08 - Comparative analysis of bioremediators under nutrient limitation and heavy metal stress using genome-scale metabolic modeling
St. Olaf College
Bioremediation, the use of organisms to remove pollutants from contaminated sites, is a promising method to improve ecosystem and human health. However, assessing an organism€s metabolic response under stressed or polluted conditions poses many obstacles, highlighting the opportunity for computational modeling as a cost-effective predictive tool. Metabolic networks are reconstructed for each organism using all known metabolic reactions and genes encoding each enzyme. Flux balance analysis (FBA) optimizes the growth rate by calculating fluxes of metabolites through the metabolic network. With increased availability of whole-genome sequences, the use of in silico modeling can be a powerful tool to predict how organisms will respond to and affect their environment. My research integrates gene expression data with metabolic models and assesses the robustness of several bioremediation agents to nutrient limitation and heavy metal stress. The comparison of experimental data to model predictions under stressed conditions allow for the quantification of how gene expression responses alter growth rate and biomass composition. Despite its promise, modeling with FBA is limited in that it calculates the maximum growth rate and cannot predict growth curves for an organism over time. However, as genome-scale modeling techniques develop, this tool will be increasingly useful in guiding future bioremediation research. In silico modeling is an effective supplement to in vitro and in vivo studies, and has many applications to reduce ecological damage and human health risks.