An Agent-Based Modeling Platform for Environmental Biotechnology

环境中的有害污染物继续威胁公众健康和环境 安全。人类接触主要污染物类别，如多氟化合物 全氟碳化物(PFC)、危险有机化合物(HOCs)和重金属，与各种 疾病，并受到严格的州和联邦环境法规的约束。 生物修复是一种低成本和环境友好的方法，许多成功的方法 用例；然而，传统的生物修复技术可能会受到不可靠、低 降解率和不完全降解率。作为超级基金网站和其他网站的利益相关者 对于水或土壤的污染，迫切需要更高效、更低成本和更可靠的 补救技术，关键是要着眼于计算技术的进步 建模以开发下一代精密工程生物修复技术。 拟议的项目建立在第一阶段的成功成果的基础上，在该阶段中，一个新的计算 设计并验证了平台，以准确预测生物修复动力学 降解地下水中的一组HOCs的多个有机体微宇宙。的基础 该平台是一种称为基于代理的建模(ABM)的方法，其中 复杂生态系统中的单个组件(例如微生物)用于预测和 优化系统级特性(例如，生物修复动力学)。 在这个二期工程中，一期开发的新型计算平台是 利用生物信息学的机器学习组件进一步改进 开发合理定制的微生物群以降解复杂污染物的数据库 混合物。模型输出的迭代实验验证是使用创新的 材料科学平台，维持不同物种在 多区处理屏障(就地)或多区生物反应器内的微生物群常数 (非原址)。该项目包括重点开发一个生物修复用例的原型， 这与传统的(非精密)生物修复系统治疗 实际受到污染的地下水。这将被执行以评估和量化 利用该项目的新型计算技术获得的预期技术和经济效益 生物技术发展的平台。 拟议项目的广泛和长期影响将是转变发展和 通过整合计算建模、机器方面的进步来实现生物修复 学习、生物信息学和材料科学。通过利用跨学科的新工具， 该项目将加快开发更精确、可靠和廉价的技术 环境整治。拟议项目的成功结果也将提供新的 为工业界和学术界提供合作机会，以更快地解决 环境中的高优先级污染物，并最终有助于减轻有害物质的影响 受环境污染影响的社区的污染物。

Hazardous pollutants in the environment continue to threaten public health and environmental safety. Human exposure to major contaminant classes, such as polyfluorinated compounds (PFCs), hazardous organic compounds (HOCs), and heavy metals, has been linked to a variety of diseases and is subject to stringent State and Federal environmental regulations. Bioremediation is a low-cost and environmentally friendly approach with many successful use-cases; however, conventional bioremediation technologies can suffer from unreliability, low degradation rates, and incomplete degradation. As stakeholders to Superfund sites and other sites with water or soil pollution urgently demand more efficient, less costly and more reliable remediation technologies, it is critical to look to advancements in computational modeling to develop next-generation, precision-engineered bioremediation technologies. The proposed project builds on successful outcomes from Phase I in which a new computational platform was designed and validated to accurately predict the bioremediation kinetics of a multi-organism microcosm degrading a combination of HOCs in groundwater. The basis of this platform is an approach called agent-based modeling (ABM), where the functions of individual components (e.g. microorganisms) within complex ecosystems are used to predict and optimize system-level properties (e.g. bioremediation kinetics). In this Phase II project, the novel computational platform developed in Phase I is further improved with a machine learning component that leverages bioinformatics databases to develop rationally tailored microbiomes for degrading complex pollutant mixtures. Iterative experimental validation of model outputs is conducted using an innovative materials science platform that maintains the relative concentration of different species in the microbiome constant within the multi-zone treatment barrier (in-situ) or multi-zone bioreactor (ex-situ). The project includes focused development of a prototype for one bioremediation use-case, which is directly compared to a conventional (non-precision) bioremediation system treating actual contaminated groundwater. This will be performed in order to assess and quantify the expected technical and economic benefits of harnessing the project's novel computational platform in biotechnology development. The broad, long-term impact of the proposed project will be to transform the development and implementation of bioremediation by integrating advancements in computational modeling, machine learning, bioinformatics, and materials science. By leveraging novel tools across disciplines, the project will accelerate the development of more precise, reliable and inexpensive technologies for environmental remediation. The successful outcome of the proposed project will also provide new collaborative opportunities for industry and academia to more rapidly address the remediation of high-priority pollutants in the environment, and ultimately help mitigate the effects of hazardous pollutants on communities impacted by the presence of environmental contamination.