MICROVI BIOTECH, INC. — Department of Health and Human Services SBIR Phase II: R

Description

Hazardous pollutants in the environment continue to threaten public health and environmentalsafety. Human exposure to major contaminant classes, such as polyfluorinated compounds(PFCs), hazardous organic compounds (HOCs), and heavy metals, has been linked to a variety ofdiseases and is subject to stringent State and Federal environmental regulations.Bioremediation is a low-cost and environmentally friendly approach with many successfuluse-cases; however, conventional bioremediation technologies can suffer from unreliability, lowdegradation rates, and incomplete degradation. As stakeholders to Superfund sites and other siteswith water or soil pollution urgently demand more efficient, less costly and more reliableremediation technologies, it is critical to look to advancements in computationalmodeling to develop next-generation, precision-engineered bioremediation technologies. The proposed project builds on successful outcomes from Phase I in which a new computationalplatform was designed and validated to accurately predict the bioremediation kinetics ofa multi-organism microcosm degrading a combination of HOCs in groundwater. The basis ofthis platform is an approach called agent-based modeling (ABM), where the functions ofindividual components (e.g. microorganisms) within complex ecosystems are used to predict andoptimize system-level properties (e.g. bioremediation kinetics). In this Phase II project, the novel computational platform developed in Phase I isfurther improved with a machine learning component that leverages bioinformaticsdatabases to develop rationally tailored microbiomes for degrading complex pollutantmixtures. Iterative experimental validation of model outputs is conducted using an innovativematerials science platform that maintains the relative concentration of different species in themicrobiome 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 treatingactual contaminated groundwater. This will be performed in order to assess and quantifythe expected technical and economic benefits of harnessing the project's novel computationalplatform in biotechnology development. The broad, long-term impact of the proposed project will be to transform the development andimplementation of bioremediation by integrating advancements in computational modeling, machinelearning, bioinformatics, and materials science. By leveraging novel tools across disciplines, theproject will accelerate the development of more precise, reliable and inexpensive technologies forenvironmental remediation. The successful outcome of the proposed project will also provide newcollaborative opportunities for industry and academia to more rapidly address the remediation ofhigh-priority pollutants in the environment, and ultimately help mitigate the effects of hazardouspollutants on communities impacted by the presence of environmental contamination.