...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Theoretical Biology and Medical Modelling >Using an agent-based model to analyze the dynamic communication network of the immune response
【24h】

Using an agent-based model to analyze the dynamic communication network of the immune response

机译：使用基于代理的模型来分析免疫应答的动态通信网络

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background The immune system behaves like a complex, dynamic network with interacting elements including leukocytes, cytokines, and chemokines. While the immune system is broadly distributed, leukocytes must communicate effectively to respond to a pathological challenge. The Basic Immune Simulator 2010 contains agents representing leukocytes and tissue cells, signals representing cytokines, chemokines, and pathogens, and virtual spaces representing organ tissue, lymphoid tissue, and blood. Agents interact dynamically in the compartments in response to infection of the virtual tissue. Agent behavior is imposed by logical rules derived from the scientific literature. The model captured the agent-to-agent contact history, and from this the network topology and the interactions resulting in successful versus failed viral clearance were identified. This model served to integrate existing knowledge and allowed us to examine the immune response from a novel perspective directed at exploiting complex dynamics, ultimately for the design of therapeutic interventions. Results Analyzing the evolution of agent-agent interactions at incremental time points from identical initial conditions revealed novel features of immune communication associated with successful and failed outcomes. There were fewer contacts between agents for simulations ending in viral elimination (win) versus persistent infection (loss), due to the removal of infected agents. However, early cellular interactions preceded successful clearance of infection. Specifically, more Dendritic Agent interactions with TCell and BCell Agents, and more BCell Agent interactions with TCell Agents early in the simulation were associated with the immune win outcome. The Dendritic Agents greatly influenced the outcome, confirming them as hub agents of the immune network. In addition, unexpectedly high frequencies of Dendritic Agent-self interactions occurred in the lymphoid compartment late in the loss outcomes. Conclusions An agent-based model capturing several key aspects of complex system dynamics was used to study the emergent properties of the immune response to viral infection. Specific patterns of interactions between leukocyte agents occurring early in the response significantly improved outcome. More interactions at later stages correlated with persistent inflammation and infection. These simulation experiments highlight the importance of commonly overlooked aspects of the immune response and provide insight into these processes at a resolution level exceeding the capabilities of current laboratory technologies.
机译：背景技术免疫系统的行为就像一个复杂的动态网络，具有相互作用的元素，包括白细胞，细胞因子和趋化因子。尽管免疫系统分布广泛，但白细胞必须有效沟通以应对病理挑战。基本免疫模拟器2010包含代表白细胞和组织细胞的物质，代表细胞因子，趋化因子和病原体的信号以及代表器官组织，淋巴组织和血液的虚拟空间。试剂响应虚拟组织的感染而在隔室中动态相互作用。代理行为是由来自科学文献的逻辑规则强加的。该模型捕获了代理之间的接触历史，并由此确定了网络拓扑结构以及导致成功或失败病毒清除的相互作用。该模型有助于整合现有知识，使我们能够从新颖的角度检查免疫反应，以开发复杂的动力学为目标，最终用于治疗性干预的设计。结果分析了相同初始条件下增量时间点的药物-药物相互作用的演变过程，揭示了免疫交流与成功和失败结果相关的新特征。由于去除了受感染的试剂，用于病毒消除（成功）与持续感染（丢失）的模拟试剂之间的联系较少。但是，早期的细胞相互作用先于成功清除感染。具体而言，在模拟早期，更多的树突状细胞与TCell和BCell药物相互作用，以及更多的BCell药物与TCell药物相互作用与免疫获胜相关。树突状剂极大地影响了结果，证实了它们是免疫网络的枢纽剂。另外，在损失结果的晚期，在淋巴室中发生了意外的高频率的树突剂自身相互作用。结论使用了一种基于代理的模型来捕获复杂系统动力学的几个关键方面，以研究对病毒感染的免疫应答的新兴特征。在反应早期发生的白细胞药物之间的相互作用的特定模式可显着改善结果。后期更多的相互作用与持续的炎症和感染有关。这些模拟实验强调了免疫反应中通常被忽视的方面的重要性，并以超出当前实验室技术能力的分辨率级别洞察了这些过程。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号