Metabolic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem

  • Chao Ye
  • Nan Xu
  • Xiulai Chen
  • Liming LiuEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1716)


Microbial communities are widespread in the environment, and to isolate and identify species or to determine relations among microorganisms, some ‘omics methods like metagenomics, proteomics, and metabolomics have been used. When combined with various ‘omics data, models known as artificial microbial ecosystems (AME) are powerful methods that can make functional predictions about microbial communities. Reconstruction of an AME model is the first step for model analysis. Many techniques have been applied to the construction of AME models, e.g., the compartmentalization approach, community objectives method, and dynamic analysis approach. Of these approaches, species compartmentalization is the most relevant to genetics. Besides, some algorithms have been developed for the analysis of AME models. In this chapter, we present a general protocol for the use of the species compartmentalization method to reconstruct a model of microbial communities. Then, the analysis of an AME is discussed.

Key words

Microbial communities Omics methods Artificial microbial ecosystem models 


  1. 1.
    Caumette P, Bertrand J-C, Normand P (2015) Some historical elements of microbial ecology. In: Bertrand J-C, Caumette P, Lebaron P, Matheron R, Normand P, Sime-Ngando T (eds) Environmental microbiology: fundamentals and applications. Springer, New York, pp 9–24Google Scholar
  2. 2.
    Bowler C, Karl DM, Colwell RR (2009) Microbial oceanography in a sea of opportunity. Nature 459(7244):180–184CrossRefPubMedGoogle Scholar
  3. 3.
    Sun X, Gao Y, Yang Y (2013) Recent advancement in microbial environmental research using metagenomics tools. Biodivers Sci 21(4):393–400Google Scholar
  4. 4.
    Yamada T (2011) Enterotypes of the human gut microbiome. Nature 473(7346):174–180CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Chikere CB, Okpokwasili GC, Chikere BO (2011) Monitoring of microbial hydrocarbon remediation in the soil. 3 Biotech 1(3):117–138CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Singh BK, Bardgett RD, Smith P, Reay DS (2010) Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat Rev Microbiol 8(11):779–790CrossRefPubMedGoogle Scholar
  7. 7.
    Wilmes P, Wexler M, Bond PL (2008) Metaproteomics provides functional insight into activated sludge wastewater treatment. PLoS One 3(3):e1778CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Daviss B (2005) Growing pains for metabolomics. Scientist 19(8):25–28Google Scholar
  9. 9.
    Ma Q, Zhou J, Zhang WW, Meng XX, Sun JW, Yuan YJ (2011) Integrated proteomic and metabolomic analysis of an artificial microbial community for two-step production of vitamin C. PLoS One 6(10):e26108CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Ye C, Xu N, Dong C, Ye Y, Zou X, Chen X, Guo F, Liu L (2017) IMGMD: a platform for the integration and standardisation of In silico Microbial Genome-scale metabolic models. Sci Rep-UK 7(1):727CrossRefGoogle Scholar
  11. 11.
    McCloskey D, Palsson BØ, Feist AM (2013) Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol Syst Biol 9(1):661CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Oberhardt MA, Palsson BØ, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5(1):320PubMedPubMedCentralGoogle Scholar
  13. 13.
    Durot M, Bourguignon PY, Schachter V (2009) Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 33(1):164–190CrossRefPubMedGoogle Scholar
  14. 14.
    Ranganathan S, Tee TW, Chowdhury A, Zomorrodi AR, Yoon JM, Fu Y, Shanks JV, Maranas CD (2012) An integrated computational and experimental study for overproducing fatty acids in Escherichia coli. Metab Eng 14(6):687–704CrossRefPubMedGoogle Scholar
  15. 15.
    Nakahigashi K, Toya Y, Ishii N, Soga T, Hasegawa M, Watanabe H, Takai Y, Honma M, Mori H, Tomita M (2009) Systematic phenome analysis of Escherichia coli multiple‐knockout mutants reveals hidden reactions in central carbon metabolism. Mol Syst Biol 5(1):306PubMedPubMedCentralGoogle Scholar
  16. 16.
    Fong SS, Palsson BO (2004) Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat Genet 36(10):1056–1058CrossRefPubMedGoogle Scholar
  17. 17.
    Ibarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420(6912):186–189CrossRefPubMedGoogle Scholar
  18. 18.
    Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:119CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Selvarasu S, Ow DSW, Lee SY, Lee MM, Oh SKW, Karimi IA, Lee DY (2009) Characterizing Escherichia coli DH5 alpha Growth and Metabolism in a complex medium using genome- scale flux analysis. Biotechnol Bioeng 102(3):923–934CrossRefPubMedGoogle Scholar
  20. 20.
    Segre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99(23):15112–15117CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Nishikawa T, Gulbahce N, Motter AE (2008) Spontaneous reaction silencing in metabolic optimization. PLoS Comput Biol 4(12):e1000236CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Ghim CM, Goh KI, Kahng B (2005) Lethality and synthetic lethality in the genome-wide metabolic network of Escherichia coli. J Theor Biol 237(4):401–411CrossRefPubMedGoogle Scholar
  23. 23.
    Motter AE, Gulbahce N, Almaas E, Barabasi AL (2008) Predicting synthetic rescues in metabolic networks. Mol Syst Biol 4:168CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Kim DH, Motter AE (2009) Slave nodes and the controllability of metabolic networks. New J Phys 11:113047CrossRefGoogle Scholar
  25. 25.
    Pal C, Papp B, Lercher MJ, Csermely P, Oliver SG, Hurst LD (2006) Chance and necessity in the evolution of minimal metabolic networks. Nature 440(7084):667–670CrossRefPubMedGoogle Scholar
  26. 26.
    Yizhak K, Tuller T, Papp B, Ruppin E (2011) Metabolic modeling of endosymbiont genome reduction on a temporal scale. Mol Syst Biol 7:479CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Notebaart RA, Kensche PR, Huynen MA, Dutilh BE (2009) Asymmetric relationships between proteins shape genome evolution. Genome Biol 10(2):R19CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Pal C, Papp B, Lercher MJ (2005) Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat Genet 37(12):1372–1375CrossRefPubMedGoogle Scholar
  29. 29.
    Pal C, Papp B, Lercher MJ (2005) Horizontal gene transfer depends on gene content of the host. Bioinformatics 21:222–223CrossRefGoogle Scholar
  30. 30.
    Ye C, Zou W, Xu N, Liu L (2014) Metabolic model reconstruction and analysis of an artificial microbial ecosystem for vitamin C production. J Biotechnol 182–183:61–67CrossRefPubMedGoogle Scholar
  31. 31.
    Stolyar S, Van Dien S, Hillesland KL, Pinel N, Lie TJ, Leigh JA, Stahl DA (2007) Metabolic modeling of a mutualistic microbial community. Mol Syst Biol 3:92CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Biggs MB, Medlock GL, Kolling GL, Papin JA (2015) Metabolic network modeling of microbial communities. Wiley Interdiscip Rev Syst Biol Med 7(5):317–334CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Zhuang K, Izallalen M, Mouser P, Richter H, Risso C, Mahadevan R, Lovley DR (2010) Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J 5(2):305–316CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, Jamshidi N (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 6(1):422PubMedPubMedCentralGoogle Scholar
  35. 35.
    Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, Edwards RA, Gerdes S, Parrello B, Shukla M (2014) The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res 42(D1):D206–D214CrossRefPubMedGoogle Scholar
  36. 36.
    Agren R, Liu LM, Shoaie S, Vongsangnak W, Nookaew I, Nielsen J (2013) The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol 9(3):e1002980CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S (2011) Quantitative prediction of cellular metabolism with constraint- based models: the COBRA Toolbox v2. 0. Nat Protoc 6(9):1290–1307CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Swainston N, Smallbone K, Mendes P, Kell D, Paton N (2011) The SuBliMinaL toolbox: automating steps in the reconstruction of metabolic networks. J Integr Bioinform 8(2):186–202CrossRefPubMedGoogle Scholar
  39. 39.
    Ravikrishnan A, Raman K (2015) Critical assessment of genome-scale metabolic networks: the need for a unified standard. Brief Bioinform 16(6):1057–1068CrossRefPubMedGoogle Scholar
  40. 40.
    Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44(D1):D457–D462CrossRefPubMedGoogle Scholar
  41. 41.
    Chang A, Schomburg I, Placzek S, Jeske L, Ulbrich M, Xiao M, Sensen CW, Schomburg D (2014) BRENDA in 2015: exciting developments in its 25th year of existence. Nucleic Acids Res 43(D1):D439–D446CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Saier MH, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G (2015) The transporter classification database (TCDB): recent advances. Nucleic Acids Res 44(D1):D372–D379CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Mueller LA (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 44(D1):D471–D480CrossRefPubMedGoogle Scholar
  44. 44.
    Keseler IM, Mackie A, Peralta-Gil M, Santos-Zavaleta A, Gama-Castro S, Bonavides-Martínez C, Fulcher C, Huerta AM, Kothari A, Krummenacker M (2013) EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res 41(D1):D605–D612CrossRefPubMedGoogle Scholar
  45. 45.
    Sheppard TK, Hitz BC, Engel SR, Song G, Balakrishnan R, Binkley G, Costanzo MC, Dalusag KS, Demeter J, Hellerstedt ST (2015) The Saccharomyces genome database variant viewer. Nucleic Acids Res 44(D1):D698–D702CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Nancy YY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, Sahinalp SC, Ester M, Foster LJ (2010) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26(13):1608–1615CrossRefGoogle Scholar
  47. 47.
    Yu CS, Chen YC, Lu CH, Hwang JK (2006) Prediction of protein subcellular localization. Proteins 64(3):643–651CrossRefPubMedGoogle Scholar
  48. 48.
    Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5(1):93–121CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    King ZA, Lu J, Drager A, Miller P, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, Lewis NE (2016) BiGG Models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44(D1):D515–D522CrossRefPubMedGoogle Scholar
  50. 50.
    Stein L (2001) Genome annotation: from sequence to biology. Nat Rev Genet 2(7):493–503CrossRefPubMedGoogle Scholar
  51. 51.
    Yandell M, Ence D (2012) A beginner's guide to eukaryotic genome annotation. Nat Rev Genet 13(5):329–342CrossRefPubMedGoogle Scholar
  52. 52.
    Stothard P, Wishart DS (2006) Automated bacterial genome analysis and annotation. Curr Opin Microbiol 9(5):505–510CrossRefPubMedGoogle Scholar
  53. 53.
    Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35(Web Server):W182–W185CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Formsma K, Gerdes S, Glass EM, Kubal M (2008) The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9(1):75–89CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Devoid S, Overbeek R, DeJongh M, Vonstein V, Best AA, Henry C (2013) Automated genome annotation and metabolic model reconstruction in the SEED and Model SEED. Methods Mol Biol 985:17–45CrossRefPubMedGoogle Scholar
  56. 56.
    Kim TY, Sohn SB, Kim YB, Kim WJ, Lee SY (2012) Recent advances in reconstruction and applications of genome-scale metabolic models. Curr Opin Biotechnol 23(4):617–623CrossRefPubMedGoogle Scholar
  57. 57.
    Sacher O, Reitz M, Gasteiger J (2009) Investigations of enzyme-catalyzed reactions based on physicochemical descriptors applied to hydrolases. J Chem Inf Model 49(6):1525–1534CrossRefPubMedGoogle Scholar
  58. 58.
    Horton P, Park K-J, Obayashi T, Fujita N, Harada H, Adams-Collier C, Nakai K (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35(suppl 2):W585–W587CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    CS Y, Lin CJ, Hwang JK (2004) Predicting subcellular localization of proteins for Gram- negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 13(5):1402–1406CrossRefGoogle Scholar
  60. 60.
    Moretti S, Martin O, Tran TV, Bridge A, Morgat A, Pagni M (2016) MetaNetX/MNXref - reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks. Nucleic Acids Res 44(D1):D523–D526CrossRefPubMedGoogle Scholar
  61. 61.
    Zomorrodi AR, Maranas CD (2012) OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput Biol 8(2):e1002363CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Zomorrodi AR, Islam MM, Maranas CD (2014) d-OptCom: dynamic multi-level and multi- objective metabolic modeling of microbial communities. ACS Synth Biol 3(4):247–257CrossRefPubMedGoogle Scholar
  63. 63.
    Khandelwal RA, Olivier BG, Roling WFM, Teusink B, Bruggeman FJ (2013) Community flux balance analysis for microbial consortia at balanced growth. PLoS One 8(5):e64567CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Food Science and Technology, Jiangnan UniversityWuxiChina

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