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Bayesian Networks to Model Pseudomonas aeruginosa Survival Mechanism and Identify Low Nutrient Response Genes in Water

  • Bertrand SodjahinEmail author
  • Vivekanandan Suresh Kumar
  • Shawn Lewenza
  • Shauna Reckseidler-Zenteno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10233)

Abstract

Pseudomonas aeruginosa is an organism notable for its ubiquity in the ecosystem and its resistance to antibiotics. It is an environmental bacterium that is a common cause of hospital-acquired infections. Identifying its survival mechanism is critical for designing preventative and curative measures. Also, understanding this mechanism is beneficial because P. aeruginosa and other related organisms are capable of bioremediation. To address this practical problem, we proceeded by decomposition into multiple learnable components, two of which are presented in this paper. With unlabeled data collected from P. aeruginosa gene expression response to low nutrient water, a Bayesian Machine Learning methodology was implemented, and we created an optimal regulatory network model of the survival mechanism. Subsequently, node influence techniques were used to computationally infer a group of twelve genes as key orchestrators of the observed survival phenotype. These results are biologically plausible, and are of great contribution to the overall goal of apprehending P. aeruginosa survival mechanism in nutrient depleted water environment.

Keywords

Machine learning Bayesian networks Gene expression Bacteria Pseudomonas aeruginosa 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Bertrand Sodjahin
    • 1
    Email author
  • Vivekanandan Suresh Kumar
    • 1
  • Shawn Lewenza
    • 2
    • 3
  • Shauna Reckseidler-Zenteno
    • 2
    • 3
  1. 1.School of Computing and Information SystemsAthabasca UniversityAthabascaCanada
  2. 2.Faculty of Science and Technology, Center for ScienceAthabasca UniversityAthabascaCanada
  3. 3.Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada

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