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.

摘要 疾病通常是复杂的非线性网络系统中多重故障的结果， 从分子到细胞再到器官和有机体的多层生物组织 程度.免疫系统也不例外。它对外来刺激的适当反应取决于 各种类型的细胞和细胞因子之间的网络样相互作用作为它们的通信 调解员免疫系统的细胞间水平的复杂性进一步加剧， 每个细胞内类似复杂的生物和生物化学网络（代谢、基因 条例等）负责单细胞水平的动态和决策。 尽管在系统免疫学方面进行了大量的研究工作，但现有的计算模型 仅限于单个分子或细胞尺度的网络模型和/或集中于单一疾病 免疫系统的一小部分。在此，我们建议开发一个系统级， 免疫系统所需的全面、综合的计算框架， 更好地理解和预测免疫系统在疾病背景下的复杂行为， 相关疗法。这一框架将整合各个层面的数据和知识， 生物组织，捕捉非线性动力学，并纳入和促进机械 认识这样一个框架有可能使询问的动态和 复杂的分子、细胞和疾病网络的涌现特性， 调节免疫系统。这种计算资源将提供一个广泛的环境， 科学界，包括分子实验学家，临床医生，转化科学家， 计算生物学家此外，本集团将利用综合模式， 了解免疫系统的基本特性，包括免疫记忆， 最后，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的CD 4+效应T细胞的其他细胞因子谱 并且潜在的表型表现出为免疫应答提供更多动力学的灵活性。 例如，我们期望识别出能够与细胞外信号结合的特定组合， 刺激一种类型的CD 4 + T细胞转变为另一种类型，并识别新的CD 4 + T细胞模式 细胞因子谱可以对应于另外的T细胞类型。