Cancer Informatics
Cancer is frequently caused by the deregulation of one or more complex signalling cascades involving multiple cellular components. To successfully treat breast cancer with minimum side effects we must fully understand the network of interactions between cellular components. A key step in building this understanding is the development of a comprehensive combinatorial analysis of data from different experimental sources. This requires not only a wide range of experimental approaches, but also a central, integrated database and associated analysis and data-mining tools.
We have recently developed a data integration platform to facilitate mapping and analysis of networks from RNAi and gene expression data. This platform will now be extended to cross-correlate diverse biological datasets to facilitate the detailed mapping of key pathways involved in breast cancer tumourigenesis and metastasis development, creating a dynamic normal breast and breast cancer ‘interactome’. We will project information onto this network, including kinetic data that will allow mathematical modelling of dynamic system behaviour. We will also model docking of ligands to protein structures of key network components to understand the specificity of drug binding and to guide development of new and better lead compounds.
This computational integration and mathematical modelling approach should ultimately aid disease diagnosis, prognosis and treatment prediction.
