@article {464,
	title = {Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation.},
	journal = {Microb Biotechnol},
	volume = {2},
	year = {2009},
	month = {2009 Mar},
	pages = {274-86},
	abstract = {The increasing availability of the genome sequences of microorganisms involved in important bioremediation processes makes it feasible to consider developing genome-scale models that can aid in predicting the likely outcome of potential subsurface bioremediation strategies. Previous studies of the in situ bioremediation of uranium-contaminated groundwater have demonstrated that Geobacter species are often the dominant members of the groundwater community during active bioremediation and the primary organisms catalysing U(VI) reduction. Therefore, a genome-scale, constraint-based model of the metabolism of Geobacter sulfurreducens was coupled with the reactive transport model HYDROGEOCHEM in an attempt to model in situ uranium bioremediation. In order to simplify the modelling, the influence of only three growth factors was considered: acetate, the electron donor added to stimulate U(VI) reduction; Fe(III), the electron acceptor primarily supporting growth of Geobacter; and ammonium, a key nutrient. The constraint-based model predicted that growth yields of Geobacter varied significantly based on the availability of these three growth factors and that there are minimum thresholds of acetate and Fe(III) below which growth and activity are not possible. This contrasts with typical, empirical microbial models that assume fixed growth yields and the possibility for complete metabolism of the substrates. The coupled genome-scale and reactive transport model predicted acetate concentrations and U(VI) reduction rates in a field trial of in situ uranium bioremediation that were comparable to the predictions of a calibrated conventional model, but without the need for empirical calibration, other than specifying the initial biomass of Geobacter. These results suggest that coupling genome-scale metabolic models with reactive transport models may be a good approach to developing models that can be truly predictive, without empirical calibration, for evaluating the probable response of subsurface microorganisms to possible bioremediation approaches prior to implementation.},
	keywords = {Acetates, Biodegradation, Environmental, Biological Transport, Genome, Bacterial, Geobacter, Iron, Models, Biological, Uranium},
	issn = {1751-7915},
	doi = {10.1111/j.1751-7915.2009.00087.x},
	author = {Scheibe, Timothy D and Mahadevan, Radhakrishnan and Fang, Yilin and Garg, Srinath and Long, Philip E and Lovley, Derek R}
}
@article {466,
	title = {Genome-scale constraint-based modeling of Geobacter metallireducens.},
	journal = {BMC Syst Biol},
	volume = {3},
	year = {2009},
	month = {2009},
	pages = {15},
	abstract = {BACKGROUND: Geobacter metallireducens was the first organism that can be grown in pure culture to completely oxidize organic compounds with Fe(III) oxide serving as electron acceptor. Geobacter species, including G. sulfurreducens and G. metallireducens, are used for bioremediation and electricity generation from waste organic matter and renewable biomass. The constraint-based modeling approach enables the development of genome-scale in silico models that can predict the behavior of complex biological systems and their responses to the environments. Such a modeling approach was applied to provide physiological and ecological insights on the metabolism of G. metallireducens.

RESULTS: The genome-scale metabolic model of G. metallireducens was constructed to include 747 genes and 697 reactions. Compared to the G. sulfurreducens model, the G. metallireducens metabolic model contains 118 unique reactions that reflect many of G. metallireducens{\textquoteright} specific metabolic capabilities. Detailed examination of the G. metallireducens model suggests that its central metabolism contains several energy-inefficient reactions that are not present in the G. sulfurreducens model. Experimental biomass yield of G. metallireducens growing on pyruvate was lower than the predicted optimal biomass yield. Microarray data of G. metallireducens growing with benzoate and acetate indicated that genes encoding these energy-inefficient reactions were up-regulated by benzoate. These results suggested that the energy-inefficient reactions were likely turned off during G. metallireducens growth with acetate for optimal biomass yield, but were up-regulated during growth with complex electron donors such as benzoate for rapid energy generation. Furthermore, several computational modeling approaches were applied to accelerate G. metallireducens research. For example, growth of G. metallireducens with different electron donors and electron acceptors were studied using the genome-scale metabolic model, which provided a fast and cost-effective way to understand the metabolism of G. metallireducens.

CONCLUSION: We have developed a genome-scale metabolic model for G. metallireducens that features both metabolic similarities and differences to the published model for its close relative, G. sulfurreducens. Together these metabolic models provide an important resource for improving strategies on bioremediation and bioenergy generation.},
	keywords = {Biodegradation, Environmental, Biomass, Computer Simulation, Ecosystem, Electron Transport, Energy Metabolism, Genome, Bacterial, Geobacter, Iron, Metabolic Networks and Pathways, Models, Biological, Models, Genetic, Mutation, Phenotype, Species Specificity, Systems Biology},
	issn = {1752-0509},
	doi = {10.1186/1752-0509-3-15},
	author = {Sun, Jun and Sayyar, Bahareh and Butler, Jessica E and Pharkya, Priti and Fahland, Tom R and Famili, Iman and Schilling, Christophe H and Lovley, Derek R and Mahadevan, Radhakrishnan}
}
