HBCU-RISE: Bridging Quantitative Science with Biological Research: Jumpstarting Computational Systems Biology Research at PVAMU

点击翻译按钮获取中文摘要

The Historically Black Colleges and Universities Research Infrastructure for Science and Engineering (HBCU-RISE) activity within the Centers of Research Excellence in Science and Technology (CREST) program supports the development of research capabilities at HBCUs that offer doctoral degrees in science and engineering disciplines. HBCU-RISE projects have a direct connection to the long-term plans of the host department(s) and the institutional mission, and plans for expanding institutional research capacity as well as increasing the production of doctoral students in science and engineering. With support from the National Science Foundation, Prairie View A&M University (PVAMU) aims to provide innovative solutions to more effective and efficient drug development by bridging quantitative research with biomedical science. The project aims to 1) jumpstart computational biology research to stimulate students' interest and enhance the PhD program in Electrical Engineering, 2) improve student enrollment and retention, and 3) attract more minority students to pursue graduate study, especially doctoral degrees. This project is aligned with the mission of the institution and the goals of the Electrical and Computer Engineering (ECE) Department. The proposed activities will support the ECE department in building a strong research program in computational biology, thus achieving the goals of enhancing the PhD program in the ECE department and broadening participation in computational biology at PVAMU. The proposed project will greatly improve African American involvement in cutting edge research that is extremely valuable to the nation. The aim of this project is to study and analyze the dynamic evolution of drug/cell interactions using biomedical big data, including both public domain data and dynamic time series data from systematic drug perturbations experiments. Innovative image processing, machine learning, dynamic modeling and control techniques are proposed to help understand the genetic regulation of cancer cells and the mechanism of action of molecularly targeted agents on gene regulation. Specifically, combining the information from robust image feature extraction using advanced image processing techniques (Thrust 1) with candidate drug targets and the identification of drug treatments identified using a novel network-based computational tool, Evaluation of Differential DependencY (EDDY; Thrust 2). Dynamic modeling and analysis of drug response in critical biological pathways will be carried out in Thrust 3. Equipped with the knowledge extracted from biomedical big data obtained in Thrust 2 and a predictive preclinical model that reveal how biological regulatory networks react when perturbed from time series data in Thrust 3, novel therapeutic interventions will be designed in Thrust 4 using advanced control theory. Findings from this study will provide innovative solutions to more effective and efficient drug development by bridging quantitative research with biomedical science. This project will be conducted in collaboration with the TEES-AgriLife Center for Bioinformatics and Genomics Systems Engineering (CBGSE) at Texas A&M University and the Translational Genomic Research Institute (TGen). The knowledge gained from this project will be disseminated broadly to a community of scientists and engineers.