Genome-scale constraint-based modeling of Geobacter metallireducens
© Sun et al; licensee BioMed Central Ltd. 2009
Received: 26 September 2008
Accepted: 28 January 2009
Published: 28 January 2009
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.
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' 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.
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.
Geobacter species are environmentally significant because of their capacity for dissimilatory Fe(III) reduction . They can conserve energy for growth by completely oxidizing organic compounds to carbon dioxide coupled to Fe(III) reduction and have been found to be ubiquitous in subsurface environments [2–6]. In addition to Fe(III) reduction, Geobacter species can also reduce a variety of toxic and radioactive metals, thus can be applied to efficient bioremediation of uranium, plutonium, technetium, and vanadium [7–9]. Geobacter species can also transfer electrons to electrodes to conserve energy for growth [10, 11]. It has been demonstrated that Geobacter sulfurreducens produces electrically conductive pili that function as nanowires to promote electron transfer to insoluble electron acceptors such as Fe(III) oxide and electrodes [12, 13]. Therefore, Geobacter species have been utilized to harvest electricity from waste organic matter [10, 14] and as a biocatalyst in microbial fuel cell applications [15, 16].
Geobacter metallireducens was the first organism in pure culture that could oxidize organic compounds with Fe(III) oxide serving as electron acceptor [2, 17]. This strict anaerobe can utilize a wide range of organic compounds as electron donors, including acetate, ethanol, propionate, butyrate, pyruvate, propanol, and butanol . More importantly, G. metallireducens was found to completely oxidize monoaromatic compounds such as toluene, phenol, cresol, benzoate, benzaldehyde, and benzylalcohol coupled to Fe(III) reduction [2, 18]. Several recent studies suggested a core benzoyl-CoA degradation pathway in the utilization of these aromatic compounds [19–22]. G. metallireducens can also use nitrate as electron acceptor [2, 23].
Constraint-based modeling 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 successfully applied to provide physiological and ecological insights on the metabolism of G. sulfurreducens , and has been used to optimize its applications in energy production and bioremediation . Due to its wide range of electron donors and acceptors, G. metallireducens has more metabolic capabilities and therefore more potential applications than G. sulfurreducens. The genome sequence of G. metallireducens was recently completed http://www.jgi.doe.gov/. Here, we report the development of a genome-scale metabolic model of G. metallireducens and the application of the model to study its metabolism.
Metabolic network reconstruction
The G. metallireducens metabolic network was reconstructed by a modified version of previously published procedure . The reconstruction was carried out in SimPheny (Genomatica, Inc., CA) from the annotated open reading frames (ORFs) encoded in the G. metallireducens genome. The sequence similarity search (BLAST) results of the G. metallireducens genome with the genomes of several high-quality genome-scale metabolic models were utilized to create a draft model that served to accelerate the reconstruction of the genome-scale metabolic model. The reactions and genes in the draft model were manually reviewed using the gene annotations and the available biochemical and physiological information. The biomass demand reaction based on biomass composition and maintenance parameters in the published G. sulfurreducens model were used in the reconstructed G. metallireducens model. The resulting network was then subjected to the gap filling process to allow biomass formation under physiological growth conditions. For gap filling, simulations were performed to determine if the network could synthesize every single component of the biomass and the missing reactions in the pathways were identified. These reactions were reviewed for gene association, or added as non-gene associated reactions to enable the formation of biomass by the reconstructed network under physiological conditions. The reconstructed model was then used to generate a set of experimentally testable hypotheses and predictions. The experimental findings were in turn used to further refine and expand the reconstructed model in an iterative process.
In silico analysis of metabolism
The metabolic capabilities of the G. metallireducens model were calculated using flux balance analysis through linear optimization  in SimPheny. For growth simulations, biomass synthesis was selected as the objective function to be maximized. For energy requirement simulations, the ATP maintenance requirement reaction was selected as the objective function to be maximized. The simulations resulted in flux values in unit of mmol/g dry weight (gdw)/h. All simulations were of anaerobic growth on minimal media, where the following external metabolites were allowed to freely enter and leave the network: CO2, H+, H2O, K+, Mg2+, NH4+, PO43-, and SO42-. The electron donors or electron acceptors tested were allowed a maximum uptake rate into the network as specified in the results. All other external metabolites were only allowed to leave the system. Flux variability analysis was carried out in SimPheny using method described before .
In silico deletion analysis was carried out for growth with acetate as the electron donor and Fe(III) or fumarate as the electron acceptor with acetate as the limiting nutrient. Maximization of biomass synthesis was the objective function. Deletions resulting in reduced growth compared to wild type were categorized as intermediate phenotype.
Strains and culture conditions
The G. metallireducens strain used in the growth experiments was constructed with a dicarboxylic acid transporter from G. sulfurreducens that grew with fumarate as the sole electron acceptor . The strain was cultured with appropriate electron donors in the NBAF medium that contained 4.64 g/l fumaric acid, 0.42 g/l KH2PO4, 0.22 g/l K2HPO4, 0.20 g/l NH4Cl, 0.38 g/l KCl, 0.36 g/l NaCl, 0.04 g/l CaCl-H22O, 0.12 g/l MgSO4-7H2O, 1.8 g/l NaHCO3, 0.5 g/l Na2CO3-H2O, and 1 μM Na2SeO4. The NBAF medium was supplemented with 15 ml/l vitamin mixtures and 10 ml/l mineral mixtures [29, 30], and was adjusted to pH 7.0. For growth experiments, the electron donors were added separately from the prepared stocks to the NBAF medium. Final electron donor concentration in the NBAF medium was fixed to 25 mM for both ethanol and pyruvate.
Stock solutions of ethanol and pyruvate were prepared, filtered with 0.2 μm filters, bubbled with N2, and capped separately. For the growth experiments, serum bottles containing the culture medium were flushed with N2:CO2 (80/20) to remove any trace of oxygen in the bottles, capped with thick butyl-rubber stoppers, and autoclaved.
For growth with benzoate, G. metallireducens cultures were grown in triplicate at 30°C in anaerobic continuous culture vessels as previously described . Defined, bicarbonate-buffered media with 1.0 mM benzoate as the limiting electron donor and Fe(III) citrate as the electron acceptor was provided at a dilution rate of 0.05 h-1. At steady state, protein concentration was 8.2 (± 0.2) mg/L and Fe(II) concentration was 30.3 (± 0.4) mM. Fe(II) was determined using the ferrozine assay as previously described .
Samples for organic acid analysis were filtered using 0.2 μm filters and stored at -20°C. The samples were analyzed together using an HPLC (Dionex, Sunnyvale, CA) with a mobile phase of 0.5 mM H2SO4 at a flow rate of 0.3 ml/min. Peaks were identified and quantified by comparing to those obtained from the standards of ethanol, pyruvate, and fumarate. HPLC data were used to estimate the time profiles of the electron donor and electron acceptor concentrations in samples of G. metallireducens in NBAF media.
Results and discussion
Metabolic network reconstruction
A draft model of G. metallireducens was built by using pair-wise BLASTp comparison of the G. metallireducens genome with the genomes of the several high-quality base models in Genomatica model database including previously published G. sulfurreducens , Escherichia coli [26, 32, 33]and Bacillus subtilis  models. The G. metallireducens draft model comprised 514 reactions. Among the base models used, G. sulfurreducens contributed 93% of the top BLASTp matches; this confirmed the close relationship between these two organisms. The G. metallireducens draft model captured significant portions of central metabolism, and the biosynthetic pathways for amino acids, nucleotides, and lipids.
The reactions and their gene associations in the draft model of G. metallireducens were evaluated manually based on gene annotations, published biochemical and physiological information, and external references as previously described . The remaining genes were also reviewed for inclusion in the reconstructed network. A biomass demand reaction based on the combination of biomass components that were experimentally determined in G. metallireducens and represented in the published G. sulfurreducens model  was used in G. metallireducens model. Similarly, the energy parameters such as growth-associated energy requirements in the published G. sulfurreducens model  were used in the G. metallireducens model for the close relationship between these two organisms.
The unique metabolic capabilities of G. metallireducens to degrade monoaromatic compounds were reconstructed in the metabolic model. Monoaromatic compounds such as toluene, phenol, cresol, benzoate, benzaldehyde, and benzylalcohol are converted into benzoyl-CoA and then through the benzoyl-CoA degradation pathway to acetyl-CoA [19–22]. Specifically, benzylalcohol and benzaldehyde are oxidized by dehydrogenases to benzoate, which is then converted into benzoyl-CoA by benzoate CoA ligase, whereas cresol and phenol are converted to 4-hydroxybenzoate and then reduced to benzoyl-CoA through 4-hydroxybenzoyl-CoA. Toluene is converted to benzoyl-CoA via benzylsuccinyl-CoA.
For gap filling, the ability of the metabolic network to synthesize a full complement of amino acids, nucleotides, lipids, carbohydrates, and cofactors from a minimal medium containing the known electron donors and acceptors was assessed. The missing reactions in the pathways were identified and reviewed. Some missing reactions were associated with G. metallireducens genes based on biochemical or genomic evidences and were included in the reconstructed network. Other missing reactions were added to the model as non-gene associated reactions to enable the reconstructed network to synthesize metabolites for biomass formation. The reconstructed network contains 30 non-gene associated reactions with different justification. These non-gene associated reactions fell into several categories: 2 reactions, 2-Oxo-4-methyl-3-carboxypentanoate decarboxylation and L-glutamate 5-semialdehyde dehydratase, are non-enzymatic conversions that happen spontaneously under physiological conditions; 4 gas diffusion processes allow the transport of these gases; 1 reaction is for ATP maintenance requirement; 5 transporter reactions for electron donors ensure consistency with growth results; and 18 non-gene associated reactions are required for biomass formation under known growth conditions (see Additional file 1 for details). Non-gene associated reactions in the latter two categories are presumptive metabolic functions encoded potentially by unknown genes, and thus will be subjected for further genomic and biochemical investigation in the future.
Metabolic network of G. metallireducens
Characteristics of the G. metallireducens genome-scale metabolic model compared with the G. sulfurreducens model.
Comparison of reactions in G. metallireducens and G. sulfurreducens metabolic models.
Amino acid and related molecules
Carbohydrate and related molecules
Cofactors and prosthetic group
Lipids and cell walls
Nucleotide and nucleic acids
G. metallireducens can utilize a much wider range of electron donors and acceptors [2, 18, 23] than G. sulfurreducens, which uses only acetate, H2 and lactate as the electron donors. The G. metallireducens metabolic model contains 118 unique reactions out found in the G. sulfurreducens model. Many of these unique reactions reflect of the diversity of G. metallireducens' metabolic capabilities. For example, the G. metallireducens model contains 32 unique reactions involved in the degradation pathways of aromatic compounds. G. metallireducens can also utilize several substrates other than the aromatic compounds that G. sulfurreducens does not use. G. metallireducens contains several alcohol dehydrogenase genes with substrate specificities for ethanol, propanol, and butanol that are not believed to be present in G. sulfurreducens. The enzymes coded by these genes catalyze several unique reactions that are key steps in the utilization of these alcohol substrates. The corresponding transporter reactions were also added to the G. metallireducens model, but not in the G. sulfurreducens model. Similarly, a butyrate kinase reaction unique to the G. metallireducens model allows the utilization of butyrate. These unique reactions in the G. metallireducens model enable the growth of the G. metallireducens model on a wide range of substrates and has accurately captured the known physiological characteristics of G. metallireducens [2, 18, 23].
In silico characterization of G. metallireducens metabolism
When biomass yields were calculated based on acceptor consumed, pyruvate resulted in the highest biomass yield per mol of electron acceptor under all conditions, suggesting that pyruvate may have advantages over other substrates in electron acceptor limiting environments. Acetate and ethanol had similar biomass yield per electron acceptor compared to the aromatic compounds, suggesting that they may produce the same amount of biomass when limited to same amount of electron acceptors in growth medium. Therefore, the modeling study rapidly predicted the growth yields of G. metallireducens under varying nutrient conditions.
Comparison of G. metallireducens metabolic model to G. sulfurreducens model
G. metallireducens also contains genes for several pathways in central metabolism that do not have corresponding homologues in G. sulfurreducens. Therefore, the unique reactions associated with these genes may provide specific metabolic capacities in the G. metallireducens model. For example, G. metallireducens is known to use nitrate as an electron acceptor [2, 23] and the model predicts such capability. The G. metallireducens network has transporters for nitrate uptake via nitrite antiport and the nitrate reductase (cytochrome c) to reduce nitrate, which are not present in G. sulfurreducens. These two reactions together allow electrons from cytochrome c to be transferred to nitrate. Nitrate is reduced and the resulting intracellular nitrite is exchanged with extracellular nitrate using the antiporter. The G. metallireducens model also contains a nitrite proton antiporter and nitrite reductase that further reduce nitrite to ammonium and allows the utilization of nitrite.
Other reactions that are not present in the G. sulfurreducens model include the glucose 6-phosphate dehydrogenase, 6-phosphogluconolactonase, and phosphogluconate dehydrogenase, which is a part of the oxidative branch of the pentose phosphate pathway. This branch provides an efficient way to produce D-ribose-5-phosphate and is an important source of NADPH. However, simulations of G. metallireducens growth predict that G. metallireducens can produce D-ribose-5-phosphate by using glyceraldehyde 3-phosphate and D-fructose-6-phosphate to produce D-xylulose 5-phosphate through transketolase and transaldolase, and then converting D-xylulose 5-phosphate to D-ribose-5-phosphate, similar as simulation of G. sulfurreducens growth. Simulations also suggest that G. metallireducens can generate NADPH through isocitrate dehydrogenase (NADP) and other reactions with NADP as cofactor in a manner similar to the G. sulfurreducens network. There was no significant change in the expression levels of these genes during growth with acetate vs. benzoate couples with Fe(III) reduction. The exact role of this oxidative branch of pentose pathway in G. metallireducens requires further examination.
ATP-consuming futile cycles involve multiple reactions allowing the interconversion between metabolites with a net ATP consumption, and can decrease growth. However, it is hypothesized that these futile cycles balance the metabolite pools to make other key reactions thermodynamically feasible . Recent 13C-labeling studies in G. metallireducens confirmed the existence of an ATP-consuming futile cycle between pyruvate and phosphoenolpyruvate .
G. metallireducens growth with electron donors
Microarray data for the above growth conditions were not readily available. Instead, we analyzed the microarray data for G. metallireducens growing with benzoate versus acetate . Among G. metallireducens genes that were significantly up-regulated (> 50%) by growth with benzoate versus acetate, genes encoding for ACS, PPC, and ACOAH were up-regulated by 161% to 270%. The up-regulation of these genes encoding for the energy-inefficient reactions during growth with the complex substrate benzoate indicated the involvement of these energy-inefficient reactions in the metabolism of G. metallireducens when high-energy substrate benzoate is consumed. It is likely that similar up-regulation of these genes occurs during growth with pyruvate and not with ethanol.
Simulations of the G. metallireducens growth were performed using maximal biomass yield as the objective function, usually to be true in natural growth conditions where nutrients are limited. However, optimizing biomass yield may not always be the growth strategy of choice and recent studies have illustrated that under conditions of nutrient excess, maximizing the ATP production might be the chosen growth strategy . Growth with high-energy substrates may also lead to a growth strategy of maximal ATP production. Under this growth strategy, the energetically inefficient reactions can be advantageous for utilizing the ATP produced. The abundance of these energetically inefficient reactions in G. metallireducens suggests that the evolution of G. sulfurreducens and G. metallireducens might have occurred in environments with different nutrient levels. In this scenario, G. sulfurreducens probably evolved in predominantly acetate limiting environments, whereas G. metallireducens probably evolved in environments with nutrient excess or with complex nutrients available.
Growth simulations of G. metallireducens using nitrate as electron acceptor
Flux distribution comparison between model prediction and 13C labeling results
Flux variability analysis defines a feasible range of fluxes for each individual reaction . A flux variability analysis under the same constraints indicated that most flux values determined by 13C labeling experiments were within such feasible ranges (data not shown), and validated the consistency between the experimental and predicted results. These results suggested that in silico growth simulation optimized for biomass formation and flux variability analysis to define the feasible flux ranges together provided a fast and easy alternative method to estimate flux distribution for the metabolism of G. metallireducens.
Functional analysis of G. metallireducens mutant phenotype
Environmental pollution and sustainable energy are among the most important challenges that the world is facing in the 21st century. Consequently, these areas have attracted significant research efforts. In particular, Geobacter species are being extensively studied for their applications in bioremediation and bioelectricity production. However, similarities and differences in the metabolism and physiology of Geobacter species have not been well characterized. In this report, we have developed a genome-scale metabolic model for G. metallireducens to accelerate discovery and gain insight into its metabolism. Together with the published G. sulfurreducens model, the G. metallireducens metabolic model provides an important resource for the improving strategies for bioremediation and bioenergy generation.
The reconstructed metabolic model of G. metallireducens was used to gain insight into the metabolism of this bacterium. The G. metallireducens model is metabolically distinct from the G. sulfurreducens model, largely due to the wider range of G. metallireducens substrate utilization. The G. metallireducens metabolic model contains many additional reactions reflecting these specific metabolic capabilities that the G. sulfurreducens model does not have. In silico modeling of these additional metabolic capabilities can be used to understand how these substrates are utilized by G. metallireducens and how these capabilities can be applied in bioremediation and bioelectricity production.
Detailed examination of the G. metallireducens model suggested 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 with pyruvate was lower than the predicted optimal in silico biomass yield, and microarray data of G. metallireducens growing with benzoate and acetate indicated that genes encoding these unique reactions were up-regulated by benzoate. These results suggested that the energy-inefficient reactions were likely turned off during G. metallireducens growth with acetate to optimize biomass yield, but were up-regulated during growth with complex electron donors to improve flux for rapid energy generation. Thus, the evolution of G. sulfurreducens and G. metallireducens might have occurred in environments with different nutrient levels: G. sulfurreducens in a predominantly acetate limiting environments, whereas G. metallireducens probably in environments in the presence of complex nutrients or nutrient abundance. These results will help understand the physiology of these Geobacter species in the subsurface environments.
Furthermore, several in silico computational modeling approaches were applied to accelerate G. metallireducens research. For example, growth of G. metallireducens with different electron donors and electron acceptors were simulated using the genome-scale metabolic model. These simulations provided an easy and cost-effective way further understanding the metabolism of G. metallireducens. Flux distribution was compared between in silico prediction and 13C labeling flux analysis results, suggesting that in silico prediction could provide a fast alternative method to estimate metabolic fluxes. Finally, the deletion analysis of the G. metallireducens metabolic model predicts phenotypes of gene knock-outs systematically and quickly. It is also important to understand that the testable hypotheses and predictions generated by in silico computational modeling with the reconstructed model should be evaluated experimentally. The experimental findings will in turn further refine and expand the reconstructed model, as well as improve our understanding of the Geobacter metabolism, in an iterative fashion.
gram dry weight
open reading frame
phosphoenolpyruvate carboxykinase (GTP)
open reading frames
g dry weight
This research was supported by the Office of Science (BER), Genomics:GTL Program of the U.S. Department of Energy, Grant No. DE-FC02-02ER63446. We thank Olivia Bui for drawing a metabolic map for the model, and John D. Trawick for critical reading of the manuscript.
- Lovley DR, Phillips EJ: Novel Mode of Microbial Energy Metabolism: Organic Carbon Oxidation Coupled to Dissimilatory Reduction of Iron or Manganese. Appl Environ Microbiol. 1988, 54: 1472-1480.PubMed CentralPubMedGoogle Scholar
- Lovley DR, Giovannoni SJ, White DC, Champine JE, Phillips EJ, Gorby YA, Goodwin S: Geobacter metallireducens gen. nov. sp. nov., a microorganism capable of coupling the complete oxidation of organic compounds to the reduction of iron and other metals. Arch Microbiol. 1993, 159: 336-344. 10.1007/BF00290916View ArticlePubMedGoogle Scholar
- Caccavo F, Lonergan DJ, Lovley DR, Davis M, Stolz JF, McInerney MJ: Geobacter sulfurreducens sp. nov., a hydrogen- and acetate-oxidizing dissimilatory metal-reducing microorganism. Appl Environ Microbiol. 1994, 60: 3752-3759.PubMed CentralPubMedGoogle Scholar
- Lovley DR, Holmes DE, Nevin KP: Dissimilatory Fe(III) and Mn(IV) reduction. Adv Microb Physiol. 2004, 49: 219-286. 10.1016/S0065-2911(04)49005-5View ArticlePubMedGoogle Scholar
- Nevin KP, Holmes DE, Woodard TL, Hinlein ES, Ostendorf DW, Lovley DR: Geobacter bemidjiensis sp. nov. and Geobacter psychrophilus sp. nov., two novel Fe(III)-reducing subsurface isolates. Int J Syst Evol Microbiol. 2005, 55: 1667-1674. 10.1099/ijs.0.63417-0View ArticlePubMedGoogle Scholar
- Shelobolina ES, Nevin KP, Blakeney-Hayward JD, Johnsen CV, Plaia TW, Krader P, Woodard T, Holmes DE, Vanpraagh CG, Lovley DR: Geobacter pickeringii sp. nov., Geobacter argillaceus sp. nov. and Pelosinus fermentans gen. nov., sp. nov., isolated from subsurface kaolin lenses. Int J Syst Evol Microbiol. 2007, 57: 126-135. 10.1099/ijs.0.64221-0View ArticlePubMedGoogle Scholar
- Lloyd JR, Sole VA, Van Praagh CV, Lovley DR: Direct and Fe(II)-mediated reduction of technetium by Fe(III)-reducing bacteria. Appl Environ Microbiol. 2000, 66: 3743-3749. 10.1128/AEM.66.9.3743-3749.2000PubMed CentralView ArticlePubMedGoogle Scholar
- Lloyd JR, Chesnes J, Glasauer S, Bunker DJ, Livens FR, Lovley DR: Reduction of actinides and fission products by Fe(III)-reducing bacteria. Geomicrobiology Journal. 2002, 19: 103-120. 10.1080/014904502317246200.View ArticleGoogle Scholar
- Ortiz-Bernad I, Anderson RT, Vrionis HA, Lovley DR: Vanadium respiration by Geobacter metallireducens: novel strategy for in situ removal of vanadium from groundwater. Appl Environ Microbiol. 2004, 70: 3091-3095. 10.1128/AEM.70.5.3091-3095.2004PubMed CentralView ArticlePubMedGoogle Scholar
- Bond DR, Holmes DE, Tender LM, Lovley DR: Electrode-reducing microorganisms that harvest energy from marine sediments. Science. 2002, 295: 483-485. 10.1126/science.1066771View ArticlePubMedGoogle Scholar
- Bond DR, Lovley DR: Electricity production by Geobacter sulfurreducens attached to electrodes. Appl Environ Microbiol. 2003, 69: 1548-1555. 10.1128/AEM.69.3.1548-1555.2003PubMed CentralView ArticlePubMedGoogle Scholar
- Reguera G, McCarthy KD, Mehta T, Nicoll JS, Tuominen MT, Lovley DR: Extracellular electron transfer via microbial nanowires. Nature. 2005, 435: 1098-1101. 10.1038/nature03661View ArticlePubMedGoogle Scholar
- Reguera G, Nevin KP, Nicoll JS, Covalla SF, Woodard TL, Lovley DR: Biofilm and nanowire production leads to increased current in Geobacter sulfurreducens fuel cells. Appl Environ Microbiol. 2006, 72: 7345-7348. 10.1128/AEM.01444-06PubMed CentralView ArticlePubMedGoogle Scholar
- Holmes DE, Bond DR, O'Neil RA, Reimers CE, Tender LR, Lovley DR: Microbial communities associated with electrodes harvesting electricity from a variety of aquatic sediments. Microb Ecol. 2004, 48: 178-190. 10.1007/s00248-003-0004-4View ArticlePubMedGoogle Scholar
- Lovley DR: Bug juice: harvesting electricity with microorganisms. Nat Rev Microbiol. 2006, 4: 497-508. 10.1038/nrmicro1442View ArticlePubMedGoogle Scholar
- Lovley DR: Microbial fuel cells: novel microbial physiologies and engineering approaches. Curr Opin Biotechnol. 2006, 17: 327-332. 10.1016/j.copbio.2006.04.006View ArticlePubMedGoogle Scholar
- Lovley DR, Baedecker M, Lonergan DJ, Cozzarelli I, Phillips EJP, Siegel D: Oxidation of aromatic contaminants coupled to microbial iron reduction. Nature. 1989, 339: 297-300. 10.1038/339297a0.View ArticleGoogle Scholar
- Lovley DR, Lonergan DJ: Anaerobic Oxidation of Toluene, Phenol, and p-Cresol by the Dissimilatory Iron-Reducing Organism, GS-15. Appl Environ Microbiol. 1990, 56: 1858-1864.PubMed CentralPubMedGoogle Scholar
- Kane SR, Beller HR, Legler TC, Anderson RT: Biochemical and genetic evidence of benzylsuccinate synthase in toluene-degrading, ferric iron-reducing Geobacter metallireducens. Biodegradation. 2002, 13: 149-154. 10.1023/A:1020454831407View ArticlePubMedGoogle Scholar
- Wischgoll S, Heintz D, Peters F, Erxleben A, Sarnighausen E, Reski R, van Dorsselaer A, Boll M: Gene clusters involved in anaerobic benzoate degradation of Geobacter metallireducens. Mol Microbiol. 2005, 58: 1238-1252.View ArticlePubMedGoogle Scholar
- Butler JE, He Q, Nevin KP, He Z, Zhou J, Lovley DR: Genomic and microarray analysis of aromatics degradation in Geobacter metallireducens and comparison to a Geobacter isolate from a contaminated field site. BMC Genomics. 2007, 8: 180- 10.1186/1471-2164-8-180PubMed CentralView ArticlePubMedGoogle Scholar
- Peters F, Heintz D, Johannes J, van Dorsselaer A, Boll M: Genes, enzymes, and regulation of para-cresol metabolism in Geobacter metallireducens. J Bacteriol. 2007, 189: 4729-4738. 10.1128/JB.00260-07PubMed CentralView ArticlePubMedGoogle Scholar
- Senko JM, Stolz JF: Evidence for iron-dependent nitrate respiration in the dissimilatory iron-reducing bacterium Geobacter metallireducens. Appl Environ Microbiol. 2001, 67: 3750-3752. 10.1128/AEM.67.8.3750-3752.2001PubMed CentralView ArticlePubMedGoogle Scholar
- Mahadevan R, Bond DR, Butler JE, Esteve-Nunez A, Coppi MV, Palsson BO, Schilling CH, Lovley DR: Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling. Appl Environ Microbiol. 2006, 72: 1558-1568. 10.1128/AEM.72.2.1558-1568.2006PubMed CentralView ArticlePubMedGoogle Scholar
- Izallalen M, Mahadevan R, Burgard A, Postier B, Didonato R, Sun J, Schilling CH, Lovley DR: Geobacter sulfurreducens strain engineered for increased rates of respiration. Metab Eng. 2008, 10: 267-275. 10.1016/j.ymben.2008.06.005View ArticlePubMedGoogle Scholar
- Edwards JS, Palsson BO: The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci USA. 2000, 97: 5528-5533. 10.1073/pnas.97.10.5528PubMed CentralView ArticlePubMedGoogle Scholar
- Reed JL, Palsson BO: Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res. 2004, 14: 1797-1805. 10.1101/gr.2546004PubMed CentralView ArticlePubMedGoogle Scholar
- Butler JE, Glaven RH, Esteve-Nunez A, Nunez C, Shelobolina ES, Bond DR, Lovley DR: Genetic characterization of a single bifunctional enzyme for fumarate reduction and succinate oxidation in Geobacter sulfurreducens and engineering of fumarate reduction in Geobacter metallireducens. J Bacteriol. 2006, 188: 450-455. 10.1128/JB.188.2.450-455.2006PubMed CentralView ArticlePubMedGoogle Scholar
- Esteve-Nunez A, Nunez C, Lovley DR: Preferential Reduction of Fe(III) over Fumarate by Geobacter sulfurreducens. J Bacteriol. 2004, 186: 2897-2899. 10.1128/JB.186.9.2897-2899.2004PubMed CentralView ArticlePubMedGoogle Scholar
- Esteve-Nunez A, Rothermich A, Sharma M, Lovley DR: Growth of Geobacter sulfurreducens Under Nutrient-Limiting Conditions in Continuous Culture. Environmental Microbiology. 2005, 7: 641-648. 10.1111/j.1462-2920.2005.00731.xView ArticlePubMedGoogle Scholar
- Lovley DR, Phillips EJ: Rapid Assay for Microbially Reducible Ferric Iron in Aquatic Sediments. Appl Environ Microbiol. 1987, 53: 1536-1540.PubMed CentralPubMedGoogle Scholar
- Reed JL, Vo TD, Schilling CH, Palsson BO: An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 2003, 4: R54- 10.1186/gb-2003-4-9-r54PubMed CentralView ArticlePubMedGoogle Scholar
- Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BO: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol. 2007, 3: 121- 10.1038/msb4100155PubMed CentralView ArticlePubMedGoogle Scholar
- Oh YK, Palsson BO, Park SM, Schilling CH, Mahadevan R: Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem. 2007, 282 (39): 28791-9. 10.1074/jbc.M703759200View ArticlePubMedGoogle Scholar
- Covert MW, Schilling CH, Famili I, Edwards JS, Goryanin II, Selkov E, Palsson BO: Metabolic modeling of microbial strains in silico. Trends Biochem Sci. 2001, 26: 179-186. 10.1016/S0968-0004(00)01754-0View ArticlePubMedGoogle Scholar
- Boll M, Albracht SS, Fuchs G: Benzoyl-CoA reductase (dearomatizing), a key enzyme of anaerobic aromatic metabolism. A study of adenosinetriphosphatase activity, ATP stoichiometry of the reaction and EPR properties of the enzyme. Eur J Biochem. 1997, 244: 840-851. 10.1111/j.1432-1033.1997.00840.xView ArticlePubMedGoogle Scholar
- Tang YJ, Chakraborty R, Martin HG, Chu J, Hazen TC, Keasling JD: Flux analysis of central metabolic pathways in Geobacter metallireducens during reduction of soluble Fe(III)-nitrilotriacetic acid. Appl Environ Microbiol. 2007, 73: 3859-3864. 10.1128/AEM.02986-06PubMed CentralView ArticlePubMedGoogle Scholar
- Schuetz R, Kuepfer L, Sauer U: Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol. 2007, 3: 119- 10.1038/msb4100162PubMed CentralView ArticlePubMedGoogle Scholar
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