An Gary
Department of Surgery, University of Chicago , Chicago, IL , USA.
Front Immunol. 2015 Nov 6;6:561. doi: 10.3389/fimmu.2015.00561. eCollection 2015.
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.
基于主体的建模已被用于描述与炎症和免疫反应相关的嵌套控制回路和非线性动力学,特别是作为一种可视化假定机制假说的手段。这个过程被称为动态知识表示,在当前数据丰富和假说丰富的生物医学研究环境中,对于促进检验和可能证伪假说的能力起着关键作用。重要的是,动态计算建模有助于识别有用的抽象概念,这是贯穿物理科学的一项基本科学原则。认识到抽象概念的关键科学作用,为生物学中倾向于将全面描述作为生物知识的主要表现形式提供了一种智力和方法论上的制衡。移植免疫学是另一个例子,说明了在确定对炎症/免疫反应有足够的理解以便开发和完善临床有效干预措施方面所面临的挑战。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,最显著的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/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。描述免疫细胞群体的动态以及导致移植排斥或耐受的交互反馈回路极具挑战性,但如果要实现诱导移植耐受的合理调节,这是必要的。本文介绍了实体器官基于主体的模型(SOTABM),作为基于主体的模型(ABM)的一个初始示例,该模型抽象地再现了对SOT免疫反应的细胞和分子成分。尽管SOTABM具有抽象性质,但它能够定性地再现急性排斥反应以及通过免疫抑制对急性排斥反应的抑制,以产生移植耐受。SOTABM旨在作为一个初始示例,展示ABM如何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,从而有可能作为诱导移植耐受的控制策略研究和开发的有用辅助工具。