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Microbial interactions: from networks to models

Key Points

  • 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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Figure 1: Summary of ecological interactions between members of different species.
Figure 2: Principle of similarity- and regression-based network inference.
Figure 3: Examples for the prediction of pairwise versus complex relationships.
Figure 4: Impact of the similarity measure on the network inference result.


  1. 1

    Lidicker, W. Z. A. Clarification of interactions in ecological systems. Bioscience 29, 475–477 (1979).

    Article  Google Scholar 

  2. 2

    Rodríguez-Martínez, J. M. & Pascual, A. Antimicrobial resistance in bacterial biofilms. Rev. Med. Microbiol. 17, 65–75 (2006).

    Article  Google Scholar 

  3. 3

    Woyke, T. et al. Symbiotic insights through metagenomic analysis of a microbial consortium. Nature 443, 950–955 (2006).

    Article  CAS  Google Scholar 

  4. 4

    Leschine, S. B. Cellulose degradation in anaerobic environments. Annu. Rev. Microbiol. 49, 399–426 (1995).

    Article  CAS  PubMed  Google Scholar 

  5. 5

    Gause, G. F. The Struggle for Existence (Williams & Wilkins, 1934).

    Book  Google Scholar 

  6. 6

    Raes, J., Foerstner, K. U. & Bork, P. Get the most out of your metagenome: computational analysis of environmental sequence data. Curr. Opin. Microbiol. 10, 1–9 (2007).

    Article  CAS  Google Scholar 

  7. 7

    Dubelaar, G. B. J. & Jonker, R. R. Flow cytometry as a tool for the study of phytoplankton. Sci. Mar. 64, 135–156 (2000).

    Article  Google Scholar 

  8. 8

    Palmer, C., Bik, E. M., DiGiulio, D. B., Relman, D. A. & Brown, P. O. Development of the human infant intestinal microbiota. PLoS Biol. 5, 1556–1573 (2007).

    Article  CAS  Google Scholar 

  9. 9

    Lane, D. J. et al. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc. Natl Acad. Sci. USA 82, 6955–6959 (1985).

    Article  CAS  Google Scholar 

  10. 10

    Hamady, M. & Knight, R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 19, 1141–1152 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Tringe, S. G. et al. Comparative metagenomics of microbial communities. Science 308, 554–557 (2005).

    Article  CAS  PubMed  Google Scholar 

  12. 12

    Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Cole, J. R. et al. The ribosomal database project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141–D145 (2009).

    Article  CAS  PubMed  Google Scholar 

  14. 14

    DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Gonzalez, A. & Knight, R. Advancing analytical algorithms and pipelines for billions of microbial sequences. Curr. Opin. Biotechnol. 23, 64–71 (2012).

    Article  CAS  PubMed  Google Scholar 

  16. 16

    De Smet, R. & Marchal, K. Advantages and limitations of current network inference methods. Nature Rev. Microbiol. 8, 717–729 (2010).

    Article  CAS  Google Scholar 

  17. 17

    Veiga, D. F. T., Dutta, B. & Balázsi, G. Network inference and network response identification: moving genome-scale data to the next level of biological discovery. Mol. Biosyst. 6, 469–480 (2010).

    Article  CAS  PubMed  Google Scholar 

  18. 18

    Bonneau, R. et al. A predictive model for transcriptional control of physiology in a free living cell. Cell 131, 1354–1365 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. 19

    Szklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, D561–D568 (2011).

    Article  CAS  Google Scholar 

  20. 20

    Milns, I., Beale, C. M. & Smith, V. A. Revealing ecological networks using Bayesian network inference algorithms. Ecology 91, 1892–1899 (2010).

    Article  PubMed  Google Scholar 

  21. 21

    Agrawal, R., Imielinski, T. & Swami, A. Mining association rules between sets of items in large databases. ACM SIGMOD Record 22, 207–216 (1993).

    Article  Google Scholar 

  22. 22

    Chaffron, S., Rehrauer, H., Pernthaler, J. & Von Mering, C. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 20, 947–959 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Eiler, A., Heinrich, F. & Bertilsson, S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 6, 330–342 (2012).

    Article  CAS  PubMed  Google Scholar 

  24. 24

    Steele, J. A. et al. Marine bacterial, archaeal and protistan association networks reveal ecological linkages. ISME J. 5, 1414–1425 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25

    Fuhrman, J. A. Microbial community structure and its functional implications. Nature 459, 193–199 (2009).

    Article  CAS  Google Scholar 

  26. 26

    Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth's biogeochemical cycles. Science 320, 1034–1039 (2008).

    Article  CAS  PubMed  Google Scholar 

  27. 27

    Hecker, M., Lambeck, S., Toepfer, S., van Someren, E. & Guthke, R. Gene regulatory network inference: data integration in dynamic models—a review. Biosystems 96, 86–103 (2009).

    Article  CAS  PubMed  Google Scholar 

  28. 28

    May, R. M. Stability and Complexity in Model Ecosystems (Princeton Univ. Press, 1973).

    Google Scholar 

  29. 29

    Mounier, J. et al. Microbial interactions within a cheese microbial community. Appl. Environ. Microbiol. 74, 172–181 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. 30

    Hoffmann, K. H. et al. Power law rank-abundance models for marine phage communities. FEMS Microbiol. Lett. 273, 224–228 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. 31

    Thingstad, T. F. Elements of a theory for the mechanisms controlling abundance, diversity and biogeochemical role of lytic bacterial viruses in aquatic systems. Limnol. Oceanogr. 45, 1320–1328 (2000).

    Article  Google Scholar 

  32. 32

    Rodriguez-Brito, B. et al. Viral and microbial community dynamics in four aquatic environments. ISME J. 4, 739–751 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33

    Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6, 343–351 (2012).

    Article  CAS  Google Scholar 

  34. 34

    Zhou, J. et al. Functional molecular ecological networks. mBio 1, e00169–e00110 (2010).

    PubMed  PubMed Central  Google Scholar 

  35. 35

    Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).

    Article  CAS  PubMed  Google Scholar 

  36. 36

    Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

  38. 38

    Freilich, S. et al. The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res. 38, 3857–3868 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Ruan, Q. et al. Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors. Bioinformatics 22, 2532–2538 (2006).

    Article  CAS  Google Scholar 

  40. 40

    Fuhrman, J. A. & Steele, J. A. Community structure of marine bacterioplankton: patterns, networks, and relationships to function aquat. Microb. Ecol. 53, 69–81 (2008).

    Article  Google Scholar 

  41. 41

    The Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012).

  42. 42

    Faust, K. & Sathirapongsasuti, J. F. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. (in the press).

  43. 43

    Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44

    Kolenbrander, P. E. et al. Communication among oral bacteria. Microbiol. Mol. Biol. Rev. 66, 486–505 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108, 4680–4687 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46

    Jumpstart Consortium Human Microbiome Project Data Generation Working Group. Evaluation of 16S rDNA-based community profiling for human microbiome research. PLoS ONE 7, e39315 (2012).

  47. 47

    Morgan, J. L., Darling, A. E. & Eisen, J. A. Metagenomic sequencing of an in vitro-simulated microbial community. PLoS ONE 5, e10209 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Raes, J. & Bork, P. Molecular eco-systems biology: towards an understanding of community function. Nature Rev. Microbiol. 6, 693–699 (2008).

    Article  CAS  Google Scholar 

  49. 49

    Aitchison, J. A concise guide to compositional data analysis. Laboratório de Estatística e Geoinformação [online], (2003).

  50. 50

    Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored ''rare biosphere''. Proc. Natl. Acad. Sci. USA 103, 12115–12120 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. 51

    Reshef, D. N. et al. Detecting novel associations in large data sets. Science 334, 1518–1524 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Kuczynski, J. et al. Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nature Methods 7, 813–819 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).

    Article  Google Scholar 

  54. 54

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  55. 55

    Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7 (Suppl. 1), S7 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Opgen-Rhein, R. & Strimmer, K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst. Biol. 1, 37 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Schaefer, J. & Strimmer, K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21, 754–764 (2005).

    Article  CAS  Google Scholar 

  58. 58

    Schaefer, J. & Strimmer, K. A. Shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Mol. Biol. 4, 32 (2005).

    Google Scholar 

  59. 59

    Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nature Commun. 2, 589 (2011).

    Article  CAS  Google Scholar 

  60. 60

    Brohée, S. et al. NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways. Nucleic Acids Res. 36, W444–W451 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Hu, Z. et al. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res. 37, W115–W121 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Smoot, M., Ono, K., Ruscheinski, J., Wang, P. & Ideker, T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431–432 (2011).

    Article  CAS  Google Scholar 

  63. 63

    Lima-Mendez, G. & van Helden, J. The powerful law of the power law and other myths in network biology. Mol. Biosyst. 5, 1482–1493 (2009).

    Article  CAS  PubMed  Google Scholar 

  64. 64

    Paine, R. T. A note on trophic complexity and community stability. Am. Nat. 103, 91–93 (1969).

    Article  Google Scholar 

  65. 65

    May, R. M. Biological populations with nonoverlapping generations: stable points, stable cycles, and chaos. Science 186, 645–647 (1974).

    Article  CAS  PubMed  Google Scholar 

  66. 66

    Becks, L., Hilker, F. M., Malchow, H., Jürgens, K. & Arndt, H. Experimental demonstration of chaos in a microbial food web. Nature 435, 1226–1229 (2005).

    Article  CAS  PubMed  Google Scholar 

  67. 67

    Harcombe, W. Novel cooperation experimentally evolved between species. Evolution 64, 2166–2172 (2010).

    PubMed  Google Scholar 

  68. 68

    Kaeberlein, T., Lewis, K. & Epstein, S. S. Isolating “uncultivable” microorganisms in pure culture in a simulated natural environment. Science 296, 1127–1129 (2002).

    Article  CAS  PubMed  Google Scholar 

  69. 69

    Nichols, D. et al. Use of ichip for high-throughput in situ cultivation of “uncultivable” microbial species. Appl. Environ. Microbiol. 76, 2445–2450 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    Valm, A. M. et al. Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc. Natl Acad. Sci. USA 108, 4152–4157 (2011).

    Article  PubMed  Google Scholar 

  71. 71

    Park, J., Kerner, A., Burns, M. A. & Lin, X. N. Microdroplet-enabled highly parallel co-cultivation of microbial communities. PLoS ONE 6, e17019 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Kim, H. J., Du, W. & Ismagilov, R. F. Complex function by design using spatially pre-structured synthetic microbial communities: degradation of pentachlorophenol in the presence of Hg(II). Integr. Biol. 3, 126–133 (2011).

    Article  CAS  Google Scholar 

  73. 73

    Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Evol. Syst. 4, 1–23 (1973).

    Article  Google Scholar 

  74. 74

    Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).

    Article  CAS  PubMed  Google Scholar 

  75. 75

    Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  76. 76

    Lewontin, R. C. The meaning of stability. Brookhaven Symp. Biol. 22, 13–23 (1969).

    CAS  PubMed  Google Scholar 

  77. 77

    Petraitis, P. S. & Dudgeon, S. R. Detection of alternative stable states in marine communities. J. Exp. Mar. Biol. Ecol. 300, 343–371 (2004).

    Article  Google Scholar 

  78. 78

    Connell, J. H. & Sousa, W. P. On the evidence needed to judge ecological stability or persistence. Am. Nat. 121, 789–824 (1983).

    Article  Google Scholar 

  79. 79

    Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Koenig, J. E. et al. Succession of microbial consortia in the developing infant gut microbiome. Proc. Natl Acad. Sci. USA 108, 4578–4585 (2011).

    Article  PubMed  Google Scholar 

  81. 81

    Fierer, N., Nemergut, D., Knight, R. & Craine, J. M. Changes through time: integrating microorganisms into the study of succession. Res. Microbiol. 161, 635–642 (2010).

    Article  Google Scholar 

  82. 82

    Trosvik, P., Stenseth, N. C. & Rudi, K. Convergent temporal dynamics of the human infant gut microbiota. ISME J. 4, 151–158 (2010).

    Article  CAS  PubMed  Google Scholar 

  83. 83

    Rosindell, J., Hubbell, S. P. & Etienne, R. S. The unified neutral theory of biodiversity and biogeography at age ten. Trends Ecol. Evol. 26, 340–348 (2011).

    Article  Google Scholar 

  84. 84

    West, S. A., Griffin, A. S., Gardner, A. & Diggle, S. P. Social evolution theory for microorganisms. Nature Rev. Microbiol. 4, 597–607 (2006).

    Article  CAS  Google Scholar 

  85. 85

    Zomorrodi, A. R. & Maranas, C. D. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput. Biol. 8, e1002363 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 86

    Karlsson, F. H., Nookaew, I., Petranovic, D. & Nielsen, J. Prospects for systems biology and modeling of the gut microbiome. Trends Biotechnol. 29, 251–258 (2011).

    Article  CAS  Google Scholar 

  87. 87

    Hastings, A. Transients: the key to long-term ecological understanding? Trends Ecol. Evol. 19, 39–45 (2004).

    Article  PubMed  Google Scholar 

  88. 88

    DeJongh, M. et al. Toward the automated generation of genome-scale metabolic networks in the SEED. BMC Bioinformatics 8, I39 (2007).

    Article  CAS  Google Scholar 

  89. 89

    Hanly, T. J. & Henson, M. A. Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures. Biotechnol. Bioeng. 108, 376–385 (2011).

    Article  CAS  PubMed  Google Scholar 

  90. 90

    Curtis, T. P., Head, I. M. & Graham, D. W. Theoretical ecology for engineering biology. Environ. Sci. Technol. 37, 64A–70A (2003).

    Article  PubMed  Google Scholar 

  91. 91

    Dunham, M. J. Synthetic ecology: a model system for cooperation. Proc. Natl Acad. Sci. USA 104, 1741–1742 (2007).

    Article  CAS  PubMed  Google Scholar 

  92. 92

    Balagaddé, F. K. et al. A synthetic Escherichia coli predator–prey ecosystem. Mol. Syst. Biol. 4, 187 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  93. 93

    Ruder, W. C., Lu, T. & Collins, J. J. Synthetic biology moving into the clinic. Science 333, 1248–1252 (2011).

    Article  CAS  PubMed  Google Scholar 

  94. 94

    Werner, J. J. et al. Bacterial community structures are unique and resilient in full-scale bioenergy systems. Proc. Natl Acad. Sci. USA 22 Feb 2011 (doi:10.1073/pnas.1015676108).

  95. 95

    Marsh, P. D. Are dental diseases examples of ecological catastrophes? Microbiology 149, 279–294 (2003).

    Article  CAS  PubMed  Google Scholar 

  96. 96

    Maloy, K. J. & Powrie, F. Intestinal homeostasis and its breakdown in inflammatory bowel disease. Nature 474, 298–306 (2011).

    Article  CAS  Google Scholar 

  97. 97

    Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).

    Article  CAS  Google Scholar 

  98. 98

    Turnbaugh, P. J., Bäckhed, F., Fulton, L. & Gordon, J. I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. 99

    Faith, J. J., McNulty, N. P., Rey, F. E. & Gordon, J. I. Predicting a human gut microbiota's response to diet in gnotobiotic mice. Science 333, 101–104 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. 100

    Abbeele, P. V.d. et al. Microbial community development in a dynamic gut model is reproducible, colon region specific, and selective for bacteroidetes and clostridium cluster IX. Appl. Environ. Microbiol. 76, 5237–5246 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 101

    Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. 102

    Diamond, J. M. in Ecology and Evolution of Communities (eds Cody, M. & Diamond, J. M.) 342–444 (Harvard Univ. Press, 1975).

    Google Scholar 

  103. 103

    Connor, E. F. & Simberloff, D. The assembly of species communities: chance or competition? Ecology 60, 1132–1140 (1979).

    Article  Google Scholar 

  104. 104

    Woodcock, S. et al. Neutral assembly of bacterial communities. FEMS Microbiol. Ecol. 62, 171–180 (2007).

    Article  CAS  PubMed  Google Scholar 

  105. 105

    Wootton, J. T. Field parameterization and experimental test of the neutral theory of biodiversity. Nature 433, 309–312 (2005).

    Article  CAS  PubMed  Google Scholar 

  106. 106

    Horner-Devine, M. C. et al. A comparison of taxon co-occurrence patterns for macro- and microorganisms. Ecology 88, 1345–1353 (2007).

    Article  PubMed  Google Scholar 

  107. 107

    Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).

    Article  PubMed  Google Scholar 

  108. 108

    Ricklefs, R. E. Community diversity: relative roles of local and regional processes. Science 235, 167–171 (1987).

    Article  CAS  PubMed  Google Scholar 

  109. 109

    Borgelt, C. & Kruse, R. in COMPSTAT 2002 — Proceedings in Computational Statistics: 15th Symposium held in Berlin, Germany, 2002 (eds Härdle, W. & Rönz, B.) 395–400 (Physica-Verlag, 2002).

    Google Scholar 

  110. 110

    Lallich, S., Teytaud, O. & Prudhomme, E. Association Rule Interestingness: Measure and Statistical Validation (eds Guillet, F. & Hamilton J. H.) (Springer, 2007).

    Google Scholar 

  111. 111

    Erdős, P. & Rényi, A. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17–61 (1960).

    Google Scholar 

  112. 112

    Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

    Article  PubMed  PubMed Central  Google Scholar 

  113. 113

    Jeong, H., Mason, S. P., Barabási, A.-L. & Oltvai, Z. N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. 114

    van Dongen, S. Graph clustering via a discrete uncoupling process. SIAM J. Matrix Analysis Appl. 30, 121–141 (2008).

    Article  Google Scholar 

  115. 115

    Clauset, A., Newman, M. E. & Moore, C. Finding community structure in very large networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 70, 066111 (2004).

    Article  CAS  PubMed  Google Scholar 

  116. 116

    Watts, D. J. & Strogatz, S. H. Collective dynamics of 'small-world' networks. Nature 393, 440–442 (1998).

    Article  CAS  PubMed  Google Scholar 

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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.

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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).

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