About the Systems Genomics laboratory
In the last decade, technological advances have driven the study of biology towards the statistical and computational sciences. We are now able to differentiate and quantify biomolecules at levels previously unimaginable, allowing us to study their interactions and relationships to health and disease in an unbiased, systems-level manner.
A corollary of this transformation is that sophisticated quantitative models can now be used to tease out underlying biological insights and pathogeneses of molecular systems. In our view, an 'organism' can be thought of as a system whose components are derived from its genome(s) and which interacts with each other and the environment in a spatial and temporal manner. We think of these components (e.g. RNAs, proteins, mobilised DNA) as operating as part of networks with the other elements (e.g. metabolites, sunlight, microorganisms). We therefore apply and develop concepts in graph theory, bioinformatics, epidemiology and biostatistics to understand how networks interact and what role they play in human diseases and traits.
Vision
Lead a world-class systems genomics research program focused on leveraging the latest genomic/biomolecular technologies and analytical techniques to alleviate the burden of diseases with immune and inflammatory aetiologies.
Our lab has a long track record of research at the interface of genomics, computer science and statistics. Overall, our aims are to utilise analytical tools to uncover insights into pathogenesis that change clinical practice; build local capacity and link it to international collaborative networks; and champion open and rapid scientific publishing.
Research focus
- Genomic prediction of common human diseases.
- Multi-omics analysis to identify and characterise new biomarkers.
- Development of new statistical/computational approaches to high-dimensional data.
Cambridge Baker Systems Genomics Initiative
We have formed the Cambridge Baker Systems Genomics Initiative in partnership with Cambridge University to significantly expand our ability to access big data to target approaches in disease prediction and personalised medicine.