Bioinformatics research on gene and protein sequences, structures and functions facilitate biomarker discovery and validation for drug response prediction. System biology research on gene/protein networks further extends the understanding of drug response mechanisms at the molecular level. Literature and medical record text mining on genes, drugs, diseases, and their associations reveals new drug targets and new predictors for drug responses.
Areas of Focus
Center investigators are working to understand mechanisms that regulate gene expression, including transcriptional, post-transcriptional and epigenetic processes. They use systems biology approaches to form functional networks from multi-omics data to identify disease genes and molecular pathways that are involved in a wide variety of diseases, including cancer, cardiovascular disease and neurological disease. Additionally, these investigators use state-of-the-art sequencing technologies to analyze omics data at the single-cell level.
Researchers in the center’s Proteomics group develop new computational methods and software tools for mass spectrometry-based protein identification, characterization and quantification. They use proteomics and other omics data to investigate mechanisms of post-transcriptional regulation and functions of protein post-translational modifications. The researchers collaborate with scientists in biological and translational research to understand protein function and identify protein biomarkers for diagnosis and prognosis of various diseases, such as cancer and type-1 diabetes.
The center’s Drug Development research group focuses on designing small molecule inhibitors for use as novel cancer therapies. They are using genomic and protein-protein interaction data from human tumors to guide structure-based virtual screening of commercial and combinatorial libraries to identify hit and lead small molecules that suppress tumor growth and metastasis.
Researchers working in the area of Pharmacogenomics and Therapeutic Response integrate demographic and clinical data with pharmacogenomics and other ‘omics’ data to individualize drug treatments in order to optimize therapeutic effects and minimize adverse drug reactions. Bioinformatics and pharmacometric approaches are used to identify factors that alter the pharmacokinetics and pharmacodynamics of drugs that are used to treat a variety of diseases at all stages of life, including in pregnant women, newborns, children, adults and the elderly.
Investigators in the center’s Data Sciences group are developing and applying advanced machine learning, artificial intelligence, data mining and statistical methods to analyze, integrate and visualize large biomedical data for both basic biological research and precision medicine applications. They use computer vision and deep learning algorithms to extract quantitative features for computational pathology and radiomics applications. Additionally, these features are integrated with multiomics data to generate new models predicting disease outcomes and biological hypothesis regarding morphogenesis. Visual analytic tools are developed to enable exploration of complicated data by scientists and clinicians. The algorithms and visualization tools have been applied to study cancers, neurological diseases and diabetes as well as brain development.