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Multi-Scale Modeling of Bacterial Bioremediation

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2007 ASAE Annual Meeting  072167.(doi:10.13031/2013.23642)
Authors:   Jennifer Mathieu, John James, James Melhuish, Paula Mahoney, Y.Meriah Arias-Thode, Marc Colosimo, Olivia Peters
Keywords:   Environmental Remediation, Heavy Metals, Simulation, Agent-Based modeling, AnyLogic, Individual-Based modeling, System Dynamics modeling, Vensim

Bacterial bioremediation of sites contaminated with heavy metals such as chromium, copper, zinc, cadmium, and lead is facilitated by adding amendments (e.g. apatite—a phosphate mineral, chitin, acetate, etc.) to stimulate indigenous bacterial growth and metal reduction. The metals can be sorbed, degraded, transformed, or immobilized by various iron- and sulfate- reducing bacteria. The interaction of bacterial species populations in bioremediation systems is hypothesized to be more efficient at metal remediation than one species of bacteria. For example, phosphate solublizing bacteria might provide increased access to phosphate allowing faster growth rates for aerobic bacterial communities. The bioremediation system is modeled here using two simulation methodologies: Agent-Based at the individual bacterium scale and System Dynamics at the population scale, with the goal of observing patterns at these different scales. A simplified estuarine environment was defined with an oxygen gradient, amendment concentrations, and metal concentrations. Three bacterial populations were selected and behavioral rules were developed based on their growth requirements. An Agent-Based model was developed based on simple rules and environment structure, and the general pattern of their behavior was validated using experimental parameters from the literature. Similarly, a population scale model was developed for the three populations. Traditionally, these modeling paradigms have been used separately to describe biological systems such as bioremediation. The strength of the Agent-Based model is the visualization of the zones of bacterial growth. The strength of the System Dynamics model is the visualization of the model structure. One possibility for model integration is in parameter estimation. Exploring the simulation space with System Dynamics and running detailed simulations using the Agent-Based model may also be beneficial in augmenting experimental data (i.e., both micro and macro) to gain a greater understanding of bioremediation systems.

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