One of my main research interests lies in the genetic and epigenetic regulation of gene expression in the context of different disease models. These regulatory mechanisms include: transcription factor binding, DNA methylation, miRNA expression, and histone modifications. An example of this can be seen in my recent work with collaborators at the Northwestern University Feinberg School of Medicine on aberrant receptor tyrosine kinase (RTK) signalling in a Sprouty gene deletion cancer model. This study details how modified RTK signalling is linked to dynamic re-programming of the enhancer landscape and provides insight into the mechanisms through which such aberrant signalling develops and is maintained.
I collaborate with clinical colleagues on topics ranging from immunology, to radiation oncology and transplant genetics. My current work on radiation oncology focuses both on understanding basic mechanisms of radiation biology as well as deriving dose-predictive metabolic markers. A study we have recently published identifies a set of metabolites which are not only highly correlated with the amount of whole-body radiation received, but that also demonstrate a unique gut microbiome signal, as indicated by perturbation to the tryptophan metabolic pathway. This interaction between the host and microbiome and its role in human health adds an important new dimension to medical research and in particular to the design of individualised treatments following radiation therapy.
My work in transplant genetics currently involves the development of statistical methods for the prediction of graft survival in kidney transplant patients. This work, which makes use of gene expression and related genomic data, is primarily focused on antibody-mediated rejection, a leading cause of graft failure, and is being carried out in collaboration with the kidney transplant program at the Albert Einstein College of Medicine in New York.
I also develop algorithms for use in the analysis of genome-wide transcription factor binding data obtained using next-generation sequencers. These algorithms typically incorporate elements of machine learning and are designed to leverage high-performance computing resources. Examples include: ChIPSOM – an improved self-organizing map neural network approach for de novo motif discovery, GMACS – a genetic algorithm for unsupervised clustering of position weight matrices, and WASP, an automated end-to-end, sample submission, LIMS, and pipeline processing system for massively parallel sequencing data.