Indiana University-Purdue University Indianapolis (IUPUI) / Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features.

Indiana University-Purdue University Indianapolis (IUPUI) / Phytoestrogens that abound in soy products, legumes, and chickpeas can induce biologic responses in animals and humans due to structural similarity to 17β-estradiol. Although experimental studies suggest that phytoestrogen intake may alter the risk of cancer and cardiovascular disease, few epidemiologic studies have investigated this research question. This dissertation investigated the associations of intake of total and individual phytoestrogens and their urinary biomarkers with these chronic conditions using data previously collected from two US national cohort studies (NHANES and PLCO). Utilizing NHANES data with urinary phytoestrogen concentrations and follow-up mortality, Cox proportional hazards regression (HR; 95% CI) were performed to evaluate the association between total cancer, cardiovascular disease, and all-cause mortality and urinary phytoestrogens. After adjustment for confounders, it was found that higher concentrations of lignans were associated with a reduced risk of death from cardiovascular disease (0.48; 0.24-0.97), whereas higher concentrations of isoflavones (2.14; 1.03-4.47) and daidzein (2.05; 1.02-4.11) were associated with an increased risk. A reduction in all-cause mortality was observed for elevated concentrations of lignans (0.65; 0.43-0.96) and enterolactone (0.65; 0.44-0.97). Utilizing PLCO data and dietary phytoestrogens, Cox proportional hazards regression examined the associations between dietary phytoestrogens and the risk of prostate cancer incidence. After adjustment for confounders, a positive association was found between dietary intake of isoflavones (1.58; 1.11-2.24), genistein (1.42; 1.02-1.98), daidzein (1.62; 1.13-2.32), and glycitein (1.53; 1.09-2.15) and the risk of advanced prostate cancer. Conversely, an inverse association existed between dietary intake of genistein and the risk of non-advanced prostate cancer (0.88; 0.78-0.99) and total prostate cancer (0.90; 0.81-1.00). C-reactive protein (CRP) concentration levels rise in response to inflammation and higher levels are a risk factor for some cancers and cardiovascular disease reported in epidemiologic studies. Logistic regression performed on NHANES data evaluated the association between CRP and urinary phytoestrogen concentrations. Higher concentrations of total and individual phytoestrogens were associated with lower concentrations of CRP. In summary, dietary intake of some phytoestrogens significantly modulates prostate cancer risk and cardiovascular disease mortality. It is possible that these associations may be in part mediated through the influence of phytoestrogen intake on circulating levels of C-reactive protein.