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首页> 外文期刊>Frontiers in Immunology >Introduction of a Framework for Dynamic Knowledge Representation of the Control Structure of Transplant Immunology: Employing the Power of Abstraction with a Solid Organ Transplant Agent-Based Model
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Introduction of a Framework for Dynamic Knowledge Representation of the Control Structure of Transplant Immunology: Employing the Power of Abstraction with a Solid Organ Transplant Agent-Based Model

机译:引入动态知识表示框架的移植免疫学控制结构的框架:利用基于实体器官移植代理的模型利用抽象的力量

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Agent-based modeling has been used to characterize the nested control loops and non-linear dynamics associated with inflammatory and immune responses, particularly as a means of visualizing putative mechanistic hypotheses. This process is termed dynamic knowledge representation and serves a critical role in facilitating the ability to test and potentially falsify hypotheses in the current data- and hypothesis-rich biomedical research environment. Importantly, dynamic computational modeling aids in identifying useful abstractions, a fundamental scientific principle that pervades the physical sciences. Recognizing the critical scientific role of abstraction provides an intellectual and methodological counterweight to the tendency in biology to emphasize comprehensive description as the primary manifestation of biological knowledge. Transplant immunology represents yet another example of the challenge of identifying sufficient understanding of the inflammatory/immune response in order to develop and refine clinically effective interventions. Advances in immunosuppressive therapies have greatly improved solid organ transplant (SOT) outcomes, most notably by reducing and treating acute rejection. The end goal of these transplant immune strategies is to facilitate effective control of the balance between regulatory T cells and the effector/cytotoxic T-cell populations in order to generate, and ideally maintain, a tolerant phenotype. Characterizing the dynamics of immune cell populations and the interactive feedback loops that lead to graft rejection or tolerance is extremely challenging, but is necessary if rational modulation to induce transplant tolerance is to be accomplished. Herein is presented the solid organ agent-based model (SOTABM) as an initial example of an agent-based model (ABM) that abstractly reproduces the cellular and molecular components of the immune response to SOT. Despite its abstract nature, the SOTABM is able to qualitatively reproduce acute rejection and the suppression of acute rejection by immunosuppression to generate transplant tolerance. The SOTABM is intended as an initial example of how ABMs can be used to dynamically represent mechanistic knowledge concerning transplant immunology in a scalable and expandable form and can thus potentially serve as useful adjuncts to the investigation and development of control strategies to induce transplant tolerance.
机译:基于代理的建模已用于表征与炎症和免疫反应相关的嵌套控制环和非线性动力学,特别是作为可视化假定的机械假设的手段。此过程称为动态知识表示,在促进测试和潜在伪造当前数据和假设丰富的生物医学研究环境中的假设的能力中起关键作用。重要的是,动态计算建模有助于识别有用的抽象,这是遍及物理科学的基本科学原理。认识到抽象的关键科学作用,为生物学趋势强调综合描述作为生物学知识的主要体现提供了智力和方法上​​的帮助。移植免疫学代表了挑战的另一个例子,即确定对炎症/免疫反应的充分理解以开发和完善临床有效的干预措施。免疫抑制疗法的进展极大地改善了实体器官移植(SOT)的结果,特别是通过减少和治疗急性排斥反应。这些移植免疫策略的最终目标是促进有效控制调节性T细胞与效应子/细胞毒性T细胞群体之间的平衡,以产生并理想地维持耐受表型。表征免疫细胞群的动力学以及导致移植物排斥或耐受的交互式反馈回路极具挑战性,但如果要实现合理的调制以诱导移植耐受,则是必需的。本文介绍了基于固体器官代理的模型(SOTABM),作为基于代理的模型(ABM)的初始示例,该模型抽象地再现了对SOT的免疫反应的细胞和分子成分。尽管具有抽象性质,但SOTABM能够定性地复制急性排斥反应,并通过免疫抑制产生移植耐受性来抑制急性排斥反应。 SOTABM旨在作为ABM如何用于以可扩展和可扩展的形式动态表示有关移植免疫学的机制知识的初始示例,因此可以潜在地用作研究和开发诱导移植耐受性的控制策略的有用辅助方法。

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