1101:, are then able to simulate the system dynamics given an initial condition. Often these rate laws contain kinetic parameters with uncertain values. In many cases it is desired to estimate these parameter values with respect to given time-series data of metabolite concentrations. The system is then supposed to reproduce the given data. For this purpose the distance between the given data set and the result of the simulation, i.e., the numerically or in few cases analytically obtained solution of the differential equation system is computed. The values of the parameters are then estimated to minimize this distance. One step further, it may be desired to estimate the mathematical structure of the differential equation system because the real rate laws are not known for the reactions within the system under study. To this end, the program
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enzymatic activity) for which there is no known protein in the genome that encodes the enzyme that facilitates the catalysis. What can also happen in semi-automatically drafted reconstructions is that some pathways are falsely predicted and don't actually occur in the predicted manner. Because of this, a systematic verification is made in order to make sure no inconsistencies are present and that all the entries listed are correct and accurate. Furthermore, previous literature can be researched in order to support any information obtained from one of the many metabolic reaction and genome databases. This provides an added level of assurance for the reconstruction that the enzyme and the reaction it catalyzes do actually occur in the organism.
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and metabolites is drafted to relate sequences and function. When an uncharacterized protein is found in the genome, its amino acid sequence is first compared to those of previously characterized proteins to search for homology. When a homologous protein is found, the proteins are considered to have a common ancestor and their functions are inferred as being similar. However, the quality of a reconstruction model is dependent on its ability to accurately infer phenotype directly from sequence, so this rough estimation of protein function will not be sufficient. A number of algorithms and bioinformatics resources have been developed for refinement of sequence homology-based assignments of protein functions:
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hypothesis-driven research. The results these experiments can uncover novel pathways and metabolic activities and decipher between discrepancies in previous experimental data. Information about the chemical reactions of metabolism and the genetic background of various metabolic properties (sequence to structure to function) can be utilized by genetic engineers to modify organisms to produce high value outputs whether those products be medically relevant like pharmaceuticals; high value chemical intermediates such as terpenoids and isoprenoids; or biotechnological outputs like biofuels, or polyhydroxybutyrates also known as bioplastics.
1163:, then the goal of metabolic reconstruction/simulation would be to determine the metabolites that are essential to the organism's proliferation inside of macrophages. If the proliferation cycle is inhibited, then the parasite would not continue to evade the host's immune system. A reconstruction model serves as a first step to deciphering the complicated mechanisms surrounding disease. These models can also look at the minimal genes necessary for a cell to maintain virulence. The next step would be to use the predictions and postulates generated from a reconstruction model and apply it to discover novel biological functions such as
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765:: An online resource for the analysis, comparison, reconstruction, and curation of genome-scale metabolic models. Users can submit genome sequences to the RAST annotation system, and the resulting annotation can be automatically piped into the ModelSEED to produce a draft metabolic model. The ModelSEED automatically constructs a network of metabolic reactions, gene-protein-reaction associations for each reaction, and a biomass composition reaction for each genome to produce a model of microbial metabolism that can be simulated using Flux Balance Analysis.
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reconstruction. An initial fast reconstruction can be developed automatically using resources like PathoLogic or ERGO in combination with encyclopedias like MetaCyc, and then manually updated by using resources like
PathwayTools. These semi-automatic methods allow for a fast draft to be created while allowing the fine tune adjustments required once new experimental data is found. It is only in this manner that the field of metabolic reconstructions will keep up with the ever-increasing numbers of annotated genomes.
681:: A bioinformatics software package that assists in the construction of pathway/genome databases such as EcoCyc. Developed by Peter Karp and associates at the SRI International Bioinformatics Research Group, Pathway Tools has several components. Its PathoLogic module takes an annotated genome for an organism and infers probable metabolic reactions and pathways to produce a new pathway/genome database. Its MetaFlux component can generate a quantitative metabolic model from that pathway/genome database using
20:
80:) into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel
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network graph from the inputs, those metabolites available to the organism from the environment, to the outputs, metabolites needed by the organism to survive. To simulate a gene knockout, the reactions enabled by the gene are removed from the network and the synthetic accessibility metric is recalculated. An increase in the total number of steps is predicted to cause lethality. Wunderlich and Mirny showed this simple, parameter-free approach predicted knockout lethality in
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is much more compact. In contrast with elementary modes and extreme pathways, which use an inner description based on generating vectors of the flux cone, MMBs are using an outer description of the flux cone. This approach is based on sets of non-negativity constraints. These can be identified with irreversible reactions, and thus have a direct biochemical interpretation. One can characterize a metabolic network by MMBs and the reversible metabolic space.
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reconstructions to find homologous genes and reactions. These homologous genes and reactions are carried over from the known reconstructions to form the draft reconstruction of the organism of interest. Tools such as ERGO, Pathway Tools and Model SEED can compile data into pathways to form a network of metabolic and non-metabolic pathways. These networks are then verified and refined before being made into a mathematical simulation.
1070:, but in contrast to elementary mode analysis and extreme pathways, only a single solution results in the end. Linear programming is usually used to obtain the maximum potential of the objective function that you are looking at, and therefore, when using flux balance analysis, a single solution is found to the optimization problem. In a flux balance analysis approach, exchange
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genome-scale metabolic models. Simply put, these models correlate metabolic genes with metabolic pathways. In general, the more information about physiology, biochemistry and genetics is available for the target organism, the better the predictive capacity of the reconstructed models. Mechanically speaking, the process of reconstructing prokaryotic and eukaryotic
910:/products that are present for other reactions within the particular pathway. This is because products in one reaction go on to become the reactants for another reaction, i.e. products of one reaction can combine with other proteins or compounds to form new proteins/compounds in the presence of different enzymes or
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Furthermore, this particular approach can accurately define if the reaction stoichiometry is in line with predictions by providing fluxes for the balanced reactions. Also, flux balance analysis can highlight the most effective and efficient pathway through the network in order to achieve a particular
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In 2009, Larhlimi and
Bockmayr presented a new approach called "minimal metabolic behaviors" for the analysis of metabolic networks. Like elementary modes or extreme pathways, these are uniquely determined by the network, and yield a complete description of the flux cone. However, the new description
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are assigned to those metabolites that enter or leave the particular network only. Those metabolites that are consumed within the network are not assigned any exchange flux value. Also, the exchange fluxes along with the enzymes can have constraints ranging from a negative to positive value (ex: -10
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A reconstruction is built by compiling data from the resources above. Database tools such as KEGG and BioCyc can be used in conjunction with each other to find all the metabolic genes in the organism of interest. These genes will be compared to closely related organisms that have already developed
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Synthetic accessibility is a simple approach to network simulation whose goal is to predict which metabolic gene knockouts are lethal. The synthetic accessibility approach uses the topology of the metabolic network to calculate the sum of the minimum number of steps needed to traverse the metabolic
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Reconstructions and their corresponding models allow the formulation of hypotheses about the presence of certain enzymatic activities and the production of metabolites that can be experimentally tested, complementing the primarily discovery-based approach of traditional microbial biochemistry with
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In order to perform a dynamic simulation with such a network it is necessary to construct an ordinary differential equation system that describes the rates of change in each metabolite's concentration or amount. To this end, a rate law, i.e., a kinetic equation that determines the rate of reaction
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The predictive aspect of a metabolic reconstruction hinges on the ability to predict the biochemical reaction catalyzed by a protein using that protein's amino acid sequence as an input, and to infer the structure of a metabolic network based on the predicted set of reactions. A network of enzymes
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available for a particular metabolic network. These are the smallest sub-networks that allow a metabolic reconstruction network to function in steady state. According to
Stelling (2002), elementary modes can be used to understand cellular objectives for the overall metabolic network. Furthermore,
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Overbeek R, Larsen N, Walunas T, D'Souza M, Pusch G, Selkov Jr, Liolios K, Joukov V, Kaznadzey D, Anderson I, Bhattacharyya A, Burd H, Gardner W, Hanke P, Kapatral V, Mikhailova N, Vasieva O, Osterman A, Vonstein V, Fonstein M, Ivanova N, Kyrpides N. (2003) The ERGO genome analysis and discovery
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Because the timescale for the development of reconstructions is so recent, most reconstructions have been built manually. However, now, there are quite a few resources that allow for the semi-automatic assembly of these reconstructions that are utilized due to the time and effort necessary for a
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A metabolic reconstruction provides a highly mathematical, structured platform on which to understand the systems biology of metabolic pathways within an organism. The integration of biochemical metabolic pathways with rapidly available, annotated genome sequences has developed what are called
1008:, it is possible to develop a ‘solution space’ where all the feasible options fall within. Then, using a kinetic model approach, a single solution that falls within the extreme pathway solution space can be determined. Therefore, in their study, Price, Reed, and Papin, use both constraint and
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An initial metabolic reconstruction of a genome is typically far from perfect due to the high variability and diversity of microorganisms. Often, metabolic pathway databases such as KEGG and MetaCyc will have "holes", meaning that there is a conversion from a substrate to a product (i.e., an
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Any new reaction not present in the databases needs to be added to the reconstruction. This is an iterative process that cycles between the experimental phase and the coding phase. As new information is found about the target organism, the model will be adjusted to predict the metabolic and
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Several inconsistencies exist between gene, enzyme, reaction databases, and published literature sources regarding the metabolic information of an organism. A reconstruction is a systematic verification and compilation of data from various sources that takes into account all of the
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Therefore, systematic verification of the initial reconstruction will bring to light several inconsistencies that can adversely affect the final interpretation of the reconstruction, which is to accurately comprehend the molecular mechanisms of the organism. Furthermore, the
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is essentially the same. Having said this, eukaryote reconstructions are typically more challenging because of the size of genomes, coverage of knowledge, and the multitude of cellular compartments. The first genome-scale metabolic model was generated in 1995 for
693:: A subscription-based service developed by Integrated Genomics. It integrates data from every level including genomic, biochemical data, literature, and high-throughput analysis into a comprehensive user friendly network of metabolic and nonmetabolic pathways.
902:, create energy costs that need to be incorporated into models. It is likely that many genes of unknown function encode proteins that repair or pre-empt metabolite damage, but most genome-scale metabolic reconstructions only include a fraction of all genes.
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studies can be performed using flux balance analysis. The enzyme that correlates to the gene that needs to be removed is given a constraint value of 0. Then, the reaction that the particular enzyme catalyzes is completely removed from the analysis.
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step also ensures that all the reactions present in the reconstruction are properly balanced. To sum up, a reconstruction that is fully accurate can lead to greater insight about understanding the functioning of the organism of interest.
800:, which contains a massive collection of medical journals. Using the link provided by ENZYME, the search can be directed towards the organism of interest, thus recovering literature on the enzyme and its use inside of the organism.
519:: Is a collection of metabolic profiles and phylogenomic information on a taxonomically diverse range of eukaryotes which provides novel facilities for viewing and comparing the metabolic profiles between organisms.
495:). After searching for a particular enzyme on the database, this resource gives you the reaction that is catalyzed. ENZYME has direct links to other gene/enzyme/literature databases such as KEGG, BRENDA, and PUBMED.
875:. Accurate metabolic reconstructions require additional information about the reversibility and preferred physiological direction of an enzyme-catalyzed reaction which can come from databases such as
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A metabolic network can be broken down into a stoichiometric matrix where the rows represent the compounds of the reactions, while the columns of the matrix correspond to the reactions themselves.
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Once proteins have been established, more information about the enzyme structure, reactions catalyzed, substrates and products, mechanisms, and more can be acquired from databases such as
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Metabolic network reconstructions and models are used to understand how an organism or parasite functions inside of the host cell. For example, if the parasite serves to compromise the
843:: Resource for the annotation of functional units in proteins. Its collection of domain models utilizes 3D structure to provide insights into sequence/structure/function relationships.
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approaches to understand the human red blood cell metabolism. In conclusion, using extreme pathways, the regulatory mechanisms of a metabolic network can be studied in further detail.
713:-files) into multiple output formats. Unlike other translators, KEGGtranslator supports a plethora of output formats, is able to augment the information in translated documents (e.g.,
130:, was reconstructed in 1998. Since then, many reconstructions have been formed. For a list of reconstructions that have been converted into a model and experimentally validated, see
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of a chemical reaction. In order to deduce what the metabolic network suggests, recent research has centered on a few approaches, such as extreme pathways, elementary mode analysis,
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as reaction database to link with the EC number predictions from CoReCo. Its automatic gap filling using atom map of all the reactions produce functional models ready for simulation.
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functions of a metabolic network. For any particular metabolic network, there is always a unique set of extreme pathways available. Furthermore, Price, Reed, and Papin, define a
1346:
Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, et al. (July 1995). "Whole-genome random sequencing and assembly of
Haemophilus influenzae Rd".
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Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL (September 2010). "High-throughput generation, optimization and analysis of genome-scale metabolic models".
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Schuster S, Fell DA, Dandekar T (March 2000). "A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks".
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provide an excellent example as to why the verification step of the project needs to be performed in significant detail. During a metabolic network reconstruction of
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Price, Reed, and Papin, from the
Palsson lab, use a method of singular value decomposition (SVD) of extreme pathways in order to understand regulation of a human
2809:
Stelling J, Klamt S, Bettenbrock K, Schuster S, Gilles ED (November 2002). "Metabolic network structure determines key aspects of functionality and regulation".
685:. Its Navigator component provides extensive query and visualization tools, such as visualization of metabolites, pathways, and the complete metabolic network.
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document, and amends missing components to fragmentary reactions within the pathway to allow simulations on those. KEGGtranslator converts these files to
475:, an encyclopedia of experimentally defined metabolic pathways and enzymes, contains 2,100 metabolic pathways and 11,400 metabolic reactions (Oct 2013).
3305:"Whole-genome sequencing and genome-scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use"
1711:"Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets"
1164:
2144:"SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools"
431:: a bioinformatics database containing information on genes, proteins, reactions, and pathways. The ‘KEGG Organisms’ section, which is divided into
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Sheikh K, Förster J, Nielsen LK (January 2005). "Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus".
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Price ND, Reed JL, Papin JA, Wiback SJ, Palsson BO (November 2003). "Network-based analysis of metabolic regulation in the human red blood cell".
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Whitaker, J.W., Letunic, I., McConkey, G.A. and
Westhead, D.R. metaTIGER: a metabolic evolution resource. Nucleic Acids Res. 2009 37: D531-8.
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Elementary mode analysis closely matches the approach used by extreme pathways. Similar to extreme pathways, there is always a unique set of
797:
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The C. elegans
Sequencing Consortium (December 1998). "Genome sequence of the nematode C. elegans: a platform for investigating biology".
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based on the concentrations of all reactants is required for each reaction. Software packages that include numerical integrators, such as
771:: algorithm for semi-automatically reconciling a pair of existing metabolic network reconstructions into a single metabolic network model.
3520:
3254:"RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms"
3152:"Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model"
935:. However, an understanding of the physiology of the organism would have revealed that due to an incomplete tricarboxylic acid pathway,
2936:"Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies"
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Papin JA, Stelling J, Price ND, Klamt S, Schuster S, Palsson BO (August 2004). "Comparison of network-based pathway analysis methods".
1762:"Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data"
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signaling pathways and regulatory network. The EcoCyc database can serve as a paradigm and model for any reconstruction. Additionally,
3477:
60:, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the
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Is a collection of 3,000 pathway/genome databases (as of Oct 2013), with each database dedicated to one organism. For example,
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Metabolic comparisons can be performed between various organisms of the same species as well as between different organisms.
511:: A knowledge base of biochemically, genetically, and genomically structured genome-scale metabolic network reconstructions.
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phenotypical output of the cell. The presence or absence of certain reactions of the metabolism will affect the amount of
851:: Provides functional analysis of proteins by classifying them into families and predicting domains and important sites.
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Francke C, Siezen RJ, Teusink B (November 2005). "Reconstructing the metabolic network of a bacterium from its genome".
779:: algorithm for automatic reconstruction of metabolic models of related species. The first version of the software used
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when evaluating whether a particular metabolic route or network is feasible and likely for a set of proteins/enzymes.
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2435:
492:
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Linster CL, Van
Schaftingen E, Hanson AD (February 2013). "Metabolite damage and its repair or pre-emption".
2256:"MetaMerge: scaling up genome-scale metabolic reconstructions with application to Mycobacterium tuberculosis"
1450:"The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities"
1180:
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1836:"Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction"
876:
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503:: A comprehensive enzyme database that allows for an enzyme to be searched by name, EC number, or organism.
2307:"Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species"
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Chaouiya C, Bérenguier D, Keating SM, Naldi A, van Iersel MP, Rodriguez N, et al. (December 2013).
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citric acid cycle. Enzymes and metabolites are the red dots and interactions between them are the lines.
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1985:"Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology"
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2601:"Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods"
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does not actually produce succinyl-CoA, and the correct reactant for that part of the reaction was
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seeks to mathematically simulate metabolism in genome-scale reconstructions of metabolic networks.
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2987:"SBMLsqueezer: a CellDesigner plug-in to generate kinetic rate equations for biochemical networks"
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Dräger A, Kronfeld M, Ziller MJ, Supper J, Planatscher H, Magnus JB, et al. (January 2009).
1205:
923:
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1983:
Karp PD, Paley SM, Krummenacker M, Latendresse M, Dale JM, Lee TJ, et al. (January 2010).
1936:"BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree"
1934:
Norsigian CJ, Pusarla N, McConn JL, Yurkovich JT, Dräger A, Palsson BO, King Z (January 2020).
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36:
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Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, et al. (January 2010).
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Castillo S, Barth D, Arvas M, Pakula TM, Pitkänen E, Blomberg P, et al. (November 2016).
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Ivanova N, Lykidis A (2009). "Metabolic
Reconstruction". (3rd ed.). pp. 607–621.
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de
Oliveira Dal'Molin CG, Quek LE, Palfreyman RW, Brumbley SM, Nielsen LK (February 2010).
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3402:- provides an open source Java API to the pathway tool BioCyc to extract Metabolic graphs.
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Pitkänen E, Jouhten P, Hou J, Syed MF, Blomberg P, Kludas J, et al. (February 2014).
1652:"Global reconstruction of the human metabolic network based on genomic and bibliomic data"
8:
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2095:"KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats"
1803:"Genome-scale modeling of Synechocystis sp. PCC 6803 and prediction of pathway insertion"
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2903:"A new constraint-based description of the steady-state flux cone of metabolic networks"
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2711:"Extreme pathway lengths and reaction participation in genome-scale metabolic networks"
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1887:"AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis"
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Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, et al. (February 2007).
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allows automatic creation of appropriate rate laws for all reactions with the network.
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1601:"Computational prediction of human metabolic pathways from the complete human genome"
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as well as elementary mode analysis and flux balance analysis in a variety of media.
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Enuh BM, Nural Yaman B, Tarzi C, Aytar Çelik P, Mutlu MB, Angione C (October 2022).
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3038:"Using the topology of metabolic networks to predict viability of mutant strains"
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1296:"A protocol for generating a high-quality genome-scale metabolic reconstruction"
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This is a visual representation of the metabolic network reconstruction process.
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at the University of Oxford, Biochemical reaction pathway inference techniques.
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Proceedings of the National Academy of Sciences of the United States of America
1509:"Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network"
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Proceedings of the National Academy of Sciences of the United States of America
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was one of the reactants for a reaction that was a part of the biosynthesis of
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2862:"gEFM: An Algorithm for Computing Elementary Flux Modes Using Graph Traversal"
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The combination of relevant metabolic and genomic information of an organism.
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The Integrated Microbial Genomes system, for genome analysis by the DOE-JGI.
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Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD (June 2004).
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Oh YK, Palsson BO, Park SM, Schilling CH, Mahadevan R (September 2007).
701:: an easy-to-use stand-alone application that can visualize and convert
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Whitmore LS, Nguyen B, Pinar A, George A, Hudson CM (September 2019).
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Chindelevitch L, Stanley S, Hung D, Regev A, Berger B (January 2012).
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A different technique to simulate the metabolic network is to perform
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2160:
1834:
Raghunathan A, Reed J, Shin S, Palsson B, Daefler S (April 2009).
1507:
Förster J, Famili I, Fu P, Palsson BØ, Nielsen J (February 2003).
499:
2866:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
2044:"Precise generation of systems biology models from KEGG pathways"
847:
742:
87:
In general, the process to build a reconstruction is as follows:
2808:
1982:
898:
and spontaneous chemical reactions can damage metabolites. This
3302:
2985:
Dräger A, Hassis N, Supper J, Schröder A, Zell A (April 2008).
2517:
2042:
Wrzodek C, Büchel F, Ruff M, Dräger A, Zell A (February 2013).
1933:
1094:
726:
714:
488:
61:
3415:
2518:
Hanson AD, Henry CS, Fiehn O, de Crécy-Lagard V (April 2016).
2141:
1087:
443:
information can be searched by typing in the enzyme of choice.
97:
Convert model into a mathematical/computational representation
3092:
2253:
754:
2482:
1345:
479:
26:
showing interactions between enzymes and metabolites in the
2520:"Metabolite Damage and Metabolite Damage Control in Plants"
1071:
972:, and a number of other constraint-based modeling methods.
780:
734:
722:
702:
3460:
2984:
2933:
1833:
1649:
880:
868:
3395:
3374:
3199:
Ivanova A, Lykidis A (2009). "Metabolic Reconstruction".
2210:
855:
750:
746:
710:
440:
3420:
2363:
859:: Database of known and predicted protein interactions.
804:
761:
524:
This table quickly compares the scope of each database.
507:
111:
3455:
3245:
3086:
2563:
2041:
461:
database on the genome and metabolic reconstruction of
3410:
2304:
1759:
1506:
864:
831:: Identifies eukaryotic orthologs by looking only at
426:
3405:
2598:
1598:
1042:
454:
450:
132:
http://sbrg.ucsd.edu/InSilicoOrganisms/OtherOrganisms
3149:
2760:
2665:
1555:
1253:
955:
3150:Fong SS, Marciniak JY, Palsson BØ (November 2003).
2599:Lewis NE, Nagarajan H, Palsson BO (February 2012).
1341:
1339:
1147:
Use in metabolic engineering for high value outputs
1125:
2859:
2708:
2092:
3757:
3467:Systems Analysis, Modelling and Prediction Group
2900:
2804:
2802:
2800:
2756:
2754:
2709:Papin JA, Price ND, Palsson BØ (December 2002).
1807:Journal of Chemical Technology and Biotechnology
1336:
900:metabolite damage, and its repair or pre-emption
439:, encompasses many organisms for which gene and
100:Evaluate and debug model through experimentation
3035:
2417:
2415:
1708:
670:
3198:
2421:
1447:
3514:
2894:
2797:
2751:
2661:
2659:
2657:
2655:
2559:
2557:
2555:
1927:
1293:
1289:
1287:
1285:
1249:
1247:
1245:
1243:
1241:
798:National Center for Biotechnology Information
796:: This is an online library developed by the
2412:
1031:elementary mode analysis takes into account
737:, SBML with qualitative modeling extension,
404:
2093:Wrzodek C, Dräger A, Zell A (August 2011).
1088:Dynamic simulation and parameter estimation
1015:
3521:
3507:
2853:
2702:
2652:
2552:
2204:
1282:
1238:
1108:
3356:system. Nucleic Acids Res. 31(1):164-71
3328:
3279:
3269:
3175:
3143:
3126:
3069:
3036:Wunderlich Z, Mirny LA (September 2006).
3012:
3002:
2961:
2951:
2918:
2877:
2734:
2648:CoBRA Methods - Constraint-based analysis
2624:
2535:
2395:
2385:
2340:
2330:
2281:
2271:
2187:
2177:
2159:
2118:
2069:
2059:
2018:
2000:
1976:
1959:
1910:
1861:
1851:
1818:
1777:
1736:
1726:
1685:
1675:
1626:
1616:
1532:
1483:
1473:
1319:
425:Kyoto Encyclopedia of Genes and Genomes (
3528:
3483:Cellnet analyzer from Klamt and von Kamp
1051:
984:metabolism. Extreme pathways are convex
808:
35:
18:
1592:
964:is a quantitative relationship between
787:
3758:
3689:Construction and management simulation
3495:A graph-based tool for EFM computation
3431:SBRI Bioinformatics Tools and Software
1390:
3502:
2537:10.1146/annurev-arplant-043015-111648
1294:Thiele I, Palsson BØ (January 2010).
805:Methodology to draft a reconstruction
717:annotations) beyond the scope of the
467:, including thorough descriptions of
126:. The first multicellular organism,
112:Genome-scale metabolic reconstruction
3725:List of computer simulation software
2860:Ullah E, Aeron S, Hassoun S (2015).
2370:built by comparative reconstruction"
1709:Jamshidi N, Palsson BØ (June 2007).
1766:The Journal of Biological Chemistry
1448:Edwards JS, Palsson BO (May 2000).
1144:Predict adaptive evolution outcomes
975:
886:
13:
3349:
1800:
1043:Minimal metabolic behaviors (MMBs)
14:
3802:
3363:
3251:
3095:"The genetic landscape of a cell"
2366:"Whole-genome metabolic model of
1079:objective function. In addition,
956:Metabolic stoichiometric analysis
523:
493:Swiss Institute of Bioinformatics
3473:efmtool provided by Marco Terzer
3209:10.1016/B978-012373944-5.00010-9
3168:10.1128/JB.185.21.6400-6408.2003
2428:10.1016/B978-012373944-5.00010-9
1126:Applications of a reconstruction
54:metabolic network reconstruction
3653:Integrated assessment modelling
3387:Case Western Reserve University
3296:
3192:
3029:
2978:
2927:
2901:Larhlimi A, Bockmayr A (2009).
2641:
2592:
2511:
2476:
2357:
2298:
2247:
2135:
2086:
2035:
1878:
1827:
1794:
1753:
1702:
1141:Analysis of synthetic lethality
68:. A reconstruction breaks down
2668:Journal of Theoretical Biology
2524:Annual Review of Plant Biology
1643:
1549:
1500:
1441:
1:
2688:10.1016/s0022-5193(03)00237-6
2578:10.1016/j.tibtech.2004.06.010
2111:10.1093/bioinformatics/btr377
1419:10.1126/science.282.5396.2012
1231:
1181:Computational systems biology
3622:Hydrological transport model
3576:Protein structure prediction
3571:Modelling biological systems
3201:Encyclopedia of Microbiology
2907:Discrete Applied Mathematics
2605:Nature Reviews. Microbiology
2332:10.1371/journal.pcbi.1003465
1221:Biochemical systems equation
996:, where through the help of
671:Tools for metabolic modeling
418:
409:
40:Metabolic network model for
16:Form of biological modelling
7:
3566:Metabolic network modelling
3062:10.1529/biophysj.105.080572
1989:Briefings in Bioinformatics
1174:
491:proteonomics server of the
447:BioCyc, EcoCyc, and MetaCyc
50:Metabolic network modelling
10:
3807:
3679:Business process modelling
3451:Stanford Genomic Resources
2374:Biotechnology for Biofuels
2311:PLOS Computational Biology
1201:Metabolic control analysis
1055:
1019:
284:Mycobacterium tuberculosis
58:metabolic pathway analysis
3712:
3666:
3640:
3584:
3551:Chemical process modeling
3536:
3271:10.1186/s12859-019-3025-9
2920:10.1016/j.dam.2008.06.039
2879:10.1109/TCBB.2015.2430344
2387:10.1186/s13068-016-0665-0
1268:10.1016/j.tim.2005.09.001
994:constraint-based approach
531:
405:Drafting a reconstruction
332:Synechocystis sp. PCC6803
3597:Chemical transport model
3561:Infectious disease model
1016:Elementary mode analysis
927:, the model showed that
212:Saccharomyces cerevisiae
3156:Journal of Bacteriology
3119:10.1126/science.1180823
2566:Trends in Biotechnology
2485:Nature Chemical Biology
2273:10.1186/gb-2012-13-1-r6
2179:10.1186/1752-0509-7-135
1677:10.1073/pnas.0610772104
1475:10.1073/pnas.97.10.5528
1368:10.1126/science.7542800
1206:Metabolic flux analysis
1109:Synthetic accessibility
937:Lactobacillus plantarum
924:Lactobacillus plantarum
3771:Biomedical engineering
3766:Biological engineering
3004:10.1186/1752-0509-2-39
2061:10.1186/1752-0509-7-15
1940:Nucleic Acids Research
1853:10.1186/1752-0509-3-38
1779:10.1074/jbc.M703759200
1728:10.1186/1752-0509-1-26
1618:10.1186/gb-2004-6-1-r2
1558:Biotechnology Progress
1312:10.1038/nprot.2009.203
1256:Trends in Microbiology
814:
487:database (part of the
356:Salmonella typhimurium
164:Haemophilus influenzae
155:Date of reconstruction
124:Haemophilus influenzae
104:The related method of
91:Draft a reconstruction
46:
33:
3730:Mathematical modeling
3674:Biopsychosocial model
2953:10.1186/1752-0509-3-5
2497:10.1038/nchembio.1141
1903:10.1104/pp.109.148817
1801:Fu P (October 2008).
1191:Flux balance analysis
1064:flux balance analysis
1058:Flux balance analysis
1052:Flux balance analysis
970:flux balance analysis
812:
683:flux-balance analysis
457:is a highly detailed
106:flux balance analysis
39:
22:
3684:Catastrophe modeling
3530:Scientific modelling
3203:. pp. 607–621.
2763:Nature Biotechnology
2213:Nature Biotechnology
788:Tools for literature
380:Arabidopsis thaliana
29:Arabidopsis thaliana
3627:Modular Ocean Model
3456:Pathway Hunter Tool
3111:2010Sci...327..425C
3054:2006BpJ....91.2304W
3042:Biophysical Journal
2991:BMC Systems Biology
2940:BMC Systems Biology
2831:10.1038/nature01166
2823:2002Natur.420..190S
2680:2003JThBi.225..185P
2617:10.1038/nrmicro2737
2323:2014PLSCB..10E3465P
2170:2013arXiv1309.1910C
2148:BMC Systems Biology
2048:BMC Systems Biology
1952:10.1093/nar/gkz1054
1840:BMC Systems Biology
1772:(39): 28791–28799.
1715:BMC Systems Biology
1668:2007PNAS..104.1777D
1466:2000PNAS...97.5528E
1411:1998Sci...282.2012.
1405:(5396): 2012–2018.
1360:1995Sci...269..496F
1186:Computer simulation
1066:. This method uses
525:
3720:Data visualization
3704:Input–output model
3617:Hydrological model
3607:Geologic modelling
3258:BMC Bioinformatics
2368:Trichoderma reesei
2011:10.1093/bib/bbp043
1068:linear programming
896:Enzyme promiscuity
815:
119:metabolic networks
72:pathways (such as
47:
34:
3753:
3752:
3632:Wildfire modeling
3612:Groundwater model
3592:Atmospheric model
3321:10.1002/mbo3.1328
3162:(21): 6400–6408.
3105:(5964): 425–431.
2913:(10): 2257–2266.
2817:(6912): 190–193.
2727:10.1101/gr.327702
2721:(12): 1889–1900.
2464:Missing or empty
2105:(16): 2314–2315.
1946:(D1): D402–D406.
1820:10.1002/jctb.2065
1570:10.1021/bp0498138
1525:10.1101/gr.234503
1460:(10): 5528–5533.
1354:(5223): 496–512.
1216:Metabolic pathway
1211:Metabolic network
668:
667:
402:
401:
308:Bacillus subtilis
78:citric acid cycle
24:Metabolic network
3798:
3745:Visual analytics
3740:Systems thinking
3658:Population model
3523:
3516:
3509:
3500:
3499:
3343:
3342:
3332:
3309:MicrobiologyOpen
3300:
3294:
3293:
3283:
3273:
3249:
3243:
3242:
3236:
3232:
3230:
3222:
3196:
3190:
3189:
3179:
3147:
3141:
3140:
3130:
3090:
3084:
3083:
3073:
3048:(6): 2304–2311.
3033:
3027:
3026:
3016:
3006:
2982:
2976:
2975:
2965:
2955:
2931:
2925:
2924:
2922:
2898:
2892:
2891:
2881:
2857:
2851:
2850:
2806:
2795:
2794:
2758:
2749:
2748:
2738:
2706:
2700:
2699:
2663:
2650:
2645:
2639:
2638:
2628:
2596:
2590:
2589:
2561:
2550:
2549:
2539:
2515:
2509:
2508:
2480:
2474:
2473:
2467:
2461:
2455:
2451:
2449:
2441:
2419:
2410:
2409:
2399:
2389:
2361:
2355:
2354:
2344:
2334:
2302:
2296:
2295:
2285:
2275:
2251:
2245:
2244:
2225:10.1038/nbt.1672
2208:
2202:
2201:
2191:
2181:
2163:
2139:
2133:
2132:
2122:
2090:
2084:
2083:
2073:
2063:
2039:
2033:
2032:
2022:
2004:
1980:
1974:
1973:
1963:
1931:
1925:
1924:
1914:
1891:Plant Physiology
1882:
1876:
1875:
1865:
1855:
1831:
1825:
1824:
1822:
1798:
1792:
1791:
1781:
1757:
1751:
1750:
1740:
1730:
1706:
1700:
1699:
1689:
1679:
1662:(6): 1777–1782.
1647:
1641:
1640:
1630:
1620:
1596:
1590:
1589:
1553:
1547:
1546:
1536:
1504:
1498:
1497:
1487:
1477:
1445:
1439:
1438:
1394:
1388:
1387:
1343:
1334:
1333:
1323:
1300:Nature Protocols
1291:
1280:
1279:
1251:
1165:drug-engineering
1028:elementary modes
1022:Elementary modes
988:that consist of
976:Extreme pathways
887:Model refinement
526:
522:
464:Escherichia coli
188:Escherichia coli
137:
136:
94:Refine the model
52:, also known as
43:Escherichia coli
3806:
3805:
3801:
3800:
3799:
3797:
3796:
3795:
3776:Systems biology
3756:
3755:
3754:
3749:
3708:
3662:
3648:Energy modeling
3636:
3580:
3556:Ecosystem model
3532:
3527:
3366:
3352:
3350:Further reading
3347:
3346:
3301:
3297:
3250:
3246:
3234:
3233:
3224:
3223:
3219:
3197:
3193:
3148:
3144:
3091:
3087:
3034:
3030:
2983:
2979:
2932:
2928:
2899:
2895:
2858:
2854:
2807:
2798:
2759:
2752:
2715:Genome Research
2707:
2703:
2664:
2653:
2646:
2642:
2597:
2593:
2562:
2553:
2516:
2512:
2481:
2477:
2465:
2463:
2453:
2452:
2443:
2442:
2438:
2420:
2413:
2362:
2358:
2317:(2): e1003465.
2303:
2299:
2252:
2248:
2209:
2205:
2140:
2136:
2091:
2087:
2040:
2036:
1981:
1977:
1932:
1928:
1883:
1879:
1832:
1828:
1799:
1795:
1758:
1754:
1707:
1703:
1648:
1644:
1597:
1593:
1554:
1550:
1513:Genome Research
1505:
1501:
1446:
1442:
1395:
1391:
1344:
1337:
1292:
1283:
1262:(11): 550–558.
1252:
1239:
1234:
1177:
1128:
1111:
1090:
1060:
1054:
1045:
1033:stoichiometrics
1024:
1018:
978:
958:
889:
807:
790:
673:
421:
412:
407:
143:Genes in Genome
114:
64:with molecular
17:
12:
11:
5:
3804:
3794:
3793:
3788:
3783:
3781:Bioinformatics
3778:
3773:
3768:
3751:
3750:
3748:
3747:
3742:
3737:
3735:Systems theory
3732:
3727:
3722:
3716:
3714:
3713:Related topics
3710:
3709:
3707:
3706:
3701:
3699:Economic model
3696:
3691:
3686:
3681:
3676:
3670:
3668:
3664:
3663:
3661:
3660:
3655:
3650:
3644:
3642:
3641:Sustainability
3638:
3637:
3635:
3634:
3629:
3624:
3619:
3614:
3609:
3604:
3599:
3594:
3588:
3586:
3582:
3581:
3579:
3578:
3573:
3568:
3563:
3558:
3553:
3548:
3546:Cellular model
3542:
3540:
3534:
3533:
3526:
3525:
3518:
3511:
3503:
3497:
3496:
3490:
3485:
3480:
3475:
3470:
3464:
3458:
3453:
3448:
3443:
3438:
3433:
3428:
3423:
3418:
3413:
3408:
3403:
3393:
3388:
3382:
3377:
3372:
3365:
3364:External links
3362:
3361:
3360:
3357:
3351:
3348:
3345:
3344:
3295:
3244:
3235:|journal=
3217:
3191:
3142:
3085:
3028:
2977:
2926:
2893:
2872:(1): 122–134.
2852:
2796:
2769:(3): 326–332.
2750:
2701:
2674:(2): 185–194.
2651:
2640:
2611:(4): 291–305.
2591:
2572:(8): 400–405.
2551:
2510:
2475:
2454:|journal=
2436:
2411:
2356:
2297:
2260:Genome Biology
2246:
2219:(9): 977–982.
2203:
2134:
2099:Bioinformatics
2085:
2034:
1975:
1926:
1897:(2): 579–589.
1877:
1826:
1813:(4): 473–483.
1793:
1752:
1701:
1642:
1605:Genome Biology
1591:
1564:(1): 112–121.
1548:
1519:(2): 244–253.
1499:
1440:
1389:
1335:
1281:
1236:
1235:
1233:
1230:
1229:
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1223:
1218:
1213:
1208:
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1198:
1193:
1188:
1183:
1176:
1173:
1149:
1148:
1145:
1142:
1139:
1136:
1133:
1132:discrepancies.
1127:
1124:
1110:
1107:
1089:
1086:
1056:Main article:
1053:
1050:
1044:
1041:
1037:thermodynamics
1020:Main article:
1017:
1014:
1006:reaction rates
982:red blood cell
977:
974:
957:
954:
888:
885:
861:
860:
852:
844:
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789:
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784:
772:
766:
758:
698:KEGGtranslator
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459:bioinformatics
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324:September 2007
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146:Genes in Model
144:
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113:
110:
102:
101:
98:
95:
92:
15:
9:
6:
4:
3:
2:
3803:
3792:
3789:
3787:
3784:
3782:
3779:
3777:
3774:
3772:
3769:
3767:
3764:
3763:
3761:
3746:
3743:
3741:
3738:
3736:
3733:
3731:
3728:
3726:
3723:
3721:
3718:
3717:
3715:
3711:
3705:
3702:
3700:
3697:
3695:
3694:Crime mapping
3692:
3690:
3687:
3685:
3682:
3680:
3677:
3675:
3672:
3671:
3669:
3665:
3659:
3656:
3654:
3651:
3649:
3646:
3645:
3643:
3639:
3633:
3630:
3628:
3625:
3623:
3620:
3618:
3615:
3613:
3610:
3608:
3605:
3603:
3602:Climate model
3600:
3598:
3595:
3593:
3590:
3589:
3587:
3585:Environmental
3583:
3577:
3574:
3572:
3569:
3567:
3564:
3562:
3559:
3557:
3554:
3552:
3549:
3547:
3544:
3543:
3541:
3539:
3535:
3531:
3524:
3519:
3517:
3512:
3510:
3505:
3504:
3501:
3494:
3491:
3489:
3486:
3484:
3481:
3479:
3476:
3474:
3471:
3468:
3465:
3462:
3459:
3457:
3454:
3452:
3449:
3447:
3444:
3442:
3441:Pathway Tools
3439:
3437:
3434:
3432:
3429:
3427:
3424:
3422:
3419:
3417:
3414:
3412:
3409:
3407:
3404:
3401:
3397:
3394:
3392:
3389:
3386:
3383:
3381:
3378:
3376:
3373:
3371:
3368:
3367:
3358:
3354:
3353:
3340:
3336:
3331:
3326:
3322:
3318:
3314:
3310:
3306:
3299:
3291:
3287:
3282:
3277:
3272:
3267:
3263:
3259:
3255:
3248:
3240:
3228:
3220:
3218:9780123739445
3214:
3210:
3206:
3202:
3195:
3187:
3183:
3178:
3173:
3169:
3165:
3161:
3157:
3153:
3146:
3138:
3134:
3129:
3124:
3120:
3116:
3112:
3108:
3104:
3100:
3096:
3089:
3081:
3077:
3072:
3067:
3063:
3059:
3055:
3051:
3047:
3043:
3039:
3032:
3024:
3020:
3015:
3010:
3005:
3000:
2996:
2992:
2988:
2981:
2973:
2969:
2964:
2959:
2954:
2949:
2945:
2941:
2937:
2930:
2921:
2916:
2912:
2908:
2904:
2897:
2889:
2885:
2880:
2875:
2871:
2867:
2863:
2856:
2848:
2844:
2840:
2836:
2832:
2828:
2824:
2820:
2816:
2812:
2805:
2803:
2801:
2792:
2788:
2784:
2780:
2776:
2775:10.1038/73786
2772:
2768:
2764:
2757:
2755:
2746:
2742:
2737:
2732:
2728:
2724:
2720:
2716:
2712:
2705:
2697:
2693:
2689:
2685:
2681:
2677:
2673:
2669:
2662:
2660:
2658:
2656:
2649:
2644:
2636:
2632:
2627:
2622:
2618:
2614:
2610:
2606:
2602:
2595:
2587:
2583:
2579:
2575:
2571:
2567:
2560:
2558:
2556:
2547:
2543:
2538:
2533:
2529:
2525:
2521:
2514:
2506:
2502:
2498:
2494:
2490:
2486:
2479:
2471:
2459:
2447:
2439:
2437:9780123739445
2433:
2429:
2425:
2418:
2416:
2407:
2403:
2398:
2393:
2388:
2383:
2379:
2375:
2371:
2369:
2360:
2352:
2348:
2343:
2338:
2333:
2328:
2324:
2320:
2316:
2312:
2308:
2301:
2293:
2289:
2284:
2279:
2274:
2269:
2265:
2261:
2257:
2250:
2242:
2238:
2234:
2230:
2226:
2222:
2218:
2214:
2207:
2199:
2195:
2190:
2185:
2180:
2175:
2171:
2167:
2162:
2157:
2153:
2149:
2145:
2138:
2130:
2126:
2121:
2116:
2112:
2108:
2104:
2100:
2096:
2089:
2081:
2077:
2072:
2067:
2062:
2057:
2053:
2049:
2045:
2038:
2030:
2026:
2021:
2016:
2012:
2008:
2003:
1998:
1994:
1990:
1986:
1979:
1971:
1967:
1962:
1957:
1953:
1949:
1945:
1941:
1937:
1930:
1922:
1918:
1913:
1908:
1904:
1900:
1896:
1892:
1888:
1881:
1873:
1869:
1864:
1859:
1854:
1849:
1845:
1841:
1837:
1830:
1821:
1816:
1812:
1808:
1804:
1797:
1789:
1785:
1780:
1775:
1771:
1767:
1763:
1756:
1748:
1744:
1739:
1734:
1729:
1724:
1720:
1716:
1712:
1705:
1697:
1693:
1688:
1683:
1678:
1673:
1669:
1665:
1661:
1657:
1653:
1646:
1638:
1634:
1629:
1624:
1619:
1614:
1610:
1606:
1602:
1595:
1587:
1583:
1579:
1575:
1571:
1567:
1563:
1559:
1552:
1544:
1540:
1535:
1530:
1526:
1522:
1518:
1514:
1510:
1503:
1495:
1491:
1486:
1481:
1476:
1471:
1467:
1463:
1459:
1455:
1451:
1444:
1436:
1432:
1428:
1424:
1420:
1416:
1412:
1408:
1404:
1400:
1393:
1385:
1381:
1377:
1373:
1369:
1365:
1361:
1357:
1353:
1349:
1342:
1340:
1331:
1327:
1322:
1317:
1313:
1309:
1306:(1): 93–121.
1305:
1301:
1297:
1290:
1288:
1286:
1277:
1273:
1269:
1265:
1261:
1257:
1250:
1248:
1246:
1244:
1242:
1237:
1227:
1224:
1222:
1219:
1217:
1214:
1212:
1209:
1207:
1204:
1202:
1199:
1197:
1194:
1192:
1189:
1187:
1184:
1182:
1179:
1178:
1172:
1170:
1169:drug delivery
1166:
1162:
1158:
1157:immune system
1153:
1146:
1143:
1140:
1137:
1134:
1130:
1129:
1123:
1121:
1120:S. cerevisiae
1117:
1106:
1104:
1100:
1099:SBMLsimulator
1096:
1085:
1082:
1081:gene knockout
1076:
1073:
1069:
1065:
1059:
1049:
1040:
1038:
1034:
1029:
1023:
1013:
1011:
1007:
1003:
999:
995:
991:
987:
986:basis vectors
983:
973:
971:
967:
963:
962:Stoichiometry
953:
950:
944:
942:
938:
934:
930:
926:
925:
920:
915:
913:
909:
903:
901:
897:
893:
884:
882:
878:
874:
870:
866:
858:
857:
853:
850:
849:
845:
842:
841:
837:
834:
830:
829:
825:
824:
823:
819:
811:
799:
795:
792:
791:
782:
778:
777:
773:
770:
767:
764:
763:
759:
756:
752:
748:
744:
740:
736:
732:
728:
724:
720:
716:
712:
708:
704:
700:
699:
695:
692:
691:
687:
684:
680:
679:
678:Pathway Tools
675:
674:
663:
660:
658:
655:
653:
650:
649:
645:
643:
640:
638:
635:
632:
631:
627:
625:
622:
620:
617:
614:
613:
609:
606:
603:
601:
598:
595:
594:
590:
587:
584:
581:
578:
575:
574:
570:
567:
564:
561:
558:
555:
554:
550:
547:
544:
541:
538:
536:
535:
528:
527:
518:
517:
513:
510:
509:
505:
502:
501:
497:
494:
490:
486:
482:
481:
477:
474:
470:
466:
465:
460:
456:
452:
448:
445:
442:
438:
434:
430:
428:
423:
422:
416:
398:
396:February 2010
395:
392:
389:
386:
383:
381:
378:
377:
374:
371:
368:
365:
362:
359:
357:
354:
353:
350:
347:
344:
341:
338:
335:
333:
330:
329:
326:
323:
320:
317:
314:
311:
309:
306:
305:
302:
299:
296:
293:
290:
287:
285:
282:
281:
278:
275:
272:
269:
266:
263:
261:
258:
257:
254:
251:
248:
245:
242:
239:
237:
234:
233:
230:
228:February 2003
227:
224:
221:
218:
215:
213:
210:
209:
206:
203:
200:
197:
194:
191:
189:
186:
185:
182:
179:
176:
173:
170:
167:
165:
162:
161:
157:
154:
151:
148:
145:
142:
139:
138:
135:
133:
129:
125:
120:
109:
107:
99:
96:
93:
90:
89:
88:
85:
83:
82:biotechnology
79:
75:
71:
67:
63:
59:
55:
51:
45:
44:
38:
31:
30:
25:
21:
3565:
3478:SBMLsqueezer
3315:(5): e1328.
3312:
3308:
3298:
3261:
3257:
3247:
3200:
3194:
3159:
3155:
3145:
3102:
3098:
3088:
3045:
3041:
3031:
2994:
2990:
2980:
2943:
2939:
2929:
2910:
2906:
2896:
2869:
2865:
2855:
2814:
2810:
2766:
2762:
2718:
2714:
2704:
2671:
2667:
2643:
2608:
2604:
2594:
2569:
2565:
2527:
2523:
2513:
2491:(2): 72–80.
2488:
2484:
2478:
2466:|title=
2377:
2373:
2367:
2359:
2314:
2310:
2300:
2263:
2259:
2249:
2216:
2212:
2206:
2151:
2147:
2137:
2102:
2098:
2088:
2051:
2047:
2037:
1995:(1): 40–79.
1992:
1988:
1978:
1943:
1939:
1929:
1894:
1890:
1880:
1843:
1839:
1829:
1810:
1806:
1796:
1769:
1765:
1755:
1718:
1714:
1704:
1659:
1655:
1645:
1608:
1604:
1594:
1561:
1557:
1551:
1516:
1512:
1502:
1457:
1453:
1443:
1402:
1398:
1392:
1351:
1347:
1303:
1299:
1259:
1255:
1226:Metagenomics
1171:techniques.
1154:
1150:
1119:
1115:
1112:
1103:SBMLsqueezer
1091:
1077:
1061:
1046:
1025:
1004:and maximum
1002:mass balance
990:steady state
979:
959:
945:
936:
929:succinyl-CoA
922:
918:
916:
904:
894:
890:
862:
854:
846:
838:
826:
820:
816:
793:
774:
768:
760:
696:
688:
676:
551:Metabolites
514:
506:
498:
485:nomenclature
483:: An enzyme
478:
468:
462:
446:
424:
413:
379:
355:
348:October 2008
331:
307:
283:
276:January 2007
260:Homo sapiens
259:
252:January 2005
236:Mus musculus
235:
211:
187:
163:
127:
123:
115:
103:
86:
57:
53:
49:
48:
41:
27:
2530:: 131–152.
1161:macrophages
998:constraints
833:in-paralogs
437:prokaryotes
152:Metabolites
3791:Metabolism
3760:Categories
3538:Biological
3264:(1): 461.
2154:(1): 135.
2002:1510.03964
1232:References
1159:by lysing
966:substrates
949:simulation
941:acetyl-CoA
933:methionine
883:database.
828:InParanoid
709:formatted
433:eukaryotes
372:April 2009
158:Reference
128:C. elegans
74:glycolysis
66:physiology
3446:metaTIGER
3421:ModelSEED
3237:ignored (
3227:cite book
2997:(1): 39.
2456:ignored (
2446:cite book
2266:(1): r6.
2161:1309.1910
2054:(1): 15.
1611:(1): R2.
1196:Fluxomics
912:catalysts
908:reactants
769:MetaMerge
762:ModelSEED
545:Reactions
516:metaTIGER
419:Databases
410:Resources
300:June 2007
180:June 1999
149:Reactions
70:metabolic
3786:Genomics
3385:PathCase
3339:36314754
3290:31500573
3186:14563875
3137:20093466
3080:16782788
3023:18447902
2972:19144170
2946:(5): 5.
2888:26886737
2839:12432396
2783:10700151
2745:12466293
2696:14575652
2635:22367118
2586:15283984
2546:26667673
2505:23334546
2406:27895706
2351:24516375
2292:22292986
2233:20802497
2198:24321545
2129:21700675
2080:23433509
2029:19955237
1970:31696234
1921:20044452
1872:19356237
1788:17573341
1747:17555602
1696:17267599
1637:15642094
1586:38627979
1578:15903248
1543:12566402
1494:10805808
1435:16873716
1384:10423613
1330:20057383
1276:16169729
1175:See also
1075:to 10).
917:Francke
873:NC-IUBMB
848:InterPro
548:Pathways
529:Database
204:May 2000
140:Organism
76:and the
3411:MetaCyc
3400:Cyclone
3330:9597258
3281:6734243
3128:5600254
3107:Bibcode
3099:Science
3071:1557581
3050:Bibcode
3014:2412839
2963:2661887
2847:4301741
2819:Bibcode
2791:7742485
2676:Bibcode
2626:3536058
2397:5117618
2380:: 252.
2342:3916221
2319:Bibcode
2283:3488975
2241:6641097
2189:3892043
2166:Bibcode
2120:3150042
2071:3623889
2020:2810111
1961:7145653
1912:2815881
1863:2678070
1738:1925256
1687:1794290
1664:Bibcode
1462:Bibcode
1427:9851916
1407:Bibcode
1399:Science
1376:7542800
1356:Bibcode
1348:Science
1321:3125167
1116:E. coli
1010:kinetic
881:MetaCyc
869:MetaCyc
743:GraphML
705:files (
596:MetaCyc
539:Enzymes
473:MetaCyc
469:E. coli
3667:Social
3488:Copasi
3426:ENZYME
3406:EcoCyc
3396:BioCyc
3391:BRENDA
3375:GeneDB
3337:
3327:
3288:
3278:
3215:
3184:
3177:219384
3174:
3135:
3125:
3078:
3068:
3021:
3011:
2970:
2960:
2886:
2845:
2837:
2811:Nature
2789:
2781:
2743:
2736:187577
2733:
2694:
2633:
2623:
2584:
2544:
2503:
2434:
2404:
2394:
2349:
2339:
2290:
2280:
2239:
2231:
2196:
2186:
2127:
2117:
2078:
2068:
2027:
2017:
1968:
1958:
1919:
1909:
1870:
1860:
1846:: 38.
1786:
1745:
1735:
1721:: 26.
1694:
1684:
1635:
1628:549063
1625:
1584:
1576:
1541:
1534:420374
1531:
1492:
1482:
1433:
1425:
1382:
1374:
1328:
1318:
1274:
1095:COPASI
1072:fluxes
919:et al.
877:BRENDA
856:STRING
794:PUBMED
776:CoReCo
757:, etc.
727:BioPAX
715:MIRIAM
633:BRENDA
615:ENZYME
576:BioCyc
532:Scope
500:BRENDA
489:ExPASy
480:ENZYME
455:EcoCyc
451:BioCyc
384:27,379
264:21,090
240:28,287
62:genome
2843:S2CID
2787:S2CID
2237:S2CID
2156:arXiv
1997:arXiv
1582:S2CID
1485:25862
1431:S2CID
1380:S2CID
1000:like
755:LaTeX
542:Genes
393:1,748
390:1,567
387:1,419
366:1,087
363:1,083
360:4,489
336:3,221
318:1,020
312:4,114
288:4,402
270:3,673
267:3,623
222:1,175
216:6,183
192:4,405
168:1,775
3493:gEFM
3436:TIGR
3416:SEED
3398:and
3380:KEGG
3370:ERGO
3335:PMID
3286:PMID
3239:help
3213:ISBN
3182:PMID
3133:PMID
3076:PMID
3019:PMID
2968:PMID
2884:PMID
2835:PMID
2779:PMID
2741:PMID
2692:PMID
2631:PMID
2582:PMID
2542:PMID
2501:PMID
2470:help
2458:help
2432:ISBN
2402:PMID
2347:PMID
2288:PMID
2229:PMID
2194:PMID
2125:PMID
2076:PMID
2025:PMID
1966:PMID
1917:PMID
1868:PMID
1784:PMID
1743:PMID
1692:PMID
1633:PMID
1574:PMID
1539:PMID
1490:PMID
1423:PMID
1372:PMID
1326:PMID
1272:PMID
1167:and
1118:and
1035:and
871:and
865:KEGG
781:KEGG
735:SBGN
723:SBML
719:KGML
707:KGML
703:KEGG
690:ERGO
651:BiGG
556:KEGG
508:BiGG
435:and
427:KEGG
246:1220
3461:IMG
3325:PMC
3317:doi
3276:PMC
3266:doi
3205:doi
3172:PMC
3164:doi
3160:185
3123:PMC
3115:doi
3103:327
3066:PMC
3058:doi
3009:PMC
2999:doi
2958:PMC
2948:doi
2915:doi
2911:157
2874:doi
2827:doi
2815:420
2771:doi
2731:PMC
2723:doi
2684:doi
2672:225
2621:PMC
2613:doi
2574:doi
2532:doi
2493:doi
2424:doi
2392:PMC
2382:doi
2337:PMC
2327:doi
2278:PMC
2268:doi
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