Lisa Alvarez-Cohen University of California Berkeley
Groundwater contamination by chlorinated solvents is an important problem at thousands of sites within the United States and worldwide. In situ bioremediation of chlorinated ethene-contaminated groundwater involves the biostimulation of indigenous Dehalococcoides bacteria and/or bioaugmentation using Dehalococcoides-containing cultures to completely reduce perchloroethene and trichloroethene to non-toxic ethene. Because these bacteria exhibit specific restrictive metabolic requirements for substrates and cofactors that can be supplied by supportive microorganisms through diverse networks of metabolic interdependencies, this project seeks to deliver systems-level predictions of interspecies electron and metabolite exchange networks that shape the structural and functional robustness of Dehalococcoides-containing microbial communities. Using a combination of engineered control of cultures in continuous-flow bioreactor systems, TCE dechlorination biokinetic modeling and high-throughput, community-level genomic analysis, this research will develop mechanistic, systems-level models under groundwater plume-relevant conditions (i.e., continuous flow with low concentrations of chlorinated ethenes) to optimize and proactively control dechlorination processes under a range of environmental scenarios, leading to a better understanding and predictive control of bioremediation processes.
Groundwater contamination by chlorinated solvents (for example, degreasing fluids and dry-cleaning agents) is an important problem at thousands of sites within the United States and worldwide. Fortunately, a type of naturally-occurring bacteria are capable of biodegrading these contaminants when they are stimulated to grow to high enough population densities, in a process called "in situ bioremediation." These bacteria live in complex microbial communities that provide many of the factors needed by the bacteria to survive and biodegrade the solvents. This project takes a systems-level approach to understanding the interactions within these microbial communities to develop an integrated picture of how complex degrading communities operate and respond to different environmental stressors. This understanding will be captured in predictive models that will be useful for environmental scientists and bioremediation practitioners to evaluate and optimize cleanup strategies for chlorinated solvents, and to provide guidance for strategies to manipulate microbial communities in the field. This project will also support science outreach activities for multiple education levels to stimulate interest in science, technology, engineering, and mathematics through (co-)curricular learning, tutoring and training services.
