Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Opinion
  • Published:

A road map for the development of community systems (CoSy) biology

Abstract

Microbial interactions are essential for all global geochemical cycles and have an important role in human health and disease. Although we possess general knowledge about the major processes within a microbial community, we are presently unable to decipher what role individual microorganisms have and how their individual actions influence others in the community. We also have limited knowledge with which to predict the effects of microbial interactions and community composition on the environment and vice versa. In this Opinion article, we describe how community systems (CoSy) biology will enable us to decode these complex relationships and will therefore improve our understanding of individual members of the community and the modes of interactions in which they engage.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Systems biology approaches for modelling single organisms and communities.
Figure 2: Three different forms of metabolic interaction that occur between microorganisms.

Similar content being viewed by others

References

  1. Zengler, K. Central role of the cell in microbial ecology. Microbiol. Mol. Biol. Rev. 73, 712–729 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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 

  4. Bordbar, A., Lewis, N. E., Schellenberger, J., Palsson, B. Ø. & Jamshidi, N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol. Syst. Biol. 6, 422 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Fleischmann, R. D. et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269, 496–512 (1995).

    Article  CAS  PubMed  Google Scholar 

  6. Edwards, J. S. & Palsson, B. O. Systems properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem. 274, 17410–17416 (1999).

    Article  CAS  PubMed  Google Scholar 

  7. Feist, A. M. & Palsson, B. Ø. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nature Biotech. 26, 659–667 (2008).

    Article  CAS  Google Scholar 

  8. Oberhardt, M. A., Palsson, B. Ø. & Papin, J. A. Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 5, 320 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Mahadevan, R., Palsson, B. Ø. & Lovley, D. R. In situ to in silico and back: elucidating the physiology and ecology of Geobacter spp. using genome-scale modelling. Nature Rev. Microbiol. 9, 39–50 (2011).

    Article  CAS  Google Scholar 

  10. Thiele, I. & Palsson, B. Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protoc. 5, 93–121 (2010).

    Article  CAS  Google Scholar 

  11. Feist, A. M., Herrgard, M. J., Thiele, I., Reed, J. L. & Palsson, B. Ø. Reconstruction of biochemical networks in microorganisms. Nature Rev. Microbiol. 7, 129–143 (2009).

    Article  CAS  Google Scholar 

  12. Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nature Biotech. 28, 245–248 (2010).

    Article  CAS  Google Scholar 

  13. Palsson, B. Metabolic systems biology. FEBS Lett. 583, 3900–3004 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Orth, J. D. & Palsson, B. Ø. Systematizing the generation of missing metabolic knowledge. Biotechnol. Bioeng. 107, 403–412 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Papp, B., Notebaart, R. A. & Pal, C. Systems-biology approaches for predicting genomic evolution. Nature Rev. Genet. 12, 591–602 (2011).

    Article  CAS  PubMed  Google Scholar 

  16. Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature Biotech. 28, 977–982 (2010).

    Article  CAS  Google Scholar 

  17. Gil, R., Silva, F. J., Pereto, J. & Moya, A. Determination of the core of a minimal bacterial gene set. Microbiol. Mol. Biol. Rev. 68, 518–537 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Thiele, I., Jamshidi, N., Fleming, R. M. & Palsson, B. Ø. Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comp. Biol. 5, e1000312 (2009).

    Article  CAS  Google Scholar 

  19. Zhang, Y. et al. Three-dimensional structural view of the central metabolic network of Thermotoga maritima. Science 325, 1544–1549 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cho, B. K. et al. The transcription unit architecture of the Escherichia coli genome. Nature Biotech. 27, 1043–1049 (2009).

    Article  CAS  Google Scholar 

  21. Qiu, Y. et al. Structural and operational complexity of the Geobacter sulfurreducens genome. Genome Res. 20, 1304–1311 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Palsson, B. & Zengler, K. The challenges of integrating multi-omic data sets. Nature Chem. Biol. 6, 787–789 (2010).

    Article  Google Scholar 

  23. Belnap, C. P. et al. Quantitative proteomic analyses of the response of acidophilic microbial communities to different pH conditions. ISME J. 5, 1152–1161 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kang, Y. et al. Transcript amplification from single bacterium for transcriptome analysis. Genome Res. 21, 925–935 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bassler, B. L. & Losick, R. Bacterially speaking. Cell 125, 237–246 (2006).

    Article  CAS  PubMed  Google Scholar 

  26. Dubey, G. P. & Ben-Yehuda, S. Intercellular nanotubes mediate bacterial communication. Cell 144, 590–600 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Summers, Z. M. et al. Direct exchange of electrons within aggregates of an evolved syntrophic coculture of anaerobic bacteria. Science 330, 1413–1415 (2010).

    Article  CAS  PubMed  Google Scholar 

  28. Samuel, B. S. et al. Genomic and metabolic adaptations of Methanobrevibacter smithii to the human gut. Proc. Natl Acad. Sci. USA 104, 10643–10648 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100 (2011).

    Article  CAS  PubMed  Google Scholar 

  30. Krembs, C., Juhl, A. R., Long, R. A. & Azam, F. Nanoscale patchiness of bacteria in lake water studied with spatial information preservation method. Limnol. Oceanogr. 43, 307–314 (1998).

    Article  Google Scholar 

  31. Jørgensen, B. B. in Marine Geochemistry 2nd edn (eds Schulz, H.D. & Zabel, M.) 173–207 (Springer, 2000).

    Book  Google Scholar 

  32. Ishii, S., Kosaka, T., Hori, K., Hotta, Y. & Watanabe, K. Coaggregation facilitates interspecies hydrogen transfer between Pelotomaculum thermopropionicum and Methanothermobacter thermautotrophicus. Appl. Environ. Microbiol. 71, 7838–7845 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Reguera, G. When microbial conversations get physical. Trends Microbiol. 19, 105–113 (2010).

    Article  CAS  Google Scholar 

  34. Overmann, J. & Schubert, K. Phototrophic consortia: model systems for symbiotic interrelations between prokaryotes. Arch. Microbiol. 177, 201–208 (2002).

    Article  CAS  PubMed  Google Scholar 

  35. Little, A. E., Robinson, C. J., Peterson, S. B., Raffa, K. F. & Handelsman, J. Rules of engagement: interspecies interactions that regulate microbial communities. Annu. Rev. Microbiol. 62, 375–401 (2008).

    Article  CAS  PubMed  Google Scholar 

  36. Rosenthal, A. Z., Matson, E. G., Eldar, A. & Leadbetter, J. R. RNA-seq reveals cooperative metabolic interactions between two termite-gut spirochete species in co-culture. ISME J. 5, 1133–1142 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Raghoebarsing, A. A. et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature 440, 918–921 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Müller, J. & Overmann, J. Close interspecies interactions between prokaryotes from sulfureous environments. Front. Microbiol. 2, 146 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Podar, M. et al. A genomic analysis of the archaeal system Ignicoccus hospitalis-Nanoarchaeum equitans. Genome Biol. 9, R158 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  41. Seeliger, S., Cord-Ruwisch, R. & Schink, B. A periplasmic and extracellular c-type cytochrome of Geobacter sulfurreducens acts as a ferric iron reductase and as an electron carrier to other acceptors or to partner bacteria. J. Bacteriol. 180, 3686–3691 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. McInerney, M. J., Bryant, M. P., Hespell, R. B. & Costerton, J. W. Syntrophomonas wolfei gen. nov. sp. nov., an anaerobic, syntrophic, fatty acid-oxidizing bacterium. Appl. Environ. Microbiol. 41, 1029–1039 (1981).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Nielsen, L. P., Risgaard-Petersen, N., Fossing, H., Christensen, P. B. & Sayama, M. Electric currents couple spatially separated biogeochemical processes in marine sediment. Nature 463, 1071–1074 (2010).

    Article  CAS  PubMed  Google Scholar 

  44. Frezza, C. et al. Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477, 225–228 (2011).

    Article  CAS  PubMed  Google Scholar 

  45. Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Woods, R. J. et al. Second-order selection for evolvability in a large Escherichia coli population. Science 331, 1433–1436 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Rainey, P. B. & Rainey, K. Evolution of cooperation and conflict in experimental bacterial populations. Nature 425, 72–74 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. Diggle, S. P., Griffin, A. S., Campbell, G. S. & West, S. A. Cooperation and conflict in quorum-sensing bacterial populations. Nature 450, 411–414 (2007).

    Article  CAS  PubMed  Google Scholar 

  49. MacLean, R. C. & Gudelj, I. Resource competition and social conflict in experimental populations of yeast. Nature 441, 498–501 (2006).

    Article  CAS  PubMed  Google Scholar 

  50. Gore, J., Youk, H. & van Oudenaarden, A. Snowdrift game dynamics and facultative cheating in yeast. Nature 459, 253–256 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Portnoy, V. A., Bezdan, D. & Zengler, K. Adaptive laboratory evolution-harnessing the power of biology for metabolic engineering. Curr. Opin. Biotechnol. 590–594 (2011).

  52. Conrad, T. M., Lewis, N. E. & Palsson, B. Ø. Microbial laboratory evolution in the era of genome-scale science. Mol. Syst. Biol. 7, 509 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Veeravalli, K., Boyd, D., Iverson, B. L., Beckwith, J. & Georgiou, G. Laboratory evolution of glutathione biosynthesis reveals natural compensatory pathways. Nature Chem. Biol. 7, 101–105 (2011).

    Article  CAS  Google Scholar 

  54. Jacobsen, A., Hendriksen, R. S., Aaresturp, F. M., Ussery, D. W. & Friis, C. The Salmonella enterica pan-genome. Microb. Ecol. 487–504 (2011).

  55. Stolyar, S. et al. Metabolic modeling of a mutualistic microbial community. Mol. Syst. Biol. 3, 92 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Hillesland, K. L. & Stahl, D. A. Rapid evolution of stability and productivity at the origin of a microbial mutualism. Proc. Natl Acad. Sci. USA 107, 2124–2129 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Klitgord, N. & Segre, D. Environments that induce synthetic microbial ecosystems. PLoS Comp. Biol. 6, e1001002 (2010).

    Article  CAS  Google Scholar 

  58. Zhuang, K. et al. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 5, 305–316 (2011).

    Article  PubMed  Google Scholar 

  59. Scheibe, T. D. et al. Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation. Microb. Biotechnol. 2, 274–286 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yoon, H. S. et al. Single-cell genomics reveals organismal interactions in uncultivated marine protists. Science 332, 714–717 (2011).

    Article  CAS  PubMed  Google Scholar 

  61. Osterman, A. & Overbeek, R. Missing genes in metabolic pathways: a comparative genomics approach. Curr. Opin. Chem. Biol. 7, 238–251 (2003).

    Article  CAS  PubMed  Google Scholar 

  62. Gilbert, J. A., Meyer, F. & Bailey, M. J. The future of microbial metagenomics (or is ignorance bliss?). ISME J. 5, 777–779 (2011).

    Article  PubMed  Google Scholar 

  63. Price, N. D., Reed, J. L. & Palsson, B. Ø. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nature Rev. Microbiol. 2, 886–897 (2004).

    Article  CAS  Google Scholar 

  64. Hsiao, T. L., Revelles, O., Chen, L., Sauer, U. & Vitkup, D. Automatic policing of biochemical annotations using genomic correlations. Nature Chem. Biol. 6, 34–40 (2010).

    Article  CAS  Google Scholar 

  65. Tettelin, H., Riley, D., Cattuto, C. & Medini, D. Comparative genomics: the bacterial pan-genome. Curr. Opin. Microbiol. 11, 472–477 (2008).

    Article  CAS  PubMed  Google Scholar 

  66. Bertics, V. J. & Ziebis, W. Bioturbation and the role of microniches for sulfate reduction in coastal marine sediments. Environ. Microbiol. 12, 3022–3034 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  Google Scholar 

  68. Watrous, J. D. & Dorrestein, P. C. Imaging mass spectrometry in microbiology. Nature Rev. Microbiol. 9, 683–694 (2011).

    Article  CAS  Google Scholar 

  69. Kim, H. J., Boedicker, J. Q., Choi, J. W. & Ismagilov, R. F. Defined spatial structure stabilizes a synthetic multispecies bacterial community. Proc. Natl Acad. Sci. USA 105, 18188–18193 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Larsen, P. E. et al. Predicted relative metabolomic turnover (PRMT) determining metabolic turnover from a coastal marine metagenomic dataset. Microb. Inform. Exp. 14, 4 (2011).

    Article  Google Scholar 

  71. Edwards, J. S. & Palsson, B. O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl Acad. Sci. USA 97, 5528–5533 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Papin, J. A., Reed, J. L. & Palsson, B. O. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. Trends Biochem. Sci. 29, 641–647 (2004).

    Article  CAS  PubMed  Google Scholar 

  73. Stein, J. L., Marsh, T. L., Wu, K. Y., Shizuya, H. & DeLong, E. F. Characterization of uncultivated prokaryotes: isolation and analysis of a 40-kilobase-pair genome fragment from a planktonic marine archaeon. J. Bacteriol. 178, 591–599 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Burgard, A. P., Pharkya, P. & Maranas, C. D. OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657 (2003).

    CAS  PubMed  Google Scholar 

  75. Béjà, O. et al. Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Science 289, 1902–1906 (2000).

    Article  PubMed  Google Scholar 

  76. Rondon, M. R. et al. Cloning the soil metagenome: a strategy for accessing the genetic and functional diversity of uncultured microorganisms. Appl. Environ. Microbiol. 66, 2541–2547 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J. & Palsson, B. O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429, 92–96 (2004).

    Article  CAS  PubMed  Google Scholar 

  78. Venter, J. C. et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66–74 (2004).

    Article  CAS  PubMed  Google Scholar 

  79. Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).

    Article  CAS  PubMed  Google Scholar 

  80. Reed, J. L. et al. Systems approach to refining genome annotation. Proc. Natl Acad. Sci. USA 103, 17480–17484 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Na, D., Kim, T. Y. & Lee, S. Y. Construction and optimization of synthetic pathways in metabolic engineering. Curr. Opin. Microbiol. 13, 363–370 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  83. Gill, S. R. et al. Metagenomic analysis of the human distal gut microbiome. Science 312, 1355–1359 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Frias-Lopez, J. et al. Microbial community gene expression in ocean surface waters. Proc. Natl Acad. Sci. USA 105, 3805–3810 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Grant, S. et al. Identification of eukaryotic open reading frames in metagenomic cDNA libraries made from environmental samples. Appl. Environ. Microbiol. 72, 135–143 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Schulze, W. X. et al. A proteomic fingerprint of dissolved organic carbon and of soil particles. Oecologia 142, 335–343 (2005).

    Article  PubMed  Google Scholar 

  87. Ram, R. J. et al. Community proteomics of a natural microbial biofilm. Science 308, 1915–1920 (2005).

    Article  CAS  PubMed  Google Scholar 

  88. 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 

Download references

Acknowledgements

This work was in part funded by the Office of Science (Biological and Environmental Research) for the US Department of Energy (grant DE-SC0004485).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Karsten Zengler.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Related links

FURTHER INFORMATION

Karsten Zengler and Bernhard O. Palsson's homepage

BiGG Database

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zengler, K., Palsson, B. A road map for the development of community systems (CoSy) biology. Nat Rev Microbiol 10, 366–372 (2012). https://doi.org/10.1038/nrmicro2763

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrmicro2763

This article is cited by

Search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology