Modeling, Machine Learning, and MicroNiche Engineering

     Friends don’t let friends bioremediate alone. Seriously! Planning and implementing bioremediation projects requires broad stakeholder cooperation and a precise blend of microbiology, analytics, and engineering to achieve successful treatment. And at a microscale, many sites require multiple microbes to tackle complex pollutant mixtures. Now, the challenge is: how do we keep these bioremediating microbes viable once we find them?

     For Microvi Biotech Inc., the answer lies in our MicroNiche Engineering™ (MNE) platform, which combines microbiology and materials science to deliver advanced sustainable solutions for water and wastewater treatment. MNE technology entrains targeted pre-grown bacteria in small proprietary polymer spheres, known as biocatalysts, to keep those specific microbes stable and functional within bioreactors. For example, Microvi’s Aerovi™ (nitrification) and Denitrovi™ (denitrification) systems achieve high rates of nitrogen removal even under heavy nutrient loads, showcasing the power of targeted microbiology. But while water and wastewater systems are well-studied, bioremediation scenarios are more complex. Bioremediation introduces variability that’s often absent in wastewater systems. Groundwater conditions vary significantly by location, and once anthropogenic pollutants are introduced, it would be easy to lose hope that there’s a microbe out there up to your (very very site-specific) task. How do we know which microbes are best suited for any given site’s challenges? Here’s where computational modeling steps in.

     Microvi’s long-time partner, Nexilico, Inc., has developed an innovative computational modeling platform for this very purpose called EnviroABM. Funded through a Phase I grant from the National Institute of Environmental Health Sciences (NIEHS), this agent-based modeling tool predicts contaminant degradation rates in multi-contaminant multi-microbe scenarios. This innovation would allow more precise engineering of bioremediation systems without costly real-world testing. Building on successful Phase I predictions, Nexilico incorporated machine learning into EnviroABM over our joint Phase II project. Through integrating public enzyme databases, the platform can match pollutants with the microbial enzymes that degrade them, identifying optimal microbial strains for specific pollutant mixtures. This allows EnviroABM to predict not just the degradation rates of complex pollutant mixtures, but also the microbes to do the job! By combining EnviroABM’s predictive power with Microvi’s MNE biocatalysts, bioremediation systems can be tailored to maintain precise microbial populations and achieve treatment goals efficiently.

     This year, we completed Phase II work with a lab-scale investigation using two parallel continuous-flow systems — one employing MNE biocatalysts and the other a traditional biofilm-based reactor. We tested the degradation of two EPA priority pollutants, Trichloroethylene (TCE) and Toluene, which pose significant public health risks. The MNE biocatalysts enabled precise microbial ratios, yielding higher degradation rates than the biofilm-based process. Importantly, the study validated Nexilico's EnviroABM predictions and demonstrated the value of combining computational modeling with advanced microbiological platforms.

     These results highlight the importance of interdisciplinary innovation and partnerships in addressing complex environmental challenges. Microvi and our collaborators are committed to leveraging these synergies to deliver impactful solutions. Want to learn more? Reach out to us at [email protected].