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

科学与技术卓越研究中心(CREST)项目中的传统黑人学院和大学科学与工程研究基础设施(HBCU-RISE)活动支持提供科学和工程学科博士学位的HBCU研究能力的发展。HBCU-RISE项目与东道国部门(S)的长期计划和机构使命以及扩大机构研究能力和增加科学和工程博士生产量的计划有直接联系。在国家科学基金会的支持下，草原景观农工大学(PVAMU)致力于通过将定量研究与生物医学科学联系起来，为更有效和更高效的药物开发提供创新解决方案。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项目，2)提高学生的入学率和保留率，3)吸引更多的少数族裔学生攻读研究生，特别是博士学位。该项目与该机构的使命以及电气和计算机工程部(欧洲经委会)的目标相一致。拟议的活动将支持欧洲经委会系建立一个强大的计算生物学研究方案，从而实现加强欧洲经委会系博士方案和扩大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尖端研究的参与度，这些研究对国家非常有价值。本项目的目的是利用生物医学大数据来研究和分析药物/细胞相互作用的动态演变，包括来自系统药物扰动实验的公共领域数据和动态时间序列数据。创新的图像处理、机器学习、动态建模和控制技术被提出，以帮助理解癌细胞的遗传调控和分子靶向药物对基因调控的作用机制。具体地说，将来自使用高级图像处理技术的稳健图像特征提取的信息(推力1)与候选药物目标以及使用一种新的基于网络的计算工具--差分依赖性评估(涡流；推力2)识别的药物治疗相结合。推力3将对关键生物途径中的药物反应进行动态建模和分析。配备从推力2获得的生物医学大数据中提取的知识，以及从推力3的时间序列数据中揭示生物调节网络在扰动时如何反应的预测性临床前模型，将在推力4中利用先进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联系起来，为更有效和更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州农工大学TES-农业生命生物信息学和基因组系统工程中心(CBGSE)和翻译基因组研究所(TGen)合作进行。从该项目中获得的知识将广泛传播给科学家和工程师群体。

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.