@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}
}
@article {430,
	title = {In situ to in silico and back: elucidating the physiology and ecology of Geobacter spp. using genome-scale modelling.},
	journal = {Nat Rev Microbiol},
	volume = {9},
	year = {2011},
	month = {2011 Jan},
	pages = {39-50},
	abstract = {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.},
	keywords = {Computer Simulation, Environment, Gene Expression Regulation, Bacterial, Genome, Bacterial, Geobacter, Models, Biological},
	issn = {1740-1534},
	doi = {10.1038/nrmicro2456},
	author = {Mahadevan, Radhakrishnan and Palsson, Bernhard {\O} 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}
}
@article {489,
	title = {Computational and experimental analysis of redundancy in the central metabolism of Geobacter sulfurreducens.},
	journal = {PLoS Comput Biol},
	volume = {4},
	year = {2008},
	month = {2008 Feb},
	pages = {e36},
	abstract = {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.},
	keywords = {Bacterial Proteins, Base Sequence, Computer Simulation, Gene Expression Regulation, Bacterial, Geobacter, Models, Biological, Molecular Sequence Data, Multienzyme Complexes, Signal Transduction},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.0040036},
	author = {Segura, Daniel and Mahadevan, Radhakrishnan and Ju{\'a}rez, Katy and Lovley, Derek R}
}
@article {498,
	title = {The degree of redundancy in metabolic genes is linked to mode of metabolism.},
	journal = {Biophys J},
	volume = {94},
	year = {2008},
	month = {2008 Feb 15},
	pages = {1216-20},
	abstract = {An understanding of the factors favoring the maintenance of duplicate genes in microbial genomes is essential for developing models of microbial evolution. A genome-scale flux-balance analysis of the metabolic network of Saccharomyces cerevisiae has suggested that gene duplications primarily provide increased enzyme dosage to enhance metabolic flux because the incidence of gene duplications in essential genes is no higher than that in nonessential genes. Here, we used genome-scale metabolic models to analyze the extent of genetic and biochemical redundancy in prokaryotes that are either specialists, with one major mode of energy generation, or generalists, which have multiple metabolic strategies for conservation of energy. Surprisingly, the results suggest that generalists, such as Escherichia coli and Bacillus subtilis, are similar to the eukaryotic generalist, S. cerevisiae, in having a low percentage (<10\%) of essential genes and few duplications of these essential genes, whereas metabolic specialists, such as Geobacter sulfurreducens and Methanosarcina barkeri, have a high percentage (>30\%) of essential genes and a high degree of genetic redundancy in these genes compared to nonessential genes. Furthermore, the specialist organisms appear to rely more on gene duplications rather than alternative-but-equivalent metabolic pathways to provide resilience to gene loss. Generalists rely more on alternative pathways. Thus, the concept that the role of gene duplications is to boost enzymatic flux rather than provide metabolic resilience may not be universal. Rather, the degree of gene duplication in microorganisms may be linked to mode of metabolism and environmental niche.},
	keywords = {Bacterial Physiological Phenomena, Bacterial Proteins, Computer Simulation, Gene Expression Regulation, Bacterial, Genes, Duplicate, Models, Biological, Signal Transduction},
	issn = {1542-0086},
	doi = {10.1529/biophysj.107.118414},
	author = {Mahadevan, R and Lovley, D R}
}
@article {682,
	title = {Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach.},
	journal = {Nucleic Acids Res},
	volume = {35},
	year = {2007},
	month = {2007 Jul},
	pages = {W506-11},
	abstract = {Biologically significant sites in a protein may be identified by contrasting the rates of synonymous (K(s)) and non-synonymous (K(a)) substitutions. This enables the inference of site-specific positive Darwinian selection and purifying selection. We present here Selecton version 2.2 (http://selecton.bioinfo.tau.ac.il), a web server which automatically calculates the ratio between K(a) and K(s) (omega) at each site of the protein. This ratio is graphically displayed on each site using a color-coding scheme, indicating either positive selection, purifying selection or lack of selection. Selecton implements an assembly of different evolutionary models, which allow for statistical testing of the hypothesis that a protein has undergone positive selection. Specifically, the recently developed mechanistic-empirical model is introduced, which takes into account the physicochemical properties of amino acids. Advanced options were introduced to allow maximal fine tuning of the server to the user{\textquoteright}s specific needs, including calculation of statistical support of the omega values, an advanced graphic display of the protein{\textquoteright}s 3-dimensional structure, use of different genetic codes and inputting of a pre-built phylogenetic tree. Selecton version 2.2 is an effective, user-friendly and freely available web server which implements up-to-date methods for computing site-specific selection forces, and the visualization of these forces on the protein{\textquoteright}s sequence and structure.},
	keywords = {Algorithms, Amino Acid Sequence, Amino Acid Substitution, Amino Acids, Animals, Bayes Theorem, Computational Biology, Computer Simulation, Humans, Internet, Models, Molecular, Molecular Sequence Data, Phylogeny, Proteins, Sequence Analysis, Protein, Software},
	issn = {1362-4962},
	doi = {10.1093/nar/gkm382},
	author = {Stern, Adi and Doron-Faigenboim, Adi and Erez, Elana and Martz, Eric and Bacharach, Eran and Pupko, Tal}
}
