An Agent-Based Modeling Platform for Environmental Biotechnology

基于代理的环境生物技术建模平台

• 批准号: 9147117
• 负责人: MOHAMMAD RK MOFRAD
• 金额: $ 22.5万
• 依托单位: MICROVI BIOTECH, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9147117
• 关键词: Academia; Aerobic; Algorithms; Behavior; Biochemical; Biodegradation; Bioinformatics; Biological; Bioremediations; Biotechnology; California; Case Study; Chemicals; Collaborations; Communities; Complex; Computer Simulation; Consult; Data; Data Set; Databases; Development; Diffusion; Ecosystem; Effectiveness; Employee Strikes; Engineering; Environment; Environmental Pollution; Equilibrium; Evaluation; Generations; Genes; Government; Government Agencies; Hand; Hazardous Substances; Heterogeneity; In Situ; Individual; Industry; Internet; Investigation; Kinetics; Knowledge; Laboratories; Left; Licensing; Marketing; Metagenomics; Methodology; Methods; Microarray Analysis; Modeling; Molecular; Outcome; Output; Performance; Persons; Phase; Plug-in; Population; Process; Property; Public Health; Reaction; Research Personnel; Safety; Site; Small Business Technology Transfer Research; Soil; System; Technology; Testing; Time; Universities; Water; Work; base; cost effective; design; empowered; experience; improved; interest; metabolomics; microbial; microbiome; microorganism; molecular scale; novel; pollutant; predictive modeling; prototype; public health relevance; remediation; research and development; research study; response; restoration; simulation; spatiotemporal; superfund site; tool; water treatment

 DESCRIPTION (provided by applicant): Hazardous compounds in waters and soils are subject to a complex, dynamic web of interactions among physical, chemical and biological constituents in the natural environment. Computational modeling has been proven indispensable to hazardous substances remediation, particularly integrated modeling of pollutant hydrogeological fate and transport. For the first time, however, advances in molecular-scale characterization have enabled new possibilities for more precise, realistic and truly predictive models for pollutant remediation. Specifically, simulations of complex microbial ecosystems ("microbiomes") associated with contaminant transformation hold great promise to direct the development of a new generation of more cost effective and reliable bioremediation solutions for a range of compounds and contaminated sites. This Phase I project aims to develop a new computational platform with the ability to predict key dynamics of natural bioremediation processes, and to leverage that information to better design remedial technologies for environmental restoration. The basis of our platform is an approach called agent-based modeling, where the behavior of individual components within complex ecosystems can calculate systems-level properties. Compared with existing computational modeling approaches, our agent-based modeling approach provides the ability to capture individual heterogeneity within complex environments, balance spatial detail with computational efficiency, and predict non-linear behaviors and kinetics across a range of spatial and temporal dynamics associated with environmental sites. The novelty of this project lies in the ability to leverage molecular and biochemical-scale parameterization to remedy the major deficiencies associated with conventional simulation approaches for pollutant biodegradation, which are often mean-field models and fitted to environmental site data. This multi-scale platform will be built, integrated and validated in an iterative fashion using microcosm studies of a contaminated environmental site. In this way, this project is designed to both contribute to increased scientifi understanding of microbiome functions in natural environments, as well as inform strategies to help further public health and environmental safety. The major outcome of this work will be a proof-of-concept of a novel, integrated and multi-scale agent-based platform for predicting the functional dynamics of environmental bioremediation. The value proposition of this project includes leveraging contemporary bioinformatics tools and databases to develop more precise, reliable and inexpensive approaches for environmental remediation. Compared with existing methods and computational models, the successful outcome of this project stands to provide benefits to a range of stakeholders, including Superfund site managers, government agencies, engineering and consulting firms, and most importantly, populations impacted by the presence of hazardous substances in their communities.

 描述（由申请人提供）：水和土壤中的有害化合物受到自然环境中物理、化学和生物成分之间复杂、动态的相互作用的影响。计算模型已被证明对于有害物质修复不可或缺，特别是污染物水文地质归宿和迁移的综合模型。然而，分子尺度表征的进步第一次为更精确、更现实和真正预测污染物修复的模型提供了新的可能性。具体来说，对与污染物转化相关的复杂微生物生态系统（“微生物组”）的模拟有望指导针对一系列化合物和污染场地开发新一代更具成本效益和可靠的生物修复解决方案。该第一阶段项目旨在开发一个新的计算平台，能够预测自然生物修复过程的关键动态，并利用该信息更好地设计环境恢复的修复技术。我们平台的基础是一种称为基于代理的建模的方法，其中复杂生态系统中各个组件的行为可以计算系统级属性。与现有的计算建模方法相比，我们基于代理的建模方法能够捕获复杂环境中的个体异质性，平衡空间细节与计算效率，并预测与环境场所相关的一系列空间和时间动态的非线性行为和动力学。该项目的新颖之处在于能够利用分子和生化尺度参数化来弥补与污染物生物降解传统模拟方法相关的主要缺陷，这些方法通常是平均场模型并适合环境现场数据。这个多尺度平台将利用受污染环境场地的微观研究以迭代方式构建、集成和验证。通过这种方式，该项目旨在既有助于增进对自然环境中微生物组功能的科学理解，又为有助于进一步公共卫生和环境安全的战略提供信息。这项工作的主要成果将是一个新颖的、集成的、多尺度的基于代理的平台的概念验证，用于预测环境生物修复的功能动态。该项目的价值主张包括利用当代生物信息学工具和数据库来开发更精确、可靠和廉价的环境修复方法。与现有方法和计算模型相比，该项目的成功结果将为一系列利益相关者带来好处，包括超级基金现场管理者、政府机构、工程和咨询公司，最重要的是，受社区中有害物质影响的人群。