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Your Environment. Your Health.

Microvi Biotechnologies, Inc.

Superfund Research Program

An Agent-Based Modeling Platform for Environmental Biotechnology

Project Leader: Fatemeh Shirazi
Grant Number: R44ES026541
Funding Period: Phase II: January 2021 - January 2023
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Summary

Hazardous compounds in water and soil continue to pose serious and widespread risks to public and environmental health. Conventional bioremediation technologies suffer from significant limitations that lead to low efficiency and/or increased costs. As a result, there is increasing demand among stakeholders for Superfund sites to provide more efficient and cost-effective technologies for remediation. Specifically, there is a need for computational platforms that can predict key dynamics of natural bioremediation processes to rapidly design remedial technologies for environmental restoration. This project is based on a prior Phase I project involving the successful development of a computational platform designed and validated to accurately predict the complex, dynamic interactions between microbial ecosystems and hazardous organic contaminants in groundwater. The basis of the platform, called EnviroABM, is an agent-based modeling (ABM) approach, where the behavior of individual components within complex ecosystems are used to calculate systems-level properties.

In Phase II, Microvi Biotechnologies, Inc is collaborating with small business Nexilico, Inc . on further improving EnviroABM. By integrating publicly available bioinformatics databases with a machine learning component they are working towards developing tailored microbiomes for degrading complex mixtures. These efforts will result in a novel platform to mitigate the effects of hazardous pollutants on public health and safety. The aims of the project are to:

1. Predict individual microorganisms that metabolize specific hazardous pollutants using structural similarity to match enzyme pathways with target pollutants.

2. Computationally propose optimal combinations of microorganisms expected to degrade representative mixtures of polyfluorinated compounds (PFCs), volatile organic compounds (VOCs), and heavy metals separately. As part of this task, the researchers will validate the broad applicability of EnviroABM through laboratory experiments supplemented with Microvi's Multi-Zone MicroNiche Engineering (MZ-MNE) platform. MZ-MNE uses biological organisms to degrade organic compounds into harmless by-products.

3. To develop and optimize a continuous-flow, laboratory-scale prototype for real-world bioremediation application using the MZ-MNE system.

For this project, Microvi is collaborating with external experts from Indiana University, the United States Geological Survey, Odell Engineering, Molecular Research LP, and Colorado State University. Phase II will leverage computational modeling, machine learning, bioinformatics, and materials science to accelerate the development of more precise, reliable, and inexpensive technologies to address the remediation of high-priority pollutants in the environment.

     

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