Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model
Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model
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DOI：
10.1016/j.jtbi.2017.07.016
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发表时间：
2017-10-07
影响因子：
2
通讯作者：
An, Gary
中科院分区：
文献类型：
--
作者：
Cockrell, Chase;An, Gary
目的：败血症每年影响美国近100万人，死亡率为28-50%，每年需要超过200亿美元的医院费用。超过四分之一世纪的研究还没有产生一个单一的可靠的诊断测试或针对败血症的治疗剂。这种不足的核心是脓毒症仍然是一种临床/生理诊断，代表了大量的分子异质性病理轨迹。由高性能计算（HPC）平台提供的计算能力的进步要求败血症的调查的演变，以尝试通过使用计算代理模型来定义传统研究（台架、临床和计算）的边界。我们提出了一种新的解释和分析方法，来自HPC资源和模拟是如何在物理科学中使用的，通过使用代理为基础的模型全身inflammation.Design识别的认识边界条件的临床脓毒症的研究：目前脓毒症的预测模型使用相关的方法，是有限的患者异质性和数据稀疏。我们通过使用HPC版本的系统级验证的基于代理的模型脓毒症，先天免疫反应ABM（IIRBM），作为代理系统，以确定脓毒症可能的行为空间的边界条件来解决这个问题。然后，我们应用先进的分析来自随机动力系统（RDS）的研究，以确定新的手段，表征系统的行为和提供洞察到传统的解释methods.Results的易处理性：行为空间的IIRABM进行了检查，模拟超过70万脓毒症患者长达90天，在一个横扫以下参数：心脏呼吸代谢弹性;微生物侵入性;微生物繁殖;和医院暴露程度。除了使用已建立的方法来描述参数空间，我们开发了两种新的方法来表征RDS的行为：概率吸引域（PBoA）和随机轨迹分析（STA）。计算生成的行为景观表现出吸引子结构周围的随机区域的行为，可以描述在一个互补的方式，通过使用PBoA和STA。吸引子边界的随机性突出了对临床脓毒症进行表征和分类的相关尝试的挑战。IIRABM等模型的HPC模拟可用于生成脓毒症行为空间的近似值，以建立“无效边界”并应用系统工程原理研究脓毒症的一般动力学特性，制定控制策略的途径。困扰脓毒症研究和治疗的问题，即临床数据稀疏和系统行为空间的实验采样不足，是几乎所有生物医学研究的基础，体现在各个层面的“生殖危机”中。HPC增强的基于模拟的研究提供了一种与物理科学（结合联合收割机实验，理论和模拟）更一致的解释策略，并有机会利用HPC的领先进展，即深度机器学习和进化计算，形成迭代科学过程的基础，以满足精准医学的全部承诺（正确的药物，正确的患者，正确的时间）。(C)2017爱思唯尔有限公司版权所有。
Objectives: Sepsis affects nearly 1 million people in the United States per year, has a mortality rate of 28-50% and requires more than $20 billion a year in hospital costs. Over a quarter century of research has not yielded a single reliable diagnostic test or a directed therapeutic agent for sepsis. Central to this insufficiency is the fact that sepsis remains a clinical/physiological diagnosis representing a multitude of molecularly heterogeneous pathological trajectories. Advances in computational capabilities offered by High Performance Computing (HPC) platforms call for an evolution in the investigation of sepsis to attempt to define the boundaries of traditional research (bench, clinical and computational) through the use of computational proxy models. We present a novel investigatory and analytical approach, derived from how HPC resources and simulation are used in the physical sciences, to identify the epistemic boundary conditions of the study of clinical sepsis via the use of a proxy agent-based model of systemic inflammation.Design: Current predictive models for sepsis use correlative methods that are limited by patient heterogeneity and data sparseness. We address this issue by using an HPC version of a system-level validated agent-based model of sepsis, the Innate Immune Response ABM (IIRBM), as a proxy system in order to identify boundary conditions for the possible behavioral space for sepsis. We then apply advanced analysis derived from the study of Random Dynamical Systems (RDS) to identify novel means for characterizing system behavior and providing insight into the tractability of traditional investigatory methods.Results: The behavior space of the IIRABM was examined by simulating over 70 million sepsis patients for up to 90 days in a sweep across the following parameters: cardio-respiratory-metabolic resilience; microbial invasiveness; microbial toxigenesis; and degree of nosocomial exposure. In addition to using established methods for describing parameter space, we developed two novel methods for characterizing the behavior of a RDS: Probabilistic Basins of Attraction (PBoA) and Stochastic Trajectory Analysis (STA). Computationally generated behavioral landscapes demonstrated attractor structures around stochastic regions of behavior that could be described in a complementary fashion through use of PBoA and STA. The stochasticity of the boundaries of the attractors highlights the challenge for correlative attempts to characterize and classify clinical sepsis.Conclusions: HPC simulations of models like the IIRABM can be used to generate approximations of the behavior space of sepsis to both establish "boundaries of futility" with respect to existing investigatory approaches and apply system engineering principles to investigate the general dynamic properties of sepsis to provide a pathway for developing control strategies. The issues that bedevil the study and treatment of sepsis, namely clinical data sparseness and inadequate experimental sampling of system behavior space, are fundamental to nearly all biomedical research, manifesting in the "Crisis of Reproducibility" at all levels. HPC-augmented simulation-based research offers an investigatory strategy more consistent with that seen in the physical sciences (which combine experiment, theory and simulation), and an opportunity to utilize the leading advances in HPC, namely deep machine learning and evolutionary computing, to form the basis of an iterative scientific process to meet the full promise of Precision Medicine (right drug, right patient, right time). (C) 2017 Elsevier Ltd. All rights reserved.