@article {410,
	title = {The design of long-term effective uranium bioremediation strategy using a community metabolic model.},
	journal = {Biotechnol Bioeng},
	volume = {109},
	year = {2012},
	month = {2012 Oct},
	pages = {2475-83},
	abstract = {Acetate amendment at uranium contaminated sites in Rifle, CO. leads to an initial bloom of Geobacter accompanied by the removal of U(VI) from the groundwater, followed by an increase of sulfate-reducing bacteria (SRBs) which are poor reducers of U(VI). One of the challenges associated with bioremediation is the decay in Geobacter abundance, which has been attributed to the depletion of bio-accessible Fe(III), motivating the investigation of simultaneous amendments of acetate and Fe(III) as an alternative bioremediation strategy. In order to understand the community metabolism of Geobacter and SRBs during artificial substrate amendment, we have created a genome-scale dynamic community model of Geobacter and SRBs using the previously described Dynamic Multi-species Metabolic Modeling framework. Optimization techniques are used to determine the optimal acetate and Fe(III) addition profile. Field-scale simulation of acetate addition accurately predicted the in situ data. The simulations suggest that batch amendment of Fe(III) along with continuous acetate addition is insufficient to promote long-term bioremediation, while continuous amendment of Fe(III) along with continuous acetate addition is sufficient to promote long-term bioremediation. By computationally minimizing the acetate and Fe(III) addition rates as well as the difference between the predicted and target uranium concentration, we showed that it is possible to maintain the uranium concentration below the environmental safety standard while minimizing the cost of chemical additions. These simulations show that simultaneous addition of acetate and Fe(III) has the potential to be an effective uranium bioremediation strategy. They also show that computational modeling of microbial community is an important tool to design effective strategies for practical applications in environmental biotechnology. Biotechnol. Bioeng. 2012; 109: 2475-2483. {\textcopyright} 2012 Wiley Periodicals, Inc.},
	issn = {1097-0290},
	doi = {10.1002/bit.24528},
	author = {Zhuang, K and Ma, E and Lovley, Derek R and Mahadevan, Radhakrishnan}
}
@article {429,
	title = {Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model.},
	journal = {J Contam Hydrol},
	volume = {122},
	year = {2011},
	month = {2011 Mar 25},
	pages = {96-103},
	abstract = {The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The modeling system is designed in such a way that constraint-based models targeting different microorganisms or competing organism communities can be easily plugged into the system. Constraint-based modeling is very costly given the size of a genome-scale reaction network. To save computation time, a binary tree is traversed to examine the concentration and solution pool generated during the simulation in order to decide whether the constraint-based model should be called. We also show preliminary results from the integrated model including a comparison of the direct and indirect coupling approaches and evaluated the ability of the approach to simulate field experiment.},
	keywords = {Biodegradation, Environmental, Biological Transport, Colorado, Computer Simulation, Genome, Bacterial, Geobacter, Models, Biological, Uranium},
	issn = {1873-6009},
	doi = {10.1016/j.jconhyd.2010.11.007},
	author = {Fang, Yilin and Scheibe, Timothy D and Mahadevan, Radhakrishnan and Garg, Srinath and Long, Philip E and Lovley, Derek R}
}
