1932

Abstract

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-082120-022836
2021-07-13
2024-12-11
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/23/1/annurev-bioeng-082120-022836.html?itemId=/content/journals/10.1146/annurev-bioeng-082120-022836&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Malik AA, Martiny JBH, Brodie EL, Martiny AC, Treseder KK, Allison SD. 2020. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J 14:1–9
    [Google Scholar]
  2. 2. 
    Graham EB, Knelman JE, Schindlbacher A, Siciliano S, Breulmann M et al. 2016. Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?. Front. Microbiol. 7:214
    [Google Scholar]
  3. 3. 
    Abatenh E, Gizaw B, Tsegaye Z, Tefera G. 2018. Microbial function on climate change - a review. Environ. Pollut. Clim. Chang. 2:147
    [Google Scholar]
  4. 4. 
    Werlang C, Cárcarmo-Oyarce G, Ribbeck K. 2019. Engineering mucus to study and influence the microbiome. Nat. Rev. Mater. 4:2134–45
    [Google Scholar]
  5. 5. 
    Sasse J, Martinoia E, Northen T. 2018. Feed your friends: Do plant exudates shape the root microbiome?. Trends Plant Sci 23:125–41
    [Google Scholar]
  6. 6. 
    Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN et al. 2018. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3:4470–80
    [Google Scholar]
  7. 7. 
    Mello FD, Braidy N, Marçal H, Guillemin G, Nabavi SM, Neilan BA. 2018. Mechanisms and effects posed by neurotoxic products of cyanobacteria/microbial eukaryotes/dinoflagellates in algae blooms: a review. Neurotox. Res. 33:1153–67
    [Google Scholar]
  8. 8. 
    Francino MP. 2016. Antibiotics and the human gut microbiome: dysbioses and accumulation of resistances. Front. Microbiol. 6:1543
    [Google Scholar]
  9. 9. 
    Vogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP et al. 2017. Gut microbiome alterations in Alzheimer's disease. Sci. Rep. 7:113537
    [Google Scholar]
  10. 10. 
    Schmidt TSB, Raes J, Bork P. 2018. The human gut microbiome: from association to modulation. Cell 172:61198–215
    [Google Scholar]
  11. 11. 
    Zheng P, Zeng B, Liu M, Chen J, Pan J et al. 2019. The gut microbiome from patients with schizophrenia modulates the glutamate-glutamine-GABA cycle and schizophrenia-relevant behaviors in mice. Sci. Adv. 5:2eaau8317
    [Google Scholar]
  12. 12. 
    Mukhtar K, Nawaz H, Abid S. 2019. Functional gastrointestinal disorders and gut-brain axis: What does the future hold?. World J. Gastroenterol. 25:5552–66
    [Google Scholar]
  13. 13. 
    Foster KR, Schluter J, Coyte KZ, Rakoff-Nahoum S. 2017. The evolution of the host microbiome as an ecosystem on a leash. Nature 548:766543–51
    [Google Scholar]
  14. 14. 
    Hibbing ME, Fuqua C, Parsek MR, Peterson SB. 2010. Bacterial competition: surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8:115–25
    [Google Scholar]
  15. 15. 
    Lawson CE, Harcombe WR, Hatzenpichler R, Lindemann SR, Löffler FE et al. 2019. Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17:725–41
    [Google Scholar]
  16. 16. 
    Venturelli OS, Carr AV, Fisher G, Hsu RH, Lau R et al. 2018. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14:6e8157
    [Google Scholar]
  17. 17. 
    Kong W, Meldgin DR, Collins JJ, Lu T. 2018. Designing microbial consortia with defined social interactions. Nat. Chem. Biol. 14:8821–29
    [Google Scholar]
  18. 18. 
    Abreu CI, Friedman J, Andersen Woltz VL, Gore J 2019. Mortality causes universal changes in microbial community composition. Nat. Commun. 10:12120
    [Google Scholar]
  19. 19. 
    Coyte KZ, Schluter J, Foster KR. 2015. The ecology of the microbiome: networks, competition, and stability. Science 350:6261663–66
    [Google Scholar]
  20. 20. 
    Stams AJM, Plugge CM. 2009. Electron transfer in syntrophic communities of anaerobic bacteria and archaea. Nat. Rev. Microbiol. 7:8568–77
    [Google Scholar]
  21. 21. 
    Henson M, Phalak P. 2017. Byproduct cross feeding and community stability in an in silico biofilm model of the gut microbiome. Processes 5:413
    [Google Scholar]
  22. 22. 
    Limdi A, Pérez-Escudero A, Li A, Gore J 2018. Asymmetric migration decreases stability but increases resilience in a heterogeneous metapopulation. Nat. Commun. 9:12969
    [Google Scholar]
  23. 23. 
    Tropini C, Earle KA, Huang KC, Sonnenburg JL. 2017. The gut microbiome: connecting spatial organization to function. Cell Host Microbe 21:4433–42
    [Google Scholar]
  24. 24. 
    Yan J, Bassler BL. 2019. Surviving as a community: antibiotic tolerance and persistence in bacterial biofilms. Cell Host Microbe 26:115–21
    [Google Scholar]
  25. 25. 
    Hartmann R, Singh PK, Pearce P, Mok R, Song B et al. 2019. Emergence of three-dimensional order and structure in growing biofilms. Nat. Phys. 15:3251–56
    [Google Scholar]
  26. 26. 
    Rooney LM, Amos WB, Hoskisson PA, McConnell G. 2020. Intra-colony channels in E. coli function as a nutrient uptake system. ISME J 14:102461–73
    [Google Scholar]
  27. 27. 
    Brethauer S, Shahab RL, Studer MH. 2020. Impacts of biofilms on the conversion of cellulose. Appl. Microbiol. Biotechnol. 104:5201–12
    [Google Scholar]
  28. 28. 
    Catania V, Lopresti F, Cappello S, Scaffaro R, Quatrini P. 2020. Innovative, ecofriendly biosorbent-biodegrading biofilms for bioremediation of oil-contaminated water. New Biotechnol 58:25–31
    [Google Scholar]
  29. 29. 
    Liu Z, Hong C-J, Yang Y, Dai L, Ho CL. 2020. Advances in bacterial biofilm management for maintaining microbiome homeostasis. Biotechnol. J. 15:10e1900320
    [Google Scholar]
  30. 30. 
    Ritz K, Young IM. 2004. Interactions between soil structure and fungi. Mycologist 18:252–59
    [Google Scholar]
  31. 31. 
    Simon A, Hervé V, Al-Dourobi A, Verrecchia E, Junier P. 2017. An in situ inventory of fungi and their associated migrating bacteria in forest soils using fungal highway columns. FEMS Microbiol. Ecol. 93:1fiw217
    [Google Scholar]
  32. 32. 
    Kohlmeier S, Smits THM, Ford RM, Keel C, Harms H, Wick LY. 2005. Taking the fungal highway: mobilization of pollutant-degrading bacteria by fungi. Environ. Sci. Technol. 39:124640–46
    [Google Scholar]
  33. 33. 
    Ng W-L, Bassler BL. 2009. Bacterial quorum-sensing network architectures. Annu. Rev. Genet. 43:197–222
    [Google Scholar]
  34. 34. 
    Zschiedrich CP, Keidel V, Szurmant H. 2016. Molecular mechanisms of two-component signal transduction. J. Mol. Biol. 428:193752–75
    [Google Scholar]
  35. 35. 
    Kell DB, Swainston N, Pir P, Oliver SG. 2015. Membrane transporter engineering in industrial biotechnology and whole cell biocatalysis. Trends Biotechnol 33:4237–46
    [Google Scholar]
  36. 36. 
    Chen J, Zhu X, Tan Z, Xu H, Tang J et al. 2014. Activating C4-dicarboxylate transporters DcuB and DcuC for improving succinate production. Appl. Microbiol. Biotechnol. 98:52197–205
    [Google Scholar]
  37. 37. 
    Zhou YJ, Yang W, Wang L, Zhu Z, Zhang S, Zhao ZK. 2013. Engineering NAD+ availability for Escherichia coli whole-cell biocatalysis: a case study for dihydroxyacetone production. Microb. Cell Factor. 12:1103
    [Google Scholar]
  38. 38. 
    Farwick A, Bruder S, Schadeweg V, Oreb M, Boles E 2014. Engineering of yeast hexose transporters to transport d-xylose without inhibition by d-glucose. PNAS 111:145159–64
    [Google Scholar]
  39. 39. 
    Darbani B, Stovicek V, Van Der Hoek SA, Borodina I 2019. Engineering energetically efficient transport of dicarboxylic acids in yeast Saccharomyces cerevisiae. PNAS 116:3919415–20
    [Google Scholar]
  40. 40. 
    Peng X, Gilmore SP, O'Malley MA. 2016. Microbial communities for bioprocessing: lessons learned from nature. Curr. Opin. Chem. Eng. 14:103–9
    [Google Scholar]
  41. 41. 
    Gilmore SP, Lankiewicz TS, Wilken SE, Brown JL, Sexton JA et al. 2019. Top-down enrichment guides in formation of synthetic microbial consortia for biomass degradation. ACS Synth. Biol. 8:92174–85
    [Google Scholar]
  42. 42. 
    Scarborough MJ, Lawson CE, Hamilton JJ, Donohue TJ, Noguera DR. 2018. Metatranscriptomic and thermodynamic insights into medium-chain fatty acid production using an anaerobic microbiome. mSystems 3:6e00221–18
    [Google Scholar]
  43. 43. 
    Gutiérrez N, Garrido D. 2019. Species deletions from microbiome consortia reveal key metabolic interactions between gut microbes. mSystems 4:4e00185–19
    [Google Scholar]
  44. 44. 
    Robinson CD, Auchtung JM, Collins J, Britton RA 2014. Epidemic Clostridium difficile strains demonstrate increased competitive fitness compared to nonepidemic isolates. Infect. Immun. 82:72815–25
    [Google Scholar]
  45. 45. 
    van Dijk EL, Jaszczyszyn Y, Naquin D, Thermes C. 2018. The third revolution in sequencing technology. Trends Genet 34:9666–81
    [Google Scholar]
  46. 46. 
    Amarasinghe SL, Su S, Dong X, Zappia L, Ritchie ME, Gouil Q. 2020. Opportunities and challenges in long-read sequencing data analysis. Genome Biol 21:130
    [Google Scholar]
  47. 47. 
    Raja HA, Miller AN, Pearce CJ, Oberlies NH. 2017. Fungal identification using molecular tools: a primer for the natural products research community. J. Nat. Prod. 80:3756–70
    [Google Scholar]
  48. 48. 
    Campanaro S, Treu L, Kougias PG, Zhu X, Angelidaki I. 2018. Taxonomy of anaerobic digestion microbiome reveals biases associated with the applied high throughput sequencing strategies. Sci. Rep. 8:11926
    [Google Scholar]
  49. 49. 
    Wilson J-J, Brandon-Mong G-J, Gan H-M, Sing K-W 2019. High-throughput terrestrial biodiversity assessments: mitochondrial metabarcoding, metagenomics or metatranscriptomics?. Mitochondrial DNA Part A 30:160–67
    [Google Scholar]
  50. 50. 
    Nayfach S, Rodriguez-Mueller B, Garud N, Pollard KS. 2016. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res 26:111612–25
    [Google Scholar]
  51. 51. 
    Scholz M, Ward DV, Pasolli E, Tolio T, Zolfo M et al. 2016. Strain-level microbial epidemiology and population genomics from shotgun metagenomics. Nat. Methods 13:5435–38
    [Google Scholar]
  52. 52. 
    Shi X, Shao C, Luo C, Chu Y, Wang J et al. 2019. Microfluidics-based enrichment and whole-genome amplification enable strain-level resolution for airway metagenomics. mSystems 4:4e00198–19
    [Google Scholar]
  53. 53. 
    Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. 2017. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res 27:4626–38
    [Google Scholar]
  54. 54. 
    Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. 2013. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat. Biotechnol. 31:6533–38
    [Google Scholar]
  55. 55. 
    Brown CT, Olm MR, Thomas BC, Banfield JF. 2016. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34:121256–63
    [Google Scholar]
  56. 56. 
    Tkacz A, Hortala M, Poole PS. 2018. Absolute quantitation of microbiota abundance in environmental samples. Microbiome 6:1110
    [Google Scholar]
  57. 57. 
    Lou J, Yang L, Wang H, Wu L, Xu J. 2018. Assessing soil bacterial community and dynamics by integrated high-throughput absolute abundance quantification. PeerJ 6:e4514
    [Google Scholar]
  58. 58. 
    Vandeputte D, Kathagen G, D'Hoe K, Vieira-Silva S, Valles-Colomer M et al. 2017. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551:7681507–11
    [Google Scholar]
  59. 59. 
    Contijoch EJ, Britton GJ, Yang C, Mogno I, Li Z et al. 2019. Gut microbiota density influences host physiology and is shaped by host and microbial factors. eLife 8:e40553
    [Google Scholar]
  60. 60. 
    Barlow JT, Bogatyrev SR, Ismagilov RF. 2020. A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities. Nat. Commun. 11:12590
    [Google Scholar]
  61. 61. 
    Sheth RU, Li M, Jiang W, Sims PA, Leong KW, Wang HH. 2019. Spatial metagenomic characterization of microbial biogeography in the gut. Nat. Biotechnol. 37:8877–83
    [Google Scholar]
  62. 62. 
    Fontana A, Campanaro S, Treu L, Kougias PG, Cappa F et al. 2018. Performance and genome-centric metagenomics of thermophilic single and two-stage anaerobic digesters treating cheese wastes. Water Res 134:181–91
    [Google Scholar]
  63. 63. 
    Thornbury M, Sicheri J, Slaine P, Getz LJ, Finlayson-Trick E et al. 2019. Characterization of novel lignocellulose-degrading enzymes from the porcupine microbiome using synthetic metagenomics. PLOS ONE 14:1e0209221
    [Google Scholar]
  64. 64. 
    Ibberson CB, Stacy A, Fleming D, Dees JL, Rumbaugh K et al. 2017. Co-infecting microorganisms dramatically alter pathogen gene essentiality during polymicrobial infection. Nat. Microbiol. 2:17079
    [Google Scholar]
  65. 65. 
    Price MN, Wetmore KM, Waters RJ, Callaghan M, Ray J et al. 2018. Mutant phenotypes for thousands of bacterial genes of unknown function. Nature 557:7706503–9
    [Google Scholar]
  66. 66. 
    Nayfach S, Shi ZJ, Seshadri R, Pollard KS, Kyrpides NC. 2019. New insights from uncultivated genomes of the global human gut microbiome. Nature 568:7753505–10
    [Google Scholar]
  67. 67. 
    Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL et al. 2020. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 48:168883–900
    [Google Scholar]
  68. 68. 
    Gómez-Godínez LJ, Fernandez-Valverde SL, Martinez Romero JC, Martínez-Romero E 2019. Metatranscriptomics and nitrogen fixation from the rhizoplane of maize plantlets inoculated with a group of PGPRs. Syst. Appl. Microbiol. 42:4517–25
    [Google Scholar]
  69. 69. 
    Abu-Ali GS, Mehta RS, Lloyd-Price J, Mallick H, Branck T et al. 2018. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol. 3:3356–66
    [Google Scholar]
  70. 70. 
    Solomon KV, Haitjema CH, Henske JK, Gilmore SP, Borges-Rivera D et al. 2016. Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes. Science 351:62781192–95
    [Google Scholar]
  71. 71. 
    Seppälä S, Wilken SE, Knop D, Solomon KV, O'Malley MA. 2017. The importance of sourcing enzymes from non-conventional fungi for metabolic engineering and biomass breakdown. Metab. Eng. 44:45–59
    [Google Scholar]
  72. 72. 
    He B, Jin S, Cao J, Mi L, Wang J 2019. Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases. Biotechnol. Biofuels 12:1153
    [Google Scholar]
  73. 73. 
    Petrova OE, Garcia-Alcalde F, Zampaloni C, Sauer K. 2017. Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes. Sci. Rep. 7:141114
    [Google Scholar]
  74. 74. 
    Fang N, Akinci-Tolun R. 2016. Depletion of ribosomal RNA sequences fromsingle-cellRNA-sequencinglibrary. Curr. Protoc. Mol. Biol. 115:17.27.1–7.27.20
    [Google Scholar]
  75. 75. 
    Wangsanuwat C, Heom KA, Liu E, O'Malley MA, Dey SS 2020. Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion. BMC Genomics 21:1717
    [Google Scholar]
  76. 76. 
    Mueller RS, Pan C. 2013. Sample handling and mass spectrometry for microbial metaproteomic analyses. Methods Enzymol 531:289–303
    [Google Scholar]
  77. 77. 
    Kleiner M. 2019. Metaproteomics: much more than measuring gene expression in microbial communities. mSystems 4:3e00115–19
    [Google Scholar]
  78. 78. 
    Chinappi M, Cecconi F. 2018. Protein sequencing via nanopore based devices: a nanofluidics perspective. J. Phys. Condens. Matter. 30:20204002
    [Google Scholar]
  79. 79. 
    Kleiner M, Thorson E, Sharp CE, Dong X, Liu D et al. 2017. Assessing species biomass contributions in microbial communities via metaproteomics. Nat. Commun. 8:1 1558.
    [Google Scholar]
  80. 80. 
    Speda J, Jonsson B-H, Carlsson U, Karlsson M. 2017. Metaproteomics-guided selection of targeted enzymes for bioprospecting of mixed microbial communities. Biotechnol. Biofuels 10:1128
    [Google Scholar]
  81. 81. 
    Vit O, Petrak J. 2017. Integral membrane proteins in proteomics. How to break open the black box?. J. Proteom. 153:8–20
    [Google Scholar]
  82. 82. 
    Emwas AHM. 2015. The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods Mol. Biol. 1277:161–93
    [Google Scholar]
  83. 83. 
    Zamboni N, Saghatelian A, Patti GJ. 2015. Defining the metabolome: size, flux, and regulation. Mol. Cell 58:4699–706
    [Google Scholar]
  84. 84. 
    Jeffryes JG, Colastani RL, Elbadawi-Sidhu M, Kind T, Niehaus TD et al. 2015. MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J. Cheminform. 7:144
    [Google Scholar]
  85. 85. 
    Long CP, Antoniewicz MR. 2019. High-resolution 13C metabolic flux analysis. Nat. Protoc. 14:102856–77
    [Google Scholar]
  86. 86. 
    Gebreselassie NA, Antoniewicz MR. 2015. 13C-metabolic flux analysis of co-cultures: a novel approach. Metab. Eng. 31:132–39
    [Google Scholar]
  87. 87. 
    Ghosh A, Nilmeier J, Weaver D, Adams PD, Keasling JD et al. 2014. A peptide-based method for 13C metabolic flux analysis in microbial communities. PLOS Comput. Biol. 10:9e1003827
    [Google Scholar]
  88. 88. 
    Antoniewicz MR. 2020. A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities. Curr. Opin. Biotechnol. 64:230–37
    [Google Scholar]
  89. 89. 
    Coyotzi S, Pratscher J, Murrell JC, Neufeld JD. 2016. Targeted metagenomics of active microbial populations with stable-isotope probing. Curr. Opin. Biotechnol. 41:1–8
    [Google Scholar]
  90. 90. 
    Egert M, Weis S, Schnell S. 2018. RNA-based stable isotope probing (RNA-SIP) to unravel intestinal host-microbe interactions. Methods 149:25–30
    [Google Scholar]
  91. 91. 
    Starr EP, Shi S, Blazewicz SJ, Probst AJ, Herman DJ et al. 2018. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6:1122
    [Google Scholar]
  92. 92. 
    Fortunato CS, Huber JA. 2016. Coupled RNA-SIP and metatranscriptomics of active chemolithoautotrophic communities at a deep-sea hydrothermal vent. ISME J 10:81925–38
    [Google Scholar]
  93. 93. 
    Radajewski S, McDonald IR, Murrell JC. 2003. Stable-isotope probing of nucleic acids: a window to the function of uncultured microorganisms. Curr. Opin. Biotechnol. 14:3296–302
    [Google Scholar]
  94. 94. 
    Seifert J, Taubert M, Jehmlich N, Schmidt F, Völker U et al. 2012. Protein-based stable isotope probing (protein-SIP) in functional metaproteomics. Mass Spectrom. Rev. 31:6683–97
    [Google Scholar]
  95. 95. 
    Mosbæk F, Kjeldal H, Mulat DG, Albertsen M, Ward AJ et al. 2016. Identification of syntrophic acetate-oxidizing bacteria in anaerobic digesters by combined protein-based stable isotope probing and metagenomics. ISME J 10:102405–18
    [Google Scholar]
  96. 96. 
    Ziels RM, Sousa DZ, Stensel HD, Beck DAC. 2018. DNA-SIP based genome-centric metagenomics identifies key long-chain fatty acid-degrading populations in anaerobic digesters with different feeding frequencies. ISME J 12:1112–23
    [Google Scholar]
  97. 97. 
    Berry D, Loy A. 2018. Stable-isotope probing of human and animal microbiome function. Trends Microbiol 26:12999–1007
    [Google Scholar]
  98. 98. 
    Schaerli Y, Hollfelder F. 2009. The potential of microfluidic water-in-oil droplets in experimental biology. Mol. Biosyst. 5:121392–404
    [Google Scholar]
  99. 99. 
    Lan F, Demaree B, Ahmed N, Abate AR. 2017. Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding. Nat. Biotechnol. 35:7640–46
    [Google Scholar]
  100. 100. 
    Sjostrom SL, Bai Y, Huang M, Liu Z, Nielsen J et al. 2014. High-throughput screening for industrial enzyme production hosts by droplet microfluidics. Lab Chip 14:4806–13
    [Google Scholar]
  101. 101. 
    Watterson WJ, Tanyeri M, Watson AR, Cham CM, Shan Y et al. 2020. Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes. eLife 9:e56998
    [Google Scholar]
  102. 102. 
    Hsu RH, Clark RL, Tan JW, Ahn JC, Gupta S et al. 2019. Microbial interaction network inference in microfluidic droplets. Cell Syst 9:3229–42.e4
    [Google Scholar]
  103. 103. 
    Kehe J, Kulesa A, Ortiz A, Ackerman CM, Thakku SG et al. 2019. Massively parallel screening of synthetic microbial communities. PNAS 116:2612804–9
    [Google Scholar]
  104. 104. 
    Ozbakir HF, Anderson NT, Fan KC, Mukherjee A. 2020. Beyond the green fluorescent protein: biomolecular reporters for anaerobic and deep-tissue imaging. Bioconjug. Chem. 31:2293–302
    [Google Scholar]
  105. 105. 
    Gupta S, Ross TD, Gomez MM, Grant JL, Romero PA, Venturelli OS. 2020. Investigating the dynamics of microbial consortia in spatially structured environments. Nat. Commun. 11:12418
    [Google Scholar]
  106. 106. 
    Zengler K, Hofmockel K, Baliga NS, Behie SW, Bernstein HC et al. 2019. EcoFABs: advancing microbiome science through standardized fabricated ecosystems. Nat. Methods 16:7567–71
    [Google Scholar]
  107. 107. 
    Jalili-Firoozinezhad S, Gazzaniga FS, Calamari EL, Camacho DM, Fadel CW et al. 2019. A complex human gut microbiome cultured in an anaerobic intestine-on-a-chip. Nat. Biomed. Eng. 3:7520–31
    [Google Scholar]
  108. 108. 
    Kumar M, Ji B, Zengler K, Nielsen J 2019. Modelling approaches for studying the microbiome. Nat. Microbiol. 4:81253–67
    [Google Scholar]
  109. 109. 
    Song HS, Cannon WR, Beliaev AS, Konopka A. 2014. Mathematical modeling of microbial community dynamics: a methodological review. Processes 2:4711–52
    [Google Scholar]
  110. 110. 
    Cao X, Hamilton JJ, Venturelli OS. 2019. Understanding and engineering distributed biochemical pathways in microbial communities. Biochemistry 58:294–107
    [Google Scholar]
  111. 111. 
    Stein RR, Bucci V, Toussaint NC, Buffie CG, Rätsch G et al. 2013. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLOS Comput. Biol. 9:12e1003388
    [Google Scholar]
  112. 112. 
    Dam P, Fonseca LL, Konstantinidis KT, Voit EO. 2016. Dynamic models of the complex microbial metapopulation of Lake Mendota. NPJ Syst. Biol. Appl. 2:116007
    [Google Scholar]
  113. 113. 
    Pusa T, Wannagat M, Sagot M-F. 2019. Metabolic games. Front. Appl. Math. Stat. 5:18
    [Google Scholar]
  114. 114. 
    Frey E. 2010. Evolutionary game theory: theoretical concepts and applications to microbial communities. Phys. A 389:4265–98
    [Google Scholar]
  115. 115. 
    Nowak MA. 2006. Evolutionary Dynamics: Exploring the Equations of Life Cambridge, MA: Harvard Univ. Press
    [Google Scholar]
  116. 116. 
    Gu C, Kim GB, Kim WJ, Kim HU, Lee SY. 2019. Current status and applications of genome-scale metabolic models. Genome Biol 20:1121
    [Google Scholar]
  117. 117. 
    Thiele I, Palsson B. 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5:193–121
    [Google Scholar]
  118. 118. 
    Orth JD, Thiele I, Palsson BØ. 2010. What is flux balance analysis?. Nat. Biotechnol. 28:3245–48
    [Google Scholar]
  119. 119. 
    Gottstein W, Olivier BG, Bruggeman FJ, Teusink B. 2016. Constraint-based stoichiometric modelling from single organisms to microbial communities. J. R. Soc. Interface 13:12420160627
    [Google Scholar]
  120. 120. 
    Gudmundsson S, Thiele I. 2010. Computationally efficient flux variability analysis. BMC Bioinform 11:489
    [Google Scholar]
  121. 121. 
    Hartmann A, Vila-Santa A, Kallscheuer N, Vogt M, Julien-Laferrière A et al. 2017. OptPipe - a pipeline for optimizing metabolic engineering targets. BMC Syst. Biol. 11:1143
    [Google Scholar]
  122. 122. 
    Zhang C, Hua Q. 2016. Applications of genome-scale metabolic models in biotechnology and systems medicine. Front. Physiol. 6:413
    [Google Scholar]
  123. 123. 
    Kim B, Kim WJ, Kim DI, Lee SY. 2015. Applications of genome-scale metabolic network model in metabolic engineering. J. Ind. Microbiol. Biotechnol. 42:3339–48
    [Google Scholar]
  124. 124. 
    Sgobba E, Wendisch VF. 2020. Synthetic microbial consortia for small molecule production. Curr. Opin. Biotechnol. 62:72–79
    [Google Scholar]
  125. 125. 
    Wang R, Zhao S, Wang Z, Koffas MA 2019. Recent advances in modular co-culture engineering for synthesis of natural products. Curr. Opin. Biotechnol. 2020:65–71
    [Google Scholar]
  126. 126. 
    Thiele I, Sahoo S, Heinken A, Hertel J, Heirendt L et al. 2020. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Mol. Syst. Biol. 16:5e8982
    [Google Scholar]
  127. 127. 
    Mendoza SN, Olivier BG, Molenaar D, Teusink B. 2019. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol 20:1158
    [Google Scholar]
  128. 128. 
    Reimers A-M, Lindhorst H, Waldherr S. 2017. A protocol for generating and exchanging (genome-scale) metabolic resource allocation models. Metabolites 7:347
    [Google Scholar]
  129. 129. 
    Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E et al. 2017. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35:181–89
    [Google Scholar]
  130. 130. 
    Babaei P, Shoaie S, Ji B, Nielsen J 2018. Challenges in modeling the human gut microbiome. Nat. Biotechnol. 36:8682–86
    [Google Scholar]
  131. 131. 
    Magnúsdóttir S, Heinken A, Fleming RMT, Thiele I. 2018. Reply to “Challenges in modeling the human gut microbiome. .” Nat. Biotechnol. 36:8686–91
    [Google Scholar]
  132. 132. 
    Norsigian CJ, Pusarla N, McConn JL, Yurkovich JT, Dräger A et al. 2020. BiGG models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic Acids Res 48:D1D402–6
    [Google Scholar]
  133. 133. 
    Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP et al. 2020. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 49:D1D1555
    [Google Scholar]
  134. 134. 
    Noronha A, Modamio J, Jarosz Y, Guerard E, Sompairac N et al. 2019. The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res 47:D1D614–24
    [Google Scholar]
  135. 135. 
    Robinson JL, Kocabaş P, Wang H, Cholley PE, Cook D et al. 2020. An atlas of human metabolism. Sci. Signal. 13:624eaaz1482
    [Google Scholar]
  136. 136. 
    Aller S, Scott A, Sarkar-Tyson M, Soyer OS. 2018. Integrated human-virus metabolic stoichiometric modelling predicts host-based antiviral targets against Chikungunya, dengue and Zika viruses. J. R. Soc. Interface 15:14620180125
    [Google Scholar]
  137. 137. 
    Kumar M, Ji B, Babaei P, Das P, Lappa D et al. 2018. Gut microbiota dysbiosis is associated with malnutrition and reduced plasma amino acid levels: lessons from genome-scale metabolic modeling. Metab. Eng. 49:128–42
    [Google Scholar]
  138. 138. 
    Lieven C, Beber ME, Olivier BG, Bergmann FT, Ataman M et al. 2020. MEMOTE for standardized genome-scale metabolic model testing. Nat. Biotechnol. 38:3272–76
    [Google Scholar]
  139. 139. 
    Kuang E, Marney M, Cuevas D, Edwards RA, Forsberg EM. 2020. Towards predicting gut microbial metabolism: integration of flux balance analysis and untargeted metabolomics. Metabolites 10:4156
    [Google Scholar]
  140. 140. 
    Tian M, Reed JL. 2018. Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis. Bioinformatics 34:223882–88
    [Google Scholar]
  141. 141. 
    Pandey V, Hadadi N, Hatzimanikatis V. 2019. Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models. PLOS Comput. Biol. 15:5e1007036
    [Google Scholar]
  142. 142. 
    Hadadi N, Pandey V, Chiappino-Pepe A, Morales M, Gallart-Ayala H et al. 2020. Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models. NPJ Syst. Biol. Appl. 6:11
    [Google Scholar]
  143. 143. 
    Stolyar S, Van Dien S, Hillesland KL, Pinel N, Lie TJ et al. 2007. Metabolic modeling of a mutualistic microbial community. Mol. Syst. Biol. 3:192
    [Google Scholar]
  144. 144. 
    Zomorrodi AR, Maranas CD. 2012. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLOS Comput. Biol. 8:2e1002363
    [Google Scholar]
  145. 145. 
    Shoaie S, Ghaffari P, Kovatcheva-Datchary P, Mardinoglu A, Sen P et al. 2015. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab 22:2320–31
    [Google Scholar]
  146. 146. 
    Khandelwal RA, Olivier BG, Ling R, Teusink W, Bruggeman BJ. 2013. Community flux balance analysis for microbial consortia at balanced growth. PLOS ONE 8:564567
    [Google Scholar]
  147. 147. 
    Chan SHJ, Simons MN, Maranas CD. 2017. SteadyCom: predicting microbial abundances while ensuring community stability. PLOS Comput. Biol. 13:5e1005539
    [Google Scholar]
  148. 148. 
    Zomorrodi AR, Segrè D. 2017. Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities. Nat. Commun. 8:11563
    [Google Scholar]
  149. 149. 
    Cai J, Tan T, Chan SHJ. 2019. Bridging evolutionary game theory and metabolic models for predicting microbial metabolic interactions. bioRxiv 623173. https://doi.org/10.1101/623173
    [Crossref]
  150. 150. 
    Zhou Y-H, Gallins P. 2019. A review and tutorial of machine learning methods for microbiome host trait prediction. Front. Genet. 10:579
    [Google Scholar]
  151. 151. 
    Namkung J. 2020. Machine learning methods for microbiome studies. J. Microbiol. 58:3206–16
    [Google Scholar]
  152. 152. 
    Zampieri G, Vijayakumar S, Yaneske E, Angione C 2019. Machine and deep learning meet genome-scale metabolic modeling. PLOS Comput. Biol. 15:7e1007084
    [Google Scholar]
  153. 153. 
    Øyås O, Stelling J. 2018. Genome-scale metabolic networks in time and space. Curr. Opin. Syst. Biol. 8:51–58
    [Google Scholar]
  154. 154. 
    Zeng H, Yang A 2020. Bridging substrate intake kinetics and bacterial growth phenotypes with flux balance analysis incorporating proteome allocation. Sci. Rep. 10:4283
    [Google Scholar]
  155. 155. 
    Nilsson A, Nielsen J, Palsson BO. 2017. Metabolic models of protein allocation call for the kinetome. Cell Syst 5:6538–41
    [Google Scholar]
  156. 156. 
    Boyarskiy S, Tullman-Ercek D. 2015. Getting pumped: membrane efflux transporters for enhanced biomolecule production. Curr. Opin. Chem. Biol. 28:15–19
    [Google Scholar]
  157. 157. 
    Lillington SP, Leggieri PA, Heom KA, O'Malley MA 2020. Nature's recyclers: anaerobic microbial communities drive crude biomass deconstruction. Curr. Opin. Biotechnol. 62:38–47
    [Google Scholar]
  158. 158. 
    Harcombe WR, Riehl WJ, Dukovski I, Granger BR, Betts A et al. 2014. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep 7:41104–15
    [Google Scholar]
  159. 159. 
    Chen J, Gomez JA, Höffner K, Phalak P, Barton PI, Henson MA. 2016. Spatiotemporal modeling of microbial metabolism. BMC Syst. Biol. 10:121
    [Google Scholar]
  160. 160. 
    Patel A, Carlson RP, Henson MA. 2019. In silico metabolic design of two-strain biofilm systems predicts enhanced biomass production and biochemical synthesis. Biotechnol. J. 14:7e1800511
    [Google Scholar]
  161. 161. 
    Phalak P, Chen J, Carlson RP, Henson MA. 2016. Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species. BMC Syst. Biol. 10:190
    [Google Scholar]
  162. 162. 
    Gorochowski TE, Matyjaszkiewicz A, Todd T, Oak N, Kowalska K et al. 2012. BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology. PLOS ONE 7:8e42790
    [Google Scholar]
  163. 163. 
    Jayathilake PG, Gupta P, Li B, Madsen C, Oyebamiji O et al. 2017. A mechanistic individual-based model of microbial communities. PLOS ONE 12:8e0181965
    [Google Scholar]
  164. 164. 
    Bauer E, Zimmermann J, Baldini F, Thiele I, Kaleta C. 2017. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLOS Comput. Biol. 13:5e1005544
    [Google Scholar]
  165. 165. 
    van Hoek MJA, Merks RMH. 2017. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC Syst. Biol. 11:156
    [Google Scholar]
  166. 166. 
    Doloman A, Varghese H, Miller CD, Flann NS. 2017. Modeling de novo granulation of anaerobic sludge. BMC Syst. Biol. 11:169
    [Google Scholar]
  167. 167. 
    David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE et al. 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505:7484559–63
    [Google Scholar]
  168. 168. 
    Johnson AJ, Vangay P, Al-Ghalith GA, Hillmann BM, Ward TL et al. 2019. Daily sampling reveals personalized diet-microbiome associations in humans. Cell Host Microbe 25:6789–802.e5
    [Google Scholar]
  169. 169. 
    Carlson JL, Erickson JM, Lloyd BB, Slavin JL. 2018. Health effects and sources of prebiotic dietary fiber. Curr. Dev. Nutr. 2:3nzy005
    [Google Scholar]
  170. 170. 
    Gilijamse PW, Hartstra AV, Levin E, Wortelboer K, Serlie MJ et al. 2020. Treatment with Anaerobutyricum soehngenii: a pilot study of safety and dose–response effects on glucose metabolism in human subjects with metabolic syndrome. NPJ Biofilms Microbiomes 6:16
    [Google Scholar]
  171. 171. 
    Piewngam P, Zheng Y, Nguyen TH, Dickey SW, Joo HS et al. 2018. Pathogen elimination by probiotic Bacillus via signalling interference. Nature 562:7728532–37
    [Google Scholar]
  172. 172. 
    Puniya AK, Salem AZM, Kumar S, Dagar SS, Griffith GW et al. 2015. Role of live microbial feed supplements with reference to anaerobic fungi in ruminant productivity: a review. J. Integr. Agric. 14:3550–60
    [Google Scholar]
  173. 173. 
    Bhardwaj D, Ansari M, Sahoo R, Tuteja N. 2014. Biofertilizers function as key player in sustainable agriculture by improving soil fertility, plant tolerance and crop productivity. Microb. Cell Factor. 13:166
    [Google Scholar]
  174. 174. 
    Maldonado-Gómez MX, Martínez I, Bottacini F, O'Callaghan A, Ventura M et al. 2016. Stable engraftment of Bifidobacterium longum AH1206 in the human gut depends on individualized features of the resident microbiome. Cell Host Microbe 20:4515–26
    [Google Scholar]
  175. 175. 
    Brandt LJ, Aroniadis OC, Mellow M, Kanatzar A, Kelly C et al. 2012. Long-term follow-up of colonoscopic fecal microbiota transplant for recurrent Clostridium difficile infection. Am. J. Gastroenterol. 107:71079–87
    [Google Scholar]
  176. 176. 
    Gupta S, Allen-Vercoe E, Petrof EO 2016. Fecal microbiota transplantation: in perspective. Therap. Adv. Gastroenterol. 9:2229–39
    [Google Scholar]
  177. 177. 
    DeFilipp Z, Bloom PP, Torres Soto M, Mansour MK, Sater MRA et al. 2019. Drug-resistant E. coli bacteremia transmitted by fecal microbiota transplant. N. Engl. J. Med. 381:212043–50
    [Google Scholar]
  178. 178. 
    Stein RR, Tanoue T, Szabady RL, Bhattarai SK, Olle B et al. 2018. Computer-guided design of optimal microbial consortia for immune system modulation. eLife 7:e30916
    [Google Scholar]
  179. 179. 
    Tanoue T, Morita S, Plichta DR, Skelly AN, Suda W et al. 2019. A defined commensal consortium elicits CD8 T cells and anti-cancer immunity. Nature 565:7741600–5
    [Google Scholar]
  180. 180. 
    Jia Y, Niu C-T, Lu Z-M, Zhang X-J, Chai L-J et al. 2020. A bottom-up approach to develop a synthetic microbial community model: application for efficient reduced-salt broad bean paste fermentation. Appl. Environ. Microbiol. 86:12e00306–20
    [Google Scholar]
  181. 181. 
    Jimenez M, Langer R, Traverso G. 2019. Microbial therapeutics: new opportunities for drug delivery. . J. Exp. Med. 216:51005–9
    [Google Scholar]
  182. 182. 
    Hwang IY, Koh E, Wong A, March JC, Bentley WE et al. 2017. Engineered probiotic Escherichia coli can eliminate and prevent Pseudomonas aeruginosa gut infection in animal models. Nat. Commun. 8:115028
    [Google Scholar]
  183. 183. 
    Thompson JA, Oliveira RA, Djukovic A, Ubeda C, Xavier KB. 2015. Manipulation of the quorum sensing signal AI-2 affects the antibiotic-treated gut microbiota. Cell Rep 10:111861–71
    [Google Scholar]
  184. 184. 
    Ryu MH, Zhang J, Toth T, Khokhani D, Geddes BA et al. 2020. Control of nitrogen fixation in bacteria that associate with cereals. Nat. Microbiol. 5:2314–30
    [Google Scholar]
  185. 185. 
    Tanna T, Ramachanderan R, Platt RJ. 2021. Engineered bacteria to report gut function: technologies and implementation. Curr. Opin. Microbiol. 59:24–33
    [Google Scholar]
  186. 186. 
    Sedighi M, Zahedi Bialvaei A, Hamblin MR, Ohadi E, Asadi A et al. 2019. Therapeutic bacteria to combat cancer; current advances, challenges, and opportunities. Cancer Med 8:63167–81
    [Google Scholar]
  187. 187. 
    Leventhal DS, Sokolovska A, Li N, Plescia C, Kolodziej SA et al. 2020. Immunotherapy with engineered bacteria by targeting the STING pathway for anti-tumor immunity. Nat. Commun. 11:12739
    [Google Scholar]
  188. 188. 
    Nemudryi AA, Valetdinova KR, Medvedev SP, Zakian SM. 2014. TALEN and CRISPR/Cas genome editing systems: tools of discovery. Acta Nat 6:2219–40
    [Google Scholar]
  189. 189. 
    Sheth RU, Cabral V, Chen SP, Wang HH. 2016. Manipulating bacterial communities by in situ microbiome engineering. Trends Genet 32:4189–200
    [Google Scholar]
  190. 190. 
    Haitjema CH, Gilmore SP, Henske JK, Solomon KV, de Groot R et al. 2017. A parts list for fungal cellulosomes revealed by comparative genomics. Nat. Microbiol. 2:817087
    [Google Scholar]
  191. 191. 
    Hamilton TA, Pellegrino GM, Therrien JA, Ham DT, Bartlett PC et al. 2019. Efficient inter-species conjugative transfer of a CRISPR nuclease for targeted bacterial killing. Nat. Commun. 10:14544
    [Google Scholar]
  192. 192. 
    Ronda C, Chen SP, Cabral V, Yaung SJ, Wang HH. 2019. Metagenomic engineering of the mammalian gut microbiome in situ. Nat. Methods 16:2167–70
    [Google Scholar]
  193. 193. 
    Howard-Varona C, Hargreaves KR, Abedon ST, Sullivan MB. 2017. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J 11:71511–20
    [Google Scholar]
  194. 194. 
    Westwater C, Kasman LM, Schofield DA, Werner PA, Dolan JW et al. 2003. Use of genetically engineered phage to deliver antimicrobial agents to bacteria: an alternative therapy for treatment of bacterial infections. Antimicrob. Agents Chemother. 47:41301–7
    [Google Scholar]
  195. 195. 
    Wilken SE, Seppälä S, Lankiewicz TS, Saxena M, Henske JK et al. 2020. Genomic and proteomic biases inform metabolic engineering strategies for anaerobic fungi. Metab. Eng. Commun. 10:e00107
    [Google Scholar]
  196. 196. 
    Wang Y, Fan L, Tuyishime P, Liu J, Zhang K et al. 2020. Adaptive laboratory evolution enhances methanol tolerance and conversion in engineered Corynebacterium glutamicum. Commun. Biol. 3:1217
    [Google Scholar]
  197. 197. 
    Reyes LH, Gomez JM, Kao KC. 2014. Improving carotenoids production in yeast via adaptive laboratory evolution. Metab. Eng. 21:26–33
    [Google Scholar]
  198. 198. 
    Wang L, Xue C, Wang L, Zhao Q, Wei W, Sun Y 2016. Strain improvement of Chlorella sp. for phenol biodegradation by adaptive laboratory evolution. Bioresour. Technol. 205:264–68
    [Google Scholar]
  199. 199. 
    Panke-Buisse K, Poole AC, Goodrich JK, Ley RE, Kao-Kniffin J. 2015. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J 9:4980–89
    [Google Scholar]
  200. 200. 
    Celiker H, Gore J. 2014. Clustering in community structure across replicate ecosystems following a long-term bacterial evolution experiment. Nat. Commun. 5:4643
    [Google Scholar]
  201. 201. 
    Scheuerl T, Hopkins M, Nowell RW, Rivett DW, Barraclough TG, Bell T. 2020. Bacterial adaptation is constrained in complex communities. Nat. Commun. 11:1754
    [Google Scholar]
  202. 202. 
    Lee JW, Chan CTY, Slomovic S, Collins JJ. 2018. Next-generation biocontainment systems for engineered organisms. Nat. Chem. Biol. 14:6530–37
    [Google Scholar]
  203. 203. 
    Mandell DJ, Lajoie MJ, Mee MT, Takeuchi R, Kuznetsov G et al. 2015. Biocontainment of genetically modified organisms by synthetic protein design. Nature 518:753755–60
    [Google Scholar]
  204. 204. 
    Shepherd ES, Deloache WC, Pruss KM, Whitaker WR, Sonnenburg JL. 2018. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature 557:7705434–38
    [Google Scholar]
  205. 205. 
    Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW 2016. GenBank. Nucleic Acids Res 44:D1D67–72
    [Google Scholar]
  206. 206. 
    O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D et al. 2016. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44:D1D733–45
    [Google Scholar]
  207. 207. 
    Amid C, Alako BTF, Balavenkataraman Kadhirvelu V, Burdett T, Burgin J et al. 2020. The European Nucleotide Archive in 2019. Nucleic Acids Res 48:D1D70–76
    [Google Scholar]
  208. 208. 
    Kodama Y, Mashima J, Kosuge T, Kaminuma E, Ogasawara O et al. 2018. DNA Data Bank of Japan: 30th anniversary. Nucleic Acids Res 46:D1D30–35
    [Google Scholar]
  209. 209. 
    Kawashima S, Katayama T, Hatanaka H, Kushida T, Takagi T. 2018. NBDC RDF portal: a comprehensive repository for semantic data in life sciences. Database 2018.bay123
    [Google Scholar]
  210. 210. 
    Kanehisa M, Goto S. 2000. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28:127–30
    [Google Scholar]
  211. 211. 
    Chen IMA, Chu K, Palaniappan K, Pillay M, Ratner A et al. 2019. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res 47:D1D666–77
    [Google Scholar]
  212. 212. 
    Grigoriev IV, Nordberg H, Shabalov I, Aerts A, Cantor M et al. 2012. The genome portal of the Department of Energy Joint Genome Institute. Nucleic Acids Res 40:D1D26–32
    [Google Scholar]
  213. 213. 
    Nordberg H, Cantor M, Dusheyko S, Hua S, Poliakov A et al. 2014. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res 42:D1D26–31
    [Google Scholar]
  214. 214. 
    Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD et al. 2012. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res 40:D1D1178–86
    [Google Scholar]
  215. 215. 
    Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O et al. 2019. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37:4420–23
    [Google Scholar]
  216. 216. 
    Krogh A, Larsson B, Von Heijne G, Sonnhammer ELL. 2001. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305:3567–80
    [Google Scholar]
  217. 217. 
    Bernsel A, Viklund H, Hennerdal A, Elofsson A. 2009. TOPCONS: consensus prediction of membrane protein topology. Nucleic Acids Res 37:W465–68
    [Google Scholar]
  218. 218. 
    Karsch-Mizrachi I, Takagi T, Cochrane G. 2018. The international nucleotide sequence database collaboration. Nucleic Acids Res 46:D1D48–51
    [Google Scholar]
  219. 219. 
    Heldin C-H, Lu B, Evans R, Gutkind JS. 2016. Signals and receptors. Cold Spring Harb. Perspect. Biol. 8:4a005900
    [Google Scholar]
  220. 220. 
    Wallin E, Von Heijne G. 2008. Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci 7:41029–38
    [Google Scholar]
  221. 221. 
    Almén MS, Nordström KJV, Fredriksson R, Schiöth HB. 2009. Mapping the human membrane proteome: a majority of the human membrane proteins can be classified according to function and evolutionary origin. BMC Biol 7:150
    [Google Scholar]
  222. 222. 
    O'Sullivan C, Burrell PC, Pasmore M, Clarke WP, Blackall LL. 2009. Application of flowcell technology for monitoring biofilm development and cellulose degradation in leachate and rumen systems. Bioresour. Technol. 100:1492–96
    [Google Scholar]
  223. 223. 
    Welch JLM, Hasegawa Y, McNulty NP, Gordon JI, Borisy GG 2017. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. PNAS 114:43E9105–14
    [Google Scholar]
  224. 224. 
    Dunham SJB, Ellis JF, Li B, Sweedler JV. 2017. Mass spectrometry imaging of complex microbial communities. Acc. Chem. Res. 50:196–104
    [Google Scholar]
  225. 225. 
    Behrens S, Lösekann T, Pett-Ridge J, Weber PK, Ng WO et al. 2008. Linking microbial phylogeny to metabolic activity at the single-cell level by using enhanced element labeling-catalyzed reporter deposition fluorescence in situ hybridization (EL-FISH) and NanoSIMS. Appl. Environ. Microbiol. 74:103143–50
    [Google Scholar]
  226. 226. 
    McGlynn SE, Chadwick GL, Kempes CP, Orphan VJ. 2015. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526:7574531–35
    [Google Scholar]
  227. 227. 
    Geva-Zatorsky N, Alvarez D, Hudak JE, Reading NC, Erturk-Hasdemir D et al. 2015. In vivo imaging and tracking of host-microbiota interactions via metabolic labeling of gut anaerobic bacteria. Nat. Med. 21:91091–100
    [Google Scholar]
  228. 228. 
    García-Bayona L, Coyne MJ, Hantman N, Montero-Llopis P, Von SS et al. 2020. Nanaerobic growth enables direct visualization of dynamic cellular processes in human gut symbionts. PNAS 117:3924484–93
    [Google Scholar]
  229. 229. 
    Chia HE, Marsh ENG, Biteen JS. 2019. Extending fluorescence microscopy into anaerobic environments. Curr. Opin. Chem. Biol. 51:98–104
    [Google Scholar]
  230. 230. 
    Orth JD, Palsson BØ. 2010. Systematizing the generation of missing metabolic knowledge. Biotechnol. Bioeng. 107:3403–12
    [Google Scholar]
  231. 231. 
    Wiback SJ, Mahadevan R, Palsson BØ. 2004. Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum. Biotechnol. Bioeng. 86:3317–31
    [Google Scholar]
  232. 232. 
    Bogaerts P, Gziri KM, Richelle A 2017. From MFA to FBA: defining linear constraints accounting for overflow metabolism in a macroscopic FBA-based dynamical model of cell cultures in bioreactor. J. Process Control 60:34–47
    [Google Scholar]
/content/journals/10.1146/annurev-bioeng-082120-022836
Loading
/content/journals/10.1146/annurev-bioeng-082120-022836
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error