CAREER: Complexity From Simplicity: Multi-scale Computational Deciphering of the Viral Life Cycle

职业：从简单到复杂：病毒生命周期的多尺度计算破译

• 批准号: 2143866
• 负责人: Elsje Pienaar
• 金额: $ 72.9万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143866&HistoricalAwards=false
• 关键词: CAREER; Complexity; Simplicity; Multi; scale

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117- 2).Viruses like Ebola and SARS-CoV-2 spread and cause disease through the combined action of multiple viral proteins working together. Limiting viral reproduction in patients will require a holistic view of how all of these proteins work together in one infected cell and across many cells in our bodies. This project will use a combination of experimental data and computer simulations to understand and predict the complex interactions that drive Ebola virus infection. Such an understanding will allow the identification of any weak points in this protein network that can be targeted with new drugs. The project will also develop new research tools to advance the use of computer simulations to accelerate viral research. The educational objectives of the project will complement the research objectives by training the next generation of scientists (from high school to graduate student level) to readily combine computational and experimental research methods. Together, the research and educational objectives will enable a comprehensive understanding of Ebola virus biology, integrated computational/experimental research tools, and a scientific workforce that can take advantage of computational technology to advance public health.The overall objective of this proposal is to identify interconnected subcellular and inter-cellular mechanisms that drive viral replication and spread within a host, using Ebola virus as a model system. Mechanistic computational models are powerful tools that generate virtual versions of real biological systems, to enable analysis of complex systems-level dynamics. In this project, mechanistic computational models, closely integrated with experimental data, will be used to identify key mechanisms in Ebola virus reproduction. New multi-dimensional analyses will be developed to elucidate coupled mechanisms, and computational predictions will be tested experimentally. Research objectives will quantify: 1) the impact of individual protein dynamics on viral production at the subcellular level using systems of ordinary differential equations; 2) the spatio-temporal impact of inter-cellular processes on cell-to-cell viral spread and proliferation using agent-based models; and 3) the combined impact of subcellular and inter-cellular mechanisms on viral replication across scales using multi-scale simulations. These simulations will be calibrated to and validated against experimental data from the Ebola virus minigenome system that allows careful isolation of individual viral proteins and steps in the viral life-cycle (e.g. transcription and assembly). The research objectives will support educational objectives at the intersection of biology and computation. The educational objectives will: integrate quantitative methods into existing biology curricula in an accessible and sustainable way; and advance interdisciplinary training in undergraduate biomedical engineering students. These objectives will be accomplished through a multi-tiered educational approach that connects students and teachers within, and between, high-school, undergraduate and graduate levels. The project will develop: 1) quantitative learning modules for biology courses; 2) international interdisciplinary undergraduate courses; and 3) interdisciplinary research training for graduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。埃博拉病毒和 SARS-CoV-2 等病毒通过多种病毒蛋白的联合作用传播并引起疾病。限制患者体内的病毒繁殖需要全面了解所有这些蛋白质如何在一个受感染的细胞中以及我们体内的许多细胞中协同工作。该项目将结合实验数据和计算机模拟来理解和预测导致埃博拉病毒感染的复杂相互作用。这种理解将有助于识别该蛋白质网络中可以用新药靶向的任何弱点。该项目还将开发新的研究工具，以推进计算机模拟的使用，以加速病毒研究。该项目的教育目标将通过培训下一代科学家（从高中到研究生水平）轻松结合计算和实验研究方法来补充研究目标。总之，研究和教育目标将使人们全面了解埃博拉病毒生物学、综合计算/实验研究工具以及能够利用计算技术促进公共卫生的科学劳动力。该提案的总体目标是确定相互关联的使用埃博拉病毒作为模型系统，驱动病毒在宿主内复制和传播的亚细胞和细胞间机制。机械计算模型是强大的工具，可以生成真实生物系统的虚拟版本，从而能够分析复杂的系统级动力学。在该项目中，机械计算模型与实验数据紧密结合，将用于确定埃博拉病毒繁殖的关键机制。将开发新的多维分析来阐明耦合机制，并通过实验测试计算预测。研究目标将量化：1）使用常微分方程系统在亚细胞水平上单个蛋白质动态对病毒生产的影响； 2）使用基于代理的模型研究细胞间过程对细胞间病毒传播和增殖的时空影响； 3）使用多尺度模拟，亚细胞和细胞间机制对病毒跨尺度复制的综合影响。这些模拟将根据埃博拉病毒小基因组系统的实验数据进行校准和验证，该系统允许仔细分离单个病毒蛋白和病毒生命周期中的步骤（例如转录和组装）。研究目标将支持生物学和计算交叉领域的教育目标。教育目标将： 以易于理解和可持续的方式将定量方法整合到现有的生物学课程中；推进生物医学工程本科生的跨学科培养。这些目标将通过多层次的教育方法来实现，该方法将高中、本科生和研究生级别内部和之间的学生和教师联系起来。该项目将开发：1）生物学课程的定量学习模块； 2）国际跨学科本科课程； 3）研究生跨学科研究培训。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。