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.

  • Perspective
  • Published:

Quantitative principles of microbial metabolism shared across scales

Nature Microbiology volume 9, pages 1940–1953 (2024)Cite this article

Subjects

Abstract

Metabolism is the complex network of chemical reactions occurring within every cell and organism, maintaining life, mediating ecosystem processes and affecting Earth’s climate. Experiments and models of microbial metabolism often focus on one specific scale, overlooking the connectivity between molecules, cells and ecosystems. Here we highlight quantitative metabolic principles that exhibit commonalities across scales, which we argue could help to achieve an integrated perspective on microbial life. Mass, electron and energy balance provide quantitative constraints on their flow within metabolic networks, organisms and ecosystems, shaping how each responds to its environment. The mechanisms underlying these flows, such as enzyme–substrate interactions, often involve encounter and handling stages that are represented by equations similar to those for cells and resources, or predators and prey. We propose that these formal similarities reflect shared principles and discuss how their investigation through experiments and models may contribute to a common language for studying microbial metabolism across scales.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: A cross-scale perspective of marine microbial systems.
Fig. 2: Mass balance imposes constraints on fluxes.
Fig. 3: Parallel metabolic organization of a schematic cell and ecosystem.
Fig. 4: Encounter and handling processes and their representation in mathematical models.
Fig. 5: Examples of using fluxes and kinetics to connect models at different scales.

Similar content being viewed by others

References

  1. Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    Article  CAS  PubMed  Google Scholar 

  3. López-Urrutia, Á., San Martin, E., Harris, R. P. & Irigoien, X. Scaling the metabolic balance of the oceans. Proc. Natl Acad. Sci. USA 103, 8739–8744 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. Smith, E. & Morowitz, H. J. Universality in intermediary metabolism. Proc. Natl Acad. Sci. USA 101, 13168–13173 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).

    Article  CAS  PubMed  Google Scholar 

  7. Lotka, A. J. Elements of Physical Biology (Williams & Wilkins, 1925).

  8. Averill, C. et al. Defending Earth’s terrestrial microbiome. Nat. Microbiol. 7, 1717–1725 (2022).

    Article  CAS  PubMed  Google Scholar 

  9. Sánchez-Baracaldo, P., Bianchini, G., Wilson, J. D. & Knoll, A. H. Cyanobacteria and biogeochemical cycles through Earth history. Trends Microbiol. 30, 143–157 (2022).

    Article  PubMed  Google Scholar 

  10. Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kleiber, M. Body size and metabolism. Hilgardia 6, 315–353 (1932).

    Article  CAS  Google Scholar 

  13. Riebesell, U., Körtzinger, A. & Oschlies, A. Sensitivities of marine carbon fluxes to ocean change. Proc. Natl Acad. Sci. USA 106, 20602–20609 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Knoop, H. et al. Flux balance analysis of cyanobacterial metabolism: the metabolic network of Synechocystis sp. PCC 6803. PLoS Comput. Biol. 9, e1003081 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Van de Waal, D. B. et al. Reversal in competitive dominance of a toxic versus non-toxic cyanobacterium in response to rising CO2. ISME J. 5, 1438–1450 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Riley, G. A. Factors controlling phytoplankton populations on Georges Bank. J. Mar. Res. 6, 54–73 (1946).

    Google Scholar 

  17. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  18. Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).

    Article  CAS  Google Scholar 

  19. Brown, J. H. & Maurer, B. A. Macroecology: the division of food and space among species on continents. Science 243, 1145–1150 (1989).

    Article  CAS  PubMed  Google Scholar 

  20. Hoffman, B. M., Lukoyanov, D., Yang, Z.-Y., Dean, D. R. & Seefeldt, L. C. Mechanism of nitrogen fixation by nitrogenase: the next stage. Chem. Rev. 114, 4041–4062 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zehr, J. P. & Kudela, R. M. Nitrogen cycle of the open ocean: from genes to ecosystems. Ann. Rev. Mar. Sci. 3, 197–225 (2011).

    Article  PubMed  Google Scholar 

  22. Ewald, J. et al. Trends in mathematical modeling of host–pathogen interactions. Cell. Mol. Life Sci. 77, 467–480 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Handel, A., La Gruta, N. L. & Thomas, P. G. Simulation modelling for immunologists. Nat. Rev. Immunol. 20, 186–195 (2020).

    Article  CAS  PubMed  Google Scholar 

  24. Heinken, A., Basile, A., Hertel, J., Thinnes, C. & Thiele, I. Genome-scale metabolic modeling of the human microbiome in the era of personalized medicine. Ann. Rev. Microbiol. 75, 199–222 (2021).

    Article  CAS  Google Scholar 

  25. Silva, L. C. R. & Lambers, H. Soil–plant–atmosphere interactions: structure, function, and predictive scaling for climate change mitigation. Plant Soil 461, 5–27 (2021).

    Article  CAS  Google Scholar 

  26. Kumar, M., Ji, B., Zengler, K. & Nielsen, J. Modelling approaches for studying the microbiome. Nat. Microbiol. 4, 1253–1267 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Maarleveld, T. R., Khandelwal, R. A., Olivier, B. G., Teusink, B. & Bruggeman, F. J. Basic concepts and principles of stoichiometric modeling of metabolic networks. Biotechnol. J. 8, 997–1008 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton Univ. Press, 2002).

  29. Welti, N. et al. Bridging food webs, ecosystem metabolism, and biogeochemistry using ecological stoichiometry theory. Front. Microbiol. 8, 1298 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Redfield, A. C. in James Johnstone Memorial 176–192 (Univ. Press of Liverpool, 1934).

  31. Rittman, B. E. & McCarty, P. Environmental Biotechnology: Principles and Applications (McGraw Hill, 2001).

  32. Vallino, J. J., Hopkinson, C. S. & Hobbie, J. E. Modeling bacterial utilization of dissolved organic matter: optimization replaces Monod growth kinetics. Limnol. Oceanogr. 41, 1591–1609 (1996).

    Article  CAS  Google Scholar 

  33. Geider, R. J. & La Roche, J. Redfield revisited: variability of C:N:P in marine microalgae and its biochemical basis. Eur. J. Phycol. 37, 1–17 (2002).

    Article  Google Scholar 

  34. Fell, D. Understanding the Control of Metabolism (Portland Press, 1997).

  35. 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 

  36. Heinrich, R. & Schuster, S. The Regulation of Cellular Systems (Springer, 1996).

  37. 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).

    Article  CAS  PubMed  Google Scholar 

  38. Holzhütter, H.-G. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem. 271, 2905–2922 (2004).

    Article  PubMed  Google Scholar 

  39. Schuetz, R., Kuepfer, L. & Sauer, U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol. 3, 119 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Machado, D., Tramontano, M., Andrejev, S. & 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 

  41. Bernstein, D. B., Sulheim, S., Almaas, E. & Segrè, D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol. 22, 64 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Seaver, S. M. D. et al. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res. 49, D575–D588 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Simensen, V. et al. Experimental determination of Escherichia coli biomass composition for constraint-based metabolic modeling. PLoS ONE 17, e0262450 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Thermodynamics-based metabolic flux analysis. Biophys. J. 92, 1792–1805 (2007).

    Article  CAS  PubMed  Google Scholar 

  46. Beard, D. A., Liang, S.-d & Qian, H. Energy balance for analysis of complex metabolic networks. Biophys. J. 83, 79–86 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zehr, J. P. & Capone, D. G. Changing perspectives in marine nitrogen fixation. Science 368, eaay9514 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Tilman, D. Resource Competition and Community Structure (MPB-17) Vol. 17 (Princeton Univ. Press, 1982).

  49. Sarkar, D. et al. Elucidation of trophic interactions in an unusual single-cell nitrogen-fixing symbiosis using metabolic modeling. PLoS Comput. Biol. 17, e1008983 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Schindler, D. W. et al. Eutrophication of lakes cannot be controlled by reducing nitrogen input: results of a 37-year whole-ecosystem experiment. Proc. Natl Acad. Sci. USA 105, 11254–11258 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Inomura, K., Bragg, J. & Follows, M. J. A quantitative analysis of the direct and indirect costs of nitrogen fixation: a model based on Azotobacter vinelandii. ISME J. 11, 166–175 (2017).

    Article  CAS  PubMed  Google Scholar 

  52. Resendis-Antonio, O., Reed, J. L., Encarnación, S., Collado-Vides, J. & Palsson, B. Ø.Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli. PLoS Comput. Biol. 3, e192 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Rhee, G.-Y. & Gotham, I. J. The effect of environmental factors on phytoplankton growth: temperature and the interactions of temperature with nutrient limitation. Limnol. Oceanogr. 26, 635–648 (1981).

    Article  CAS  Google Scholar 

  54. Finkel, Z. V. et al. Phylogenetic diversity in the macromolecular composition of microalgae. PLoS ONE 11, e0155977 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Phillips, K. N., Godwin, C. M. & Cotner, J. B. The effects of nutrient imbalances and temperature on the biomass stoichiometry of freshwater bacteria. Front. Microbiol. 8, 1692 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Harcombe, W. R. et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 7, 1104–1115 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  58. Mahadevan, R., Edwards, J. S. & Doyle, F. J. 3rd Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys. J. 83, 1331–1340 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Alon, U. An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman and Hall/CRC, 2006).

  60. Segel, L. A. & Slemrod, M. The quasi-steady-state assumption: a case study in perturbation. SIAM Rev. 31, 446–477 (1989).

    Article  Google Scholar 

  61. 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  PubMed Central  Google Scholar 

  62. Salimi, F., Zhuang, K. & Mahadevan, R. Genome-scale metabolic modeling of a clostridial co-culture for consolidated bioprocessing. Biotechnol. J. 5, 726–738 (2010).

    Article  CAS  PubMed  Google Scholar 

  63. Casey, J. R. et al. Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology. Sci. Adv. 8, eabl4930 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Régimbeau, A. et al. Contribution of genome-scale metabolic modelling to niche theory. Ecol. Lett. 25, 1352–1364 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Régimbeau, A. et al. Towards modeling genome-scale knowledge in the global ocean. Preprint at bioRxiv https://doi.org/10.1101/2023.11.23.568447 (2023).

  66. Kiørboe, T. A Mechanistic Approach to Plankton Ecology (Princeton Univ. Press, 2008).

  67. Michaelis, L. & Menten, M. I. Die kinetik der invirtinwirkung. Biochem. Z. 49, 333–369 (1913).

    CAS  Google Scholar 

  68. Monod, J. The growth of bacterial cultures. Ann. Rev. Microbiol. 3, 371–394 (1949).

    Article  CAS  Google Scholar 

  69. Holling, C. S. Some characteristics of simple types of predation and parasitism. Can. Entomol. 91, 385–398 (1959).

    Article  Google Scholar 

  70. Real, L. A. The kinetics of functional response. Am. Nat. 111, 289–300 (1977).

    Article  Google Scholar 

  71. McNickle, G. G. & Brown, J. S. When Michaelis and Menten met Holling: towards a mechanistic theory of plant nutrient foraging behaviour. AoB Plants 6, plu066 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Gavis, J. Munk and Riley revisited: nutrient diffusion transport and rates of phytoplankton growth. J. Mar. Res. 34, 161–179 (1976).

    Google Scholar 

  73. Cornish-Bowden, A. Fundamentals of Enzyme Kinetics 4th edn (Wiley-Blackwell, 2012).

  74. Han, B.-P. Photosynthesis–irradiance response at physiological level: a mechanistic model. J. Theor. Biol. 213, 121–127 (2001).

    Article  CAS  PubMed  Google Scholar 

  75. Liu, Y. Overview of some theoretical approaches for derivation of the Monod equation. Appl. Microbiol. Biotechnol. 73, 1241–1250 (2007).

    Article  CAS  PubMed  Google Scholar 

  76. Stefan, M. I. & Le Novère, N. Cooperative binding. PLoS Comput. Biol. 9, e1003106 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Weiss, J. N. The Hill equation revisited: uses and misuses. FASEB J. 11, 835–841 (1997).

    Article  CAS  PubMed  Google Scholar 

  78. Hill, A. V. The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. J. Physiol. 40, iv–vii (1910).

    Google Scholar 

  79. Holling, C. S. The functional response of invertebrate predators to prey density. Mem. Entomol. Soc. Can. 98, 5–86 (2012).

    Article  Google Scholar 

  80. Kalinkat, G., Rall, B. C., Uiterwaal, S. F. & Uszko, W. Empirical evidence of type III functional responses and why it remains rare. Front. Ecol. Evol. 11, 1033818 (2023).

    Article  Google Scholar 

  81. Armstrong McKay, D. I., Cornell, S. E., Richardson, K. & Rockström, J. Resolving ecological feedbacks on the ocean carbon sink in Earth system models. Earth Syst. Dyn. 12, 797–818 (2021).

    Article  Google Scholar 

  82. Moore, J. K., Doney, S. C. & Lindsay, K.Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model. Glob. Biogeochem. Cycle 18, GB4028 (2004).

    Article  Google Scholar 

  83. Wu, Z. et al. Modeling photosynthesis and exudation in subtropical oceans. Glob. Biogeochem. Cycle 35, e2021GB006941 (2021).

    Article  CAS  Google Scholar 

  84. Geider, R. J., MacIntyre, H. L. & Kana, T. M. Dynamic model of phytoplankton growth and acclimation: responses of the balanced growth rate and the chlorophyll a:carbon ratio to light, nutrient-limitation and temperature. Mar. Ecol. Prog. Ser. 148, 187–200 (1997).

    Article  Google Scholar 

  85. Lin, H. et al. The fate of photons absorbed by phytoplankton in the global ocean. Science 351, 264–267 (2016).

    Article  CAS  PubMed  Google Scholar 

  86. Westermark, S. & Steuer, R. Toward multiscale models of cyanobacterial growth: a modular approach. Front. Bioeng. Biotechnol. 4, 95 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Bruggeman, J. & Bolding, K. A general framework for aquatic biogeochemical models. Environ. Model. Softw. 61, 249–265 (2014).

    Article  Google Scholar 

  88. Agmon, E. et al. Vivarium: an interface and engine for integrative multiscale modeling in computational biology. Bioinformatics 38, 1972–1979 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Zavřel, T. et al. Quantitative insights into the cyanobacterial cell economy. eLife 8, e42508 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Machado, D. & Herrgård, M. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput. Biol. 10, e1003580 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Buccitelli, C. & Selbach, M. mRNAs, proteins and the emerging principles of gene expression control. Nat. Rev. Genet. 21, 630–644 (2020).

    Article  CAS  PubMed  Google Scholar 

  92. Reznik, E. et al. Genome-scale architecture of small molecule regulatory networks and the fundamental trade-off between regulation and enzymatic activity. Cell Rep. 20, 2666–2677 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Kümmel, A., Panke, S. & Heinemann, M. Putative regulatory sites unraveled by network‐embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2, 2006.0034 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Domenzain, I. et al. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat. Commun. 13, 3766 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Meiler, S. et al. Constraining uncertainties of diazotroph biogeography from nifH gene abundance. Limnol. Oceanogr. 67, 816–829 (2022).

    Article  Google Scholar 

  96. Zehr, J. P. & Riemann, L. Quantification of gene copy numbers is valuable in marine microbial ecology: a comment to Meiler et al. (2022). Limnol. Oceanogr. 68, 1406–1412 (2023).

    Article  Google Scholar 

  97. Meiler, S., Britten, G. L., Dutkiewicz, S., Moisander, P. H. & Follows, M. J. Challenges and opportunities in connecting gene count observations with ocean biogeochemical models: reply to Zehr and Riemann (2023). Limnol. Oceanogr. 68, 1413–1416 (2023).

    Article  Google Scholar 

  98. Segre, D., Vitkup, D. & Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl Acad. Sci. USA 99, 15112–15117 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Ibarra, R. U., Edwards, J. S. & Palsson, B. O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002).

    Article  CAS  PubMed  Google Scholar 

  100. Zhao, Q., Stettner, A. I., Reznik, E., Paschalidis, I. C. & Segrè, D. Mapping the landscape of metabolic goals of a cell. Genome Biol. 17, 109 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Harcombe, W. R., Delaney, N. F., Leiby, N., Klitgord, N. & Marx, C. J. The ability of flux balance analysis to predict evolution of central metabolism scales with the initial distance to the optimum. PLoS Comput. Biol. 9, e1003091 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Boon, E. et al. Interactions in the microbiome: communities of organisms and communities of genes. FEMS Microbiol. Rev. 38, 90–118 (2014).

    Article  CAS  PubMed  Google Scholar 

  103. Morris, J. J., Lenski, R. E. & Zinser, E. R.The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio 3, e00036-12 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  104. 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 

  105. Zomorrodi, A. R. & Segrè, D. Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities. Nat. Commun. 8, 1563 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Kleidon, A., Malhi, Y. & Cox, P. M. Maximum entropy production in environmental and ecological systems. Phil. Trans. R. Soc. B Biol. Sci. 365, 1297–1302 (2010).

    Article  Google Scholar 

  107. Vallino, J. J. Ecosystem biogeochemistry considered as a distributed metabolic network ordered by maximum entropy production. Phil. Trans. R. Soc. B Biol. Sci. 365, 1417–1427 (2010).

    Article  CAS  Google Scholar 

  108. Goldenfeld, N. & Woese, C. Life is physics: evolution as a collective phenomenon far from equilibrium. Annu. Rev. Condens. Matter Phys. 2, 375–399 (2011).

    Article  CAS  Google Scholar 

  109. Barve, A. & Wagner, A. A latent capacity for evolutionary innovation through exaptation in metabolic systems. Nature 500, 203–206 (2013).

    Article  CAS  PubMed  Google Scholar 

  110. Yizhak, K., Tuller, T., Papp, B. & Ruppin, E. Metabolic modeling of endosymbiont genome reduction on a temporal scale. Mol. Syst. Biol. 7, 479 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Handorf, T., Ebenhöh, O. & Heinrich, R. Expanding metabolic networks: scopes of compounds, robustness, and evolution. J. Mol. Evol. 61, 498–512 (2005).

    Article  CAS  PubMed  Google Scholar 

  112. Raymond, J. & Segre, D. The effect of oxygen on biochemical networks and the evolution of complex life. Science 311, 1764–1767 (2006).

    Article  CAS  PubMed  Google Scholar 

  113. Goldford, J. E., Hartman, H., Smith, T. F. & Segrè, D. Remnants of an ancient metabolism without phosphate. Cell 168, 1126–1134.e9 (2017).

    Article  CAS  PubMed  Google Scholar 

  114. Goldford, J. E., Hartman, H., Marsland, R. & Segrè, D. Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism. Nat. Ecol. Evol. 3, 1715–1724 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Xavier, J. C. et al. The metabolic network of the last bacterial common ancestor. Commun. Biol. 4, 413 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Chu, X. Y. et al. Plausibility of early life in a relatively wide temperature range: clues from simulated metabolic network expansion.Life (Basel) 11, 738 (2021).

    PubMed  Google Scholar 

  117. Kim, H., Smith, H. B., Mathis, C., Raymond, J. & Walker, S. I. Universal scaling across biochemical networks on Earth. Sci. Adv. 5, eaau0149 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  118. Zoccarato, L., Sher, D., Miki, T., Segrè, D. & Grossart, H.-P. A comparative whole-genome approach identifies bacterial traits for marine microbial interactions. Commun. Biol. 5, 276 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. De Vargas Roditi, L., Boyle, K. E. & Xavier, J. B. Multilevel selection analysis of a microbial social trait. Mol. Syst. Biol. 9, 684 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Damore, J. A. & Gore, J. Understanding microbial cooperation. J. Theor. Biol. 299, 31–41 (2012).

    Article  PubMed  Google Scholar 

  121. Harrington, K. I. & Sanchez, A. Eco-evolutionary dynamics of complex social strategies in microbial communities. Commun. Integr. Biol. 7, e28230 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Dundore-Arias, J. P., Michalska-Smith, M., Millican, M. & Kinkel, L. L. More than the sum of its parts: unlocking the power of network structure for understanding organization and function in microbiomes. Annu. Rev. Phytopathol. 61, 403–423 (2023).

    Article  CAS  PubMed  Google Scholar 

  123. West, G. B., Woodruff, W. H. & Brown, J. H. Allometric scaling of metabolic rate from molecules and mitochondria to cells and mammals. Proc. Natl Acad. Sci. USA 99, 2473–2478 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Enquist, B. J. et al. Scaling metabolism from organisms to ecosystems. Nature 423, 639–642 (2003).

    Article  CAS  PubMed  Google Scholar 

  125. Gardner, J. J. & Boyle, N. R.The use of genome-scale metabolic network reconstruction to predict fluxes and equilibrium composition of N-fixing versus C-fixing cells in a diazotrophic cyanobacterium, Trichodesmium erythraeum. BMC Syst. Biol. 11, 4 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Epstein, J. M. Why model? J. Artif. Soc. Soc. Simul. 11, 12 (2008).

    Google Scholar 

  127. Liefer, J. D. et al. The macromolecular basis of phytoplankton C:N:P under nitrogen starvation. Front. Microbiol. 10, 763 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Zaguri, M., Kandel, S., Rinehart, S. A., Torsekar, V. R. & Hawlena, D. Protein quantification in ecological studies: a literature review and empirical comparisons of standard methodologies. Methods Ecol. Evol. 12, 1240–1251 (2021).

    Article  Google Scholar 

  129. Omta, A. W. et al. Quantifying nutrient throughput and DOM production by algae in continuous culture. J. Theor. Biol. 494, 110214 (2020).

    Article  CAS  PubMed  Google Scholar 

  130. Çakır, T. et al. Flux balance analysis of a genome-scale yeast model constrained by exometabolomic data allows metabolic system identification of genetically different strains. Biotechnol. Progr. 23, 320–326 (2007).

    Article  Google Scholar 

  131. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Tilman, D. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58, 338–348 (1977).

    Article  CAS  Google Scholar 

  133. Fiksen, O., Follows, M. & Aksnes, D. L. Trait based models of nutrient uptake in microbes extend the michaelis menten framework. Limnol. Oceanogr. 58, 193–202 (2013).

    Article  Google Scholar 

  134. Grossowicz, M. et al. Prochlorococcus in the lab and in silico: the importance of representing exudation. Limnol. Oceanogr. 62, 818–835 (2017).

    Article  Google Scholar 

  135. Omta, A. W. et al. Extracting phytoplankton physiological traits from batch and chemostat culture data. Limnol. Oceanogr. Methods 15, 453–466 (2017).

    Article  Google Scholar 

Download references

Acknowledgements

We thank R. Braakman, J. Casey, S. Ben Tabou de Leon and O. Weissberg for critical reading of the manuscript. The joint work of the research groups of D. Sher, D. Segrè and M.J.F. was supported by the Gordon and Betty Moore Foundation (grant number GBMF #3778 to M.J.F.), Human Frontier Science Program (grant number RGP0020/2016 to D. Sher and D. Segrè), United States–Israel Binational Science Foundation (grant number 2010183 to D. Sher and M.J.F.), Israel Science Foundation (grant number 1786/20 to D. Sher) and National Science Foundation/United States–Israel Binational Science Foundation (NSFOCE-BSF 1635070 and NSF-BSF 2246707 to D. Segrè and D. Sher). M.J.F. is also grateful for support from the Simons Foundation (CBIOMES; grant number 549931 to M.J.F.). D. Segrè was also supported by the National Science Foundation Center for Chemical Currencies of a Microbial Planet (C-CoMP publication #047), National Institutes of Health (National Institute on Aging (award number UH2AG064704) and National Cancer Institute (grant number R21CA279630)) and US Department of Energy, Office of Science, Office of Biological and Environmental Research through the Microbial Community Analysis and Functional Evaluation in Soils (m-CAFEs) Science Focus Area Program under contract number DE-AC02-05CH11231 to the Lawrence Berkeley National Laboratory.

Author information

Authors and Affiliations

  1. Department of Marine Biology, Leon H. Charney School of Marine Sciences, University of Haifa, Haifa, Israel

    Daniel Sher

  2. Department of Biology, Boston University, Boston, MA, USA

    Daniel Segrè

  3. Biological Design Center, Boston University, Boston, MA, USA

    Daniel Segrè

  4. Bioinformatics Program, Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA

    Daniel Segrè

  5. Department of Physics, Boston University, Boston, MA, USA

    Daniel Segrè

  6. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Daniel Segrè

  7. Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael J. Follows

Authors
  1. Daniel Sher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Segrè
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael J. Follows
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All of the authors contributed equally to all aspects of this article.

Corresponding authors

Correspondence to Daniel Sher, Daniel Segrè or Michael J. Follows.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks T. Thingstad and Ralf Steuer for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sher, D., Segrè, D. & Follows, M.J. Quantitative principles of microbial metabolism shared across scales. Nat Microbiol 9, 1940–1953 (2024). https://doi.org/10.1038/s41564-024-01764-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-024-01764-0

Search

Advanced 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