Software for collaborative construction, simulation, and analysis of mechanistic computational models of biological systems

用于协同构建、模拟和分析生物系统机械计算模型的软件

• 批准号: 10609352
• 负责人: Tomas Helikar
• 金额: $ 22.28万
• 依托单位: UNIVERSITY OF NEBRASKA LINCOLN
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10609352
• 关键词: Architecture; Biological Markers; Biological Models; Biological Sciences; COVID-19; Cells; Client; Code; Collaborations; Communication; Communities; Complex; Computational Biology; Computer Models; Computer Systems; Computer software; Cypress; Data; Decision Making; Development; Differential Equation; Disease; Docking; Dose; Education; Environment; Feedback; Foundations; Growth; High Performance Computing; Human; Immune; Immune response; Immune system; Immunological Models; Immunologist; Industry Standard; Interdisciplinary Study; International; Internet; Mediator of activation protein; Metabolism; Methods; Modeling; Nature; Nebraska; Performance; Process; Property; Registries; Reproducibility; Research; Research Personnel; Resources; Scientist; Side; Signal Transduction; Software Design; Software Engineering; Speed; Students; System; Technology; Testing; Time; Training; Universities; Visualization; Work; base; biological systems; case-based; cell type; cloud based; computer framework; computing resources; cost; data-driven model; drug discovery; effective therapy; experimental study; gene regulatory network; genome-wide; instructor; loss of function; models and simulation; multi-scale modeling; open source; parallelization; parent grant; pathogen; predictive modeling; programs; response; service utilization; simulation; translational scientist; virtual

Project Summary The parent grant, 2R35GM119770, (“Multi-cellular and multi-scale systems modeling to understand the dynamics of the human immune system in interdisciplinary applications”) supports my research program, which aims to identify how the immune system can be rewired en masse to elicit higher-order decision-making while still enabling the system to remain otherwise “healthy.” To this end, we are leveraging a highly interdisciplinary research team (computational and experimental immunologists, software engineers, and education researchers) and collaborators to build a Virtual Immune System -- a multi-scale, multi-approach computational framework to understand better the complex dynamical nature of the immune system, identify more accurate multi-dimensional biomarkers, and identify safe and effective treatments within a reasonable time and cost. In addition to expanding the Virtual Immune system, my program continues to develop methods and technologies for data-driven model construction, visualization, computation, real-time simulations, and reproducibility to advance multi-scale modeling of the immune system and beyond, including a cloud-based collaborative modeling software, Cell Collective. Cell Collective is a core technology that enables us to continue to build collaborations and deepen our existing relationships, including with translational partners to advance the impact of our systems work on drug discovery, a large international team modeling COVID-19, and with virologists and immunologists to further validate our computational predictions experimentally. This supplement will enable us to enhance the scalability, robustness, and sustainability of Cell Collective, which is currently used by thousands of scientists and life sciences students, and instructors around the world.

项目摘要 母基金，2 R35 GM 119770，（“多细胞和多尺度系统建模，以了解动态的 人类免疫系统的跨学科应用”）支持我的研究计划，其目的是确定如何在人类免疫系统中的跨学科应用。 免疫系统可以被重新连接，以引发更高层次的决策，同时仍然使系统保持 否则就是“健康”为此，我们正在利用一个高度跨学科的研究团队（计算和 实验免疫学家，软件工程师和教育研究人员）和合作者建立一个虚拟免疫 系统-一个多尺度，多方法的计算框架，以更好地理解复杂的动力学性质， 免疫系统，识别更准确的多维生物标志物，并确定安全有效的治疗方法 在合理的时间和成本内。除了扩展虚拟免疫系统，我的程序还在继续开发 用于数据驱动的模型构建、可视化、计算、实时模拟的方法和技术， 可重复性，以推进免疫系统及其他领域的多尺度建模，包括基于云的协作 建模软件Cell Collective。Cell Collective是一项核心技术，使我们能够继续建立合作关系。 并深化我们现有的关系，包括与翻译合作伙伴的关系，以提高我们的系统工作的影响， 药物发现，一个大型国际团队模拟COVID-19，并与病毒学家和免疫学家进一步验证 我们的计算预测实验。这一补充将使我们能够增强可扩展性、鲁棒性和 细胞集体的可持续性，这是目前使用的数千名科学家和生命科学的学生， 世界各地的教师。