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: 9117595
• 负责人: Gary An
• 金额: $ 41.05万
• 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/9117595
• 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; Health; 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; big biomedical data; biological systems; body system; clinical Diagnosis; computing resources; design; interest; mathematical model; models and simulation; novel therapeutics; scale up; simulation; stem; 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; Health; 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; big biomedical data; biological systems; body system; clinical Diagnosis; computing resources; design; interest; mathematical model; models and simulation; novel therapeutics; 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（由适用提供）：基于代理的建模是一种离散的事件，面向对象的，空间解释的计算机模拟类型，它是一种越来越流行的建模方法，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。代表相互作用细胞种群的基于代理的模型（ABM）已用于检查一系列生理/病理生理系统，例如癌症，败血症，传染病，伤口愈合和胃肠道疾病。基于代理建模的演变的自然步骤是渴望发展高分辨率，解剖尺度的器官ABM，这些器官可以再现可识别的临床病理生理学。但是，在现有方法的情况下，有效的参数化（校准），表征（元模型）和验证此类模型的操作挑战是令人生畏的，即使不是计算上是可行的，如果不是在计算上棘手的话，则在现有方法上。为了帮助解决这个问题，我们建议在解剖级别的基于多尺度的代理模型上利用自动自适应模拟工作流程，以启用并使探索非常大的参数和行为空间的过程（数百亿个代理）ABMS可以代表整个器官系统。这些工作流已经在较小的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，以证明并最终为开发较大且更复杂的ABM的研究人员提供了这种能力，从根本上讲，从根本上讲，从根本上讲，改变了这些模型的使用方式和分析。我们将利用Swift任务并行脚本语言的高性能版本（SWIFT/T），对肠道组织（段）的空间显式通用通用模型的增强版本进行参数化和行为空间探索，该模型以解剖量表实现的HPC（段），即整个小肠。该项目包括使用从组织和组织得出的先前确定的目标函数在多个缩放水平下对段HPC进行参数空间表征 肠道组织的器官级特征，将段HPC移植到现有的HPC，这是一种现有的HPC ABM工具包。通过这样做，我们将扩展HPC代表复杂生物学现象的能力，并使用Swift/T和Repast HPC开发自适应模拟工作流，该工作流在REPAST HPC实施段中测试，以促进参数空间探索和通过一般建模社区的特征。