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

历史上的黑人高校科学与工程研究基础设施（HBCU-RISE）活动隶属于科学与技术卓越研究中心（CREST）项目，支持提供科学和工程学科博士学位的hbcu的研究能力发展。HBCU-RISE项目与接收部门的长期计划和机构使命，以及扩大机构研究能力和增加理工科博士生生产的计划直接相关。在美国国家科学基金会的支持下，Prairie View a&m University （PVAMU）旨在通过将定量研究与生物医学科学相结合，为更有效和高效的药物开发提供创新的解决方案。该项目旨在1)启动计算生物学研究，以激发学生的兴趣，并加强电气工程博士课程；2)提高学生入学率和保留率；3)吸引更多少数民族学生攻读研究生，特别是博士学位。这个项目与学院的使命和电气与计算机工程系的目标是一致的。拟议的活动将支持欧洲经委会建立一个强大的计算生物学研究计划，从而实现加强欧洲经委会博士课程和扩大PVAMU计算生物学参与的目标。拟议中的项目将极大地提高非裔美国人对前沿研究的参与程度，这对国家来说是极其宝贵的。该项目的目的是利用生物医学大数据，包括公共领域数据和系统药物摄动实验的动态时间序列数据，研究和分析药物/细胞相互作用的动态演变。提出了创新的图像处理、机器学习、动态建模和控制技术，以帮助了解癌细胞的遗传调控以及分子靶向药物对基因调控的作用机制。具体来说，将使用先进图像处理技术（Thrust 1）的鲁棒图像特征提取信息与候选药物靶点相结合，并使用一种新的基于网络的计算工具——差分依赖性评估（EDDY; Thrust 2）——识别药物治疗。关键生物通路中药物反应的动态建模和分析将在推力3中进行。利用从Thrust 2中获得的生物医学大数据中提取的知识，以及揭示生物调控网络在受到Thrust 3中时间序列数据干扰时如何反应的预测临床前模型，推力4将采用先进的控制理论设计新的治疗干预措施。这项研究的结果将通过将定量研究与生物医学科学相结合，为更有效和高效的药物开发提供创新的解决方案。该项目将与德克萨斯a&m大学的TEES-AgriLife生物信息学和基因组学系统工程中心（CBGSE）和转化基因组研究所（TGen）合作进行。从这个项目中获得的知识将广泛传播给科学家和工程师社区。

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.