Driving Biological Projects

Driving Biological Projects (DBPs) are central to the Center mission, both motivating and challenging computational development. The four initial DBPs refer to very common diseases with complex and heterogeneous etiology: Prostate Cancer, complications of Type 1 Diabetes, susceptibility to Type 2 Diabetes, and genetic susceptibility and phenotypic subclassification of Bipolar Disorder. More specifically, collaborations between Cores 1 and 2 with Core 3 DBP researchers focus on prostate cancer progression, including roles of EZH2 polycomb proteins in transcriptional regulation and pathways of androgen regulation and androgen independence during tumor progression; several specific mechanisms of glucose-induced oxidative stress underlying neuropathy and nephropathy in type 1 diabetes mellitus; genes involved in susceptibility to type 2 diabetes mellitus and pathways of insulin-regulated metabolic features of the disease; and genetic susceptibility to bipolar disease and identification of subgroups of patients through careful examination of data from neuroimaging, physiological variables, and neuropsychological features.

Complex diseases are the next frontier in biomedical research, requiring tools to aid the researcher in understanding the massive amounts of data and text available to address their questions. In each of these DBPs, the investigators have made major contributions to the experimental knowledge currently available in their fields and are experienced in the use of publications and databases. In order to mine and assimilate the wide range of available information, they need tools that are both sophisticated and user-friendly to generate insightful models of disease processes.

NCIBI's interaction with biomedical researchers in addressing DBP questions are improving the results that researchers see by (1) optimizing the use of human and computer capabilities, (2) processing massive amounts of text to identify biological relationships, and (3) modeling intra- and inter-cellular processes based on high-throughput data, to understand fundamental biological interactions.