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（由适用提供）：水和土壤中的危险化合物受到自然环境中物理，化学和生物构成之间相互作用的复杂而动态的网络。计算建模已被证明是危险物质修复必不可少的，尤其是污染物氢化命运和运输的综合建模。然而，第一次，分子尺度表征的进步使新的可能性为污染物修复的更精确，现实和真正的预测模型提供了新的可能性。具体而言，与污染物转化相关的复杂微生物生态系统（“微生物组”）的模拟具有很大的希望，可以指导新一代新一代更具成本效益和可靠的生物化解决方案，以针对各种化合物和受污染的地点开发。该阶段I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，并利用这些信息来更好地设计补救技术来进行环境修复。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，我们的基于代理的建模方法提供了在复杂环境中捕获个体异质性，平衡空间细节与计算效率的能力，并预测与环境部位相关的一系列空间和临时动态的非线性行为和动力学。该项目的新颖性在于能够利用分子和生化尺度参数化来记住与污染物生物降解的常规仿真方法相关的主要缺陷，这些缺陷通常是平均场模型并适合环境位点数据。该多尺度平台将使用污染的环境站点的微观研究以迭代方式建立，集成和验证。通过这种方式，该项目旨在既有助于提高对自然环境中微生物组功能的科学理解，又有助于帮助进一步的公共卫生和环境安全的信息策略。这项工作的主要结果将是基于新型，集成和多尺度代理的平台的概念，以预测环境生物修复的功能动力学。该项目的价值提议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。与现有的方法和计算模型相比，该项目的成功结果旨在为包括超级基金现场经理，政府机构，工程和咨询公司在内的一系列利益相关者提供好处，最重要的是，受到社区中有害物质存在影响的人口。