Microorganisms form various ecological relationships, ranging from mutualism to competition, that in addition to other factors (such as niche preferences and random processes) shape microbial abundances. Recently, network inference techniques have frequently been applied to microbial presence–absence or abundance data to detect significant patterns of co-presence and mutual exclusion between taxa and to represent them as a network.
In addition to predicting links between taxa and between environmental traits and taxa, the analysis of microbial association networks reveals niches, points out keystone species and indicates alternative community configurations.
However, several pitfalls in the construction and interpretation of these networks exist, ranging from data normalization to multiple test correction. Thorough evaluation is needed to determine the best-performing network inference technique.
Recent advances in the cultivation of unknown microorganisms, combinatorial labelling and parallel cultivation may soon allow systematic co-culturing and perturbation (that is, species removal) experiments.
Interaction strengths that have been obtained from static networks or that have been measured experimentally can serve as inputs for dynamic models of microbial communities, which in turn can simulate the behaviour of the system in various conditions. In the long run, dynamic models could help to engineer microbial communities.
The theory of dynamic systems can contribute to our understanding of microbial communities. For instance, alternative community states can arise as a consequence of system dynamics without being driven by environmental differences.
Metagenomics and 16S pyrosequencing have enabled the study of ecosystem structure and dynamics to great depth and accuracy. Co-occurrence and correlation patterns found in these data sets are increasingly used for the prediction of species interactions in environments ranging from the oceans to the human microbiome. In addition, parallelized co-culture assays and combinatorial labelling experiments allow high-throughput discovery of cooperative and competitive relationships between species. In this Review, we describe how these techniques are opening the way towards global ecosystem network prediction and the development of ecosystem-wide dynamic models.
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K.F. and J.R. are supported by the Research Foundation Flanders (FWO), the Flemish agency for Innovation by Science and Technology (IWT) and the Brussels Institute for Research and Innovation. We would like to acknowledge G. Lima-Mendez, S. Chaffron and all other members of the Raes laboratory, as well as D. Gonze for helpful comments and discussions. We would also like to apologize to all authors whose work could not be included owing to space restraints. In adition, we thank our reviewers, whose criticisms and suggestions helped to improve this Review.
The authors declare no competing financial interests.
In-house tool for microbial association network construction
An interaction between two species in which each species derives a benefit. Also referred to as cooperation or symbiosis by some authors; however, mutualism is preferred here because 'symbiosis' can be used in a broader sense to include all ecological relationships, and 'cooperation' can be used to designate mutualism between single organisms rather than populations.
Defined by Hutchinson as the volume in which the growth rate of an organism is larger than or equal to 1, where the volume is an abstract space with axes that correspond to abiotic and biotic factors that affect the growth rate of the species.
- Network inference
The process of reconstructing the wiring diagram of a complex system from the behaviour of its components. For microbial communities, the goal of network inference is to predict ecological relationships between microorganisms from abundance data.
Prediction of a relationship between a dependent variable (here, the abundance of a target species) and independent variables (the abundance (or abundances) of one or more independent source species, environmental traits and possibly a noise term), which are termed factors here.
- Lotka–Volterra equations
Equations that describe the dynamics of a prey–predator system. In their generalized form, the Lotka–Volterra equations can model the dynamics of more than two species and describe relationships other than prey–predator.
- Hypergeometric distribution
This distribution underlies Fisher's exact test, which is commonly used to infer networks from presence–absence data. Given the occurrences of two taxa across the samples, the test assesses the significance of the number of observed co-presences.
- Operational taxonomic unit
(OTU). Refers to bacterial and archaeal taxonomic groups that are derived by sequence clustering and that are thus specific to the samples analysed.
A type of dynamic behaviour that is characterized by irregular oscillations and a sensitivity to small differences in initial conditions, so that for two similar sets of start conditions, the system may behave entirely differently after some time has elapsed.
- Stable state
A region in (multi-dimensional) space in which the system remains and to which the system returns after a small perturbation. The stable state may be a point (also referred to as stable equilibrium or stable steady state), a limit cycle (at which the system oscillates) or may have other shapes (for example, the strange attractors of chaotic systems).
The orderly and predictable manner by which communities change over time following the colonization of a new environment.
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Faust, K., Raes, J. Microbial interactions: from networks to models. Nat Rev Microbiol 10, 538–550 (2012). https://doi.org/10.1038/nrmicro2832
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