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Ecological modelling approaches for predicting emergent properties in microbial communities

Nature Ecology & Evolution volume 6, pages 855–865 (2022)Cite this article

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Abstract

Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties—patterns or functions that cannot be deduced linearly from the properties of the constituent parts—underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka–Volterra, consumer–resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.

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Fig. 1: Examples of emergent properties arising from community complexity.
Fig. 2: Model types commonly used for studying emergent properties of microbial communities.
Fig. 3: Choosing a suitable modelling approach.

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References

  1. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Shi, Z. Gut microbiota: an important link between Western diet and chronic diseases. Nutrients 11, 2287 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  3. Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Glowacki, R. W. P. & Martens, E. C. In sickness and health: effects of gut microbial metabolites on human physiology. PLoS Pathog. 16, e1008370 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nazaries, L. et al. Evidence of microbial regulation of biogeochemical cycles from a study on methane flux and land use change. Appl. Environ. Microbiol. 79, 4031–4040 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Konopka, A. What is microbial community ecology? ISME J. 3, 1223–1230 (2009).

    Article  PubMed  Google Scholar 

  7. Flemming, H.-C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Bauer, E., Zimmermann, J., Baldini, F., Thiele, I. & Kaleta, C. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput. Biol. 13, e1005544 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. van Hoek, M. J. A. & Merks, R. M. H. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC Syst. Biol. 11, 56 (2017).

    Article  Google Scholar 

  10. Gorter, F. A., Manhart, M. & Ackermann, M. Understanding the evolution of interspecies interactions in microbial communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190256 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chen, J., Yoshinaga, M. & Rosen, B. P. The antibiotic action of methylarsenite is an emergent property of microbial communities. Mol. Microbiol. 111, 487–494 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Konstantinidis, D. et al. Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion. Mol. Syst. Biol. 17, e10189 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Park, H. et al. Artificial consortium demonstrates emergent properties of enhanced cellulosic-sugar degradation and biofuel synthesis. NPJ Biofilms Microbiomes 6, 59 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schwartzman, J. A. et al. Bacterial growth in multicellular aggregates leads to the emergence of complex lifecycles. Preprint at bioRxiv https://doi.org/10.1101/2021.11.01.466752 (2021).

  16. Levins, R. & Lewontin, R. The Dialectical Biologist (Harvard Univ. Press, 1985).

  17. Diaz, P. I. & Valm, A. M. Microbial interactions in oral communities mediate emergent biofilm properties. J. Dent. Res. 99, 18–25 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Buerger, A. N. et al. Gastrointestinal dysbiosis following diethylhexyl phthalate exposure in zebrafish (Danio rerio): altered microbial diversity, functionality, and network connectivity. Environ. Pollut. 265, 114496 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Kim, M. K., Ingremeau, F., Zhao, A., Bassler, B. L. & Stone, H. A. Local and global consequences of flow on bacterial quorum sensing. Nat. Microbiol. 1, 15005 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ebrahimi, A. & Or, D. Hydration and diffusion processes shape microbial community organization and function in model soil aggregates. Water Resour. Res. 51, 9804–9827 (2015).

    Article  Google Scholar 

  21. Falconer, R. E. et al. Microscale heterogeneity explains experimental variability and non-linearity in soil organic matter mineralisation. PLoS ONE 10, e0123774 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Marino, S., Baxter, N. T., Huffnagle, G. B., Petrosino, J. F. & Schloss, P. D. Mathematical modeling of primary succession of murine intestinal microbiota. Proc. Natl Acad. Sci. USA 111, 439–444 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. Cariboni, J., Gatelli, D., Liska, R. & Saltelli, A. The role of sensitivity analysis in ecological modelling. Ecol. Modell. 203, 167–182 (2007).

    Article  Google Scholar 

  26. Oreskes, N., Shrader-Frechette, K. & Belitz, K. Verification, validation, and confirmation of numerical models in the Earth sciences. Science 263, 641–646 (1994).

    Article  CAS  PubMed  Google Scholar 

  27. Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Hammarlund, S. P., Chacón, J. M. & Harcombe, W. R. A shared limiting resource leads to competitive exclusion in a cross-feeding system. Environ. Microbiol. 21, 759–771 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Current status and applications of genome-scale metabolic models. Genome Biol. 20, 121 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. O’Brien, E. J., Monk, J. M. & Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 161, 971–987 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Fang, X., Lloyd, C. J. & Palsson, B. O. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat. Rev. Microbiol. 18, 731–743 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Colarusso, A. V., Goodchild-Michelman, I., Rayle, M. & Zomorrodi, A. R. Computational modeling of metabolism in microbial communities on a genome-scale. Curr. Opin. Syst. Biol. 26, 46–57 (2021).

    Article  CAS  Google Scholar 

  36. García-Jiménez, B., Torres-Bacete, J. & Nogales, J. Metabolic modelling approaches for describing and engineering microbial communities. Comput. Struct. Biotechnol. J. 19, 226–246 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Frioux, C., Singh, D., Korcsmaros, T. & Hildebrand, F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput. Struct. Biotechnol. J. 18, 1722–1734 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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 

  39. Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Li, J. et al. Distinct mechanisms shape soil bacterial and fungal co-occurrence networks in a mountain ecosystem. FEMS Microbiol. Ecol. 96, fiaa030 (2020).

    Article  CAS  PubMed  Google Scholar 

  41. Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Barbier, M., Arnoldi, J.-F., Bunin, G. & Loreau, M. Generic assembly patterns in complex ecological communities. Proc. Natl Acad. Sci. USA 115, 2156–2161 (2018).

  44. Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).

    Article  CAS  PubMed  Google Scholar 

  45. Madeo, D., Comolli, L. R. & Mocenni, C. Emergence of microbial networks as response to hostile environments. Front. Microbiol. 5, 407 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Wang, B. & Allison, S. D. Emergent properties of organic matter decomposition by soil enzymes. Soil Biol. Biochem. 136, 107522 (2019).

    Article  CAS  Google Scholar 

  48. Walsh, A. M. et al. Microbial succession and flavor production in the fermented dairy beverage kefir. mSystems 1, e00052-16 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Oliveira, N. M., Niehus, R. & Foster, K. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941–17946 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Leigh, E. R. in Some Mathematical Problems in Biology (ed. Gerstenhaber, M.) 1–61 (American Mathematical Society, 1968).

  51. Nedorezov, L. The dynamics of the lynx–hare system: an application of the Lotka–Volterra model. Biophys. 61, 149–154 (2016).

    Article  CAS  Google Scholar 

  52. Mühlbauer, L. K., Schulze, M., Harpole, W. S. & Clark, A. T. gauseR: simple methods for fitting Lotka–Volterra models describing Gause’s “struggle for existence”. Ecol. Evol. 10, 13275–13283 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Belovsky, G. E. Moose and snowshoe hare competition and a mechanistic explanation from foraging theory. Oecologia 61, 150–159 (1984).

    Article  CAS  PubMed  Google Scholar 

  54. May, R. M. Limit cycles in predator–prey communities. Science 177, 900–902 (1972).

    Article  CAS  PubMed  Google Scholar 

  55. Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).

    Article  PubMed  Google Scholar 

  56. Voit, E. O., Davis, J. D. & Olivença, D. V. Inference and validation of the structure of Lotka–Volterra models. Preprint at bioXriv https://doi.org/10.1101/2021.08.14.456346 (2021).

  57. Bucci, V. & Xavier, J. B. Towards predictive models of the human gut microbiome. J. Mol. Biol. 426, 3907–3916 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Bucci, V. et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gao, X., Huynh, B.-T., Guillemot, D., Glaser, P. & Opatowski, L. Inference of significant microbial interactions from longitudinal metagenomics data. Front. Microbiol. 9, 2319 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Li, C. et al. An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data. Microbiome 7, 118 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Joseph, T. A., Shenhav, L., Xavier, J. B., Halperin, E. & Pe’er, I. Compositional Lotka–Volterra describes microbial dynamics in the simplex. PLoS Comput. Biol. 16, e1007917 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Hosoda, S., Fukunaga, T. & Hamada, M. Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model. Bioinformatics 37, i16–i24 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Remien, C. H., Eckwright, M. J. & Ridenhour, B. J. Structural identifiability of the generalized Lotka–Volterra model for microbiome studies. R. Soc. Open Sci. 8, 201378 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. White, J. R. Novel Methods for Metagenomic Analysis. PhD thesis, Univ. of Maryland (2010).

  66. Sousa, A., Frazão, N., Ramiro, R. S. & Gordo, I. Evolution of commensal bacteria in the intestinal tract of mice. Curr. Opin. Microbiol. 38, 114–121 (2017).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  68. Momeni, B., Xie, L. & Shou, W. Lotka–Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. eLife 6, e25051 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Mai, T. S. N. Impact of Metabolic Plasticity on Microbial Community Diversity and Stability. MSc thesis, Univ. of Groningen (2021).

  72. Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F. & Sanchez, A. High-order interactions distort the functional landscape of microbial consortia. PLoS Biol. 17, e3000550 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Mickalide, H. & Kuehn, S. Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community. Cell Syst. 9, 521–533.e10 (2019).

    Article  CAS  PubMed  Google Scholar 

  74. Guo, X. & Boedicker, J. Q. The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLoS Comput. Biol. 12, e1005079 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Meroz, N., Tovi, N., Sorokin, Y. & Friedman, J. Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales. Nat. Commun. 12, 2891 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Zomorrodi, A. R. & Segrè, D. Synthetic ecology of microbes: mathematical models and applications. J. Mol. Biol. 428, 837–861 (2016).

    Article  CAS  PubMed  Google Scholar 

  77. Song, H. S., Cannon, W. R., Beliaev, A. S. & Konopka, A. Mathematical modeling of microbial community dynamics: a methodological review. Processes 2, 711–752 (2014).

    Article  Google Scholar 

  78. Descheemaeker, L., Grilli, J. & de Buyl, S. Heavy-tailed abundance distributions from stochastic Lotka–Volterra models. Phys. Rev. E 104, 034404 (2021).

    Article  CAS  PubMed  Google Scholar 

  79. Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Ji, B., Herrgård, M. J. & Nielsen, J. Microbial community dynamics revisited. Nat. Comput. Sci. 1, 640–641 (2021).

    Article  Google Scholar 

  81. Abreu, C. I., Anderen Woltz, V. L., Friedman, J. & Gore, J. Microbial communities display alternative stable states in a fluctuating environment. PLoS Comput. Biol. 16, e1007934 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Xu, L., Xu, X., Kong, D., Gu, H. & Kenney T. Stochastic generalized Lotka–Volterra model with an application to learning microbial community structures. Preprint at arXiv https://doi.org/10.48550/arXiv.2009.10922 (2020).

  83. Brunner, J. D. & Chia, N. Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling. J. R. Soc. Interface 16, 20190423 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. MacArthur, R. Species packing and competitive equilibrium for many species. Theor. Popul. Biol. 1, 1–11 (1970).

    Article  CAS  PubMed  Google Scholar 

  85. Tilman, D. Resource competition and community structure. Monogr. Popul. Biol. 17, 1–296 (1982).

    CAS  PubMed  Google Scholar 

  86. Chesson, P. MacArthur’s consumer-resource model. Theor. Popul. Biol. 37, 26–38 (1990).

    Article  Google Scholar 

  87. Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Marsland, R.3rd et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Marsland, R.3rd, Cui, W. & Mehta, P. A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns. Sci. Rep. 10, 3308 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Estrela, S., Sanchez-Gorostiaga, A., Vila, J. C. & Sanchez, A. Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments. eLife 10, e65948 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Cui, W., Marsland, R. & Mehta, P. Diverse communities behave like typical random ecosystems. Phys. Rev. E 104, 034416 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Haygood, R. Coexistence in MacArthur-style consumer–resource models. Theor. Popul. Biol. 61, 215–223 (2002).

    Article  PubMed  Google Scholar 

  93. Dubinkina, V., Fridman, Y., Pandey, P. P. & Maslov, S. Multistability and regime shifts in microbial communities explained by competition for essential nutrients. eLife 8, e49720 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat. Commun. 12, 2365 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Crowther, T. W. et al. Untangling the fungal niche: the trait-based approach. Front. Microbiol. 5, 579 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Pacciani-Mori, L., Suweis, S., Maritan, A. & Giometto, A. Constrained proteome allocation affects coexistence in models of competitive microbial communities. ISME J. 15, 1458–1477 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Marsland, R. et al. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Obadia, B. et al. Probabilistic invasion underlies natural gut microbiome stability. Curr. Biol. 27, 1999–2006.e8 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. D’Andrea, R., Gibbs, T. & O’Dwyer, J. P. Emergent neutrality in consumer-resource dynamics. PLoS Comput. Biol. 16, e1008102 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Mancuso, C. P., Lee, H., Abreu, C. I., Gore, J. & Khalil, A. S. Environmental fluctuations reshape an unexpected diversity-disturbance relationship in a microbial community. eLife 10, e67175 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019).

    Article  CAS  PubMed  Google Scholar 

  103. Zakharova, L., Meyer, K. M. & Seifan, M. Trait-based modelling in ecology: a review of two decades of research. Ecol. Modell. 407, 108703 (2019).

    Article  Google Scholar 

  104. Merico, A., Brandt, G., Lan Smith, S. L. & Oliver, M. Sustaining diversity in trait-based models of phytoplankton communities. Front. Ecol. Evol. 2, 59 (2014).

  105. Grigoratou, M. et al. A trait-based modelling approach to planktonic foraminifera ecology. Biogeosciences 16, 1469–1492 (2019).

    Article  Google Scholar 

  106. Muscarella, M. E., Howey, X. M. & Lennon, J. T. Trait-based approach to bacterial growth efficiency. Environ. Microbiol. 22, 3494–3504 (2020).

    Article  CAS  PubMed  Google Scholar 

  107. Shao, P., Lynch, L., Xie, H., Bao, X. & Liang, C. Tradeoffs among microbial life history strategies influence the fate of microbial residues in subtropical forest soils. Soil Biol. Biochem. 153, 108112 (2021).

    Article  CAS  Google Scholar 

  108. Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).

    Article  CAS  PubMed  Google Scholar 

  109. Le Roux, X. et al. Predicting the responses of soil nitrite-oxidizers to multi-factorial global change: a trait-based approach. Front. Microbiol. 7, 628 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Bouskill, N. J., Tang, J., Riley, W. J. & Brodie, E. L. Trait-based representation of biological nitrification: model development, testing, and predicted community composition. Front. Microbiol. 3, 364 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Kyker-Snowman, E., Wieder, W. R., Frey, S. D. & Grandy, A. S. Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0). Geosci. Model Dev. 13, 4413–4434 (2020).

    Article  CAS  Google Scholar 

  112. Kruk, C. et al. A trait-based approach predicting community assembly and dominance of microbial invasive species. Oikos 130, 571–586 (2021).

    Article  Google Scholar 

  113. Litchman, E., Ohman, M. D. & Kiørboe, T. Trait-based approaches to zooplankton communities. J. Plankton Res. 35, 473–484 (2013).

    Article  Google Scholar 

  114. Garcia, C. A. et al. Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190254 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Moreno, A. R., Hagstrom, G. I., Primeau, F. W., Levin, S. A. & Martiny, A. C. Marine phytoplankton stoichiometry mediates nonlinear interactions between nutrient supply, temperature, and atmospheric CO2. Biogeosciences 15, 2761–2779 (2018).

    Article  CAS  Google Scholar 

  116. Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).

    Article  CAS  PubMed  Google Scholar 

  117. Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).

    Article  CAS  PubMed  Google Scholar 

  118. Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).

    Article  PubMed  Google Scholar 

  119. Bradford, M. A. et al. Quantifying microbial control of soil organic matter dynamics at macrosystem scales. Biogeochemistry 156, 19–40 (2021).

    Article  Google Scholar 

  120. Ward, B. A., Dutkiewicz, S., Moore, C. M. & Follows, M. J. Iron, phosphorus, and nitrogen supply ratios define the biogeography of nitrogen fixation. Limnol. Oceanogr. 58, 2059–2075 (2013).

    Article  CAS  Google Scholar 

  121. Zwart, J. A., Solomon, C. T. & Jones, S. E. Phytoplankton traits predict ecosystem function in a global set of lakes. Ecology 96, 2257–2264 (2015).

    Article  PubMed  Google Scholar 

  122. Nemergut, D. R., Shade, A. & Violle, C. When, where and how does microbial community composition matter. Front. Microbiol. 5, 497 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Severin, I., Östman, Ö. & Lindström, E. S. Variable effects of dispersal on productivity of bacterial communities due to changes in functional trait composition. PLoS ONE 8, e80825 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Staley, C. et al. Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover. Front. Microbiol. 5, 414 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Worden, L. Conservation of community functional structure across changes in composition in consumer-resource models. J. Theor. Biol. 493, 110239 (2020).

    Article  PubMed  Google Scholar 

  126. van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).

    Article  PubMed  Google Scholar 

  127. Song, H.-S. et al. Regulation-structured dynamic metabolic model provides a potential mechanism for delayed enzyme response in denitrification process. Front. Microbiol. 8, 1866 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Hemelrijk, C. K. & Hildenbrandt, H. Schools of fish and flocks of birds: their shape and internal structure by self-organization. Interface Focus 2, 726–737 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Hellweger, F. L., Clegg, R. J., Clark, J. R., Plugge, C. M. & Kreft, J.-U. Advancing microbial sciences by individual-based modelling. Nat. Rev. Microbiol. 14, 461–471 (2016).

    Article  CAS  PubMed  Google Scholar 

  130. Griesemer, M. & Sindi, S. S. Rules of engagement: a guide to developing agent-based models. Methods Mol. Biol. 2349, 367–380 (2022).

    Article  PubMed  Google Scholar 

  131. Jayathilake, P. G. et al. A mechanistic individual-based model of microbial communities. PLoS ONE 12, e0181965 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Clark, J. R., Daines, S. J., Lenton, T. M., Watson, A. J. & Williams, H. T. P. Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters. Ecol. Modell. 222, 3823–3837 (2011).

    Article  Google Scholar 

  133. Nadell, C. D. et al. Cutting through the complexity of cell collectives. Proc. Biol. Sci. 280, 20122770 (2013).

    PubMed  PubMed Central  Google Scholar 

  134. Allen, B., Gore, J. & Nowak, M. A. Spatial dilemmas of diffusible public goods. eLife 2, e01169 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Abs, E., Leman, H. & Ferrière, R. A multi-scale eco-evolutionary model of cooperation reveals how microbial adaptation influences soil decomposition. Commun. Biol. 3, 520 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Kreft, J.-U. et al. Mighty small: observing and modeling individual microbes becomes big science. Proc. Natl Acad. Sci. USA 110, 18027–18028 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Parise, F., Lygeros, J. & Ruess, J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Front. Environ. Sci. 3, https://doi.org/10.3389/fenvs.2015.00042 (2015).

  138. Allison, S. D. & Goulden, M. L. Consequences of drought tolerance traits for microbial decomposition in the DEMENT model. Soil Biol. Biochem. 107, 104–113 (2017).

    Article  CAS  Google Scholar 

  139. Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012).

    Article  CAS  PubMed  Google Scholar 

  140. Doloman, A., Varghese, H., Miller, C. D. & Flann, N. S. Modeling de novo granulation of anaerobic sludge. BMC Syst. Biol. 11, 69 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Gogulancea, V. et al. Individual based model links thermodynamics, chemical speciation and environmental conditions to microbial growth. Front. Microbiol. 10, 1871 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Gutierrez, M. & Rodriguez-Paton, A. Simulating multicell populations with an accelerated gro simulator. In Proc. ECAL 2017, Fourteenth European Conf. on Artificial Life, 186–188 (2017).

  143. Gutiérrez, M. et al. A new improved and extended version of the multicell bacterial simulator gro. ACS Synth. Biol. 6, 1496–1508 (2017).

    Article  PubMed  Google Scholar 

  144. Momeni, B., Waite, A. J. & Shou, W. Spatial self-organization favors heterotypic cooperation over cheating. eLife 2, e00960 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  145. Kreft, J.-U., Booth, G. & Wimpenny, J. W. T. BacSim, a simulator for individual-based modelling of bacterial colony growth. Microbiology 144, 3275–3287 (1998).

    Article  CAS  PubMed  Google Scholar 

  146. Picioreanu, C., Van Loosdrecht, M. C. & Heijnen, J. J. Effect of diffusive and convective substrate transport on biofilm structure formation: a two-dimensional modeling study. Biotechnol. Bioeng. 69, 504–515 (2000).

    Article  CAS  PubMed  Google Scholar 

  147. Lardon, L. A. et al. iDynoMiCS: next-generation individual-based modelling of biofilms. Environ. Microbiol. 13, 2416–2434 (2011).

    Article  CAS  PubMed  Google Scholar 

  148. Chacón, J. M., Möbius, W. & Harcombe, W. R. The spatial and metabolic basis of colony size variation. ISME J. 12, 669–680 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Oyebamiji, O. K. et al. Gaussian process emulation of an individual-based model simulation of microbial communities. J. Comput. Sci. 22, 69–84 (2017).

    Article  Google Scholar 

  150. Menon, R. & Korolev, K. S. Public good diffusion limits microbial mutualism. Phys. Rev. Lett. 114, 168102 (2015).

    Article  PubMed  Google Scholar 

  151. Dobay, A., Bagheri, H. C., Messina, A., Kümmerli, R. & Rankin, D. J. Interaction effects of cell diffusion, cell density and public goods properties on the evolution of cooperation in digital microbes. J. Evol. Biol. 27, 1869–1877 (2014).

    Article  CAS  PubMed  Google Scholar 

  152. Canzian, L., Zhao, K., Wong, G. C. L. & van der Schaar, M. A dynamic network formation model for understanding bacterial self-organization into micro-colonies. IEEE Trans. Mol. Biol. Multiscale Commun. 1, 76–89 (2015).

    Article  Google Scholar 

  153. Nadell, C. D., Foster, K. R. & Xavier, J. B. Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comput. Biol. 6, e1000716 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  154. Mendoza, S. N., Olivier, B. G., Molenaar, D. & Teusink, B. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol. 20, 158 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Feist, A. M. & Palsson, B. O. The biomass objective function. Curr. Opin. Microbiol. 13, 344–349 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol. 6, 196–208 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Dukovski, I. et al. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat. Protoc. 16, 5030–5082 (2021).

    Article  CAS  PubMed  Google Scholar 

  159. Varahan, S., Sinha, V., Walvekar, A., Krishna, S. & Laxman, S. Resource plasticity-driven carbon-nitrogen budgeting enables specialization and division of labor in a clonal community. eLife 9, e57609 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Angeles-Martinez, L. & Hatzimanikatis, V. Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities. PLoS Comput. Biol. 17, e1009140 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 22, 81 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Zorrilla, F., Buric, F., Patil, K. R. & Zelezniak, A. metaGEM: reconstruction of genome scale metabolic models directly from metagenomes. Nucleic Acids Res. 49, e126 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Ponomarova, O. et al. Yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).

    Article  CAS  PubMed  Google Scholar 

  165. Borer, B., Ataman, M., Hatzimanikatis, V. & Or, D. Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH). PLoS Comput. Biol. 15, e1007127 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Labhsetwar, P., Cole, J. A., Roberts, E., Price, N. D. & Luthey-Schulten, Z. A. Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc. Natl Acad. Sci. USA 110, 14006–14011 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405.e21 (2018).

    Article  CAS  PubMed  Google Scholar 

  169. Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  170. Hamilton, J. J. et al. Metabolic network analysis and metatranscriptomics reveal auxotrophies and nutrient sources of the cosmopolitan freshwater microbial lineage acI. mSystems 2, e00091-17 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).

    Article  CAS  PubMed  Google Scholar 

  172. IPCC. Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2020).

  173. Pacciani-Mori, L., Giometto, A., Suweis, S. & Maritan, A. Dynamic metabolic adaptation can promote species coexistence in competitive microbial communities. PLoS Comput. Biol. 16, e1007896 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 866028) and from the UK Medical Research Council (project no. MC_UU_00025/11). S.M. thanks the Swiss National Science Foundation for funding the Swiss National Centre of Competence in Research Microbiomes and an Eccellenza project and the ERC for starting grant no. 715097. W.H. received funding from the National Institutes of Health through grant no. R01-GM121498. S.S. received funding from the Portuguese Foundation for Science and Technology under the scope of a PhD grant (no. SFRH/BD/121695/2016) and the strategic funding of the UIDB/04469/2020 unit.

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Authors and Affiliations

  1. Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK

    Naomi Iris van den Berg & Kiran R. Patil

  2. Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway

    Daniel Machado

  3. Centre of Biological Engineering, University of Minho, Braga, Portugal

    Sophia Santos

  4. Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal

    Isabel Rocha

  5. Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA

    Jeremy Chacón & William Harcombe

  6. Département de Microbiologie Fondamentale, University of Lausanne, Lausanne, Switzerland

    Sara Mitri

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Contributions

N.I.V.D.B. conceived the review, carried out the literature survey and wrote the manuscript. D.M., S.S. and I.R. contributed to the section on the genome-scale metabolic models. J.C., W.H. and S.M. helped with the literature survey and contributed to the manuscript structuring and writing. K.R.P. conceived the review, helped with the literature survey and wrote the manuscript.

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van den Berg, N.I., Machado, D., Santos, S. et al. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat Ecol Evol 6, 855–865 (2022). https://doi.org/10.1038/s41559-022-01746-7

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