Center for Proteomics and Bioinformatics:
Systems Biology of Human Disease
Biology is presently undergoing a transformation to an information science. This is being driven by technological advances that have enabled us to measure global changes in the abundance or alteration of many biomolecules (e.g. DNA, mRNA, SNPs, PTMs, etc.), changes which provide clues to the causes of a variety of expressed phenotypes. Equally important to this transformation is our ability to integrate these high-dimensional data within computational frameworks and analyze them in the context of protein interactions, the most immediate cause of a disease phenotype. With respect to complex, polygenic human diseases, it is believed that the integration of all the data of a given cellular state will lead to improved candidate markers of disease, new drug targets, and ultimately to improvements in the clinical stratification of patients afflicted with these diseases. However, for the foreseeable future it will continue to be important that researchers validate the findings of various computational approaches by traditional wet-bench assays, in cell culture or relevant animal models.
My research goals involve developing integrative computational approaches for finding improved molecular markers of a variety of human diseases. In particular, I am interested in the relatively nascent field of network biology, and the advantages it promises for moving us beyond traditional single-gene markers of disease. In addition, I am eager to incorporate the wealth of high-dimensional data being captured by proteomics screens, for instance from disease vs. control experiments, and using these data as a powerful source of cellular information driving the integrative, computational approach. Compared to a rich literature of genomic approaches in this area, similar proteomic approaches are far fewer, largely because quantitative proteome-wide profiling has not been feasible, but this is rapidly changing (1).
(1) Cox J, Mann M, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008 Dec;26(12):1367-72.