DNA micro-array technology and the sequencing of the human genome have advanced to the point that it is now possible to monitor gene expression levels on a genomic scale. These data hold the key to fundamental understanding of biological processes on the molecular level, which will revolutionize medical diagnosis and treatment of diseases and cancer, and the design of effective drugs. Despite the progress in gene expression technology, its promised benefits for the future of molecular genetics and medicine will not materialize without appropriate tools for data analysis and information extraction. Without means of detecting macroscopic patterns in the gene expression data that correspond to specific cellular processes or phenotype characteristics, these data are, and will remain, incomprehensible. Little has been done so far toward the analysis of these new data, which requires the development of new analytical and computational tools or the adaptation of existing tools from other scientific disciplines. These tools should be independent of any model for the description of gene expression, since no such predictive model is available. They should be suitable for making use of the large quantities of data and at the same time reducing the complexity of the data to make them comprehensible. In this project, these much-needed tools for genome-wide gene expression data analysis will be developed. Analytical and computational methods which are already successful in describing the physical world, from quantum mechanics to image processing, will be used. Applying these tools to gene expression data obtained in the labs of Profs. Botstein and Brown, patterns that correspond to specific molecular mechanisms and phenotype characteristics will be identified. The goal of this project is to create predictive models for diagnosis and treatment of diseases and cancer. These models may eventually help elucidate the first principles of biological processes on the molecular level.
