Project Description
Methicillin-Resistant Staphylococcus aureus (MRSA) is a bacterial pathogen that counteracts harsh environments by rapidly remodelling its transcriptome.Ribonucleases (RNases) are vital in altering MRSA gene expression, making them attractive targets for antimicrobial development. However, precisely how their RNase activities contribute to host infection is poorly understood.
Methicillin-Resistant Staphylococcus aureus (MRSA) is a bacterial pathogen that counteracts harsh environments by rapidly remodelling its transcriptome.Ribonucleases (RNases) are vital in altering MRSA gene expression, making them attractive targets for antimicrobial development. However, precisely how their RNase activities contribute to host infection is poorly understood.
The project aims to develop machine learning methods to analyze genome-wide time series of kinetic CRAC data, which is a novel technology for RNase-RNA UV-crosslinking. The project will build on our recent work using Gaussian process clustering, which already gave evidence that RNases differentially degrade RNAs in a temporally coordinated manner. Here, we will generalize the methodology to move from pairwise comparison of RNase binding profiles to the concerted response by all major RNases of MRSA.
The project will be an excellent opportunity to work on a medically relevant organism, learn about host-pathogen interactions and become well-versed in cutting-edge systems biology and machine learning methods.
