Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).
Google Scholar
Shi, Z. Gut microbiota: an important link between Western diet and chronic diseases. Nutrients 11, 2287 (2019).
Google Scholar
Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).
Google Scholar
Glowacki, R. W. P. & Martens, E. C. In sickness and health: effects of gut microbial metabolites on human physiology. PLoS Pathog. 16, e1008370 (2020).
Google Scholar
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).
Google Scholar
Konopka, A. What is microbial community ecology? ISME J. 3, 1223–1230 (2009).
Google Scholar
Flemming, H.-C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).
Google Scholar
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).
Google Scholar
Konstantinidis, D. et al. Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion. Mol. Syst. Biol. 17, e10189 (2021).
Google Scholar
Park, H. et al. Artificial consortium demonstrates emergent properties of enhanced cellulosic-sugar degradation and biofuel synthesis. NPJ Biofilms Microbiomes 6, 59 (2020).
Google Scholar
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).
Levins, R. & Lewontin, R. The Dialectical Biologist (Harvard Univ. Press, 1985).
Diaz, P. I. & Valm, A. M. Microbial interactions in oral communities mediate emergent biofilm properties. J. Dent. Res. 99, 18–25 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Falconer, R. E. et al. Microscale heterogeneity explains experimental variability and non-linearity in soil organic matter mineralisation. PLoS ONE 10, e0123774 (2015).
Google Scholar
Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).
Google Scholar
Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).
Google Scholar
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).
Google Scholar
Cariboni, J., Gatelli, D., Liska, R. & Saltelli, A. The role of sensitivity analysis in ecological modelling. Ecol. Modell. 203, 167–182 (2007).
Google Scholar
Oreskes, N., Shrader-Frechette, K. & Belitz, K. Verification, validation, and confirmation of numerical models in the Earth sciences. Science 263, 641–646 (1994).
Google Scholar
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).
Google Scholar
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).
Google Scholar
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).
Google Scholar
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).
Google Scholar
Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).
Google Scholar
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).
Google Scholar
O’Brien, E. J., Monk, J. M. & Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 161, 971–987 (2015).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).
Google Scholar
Li, J. et al. Distinct mechanisms shape soil bacterial and fungal co-occurrence networks in a mountain ecosystem. FEMS Microbiol. Ecol. 96, fiaa030 (2020).
Google Scholar
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).
Google Scholar
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).
Google Scholar
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).
Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).
Google Scholar
Madeo, D., Comolli, L. R. & Mocenni, C. Emergence of microbial networks as response to hostile environments. Front. Microbiol. 5, 407 (2014).
Google Scholar
Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).
Google Scholar
Wang, B. & Allison, S. D. Emergent properties of organic matter decomposition by soil enzymes. Soil Biol. Biochem. 136, 107522 (2019).
Google Scholar
Walsh, A. M. et al. Microbial succession and flavor production in the fermented dairy beverage kefir. mSystems 1, e00052-16 (2016).
Google Scholar
Oliveira, N. M., Niehus, R. & Foster, K. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941–17946 (2014).
Google Scholar
Leigh, E. R. in Some Mathematical Problems in Biology (ed. Gerstenhaber, M.) 1–61 (American Mathematical Society, 1968).
Nedorezov, L. The dynamics of the lynx–hare system: an application of the Lotka–Volterra model. Biophys. 61, 149–154 (2016).
Google Scholar
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).
Google Scholar
Belovsky, G. E. Moose and snowshoe hare competition and a mechanistic explanation from foraging theory. Oecologia 61, 150–159 (1984).
Google Scholar
May, R. M. Limit cycles in predator–prey communities. Science 177, 900–902 (1972).
Google Scholar
Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).
Google Scholar
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).
Bucci, V. & Xavier, J. B. Towards predictive models of the human gut microbiome. J. Mol. Biol. 426, 3907–3916 (2014).
Google Scholar
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).
Google Scholar
Bucci, V. et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).
Google Scholar
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).
Google Scholar
Li, C. et al. An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data. Microbiome 7, 118 (2019).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
White, J. R. Novel Methods for Metagenomic Analysis. PhD thesis, Univ. of Maryland (2010).
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).
Google Scholar
Mounier, J. et al. Microbial interactions within a cheese microbial community. Appl. Environ. Microbiol. 74, 172–181 (2008).
Google Scholar
Momeni, B., Xie, L. & Shou, W. Lotka–Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. eLife 6, e25051 (2017).
Google Scholar
Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).
Google Scholar
Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).
Google Scholar
Mai, T. S. N. Impact of Metabolic Plasticity on Microbial Community Diversity and Stability. MSc thesis, Univ. of Groningen (2021).
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Zomorrodi, A. R. & Segrè, D. Synthetic ecology of microbes: mathematical models and applications. J. Mol. Biol. 428, 837–861 (2016).
Google Scholar
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).
Google Scholar
Descheemaeker, L., Grilli, J. & de Buyl, S. Heavy-tailed abundance distributions from stochastic Lotka–Volterra models. Phys. Rev. E 104, 034404 (2021).
Google Scholar
Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285 (2016).
Google Scholar
Ji, B., Herrgård, M. J. & Nielsen, J. Microbial community dynamics revisited. Nat. Comput. Sci. 1, 640–641 (2021).
Google Scholar
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).
Google Scholar
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).
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).
Google Scholar
MacArthur, R. Species packing and competitive equilibrium for many species. Theor. Popul. Biol. 1, 1–11 (1970).
Google Scholar
Tilman, D. Resource competition and community structure. Monogr. Popul. Biol. 17, 1–296 (1982).
Google Scholar
Chesson, P. MacArthur’s consumer-resource model. Theor. Popul. Biol. 37, 26–38 (1990).
Google Scholar
Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).
Google Scholar
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).
Google Scholar
Marsland, R.3rd, Cui, W. & Mehta, P. A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns. Sci. Rep. 10, 3308 (2020).
Google Scholar
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).
Google Scholar
Cui, W., Marsland, R. & Mehta, P. Diverse communities behave like typical random ecosystems. Phys. Rev. E 104, 034416 (2021).
Google Scholar
Haygood, R. Coexistence in MacArthur-style consumer–resource models. Theor. Popul. Biol. 61, 215–223 (2002).
Google Scholar
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).
Google Scholar
Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat. Commun. 12, 2365 (2021).
Google Scholar
Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).
Google Scholar
Crowther, T. W. et al. Untangling the fungal niche: the trait-based approach. Front. Microbiol. 5, 579 (2014).
Google Scholar
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).
Google Scholar
Marsland, R. et al. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).
Google Scholar
Obadia, B. et al. Probabilistic invasion underlies natural gut microbiome stability. Curr. Biol. 27, 1999–2006.e8 (2017).
Google Scholar
D’Andrea, R., Gibbs, T. & O’Dwyer, J. P. Emergent neutrality in consumer-resource dynamics. PLoS Comput. Biol. 16, e1008102 (2020).
Google Scholar
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).
Google Scholar
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019).
Google Scholar
Zakharova, L., Meyer, K. M. & Seifan, M. Trait-based modelling in ecology: a review of two decades of research. Ecol. Modell. 407, 108703 (2019).
Google Scholar
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).
Grigoratou, M. et al. A trait-based modelling approach to planktonic foraminifera ecology. Biogeosciences 16, 1469–1492 (2019).
Google Scholar
Muscarella, M. E., Howey, X. M. & Lennon, J. T. Trait-based approach to bacterial growth efficiency. Environ. Microbiol. 22, 3494–3504 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Kruk, C. et al. A trait-based approach predicting community assembly and dominance of microbial invasive species. Oikos 130, 571–586 (2021).
Google Scholar
Litchman, E., Ohman, M. D. & Kiørboe, T. Trait-based approaches to zooplankton communities. J. Plankton Res. 35, 473–484 (2013).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).
Google Scholar
Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).
Google Scholar
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).
Google Scholar
Bradford, M. A. et al. Quantifying microbial control of soil organic matter dynamics at macrosystem scales. Biogeochemistry 156, 19–40 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Nemergut, D. R., Shade, A. & Violle, C. When, where and how does microbial community composition matter. Front. Microbiol. 5, 497 (2014).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Worden, L. Conservation of community functional structure across changes in composition in consumer-resource models. J. Theor. Biol. 493, 110239 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Griesemer, M. & Sindi, S. S. Rules of engagement: a guide to developing agent-based models. Methods Mol. Biol. 2349, 367–380 (2022).
Google Scholar
Jayathilake, P. G. et al. A mechanistic individual-based model of microbial communities. PLoS ONE 12, e0181965 (2017).
Google Scholar
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).
Google Scholar
Nadell, C. D. et al. Cutting through the complexity of cell collectives. Proc. Biol. Sci. 280, 20122770 (2013).
Google Scholar
Allen, B., Gore, J. & Nowak, M. A. Spatial dilemmas of diffusible public goods. eLife 2, e01169 (2013).
Google Scholar
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).
Google Scholar
Kreft, J.-U. et al. Mighty small: observing and modeling individual microbes becomes big science. Proc. Natl Acad. Sci. USA 110, 18027–18028 (2013).
Google Scholar
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).
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).
Google Scholar
Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012).
Google Scholar
Doloman, A., Varghese, H., Miller, C. D. & Flann, N. S. Modeling de novo granulation of anaerobic sludge. BMC Syst. Biol. 11, 69 (2017).
Google Scholar
Gogulancea, V. et al. Individual based model links thermodynamics, chemical speciation and environmental conditions to microbial growth. Front. Microbiol. 10, 1871 (2019).
Google Scholar
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).
Gutiérrez, M. et al. A new improved and extended version of the multicell bacterial simulator gro. ACS Synth. Biol. 6, 1496–1508 (2017).
Google Scholar
Momeni, B., Waite, A. J. & Shou, W. Spatial self-organization favors heterotypic cooperation over cheating. eLife 2, e00960 (2013).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Lardon, L. A. et al. iDynoMiCS: next-generation individual-based modelling of biofilms. Environ. Microbiol. 13, 2416–2434 (2011).
Google Scholar
Chacón, J. M., Möbius, W. & Harcombe, W. R. The spatial and metabolic basis of colony size variation. ISME J. 12, 669–680 (2018).
Google Scholar
Oyebamiji, O. K. et al. Gaussian process emulation of an individual-based model simulation of microbial communities. J. Comput. Sci. 22, 69–84 (2017).
Google Scholar
Menon, R. & Korolev, K. S. Public good diffusion limits microbial mutualism. Phys. Rev. Lett. 114, 168102 (2015).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).
Google Scholar
Feist, A. M. & Palsson, B. O. The biomass objective function. Curr. Opin. Microbiol. 13, 344–349 (2010).
Google Scholar
Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol. 6, 196–208 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Angeles-Martinez, L. & Hatzimanikatis, V. Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities. PLoS Comput. Biol. 17, e1009140 (2021).
Google Scholar
Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 22, 81 (2021).
Google Scholar
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).
Google Scholar
Ponomarova, O. et al. Yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357.e6 (2017).
Google Scholar
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).
Google Scholar
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).
Google Scholar
Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).
Google Scholar
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).
Google Scholar
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).
Google Scholar
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).
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).
Google Scholar
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