Managing the Data Explosion in Post-Genomic Biology with Fast Bayesian Computational Methods

Rapid technological advances in molecular biology are providing an unprecedented opportunity to investigate the basic processes of life. This `post-genomic' phase of molecular biology has resulted in an explosion of typically high dimensional structured data from new technologies for transcriptomics (microarrays), proteomics and metabolomics. Such data requires novel mathematical, statistical and computational methods for their interpretation and analysis. This proposal focuses on the development of statistical and computational methods for the analysis of such data, using novel approaches from the fields of machine learning and nonparametric Bayesian statistics. The project involves a close collaboration of scientists with expertise in machine learning and statistics, bioinformatics and molecular biology. The new software tools will be developed in the context of real-world scientific problems, such as: elucidating signalling networks in plant stress responses; metabolic regulation in the bacteria Streptomyces, major producers of antibiotics and delineating the molecular mechanisms contributing to mitochondrial dysfunction in obesity and diabetes. The scientific goal of the project will be to apply these novel methods to modelling bioinformatics data, but the methods developed will be broadly applicable across a number of fields.