eMB: Bridging the Gap Between Agent Based Models of Complex Biological Phenomena and Real-World Data Using Surrogate Models

eMB：使用代理模型弥合基于代理的复杂生物现象模型与真实世界数据之间的差距

• 批准号: 2324818
• 负责人: Trachette Jackson
• 金额: $ 60万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324818&HistoricalAwards=false
• 关键词: eMB; Bridging; Gap; Between; Agent

Much has happened in the past three years - to us as individuals, as a united nation, and as one world. The consequences of human-induced transformations on our environment and the reciprocal impact of the changing global environment on humanity have been profound. To address these challenges and improve human well-being, researchers, scientists, and engineers are generating large amounts of data on the evolving condition of our world and its inhabitants in novel, multidimensional forms. Unfortunately, existing mathematical, statistical, and computational techniques offer only partial tractability in comprehending these complex datasets. New, thoughtfully developed mathematical methods and modeling approaches are desperately needed to gain a deep and robust understanding of these data for human benefit and to mitigate human harm. The successful completion of this project will result in a robust and scalable computational framework for constraining large parameter spaces in agent-based models with real-world data. Agent-based models are widely recognized as a powerful computational framework for advancing our understanding of human disease, human-society interactions, and environmental systems. However, their inherent stochasticity and prohibitive computational expense pose significant barriers to integrating such models with real-world data. The new approach will provide a much-needed platform for exploring parameter uncertainty and sensitivity in multiscale agent-based models representing complex biological phenomena. Ultimately, the new methods developed here will result in a scalable mathematical tool for operationalizing computationally complex models designed to solve formidable biological problems that are of great interest to biologists, ecologists, clinicians, and health policymakers.Unlocking the full potential of computationally complex mathematical models to advance our understanding of interconnected biological systems urgently requires techniques for integrating these models with multifaceted real-world data. Multiscale agent-based models (ABMs) are widely recognized as a powerful computation framework for advancing our understanding of systems ranging from molecular, cellular, and tissue dynamics to human-society interactions, infectious diseases, and ecological systems. However, to make meaningful, reliable quantitative predictions and to gain mechanistic insight, ABMs must be integrated with real-world data through model parameterization and calibration. Unfortunately, robust, scalable techniques for addressing the challenges posed by the inherent stochasticity and heavy computational requirements of an ABM in integrating it with real-world data are sorely lacking. Hence, there is a critical need to develop new theoretical and computational approaches to bridge this gap between ABM parameters and real-world data. This project develops a new computational framework for parameter estimation, uncertainty quantification, and sensitivity analysis of multiscale ABMs informed by noisy, sparse, and multifaceted real-world data. The method utilizes explicitly formulated and mechanistic surrogate models simultaneously inferred from both the ABM formulation and the data to link the two in previously impossible ways. The approach will open new possibilities for ABMs representing complex biological phenomena to uncover how data sets can hide unexpected or counter-intuitive underlying mechanisms that have profound implications for predicted outcomes and planned interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的三年中，发生了很多事情 - 对我们个人，作为一个联合国和一个世界而言。人类引起的转变对我们环境的后果以及全球环境对人类不断变化的相互影响的影响是深远的。为了应对这些挑战并改善人类的福祉，研究人员，科学家和工程师正在以新颖的多维形式产生大量有关我们世界及其居民不断发展的状况的数据。不幸的是，现有的数学，统计和计算技术仅在理解这些复杂数据集时提供了部分障碍。迫切需要进行新的，周到的数学方法和建模方法，以获得对这些数据的深刻而强烈的理解，以使人类的利益和减轻人类伤害。该项目的成功完成将导致一个可靠，可扩展的计算框架，用于用实际数据来约束基于代理的模型中的大参数空间。基于代理的模型被广泛认为是一种强大的计算框架，用于促进我们对人类疾病，人类社会相互作用和环境系统的理解。但是，它们固有的随机性和过度的计算费用构成了将这些模型与现实数据相结合的重大障碍。这种新方法将为代表复杂生物学现象的基于多尺度代理的模型中的参数不确定性和灵敏度提供急需的平台。最终，此处开发的新方法将产生可扩展的数学工具，用于操作旨在解决生物学家，生态学家，临床医生和健康决策者的强大生物学问题的计算复杂模型。毫无疑问，这些模型可以促进我们对互联的生物学系统的了解，从而使计算上的复杂模型的全部潜力促进了这些模型，以促进我们对这些模型进行互补的模型，这些模型将这些模型集成了这些模型。 基于多尺度代理的模型（ABM）被广泛认为是一种强大的计算框架，用于促进我们对从分子，细胞和组织动力学到人类社会相互作用，传染病和生态系统等系统的理解。 但是，为了做出有意义的，可靠的定量预测并获得机械洞察力，必须通过模型参数化和校准将ABM与现实世界中的数据集成在一起。不幸的是，非常有力的，可扩展的技术，可解决ABM与现实世界数据集成在一起的固有随机性和繁重的计算要求所带来的挑战。因此，迫切需要开发新的理论和计算方法来弥合ABM参数和现实世界数据之间的差距。 该项目为参数估计，不确定性量化以及多尺度ABM的敏感性分析开发了一个新的计算框架，这些框架以嘈杂，稀疏和多方面的现实世界数据为导入。 该方法利用从ABM公式和数据同时推断出明确的配制和机械替代模型，以先前不可能的方式链接两者。这种方法将为代表复杂生物学现象的ABM打开新的可能性，以揭示数据集如何隐藏意外或反直觉的潜在机制，这些机制对预测的结果和计划的干预措施具有深远的影响。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的crietia crietia criperia criperia criperia criperia criperia criperia criperia criperia cripitia cripitia cripitia cripitia recectia rection the奖项。