Our lab applies computational and mathematical techniques to understand biological systems that generally have clinical relevance.
One area of research is modelling bacteria at the atomic, molecular and population levels. Bacteria are relatively simple and so we can attempt to gain a mechanistic-level understanding of their working. Gaining such an understanding is clinicially important in terms of finding new approaches to tackle drug-resistant strains.
A different area of our research looks at more complex systems where gaining a mechanistic understanding is not viable. In particular we are interested in neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntington's disease. We approach understanding these complex systems by integrating different types of high-throughput data and applying a variety of machine learning and network-based approaches to search for patterns in the data that could suggest different disease mechanisms. Again we try to integrate data both at the molecular level within neurons (e.g. genomics and transcriptomics data) and also at the population level (i.e. whole brain using MRI and EEG data).
The aim of this project is to understand changes in the population level behaviour of Streptococcus pneumonia (such as competence, fractricide and biofilm formation) from changes occurring at the molecular level within the quorum signalling pathway. To accomplish this we are integrating genomic data, cis-regulatory motif, microarray, ODE-based modelling of the signalling and gene regulation pathways and agent-based modelling approaches. This is a collaborative project with Dr Bambos Charalambous in Department of Infection.
Signalling between the ER and mitochondria under stress conditions are important mechanisms within cancer and neurodegeration - leading to either adaptation or apoptosis. The aim of this project is to determine pathways involved in these processes by applying machine learning to public microarray data over cancer cell lines. Any tentative pathways will then be experimentally confirmed within cell cultures. This is a collaborative CoMPLEX project with Dr Gyorgy Szabadkai in Cell & Development Biology.
Purine metabolism is implicated in a variety of cancers such as acute myeloid cancer where drug treatments which inhibit IMPDH, one of the key purine metabolic enzymes, have been shown to stop cell proliferation. The aim of this project is to develop ODE models, parameterized using literature and RNASeq data, to predict the overall metabolic effects of inhibiting particular enzymes, potentially revealing other drug targets within the network. This is a collaborative CoMPLEX project with Dr Geraint Thomas in Cell & Development Biology.