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.
Contaminated soils and waters continue to threaten public health and safety. This project aims to increase the effectiveness of clean-up efforts through the development of a novel computational platform for predicting the complex, dynamic interactions between microbial ecosystems and hazardous contaminants-of-concern in the environment.
