Multi-cellular and multi-scale systems modeling to understand the dynamics of the human immune system in interdisciplinary applications

多细胞和多尺度系统建模，以了解跨学科应用中人体免疫系统的动态

• 批准号: 10543785
• 负责人: Tomas Helikar
• 金额: $ 37.13万
• 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/10543785
• 关键词: Behavior; Biochemical; Biological; Biological Markers; Biological Models; Brain; CD4 Positive T Lymphocytes; COVID-19; Cells; Collaborations; Communication; Communities; Complex; Computer software; Decision Making; Dimensions; Disease; Education; Etiology; Funding; Gene Expression Regulation; Human; Immune; Immune response; Immune system; Immunological Models; Immunologist; Interdisciplinary Study; International; Laboratories; Mediator; Metabolism; Methods; Modeling; Multiomic Data; Nature; Pathology; Pharmacotherapy; Property; Reproducibility; Research; Research Personnel; Software Engineering; Stimulus; System; Technology; Time; Visualization; Work; cell type; computer framework; cost; cytokine; data-driven model; design; drug discovery; effective therapy; human model; multi-scale modeling; pathogen; predictive modeling; programs; response; simulation; translational scientist; virtual

PROJECT SUMMARY/ABSTRACT The immune system is arguably the second most complex human system after the brain. Its proper response to foreign stimuli is governed by network-like interactions among various types of cells and cytokines as their communication mediators. The complexity at the inter-cellular level of the immune system is further exacerbated by the similarly complex biological and biochemical networks within each cell (metabolism, gene regulation, etc.) responsible for the dynamics and decision-making at the single-cell level. Such multiscale complexity makes it incredibly challenging to understand the complete etiology and pathology of immune-system-related diseases. My research program 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, my research program is 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 the next five years, in addition to expanding the Virtual Immune system, my program will continue 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. We will continue to decipher the dynamics of the immune system under various pathologies and the re-programmability of CD4+ T cells under the milieu of their microenvironments. My laboratory will continue to iteratively predict, validate, and refine predictions generated using the systems approaches and technologies. To do this, we will generate our multi-omics data to more precisely validate immune system behaviors and apply our findings to refine the computational approaches directly. My team will continue to build collaborations and deepen our existing relationships, including with translational partners to advance the impact of our systems work on drug discovery, the international team modeling COVID-19, and with virologists and immunologists to further validate our computational predictions experimentally.

项目摘要/摘要 免疫系统可以说是仅次于大脑的第二大部分人类系统。 对外国刺激的适当反应受各种类型之间类似网络的互动的控制 细胞和细胞因子作为通信介质的。细胞间的复杂性 相似复杂的生物学和 每个细胞内的生化网络（代谢，基因调节等）负责 单细胞级别的动态和决策。这样的多尺度复杂性使得 令人难以置信的挑战，要了解完整的病因和病理学 免疫系统相关疾病。我的研究计划旨在确定如何免疫 可以集合系统重新建立，以引发高阶决策，同时仍使得 保持原本“健康”的系统。为此，我的研究计划正在利用高度 跨学科研究团队（计算和实验免疫学家，软件 工程师和教育研究人员）和合作者建立虚拟免疫系统 - 一种 多尺度的多核算计算框架，以更好地了解复杂 免疫系统的动态性质，确定更准确的多维生物标志物， 并在合理的时间和成本内确定安全有效的治疗方法。在接下来的五个 多年，除了扩大虚拟免疫系统外，我的计划还将继续发展 数据驱动模型构建，可视化，计算的方法和技术 实时仿真和可再现性，以推进免疫系统的多尺度建模 及以后。我们将继续在各种下继续破译免疫系统的动态 CD4+ T细胞在其环境下的病理和重新编程性 微环境。我的实验室将继续迭代预测，验证和完善 使用系统方法和技术产生的预测。为此，我们将 生成我们的多媒体数据以更精确地验证免疫系统行为并应用 我们的发现直接完善计算方法。我的团队将继续建立 合作并加深了我们现有的关系，包括与转化合作伙伴 推进我们系统对药物发现的影响，国际团队建模 COVID-19，以及病毒学家和免疫学家，以进一步验证我们的计算 实验预测。