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
Part of the Computational Biology book series (COBO, volume 27)


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.



This study was supported by fellowships from CAPES to FMF and from the Oswaldo Cruz Institute ( to TG. We also thank FAPERJ and CAPES for financial support.


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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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