regulated by different spatio-temporal phenomena. We finally discuss promising examples of methods proposing the integration of differential equations with constraint-based stoichiometric models and argue that more work is needed in this direction.

KEYWORDS:

constraint-based models; dynamic models; mathematical models; microbial communities

PMID:
29540507
PMCID:
PMC5906705
DOI:
10.1042/BST20170265
[Indexed for MEDLINE]
Free PMC Article

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