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Modelling approaches for studying the microbiome

Abstract

Advances in metagenome sequencing of the human microbiome have provided a plethora of new insights and revealed a close association of this complex ecosystem with a range of human diseases. However, there is little knowledge about how the different members of the microbial community interact with each other and with the host, and we lack basic mechanistic understanding of these interactions related to health and disease. Mathematical modelling has been demonstrated to be highly advantageous for gaining insights into the dynamics and interactions of complex systems and in recent years, several modelling approaches have been proposed to enhance our understanding of the microbiome. Here, we review the latest developments and current approaches, and highlight how different modelling strategies have been applied to unravel the highly dynamic nature of the human microbiome. Furthermore, we discuss present limitations of different modelling strategies and provide a perspective of how modelling can advance understanding and offer new treatment routes to impact human health.

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Fig. 1: Schematic illustration of ODE-based modelling accounting for phenotypic traits of microorganisms.
Fig. 2: Modelling strategies based on sequence-read abundance.
Fig. 3: Fundamentals of GEMs.
Fig. 4: Community-level analysis of gut microbiome based on GEMs.
Fig. 5: Applications of mathematical modelling in the systems-level analysis of human gut microbiome.

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References

  1. Lozupone, C., Stombaugh, J., Gordon, J. & Jansson, J. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gilbert, J. A. et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535, 94–103 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Van Wey, A. S. et al. Monoculture parameters successfully predict coculture growth kinetics of Bacteroides thetaiotaomicron and two Bifidobacterium strains. Int. J. Food Microbiol. 191, 172–181 (2014).

    Article  PubMed  Google Scholar 

  4. Muñoz-Tamayo, R. et al. Kinetic modelling of lactate utilization and butyrate production by key human colonic bacterial species. FEMS Microbiol. Ecol. 76, 615–624 (2011).

    Article  PubMed  CAS  Google Scholar 

  5. White, R. A., Callister, S. J., Moore, R. J., Baker, E. S. & Jansson, J. K. The past, present and future of microbiome analyses. Nat. Protoc. 11, 2049–2053 (2016).

    Article  CAS  Google Scholar 

  6. Arnold, J. W., Roach, J. & Azcarate-Peril, M. A. Emerging technologies for gut microbiome research. Trends Microbiol. 24, 887–901 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Amaretti, A. et al. Kinetics and metabolism of Bifidobacterium adolescentis MB 239 growing on glucose, galactose, lactose, and galactooligosaccharides. Appl. Environ. Microbiol. 73, 3637–3644 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lagier, J. C. et al. The rebirth of culture in microbiology through the example of culturomics to study human gut microbiota. Clin. Microbiol. Rev. 28, 237–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Tomlin, C. J. & Axelrod, J. D. Biology by numbers: mathematical modelling in developmental biology. Nat. Rev. Genet. 8, 331–340 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Fabien, B. Analytical System Dynamics (Springer, 2009).

  11. Van Wey, A. S., Lovatt, S. J., Roy, N. C. & Shorten, P. R. Determination of potential metabolic pathways of human intestinal bacteria by modeling growth kinetics from cross-feeding dynamics. Food Res. Int. 88, 207–216 (2016).

    Article  CAS  Google Scholar 

  12. Muñoz-Tamayo, R., Laroche, B., Walter, É., Doré, J. & Leclerc, M. Mathematical modelling of carbohydrate degradation by human colonic microbiota. J. Theor. Biol. 266, 189–201 (2010).

    Article  PubMed  CAS  Google Scholar 

  13. Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, 31–36 (2013).

    Article  CAS  Google Scholar 

  16. Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).

    Article  CAS  PubMed  Google Scholar 

  17. Mounier, J. et al. Microbial interactions within a cheese microbial community. Appl. Environ. Microbiol. 74, 172–181 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shoaie, S. et al. Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci. Rep. 3, 2532 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. El-Semman, I. E. et al. Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2–32 and Faecalibacterium prausnitzii A2–165 and their interaction. BMC Syst. Biol. 8, 41 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Steinway, S. N., Biggs, M. B., Loughran, T. P., Papin, J. A. & Albert, R. Inference of network dynamics and metabolic interactions in the gut microbiome. PLoS Comput. Biol. 11, e1004338 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Shoaie, S. et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320–331 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Heinken, A. & Thiele, I. Anoxic conditions promote species-specific mutualism between gut microbes in silico. Appl. Environ. Microbiol. 81, 4049–4061 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lidicker, W. Z. A clarification of interactions in ecological systems. Bioscience 29, 475–477 (1979).

    Article  Google Scholar 

  26. Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2016).

    Article  PubMed  CAS  Google Scholar 

  27. Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Karlsson, F. H., Nookaew, I., Petranovic, D. & Nielsen, J. Prospects for systems biology and modeling of the gut microbiome. Trends Biotechnol. 29, 251–258 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Bonabeau, E. Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl Acad. Sci. USA 99, 7280–7287 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Shashkova, T. et al. Agent based modeling of human gut microbiome interactions and perturbations. PLoS ONE 11, e0148386 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Pinto, F., Medina, D. A., Pérez-Correa, J. R. & Garrido, D. Modeling metabolic interactions in a consortium of the infant gut microbiome. Front. Microbiol. 8, 2507 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ruan, Q. et al. Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors. Bioinformatics 22, 2532–2538 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Deng, Y. et al. Molecular ecological network analyses. BMC Bioinformatics 13, 133 (2012).

    Article  Google Scholar 

  34. Hoffmann, C. et al. Archaea and fungi of the human gut microbiome: correlations with diet and bacterial residents. PLoS ONE 8, e66019 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945).

    Article  Google Scholar 

  36. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–109 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Faust, K. & Raes, J. CoNet app: inference of biological association networks using Cytoscape. F1000 5, 1519 (2016).

    Article  Google Scholar 

  40. Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).

    Article  CAS  PubMed  Google Scholar 

  41. Meinshausen, N. & Bühlmann, P. High-dimensional graphs and variable selection with the Lasso. Ann. Stat. 34, 1436–1462 (2006).

    Article  Google Scholar 

  42. Bonneau, R. et al. The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 7, R36 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Friedman, J., Hastie, T. & Tibshirani, R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432–441 (2008).

    Article  PubMed  Google Scholar 

  44. Banerjee, O. & Ghaoui, L. El & D’Aspremont, A. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. J. Mach. Learn. Res. 9, 485–516 (2008).

    Google Scholar 

  45. Sarkar, S. K. & Chang, C. K. The simes method for multiple hypothesis testing with positively dependent test statistics. J. Am. Stat. Assoc. 92, 1601–1608 (1997).

    Article  Google Scholar 

  46. Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Marino, S., Baxter, N. T., Huffnagle, G. B., Petrosino, J. F. & Schloss, P. D. Mathematical modeling of primary succession of murine intestinal microbiota. Proc. Natl Acad. Sci. USA 111, 439–444 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Weiss, S. et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 10, 1669–1681 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Faust, K. et al. Signatures of ecological processes in microbial community time series. Microbiome 6, 120 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Bucci, V. & Xavier, J. B. Towards predictive models of the human gut microbiome. J. Mol. Biol. 426, 3907–3916 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Greenblum, S., Turnbaugh, P. J. & Borenstein, E. Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc. Natl Acad. Sci. USA 109, 594–599 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Chan, S. H. J., Simons, M. N. & Maranas, C. D. SteadyCom: predicting microbial abundances while ensuring community stability. PLoS Comput. Biol. 13, e1005539 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Shoaie, S. et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320–331 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Zengler, K. & Palsson, B. O. A road map for the development of community systems (CoSy) biology. Nat. Rev. Microbiol. 10, 366–372 (2012).

    Article  CAS  PubMed  Google Scholar 

  58. Oberhardt, M. A., Palsson, B. & Papin, J. A. Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 5, 320 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Lewis, N. E., Nagarajan, H. & Palsson, B. O. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 10, 291–305 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Levy, S. E. & Myers, R. M. Advancements in next-generation sequencing. Annu. Rev. Genom. Hum. Genet. 17, 95–115 (2016).

    Article  CAS  Google Scholar 

  62. Heinken, A., Sahoo, S., Fleming, R. M. T. & Thiele, I. Systems-level characterization of a host–microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4, 28–40 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977–982 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Zomorrodi, A. R. & Maranas, C. D. OptCom: A multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput. Biol. 8, e1002363 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Khandelwal, R. A., Olivier, B. G., Röling, W. F. M., Teusink, B. & Bruggeman, F. J. Community flux balance analysis for microbial consortia at balanced growth. PLoS ONE 8, e64567 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lozupone, C., Stomabaugh, J., Gordon, J., Jansson, J. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  69. David, L. A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2015).

    Article  CAS  Google Scholar 

  70. Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Kumar, M. et al. Gut microbiota dysbiosis is associated with malnutrition and reduced plasma amino acid levels: Lessons from genome-scale metabolic modeling. Metab. Eng. 49, 128–142 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Babaei, P., Shoaie, S., Ji, B. & Nielsen, J. Challenges in modeling the human gut microbiome. Nat. Biotechnol. 36, 682–686 (2018).

    Article  CAS  PubMed  Google Scholar 

  73. Garza, D. R., Van Verk, M. C., Huynen, M. A. & Dutilh, B. E. Towards predicting the environmental metabolome from metagenomics with a mechanistic model. Nat. Microbiol. 3, 456–460 (2018).

    Article  CAS  PubMed  Google Scholar 

  74. Diener, C. & Resendis-Antonio, O. Micom: metagenome-scale modeling to infer metabolic interactions in the microbiota. Preprint at https://www.biorxiv.org/content/10.1101/361907v2 (2018).

  75. Harrison, R., Papp, B., Pál, C., Oliver, S. G. & Delneri, D. Plasticity of genetic interactions in metabolic networks of yeast. Proc. Natl Acad. Sci. USA 104, 2307–2312 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat. Genet. 43, 656–662 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Mani, R., St.Onge, R. P., Hartman, J. L., Giaever, G. & Roth, F. P. Defining genetic interaction. Proc. Natl Acad. Sci. USA 105, 3461–3466 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).

    Article  CAS  PubMed  Google Scholar 

  79. Bauer, E., Zimmermann, J., Baldini, F., Thiele, I. & Kaleta, C. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput. Biol. 13, e1005544 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Glushchenko, O. et al. VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota. Bioinformatics https://doi.org/10.1093/bioinformatics/btz154 (2019).

    Article  PubMed  Google Scholar 

  81. Weston, B., Fogal, B., Cook, D. & Dhurjati, P. An agent-based modeling framework for evaluating hypotheses on risks for developing autism: effects of the gut microbial environment. Med. Hypotheses 84, 395–401 (2015).

    Article  PubMed  Google Scholar 

  82. An, G., Mi, Q., Dutta-Moscato, J. & Vodovotz, Y. Agent-based models in translational systems biology. WIREs Syst. Biol. Med. 1, 159–171 (2009).

    Article  CAS  Google Scholar 

  83. Mardinoglu, A. et al. The gut microbiota modulates host amino acid and glutathione metabolism in mice. Mol. Syst. Biol. 11, 834–834 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Arora, T. et al. Diabetes-associated microbiota in fa/fa rats is modified by Roux-en-Y gastric bypass. ISME J. 11, 2035–2046 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Arora, T. et al. Microbially produced glucagon-like peptide 1 improves glucose tolerance in mice. Mol. Metab. 5, 725–730 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Darzi, Y., Falony, G., Vieira-Silva, S. & Raes, J. Towards biome-specific analysis of meta-omics data. ISME J. 10, 1025–1028 (2016).

    Article  CAS  PubMed  Google Scholar 

  87. Wang, Y., Eddy, J. A. & Price, N. D. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst. Biol. 6, 153 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Agren, R. et al. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput. Biol. 8, e1002518 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Schultz, A. & Qutub, A. A. Reconstruction of tissue-specific metabolic networks using CORDA. PLoS Comput. Biol. 12, e1004808 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Zhalnina, K., Zengler, K., Newman, D. & Northen, T. R. Need for laboratory ecosystems to unravel the structures and functions of soil microbial communities mediated by chemistry. mBio 9, e01175–18 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Petriz, B. A. & Franco, O. L. Metaproteomics as a complementary approach to gut microbiota in health and disease. Front. Chem. 5, 4 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. de Haffmann, E. Tandem mass spectrometry: a primer. J. Mass Spectrom. 31, 129–137 (1996).

    Article  Google Scholar 

  93. Benesty, J., Chen, J., Huang, Y. & Cohen, I. Noise reduction in speech processing (Springer, 2009).

  94. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Huson, D., Auch, A., Qi, J. & Schuster, S. MEGAN analysis of metagenome data. Genome Res. 17, 377–386 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Karlsson, F. H., Nookaew, I. & Nielsen, J. Metagenomic data utilization and analysis (MEDUSA) and construction of a global gut microbial gene catalogue. PLoS Comput. Biol. 10, e1003706 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Meyer, F. et al. The metagenomics RAST server — a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9, 386 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Kultima, J. R. et al. MOCAT: A metagenomics assembly and gene prediction toolkit. PLoS ONE 7, e47656 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  101. Kultima, J. R. et al. MOCAT2: A metagenomic assembly, annotation and profiling framework. Bioinformatics 32, 2520–2523 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Oulas, A. et al. Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinform. Biol. Insights 9, 75–88 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Kunin, V., Copeland, A., Lapidus, A., Mavromatis, K. & Hugenholtz, P. A bioinformatician’s guide to metagenomics. Microbiol. Mol. Biol. Rev. 72, 557–578 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Escobar-Zepeda, A. et al. Analysis of sequencing strategies and tools for taxonomic annotation: defining standards for progressive metagenomics. Sci. Rep. 8, 12034 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Lillacci, G. & Khammash, M. Parameter estimation and model selection in computational biology. PLoS Comput. Biol. 6, e1000696 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Thiele, I. & Palsson, B. Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93–121 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Chan, S. H. J., Cai, J., Wang, L., Simons-Senftle, M. N. & Maranas, C. D. Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models. Bioinformatics 33, 3603–3609 (2017).

    Article  CAS  PubMed  Google Scholar 

  108. Henry, C. S., Jankowski, M. D., Broadbelt, L. J. & Hatzimanikatis, V. Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys. J. 90, 1453–1461 (2006).

    Article  CAS  PubMed  Google Scholar 

  109. Agren, R. et al. The RAVEN Toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput. Biol. 9, e1002980 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).

    Article  CAS  PubMed  Google Scholar 

  112. Tian, M. & Reed, J. L. Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis. Bioinformatics 34, 3882–3888 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Sung, J., Hale, V., Merkel, A. C., Kim, P. J. & Chia, N. Metabolic modeling with big data and the gut microbiome. Appl. Transl. Genom. 10, 10–15 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Levy, R. & Borenstein, E. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc. Natl Acad. Sci. USA 110, 12804–12809 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Embree, M., Liu, J. K., Al-Bassam, M. M. & Zengler, K. Networks of energetic and metabolic interactions define dynamics in microbial communities. Proc. Natl Acad. Sci. USA 112, 15450–15455 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Nagarajan, H. et al. Characterization and modelling of interspecies electron transfer mechanisms and microbial community dynamics of a syntrophic association. Nat. Commun. 4, 2809 (2013).

    Article  PubMed  CAS  Google Scholar 

  117. Karp, P. D., Paley, S. & Romero, P. The pathway tools software. Bioinformatics 18, 225–232 (2002).

    Article  Google Scholar 

  118. Arakawa, K., Yamada, Y., Shinoda, K., Nakayama, Y. & Tomita, M. GEM system: automatic prototyping of cell-wide metabolic pathway models from genomes. BMC Bioinformatics 7, 168 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  119. Pitkänen, E. et al. Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species. PLoS Comput. Biol. 10, e1003465 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Dias, O., Rocha, M., Ferreira, E. C. & Rocha, I. Reconstructing high-quality large-scale metabolic models with merlin. Methods Mol. Biol. 1716, 1–36 (2018).

    Article  CAS  PubMed  Google Scholar 

  121. Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Swainston, N. et al. The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks. J. Integr. Bioinform. 8, 186 (2011).

    Article  PubMed  Google Scholar 

  123. Becker, S. A. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat. Protoc. 2, 727–738 (2007).

    Article  CAS  PubMed  Google Scholar 

  124. Ebrahim, A., Palsson, J. A. L. B. O. & Hyduke, D. R. COBRApy: constraints-based reconstruction and analysis for Python. BMC Syst. Biol. 7, 74 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Heirendt, L., Thiele, I. & Fleming, R. M. T. DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics 33, 1421–1423 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Olivier, B. G., Rohwer, J. M. & Hofmeyr, J. H. S. Modelling cellular systems with PySCeS. Bioinformatics 21, 560–561 (2005).

    Article  CAS  PubMed  Google Scholar 

  127. Gelius-Dietrich, G., Desouki, A. A., Fritzemeier, C. J. & Lercher, M. J. Sybil-efficient constraint-based modelling in R. BMC Syst. Biol. 7, 125 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Seif, Y. et al. Genome-scale metabolic reconstructions of multiple Salmonella strains reveal serovar-specific metabolic traits. Nat. Commun. 9, 3771 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  129. Monk, J. M. et al. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc. Natl Acad. Sci. USA 110, 20338–20343 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. Imelfort, M. et al. GroopM: an automated tool for the recovery of population genomes from related metagenomes. PeerJ 2, e603 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Tramontano, M. et al. Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies. Nat. Microbiol. 3, 514–522 (2018).

    Article  CAS  PubMed  Google Scholar 

  133. Lieven, C. et al. Memote: A community driven effort towards a standardized genome-scale metabolic model test suite. Preprint at https://www.biorxiv.org/content/10.1101/350991v1 (2018).

  134. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

    Article  CAS  PubMed  Google Scholar 

  135. Schellenberger, J., Park, J. O., Conrad, T. M. & Palsson, B. Ø. BiGG: a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11, 213 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  136. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 44, D471–D480 (2016).

    Article  CAS  PubMed  Google Scholar 

  137. Benedict, M. N., Mundy, M. B., Henry, C. S., Chia, N. & Price, N. D. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models. PLoS Comput. Biol. 10, e1003882 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  138. Latendresse, M. Efficiently gap-filling reaction networks. BMC Bioinformatics 15, 225 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We acknowledge financial support from Knut and Alice Wallenberg Foundation, the Novo Nordisk Foundation (grant no. NNF10CC1016517), Vetenskapsrådet, Bill & Melinda Gates Foundation (grant no. OPP1127499), MetaCardis (grant no. HEALTH-F4-2012-305312), FORMAS and the Swedish Foundation for Strategic Research.

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M.K., B.J. and J.N. collectively conceptualized the manuscript. M.K., B.J., K.Z. and J.N. wrote the manuscript.

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Correspondence to Jens Nielsen.

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Kumar, M., Ji, B., Zengler, K. et al. Modelling approaches for studying the microbiome. Nat Microbiol 4, 1253–1267 (2019). https://doi.org/10.1038/s41564-019-0491-9

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