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Computational Modeling of Multidrug-Resistant Bacteria

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Theoretical and Applied Aspects of Systems Biology

Part of the book series: Computational Biology ((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.

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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.

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Authors and Affiliations

  1. Fundação Oswaldo Cruz – FIOCRUZ, Rio de Janeiro, Brazil

    Fabricio Alves Barbosa da Silva, Fernando Medeiros Filho, Thiago Castanheira Merigueti, Thiago Giannini, Laura Machado de Faria, Ana Paula Barbosa do Nascimento, Floriano Paes Silva Jr. & Ana Paula D’A. Carvalho-Assef

  2. Laboratório Nacional de Computação Científica – LNCC/MCTI, Petrópolis, Brazil

    Marcelo Trindade dos Santos

  3. Centro Federal de Educação Tecnológica Celso Suckow da Fonseca – CEFET/RJ, Rio de Janeiro, Brazil

    Kele Teixeira Belloze

  4. Universidade do Estado do Rio de Janeiro – UERJ, Rio de Janeiro, Brazil

    Rafaela Brum, Rodolpho Mattos Albano & Maria Clicia Stelling de Castro

  5. Instituto de Física, Universidade Federal Fluminense, Niterói, Brazil

    Marcio Argollo de Menezes

  6. Instituto Nacional de Ciência e Tecnologia de Sistemas Complexos, INCT-SC, Rio de Janeiro, Brazil

    Marcio Argollo de Menezes

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  1. Fabricio Alves Barbosa da Silva
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  2. Fernando Medeiros Filho
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  10. Rodolpho Mattos Albano
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  14. Ana Paula D’A. Carvalho-Assef
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Correspondence to Fabricio Alves Barbosa da Silva .

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Editors and Affiliations

  1. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Fabricio Alves Barbosa da Silva

  2. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Nicolas Carels

  3. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Floriano Paes Silva Junior

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da Silva, F.A.B. et al. (2018). Computational Modeling of Multidrug-Resistant Bacteria. In: Alves Barbosa da Silva, F., Carels, N., Paes Silva Junior, F. (eds) Theoretical and Applied Aspects of Systems Biology. Computational Biology, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-74974-7_11

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