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Computational Modeling of Multidrug-Resistant Bacteria

  • Fabricio Alves Barbosa da SilvaEmail author
  • Fernando Medeiros Filho
  • Thiago Castanheira Merigueti
  • Thiago Giannini
  • Rafaela Brum
  • Laura Machado de Faria
  • Ana Paula Barbosa do Nascimento
  • Kele Teixeira Belloze
  • Floriano Paes SilvaJr.
  • Rodolpho Mattos Albano
  • Marcelo Trindade dos Santos
  • Maria Clicia Stelling de Castro
  • Marcio Argollo de Menezes
  • Ana Paula D’A. Carvalho-Assef
Chapter
Part of the Computational Biology book series (COBO, volume 27)

Abstract

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology, and computational approaches have been increasingly employed to tackle this task. In this chapter, we describe current efforts by FIOCRUZ and partners to develop integrated computational models of multidrug-resistant bacteria. The bacterium chosen as the main focus of this effort is Pseudomonas aeruginosa, an opportunistic pathogen associated with a broad spectrum of infections in humans. Nowadays, P. aeruginosa is one of the main problems of healthcare-associated infections (HAI) in the world, because of its great capacity of survival in hospital environments and its intrinsic resistance to many antibiotics. Our overall research objective is to use integrated computational models to accurately predict a wide range of observable cellular behaviors of multidrug-resistant P. aeruginosa CCBH4851, which is a strain belonging to the clone ST277, endemic in Brazil. In this chapter, after a brief introduction to P. aeruginosa biology, we discuss the construction of metabolic and gene regulatory networks of P. aeruginosa CCBH 4851 from its genome. We also illustrate how these networks can be integrated into a single model, and we discuss methods for identifying potential therapeutic targets through integrated models.

Notes

Acknowledgment

This study was supported by fellowships from CAPES to FMF and from the Oswaldo Cruz Institute (https://pgbcs.ioc.fiocruz.br/) to TG. We also thank FAPERJ and CAPES for financial support.

References

  1. 1.
    Brasil. Ministério da Saúde. Agência Nacional de Vigilância Sanitária. Boletim de segurança do paciente e qualidade em serviços de saúde n° 14: Avaliação dos indicadores nacionais das Infecções Relacionadas à Assistência à Saúde (IRAS) e resistência microbiana do ano de 2015. Brasília (DF): Ministério da Saúde. (In portuguese) Available at: (https://www20.anvisa.gov.br/segurancadopaciente/index.php/publicacoes/item/boletim-de-seguranca-do-paciente-e-qualidade-em-servicos-de-saude-n-13-avaliacao-dos-indicadores-nacionais-das-infeccoes-relacionadas-a-assistencia-a-saude-iras-e-resistencia-microbiana-do-ano-de-2015) 2016.
  2. 2.
    World Health Organization. Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. (http://www.who.int/medicines/publications/WHO-PPL-Short_Summary_25Feb-ET_NM_WHO.pdf?ua=1) 2017.
  3. 3.
    Covert M, Xiao N, Chen T, Karr J. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics. 2008;24(18):2044–50.CrossRefGoogle Scholar
  4. 4.
    Silveira M, Albano R, Asensi M, Assef A. The draft genome sequence of multidrug-resistant Pseudomonas aeruginosa strain CCBH4851, a nosocomial isolate belonging to clone SP (ST277) that is prevalent in Brazil. Mem Inst Oswaldo Cruz. 2014;109(8):1086–7.CrossRefGoogle Scholar
  5. 5.
    Carrera J, Covert M. Why build whole-cell models? Trends Cell Biol. 2015;25(12):719–22.CrossRefGoogle Scholar
  6. 6.
    Karr J, Sanghvi J, Macklin D, Gutschow M, Jacobs J, Bolival B, et al. A whole-cell computational model predicts phenotype from genotype. Cell. 2012;150(2):389–401.CrossRefGoogle Scholar
  7. 7.
    Pier GB, Ramphal R. Pseudomonas aeruginosa. In: Mandell GL, Bennett JE, Dolin R, editors. Mandell, Douglas, and Bennett’s principles and practice of infectious diseases. 7th ed. Philadelphia: Churchill Livingstone Elsevier; 2010. p. 2835–60.CrossRefGoogle Scholar
  8. 8.
    Driscoll J, Brody S, Kollef M. The epidemiology, pathogenesis and treatment of Pseudomonas aeruginosa infections. Drugs. 2007;67(3):351–68.CrossRefGoogle Scholar
  9. 9.
    Lee K, Yoon SS. Pseudomonas aeruginosa biofilm, a programmed bacterial life for fitness. J Microbiol Biotechnol. 2017;27(6):1053–64.Google Scholar
  10. 10.
    Balasubramanian D, Schneper L, Kumari H, Mathee K. A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence. Nucleic Acids Res. 2012;41(1):1–20.CrossRefGoogle Scholar
  11. 11.
    Engel J, Balachandran P. Role of Pseudomonas aeruginosa type III effectors in disease. Curr Opin Microbiol. 2009;12(1):61–6.CrossRefGoogle Scholar
  12. 12.
    Tomita M, Hashimoto K, Takahashi K, Shimizu TS, Matsuzaki Y, Miyoshi F, Saio K, Tanida S, Yugi K, Venter J, Hutchison CA. E-CELL: software environment for whole-cell simulation. Bioinformatics. 1999;15(1):72–84.CrossRefGoogle Scholar
  13. 13.
    Kerr K, Snelling A. Pseudomonas aeruginosa: a formidable and ever-present adversary. J Hosp Infect. 2009;73(4):338–44.CrossRefGoogle Scholar
  14. 14.
    Kung V, Ozer E, Hauser A. The accessory genome of Pseudomonas aeruginosa. Microbiol Mol Biol Rev. 2010;74(4):621–41.CrossRefGoogle Scholar
  15. 15.
    Vallet-Gely I, Boccard F. Chromosomal organization and segregation in Pseudomonas aeruginosa. PLoS Genet. 2013;9(5):e1003492.CrossRefGoogle Scholar
  16. 16.
    Silveira M, Albano R, Asensi M, Carvalho-Assef A. Description of genomic islands associated to the multidrug-resistant Pseudomonas aeruginosa clone ST277. Infect Genet Evol. 2016;42:60–5.CrossRefGoogle Scholar
  17. 17.
    Oliver A, Mulet X, López-Causapé C, Juan C. The increasing threat of Pseudomonas aeruginosa high-risk clones. Drug Resist Updat. 2015;21–22:41–59.CrossRefGoogle Scholar
  18. 18.
    Cornaglia G, Giamarellou H, Rossolini G. Metallo-β-lactamases: a last frontier for β-lactams? Lancet Infect Dis. 2011;11(5):381–93.CrossRefGoogle Scholar
  19. 19.
    Nascimento A, Ortiz M, Martins W, Morais G, Fehlberg L, Almeida L, et al. Intraclonal genome stability of the metallo-β-lactamase SPM-1-producing Pseudomonas aeruginosa ST277, an endemic clone disseminated in brazilian hospitals. Front Microbiol. 2016;7:1946.CrossRefGoogle Scholar
  20. 20.
    Cavalcanti F, Almeida A, Vilela M, Morais M, Morais JM. Changing the epidemiology of carbapenem-resistant Pseudomonas aeruginosa in a Brazilian teaching hospital: the replacement of São Paulo metallo-β-lactamase-producing isolates. Mem Inst Oswaldo Cruz. 2012;107(3):420–3.CrossRefGoogle Scholar
  21. 21.
    Gales A, Menezes L, Silbert S, Sader H. Dissemination in distinct Brazilian regions of an epidemic carbapenem-resistant Pseudomonas aeruginosa producing SPM metallo- β-lactamase. J Antimicrob Chemother. 2003;52(4):699–702.CrossRefGoogle Scholar
  22. 22.
    Fonseca E, Freitas F, Vicente A. The Colistin-only sensitive Brazilian Pseudomonas aeruginosa clone SP (sequence type 277) is spread worldwide. Antimicrob Agents Chemother. 2010;54(6):2743.CrossRefGoogle Scholar
  23. 23.
    Salabi A, Toleman M, Weeks J, Bruderer T, Frei R, Walsh T. First report of the metallo- β-lactamase SPM-1 in Europe. Antimicrob Agents Chemother. 2009;54(1):582.CrossRefGoogle Scholar
  24. 24.
    Hopkins K, Findlay J, Mustafa N, Pike R, Parsons H, Wright L, et al. SPM-1 metallo-β-lactamase-producing Pseudomonas aeruginosa ST277 in the UK. J Med Microbiol. 2016;65(7):696–7.CrossRefGoogle Scholar
  25. 25.
    Galán-Vásquez E, Luna B, Martínez-Antonio A. The regulatory network of Pseudomonas aeruginosa. Microb Inf Exp. 2011;1(1):3.CrossRefGoogle Scholar
  26. 26.
    Babaei P, Ghasemi-Kahrizsangi T, Marashi S. Modeling the differences in biochemical capabilities of pseudomonas species by flux balance analysis: how good are genome-scale metabolic networks at predicting the differences? Sci World J. 2014;2014:1–11.Google Scholar
  27. 27.
    Brent M. Genome annotation past, present, and future: how to define an ORF at each locus. Genome Res. 2005;15(12):1777–86.CrossRefGoogle Scholar
  28. 28.
    Richardson E, Watson M. The automatic annotation of bacterial genomes. Brief Bioinform. 2012;14(1):1–12.CrossRefGoogle Scholar
  29. 29.
    Verli H. Bioinformática: da biologia à flexibilidade molecular. 1st ed. São Paulo: SBBq; 2014.Google Scholar
  30. 30.
    Campbell M, Yandell M. An introduction to genome annotation. Curr Protocol Bioinforma. 2015;52:4.1.1–4.1.17.CrossRefGoogle Scholar
  31. 31.
    Delcher A. Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 1999;27(23):4636–41.CrossRefGoogle Scholar
  32. 32.
    Besemer J, Lomsadze A, Borodovsky M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001;29(12):2607–18.CrossRefGoogle Scholar
  33. 33.
    Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki E, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44(14):6614–24.CrossRefGoogle Scholar
  34. 34.
    Lagesen K, Hallin P, Rødland E, Stærfeldt H, Rognes T, Ussery D. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35(9):3100–8.CrossRefGoogle Scholar
  35. 35.
    Lowe T, Eddy S. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25(5):955–64.CrossRefGoogle Scholar
  36. 36.
    Laslett D, Canback B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 2004;32(1):11–6.CrossRefGoogle Scholar
  37. 37.
    Kinouchi M, Kurokawa K. [Special issue: fact databases and freewares] tRNAfinder: a software system to find all tRNA genes in the DNA sequence based on the cloverleaf secondary structure. J Comput Aided Chem. 2006;7:116–24.CrossRefGoogle Scholar
  38. 38.
    Overbeek R, Olson R, Pusch G, Olsen G, Davis J, Disz T, et al. The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res. 2013;42(D1):D206–14.CrossRefGoogle Scholar
  39. 39.
    Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9.CrossRefGoogle Scholar
  40. 40.
    Zimin A, Marçais G, Puiu D, Roberts M, Salzberg S, Yorke J. The MaSuRCA genome assembler. Bioinformatics. 2013;29(21):2669–77.CrossRefGoogle Scholar
  41. 41.
    Otto T, Dillon G, Degrave W, Berriman M. RATT: rapid annotation transfer tool. Nucleic Acids Res. 2011;39(9):e57.CrossRefGoogle Scholar
  42. 42.
    Thiele I, Palsson B. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5(1):93–121.CrossRefGoogle Scholar
  43. 43.
    The Uniprot Consortium: the universal protein knowledgebase. Nucleic Acids Res. 2017;45(D1):D158–D169.Google Scholar
  44. 44.
    Barthelmes J, Ebeling C, Chang A, Schomburg I, Schomburg D. BRENDA, AMENDA and FRENDA: the enzyme information system in 2007. Nucleic Acids Res. 2007;35(Database):D511–4.CrossRefGoogle Scholar
  45. 45.
    Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita K, Itoh M, Kawashima S, et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006;34(90001):D354–7.CrossRefGoogle Scholar
  46. 46.
    Heavner B, Price N. Transparency in metabolic network reconstruction enables scalable biological discovery. Curr Opin Biotechnol. 2015;34:105–9.CrossRefGoogle Scholar
  47. 47.
    Oberhardt M, Puchalka J, Fryer K, Martins dos Santos V, Papin J. Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol. 2008;190(8):2790–803.CrossRefGoogle Scholar
  48. 48.
    Vital-Lopez F, Reifman J, Wallqvist A. Biofilm formation mechanisms of Pseudomonas aeruginosa predicted via genome-scale kinetic models of bacterial metabolism. PLoS Comput Biol. 2015;11(10):e1004452.CrossRefGoogle Scholar
  49. 49.
    Bartell J, Blazier A, Yen P, Thøgersen J, Jelsbak L, Goldberg J, et al. Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat Commun. 2017;8:14631.CrossRefGoogle Scholar
  50. 50.
    Moreno-Hagelsieb G, Latimer K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics. 2008;24(3):319–24.CrossRefGoogle Scholar
  51. 51.
    Novichkov P, Kazakov A, Ravcheev D, Leyn S, Kovaleva G, Sutormin R, et al. RegPrecise 3.0 – a resource for genome-scale exploration of transcriptional regulation in bacteria. BMC Genomics. 2013;14(1):745.CrossRefGoogle Scholar
  52. 52.
    Bailey T, Boden M, Buske F, Frith M, Grant C, Clementi L, et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009;37.(Web Server:W202–8.CrossRefGoogle Scholar
  53. 53.
    Hwang S, Kim C, Ji S, Go J, Kim H, Yang S, et al. Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa. Sci Rep. 2016;6(1):26223.CrossRefGoogle Scholar
  54. 54.
    Jeong H, Mason S, Barabási A, Oltvai Z. Lethality and centrality in protein networks. Nature. 2001;411(6833):41–2.CrossRefGoogle Scholar
  55. 55.
    Shannon P, Markiel A, Owen O, Nitin SB, Jonathan TW, Daniel R, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.CrossRefGoogle Scholar
  56. 56.
    Trindade dos Santos M, Nascimento A, Medeiros Filho F, Silva F. Modeling gene transcriptional regulation. Theor Appl Asp Syst Biol. 2018;27:27–39.Google Scholar
  57. 57.
    Carrera J, Estrela R, Luo J, Rai N, Tsoukalas A, Tagkopoulos I. An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol Syst Biol. 2014;10(7):735.CrossRefGoogle Scholar
  58. 58.
    Goldberg A, Chew Y, Karr J. Toward scalable whole-cell modeling of human cells. Proceedings of the 2016 annual ACM Conference on SIGSIM Principles of Advanced Discrete Simulation – SIGSIM-PADS ‘16. 2016.Google Scholar
  59. 59.
    Covert M, Palsson B. Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J Biol Chem. 2002;277(31):28058–64.CrossRefGoogle Scholar
  60. 60.
    Karr J, Sanghvi J, Macklin D, Arora A, Covert M. WholeCellKB: model organism databases for comprehensive whole-cell models. Nucleic Acids Res. 2013;41(D1):D787–92.CrossRefGoogle Scholar
  61. 61.
    Karr J, Phillips N, Covert M. WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database. 2014;2014:bau095.CrossRefGoogle Scholar
  62. 62.
    Lee R, Karr J, Covert M. WholeCellViz: data visualization for whole-cell models. BMC Bioinf. 2013;14(1):253.CrossRefGoogle Scholar
  63. 63.
    Waltemath D, Karr J, Bergmann F, Chelliah V, Hucka M, Krantz M, et al. Toward community standards and software for whole-cell modeling. IEEE Trans Biomed Eng. 2016;63(10):2007–14.CrossRefGoogle Scholar
  64. 64.
    Ottino J. Engineering complex systems. Nature. 2004;427(6973):399.CrossRefGoogle Scholar
  65. 65.
    Carothers C, Bauer D, Pearce S. ROSS: a high-performance, low-memory, modular time warp system. J Parallel Distrib Comput. 2002;62(11):1648–69.zbMATHCrossRefGoogle Scholar
  66. 66.
    Macklin D, Ruggero N, Covert M. The future of whole-cell modeling. Curr Opin Biotechnol. 2014;28:111–5.CrossRefGoogle Scholar
  67. 67.
    Abreu R, Castro M, Silva F. Simulation step size analysis of a whole-cell computational model of bacteria. AIP Conf Proc. 2016;1790(1):100014.CrossRefGoogle Scholar
  68. 68.
    Hansen J. GNU octave beginner’s guide. Birmingham: Packt Publishing; 2011.Google Scholar
  69. 69.
    McPhillie M, Cain R, Narramore S, Fishwick C, Simmons K. Computational methods to identify new antibacterial targets. Chem Biol Drug Des. 2015;85(1):22–9.CrossRefGoogle Scholar
  70. 70.
    Pujol A, Mosca R, Farrés J, Aloy P. Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol Sci. 2010;31(3):115–23.CrossRefGoogle Scholar
  71. 71.
    Schadt E, Friend S, Shaywitz D. A network view of disease and compound screening. Nat Rev Drug Discov. 2009;8(4):286–95.CrossRefGoogle Scholar
  72. 72.
    Xie L, Li J, Xie L, Bourne P. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput Biol. 2009;5(5):e1000387.CrossRefGoogle Scholar
  73. 73.
    Murabito E, Smallbone K, Swinton J, Westerhoff H, Steuer R. A probabilistic approach to identify putative drug targets in biochemical networks. J R Soc Interface. 2010;8(59):880–95.CrossRefGoogle Scholar
  74. 74.
    Rienksma R, Suarez-Diez M, Spina L, Schaap P. Martins dos Santos V. Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets. Semin Immunol. 2014;26(6):610–22.CrossRefGoogle Scholar
  75. 75.
    Kozakov D, Hall D, Napoleon R, Yueh C, Whitty A, Vajda S. New frontiers in druggability. J Med Chem. 2015;58(23):9063–88.CrossRefGoogle Scholar
  76. 76.
    Vashisht R, Bhat A, Kushwaha S, Bhardwaj A, Consortium O, Brahmachari S. Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets. J Transl Med. 2014;12(1):263–81.Google Scholar
  77. 77.
    Chaudhury S, Abdulhameed M, Singh N, Tawa G, D’haeseleer P, Zemla A, et al. Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction. PLoS One. 2013;8(5):e63369.CrossRefGoogle Scholar
  78. 78.
    Lewis N, Nagarajan H, Palsson B. Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012;10(4):291–305.CrossRefGoogle Scholar
  79. 79.
    Becker S, Palsson B. Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol. 2008;4(5):e1000082.MathSciNetCrossRefGoogle Scholar
  80. 80.
    Shlomi T, Cabili M, Herrgård M, Palsson B, Ruppin E. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol. 2008;26(9):1003–10.CrossRefGoogle Scholar
  81. 81.
    Colijn C, Brandes A, Zucker J, Lun D, Weiner B, Farhat M, et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol. 2009;5(8):e1000489.MathSciNetCrossRefGoogle Scholar
  82. 82.
    Zur H, Ruppin E, Shlomi T. iMAT: an integrative metabolic analysis tool. Bioinformatics. 2010;26(24):3140–2.CrossRefGoogle Scholar
  83. 83.
    Chandrasekaran S, Price N. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci. 2010;107(41):17845–50.CrossRefGoogle Scholar
  84. 84.
    Brandes A, Lun D, Ip K, Zucker J, Colijn C, Weiner B, et al. Inferring carbon sources from gene expression profiles using metabolic flux models. PLoS One. 2012;7(5):e36947.CrossRefGoogle Scholar
  85. 85.
    Ma S, Minch K, Rustad T, Hobbs S, Zhou S, Sherman D, et al. Integrated modeling of gene regulatory and metabolic networks in Mycobacterium tuberculosis. PLoS Comput Biol. 2015;11(11):e1004543.CrossRefGoogle Scholar
  86. 86.
    Garay C, Dreyfuss J, Galagan J. Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis. BMC Syst Biol. 2015;9(1):57.CrossRefGoogle Scholar
  87. 87.
    Toleman MA, Simm AM, Murphy TA, Gales AC, Biedenbach DJ, Jones RN, Walsh TR, Molecular characterization of SPM-1, a novel metallo-?-lactamase isolated in Latin America: report from the SENTRY antimicrobial surveillance programme. J Antimicrob Chemother. 2002;50(5):673–9.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Fabricio Alves Barbosa da Silva
    • 1
    Email author
  • Fernando Medeiros Filho
    • 1
  • Thiago Castanheira Merigueti
    • 1
  • Thiago Giannini
    • 1
  • Rafaela Brum
    • 4
  • Laura Machado de Faria
    • 1
  • Ana Paula Barbosa do Nascimento
    • 1
  • Kele Teixeira Belloze
    • 3
  • Floriano Paes SilvaJr.
    • 1
  • Rodolpho Mattos Albano
    • 4
  • Marcelo Trindade dos Santos
    • 2
  • Maria Clicia Stelling de Castro
    • 4
  • Marcio Argollo de Menezes
    • 5
    • 6
  • Ana Paula D’A. Carvalho-Assef
    • 1
  1. 1.Fundação Oswaldo Cruz – FIOCRUZRio de JaneiroBrazil
  2. 2.Laboratório Nacional de Computação Científica – LNCC/MCTIPetrópolisBrazil
  3. 3.Centro Federal de Educação Tecnológica Celso Suckow da Fonseca – CEFET/RJRio de JaneiroBrazil
  4. 4.Universidade do Estado do Rio de Janeiro – UERJRio de JaneiroBrazil
  5. 5.Instituto de Física, Universidade Federal FluminenseNiteróiBrazil
  6. 6.Instituto Nacional de Ciência e Tecnologia de Sistemas Complexos, INCT-SCRio de JaneiroBrazil

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