Department of Surgery, University of Chicago, Chicago, Illinois, United States of America.
PLoS Comput Biol. 2018 Feb 15;14(2):e1005876. doi: 10.1371/journal.pcbi.1005876. eCollection 2018 Feb.
Sepsis, a manifestation of the body's inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical "sepsis," and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true "precision control" of sepsis.
脓毒症是机体对损伤和感染的炎症反应的表现，其死亡率在 28%-50%之间，每年影响美国约 100 万患者。目前，尚无针对导致脓毒症的细胞/分子过程的治疗方法能够在临床环境中控制这种疾病进程。我们认为，这在很大程度上是由于构成临床“脓毒症”的临床轨迹存在相当大的异质性，而确定如何将该系统控制回健康状态需要应用来自动力系统领域的概念。在这项工作中，我们认为人类免疫系统是一个随机动力系统，并使用先天免疫反应的基于代理的模型（先天免疫反应代理模型或 IIRABM）作为替代、代理系统来研究其潜在的可控性。使用 IIRABM 进行的模拟实验解释了为什么在单个或少数时间点进行单个/有限的细胞因子扰动不太可能显著提高脓毒症的死亡率。然后，我们使用遗传算法 (GA) 探索和描述随机免疫动力系统的多靶向控制策略，将其从持续的、不可恢复的炎症状态（在功能上相当于全身炎症反应综合征 (SIRS) 或脓毒症的临床状态）引导到健康状态。我们在单个参数集上使用多个随机副本对 GA 进行训练，并表明虽然计算结果具有良好的泛化能力，但需要更先进的策略来实现适应性个性化医疗的目标。这项评估控制脓毒症简化替代模型所需干预程度的工作提供了对临床挑战范围的深入了解，并可以作为实现脓毒症真正“精确控制”的指导。
