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Microfluidic and mathematical modeling of aquatic microbial communities

Analytical and Bioanalytical Chemistry volume 413, pages 2331–2344 (2021)Cite this article

Abstract

Aquatic microbial communities contribute fundamentally to biogeochemical transformations in natural ecosystems, and disruption of these communities can lead to ecological disasters such as harmful algal blooms. Microbial communities are highly dynamic, and their composition and function are tightly controlled by the biophysical (e.g., light, fluid flow, and temperature) and biochemical (e.g., chemical gradients and cell concentration) parameters of the surrounding environment. Due to the large number of environmental factors involved, a systematic understanding of the microbial community-environment interactions is lacking. In this article, we show that microfluidic platforms present a unique opportunity to recreate well-defined environmental factors in a laboratory setting in a high throughput way, enabling quantitative studies of microbial communities that are amenable to theoretical modeling. The focus of this article is on aquatic microbial communities, but the microfluidic and mathematical models discussed here can be readily applied to investigate other microbiomes.

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References

  1. Pomati F, Matthews B, Jokela J, Schildknecht A, Ibelings BW. Effects of re-oligotrophication and climate warming on plankton richness and community stability in a deep mesotrophic lake. Oikos. 2012;121(8):1317–27. https://doi.org/10.1111/j.1600-0706.2011.20055.x.

    CAS  Article  Google Scholar 

  2. Huisman J, Codd GA, Paerl HW, Ibelings BW, Verspagen JMH, Visser PM. Cyanobacterial blooms. Nat Rev Microbiol. 2018;16(8):471–83. https://doi.org/10.1038/s41579-018-0040-1.

    CAS  Article  PubMed  Google Scholar 

  3. Wells ML, Trainer VL, Smayda TJ, Karlson BSO, Trick CG, Kudela RM, et al. Harmful algal blooms and climate change: learning from the past and present to forecast the future. Harmful Algae. 2015;49:68–93. https://doi.org/10.1016/j.hal.2015.07.009.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Gobler CJ. Climate change and harmful algal blooms: insights and perspective. Harmful Algae. 2020;91:101731. https://doi.org/10.1016/j.hal.2019.101731.

    Article  PubMed  Google Scholar 

  5. Nai C, Meyer V. From axenic to mixed cultures: technological advances accelerating a paradigm shift in microbiology. Trends Microbiol. 2018;26(6):538–54. https://doi.org/10.1016/j.tim.2017.11.004.

    CAS  Article  PubMed  Google Scholar 

  6. Burmeister A, Grünberger A. Microfluidic cultivation and analysis tools for interaction studies of microbial co-cultures. Curr Opin Biotechnol. 2020;62:106–15. https://doi.org/10.1016/j.copbio.2019.09.001.

    CAS  Article  PubMed  Google Scholar 

  7. Yang Y-T, Wang CY. Review of microfluidic photobioreactor technology for metabolic engineering and synthetic biology of cyanobacteria and microalgae. Micromachines (Basel). 2016;7(10):185. https://doi.org/10.3390/mi7100185.

    CAS  Article  Google Scholar 

  8. Rusconi R, Garren M, Stocker R. Microfluidics expanding the frontiers of microbial ecology. In: Dill KA, editor. Annual Review of Biophysics, Vol 43. Annu Rev Biophys, 2014. p. 65–91.

  9. Zhu CM, Zhang JY, Guan R, Hale L, Chen N, Li M, et al. Alternate succession of aggregate-forming cyanobacterial genera correlated with their attached bacteria by co-pathways. Sci Total Environ. 2019;688:867–79. https://doi.org/10.1016/j.scitotenv.2019.06.150.

    CAS  Article  PubMed  Google Scholar 

  10. Li J, Dittrich M. Dynamic polyphosphate metabolism in cyanobacteria responding to phosphorus availability. Environ Microbiol. 2019;21(2):572–83. https://doi.org/10.1111/1462-2920.14488.

    CAS  Article  PubMed  Google Scholar 

  11. Liu YM, Li L, Jia RB. The optimum resource ratio (N:P) for the growth of Microcystis aeruginosa with abundant nutrients. Procedia Environ Sci. 2011;10:2134–40. https://doi.org/10.1016/j.proenv.2011.09.334.

    CAS  Article  Google Scholar 

  12. Banares-Espana E, Kromkamp JC, Lopez-Rodas V, Costas E, Flores-Moya A. Photoacclimation of cultured strains of the cyanobacterium Microcystis aeruginosa to high-light and low-light conditions. FEMS Microbiol Ecol. 2013;83(3):700–10. https://doi.org/10.1111/1574-6941.12025.

    CAS  Article  PubMed  Google Scholar 

  13. Kaebernick M, Neilan BA, Borner T, Dittmann E. Light and the transcriptional response of the microcystin biosynthesis gene cluster. Appl Environ Microbiol. 2000;66(8):3387–92. https://doi.org/10.1128/Aem.66.8.3387-3392.2000.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Xiao M, Willis A, Burford MA. Differences in cyanobacterial strain responses to light and temperature reflect species plasticity. Harmful Algae. 2017;62:84–93. https://doi.org/10.1016/j.hal.2016.12.008.

    CAS  Article  PubMed  Google Scholar 

  15. Richter LV, Mansfeldt CB, Kuan MM, Cesare AE, Menefee ST, Richardson RE, et al. Altered microbiome leads to significant phenotypic and transcriptomic differences in a lipid accumulating chlorophyte. Environ Sci Technol. 2018;52(12):6854–63. https://doi.org/10.1021/acs.est.7b06581.

    CAS  Article  PubMed  Google Scholar 

  16. Reynolds CS. The ecology of phytoplankton. ecology, biodiversity, and conservation. Cambridge: Cambridge University Press; 2006.

    Book  Google Scholar 

  17. Fischer R, Andersen T, Hillebrand H, Ptacnik R. The exponentially fed batch culture as a reliable alternative to conventional chemostats. Limnol Oceanogr Methods. 2014;12(7):432–40. https://doi.org/10.4319/lom.2014.12.432.

    Article  Google Scholar 

  18. Yamane T, Kishimoto M, Yoshida F. Semi-batch culture of methanol-assimilating bacteria with exponentially increased methanol feed. 1976;v. 54.

  19. Stewart EJ. Growing unculturable bacteria. J Bacteriol. 2012;194(16):4151. https://doi.org/10.1128/JB.00345-12.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Kim HS, Devarenne TP, Han A. A high-throughput microfluidic single-cell screening platform capable of selective cell extraction. Lab Chip. 2015;15(11):2467–75. https://doi.org/10.1039/c4lc01316f.

    CAS  Article  PubMed  Google Scholar 

  21. Luke CS, Selimkhanov J, Baumgart L, Cohen SE, Golden SS, Cookson NA, et al. A microfluidic platform for long-term monitoring of algae in a dynamic environment. ACS Synth Biol. 2016;5(1):8–14. https://doi.org/10.1021/acssynbio.5b00094.

    CAS  Article  PubMed  Google Scholar 

  22. Westerwalbesloh C, Brehl C, Weber S, Probst C, Widzgowski J, Grünberger A, et al. A microfluidic photobioreactor for simultaneous observation and cultivation of single microalgal cells or cell aggregates. PLoS One. 2019;14(4):e0216093. https://doi.org/10.1371/journal.pone.0216093.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Chen Y, Kim JK, Hirning AJ, Josić K, Bennett MR. SYNTHETIC BIOLOGY. Emergent genetic oscillations in a synthetic microbial consortium. Science. 2015;349(6251):986–9. https://doi.org/10.1126/science.aaa3794.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Alnahhas RN, Winkle JJ, Hirning AJ, Karamched B, Ott W, Josić K, et al. Spatiotemporal dynamics of synthetic microbial consortia in microfluidic devices. ACS Synth Biol. 2019;8(9):2051–8. https://doi.org/10.1021/acssynbio.9b00146.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Graham PJ, Riordon J, Sinton D. Microalgae on display: a microfluidic pixel-based irradiance assay for photosynthetic growth. Lab Chip. 2015;15(15):3116–24. https://doi.org/10.1039/c5lc00527b.

    CAS  Article  PubMed  Google Scholar 

  26. Kim BJ, Richter LV, Hatter N, Tung CK, Ahner BA, Wu MM. An array microhabitat system for high throughput studies of microalgal growth under controlled nutrient gradients. Lab Chip. 2015;15(18):3687–94. https://doi.org/10.1039/c5lc00727e.

    CAS  Article  PubMed  Google Scholar 

  27. Guo X, Silva KPT, Boedicker JQ. Single-cell variability of growth interactions within a two-species bacterial community. Phys Biol. 2019;16(3):036001. https://doi.org/10.1088/1478-3975/ab005f.

    CAS  Article  PubMed  Google Scholar 

  28. Ohan J, Pelle B, Nath P, Huang JH, Hovde B, Vuyisich M, et al. High-throughput phenotyping of cell-to-cell interactions in gel microdroplet pico-cultures. BioTechniques. 2019;66(5):218–24. https://doi.org/10.2144/btn-2018-0124.

    CAS  Article  PubMed  Google Scholar 

  29. Kehe J, Kulesa A, Ortiz A, Ackerman CM, Thakku SG, Sellers D, et al. Massively parallel screening of synthetic microbial communities. Proc Natl Acad Sci U S A. 2019;116(26):12804–9. https://doi.org/10.1073/pnas.1900102116.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Kürsten D, Möller F, Gross GA, Lenk C, Visaveliya N, Schüler T, et al. Identification of response classes from heavy metal-tolerant soil microbial communities by highly resolved concentration-dependent screenings in a microfluidic system. Methods Ecol Evol. 2015;6(5):600–9. https://doi.org/10.1111/2041-210X.12344.

    Article  Google Scholar 

  31. Pan J, Stephenson AL, Kazamia E, Huck WTS, Dennis JS, Smith AG, et al. Quantitative tracking of the growth of individual algal cells in microdroplet compartments. Integr Biol. 2011;3(10):1043–51. https://doi.org/10.1039/c1ib00033k.

    Article  Google Scholar 

  32. Au SH, Shih SC, Wheeler AR. Integrated microbioreactor for culture and analysis of bacteria, algae and yeast. Biomed Microdevices. 2011;13(1):41–50. https://doi.org/10.1007/s10544-010-9469-3.

    CAS  Article  PubMed  Google Scholar 

  33. Hesselman MC, Odoni DI, Ryback BM, de Groot S, van Heck RGA, Keijsers J, et al. A multi-platform flow device for microbial (co-) cultivation and microscopic analysis. PLoS One. 2012;7(5):e36982. https://doi.org/10.1371/journal.pone.0036982.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Burmeister A, Hilgers F, Langner A, Westerwalbesloh C, Kerkhoff Y, Tenhaef N, et al. A microfluidic co-cultivation platform to investigate microbial interactions at defined microenvironments. Lab Chip. 2019;19(1):98–110. https://doi.org/10.1039/C8LC00977E.

    CAS  Article  Google Scholar 

  35. Kim HJ, Boedicker JQ, Choi JW, Ismagilov RF. Defined spatial structure stabilizes a synthetic multispecies bacterial community. Proc Natl Acad Sci U S A. 2008;105(47):18188–93. https://doi.org/10.1073/pnas.0807935105.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pham PLH, Rooholghodos SA, Choy JS, Luo X. Constructing synthetic ecosystems with biopolymer fluitrodes. Adv Biosyst. 2018;2(3):1700180. https://doi.org/10.1002/adbi.201700180.

    CAS  Article  Google Scholar 

  37. Moffitt JR, Lee JB, Cluzel P. The single-cell chemostat: an agarose-based, microfluidic device for high-throughput, single-cell studies of bacteria and bacterial communities. Lab Chip. 2012;12(8):1487–94. https://doi.org/10.1039/c2lc00009a.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Lu Y-C, Chu T, Hall MS, Fu D-J, Shi Q, Chiu A, et al. Physical confinement induces malignant transformation in mammary epithelial cells. Biomaterials. 2019;217:119307. https://doi.org/10.1016/j.biomaterials.2019.119307.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Ma M, Chiu A, Sahay G, Doloff JC, Dholakia N, Thakrar R, et al. Core–shell hydrogel microcapsules for improved islets encapsulation. Adv Healthc Mater. 2013;2(5):667–72. https://doi.org/10.1002/adhm.201200341.

    CAS  Article  PubMed  Google Scholar 

  40. Connell JL, Ritschdorff ET, Whiteley M, Shear JB. 3D printing of microscopic bacterial communities. Proc Natl Acad Sci. 2013;110(46):18380. https://doi.org/10.1073/pnas.1309729110.

    CAS  Article  PubMed  Google Scholar 

  41. Chiou PY, Ohta AT, Wu MC. Massively parallel manipulation of single cells and microparticles using optical images. Nature. 2005;436(7049):370–2. https://doi.org/10.1038/nature03831.

    CAS  Article  PubMed  Google Scholar 

  42. Lam AT, Samuel-Gama KG, Griffin J, Loeun M, Gerber LC, Hossain Z, et al. Device and programming abstractions for spatiotemporal control of active micro-particle swarms. Lab Chip. 2017;17(8):1442–51. https://doi.org/10.1039/c7lc00131b.

    CAS  Article  PubMed  Google Scholar 

  43. Tsang ACH, Lam AT, Riedel-Kruse IH. Polygonal motion and adaptable phototaxis via flagellar beat switching in the microswimmer Euglena gracilis. Nat Phys. 2018;14(12):1216–22. https://doi.org/10.1038/s41567-018-0277-7.

    CAS  Article  Google Scholar 

  44. Frangipane G, Dell'Arciprete D, Petracchini S, Maggi C, Saglimbeni F, Bianchi S, et al. Dynamic density shaping of photokinetic E. coli. Elife. 2018;7:e36608. https://doi.org/10.7554/eLife.36608.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Jung EE, Kalontarov M, Doud DFR, Ooms MD, Angenent LT, Sinton D, et al. Slab waveguide photobioreactors for microalgae based biofuel production. Lab Chip. 2012;12(19):3740–5. https://doi.org/10.1039/C2LC40490G.

    CAS  Article  PubMed  Google Scholar 

  46. Ooms MD, Bajin L, Sinton D. Culturing photosynthetic bacteria through surface plasmon resonance. Appl Phys Lett. 2012;101(25):253701. https://doi.org/10.1063/1.4771990.

    CAS  Article  Google Scholar 

  47. Ooms MD, Jeyaram Y, Sinton D. Wavelength-selective plasmonics for enhanced cultivation of microalgae. Appl Phys Lett. 2015;106(6):063902. https://doi.org/10.1063/1.4908259.

    CAS  Article  Google Scholar 

  48. Chen M, Mertiri T, Holland T, Basu AS. Optical microplates for high-throughput screening of photosynthesis in lipid-producing algae. Lab Chip. 2012;12(20):3870–4. https://doi.org/10.1039/C2LC40478H.

    Article  PubMed  Google Scholar 

  49. Heo J, Cho D-H, Ramanan R, Oh H-M, Kim H-S. PhotoBiobox: a tablet sized, low-cost, high throughput photobioreactor for microalgal screening and culture optimization for growth, lipid content and CO2 sequestration. Biochem Eng J. 2015;103:193–7. https://doi.org/10.1016/j.bej.2015.07.013.

    CAS  Article  Google Scholar 

  50. Kopparthy VL, Crews ND. A versatile oscillating-flow microfluidic PCR system utilizing a thermal gradient for nucleic acid analysis. Biotechnol Bioeng. 2020;117(5):1525–32. https://doi.org/10.1002/bit.27278.

    CAS  Article  PubMed  Google Scholar 

  51. Shi HH, Nie KX, Dong B, Long MQ, Xu H, Liu ZC. Recent progress of microfluidic reactors for biomedical applications. Chem Eng J. 2019;361:635–50. https://doi.org/10.1016/j.cej.2018.12.104.

    CAS  Article  Google Scholar 

  52. Barman U, Wiederkehr RS, Fiorini P, Lagae L, Jones B. A comprehensive methodology for design and development of an integrated microheater for on-chip DNA amplification. J Micromech Microeng. 2018;28(8):11. https://doi.org/10.1088/1361-6439/aabd2c.

    CAS  Article  Google Scholar 

  53. Park J, Park H. Thermal cycling characteristics of a 3D-printed serpentine microchannel for DNA amplification by polymerase chain reaction. Sensors Actuators Phys. 2017;268:183–7. https://doi.org/10.1016/j.sna.2017.10.044.

    CAS  Article  Google Scholar 

  54. Kanai T, Nakai H, Yamada A, Fukuyama M, Weitz DA. Preparation of monodisperse hybrid gel particles with various morphologies via flow rate and temperature control. Soft Matter. 2019;15(35):6934–7. https://doi.org/10.1039/c9sm00500e.

    CAS  Article  PubMed  Google Scholar 

  55. Lei ZL, Xie DC, Mbogba MK, Chen ZR, Tian CH, Xu L, et al. A microfluidic platform with cell-scale precise temperature control for simultaneous investigation of the osmotic responses of multiple oocytes. Lab Chip. 2019;19(11):1929–40. https://doi.org/10.1039/c9lc00107g.

    CAS  Article  PubMed  Google Scholar 

  56. Fang CF, Ji FJ, Shu ZQ, Gao DY. Determination of the temperature-dependent cell membrane permeabilities using microfluidics with integrated flow and temperature control. Lab Chip. 2017;17(5):951–60. https://doi.org/10.1039/c6lc01523a.

    CAS  Article  PubMed  Google Scholar 

  57. Peng J, Fang CF, Ren S, Pan JJ, Jia YD, Shu ZQ, et al. Development of a microfluidic device with precise on-chip temperature control by integrated cooling and heating components for single updates cell-based analysis. Int J Heat Mass Transf. 2019;130:660–7. https://doi.org/10.1016/j.ijheatmasstransfer.2018.10.135.

    CAS  Article  Google Scholar 

  58. Erdem EY, Cheng JC, Doyle FM, Pisano AP. Multi-temperature zone, droplet-based microreactor for increased temperature control in nanoparticle synthesis. Small. 2014;10(6):1076–80. https://doi.org/10.1002/smll.201302379.

    CAS  Article  PubMed  Google Scholar 

  59. Han D, Jang Y-C, Oh S-N, Chand R, Lim K-T, Kim K-I, et al. MCU based real-time temperature control system for universal microfluidic PCR chip. Microsyst Technol. 2014;20(3):471–6. https://doi.org/10.1007/s00542-013-1970-1.

    CAS  Article  Google Scholar 

  60. Munoz-Garcia J, Babic J, Coudreuse D. Drug delivery and temperature control in microfluidic chips during live-cell imaging experiments. In: Piel M, Fletcher D, Doh J, editors. Microfluidics in Cell Biology, Pt B: Microfluidics in Single Cells. Methods in Cell Biology, 2018. p. 3–28.

  61. Zhu JY, Suarez SA, Thurgood P, Nguyen N, Mohammed M, Abdelwahab H, et al. Reconfigurable, self-sufficient convective heat exchanger for temperature control of microfluidic systems. Anal Chem. 2019;91(24):15784–90. https://doi.org/10.1021/acs.analchem.9b04066.

    CAS  Article  PubMed  Google Scholar 

  62. Fang C, Lee D, Stober B, Fuller GG, Shen AQ. Integrated microfluidic platform for instantaneous flow and localized temperature control. RSC Adv. 2015;5(104):85620–9. https://doi.org/10.1039/C5RA19944A.

    CAS  Article  Google Scholar 

  63. Hoera C, Ohla S, Shu Z, Beckert E, Nagl S, Belder D. An integrated microfluidic chip enabling control and spatially resolved monitoring of temperature in micro flow reactors. Anal Bioanal Chem. 2015;407(2):387–96. https://doi.org/10.1007/s00216-014-8297-3.

    CAS  Article  PubMed  Google Scholar 

  64. Fornells E, Murray E, Waheed S, Morrin A, Diamond D, Paull B, et al. Integrated 3D printed heaters for microfluidic applications: ammonium analysis within environmental water. Anal Chim Acta. 2020;1098:94–101. https://doi.org/10.1016/j.aca.2019.11.025.

    CAS  Article  PubMed  Google Scholar 

  65. Moazami E, Perry JM, Soffer G, Husser MC, Shih SCC. Integration of world-to-chip interfaces with digital microfluidics for bacterial transformation and enzymatic assays. Anal Chem. 2019;91(8):5159–68. https://doi.org/10.1021/acs.analchem.8b05754.

    CAS  Article  PubMed  Google Scholar 

  66. Mukhitov N, Yi L, Schrell AM, Roper MG. Optimization of a microfluidic electrophoretic immunoassay using a Peltier cooler. J Chromatogr A. 2014;1367:154–60. https://doi.org/10.1016/j.chroma.2014.09.040.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. Saunders DC, Holst GL, Phaneuf CR, Pak N, Marchese M, Sondej N, et al. Rapid, quantitative, reverse transcription PCR in a polymer microfluidic chip. Biosens Bioelectron. 2013;44:222–8. https://doi.org/10.1016/j.bios.2013.01.019.

    CAS  Article  PubMed  Google Scholar 

  68. Phaneuf CR, Pak N, Forest CR. Modeling radiative heating of liquids in microchip reaction chambers. Sensors Actuators A Phys. 2011;167(2):531–6. https://doi.org/10.1016/j.sna.2011.02.002.

    CAS  Article  Google Scholar 

  69. Liu F, Yazdani M, Ahner BA, Wu M. An array microhabitat device with dual gradients revealed synergistic roles of nitrogen and phosphorous in the growth of microalgae. Lab Chip. 2020;20(4):798–805. https://doi.org/10.1039/C9LC01153F.

    CAS  Article  PubMed  Google Scholar 

  70. Bae S, Kim CW, Choi JS, Yang JW, Seo TS. An integrated microfluidic device for the high-throughput screening of microalgal cell culture conditions that induce high growth rate and lipid content. Anal Bioanal Chem. 2013;405(29):9365–74. https://doi.org/10.1007/s00216-013-7389-9.

    CAS  Article  PubMed  Google Scholar 

  71. Yang CC, Wen RC, Shen CR, Yao DJ. Using a microfluidic gradient generator to characterize BG-11 medium for the growth of cyanobacteria Synechococcus elongatus PCC7942. Micromachines (Basel). 2015;6(11):1755–67. https://doi.org/10.3390/mi6111454.

    Article  Google Scholar 

  72. Park HY, Qiu X, Rhoades E, Korlach J, Kwok LW, Zipfel WR, et al. Achieving uniform mixing in a microfluidic device: hydrodynamic focusing prior to mixing. Anal Chem. 2006;78(13):4465–73. https://doi.org/10.1021/ac060572n.

    CAS  Article  PubMed  Google Scholar 

  73. Li Q, Lin F, Yang C, Wang J, Lin Y, Shen M, et al. A large-scale comparative metagenomic study reveals the functional interactions in six bloom-forming Microcystis-epibiont communities. Front Microbiol. 2018;9:746. https://doi.org/10.3389/fmicb.2018.00746.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Kim M, Shin B, Lee J, Park HY, Park W. Culture-independent and culture-dependent analyses of the bacterial community in the phycosphere of cyanobloom-forming Microcystis aeruginosa. Sci Rep-Uk. 2019;9(1):20416. https://doi.org/10.1038/s41598-019-56882-1.

    CAS  Article  Google Scholar 

  75. Bell W, Mitchell R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol Bull. 1972;143(2):265–77. https://doi.org/10.2307/1540052.

    Article  Google Scholar 

  76. Mayali X. Editorial: Metabolic interactions between bacteria and phytoplankton. Front Microbiol. 2018;9:727. https://doi.org/10.3389/fmicb.2018.00727.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Leão PN, Ramos V, Vale M, Machado JP, Vasconcelos VM. Microbial community changes elicited by exposure to cyanobacterial allelochemicals. Microb Ecol. 2012;63(1):85–95. https://doi.org/10.1007/s00248-011-9939-z.

    CAS  Article  PubMed  Google Scholar 

  78. Briand E, Reubrecht S, Mondeguer F, Sibat M, Hess P, Amzil Z, et al. Chemically mediated interactions between Microcystis and Planktothrix: impact on their growth, morphology and metabolic profiles. Environ Microbiol. 2019;21(5):1552–66. https://doi.org/10.1111/1462-2920.14490.

    CAS  Article  PubMed  Google Scholar 

  79. Leão PN, Pereira AR, Liu W-T, Ng J, Pevzner PA, Dorrestein PC, et al. Synergistic allelochemicals from a freshwater cyanobacterium. Proc Natl Acad Sci. 2010;107(25):11183. https://doi.org/10.1073/pnas.0914343107.

    Article  PubMed  Google Scholar 

  80. Song H, Lavoie M, Fan X, Tan H, Liu G, Xu P, et al. Allelopathic interactions of linoleic acid and nitric oxide increase the competitive ability of Microcystis aeruginosa. ISME J. 2017;11(8):1865–76. https://doi.org/10.1038/ismej.2017.45.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  81. Chia MA, Kramer BJ, Jankowiak JG, Bittencourt-Oliveira MDC, Gobler CJ. The individual and combined effects of the cyanotoxins, anatoxin-a and microcystin-LR, on the growth, toxin production, and nitrogen fixation of prokaryotic and eukaryotic algae. Toxins (Basel). 2019;11(1). https://doi.org/10.3390/toxins11010043.

  82. Kirchman DL, Suzuki Y, Garside C, Ducklow HW. High turnover rates of dissolved organic carbon during a spring phytoplankton bloom. Nature. 1991;352(6336):612–4. https://doi.org/10.1038/352612a0.

    CAS  Article  Google Scholar 

  83. Obernosterer I, Catala P, Lebaron P, West NJ. Distinct bacterial groups contribute to carbon cycling during a naturally iron fertilized phytoplankton bloom in the Southern Ocean. Limnol Oceanogr. 2011;56(6):2391–401. https://doi.org/10.4319/lo.2011.56.6.2391.

    CAS  Article  Google Scholar 

  84. Landa M, Blain S, Christaki U, Monchy S, Obernosterer I. Shifts in bacterial community composition associated with increased carbon cycling in a mosaic of phytoplankton blooms. SME J. 2016;10(1):39–50. https://doi.org/10.1038/ismej.2015.105.

    CAS  Article  Google Scholar 

  85. Samo TJ, Kimbrel JA, Nilson DJ, Pett-Ridge J, Weber PK, Mayali X. Attachment between heterotrophic bacteria and microalgae influences symbiotic microscale interactions. Environ Microbiol. 2018;20(12):4385–400. https://doi.org/10.1111/1462-2920.14357.

    CAS  Article  PubMed  Google Scholar 

  86. Cook KV, Li C, Cai H, Krumholz LR, Hambright KD, Paerl HW, et al. The global Microcystis interactome. Limnol Oceanogr. 2020;65(Suppl 1):S194–207. https://doi.org/10.1002/lno.11361.

    Article  PubMed  Google Scholar 

  87. Jiang L, Yang L, Xiao L, Shi X, Gao G, Qin B. Quantitative studies on phosphorus transference occuring between Microcystis aeruginosa and its attached bacterium (Pseudomonas sp.). In: Qin B, Liu Z, Havens K, editors. Eutrophication of shallow lakes with special reference to Lake Taihu, China. Dordrecht: Springer Netherlands; 2007. p. 161–5.

    Chapter  Google Scholar 

  88. Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522(7554):98–101. https://doi.org/10.1038/nature14488.

    CAS  Article  PubMed  Google Scholar 

  89. Amin SA, Green DH, Hart MC, Küpper FC, Sunda WG, Carrano CJ. Photolysis of iron–siderophore chelates promotes bacterial–algal mutualism. Proc Natl Acad Sci. 2009;106(40):17071–6. https://doi.org/10.1073/pnas.0905512106.

    Article  PubMed  Google Scholar 

  90. Tang YZ, Koch F, Gobler CJ. Most harmful algal bloom species are vitamin B1 and B12 auxotrophs. Proc Natl Acad Sci. 2010;107(48):20756–61. https://doi.org/10.1073/pnas.1009566107.

    Article  PubMed  Google Scholar 

  91. Croft MT, Lawrence AD, Raux-Deery E, Warren MJ, Smith AG. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature. 2005;438(7064):90–3. https://doi.org/10.1038/nature04056.

    CAS  Article  PubMed  Google Scholar 

  92. Kazamia E, Czesnick H, Nguyen TT, Croft MT, Sherwood E, Sasso S, et al. Mutualistic interactions between vitamin B12 -dependent algae and heterotrophic bacteria exhibit regulation. Environ Microbiol. 2012;14(6):1466–76. https://doi.org/10.1111/j.1462-2920.2012.02733.x.

    CAS  Article  PubMed  Google Scholar 

  93. Grant MAA, Kazamia E, Cicuta P, Smith AG. Direct exchange of vitamin B12 is demonstrated by modelling the growth dynamics of algal-bacterial cocultures. ISME J. 2014;8(7):1418–27. https://doi.org/10.1038/ismej.2014.9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  94. Helliwell KE, Lawrence AD, Holzer A, Kudahl UJ, Sasso S, Kräutler B, et al. Cyanobacteria and eukaryotic algae use different chemical variants of vitamin B12. Curr Biol. 2016;26(8):999–1008. https://doi.org/10.1016/j.cub.2016.02.041.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  95. Meyer N, Bigalke A, Kaulfuß A, Pohnert G. Strategies and ecological roles of algicidal bacteria. FEMS Microbiol Rev. 2017;41(6):880–99. https://doi.org/10.1093/femsre/fux029.

    CAS  Article  PubMed  Google Scholar 

  96. Zhou S, Yin H, Tang S, Peng H, Yin D, Yang Y, et al. Physiological responses of Microcystis aeruginosa against the algicidal bacterium Pseudomonas aeruginosa. Ecotoxicol Environ Saf. 2016;127:214–21. https://doi.org/10.1016/j.ecoenv.2016.02.001.

    CAS  Article  PubMed  Google Scholar 

  97. Weiss G, Kovalerchick D, Lieman-Hurwitz J, Murik O, De Philippis R, Carmeli S, et al. Increased algicidal activity of Aeromonas veronii in response to Microcystis aeruginosa: interspecies crosstalk and secondary metabolites synergism. Environ Microbiol. 2019;21(3):1140–50. https://doi.org/10.1111/1462-2920.14561.

    CAS  Article  PubMed  Google Scholar 

  98. Guo X, Liu X, Wu L, Pan J, Yang H. The algicidal activity of Aeromonas sp. strain GLY-2107 against bloom-forming Microcystis aeruginosa is regulated by N-acyl homoserine lactone-mediated quorum sensing. Environ Microbiol. 2016;18(11):3867–83. https://doi.org/10.1111/1462-2920.13346.

    CAS  Article  PubMed  Google Scholar 

  99. Wu L, Guo X, Liu X, Yang H. NprR-NprX quorum-sensing system regulates the algicidal activity of Bacillus sp. strain S51107 against bloom-forming cyanobacterium Microcystis aeruginosa. Front Microbiol. 2017;8:1968. https://doi.org/10.3389/fmicb.2017.01968.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Rolland JL, Stien D, Sanchez-Ferandin S, Lami R. Quorum sensing and quorum quenching in the phycosphere of phytoplankton: a case of chemical interactions in ecology. J Chem Ecol. 2016;42(12):1201–11. https://doi.org/10.1007/s10886-016-0791-y.

    CAS  Article  PubMed  Google Scholar 

  101. Wu Q, Zhang Y, Li Y, Li J, Zhang X, Li P. Comparison of community composition between Microcystis colony-attached and free-living bacteria, and among bacteria attached with Microcystis colonies of various sizes in culture. Aquat Ecol. 2019;53(3):465–81. https://doi.org/10.1007/s10452-019-09702-7.

    CAS  Article  Google Scholar 

  102. Dunker S, Althammer J, Pohnert G, Wilhelm C. A fateful meeting of two phytoplankton species-chemical vs. cell-cell-interactions in co-cultures of the green algae Oocystis marsonii and the cyanobacterium Microcystis aeruginosa. Microb Ecol. 2017;74(1):22–32. https://doi.org/10.1007/s00248-016-0927-1.

    Article  PubMed  Google Scholar 

  103. Ebrahimi A, Schwartzman J, Cordero OX. Cooperation and spatial self-organization determine rate and efficiency of particulate organic matter degradation in marine bacteria. Proc Natl Acad Sci. 2019;116(46):23309. https://doi.org/10.1073/pnas.1908512116.

    CAS  Article  PubMed  Google Scholar 

  104. Chia MA, Jankowiak JG, Kramer BJ, Goleski JA, Huang IS, Zimba PV, et al. Succession and toxicity of Microcystis and Anabaena (Dolichospermum) blooms are controlled by nutrient-dependent allelopathic interactions. Harmful Algae. 2018;74:67–77. https://doi.org/10.1016/j.hal.2018.03.002.

    CAS  Article  PubMed  Google Scholar 

  105. Chen R, Li F, Liu J, Zheng H, Shen F, Xue Y, et al. The combined effects of Dolichospermum flos-aquae, light, and temperature on microcystin production by Microcystis aeruginosa. Chin J Oceanol Limnol. 2016;34(6):1173–82. https://doi.org/10.1007/s00343-016-5204-0.

    Article  Google Scholar 

  106. Barber-Lluch E, Hernández-Ruiz M, Prieto A, Fernández E, Teira E. Role of vitamin B12 in the microbial plankton response to nutrient enrichment. Mar Ecol Prog Ser. 2019;626:29–42.

    CAS  Article  Google Scholar 

  107. Nishu SD, Kang Y, Han I, Jung TY, Lee TK. Nutritional status regulates algicidal activity of Aeromonas sp. L23 against cyanobacteria and green algae. PLoS One. 2019;14(3):e0213370. https://doi.org/10.1371/journal.pone.0213370.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  108. Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10(8):538–50. https://doi.org/10.1038/nrmicro2832.

    CAS  Article  PubMed  Google Scholar 

  109. Widder S, Allen RJ, Pfeiffer T, Curtis TP, Wiuf C, Sloan WT, et al. Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME J. 2016;10(11):2557–68. https://doi.org/10.1038/ismej.2016.45.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Khandelwal RA, Olivier BG, Röling WFM, Teusink B, Bruggeman FJ. Community flux balance analysis for microbial consortia at balanced growth. PLoS One. 2013;8(5):e64567. https://doi.org/10.1371/journal.pone.0064567.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  111. Henson MA, Hanly TJ. Dynamic flux balance analysis for synthetic microbial communities. IET Syst Biol. 2014;8(5):214–29. https://doi.org/10.1049/iet-syb.2013.0021.

    Article  PubMed  Google Scholar 

  112. Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T. Interdependence of cell growth and gene expression: origins and consequences. Science. 2010;330(6007):1099–102. https://doi.org/10.1126/science.1192588.

    CAS  Article  PubMed  Google Scholar 

  113. Sharma S, Steuer R. Modelling microbial communities using biochemical resource allocation analysis. J R Soc Interface. 2019;16(160):20190474. https://doi.org/10.1098/rsif.2019.0474.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  114. Pacciani-Mori L, Giometto A, Suweis S, Maritan A. Dynamic metabolic adaptation can promote species coexistence in competitive microbial communities. PLoS Comput Biol. 2020;16(5):e1007896. https://doi.org/10.1371/journal.pcbi.1007896.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  115. MacArthur R. Species packing and competitive equilibrium for many species. Theor Popul Biol. 1970;1(1):1–11. https://doi.org/10.1016/0040-5809(70)90039-0.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  116. Dubinkina V, Fridman Y, Pandey PP, Maslov S. Multistability and regime shifts in microbial communities explained by competition for essential nutrients. Elife. 2019;8:e49720. https://doi.org/10.7554/eLife.49720.

    Article  PubMed  PubMed Central  Google Scholar 

  117. Posfai A, Taillefumier T, Wingreen NS. Metabolic trade-offs promote diversity in a model ecosystem. Phys Rev Lett. 2017;118(2):028103. https://doi.org/10.1103/PhysRevLett.118.028103.

    Article  PubMed  PubMed Central  Google Scholar 

  118. Tikhonov M, Monasson R. Collective phase in resource competition in a highly diverse ecosystem. Phys Rev Lett. 2017;118(4):048103. https://doi.org/10.1103/PhysRevLett.118.048103.

    Article  PubMed  Google Scholar 

  119. Pacciani-Mori L, Suweis S, Maritan A, Giometto A. Constrained proteome allocation affects coexistence in models of competitive microbial communities. bioRxiv. 2020:2020.01.27.921478. https://doi.org/10.1101/2020.01.27.921478.

  120. Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361(6401):469. https://doi.org/10.1126/science.aat1168.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  121. May RM. Will a large complex system be stable? Nature. 1972;238(5364):413–4. https://doi.org/10.1038/238413a0.

    CAS  Article  PubMed  Google Scholar 

  122. Allesina S, Tang S. The stabilit™ complexity relationship at age 40: a random matrix perspective. Popul Ecol. 2014;57:63–75.

    Article  Google Scholar 

  123. Bunin G. Ecological communities with Lotka-Volterra dynamics. Phys Rev E. 2017;95(4):042414. https://doi.org/10.1103/PhysRevE.95.042414.

    Article  PubMed  Google Scholar 

  124. Butler S, O’Dwyer JP. Stability criteria for complex microbial communities. Nat Commun. 2018;9(1):2970. https://doi.org/10.1038/s41467-018-05308-z.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  125. Tu C, Suweis S, Grilli J, Formentin M, Maritan A. Reconciling cooperation, biodiversity and stability in complex ecological communities. Sci Rep-Uk. 2019;9(1):5580. https://doi.org/10.1038/s41598-019-41614-2.

    CAS  Article  Google Scholar 

  126. Carrara F, Giometto A, Seymour M, Rinaldo A, Altermatt F. Experimental evidence for strong stabilizing forces at high functional diversity of aquatic microbial communities. Ecology. 2015;96(5):1340–50. https://doi.org/10.1890/14-1324.1.

    Article  PubMed  Google Scholar 

  127. Friedman J, Higgins LM, Gore J. Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol. 2017;1(5):109. https://doi.org/10.1038/s41559-017-0109.

    Article  PubMed  Google Scholar 

  128. Momeni B, Xie L, Shou W. Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. Elife. 2017;6:e25051. https://doi.org/10.7554/eLife.25051.

    Article  PubMed  PubMed Central  Google Scholar 

  129. Carrara F, Giometto A, Seymour M, Rinaldo A, Altermatt F. Inferring species interactions in ecological communities: a comparison of methods at different levels of complexity. Methods Ecol Evol. 2015;6(8):895–906. https://doi.org/10.1111/2041-210X.12363.

    Article  Google Scholar 

  130. Angell IL, Rudi K. A game theory model for gut bacterial nutrient utilization strategies during human infancy. Proc Biol Sci. 2020;287(1931):20200824. https://doi.org/10.1098/rspb.2020.0824.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Gore J, Youk H, van Oudenaarden A. Snowdrift game dynamics and facultative cheating in yeast. Nature. 2009;459(7244):253–6. https://doi.org/10.1038/nature07921.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  132. Goyal A, Dubinkina V, Maslov S. Multiple stable states in microbial communities explained by the stable marriage problem. ISME J. 2018;12(12):2823–34. https://doi.org/10.1038/s41396-018-0222-x.

    Article  PubMed  PubMed Central  Google Scholar 

  133. Grilli J, Adorisio M, Suweis S, Barabás G, Banavar JR, Allesina S, et al. Feasibility and coexistence of large ecological communities. Nat Commun. 2017;8(1):14389. https://doi.org/10.1038/ncomms14389.

    CAS  Article  PubMed Central  Google Scholar 

  134. Kéfi S, Holmgren M, Scheffer M. When can positive interactions cause alternative stable states in ecosystems? Funct Ecol. 2016;30(1):88–97. https://doi.org/10.1111/1365-2435.12601.

    Article  Google Scholar 

  135. Landi P, Minoarivelo HO, Brännström Å, Hui C, Dieckmann U. Complexity and stability of ecological networks: a review of the theory. Popul Ecol. 2018;60(4):319–45. https://doi.org/10.1007/s10144-018-0628-3.

    Article  Google Scholar 

  136. Serván CA, Capitán JA, Grilli J, Morrison KE, Allesina S. Coexistence of many species in random ecosystems. Nat Ecol Evol. 2018;2(8):1237–42. https://doi.org/10.1038/s41559-018-0603-6.

    Article  PubMed  Google Scholar 

  137. Pusa T, Wannagat M, Sagot M-F. Metabolic games. Front Appl Math Stat. 2019;5:18.

    Article  Google Scholar 

  138. Zomorrodi AR, Segrè D. Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities. Nat Commun. 2017;8(1):1563. https://doi.org/10.1038/s41467-017-01407-5.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  139. Carrara F, Altermatt F, Rodriguez-Iturbe I, Rinaldo A. Dendritic connectivity controls biodiversity patterns in experimental metacommunities. Proc Natl Acad Sci. 2012;109(15):5761. https://doi.org/10.1073/pnas.1119651109.

    Article  PubMed  Google Scholar 

  140. Giometto A, Altermatt F, Rinaldo A. Demographic stochasticity and resource autocorrelation control biological invasions in heterogeneous landscapes. Oikos. 2017;126(11):1554–63. https://doi.org/10.1111/oik.04330.

    Article  Google Scholar 

  141. Park S, Wolanin PM, Yuzbashyan EA, Lin H, Darnton NC, Stock JB, et al. Influence of topology on bacterial social interaction. Proc Natl Acad Sci. 2003;100(24):13910. https://doi.org/10.1073/pnas.1935975100.

    CAS  Article  PubMed  Google Scholar 

  142. van Vliet S, Hol FJH, Weenink T, Galajda P, Keymer JE. The effects of chemical interactions and culture history on the colonization of structured habitats by competing bacterial populations. BMC Microbiol. 2014;14(1):116. https://doi.org/10.1186/1471-2180-14-116.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  143. Gupta S, Ross TD, Gomez MM, Grant JL, Romero PA, Venturelli OS. Investigating the dynamics of microbial consortia in spatially structured environments. Nat Commun. 2020;11(1):2418. https://doi.org/10.1038/s41467-020-16200-0.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  144. Paerl HW, Barnard MA. Mitigating the global expansion of harmful cyanobacterial blooms: moving targets in a human- and climatically-altered world. Harmful Algae. 2020;96:101845. https://doi.org/10.1016/j.hal.2020.101845.

    CAS  Article  PubMed  Google Scholar 

  145. Wells ML, Karlson B, Wulff A, Kudela R, Trick C, Asnaghi V, et al. Future HAB science: directions and challenges in a changing climate. Harmful Algae. 2020;91:101632. https://doi.org/10.1016/j.hal.2019.101632.

    Article  PubMed  Google Scholar 

  146. Boyd PW, Collins S, Dupont S, Fabricius K, Gattuso J-P, Havenhand J, et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—a review. Glob Chang Biol. 2018;24(6):2239–61. https://doi.org/10.1111/gcb.14102.

    Article  PubMed  Google Scholar 

  147. Paerl HW, Otten TG, Kudela R. Mitigating the expansion of harmful algal blooms across the freshwater-to-marine continuum. Environ Sci Technol. 2018;52(10):5519–29. https://doi.org/10.1021/acs.est.7b05950.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

MW benefited from discussions at the workshop sponsored by the Kavli Institute of theoretical physics on Active matter 2020 at UC Santa Barbara. MW and FL thank Beth Ahner and Hans Paerl for helpful discussions.

Funding

This work is supported by the USDA National Institute of Food and Agriculture, AFRI project [2016-08830], the Academic Venture Fund from the Cornell Atkinson Center for Sustainability, and The New York State Hatch fund.

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  1. Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, 14853, USA

    Fangchen Liu & Mingming Wu

  2. School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, 14853, USA

    Andrea Giometto

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Liu, F., Giometto, A. & Wu, M. Microfluidic and mathematical modeling of aquatic microbial communities. Anal Bioanal Chem 413, 2331–2344 (2021). https://doi.org/10.1007/s00216-020-03085-7

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Keywords

  • Microbial community
  • Microfluidics
  • Mathematical modeling
  • Algal bloom
  • Phytoplankton