Our research

Bioinformatic and systems biology methods can be used to help understand disease. A major challenge facing medical science at present is the integration of vast and rapidly growing volumes of information into a holistic understanding of disease. Most projects in this area involve collaboration between specialists in compuational biology, applied mathematics, statistics, genetics, as well as pathologists, physicians and surgeons.

Simply combining `omics information from different sources, or simply generating mathematical models of the molecular aspects of a disease are unsatisfying, no matter how elegant the methods used are! Instead, evaluation of this type of research by laboratory experiments or clinical trials is required to translate `omics information into improved biological understanding or clinical practice.

Random mutation and selection give rise to genetic evolution when genes are transmitted vertically from generation to generation. Similarly cultural evolution is caused by partial transmission of information vertically and also horizontal transmission between individuals. Phylogenetic methods allow us to test different hypotheses concerning the parameters of evolution of these cultural traits. Recent applications of phylogenetic methods have included analysis of birdsong and the use of Bayesian approaches to simulate possible migration pathways of the first Lapita settlers entering Near Oceania.

The Institute is proud to have strong links with the Centre for Computational Evolution directed by Prof Alexei Drummond.

The quantitative investigation of complex biological systems is a rapidly growing area of research, driven by the availability of ever more powerful analytical and computational tools, and by an increasing awareness of the contribution of emergent properties to the behaviour of whole systems.

With the rapid increase in availability of genomic data, there has been a need for methodologies to help understand the underlying dynamics of genome biology. Information can be inferred from the patterns of molecular sequence evolution. We have developed techniques which identify and utilise such patterns. These include methods to predict the function of genes, to illustrate co-evolution of genes and to detect selection that has occurred on sequences in specific lineages.