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

摘要 疾病通常是复杂的、非线性的网络系统中多个故障的结果 生物组织的多层次，从分子到细胞再到器官和有机体 级别。免疫系统也不例外。它对外来刺激的适当反应是由 不同类型的细胞和细胞因子之间的网络状相互作用作为它们的通讯 调解人。免疫系统细胞间水平的复杂性进一步加剧了 每个细胞内类似复杂的生物和生化网络(新陈代谢、基因 规例等)负责单细胞层面的动态和决策。 尽管在系统免疫学方面做了大量的研究工作，但现有的计算模型 仅限于单个分子或细胞级别的网络模型和/或专注于单一疾病 在免疫系统的一小部分内。在这里，我们建议开发一种系统级的， 全面、综合的免疫系统计算框架，需要 更好地理解和预测免疫系统在疾病和 联合疗法。该框架将跨不同级别的数据和知识集成 生物组织，捕捉非线性动力学，并结合和促进机械学 理解。这样的框架有可能使得能够询问动态和 复杂的分子、细胞和疾病网络的紧急性质，引起和 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、翻译科学家和 计算生物学家。此外，我们集团将利用综合模式更好地 了解构成免疫系统基础的新特性，包括免疫记忆， 最后，我们还将研究T细胞的能力、可塑性和丰度 差异化。我们假设存在定义新的CD4+效应T细胞的其他细胞因子 潜在的表型表现出灵活性，为免疫反应提供了更多的动力。 例如，我们希望确定细胞外信号的特定组合，从而能够 刺激一种类型的CD4+T细胞转换为另一种类型，以及识别新的模式 可能与其他T细胞类型相对应的细胞因子谱。

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.