Genetic and Epigenetic Networks in Cancer


Despite major advances in molecular biology and genetics, our understanding of the molecular networks that control cell function, and the way in which dysregulation of these networks promotes diseases, including cancer, remains far from complete. Developing avenues for new therapies and diagnostic tests relies on our comprehension of the molecular mechanisms regulating the progress of disease. A Systems Biology approach to studying disease progression is to construct network models from high-quality, high-throughput molecular data, and to interrogate these networks through computational analysis.

We are combining novel methods developed at the Systems Biology Laboratory with other techniques for network-based analysis, and applying this approach to datasets in a variety of cancers and tumour types to uncover potential targets for further investigation. We are also integrating clinical information such as patient history, survival, tumor grade and age, with molecular data to improve the predictive power and clinical applicability of this approach.

Recent Publications:

J. Cursons, K.A. Pillman, K.G. Scheer, P.A. Gregory, M. Foroutan, S. Hediyeh-Zadeh, J. Toubia, E.J. Crampin, G.J. Goodall, C.P. Bracken, M.J. Davis (2018). Combinatorial Targeting by MicroRNAs Co-ordinates Post-transcriptional Control of EMT. Cell Systems 7, 77–91

D. Lin, A. Kan, J. Gao, E.J. Crampin, P.D. Hodgkin, S.H. Naik (2018). DiSNE Movie Visualization and Assessment of Clonal Kinetics Reveals Multiple Trajectories of Dendritic Cell Development. Cell Reports 22 (10), 2557–2566

D.M. Budden, E.J. Crampin (2016). Distributed gene expression modelling for exploring variability in epigenetic function. BMC Bioinformatics 17:446

D.M. Budden, E.J. Crampin (2016). Information theoretic inference of biological networks from continuous-valued data. BMC Systems Biology 10:89