An Agent-Based Modeling Platform for Environmental Biotechnology

 描述(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、动态的相互作用网络的影响。计算模拟已被证明是有害物质修复所不可缺少的，特别是污染物水文地质去向和迁移的综合模拟。然而，分子尺度表征的进步第一次为污染物修复提供了更精确、更现实和真正可预测的模型的新可能性。具体地说，与污染物转化相关的复杂微生物生态系统(“微生物群”)的模拟很有希望指导针对一系列化合物和污染场地的新一代更具成本效益和更可靠的生物修复解决方案的开发。这个第一阶段的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，并利用这些信息更好地设计环境恢复的补救技术。我们平台的基础是一种称为基于代理的建模方法，其中复杂生态系统中的单个组件的行为可以计算系统级的属性。与现有的计算建模方法相比，我们的基于代理的建模方法能够捕捉复杂环境中的个体异质性，平衡空间细节和计算效率，并预测与环境站点相关的一系列空间和时间动力学的非线性行为和动力学。该项目的创新之处在于能够利用分子和生物化学尺度的参数化来弥补与污染物生物降解的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，并与环境现场数据相适应。这一多尺度平台将以迭代的方式建立、集成和验证，使用对一个受污染的环境场地进行的微观研究。通过这种方式，该项目的设计既有助于增进对自然环境中微生物组功能的科学了解，又有助于制定有助于进一步促进公共健康和环境安全的战略。这项工作的主要成果将是一个新的、集成的和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、可靠和廉价的环境补救方法。与现有的方法和计算模型相比，该项目的成功结果将使一系列利益相关者受益，包括超级基金网站经理、政府机构、工程和咨询公司，以及最重要的是，受其社区存在危险物质影响的人群。

 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.