A predictive multi-scale model of the immune system: An integrated computational resource for interdisciplinary applications.

免疫系统的预测性多尺度模型：跨学科应用的集成计算资源。

• 批准号: 9142820
• 负责人: Tomas Helikar
• 金额: $ 35.68万
• 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 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9142820
• 关键词: Behavior; Biochemical; Biological; Biological Models; CD4 Positive T Lymphocytes; Cells; Cellular Metabolic Process; Communication; Communities; Complex; Computer Simulation; Data; Decision Making; Disease; Environment; Exhibits; Gene Expression Regulation; Immune; Immune response; Immune system; Immunology; Individual; Knowledge; Mediator of activation protein; Memory; Modeling; Molecular; Nonlinear Dynamics; Organ; Pattern; Pharmacotherapy; Phenotype; Property; Research; Scientist; Signal Transduction; Stimulus; System; T cell differentiation; T-Lymphocyte; abstracting; cell type; computer framework; computing resources; cytokine; design; extracellular; flexibility; multi-scale modeling; network models; novel; pathogen; response; simulation; virtual

Abstract Diseases are often a result of multiple malfunctions in complex, nonlinear network systems that span multiple layers of biological organization, ranging from molecular to cellular to organ and organismal levels. The immune system is no exception. 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.) that are responsible for the dynamics and decision-making at the single-cell level. Despite substantive research efforts in systems immunology, existing computational models are limited to network models at individual molecular or cellular scales and/or focus on a single disease within a small part of the immune system. Herein, we propose to develop a systems-level, comprehensive, and integrative computational framework for the immune system that is needed to better understand and predict complex behavior of the immune system in the context of diseases and associated therapies. This framework will integrate data and knowledge across various levels of biological organization, capture nonlinear dynamics, and incorporate and facilitate mechanistic understanding. Such a framework has the potential to enable the interrogation of the dynamics and emergent properties of complex molecular, cellular, and disease networks that give rise to and regulate the immune system. This computational resource will provide a broad environment to a range of scientific communities, including molecular experimentalists, clinicians, translational scientists, and computational biologists. Furthermore, our group will utilize the comprehensive model to better understand emergent properties that underlie the immune system, including immune memory, adaptation, etc. Finally, we will also investigate the capacity, plasticity, and richness of T-cell differentiation. We hypothesize that additional cytokine profiles defining new CD4+ effector T cells exist and that the underlying phenotypes exhibit flexibility to provide more dynamics to immune response. For example, we expect to identify specific combinations of extracellular signals that are able to stimulate one type of CD4+ T cells to switch to another type, as well as identify novel patterns of cytokine profiles that may correspond to additional T cell types.

抽象的 疾病通常是复杂的非线性网络系统中多个故障的结果 生物组织的多层，从分子到细胞到器官和有机体 水平。免疫系统也不例外。它对外国刺激的适当反应受 各种类型的细胞和细胞因子之间的网络状相互作用作为交流 调解人。免疫系统的细胞间水平的复杂性进一步加剧了 每个细胞中类似复杂的生物学和生化网络（代谢，基因 法规等）负责单细胞级别的动态和决策。 尽管在系统免疫学方面进行了实质性研究，但现有的计算模型是 仅限于单个分子或细胞量表的网络模型和/或专注于单个疾病 在免疫系统的一小部分内。本文中，我们建议开发系统级， 免疫系统的全面且综合的计算框架 更好地理解和预测免疫系统在疾病和 相关疗法。该框架将整合各个级别的数据和知识 生物组织，捕获非线性动力学，并结合并促进机理 理解。这样的框架有可能使动态审问 复杂的分子，细胞和疾病网络的新兴特性产生和 调节免疫系统。该计算资源将为一个范围提供广泛的环境 科学社区，包括分子实验家，临床医生，翻译科学家和 计算生物学家。此外，我们的小组将利用综合模型来更好 了解免疫系统（包括免疫记忆）构成的新兴特性， 最终，我们还将研究T细胞的能力，可塑性和丰富性 分化。我们假设存在定义新CD4+效应T细胞的其他细胞因子曲线 并且基础表型表现出灵活性，可以为免疫反应提供更多动力。 例如，我们希望确定能够能够的特定组合 刺激一种类型的CD4+ T细胞切换到另一种类型，并确定新的模式 可能与其他T细胞类型相对应的细胞因子谱。