Adaptive Simulation to Enable Anatomic-scale Agent-based

自适应模拟以实现基于代理的解剖规模

• 批准号: 8945167
• 负责人: Gary An
• 金额: $ 41.91万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8945167
• 关键词: Address; Anatomy; Behavior; Big Data; Biological Phenomena; Biological Process; Calibration; Cells; Clinical; Communicable Diseases; Communities; Complex; Computer Simulation; Computer software; Data; Disease; Enteral; Environment; Event; Evolution; Foundations; Functional disorder; Gastrointestinal Diseases; Goals; Heart Diseases; High Performance Computing; Histology; Image; Intestines; Knowledge; Language; Large Intestine; Malignant Neoplasms; Methods; Modeling; Molecular; Organ; Pathway interactions; Pattern; Performance; Phenotype; Physiological; Population; Process; Programming Languages; Research Personnel; Resolution; Running; Sampling; Sepsis; Small Intestines; Space Explorations; Specific qualifier value; System; Techniques; Testing; Therapeutic Agents; Time; Tissues; Validation; Wound Healing; base; biological systems; body system; clinical Diagnosis; computing resources; design; interest; mathematical model; models and simulation; novel therapeutics; public health relevance; scale up; simulation; stem

 DESCRIPTION (provided by applicant): Agent-based modeling is a discrete-event, object-oriented, spatially-explicit type of computer simulation that is an increasingly popular modeling method for converting the correlations identified from Big Data into dynamic representations of mechanistic knowledge. Agent-based models (ABMs) representing populations of interacting cells have been used to examine a range of physiological/pathophysiological systems such as cancer, sepsis, infectious disease, wound healing, and gastrointestinal disease. A natural step in the evolution of agent-based modeling is the desire to develop high-resolution, anatomic-scale organ ABMs that can reproduce recognizable clinical pathophysiology. However, the operational challenge of effectively parameterizing (calibrating), characterizing (meta-modeling) and validating such models are daunting, if not computationally intractable as a practical issue, given existing methods. To help address this issue, we propose to utilize automated adaptive simulation workflows on anatomic-level multi-scale agent-based models to enable and make tractable the process of exploring the parameter and behavior spaces of very large (hundreds of billions of agents) ABMs able to represent entire organ systems. These workflows, already tested in the characterization of smaller scale ABMs, will be extended to state-of- the-art high performance computing (HPC) environments in order to demonstrate and eventually provide this capability to researchers developing larger and more complex ABMs, fundamentally changing how such models are used and analyzed. We will utilize a high-performance version of the Swift task-parallel scripting language (Swift/T), to perform parameterization and behavior-space exploration of an enhanced version of the Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT) HPC implemented at anatomic scale, i.e., the entire small and large intestine. This project includes performing parameter-space characterization of SEGMEnT HPC at multiple scaling levels using previously identified objective functions derived from tissue- and organ-level features of intestinal tissue, porting SEGMEnT HPC to Repast HPC, an existing HPC-capable ABM toolkit. In doing so we will expand Repast HPC's ability to represent complex biological phenomena, and develop adaptive simulation workflows using Swift/T and Repast HPC, tested in the Repast HPC implementation of SEGMEnT, in order to facilitate parameter space exploration and characterization by the general modeling community.

 描述（由申请人提供）：基于代理的建模是离散事件、面向对象、空间显式类型的计算机模拟，其是用于将从大数据识别的相关性转换为机械知识的动态表示的日益流行的建模方法。基于代理的模型（ABM）表示相互作用的细胞群体已被用来检查一系列的生理/病理生理系统，如癌症，败血症，传染病，伤口愈合，和胃肠道疾病。基于代理的建模的发展的一个自然步骤是希望开发高分辨率，解剖尺度的器官ABM，可以重现可识别的临床病理生理。然而，有效地参数化（校准），表征（元建模）和验证这些模型的操作挑战是艰巨的，如果不是计算上棘手的实际问题，给定现有的方法。为了帮助解决这个问题，我们建议在解剖学水平的多尺度基于代理的模型上利用自动自适应仿真工作流程，以使探索能够代表整个器官系统的非常大的（数千亿代理）ABM的参数和行为空间的过程变得易于处理。这些工作流程已经在小规模ABM的表征中进行了测试，将扩展到最先进的高性能计算（HPC）环境中，以展示并最终为开发更大，更复杂的ABM的研究人员提供这种能力，从根本上改变这些模型的使用和分析方式。我们将利用高性能版本的Swift任务并行脚本语言（Swift/T），对在解剖尺度上实现的增强版肠道组织空间显式通用模型（SEGMEnT）HPC进行参数化和行为空间探索，即，整个小肠和大肠。该项目包括使用先前确定的来自组织的目标函数在多个缩放级别上执行SEGMEnT HPC的参数空间表征， 肠组织的器官级特征，将SEGMEnT HPC移植到Repast HPC（一种现有的支持HPC的ABM工具包）。在此过程中，我们将扩展Repast HPC表示复杂生物现象的能力，并使用Swift/T和Repast HPC开发自适应仿真工作流程，在SEGMEnT的Repast HPC实现中进行测试，以促进一般建模社区的参数空间探索和表征。