Download Document

Pseudomonas aeruginosa is a bacterial organism notable for its ubiquity in the ecosystem and its
capacity to resist antibiotics. It can survive at length in any environment. This is of particular medical
concern because P. aeruginosa can live on hospital surfaces and in the water supply. Being a common
origin of hospital-acquired infections, it causes various diseases. Not only 40% of mechanically ventilated
patients with P. aeruginosa succumb to their condition, this bacterium also affects animals and plants. It
has been shown that P. aeruginosa can be isolated from water in a number of intensive care units.
Understanding how it survives in water is important in designing strategies for the prevention and
treatment of infections caused. Furthermore, identifying the survival mechanism in the absence of
nutrients is beneficial because P. aeruginosa and related organisms are capable of bioremediation. We
hypothesize that P. aeruginosa is capable of long-term survival due to the presence of particular genes
which encode for persistence proteins. With readily available P. aeruginosa gene expression data from
previous works, here we propose Machine Learning, a Computer Science technique, to identify genes
involved in this bacterium’s survival, by analyzing their response to low nutrient water. We then
establish genes interaction and regulatory network that are conducive to the comprehension of the
survival mechanism. Subsequently, we develop a P. aeruginosa persistence model from which general
bacteria persistence model is inferred. With Inductive Logic Programming (ILP), we also aspire to
investigate possible unknown environmental risk factors associated with P. aeruginosa.