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.
Contaminated soils and waters continue to threaten public health and safety. This project builds on the development of a novel computational platform for predicting the complex, dynamic interactions between microbial ecosystems and hazardous contaminants-of-concern in the environment, and to utilize this information to develop improved engineered remediation biotechnologies.
