<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fang, Yilin</style></author><author><style face="normal" font="default" size="100%">Scheibe, Timothy D</style></author><author><style face="normal" font="default" size="100%">Mahadevan, Radhakrishnan</style></author><author><style face="normal" font="default" size="100%">Garg, Srinath</style></author><author><style face="normal" font="default" size="100%">Long, Philip E</style></author><author><style face="normal" font="default" size="100%">Lovley, Derek R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model.</style></title><secondary-title><style face="normal" font="default" size="100%">J Contam Hydrol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Contam. Hydrol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biodegradation, Environmental</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Transport</style></keyword><keyword><style  face="normal" font="default" size="100%">Colorado</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Geobacter</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Uranium</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Mar 25</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">96-103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">1-4</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21172725?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhuang, Kai</style></author><author><style face="normal" font="default" size="100%">Izallalen, Mounir</style></author><author><style face="normal" font="default" size="100%">Mouser, Paula</style></author><author><style face="normal" font="default" size="100%">Richter, Hanno</style></author><author><style face="normal" font="default" size="100%">Risso, Carla</style></author><author><style face="normal" font="default" size="100%">Mahadevan, Radhakrishnan</style></author><author><style face="normal" font="default" size="100%">Lovley, Derek R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments.</style></title><secondary-title><style face="normal" font="default" size="100%">ISME J</style></secondary-title><alt-title><style face="normal" font="default" size="100%">ISME J</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acetates</style></keyword><keyword><style  face="normal" font="default" size="100%">Anaerobiosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Biodegradation, Environmental</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomass</style></keyword><keyword><style  face="normal" font="default" size="100%">Comamonadaceae</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Geobacter</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Nitrogen Fixation</style></keyword><keyword><style  face="normal" font="default" size="100%">Quaternary Ammonium Compounds</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Ribosomal, 16S</style></keyword><keyword><style  face="normal" font="default" size="100%">Uranium</style></keyword><keyword><style  face="normal" font="default" size="100%">Water Microbiology</style></keyword><keyword><style  face="normal" font="default" size="100%">Water Pollutants, Radioactive</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">305-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The advent of rapid complete genome sequencing, and the potential to capture this information in genome-scale metabolic models, provide the possibility of comprehensively modeling microbial community interactions. For example, Rhodoferax and Geobacter species are acetate-oxidizing Fe(III)-reducers that compete in anoxic subsurface environments and this competition may have an influence on the in situ bioremediation of uranium-contaminated groundwater. Therefore, genome-scale models of Geobacter sulfurreducens and Rhodoferax ferrireducens were used to evaluate how Geobacter and Rhodoferax species might compete under diverse conditions found in a uranium-contaminated aquifer in Rifle, CO. The model predicted that at the low rates of acetate flux expected under natural conditions at the site, Rhodoferax will outcompete Geobacter as long as sufficient ammonium is available. The model also predicted that when high concentrations of acetate are added during in situ bioremediation, Geobacter species would predominate, consistent with field-scale observations. This can be attributed to the higher expected growth yields of Rhodoferax and the ability of Geobacter to fix nitrogen. The modeling predicted relative proportions of Geobacter and Rhodoferax in geochemically distinct zones of the Rifle site that were comparable to those that were previously documented with molecular techniques. The model also predicted that under nitrogen fixation, higher carbon and electron fluxes would be diverted toward respiration rather than biomass formation in Geobacter, providing a potential explanation for enhanced in situ U(VI) reduction in low-ammonium zones. These results show that genome-scale modeling can be a useful tool for predicting microbial interactions in subsurface environments and shows promise for designing bioremediation strategies.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/20668487?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mahadevan, Radhakrishnan</style></author><author><style face="normal" font="default" size="100%">Palsson, Bernhard Ø</style></author><author><style face="normal" font="default" size="100%">Lovley, Derek R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">In situ to in silico and back: elucidating the physiology and ecology of Geobacter spp. using genome-scale modelling.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Rev Microbiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat. Rev. Microbiol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Environment</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Geobacter</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">39-50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">There is a wide diversity of unexplored metabolism encoded in the genomes of microorganisms that have an important environmental role. Genome-scale metabolic modelling enables the individual reactions that are encoded in annotated genomes to be organized into a coherent whole, which can then be used to predict metabolic fluxes that will optimize cell function under a range of conditions. In this Review, we summarize a series of studies in which genome-scale metabolic modelling of Geobacter spp. has resulted in an in-depth understanding of their central metabolism and ecology. A similar iterative modelling and experimental approach could accelerate elucidation of the physiology and ecology of other microorganisms inhabiting a diversity of environments, and could guide optimization of the practical applications of these species.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21132020?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Scheibe, Timothy D</style></author><author><style face="normal" font="default" size="100%">Mahadevan, Radhakrishnan</style></author><author><style face="normal" font="default" size="100%">Fang, Yilin</style></author><author><style face="normal" font="default" size="100%">Garg, Srinath</style></author><author><style face="normal" font="default" size="100%">Long, Philip E</style></author><author><style face="normal" font="default" size="100%">Lovley, Derek R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation.</style></title><secondary-title><style face="normal" font="default" size="100%">Microb Biotechnol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Microb Biotechnol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acetates</style></keyword><keyword><style  face="normal" font="default" size="100%">Biodegradation, Environmental</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Transport</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Geobacter</style></keyword><keyword><style  face="normal" font="default" size="100%">Iron</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Uranium</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">274-86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21261921?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Risso, Carla</style></author><author><style face="normal" font="default" size="100%">Sun, Jun</style></author><author><style face="normal" font="default" size="100%">Zhuang, Kai</style></author><author><style face="normal" font="default" size="100%">Mahadevan, Radhakrishnan</style></author><author><style face="normal" font="default" size="100%">DeBoy, Robert</style></author><author><style face="normal" font="default" size="100%">Ismail, Wael</style></author><author><style face="normal" font="default" size="100%">Shrivastava, Susmita</style></author><author><style face="normal" font="default" size="100%">Huot, Heather</style></author><author><style face="normal" font="default" size="100%">Kothari, Sagar</style></author><author><style face="normal" font="default" size="100%">Daugherty, Sean</style></author><author><style face="normal" font="default" size="100%">Bui, Olivia</style></author><author><style face="normal" font="default" size="100%">Schilling, Christophe H</style></author><author><style face="normal" font="default" size="100%">Lovley, Derek R</style></author><author><style face="normal" font="default" size="100%">Methé, Barbara A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-scale comparison and constraint-based metabolic reconstruction of the facultative anaerobic Fe(III)-reducer Rhodoferax ferrireducens.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Comamonadaceae</style></keyword><keyword><style  face="normal" font="default" size="100%">Comparative Genomic Hybridization</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Ferric Compounds</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Oxidation-Reduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">447</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Rhodoferax ferrireducens is a metabolically versatile, Fe(III)-reducing, subsurface microorganism that is likely to play an important role in the carbon and metal cycles in the subsurface. It also has the unique ability to convert sugars to electricity, oxidizing the sugars to carbon dioxide with quantitative electron transfer to graphite electrodes in microbial fuel cells. In order to expand our limited knowledge about R. ferrireducens, the complete genome sequence of this organism was further annotated and then the physiology of R. ferrireducens was investigated with a constraint-based, genome-scale in silico metabolic model and laboratory studies.

RESULTS: The iterative modeling and experimental approach unveiled exciting, previously unknown physiological features, including an expanded range of substrates that support growth, such as cellobiose and citrate, and provided additional insights into important features such as the stoichiometry of the electron transport chain and the ability to grow via fumarate dismutation. Further analysis explained why R. ferrireducens is unable to grow via photosynthesis or fermentation of sugars like other members of this genus and uncovered novel genes for benzoate metabolism. The genome also revealed that R. ferrireducens is well-adapted for growth in the subsurface because it appears to be capable of dealing with a number of environmental insults, including heavy metals, aromatic compounds, nutrient limitation and oxidative stress.

CONCLUSION: This study demonstrates that combining genome-scale modeling with the annotation of a new genome sequence can guide experimental studies and accelerate the understanding of the physiology of under-studied yet environmentally relevant microorganisms.</style></abstract><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19772637?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sun, Jun</style></author><author><style face="normal" font="default" size="100%">Sayyar, Bahareh</style></author><author><style face="normal" font="default" size="100%">Butler, Jessica E</style></author><author><style face="normal" font="default" size="100%">Pharkya, Priti</style></author><author><style face="normal" font="default" size="100%">Fahland, Tom R</style></author><author><style face="normal" font="default" size="100%">Famili, Iman</style></author><author><style face="normal" font="default" size="100%">Schilling, Christophe H</style></author><author><style face="normal" font="default" size="100%">Lovley, Derek R</style></author><author><style face="normal" font="default" size="100%">Mahadevan, Radhakrishnan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-scale constraint-based modeling of Geobacter metallireducens.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Syst Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biodegradation, Environmental</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomass</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Ecosystem</style></keyword><keyword><style  face="normal" font="default" size="100%">Electron Transport</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy Metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Geobacter</style></keyword><keyword><style  face="normal" font="default" size="100%">Iron</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Species Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems Biology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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' 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.</style></abstract><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19175927?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Segura, Daniel</style></author><author><style face="normal" font="default" size="100%">Mahadevan, Radhakrishnan</style></author><author><style face="normal" font="default" size="100%">Juárez, Katy</style></author><author><style face="normal" font="default" size="100%">Lovley, Derek R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational and experimental analysis of redundancy in the central metabolism of Geobacter sulfurreducens.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Comput. Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bacterial Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Geobacter</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multienzyme Complexes</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">e36</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Previous model-based analysis of the metabolic network of Geobacter sulfurreducens suggested the existence of several redundant pathways. Here, we identified eight sets of redundant pathways that included redundancy for the assimilation of acetate, and for the conversion of pyruvate into acetyl-CoA. These equivalent pathways and two other sub-optimal pathways were studied using 5 single-gene deletion mutants in those pathways for the evaluation of the predictive capacity of the model. The growth phenotypes of these mutants were studied under 12 different conditions of electron donor and acceptor availability. The comparison of the model predictions with the resulting experimental phenotypes indicated that pyruvate ferredoxin oxidoreductase is the only activity able to convert pyruvate into acetyl-CoA. However, the results and the modeling showed that the two acetate activation pathways present are not only active, but needed due to the additional role of the acetyl-CoA transferase in the TCA cycle, probably reflecting the adaptation of these bacteria to acetate utilization. In other cases, the data reconciliation suggested additional capacity constraints that were confirmed with biochemical assays. The results demonstrate the need to experimentally verify the activity of key enzymes when developing in silico models of microbial physiology based on sequence-based reconstruction of metabolic networks.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18266464?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Izallalen, Mounir</style></author><author><style face="normal" font="default" size="100%">Mahadevan, Radhakrishnan</style></author><author><style face="normal" font="default" size="100%">Burgard, Anthony</style></author><author><style face="normal" font="default" size="100%">Postier, Bradley</style></author><author><style face="normal" font="default" size="100%">DiDonato, Raymond</style></author><author><style face="normal" font="default" size="100%">Sun, Jun</style></author><author><style face="normal" font="default" size="100%">Schilling, Christopher H</style></author><author><style face="normal" font="default" size="100%">Lovley, Derek R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geobacter sulfurreducens strain engineered for increased rates of respiration.</style></title><secondary-title><style face="normal" font="default" size="100%">Metab Eng</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Metab. Eng.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adenosine Triphosphate</style></keyword><keyword><style  face="normal" font="default" size="100%">Bacterial Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Biodegradation, Environmental</style></keyword><keyword><style  face="normal" font="default" size="100%">Citric Acid Cycle</style></keyword><keyword><style  face="normal" font="default" size="100%">Electron Transport</style></keyword><keyword><style  face="normal" font="default" size="100%">Geobacter</style></keyword><keyword><style  face="normal" font="default" size="100%">Metals</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">NADH Dehydrogenase</style></keyword><keyword><style  face="normal" font="default" size="100%">NADP</style></keyword><keyword><style  face="normal" font="default" size="100%">Oxygen Consumption</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphates</style></keyword><keyword><style  face="normal" font="default" size="100%">Proton-Translocating ATPases</style></keyword><keyword><style  face="normal" font="default" size="100%">Radioactive Pollutants</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Sep</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">267-75</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Geobacter species are among the most effective microorganisms known for the bioremediation of radioactive and toxic metals in contaminated subsurface environments and for converting organic compounds to electricity in microbial fuel cells. However, faster rates of electron transfer could aid in optimizing these processes. Therefore, the Optknock strain design methodology was applied in an iterative manner to the constraint-based, in silico model of Geobacter sulfurreducens to identify gene deletions predicted to increase respiration rates. The common factor in the Optknock predictions was that each resulted in a predicted increase in the cellular ATP demand, either by creating ATP-consuming futile cycles or decreasing the availability of reducing equivalents and inorganic phosphate for ATP biosynthesis. The in silico model predicted that increasing the ATP demand would result in higher fluxes of acetate through the TCA cycle and higher rates of NADPH oxidation coupled with decreases in flux in reactions that funnel acetate toward biosynthetic pathways. A strain of G. sulfurreducens was constructed in which the hydrolytic, F(1) portion of the membrane-bound F(0)F(1) (H(+))-ATP synthase complex was expressed when IPTG was added to the medium. Induction of the ATP drain decreased the ATP content of the cell by more than half. The cells with the ATP drain had higher rates of respiration, slower growth rates, and a lower cell yield. Genome-wide analysis of gene transcript levels indicated that when the higher rate of respiration was induced transcript levels were higher for genes involved in energy metabolism, especially in those encoding TCA cycle enzymes, subunits of the NADH dehydrogenase, and proteins involved in electron acceptor reduction. This was accompanied by lower transcript levels for genes encoding proteins involved in amino acid biosynthesis, cell growth, and motility. Several changes in gene expression that involve processes not included in the in silico model were also detected, including increased expression of a number of redox-active proteins, such as c-type cytochromes and a putative multicopper outer-surface protein. The results demonstrate that it is possible to genetically engineer increased respiration rates in G. sulfurreducens in accordance with predictions from in silico metabolic modeling. To our knowledge, this is the first report of metabolic engineering to increase the respiratory rate of a microorganism.</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18644460?dopt=Abstract</style></custom1></record></records></xml>