BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities

PLoS Computational Biology

Volumn 13, Issue 5, 2017, Pages

  Bauer, Eugen ^a   Zimmermann, Johannes ^b   Baldini, Federico ^a   Thiele, Ines ^a   Kaleta, Christoph ^b  

^a UNIVERSITY OF LUXEMBOURG   (Luxembourg)

^b UNIVERSITY OF KIEL   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIA; METABOLISM;

CELLULAR POPULATIONS; CONSTRAINT-BASED MODELING; CONSTRAINTS-BASED MODELING; HUMAN GUTS; HUMAN HEALTH; INDIVIDUAL-BASED; METABOLIC INTERACTIONS; METABOLIC MODELLING; MICROBIAL COMMUNITIES; MODEL COMMUNITIES;

BIOFILMS;

GLYCAN;

ARTICLE; BIOFILM; BIOLOGICAL MODEL; BIOMASS FERMENTATION; COMPUTER SYSTEM; GENETIC HETEROGENEITY; HUMAN; INTESTINE; METABOLIC FLUX ANALYSIS; METABOLIC MODEL; MICROBIAL COMMUNITY; MICROBIAL METABOLISM; NONHUMAN; ORGANISMAL INTERACTION; PHENOTYPE; PSEUDOMONAS AERUGINOSA; SPATIAL ANALYSIS; TEMPORAL ANALYSIS; INTESTINE FLORA; METABOLISM; MICROBIAL CONSORTIUM; PHYSIOLOGY; PROCEDURES;

BIOFILMS; GASTROINTESTINAL MICROBIOME; HUMANS; METABOLIC FLUX ANALYSIS; MICROBIAL CONSORTIA; MICROBIAL INTERACTIONS; MODELS, BIOLOGICAL; PSEUDOMONAS AERUGINOSA;

EID: 85020127804     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005544     Document Type: Article

References (79)

1. Zengler K, Palsson BØ, A road map for the development of community systems (CoSy) biology. Nature Reviews Microbiology. 2012;10(5):366–372. 22450377
2. Cardinale BJ, Palmer MA, Swan CM, Brooks S, Poff NL, The influence of substrate heterogeneity on biofilm metabolism in a stream ecosystem. Ecology. 2002;83(2):412–422. doi: 10.1890/0012-9658(2002)083%5B0412:TIOSHO%5D2.0.CO;2
3. Stewart PS, Franklin MJ, Physiological heterogeneity in biofilms. Nature Reviews Microbiology. 2008;6(3):199–210. doi: 10.1038/nrmicro1838 18264116
4. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, et al. Diversity of the human intestinal microbial flora. Science. 2005;308(5728):1635–1638. doi: 10.1126/science.1110591 15831718
5. Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI, Human nutrition, the gut microbiome and the immune system. Nature. 2011;474(7351):327–336. doi: 10.1038/nature10213 21677749
6. Wong JM, De Souza R, Kendall CW, Emam A, Jenkins DJ, Colonic health: fermentation and short chain fatty acids. Journal of Clinical Gastroenterology. 2006;40(3):235–243. doi: 10.1097/00004836-200603000-00015 16633129
7. McGuckin MA, Lindén SK, Sutton P, Florin TH, Mucin dynamics and enteric pathogens. Nature Reviews Microbiology. 2011;9(4):265–278. doi: 10.1038/nrmicro2538 21407243
8. Germerodt S, Bohl K, Lück A, Pande S, Schröter A, Kaleta C, et al. Pervasive Selection for Cooperative Cross-Feeding in Bacterial Communities. PLOS Computational Biology. 2016;12(6):e1004986. doi: 10.1371/journal.pcbi.1004986 27314840
9. Pande S, Merker H, Bohl K, Reichelt M, Schuster S, de Figueiredo LF, et al. Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria. The ISME Journal. 2013;8(5):953–962. doi: 10.1038/ismej.2013.211 24285359
10. Belenguer A, Duncan SH, Calder AG, Holtrop G, Louis P, Lobley GE, et al. Two routes of metabolic cross-feeding between Bifidobacterium adolescentis and butyrate-producing anaerobes from the human gut. Applied and Environmental Microbiology. 2006;72(5):3593–3599. doi: 10.1128/AEM.72.5.3593-3599.2006 16672507
11. Liu J, Prindle A, Humphries J, Gabalda-Sagarra M, Asally M, Dong-yeon DL, et al. Metabolic co-dependence gives rise to collective oscillations within biofilms. Nature. 2015;523(7562):550–554. doi: 10.1038/nature14660 26200335
12. Parkins MD, Ceri H, Storey DG, Pseudomonas aeruginosa GacA, a factor in multihost virulence, is also essential for biofilm formation. Molecular Microbiology. 2001;40(5):1215–1226. doi: 10.1046/j.1365-2958.2001.02469.x 11401724
13. Stewart PS, Costerton JW, Antibiotic resistance of bacteria in biofilms. The Lancet. 2001;358(9276):135–138. doi: 10.1016/S0140-6736(01)05321-1
14. Lewis NE, Nagarajan H, Palsson BO, Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012;10(4):291–305. doi: 10.1038/nrmicro2737 22367118
15. Thiele I, Palsson BØ, A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protocols. 2010;5(1):93–121. doi: 10.1038/nprot.2009.203 20057383
16. Orth JD, Thiele I, Palsson BØ, What is flux balance analysis?Nature Biotechnology. 2010;28(3):245–248. doi: 10.1038/nbt.1614 20212490
17. Zomorrodi AR, Segrè D, Synthetic ecology of microbes: mathematical models and applications. Journal of molecular biology. 2016;428(5):837–861. doi: 10.1016/j.jmb.2015.10.019 26522937
18. Klitgord N, Segre D, Environments that induce synthetic microbial ecosystems. PLoS Computational Biology. 2010;6(11):e1001002. doi: 10.1371/journal.pcbi.1001002 21124952
19. Heinken A, Thiele I, Anoxic conditions promote species-specific mutualism between gut microbes in silico. Applied and Environmental Microbiology. 2015;81(12):4049–4061. doi: 10.1128/AEM.00101-15 25841013
20. Shoaie S, Ghaffari P, Kovatcheva-Datchary P, Mardinoglu A, Sen P, Pujos-Guillot E, et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell metabolism. 2015;22(2):320–331. doi: 10.1016/j.cmet.2015.07.001 26244934
21. Zhuang K, Izallalen M, Mouser P, Richter H, Risso C, Mahadevan R, et al. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. The ISME Journal. 2011;5(2):305–316. doi: 10.1038/ismej.2010.117 20668487
22. Louca S, Doebeli M, Calibration and analysis of genome-based models for microbial ecology. eLife. 2015;4:e08208. doi: 10.7554/eLife.08208 26473972
23. Zomorrodi AR, Islam MM, Maranas CD, d-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities. ACS Synthetic Biology. 2014;3(4):247–257. doi: 10.1021/sb4001307 24742179
24. Harcombe WR, Riehl WJ, Dukovski I, Granger BR, Betts A, Lang AH, et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Reports. 2014;7(4):1104–1115. doi: 10.1016/j.celrep.2014.03.070 24794435
25. Biggs MB, Papin JA, Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation. PloS One. 2013;8(10):e78011. doi: 10.1371/journal.pone.0078011 24147108
26. Holland JH, Complex Adaptive Systems. Daedalus, The MIT Press on behalf of American Academy of Arts & Sciences. 1992;121(1):17–30.
27. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, et al. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science. 2005;310(5750):987–991. doi: 10.1126/science.1116681 16284171
28. Kaul H, Ventikos Y, Investigating biocomplexity through the agent-based paradigm. Briefings in Bioinformatics. 2015;16(1):137–152. doi: 10.1093/bib/bbt077 24227161
29. Eddelbuettel D, François R, Allaire J, Chambers J, Bates D, Ushey K, Rcpp: Seamless R and C++ integration. Journal of Statistical Software. 2011;40(8):1–18. doi: 10.18637/jss.v040.i08
30. Bauer E, Laczny CC, Magnusdottir S, Wilmes P, Thiele I, Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires. Microbiome. 2015;3(1):1. doi: 10.1186/s40168-015-0121-6
31. Muralidharan V, Rinker K, Hirsh I, Bouwer E, Kelly R, Hydrogen transfer between methanogens and fermentative heterotrophs in hyperthermophilic cocultures. Biotechnology and bioengineering. 1997;56(3):268–278. doi: 10.1002/(SICI)1097-0290(19971105)56:3%3C268::AID-BIT4%3E3.0.CO;2-H 18636642
32. Milne CB, Eddy JA, Raju R, Ardekani S, Kim PJ, Senger RS, et al. Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052. BMC Syst Biol. 2011;5:130. doi: 10.1186/1752-0509-5-130 21846360
33. Gorochowski TE, Matyjaszkiewicz A, Todd T, Oak N, Kowalska K, Reid S, et al. BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology. PloS One. 2012;7(8):e42790. doi: 10.1371/journal.pone.0042790 22936991
34. Shashkova T, Popenko A, Tyakht A, Peskov K, Kosinsky Y, Bogolubsky L, et al. Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations. PloS One. 2016;11(2):e0148386. doi: 10.1371/journal.pone.0148386 26894828
35. Lee J, Yun H, Feist AM, Palsson BØ, Lee SY, Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network. Applied microbiology and biotechnology. 2008;80(5):849–862. doi: 10.1007/s00253-008-1654-4 18758767
36. Gonnerman MC, Benedict MN, Feist AM, Metcalf WW, Price ND, Genomically and biochemically accurate metabolic reconstruction of Methanosarcina barkeri Fusaro, iMG746. Biotechnology journal. 2013;8(9):1070–1079. doi: 10.1002/biot.201200266 23420771
37. LaBauve AE, Wargo MJ, Growth and laboratory maintenance of Pseudomonas aeruginosa. Curr Protoc Microbiol. 2012;Chapter 6:Unit 6E.1. doi: 10.1002/9780471729259.mc06e01s25 22549165
38. Eschbach M, Schreiber K, Trunk K, Buer J, Jahn D, Schobert M, Long-term anaerobic survival of the opportunistic pathogen Pseudomonas aeruginosa via pyruvate fermentation. Journal of Bacteriology. 2004;186(14):4596–4604. doi: 10.1128/JB.186.14.4596-4604.2004 15231792
39. Robert M, Mercade M, Bosch M, Parra J, Espuny M, Manresa M, et al. Effect of the carbon source on biosurfactant production byPseudomonas aeruginosa 44T1. Biotechnology Letters. 1989;11(12):871–874. doi: 10.1007/BF01026843
40. Smyth P, Clarke PH, Catabolite repression of Pseudomonas aeruginosa amidase: the effect of carbon source on amidase synthesis. Microbiology. 1975;90(1):81–90.
41. King AD, Nagel CW, Influence of carbon dioxide upon the metabolism of Pseudomonas aeruginosa. Journal of Food Science. 1975;40(2):362–366. doi: 10.1111/j.1365-2621.1975.tb02202.x
42. Gill C, Tan K, Effect of carbon dioxide on growth of Pseudomonas fluorescens. Applied and Environmental Microbiology. 1979;38(2):237–240. 117751
43. Schobert M, Jahn D, Anaerobic physiology of Pseudomonas aeruginosa in the cystic fibrosis lung. International Journal of Medical Microbiology. 2010;300(8):549–556. doi: 10.1016/j.ijmm.2010.08.007 20951638
44. Carlson CA, Ingraham JL, Comparison of denitrification by Pseudomonas stutzeri, Pseudomonas aeruginosa, and Paracoccus denitrificans. Applied and Environmental Microbiology. 1983;45(4):1247–1253. 6407395
45. Vander Wauven C, Pierard A, Kley-Raymann M, Haas D, Pseudomonas aeruginosa mutants affected in anaerobic growth on arginine: evidence for a four-gene cluster encoding the arginine deiminase pathway. Journal of Bacteriology. 1984;160(3):928–934. 6438064
46. Borriello G, Werner E, Roe F, Kim AM, Ehrlich GD, Stewart PS, Oxygen Limitation Contributes to Antibiotic Tolerance of Pseudomonas aeruginosa in Biofilms. Antimicrobial Agents and Chemotherapy. 2004;48(7):2659–2664. doi: 10.1128/AAC.48.7.2659-2664.2004 15215123
47. Becker N, Kunath J, Loh G, Blaut M, Human intestinal microbiota: characterization of a simplified and stable gnotobiotic rat model. Gut Microbes. 2011;2(1):25–33. doi: 10.4161/gmic.2.1.14651 21637015
48. Bouhnik Y, Alain S, Attar A, Flourié B, Raskine L, Sanson-Le Pors MJ, et al. Bacterial populations contaminating the upper gut in patients with small intestinal bacterial overgrowth syndrome. The American Journal of Gastroenterology. 1999;94(5):1327–1331. doi: 10.1111/j.1572-0241.1999.01016.x 10235214
49. Koropatkin NM, Cameron EA, Martens EC, How glycan metabolism shapes the human gut microbiota. Nature reviews Microbiology. 2012;10(5):323–335. doi: 10.1038/nrmicro2746 22491358
50. Earle KA, Billings G, Sigal M, Lichtman JS, Hansson GC, Elias JE, et al. Quantitative imaging of gut microbiota spatial organization. Cell Host & Microbe. 2015;18(4):478–488. doi: 10.1016/j.chom.2015.09.002
51. Collado MC, Derrien M, Isolauri E, de Vos WM, Salminen S, Intestinal integrity and Akkermansia muciniphila, a mucin-degrading member of the intestinal microbiota present in infants, adults, and the elderly. Applied and Environmental Microbiology. 2007;73(23):7767–7770. doi: 10.1128/AEM.01477-07 17933936
52. Swidsinski A, Weber J, Loening-Baucke V, Hale LP, Lochs H, Spatial organization and composition of the mucosal flora in patients with inflammatory bowel disease. Journal of Clinical Microbiology. 2005;43(7):3380–3389. doi: 10.1128/JCM.43.7.3380-3389.2005 16000463
53. Schluter J, Foster KR, The evolution of mutualism in gut microbiota via host epithelial selection. PLoS Biology. 2012;10(11):e1001424. doi: 10.1371/journal.pbio.1001424 23185130
54. Flint HJ, Duncan SH, Scott KP, Louis P, Interactions and competition within the microbial community of the human colon: links between diet and health. Environmental Microbiology. 2007;9(5):1101–1111. doi: 10.1111/j.1462-2920.2007.01281.x 17472627
55. Ferreyra JA, Wu KJ, Hryckowian AJ, Bouley DM, Weimer BC, Sonnenburg JL, Gut microbiota-produced succinate promotes C. difficile infection after antibiotic treatment or motility disturbance. Cell Host & Microbe. 2014;16(6):770–777. doi: 10.1016/j.chom.2014.11.003
56. Cummings J, Pomare E, Branch W, Naylor C, Macfarlane G, Short chain fatty acids in human large intestine, portal, hepatic and venous blood. Gut. 1987;28(10):1221–1227. doi: 10.1136/gut.28.10.1221 3678950
57. Ji B, Nielsen J, From next-generation sequencing to systematic modeling of the gut microbiome. Frontiers in genetics. 2015;6. doi: 10.3389/fgene.2015.00219 26157455
58. Gelius-Dietrich G, Desouki AA, Fritzemeier CJ, Lercher MJ, sybil–Efficient constraint-based modelling in R. BMC Systems Biology. 2013;7(1):125. doi: 10.1186/1752-0509-7-125 24224957
59. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular Systems Biology. 2010;6(1):390. doi: 10.1038/msb.2010.47 20664636
60. Michaelis L, Menten ML, Die Kinetik der Invertinwirkung. Biochemische Zeitschrift. 1913;49(333-369):352.
61. Adler J, Dahl MM, A method for measuring the motility of bacteria and for comparing random and non-random motility. Microbiology. 1967;46(2):161–173.
62. Middelboe M, Jørgensen NO, Viral lysis of bacteria: an important source of dissolved amino acids and cell wall compounds. Journal of the Marine Biological Association of the United Kingdom. 2006;86(03):605–612. doi: 10.1017/S0025315406013518
63. Monod J, The growth of bacterial cultures. Annual Reviews in Microbiology. 1949;3(1):371–394. doi: 10.1146/annurev.mi.03.100149.002103
64. Stewart PS, Diffusion in biofilms. Journal of Bacteriology. 2003;185(5):1485–1491. doi: 10.1128/JB.185.5.1485-1491.2003 12591863
65. Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, et al. BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Research. 2004;32(suppl 1):D431–D433. doi: 10.1093/nar/gkh081 14681450
66. GNU Linear Programming Kit (GLPK);. Available from: http://www.gnu.org/software/glpk/.
67. COIN OR Clp;. Available from: https://projects.coin-or.org/Clp/.
68. IBM ILOG CPLEX;. Available from: https://www.ibm.com/developerworks/university/academicinitiative/.
69. Gurobi;. Available from: http://www.gurobi.com.
70. Soetaert K, Meysman F. ReacTran: Reactive transport modelling in 1D, 2D and 3D. R Package version. 2010;1.
71. Soetaert K, Petzoldt T, Setzer RW, et al. Solving differential equations in R: package deSolve. Journal of Statistical Software. 2010;33(9):1–25. doi: 10.18637/jss.v033.i09
72. Milo R, Jorgensen P, Moran U, Weber G, Springer M, BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Research. 2010;38(suppl 1):D750–D753. doi: 10.1093/nar/gkp889 19854939
73. Prats R, De Pedro M, Normal growth and division of Escherichia coli with a reduced amount of murein. Journal of Bacteriology. 1989;171(7):3740–3745. doi: 10.1128/jb.171.7.3740-3745.1989 2500418
74. Loferer-Krößbacher M, Klima J, Psenner R, Determination of bacterial cell dry mass by transmission electron microscopy and densitometric image analysis. Applied and Environmental Microbiology. 1998;64(2):688–694. 9464409
75. Gosset G, Improvement of Escherichia coli production strains by modification of the phosphoenolpyruvate: sugar phosphotransferase system. Microbial Cell Factories. 2005;4(1):1.
76. Oberhardt MA, Puchałka J, Fryer KE, Dos Santos VAM, Papin JA, Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. Journal of Bacteriology. 2008;190(8):2790–2803. doi: 10.1128/JB.01583-07 18192387
77. Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E, Noronha A, et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nature Biotechnology. 2016; 27893703
78. Mahadevan R, Schilling C, The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metabolic Engineering. 2003;5(4):264–276. doi: 10.1016/j.ymben.2003.09.002 14642354
79. Smirnov A, Sklan D, Uni Z, Mucin dynamics in the chick small intestine are altered by starvation. The Journal of Nutrition. 2004;134(4):736–742. 15051819