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A Brief Review on the Ecological Network Analysis with Applications in the Emerging Medical Ecology

  • Zhanshan (Sam) MaEmail author
  • Chengchen Zhang
  • Qingpeng Zhang
  • Jie Li
  • Lianwei Li
  • Linyi Qi
  • Xianghong Yang
Part of the Springer Protocols Handbooks book series (SPH)

Abstract

Ecology studies the relationship between organisms and their environment, and a well-accepted paradigm of ecological science is to divide the subject into molecular ecology, autecology, population ecology, community ecology, ecosystem ecology, and landscape ecology, according to the scale of investigation. Network analysis can arguably be a powerful tool for any scale of the ecological research because it is particularly suitable for investigating the complex relationships in multivariate and multidimensional settings. In this article, we present a glimpse of the state-of-the-art research in ecological network analysis, focusing on the species interaction network (SIN) in the human microbiome. Our choice of the focus takes advantage of the recent revolutionary metagenomic technology that has open unprecedented opportunities to the study of microbial communities, especially the human microbiome. We also present a case study to demonstrate the analysis of SINs in both healthy and diseased oral microbiomes. The analysis reveals striking characteristic changes in the human oral microbiome associated with the transition from healthy regime to periodontitis regime.

Keywords:

Ecological networks Human microbiome Metagenomics Microbial networks Species interaction networks 

Notes

Acknowledgements

We thank Prof. Mihai Pop (University of Maryland) for generously providing us the OTU tables of the oral microbiome used for the case study. This research received funding from the following sources: National Science Foundation of China (Grants No. 61175071 & No. 71473243). “The Exceptional Scientists Program and Top Oversea Scholars Program” of Yunnan Province, “The Innovative Research Team on the Synthesis of Natural Evolution and Computational Evolution.”

References

  1. 1.
    Pascual M, Dunne JA (2006) Ecological networks: linking structure to dynamics in food webs. Oxford University Press, New YorkGoogle Scholar
  2. 2.
    Montoya JM, Pimm SL, Solé RV (2006) Ecological networks and their fragility. Nature 442:259–264CrossRefPubMedGoogle Scholar
  3. 3.
    Ings TC, Montoya JM, Bascompte J et al (2009) Ecological networks – beyond food webs. J Anim Ecol 78:253–269CrossRefPubMedGoogle Scholar
  4. 4.
    Bastolla U, Fortuna MA, Pascual-García A et al (2009) The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458:1018–1020CrossRefPubMedGoogle Scholar
  5. 5.
    Bascompte J (2010) Structure and dynamics of ecological networks. Science 329:765–766CrossRefPubMedGoogle Scholar
  6. 6.
    Greenblum S, Turnbaugh PJ, Borenstein E (2012) Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci U S A 109:594–599CrossRefPubMedGoogle Scholar
  7. 7.
    Smillie CS, Smith MB, Friedman J et al (2011) Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480:241–244. doi: 10.1038/nature10571 CrossRefPubMedGoogle Scholar
  8. 8.
    Pocock MJO, Evans DM, Memmott J (2012) The robustness and restoration of a network of ecological networks. Science 335:973–977CrossRefPubMedGoogle Scholar
  9. 9.
    Faust K, Raes J (2012) Microbial interactions: from networks to models. Nat Rev Microbiol 10:538–550CrossRefPubMedGoogle Scholar
  10. 10.
    Faust K, Sathirapongsasuti JF, Izard J et al (2012) Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol 8, e1002606CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Suweis S, Simini F, Banavar JR, Maritan A (2013) Emergence of structural and dynamical properties of ecological mutualistic networks. Nature 500:449–452CrossRefPubMedGoogle Scholar
  12. 12.
    Heleno R, Garcia C, Jordano P et al (2014) Ecological networks: delving into the architecture of biodiversity. Biol Lett 10:20131000. doi: 10.1098/rsbl.2013.1000 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Tung J, Barreiro LB, Burns MB et al (2015) Social networks predict gut microbiome composition in wild baboons. eLife. doi: 10.7554/eLife.05224 Google Scholar
  14. 14.
    Sam Ma Z, Guan Q, Ye C et al (2015) Network analysis suggests a potentially “evil” alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities. Sci Rep 5:8275. doi: 10.1038/srep08275 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Elton CS (1927) Animal ecology. Sidwich & Jackson, LondonGoogle Scholar
  16. 16.
    Lindeman RL (1991) The trophic-dynamic aspect of ecology. Bull Math Biol 53:167–191CrossRefGoogle Scholar
  17. 17.
    May RM (2001) Stability and complexity in model ecosystems. Princeton University Press, Princeton, NJGoogle Scholar
  18. 18.
    May RM (1983) Ecology: the structure of food webs. Nature 301:566–568. doi: 10.1038/301566a0 CrossRefGoogle Scholar
  19. 19.
    Cohen JE, Newman CM (1985) A stochastic theory of community food webs: I. Models and aggregated data. Proc R Soc Lond Ser B, containing papers of a biological character Royal Society (Great Britain) 224:421–448Google Scholar
  20. 20.
    Cohen JE (1990) A stochastic theory of community food webs. VI. Heterogeneous alternatives to the cascade model. Theor Popul Biol 37:55–90CrossRefGoogle Scholar
  21. 21.
    Turnbaugh PJ, Ley RE, Hamady M et al (2007) The human microbiome project. Nature 449:804–810CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Human Microbiome Project Consortium (2012) A framework for human microbiome research. Nature 486:215–221CrossRefGoogle Scholar
  23. 23.
    Human Microbiome Project Consortium (2012) Structure, function and diversity of the healthy human microbiome. Nature 486:207–214. doi: 10.1038/nature11234 CrossRefGoogle Scholar
  24. 24.
    Gilbert JA, Meyer F, Antonopoulos D et al (2010) Meeting report: the terabase metagenomics workshop and the vision of an earth microbiome project. Stand Genomic Sci 3:243–248CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Gilbert JA, O’Dor R, King N, Vogel TM (2011) The importance of metagenomic surveys to microbial ecology: or why Darwin would have been a metagenomic scientist. Microb Inf Exp 1:5. doi: 10.1186/2042-5783-1-5 CrossRefGoogle Scholar
  26. 26.
    Prosser JI, Bohannan BJM, Curtis TP et al (2007) The role of ecological theory in microbial ecology. Nat Rev Microbiol 5:384–392. doi: 10.1038/nrmicro1643 CrossRefPubMedGoogle Scholar
  27. 27.
    Costello EK, Stagaman K, Dethlefsen L et al (2012) The application of ecological theory toward an understanding of the human microbiome. Science 336:1255–1262. doi: 10.1126/science.1224203 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71:8228–8235. doi: 10.1128/AEM.71.12.8228-8235.2005 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Erdos P, Renyi A (1959) On random graphs. Publicationes Mathematicae, DebrecenGoogle Scholar
  30. 30.
    Erdős P, Rényi A (1960) On the evolution of random graphs. In: Publication of the Mathematical Institute of the Hungarian Academy of Sciences. pp 17–61Google Scholar
  31. 31.
    Bondy J, Murty U (1976) Graph theory with applications. Elsevier Science Ltd/North-Holland, New YorkCrossRefGoogle Scholar
  32. 32.
    Newman MEJ, Watts DJ, Strogatz SH (2002) Random graph models of social networks. Proc Natl Acad Sci U S A 99:2566–2572. doi: 10.1073/pnas.012582999 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Barabási A, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512CrossRefPubMedGoogle Scholar
  34. 34.
    Albert R, Barabási A (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97CrossRefGoogle Scholar
  35. 35.
    Schwöbbermeyer H (2008) Network motifs. In: Junker BH, Schreiber F (eds) Analysis of biological networks. Wiley, Hoboken, NJ, pp 85–111CrossRefGoogle Scholar
  36. 36.
    Watts DJ, Strogatz SH (1998) Collective dynamics of “small-world” networks. Nature 393:440–442. doi: 10.1038/30918 CrossRefPubMedGoogle Scholar
  37. 37.
    Taylor LR (1961) Aggregation, variance and the mean. Nature 189:732–735. doi: 10.1038/189732a0 CrossRefGoogle Scholar
  38. 38.
    Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:3448–3449. doi: 10.1093/bioinformatics/bti551 CrossRefPubMedGoogle Scholar
  41. 41.
    Taylor RC, Shah A, Treatman C, Blevins M (2006) SEBINI: Software Environment for Biological Network Inference. Bioinformatics 22:2706–2708CrossRefPubMedGoogle Scholar
  42. 42.
    Csardi G, Nepusz T (2005) The Igraph software package for complex network research. I J Complex Sys (5):1–9Google Scholar
  43. 43.
    Hagberg A, Schult D, Swart P (2008) Exploring network structure, dynamics, and function using NetworkX. No. LA-UR-08-05495, Los Alamos National Laboratory (LANL)Google Scholar
  44. 44.
    Thomas S, Bonchev D (2010) A survey of current software for network analysis in molecular biology. Hum Genomics 4:353–360. doi: 10.1186/1479-7364-4-5-353 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Durrett R (2006) Random graph dynamics. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  46. 46.
    Stouffer D (2010) Scaling from individuals to networks in food webs. Functional Ecology 24:44–51CrossRefGoogle Scholar
  47. 47.
    Lotka AJ (1925) Elements of physical biology. Williams & Wilkins, BaltimoreGoogle Scholar
  48. 48.
    Volterra V (1926) Variazioni e fluttuazioni del numero d’individui in specie animali conviventi. Mem R Accad Naz dei Lincei Ser VI 2:31–113Google Scholar
  49. 49.
    Gaedke U (2008) Ecological networks. In: Junker BH, Schreiber F (eds) Analysis of biological networks. Wiley, Hoboken, NJ, pp 283–304CrossRefGoogle Scholar
  50. 50.
    Dunne JA, Williams RJ, Martinez ND (2002) Food-web structure and network theory: the role of connectance and size. Proc Natl Acad Sci U S A 99:12917–12922. doi: 10.1073/pnas.192407699 CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Dunne JA, Williams RJ, Martinez ND (2002) Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol Lett 5:558–567CrossRefGoogle Scholar
  52. 52.
    Dunne JA (2005) The network structure of food webs. In: Pascual M, Dunne JA (eds) Ecological networks: linking structure to dynamics in food webs. Oxford University Press, OxfordGoogle Scholar
  53. 53.
    Ma Z, Krings AW (2011) Dynamic hybrid fault modeling and extended evolutionary game theory for reliability, survivability and fault tolerance analyses. IEEE Trans Reliab 60:180–196. doi: 10.1109/TR.2011.2104997 CrossRefGoogle Scholar
  54. 54.
    MacArthur R (1955) Fluctuations of animal populations and a measure of community stability. Ecology 36:533–536. doi: 10.2307/1929601 CrossRefGoogle Scholar
  55. 55.
    Pepper JW, Rosenfeld S (2012) The emerging medical ecology of the human gut microbiome. Trends Ecol Evol 27:381–384CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Ma ZS (2012) A note on extending Taylor’s power law for characterizing human microbial communities: inspiration from comparative studies on the distribution patterns of insects and galaxies, and as a case study for medical ecology. http://adsabs.harvard.edu/abs/2012arXiv1205.3504M
  57. 57.
    Palmer C, Bik EM, DiGiulio DB et al (2007) Development of the human infant intestinal microbiota. PLoS Biol 5, e177CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Markowitz VM, Ivanova NN, Szeto E et al (2008) IMG/M: a data management and analysis system for metagenomes. Nucleic Acids Res 36:D534–D538. doi: 10.1093/nar/gkm869 CrossRefPubMedGoogle Scholar
  59. 59.
    Glass EM, Wilkening J, Wilke A et al (2010) Using the metagenomics RAST server (MG-RAST) for analyzing shotgun metagenomes. Cold Spring Harb Protoc 2010. doi: 10.1101/pdb.prot5368
  60. 60.
    Wooley JC, Godzik A, Friedberg I (2010) A primer on metagenomics. PLoS Comput Biol. doi: 10.1371/journal.pcbi.1000667 Google Scholar
  61. 61.
    Scholz MB, Lo C-C, Chain PS (2012) Next generation sequencing and bioinformatic bottlenecks: the current state of metagenomic data analysis. Curr Opin Biotechnol 23:9–15CrossRefPubMedGoogle Scholar
  62. 62.
    Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. doi: 10.1038/nmeth.f.303 CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Schloss PD, Westcott SL, Ryabin T et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537. doi: 10.1128/AEM.01541-09 CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Junker BH, Schreiber F (2008) Analysis of biological networks. Wiley-InterScience, Hoboken, NJCrossRefGoogle Scholar
  65. 65.
    Breitkreutz B-J, Stark C, Tyers M (2003) Osprey: a network visualization system. Genome Biol 4:R22CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Hu Z, Mellor J, Wu J et al (2005) VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res 33:W352CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    igraph – Network analysis software. http://igraph.org/. Accessed 13 Nov 2015
  68. 68.
    Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. Proceedings of international Association for the Advancement of Artificial Intelligence (www.aaai.org). conference on weblogs and social media
  69. 69.
    Brusco M, Doreian P, Mrvar A, Steinley D (2011) Two algorithms for relaxed structural balance partitioning: linking theory, models, and data to understand social network phenomena. Sociol Methods Res 40:57–87. doi: 10.1177/0049124110384947 CrossRefGoogle Scholar
  70. 70.
    Carley KM (2014) ORA: a toolkit for dynamic network analysis and visualization. In: Rokne PJ, Alhajj PR (eds) Encyclopedia of social network analysis and mining. Springer, New York, pp 1219–1228Google Scholar
  71. 71.
    Kashtan N, Itzkovitz S, Milo R, Alon U (2002) Mfinder tool guide. Technical report, Department of Molecular Cell Biology and Computer Science & Applied Mathematics, Weizman Institute of ScienceGoogle Scholar
  72. 72.
    Schreiber F, Schwöbbermeyer H (2005) MAVisto: a tool for the exploration of network motifs. Bioinformatics 21:3572–3574. doi: 10.1093/bioinformatics/bti556 CrossRefPubMedGoogle Scholar
  73. 73.
    Wernicke S, Rasche F (2015) FANMOD: a tool for fast network motif detection. http://bioinformatics.oxfordjournals.org. Accessed 13 Nov 2015
  74. 74.
    Sahraeian SME, Yoon B-J (2012) RESQUE: network reduction using semi-Markov random walk scores for efficient querying of biological networks. Bioinformatics 28:2129–2136. doi: 10.1093/bioinformatics/bts341 CrossRefPubMedGoogle Scholar
  75. 75.
    Kepes F (2007) Biological networks. World Scientific, SingaporeCrossRefGoogle Scholar
  76. 76.
    Butenko S et al (2009) Clustering challenges in biological networks. World Scientific, SingaporeCrossRefGoogle Scholar
  77. 77.
    Dehmer M, Emmert-Streib F (2009) Analysis of complex networks: from biology to linguistics. Wiley-VCH Verlag, WeinheimCrossRefGoogle Scholar
  78. 78.
    Networks: an introduction. http://www-personal.umich.edu/~mejn/networks-an-introduction/. Accessed 13 Nov 2015
  79. 79.
    Cagney G, Emili A (2011) Network biology: methods and applications. Humana Press, New York, NYCrossRefGoogle Scholar
  80. 80.
    Liu B, Faller LL, Klitgord N et al (2012) Deep sequencing of the oral microbiome reveals signatures of periodontal disease. PLoS One. doi: 10.1371/journal.pone.0037919 Google Scholar
  81. 81.
    Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci U S A 99:7821–7826CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256CrossRefGoogle Scholar
  83. 83.
    Newman MEJ (2010) Networks: an introduction. Oxford University Press, OxfordCrossRefGoogle Scholar
  84. 84.
    Newman MEJ, Barabási AL, Watts DJ (eds) (2006) The structure and dynamics of networks. Princeton University Press, Princeton, NJGoogle Scholar
  85. 85.
    Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Barabási AL (2013) Network science. Philos Trans R Soc 371:1–3CrossRefGoogle Scholar
  87. 87.
  88. 88.
  89. 89.
  90. 90.
  91. 91.
  92. 92.
  93. 93.
  94. 94.
    Tulip Website. http://tulip.labri.fr/TulipDrupal/?q=tutorials. Accessed 2 Sept 2015

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Zhanshan (Sam) Ma
    • 1
    Email author
  • Chengchen Zhang
    • 2
  • Qingpeng Zhang
    • 3
  • Jie Li
    • 1
  • Lianwei Li
    • 1
  • Linyi Qi
    • 1
  • Xianghong Yang
    • 4
  1. 1.Computational Biology and Medical Ecology Lab, State Key Lab of Genetic Resources and EvolutionKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
  2. 2.Department of MedicineColumbia University Medical CenterNew YorkUSA
  3. 3.Department of Systems Engineering and Engineering ManagementCity University of Hong KongKowloon TongChina
  4. 4.Department of Dental MedicineYan An Affiliated Hospital of Kunming Medical UniversityKunmingChina

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