Mining multi-omics interaction data to reveal the determinants and evolution of host-pathogen disease susceptibility.

The relationship between pathogens and their host is often complex and their evolutionary arms race intricate. Subclinical infections are a common occurrence; host organisms are infected by a normally disease-inducing pathogen, but no symptoms are displayed. This allows pathogens to establish natural reservoirs of asymptomatic carriers that can aid in their transmission to those hosts that are susceptible to the disease. The goal of this fundamental research project is to gain understanding of the general molecular mechanisms that underlie why some animal species - or even some individuals - remain mostly asymptomatic following infection with specific pathogens, while others progress into symptomatic disease. To this end, a large collection of pathogen-host interaction networks will be established for both symptomatic and asymptomatic hosts. State-of-the-art data mining methods will then be applied to discover rules and patterns in the interaction network that are associated with disease susceptibility. Finally, these patterns will be filtered and validated using integrated multi-level 'omics information derived from both the pathogen and the host species. The results of this project will lead to both novel methodology to tackle previously uncharacterised host-pathogen interactions and deliver fundamental new insights in the biological drivers of disease susceptibility.

Keywords:DATA MINING

Disciplines:Scientific computing, Bioinformatics and computational biology, Public health care, Public health services

Project type:Collaboration project