Microarray technology has become an important and powerful tool for high-throughput analysis of global gene expression and has applications in almost every field of biomedical research. Genome-wide association studies are increasingly used to identify the gene expression signature of a disease, understand the molecular mechanisms underlying the disease phenotype and identify new drug targets. However, most common bioinformatics approaches used to analyse gene expression are unable to handle time-course data, are not suitable for analyzing short, non-uniformly sampled time-series and do not fully explore the functional relationship between disease pathology and the dynamics and robustness of gene expression.
This project focuses on the development of control theoretical approaches for modelling, analysis and interpretation of dynamic gene expression data sets as well as for biomarker discovery.
Experimental data is provided by collaborators in Cardiovascular Science and the Centre for Stem Cell Biology.