Identification of adaptive mechanisms leading to reduced antibiotic susceptibility in bacterial biofilms using experimental evolution and machine learning approaches

01 January 2020 → Ongoing
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Machine learning and decision making
    • Medicinal and biomolecular chemistry not elsewhere classified
  • Medical and health sciences
    • Bacteriology
    • Infectious diseases
Biofilm microbial evolution antibiotic resistance machine learning
Project description

Because many mechanisms of reduced sensitivity in bacterial biofilms are still unknown, it is impossible to predict resistance. In this project we will allow bacteria to evolve in vitro in the presence of antibiotics, in order to map all mutations, differences in gene expression and relevant phenotypic characteristics. This will allow to develop a prediction algorithm using machine learning.