Our lab focuses on applying mathematical, statistical and computational approaches to understand clinically relevant biological systems with the aim to integrate diverse data sources and techniques to develop models that can predict the normal and dysfunctional behaviour.
A number of ongoing projects in the lab include:
- Modelling the quorum mechanism of Streptococcus pneumonia. 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.
- Modelling the phosphatidylinositol (PI) / Calcium signalling in human and its control by Arf proteins. The PI cycle is a metabolic signalling pathway which is stimulated by a diverse range of hormones and growth factors and controls cell growth and differentiation. Arf1 and Arf6 are small GTPases which have been shown to stimulate PLD activity and thus regulate the PI signalling pathway. Arf exists in both extracellular and membrane-attached states with the enzymatic process being membrane-bound. This membrane-bound reaction provides modelling challenges. We are developing predictive ODE-based models with parameters determined both via literature searching and fitting experimental NMR data with species identified via QM calculations using Gaussian. This is a collaborative CoMPLEX project with Dr Geraint Thomas in Cell & Development Biology.
- Understanding the ER/mitochondrial intra-cellular signalling pathways in human. 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.
- Modelling the purine metabolic pathway in human. 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.
- Protein structure modelling for misfolding diseases. The lab has a general interest in neurodegenerative diseases involving protein misfolding and amyloid formation such as Huntington’s disease. The signalling and metabolic pathways mentioned previously are all implicated in these diseases and we also have developed sequence-based predictors of amyloidogenesis using machine learning approaches to better understand this process.