Cho, I. & Blaser, M. J. The human microbiome: on the interface of well being and illness. Nat. Rev. Genet. 13, 260–270 (2012).
Shi, Z. Intestine microbiota: an vital hyperlink between Western weight-reduction plan and power illnesses. Vitamins 11, 2287 (2019).
Lynch, S. V. & Pedersen, O. The human intestinal microbiome in well being and illness. N. Engl. J. Med. 375, 2369–2379 (2016).
Glowacki, R. W. P. & Martens, E. C. In illness and well being: results of intestine microbial metabolites on human physiology. PLoS Pathog. 16, e1008370 (2020).
Nazaries, L. et al. Proof of microbial regulation of biogeochemical cycles from a research on methane flux and land use change. Appl. Environ. Microbiol. 79, 4031–4040 (2013).
Konopka, A. What’s microbial group ecology? ISME J. 3, 1223–1230 (2009).
Flemming, H.-C. et al. Biofilms: an emergent type of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).
Bauer, E., Zimmermann, J., Baldini, F., Thiele, I. & Kaleta, C. BacArena: individual-based metabolic modeling of heterogeneous microbes in advanced communities. PLoS Comput. Biol. 13, e1005544 (2017).
van Hoek, M. J. A. & Merks, R. M. H. Emergence of microbial range because of cross-feeding interactions in a spatial mannequin of intestine microbial metabolism. BMC Syst. Biol. 11, 56 (2017).
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).
Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).
Chen, J., Yoshinaga, M. & Rosen, B. P. The antibiotic motion of methylarsenite is an emergent property of microbial communities. Mol. Microbiol. 111, 487–494 (2019).
Konstantinidis, D. et al. Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion. Mol. Syst. Biol. 17, e10189 (2021).
Park, H. et al. Synthetic consortium demonstrates emergent properties of enhanced cellulosic-sugar degradation and biofuel synthesis. NPJ Biofilms Microbiomes 6, 59 (2020).
Schwartzman, J. A. et al. Bacterial progress in multicellular aggregates results in the emergence of advanced 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).
Buerger, A. N. et al. Gastrointestinal dysbiosis following diethylhexyl phthalate publicity in zebrafish (Danio rerio): altered microbial range, performance, and community connectivity. Environ. Pollut. 265, 114496 (2020).
Kim, M. Okay., Ingremeau, F., Zhao, A., Bassler, B. L. & Stone, H. A. Native and international penalties of move on bacterial quorum sensing. Nat. Microbiol. 1, 15005 (2016).
Ebrahimi, A. & Or, D. Hydration and diffusion processes form microbial group group and performance in mannequin soil aggregates. Water Resour. Res. 51, 9804–9827 (2015).
Falconer, R. E. et al. Microscale heterogeneity explains experimental variability and non-linearity in soil natural matter mineralisation. PLoS ONE 10, e0123774 (2015).
Fredrickson, J. Okay. Ecological communities by design. Science 348, 1425–1427 (2015).
Singer, E. et al. Subsequent technology sequencing information of an outlined microbial mock group. Sci. Knowledge 3, 160081 (2016).
Marino, S., Baxter, N. T., Huffnagle, G. B., Petrosino, J. F. & Schloss, P. D. Mathematical modeling of main succession of murine intestinal microbiota. Proc. Natl Acad. Sci. USA 111, 439–444 (2014).
Cariboni, J., Gatelli, D., Liska, R. & Saltelli, A. The position of sensitivity evaluation in ecological modelling. Ecol. Modell. 203, 167–182 (2007).
Oreskes, N., Shrader-Frechette, Okay. & Belitz, Okay. Verification, validation, and affirmation of numerical fashions within the Earth sciences. Science 263, 641–646 (1994).
Machado, D., Andrejev, S., Tramontano, M. & Patil, Okay. R. Quick automated reconstruction of genome-scale metabolic fashions for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).
Hammarlund, S. P., Chacón, J. M. & Harcombe, W. R. A shared limiting useful resource results in aggressive exclusion in a cross-feeding system. Environ. Microbiol. 21, 759–771 (2019).
Coyte, Okay. Z., Schluter, J. & Foster, Okay. R. The ecology of the microbiome: networks, competitors, and stability. Science 350, 663–666 (2015).
Machado, D. et al. Polarization of microbial communities between aggressive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Present standing and purposes of genome-scale metabolic fashions. Genome Biol. 20, 121 (2019).
O’Brien, E. J., Monk, J. M. & Palsson, B. O. Utilizing genome-scale fashions to foretell organic capabilities. Cell 161, 971–987 (2015).
Fang, X., Lloyd, C. J. & Palsson, B. O. Reconstructing organisms in silico: genome-scale fashions and their rising purposes. Nat. Rev. Microbiol. 18, 731–743 (2020).
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).
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).
Frioux, C., Singh, D., Korcsmaros, T. & Hildebrand, F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities within the period of metagenome-assembled genomes. Comput. Struct. Biotechnol. J. 18, 1722–1734 (2020).
Chaffron, S., Rehrauer, H., Pernthaler, J. & von Mering, C. A world community of coexisting microbes from environmental and whole-genome sequence information. Genome Res. 20, 947–959 (2010).
Faust, Okay. & Raes, J. Microbial interactions: from networks to fashions. Nat. Rev. Microbiol. 10, 538–550 (2012).
Li, J. et al. Distinct mechanisms form soil bacterial and fungal co-occurrence networks in a mountain ecosystem. FEMS Microbiol. Ecol. 96, fiaa030 (2020).
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Entrance. Microbiol. 5, 219 (2014).
Stein, R. R. et al. Ecological modeling from time-series inference: perception into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).
Barbier, M., Arnoldi, J.-F., Bunin, G. & Loreau, M. Generic meeting patterns in advanced 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 group meeting. Curr. Biol. 30, R1176–R1188 (2020).
Madeo, D., Comolli, L. R. & Mocenni, C. Emergence of microbial networks as response to hostile environments. Entrance. Microbiol. 5, 407 (2014).
Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).
Wang, B. & Allison, S. D. Emergent properties of natural matter decomposition by soil enzymes. Soil Biol. Biochem. 136, 107522 (2019).
Walsh, A. M. et al. Microbial succession and taste manufacturing within the fermented dairy beverage kefir. mSystems 1, e00052-16 (2016).
Oliveira, N. M., Niehus, R. & Foster, Okay. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941–17946 (2014).
Leigh, E. R. in Some Mathematical Issues in Biology (ed. Gerstenhaber, M.) 1–61 (American Mathematical Society, 1968).
Nedorezov, L. The dynamics of the lynx–hare system: an software of the Lotka–Volterra mannequin. Biophys. 61, 149–154 (2016).
Mühlbauer, L. Okay., Schulze, M., Harpole, W. S. & Clark, A. T. gauseR: easy strategies for becoming Lotka–Volterra fashions describing Gause’s “wrestle for existence”. Ecol. Evol. 10, 13275–13283 (2020).
Belovsky, G. E. Moose and snowshoe hare competitors and a mechanistic rationalization from foraging idea. Oecologia 61, 150–159 (1984).
Could, R. M. Restrict cycles in predator–prey communities. Science 177, 900–902 (1972).
Friedman, J., Higgins, L. M. & Gore, J. Group construction follows easy meeting guidelines in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).
Voit, E. O., Davis, J. D. & Olivença, D. V. Inference and validation of the construction of Lotka–Volterra fashions. Preprint at bioXriv https://doi.org/10.1101/2021.08.14.456346 (2021).
Bucci, V. & Xavier, J. B. In direction of predictive fashions of the human intestine microbiome. J. Mol. Biol. 426, 3907–3916 (2014).
Fisher, C. Okay. & Mehta, P. Figuring out keystone species within the human intestine microbiome from metagenomic timeseries utilizing sparse linear regression. PLoS ONE 9, e102451 (2014).
Bucci, V. et al. MDSINE: Microbial Dynamical Methods INference Engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).
Gao, X., Huynh, B.-T., Guillemot, D., Glaser, P. & Opatowski, L. Inference of serious microbial interactions from longitudinal metagenomics information. Entrance. Microbiol. 9, 2319 (2018).
Li, C. et al. An expectation-maximization algorithm allows correct ecological modeling utilizing longitudinal microbiome sequencing information. Microbiome 7, 118 (2019).
Joseph, T. A., Shenhav, L., Xavier, J. B., Halperin, E. & Pe’er, I. Compositional Lotka–Volterra describes microbial dynamics within the simplex. PLoS Comput. Biol. 16, e1007917 (2020).
Hosoda, S., Fukunaga, T. & Hamada, M. Umibato: estimation of time-varying microbial interplay utilizing continuous-time regression hidden Markov mannequin. Bioinformatics 37, i16–i24 (2021).
Remien, C. H., Eckwright, M. J. & Ridenhour, B. J. Structural identifiability of the generalized Lotka–Volterra mannequin for microbiome research. R. Soc. Open Sci. 8, 201378 (2021).
White, J. R. Novel Strategies for Metagenomic Evaluation. PhD thesis, Univ. of Maryland (2010).
Sousa, A., Frazão, N., Ramiro, R. S. & Gordo, I. Evolution of commensal micro organism within the intestinal tract of mice. Curr. Opin. Microbiol. 38, 114–121 (2017).
Mounier, J. et al. Microbial interactions inside a cheese microbial group. Appl. Environ. Microbiol. 74, 172–181 (2008).
Momeni, B., Xie, L. & Shou, W. Lotka–Volterra pairwise modeling fails to seize numerous pairwise microbial interactions. eLife 6, e25051 (2017).
Hoek, T. A. et al. Useful resource availability modulates the cooperative and aggressive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).
Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).
Mai, T. S. N. Impression of Metabolic Plasticity on Microbial Group Variety and Stability. MSc thesis, Univ. of Groningen (2021).
Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F. & Sanchez, A. Excessive-order interactions distort the practical panorama of microbial consortia. PLoS Biol. 17, e3000550 (2019).
Mickalide, H. & Kuehn, S. Greater-order interplay between species inhibits bacterial invasion of a phototroph-predator microbial group. Cell Syst. 9, 521–533.e10 (2019).
Guo, X. & Boedicker, J. Q. The contribution of high-order metabolic interactions to the worldwide exercise of a four-species microbial group. PLoS Comput. Biol. 12, e1005079 (2016).
Meroz, N., Tovi, N., Sorokin, Y. & Friedman, J. Group composition of microbial microcosms follows easy meeting guidelines at evolutionary timescales. Nat. Commun. 12, 2891 (2021).
Zomorrodi, A. R. & Segrè, D. Artificial ecology of microbes: mathematical fashions and purposes. J. Mol. Biol. 428, 837–861 (2016).
Tune, H. S., Cannon, W. R., Beliaev, A. S. & Konopka, A. Mathematical modeling of microbial group dynamics: a methodological assessment. Processes 2, 711–752 (2014).
Descheemaeker, L., Grilli, J. & de Buyl, S. Heavy-tailed abundance distributions from stochastic Lotka–Volterra fashions. Phys. Rev. E 104, 034404 (2021).
Bairey, E., Kelsic, E. D. & Kishony, R. Excessive-order species interactions form ecosystem range. Nat. Commun. 7, 12285 (2016).
Ji, B., Herrgård, M. J. & Nielsen, J. Microbial group dynamics revisited. Nat. Comput. Sci. 1, 640–641 (2021).
Abreu, C. I., Anderen Woltz, V. L., Friedman, J. & Gore, J. Microbial communities show different steady states in a fluctuating surroundings. PLoS Comput. Biol. 16, e1007934 (2020).
Xu, L., Xu, X., Kong, D., Gu, H. & Kenney T. Stochastic generalized Lotka–Volterra mannequin with an software to studying microbial group constructions. Preprint at arXiv https://doi.org/10.48550/arXiv.2009.10922 (2020).
Brunner, J. D. & Chia, N. Metabolite-mediated modelling of microbial group dynamics captures emergent behaviour extra successfully than species–species modelling. J. R. Soc. Interface 16, 20190423 (2019).
MacArthur, R. Species packing and aggressive equilibrium for a lot of species. Theor. Popul. Biol. 1, 1–11 (1970).
Tilman, D. Useful resource competitors and group construction. Monogr. Popul. Biol. 17, 1–296 (1982).
Chesson, P. MacArthur’s consumer-resource mannequin. Theor. Popul. Biol. 37, 26–38 (1990).
Goldford, J. E. et al. Emergent simplicity in microbial group meeting. Science 361, 469–474 (2018).
Marsland, R.third et al. Out there power fluxes drive a transition within the range, stability, and practical construction of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).
Marsland, R.third, Cui, W. & Mehta, P. A minimal mannequin for microbial biodiversity can reproduce experimentally noticed ecological patterns. Sci. Rep. 10, 3308 (2020).
Estrela, S., Sanchez-Gorostiaga, A., Vila, J. C. & Sanchez, A. Nutrient dominance governs the meeting of microbial communities in blended nutrient environments. eLife 10, e65948 (2021).
Cui, W., Marsland, R. & Mehta, P. Various communities behave like typical random ecosystems. Phys. Rev. E 104, 034416 (2021).
Haygood, R. Coexistence in MacArthur-style shopper–useful resource fashions. Theor. Popul. Biol. 61, 215–223 (2002).
Dubinkina, V., Fridman, Y., Pandey, P. P. & Maslov, S. Multistability and regime shifts in microbial communities defined by competitors for important vitamins. eLife 8, e49720 (2019).
Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial group responses to environmental complexity. Nat. Commun. 12, 2365 (2021).
Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in numerous microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).
Crowther, T. W. et al. Untangling the fungal area of interest: the trait-based method. Entrance. Microbiol. 5, 579 (2014).
Pacciani-Mori, L., Suweis, S., Maritan, A. & Giometto, A. Constrained proteome allocation impacts coexistence in fashions of aggressive microbial communities. ISME J. 15, 1458–1477 (2021).
Marsland, R. et al. The Group Simulator: a Python package deal for microbial ecology. PLoS ONE 15, e0230430 (2020).
Obadia, B. et al. Probabilistic invasion underlies pure intestine microbiome stability. Curr. Biol. 27, 1999–2006.e8 (2017).
D’Andrea, R., Gibbs, T. & O’Dwyer, J. P. Emergent neutrality in consumer-resource dynamics. PLoS Comput. Biol. 16, e1008102 (2020).
Mancuso, C. P., Lee, H., Abreu, C. I., Gore, J. & Khalil, A. S. Environmental fluctuations reshape an sudden diversity-disturbance relationship in a microbial group. eLife 10, e67175 (2021).
Lajoie, G. & Kembel, S. W. Taking advantage of trait-based approaches for microbial ecology. Traits Microbiol. 27, 814–823 (2019).
Zakharova, L., Meyer, Okay. M. & Seifan, M. Trait-based modelling in ecology: a assessment of twenty years of analysis. Ecol. Modell. 407, 108703 (2019).
Merico, A., Brandt, G., Lan Smith, S. L. & Oliver, M. Sustaining range in trait-based fashions of phytoplankton communities. Entrance. Ecol. Evol. 2, 59 (2014).
Grigoratou, M. et al. A trait-based modelling method to planktonic foraminifera ecology. Biogeosciences 16, 1469–1492 (2019).
Muscarella, M. E., Howey, X. M. & Lennon, J. T. Trait-based method to bacterial progress effectivity. Environ. Microbiol. 22, 3494–3504 (2020).
Shao, P., Lynch, L., Xie, H., Bao, X. & Liang, C. Tradeoffs amongst microbial life historical past methods affect the destiny of microbial residues in subtropical forest soils. Soil Biol. Biochem. 153, 108112 (2021).
Malik, A. A. et al. Defining trait-based microbial methods with penalties for soil carbon biking underneath local weather change. ISME J. 14, 1–9 (2020).
Le Roux, X. et al. Predicting the responses of soil nitrite-oxidizers to multi-factorial international change: a trait-based method. Entrance. Microbiol. 7, 628 (2016).
Bouskill, N. J., Tang, J., Riley, W. J. & Brodie, E. L. Trait-based illustration of organic nitrification: mannequin improvement, testing, and predicted group composition. Entrance. Microbiol. 3, 364 (2012).
Kyker-Snowman, E., Wieder, W. R., Frey, S. D. & Grandy, A. S. Stoichiometrically coupled carbon and nitrogen biking within the MIcrobial-MIneral Carbon Stabilization mannequin model 1.0 (MIMICS-CN v1.0). Geosci. Mannequin Dev. 13, 4413–4434 (2020).
Kruk, C. et al. A trait-based method predicting group meeting and dominance of microbial invasive species. Oikos 130, 571–586 (2021).
Litchman, E., Ohman, M. D. & Kiørboe, T. Trait-based approaches to zooplankton communities. J. Plankton Res. 35, 473–484 (2013).
Garcia, C. A. et al. Linking regional shifts in microbial genome adaptation with floor ocean biogeochemistry. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190254 (2020).
Moreno, A. R., Hagstrom, G. I., Primeau, F. W., Levin, S. A. & Martiny, A. C. Marine phytoplankton stoichiometry mediates nonlinear interactions between nutrient provide, temperature, and atmospheric CO2. Biogeosciences 15, 2761–2779 (2018).
Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a mannequin ocean. Science 315, 1843–1846 (2007).
Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).
Ratzke, C., Barrere, J. & Gore, J. Energy of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).
Bradford, M. A. et al. Quantifying microbial management of soil natural matter dynamics at macrosystem scales. Biogeochemistry 156, 19–40 (2021).
Ward, B. A., Dutkiewicz, S., Moore, C. M. & Follows, M. J. Iron, phosphorus, and nitrogen provide ratios outline the biogeography of nitrogen fixation. Limnol. Oceanogr. 58, 2059–2075 (2013).
Zwart, J. A., Solomon, C. T. & Jones, S. E. Phytoplankton traits predict ecosystem perform in a worldwide set of lakes. Ecology 96, 2257–2264 (2015).
Nemergut, D. R., Shade, A. & Violle, C. When, the place and the way does microbial group composition matter. Entrance. Microbiol. 5, 497 (2014).
Severin, I., Östman, Ö. & Lindström, E. S. Variable results of dispersal on productiveness of bacterial communities because of adjustments in practical trait composition. PLoS ONE 8, e80825 (2013).
Staley, C. et al. Core practical traits of bacterial communities within the Higher Mississippi River present restricted variation in response to land cowl. Entrance. Microbiol. 5, 414 (2014).
Worden, L. Conservation of group practical construction throughout adjustments in composition in consumer-resource fashions. J. Theor. Biol. 493, 110239 (2020).
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).
Tune, H.-S. et al. Regulation-structured dynamic metabolic mannequin offers a possible mechanism for delayed enzyme response in denitrification course of. Entrance. Microbiol. 8, 1866 (2017).
Hemelrijk, C. Okay. & Hildenbrandt, H. Faculties of fish and flocks of birds: their form and inside construction by self-organization. Interface Focus 2, 726–737 (2012).
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).
Griesemer, M. & Sindi, S. S. Guidelines of engagement: a information to growing agent-based fashions. Strategies Mol. Biol. 2349, 367–380 (2022).
Jayathilake, P. G. et al. A mechanistic individual-based mannequin of microbial communities. PLoS ONE 12, e0181965 (2017).
Clark, J. R., Daines, S. J., Lenton, T. M., Watson, A. J. & Williams, H. T. P. Particular person-based modelling of adaptation in marine microbial populations utilizing genetically outlined physiological parameters. Ecol. Modell. 222, 3823–3837 (2011).
Nadell, C. D. et al. Chopping by means of the complexity of cell collectives. Proc. Biol. Sci. 280, 20122770 (2013).
Allen, B., Gore, J. & Nowak, M. A. Spatial dilemmas of diffusible public items. eLife 2, e01169 (2013).
Abs, E., Leman, H. & Ferrière, R. A multi-scale eco-evolutionary mannequin of cooperation reveals how microbial adaptation influences soil decomposition. Commun. Biol. 3, 520 (2020).
Kreft, J.-U. et al. Mighty small: observing and modeling particular person microbes turns into large science. Proc. Natl Acad. Sci. USA 110, 18027–18028 (2013).
Parise, F., Lygeros, J. & Ruess, J. Bayesian inference for stochastic individual-based fashions of ecological programs: a pest management simulation research. Entrance. Environ. Sci. 3, https://doi.org/10.3389/fenvs.2015.00042 (2015).
Allison, S. D. & Goulden, M. L. Penalties of drought tolerance traits for microbial decomposition within the DEMENT mannequin. Soil Biol. Biochem. 107, 104–113 (2017).
Allison, S. D. A trait-based method for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012).
Doloman, A., Varghese, H., Miller, C. D. & Flann, N. S. Modeling de novo granulation of anaerobic sludge. BMC Syst. Biol. 11, 69 (2017).
Gogulancea, V. et al. Particular person primarily based mannequin hyperlinks thermodynamics, chemical speciation and environmental circumstances to microbial progress. Entrance. Microbiol. 10, 1871 (2019).
Gutierrez, M. & Rodriguez-Paton, A. Simulating multicell populations with an accelerated gro simulator. In Proc. ECAL 2017, Fourteenth European Conf. on Synthetic Life, 186–188 (2017).
Gutiérrez, M. et al. A brand new improved and prolonged model of the multicell bacterial simulator gro. ACS Synth. Biol. 6, 1496–1508 (2017).
Momeni, B., Waite, A. J. & Shou, W. Spatial self-organization favors heterotypic cooperation over dishonest. eLife 2, e00960 (2013).
Kreft, J.-U., Sales space, G. & Wimpenny, J. W. T. BacSim, a simulator for individual-based modelling of bacterial colony progress. Microbiology 144, 3275–3287 (1998).
Picioreanu, C., Van Loosdrecht, M. C. & Heijnen, J. J. Impact of diffusive and convective substrate transport on biofilm construction formation: a two-dimensional modeling research. Biotechnol. Bioeng. 69, 504–515 (2000).
Lardon, L. A. et al. iDynoMiCS: next-generation individual-based modelling of biofilms. Environ. Microbiol. 13, 2416–2434 (2011).
Chacón, J. M., Möbius, W. & Harcombe, W. R. The spatial and metabolic foundation of colony measurement variation. ISME J. 12, 669–680 (2018).
Oyebamiji, O. Okay. et al. Gaussian course of emulation of an individual-based mannequin simulation of microbial communities. J. Comput. Sci. 22, 69–84 (2017).
Menon, R. & Korolev, Okay. S. Public good diffusion limits microbial mutualism. Phys. Rev. Lett. 114, 168102 (2015).
Dobay, A., Bagheri, H. C., Messina, A., Kümmerli, R. & Rankin, D. J. Interplay results of cell diffusion, cell density and public items properties on the evolution of cooperation in digital microbes. J. Evol. Biol. 27, 1869–1877 (2014).
Canzian, L., Zhao, Okay., Wong, G. C. L. & van der Schaar, M. A dynamic community formation mannequin for understanding bacterial self-organization into micro-colonies. IEEE Trans. Mol. Biol. Multiscale Commun. 1, 76–89 (2015).
Nadell, C. D., Foster, Okay. R. & Xavier, J. B. Emergence of spatial construction in cell teams and the evolution of cooperation. PLoS Comput. Biol. 6, e1000716 (2010).
Mendoza, S. N., Olivier, B. G., Molenaar, D. & Teusink, B. A scientific evaluation of present genome-scale metabolic reconstruction instruments. Genome Biol. 20, 158 (2019).
Orth, J. D., Thiele, I. & Palsson, B. Ø. What’s flux steadiness evaluation? Nat. Biotechnol. 28, 245–248 (2010).
Feist, A. M. & Palsson, B. O. The biomass goal perform. Curr. Opin. Microbiol. 13, 344–349 (2010).
Blasche, S. et al. Metabolic cooperation and spatiotemporal area of interest partitioning in a kefir microbial group. Nat. Microbiol. 6, 196–208 (2021).
Dukovski, I. et al. A metabolic modeling platform for the computation of microbial ecosystems in time and house (COMETS). Nat. Protoc. 16, 5030–5082 (2021).
Varahan, S., Sinha, V., Walvekar, A., Krishna, S. & Laxman, S. Useful resource plasticity-driven carbon-nitrogen budgeting allows specialization and division of labor in a clonal group. eLife 9, e57609 (2020).
Angeles-Martinez, L. & Hatzimanikatis, V. Spatio-temporal modeling of the crowding circumstances and metabolic variability in microbial communities. PLoS Comput. Biol. 17, e1009140 (2021).
Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: knowledgeable prediction of bacterial metabolic pathways and reconstruction of correct metabolic fashions. Genome Biol. 22, 81 (2021).
Zorrilla, F., Buric, F., Patil, Okay. R. & Zelezniak, A. metaGEM: reconstruction of genome scale metabolic fashions immediately from metagenomes. Nucleic Acids Res. 49, e126 (2021).
Ponomarova, O. et al. Yeast creates a distinct segment for symbiotic lactic acid micro organism by means of nitrogen overflow. Cell Syst. 5, 345–357.e6 (2017).
Foster, Okay. R. & Bell, T. Competitors, not cooperation, dominates interactions amongst culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).
Borer, B., Ataman, M., Hatzimanikatis, V. & Or, D. Modeling metabolic networks of particular person bacterial brokers in heterogeneous and dynamic soil habitats (IndiMeSH). PLoS Comput. Biol. 15, e1007127 (2019).
Labhsetwar, P., Cole, J. A., Roberts, E., Value, N. D. & Luthey-Schulten, Z. A. Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli inhabitants. Proc. Natl Acad. Sci. USA 110, 14006–14011 (2013).
Kehe, J. et al. Optimistic interactions are widespread amongst culturable micro organism. Sci. Adv. 7, eabi7159 (2021).
Zmora, N. et al. Personalised intestine mucosal colonization resistance to empiric probiotics is related to distinctive host and microbiome options. Cell 174, 1388–1405.e21 (2018).
Korem, T. et al. Progress dynamics of intestine microbiota in well being and illness inferred from single metagenomic samples. Science 349, 1101–1106 (2015).
Hamilton, J. J. et al. Metabolic community evaluation and metatranscriptomics reveal auxotrophies and nutrient sources of the cosmopolitan freshwater microbial lineage acI. mSystems 2, e00091-17 (2017).
Guerra, C. A. et al. Monitoring, focusing on, and conserving soil biodiversity. Science 371, 239–241 (2021).
IPCC. Local weather Change and Land: an IPCC Particular Report on Local weather Change, Desertification, Land Degradation, Sustainable Land Administration, Meals Safety, and Greenhouse Gasoline Fluxes in Terrestrial Ecosystems (IPCC, 2020).
Pacciani-Mori, L., Giometto, A., Suweis, S. & Maritan, A. Dynamic metabolic adaptation can promote species coexistence in aggressive microbial communities. PLoS Comput. Biol. 16, e1007896 (2020).