# An Agent-Based Modeling Platform for Environmental Biotechnology

> **NIH NIH R44** · MICROVI BIOTECH, INC. · 2021 · $630,992

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10158243
- **Project number:** 2R44ES026541-02
- **Recipient organization:** MICROVI BIOTECH, INC.
- **Principal Investigator:** Fatemeh Shirazi
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $630,992
- **Award type:** 2
- **Project period:** 2016-09-30 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10158243

## Citation

> US National Institutes of Health, RePORTER application 10158243, An Agent-Based Modeling Platform for Environmental Biotechnology (2R44ES026541-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10158243. Licensed CC0.

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