SBIR-STTR Award

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.

Public Health Relevance Statement:


Public Health Relevance:
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.

Project Terms:
Academia; Aerobic; Algorithms; base; Behavior; Biochemical; Biodegradation; Bioinformatics; Biological; Bioremediations; Biotechnology; California; Case Study; Chemicals; Collaborations; Communities; Complex; Computer Simulation; Consult; cost effective; Data; Data Set; Databases; design; Development; Diffusion; Ecosystem; Effectiveness; Employee Strikes; empowered; Engineering; Environment; Environmental Pollution; Equilibrium; Evaluation; experience; Generations; Genes; Government; Government Agencies; Hand; Hazardous Substances; Heterogeneity; improved; In Situ; Individual; Industry; interest; Internet; Investigation; Kinetics; Knowledge; Laboratories; Left; Licensing; Marketing; metabolomics; Metagenomics; Methodology; Methods; Microarray Analysis; microbial; microbiome; microorganism; Modeling; Molecular; molecular scale; novel; Outcome; Output; Performance; Persons; Phase; Plug-in; pollutant; Population; predictive modeling; Process; Property; prototype; Public Health; public health relevance; Reaction; remediation; research and development; Research Personnel; research study; response; restoration; Safety; simulation; Site; Small Business Technology Transfer Research; Soil; spatiotemporal; superfund site; System; Technology; Testing; Time; tool; Universities; Water; water treatment; Work

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.

Public Health Relevance Statement:
PROJECT NARRATIVE 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.

Project Terms: