About the project
Interested in applying AI to solve real-world biological and security challenges? This project will bring together systems biology, machine learning and network science to build predictive models of host-pathogen interactions. You will develop cutting-edge AI approaches that could transform how we respond to novel infectious and biological threats.
The human body is a complex adaptive and hierarchical system of nested biological networks. These networks continuously respond to a multitude of external and internal signals, ranging from pathogen (bacterial, viral) exposure and environmental stressors to therapeutic interventions.
The ability to predict how biological networks respond to external perturbations has important applications across healthcare, defence and biosecurity. Biological threats, whether naturally occurring, engineered, or previously unknown, represent a significant risk to military personnel, critical infrastructure, and civilian populations. Rapidly understanding how a novel biological agent interacts with the human host and how the host networks respond is therefore a major challenge.
This project focuses on developing tools for prediction of how these networks respond to biological threats such as bacterial and viral pathogens.
Knowledge graphs provide a powerful framework for integrating, representing and interrogating heterogeneous information. Biomedical knowledge graphs integrate diverse molecular and disease-related information and are comprised of 100s of thousands of entities and millions of relations between those entities.
The project will first construct an integrated multi-omics knowledge graph incorporating host-pathogen interactions, molecular pathways, diseases, phenotypes and therapeutic interventions.
We will then develop graph foundation models and explainable AI methods to quantify how biological threats perturb molecular networks, identify pathways associated with disease severity and resilience, and predict potential intervention targets.
These approaches will provide a mechanistic framework for modelling biological responses and supporting preparedness against emerging infectious and environmental threats.
