Developing mathematical tools to identify the genes underlying disease
Next-generation sequencing (NGS) of Deoxyribonucleic Acid (DNA) from patients with diseases is revolutionising medical research stimulating rapid transition towards ‘personalised’ treatment. However, progress in both research and clinical settings is hard to achieve because the interpretation of relationships between genetic variation and disease phenotypes is extremely challenging. The reason is that the large number of genomic and functional properties are complex and heterogenous. To overcome this, simulation is an essential tool to establish the models required on genomic and functional data.
The aim of this research is to build a model that will discriminate genes most likely to contain disease variation from those less likely by identifying the genetic factors that predispose disease. Furthermore, this project involves inference using a Monte Carlo molecular simulation process. Successful analyses will be extremely valuable for identifying disease candidate genes in the context of NGS data with implications for ‘personalised’ medicine.
Transdisciplinary tags: NGCM