MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases
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Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity).
MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.
The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).
KeywordsMetabolic Model Flux Balance Analysis Atomistic Detail Pelargonic Acid Reaction Entry
A major impediment is the presence of metabolites with multiple names across databases and models, and in some cases within the same resource, which significantly slows down the pooling of information from multiple sources. Therefore, the almost unavoidable inclusion of multiple replicates of the same metabolite can lead to missed opportunities to reveal (synthetic) lethal gene deletions, repair network gaps and quantify metabolic flows. Moreover, most data sources inadvertently include some reactions that may be stoichiometrically inconsistent  and/or elementally/charge unbalanced [22, 23], which can adversely affect the prediction quality of the resulting models if used directly. Finally, a large number of metabolites in reactions are partly specified with respect to structural information and may contain generic side groups (e.g., alkyl groups -R), varying degree of a repeat unit participation in oligomers, or even just compound class identification such as "an amino acid" or "electron acceptor". Over 3% of all metabolites and 8% of all reactions in the aforementioned databases and models exhibit one or more of these problems.
There have already been a number of efforts aimed at addressing some of these limitations. The Rhea database, hosted by the European Bioinformatics Institute, aggregates reaction data primarily from IntEnz  and ENZYME , whereas Reactome.org is a collection of reactions primarily focused on human metabolism [26, 27]. Even though they crosslink their data to one or more popular databases such as KEGG, ChEBI, NCBI, Ensembl, Uniprot, etc., both retain their own representation formats. More recently, the BKM-react database is a non-redundant biochemical reaction database containing known enzyme-catalyzed reactions compiled from BRENDA, KEGG, and MetaCyc . The BKM-react database currently contains 20,358 reactions. Additionally, the contents of five frequently used human metabolic pathway databases have been compared . An important step forward for models was the BiGG database, which includes seven genome-scale models from the Palsson group in a consistent nomenclature and exportable in SBML format [29, 30, 31]. Research towards integrating genome-scale metabolic models with large databases has so far been even more limited. Notable exceptions include the partial reconciliation of the latest E. coli genome scale model i AF1260 with EcoCyc  and the aggregation of data from the Arabidopsis thaliana database and KEGG for generating genome-scale models  in a semi-automated fashion. Additionally, ReMatch integrates some metabolic models, although its primary focus is on carbon mappings for metabolic flux analysis . Also, many metabolic models retain the KEGG identifiers of metabolites and reactions extracted during their construction [35, 36]. An important recent development is the web resource Model SEED that can generate draft genome-scale metabolic models drawing from an internal database that integrates KEGG with 13 genome scale models (including six of the models in the BiGG database) . All of the reactions in Model SEED and BiGG are charge and elementally balanced.
In this paper, we describe the development and highlight applications of the web-based resource MetRxn that integrates, using internally consistent descriptions, metabolite and reaction information from 8 databases and 44 metabolic models. The MetRxn knowledgebase (as of October 2011) contains over 76,000 metabolites and 72,000 reactions (including unresolved entries) that are charge and elementally balanced. By conforming to standardized metabolite and reaction descriptions, MetRxn enables users to efficiently perform queries and comparisons across models and/or databases. For example, common metabolites and/or reactions between models and databases can rapidly be generated along with connected paths that link source to target metabolites. MetRxn supports export of models in SBML format. New models are being added as they are published or made available to us. It is available as a web-based resource at http://metrxn.che.psu.edu.
Construction and Content
Step 1: Source data acquisition
Metabolite and reaction data was downloaded from BRENDA, KEGG, BioCyc, BKM-react and other databases using a variety of methods based on protocols such as SOAP, FTP and HTTP. We preprocessed the data into flat files that were subsequently imported into the knowledgebase. All original information pertaining to metabolite name, abbreviations, metabolite geometry, related reactions, catalyzing enzyme and organism name, gene-protein-reaction associations, and compartmentalization was retained. For all 44 initial genome-scale models listed, the online information from the corresponding publications was also imported. The source codes for all parsers used in Step 1 are available on the MetRxn website.
Step 2: Source data parsing
The "raw data" from both databases and models was unified using standard SQL scripts on a MySQL server. The description schema for metabolites includes source, name, abbreviations used in the source, chemical formula, and geometry. The schema for reactions accounts for source, name, reaction string (reactants and products), organism designation, associated enzymes and genes, EC number, compartment, reversibility/direction, and pathway information. Once a source has been imported into the MySQL server, a data source-specific dictionary is created to map metabolite abbreviations onto names/synonyms and structures and metabolites to reactions.
Step 3: Metabolite charge and structural analysis
We used Marvin (Chemaxon) to analyze all 218,122 raw metabolite entries containing structural information (out of a total of 322,936, including BRENDA entries). Inconsistencies were found in 12,965 entries typically due to wrong atom connectivity, valence, bond length or stereo chemical information, which were corrected using APIs available in Marvin. A final corrected version of the metabolite geometries was calculated at a fixed pH of 7.2 and converted into standard Isomeric SMILES format. The structure/formula used corresponded to the major microspecies found during the charge calculation, which effectively rounds the charge to an integer value in accordance with previous model construction conventions. This format includes both chiral and stereo information, as it allows specification of molecular configuration [38, 39, 40]. Metabolites were also annotated with Canonical SMILES using the OpenBabel Interface from Chemspider. The canonical representation encodes only atom-atom connectivity while ignoring all conformers for a metabolite. Using bond connectivity information from the primary sources and resources such as PubChem and ChemSpider we used Canonical SMILES [41, 42] to resolve the identity of 34,984 metabolites and 32,311 reactions. Another 6,100 metabolites and 11,401 reactions involved, in various degrees, lack of full atomistic detail in their description (e.g., use an R or × as side-chains, are generic compounds like "amino acid" or "electron acceptor"). Over 25,000 duplicate metabolites and 27,000 reaction entries were identified and consolidated within the database. The metabolites and reactions present in the resolved repository were further classified with respect to the completeness of atomistic detail in their description.
Step 4: Metabolite synonyms and initial reaction reconciliation
Raw metabolite entries were assigned to Isomeric SMILES representations whenever possible. If insufficient structural information was available for a downloaded raw metabolite then it was assigned temporarily with the Canonical SMILES and revisited during the reaction reconciliation. Canonical SMILES retain atom connectivity but not stereo-specificity and are used as the basic metabolite topology descriptors as many metabolic models lack stereo-specificity information. After generating the initial metabolite associations, we identified reaction overlaps using the reaction synonyms and reaction strings along with the metabolite SMILES representations. Directionality and cofactor usage were temporarily ignored. During this step, reactions were flagged as single-compartment or two-compartment (i.e., transport reactions). MetRxn internally retains the original compartment designations, but currently only displays these simplified compartment designations. In analogy to metabolites, reactions were grouped into families that shared participants but in the source data sets occurred in different compartments or differed only in protonation.
Step 5: Reaction charge and elemental balancing
Once metabolites were assigned correct elemental composition and protonation states, reactions were charge and elementally balanced. To this end, for charge balancing we relied on a linear programming representation that minimizes the difference in the sum of the charge of the reactants and the sum of the charge on the products. The complete formulation is provided in the documentation at MetRxn.
Step 6: Iterative reaction reconciliation
Reactions with one (or more) unresolved reactants and/or products were string compared against the entire resolved collection of reactions. This step was successively executed as newly resolved metabolites and reactions could enable the resolution of previously unresolved ones. After the first pass 164 metabolites were resolved, while subsequent passes (up to 18 for some models) helped resolved a total of 8,720 entries. Reactions with significant (but not complete) overlapping sets of reactants/products are additionally sent to the curator GUI including phonetic information. Briefly, the phonetic tokens of synonyms with known structures were compared against the ones without any associated structure. The algorithm suppresses keywords/tokens depicting stereo information such as cis, trans, L-, D-, alpha, beta, gamma, and numerical entries because they change the phonetic signature of the synonym under investigation. In addition, the algorithm ignores non-chemistry related words (e.g., use, for, experiment) that are found in some metabolite names. Certain tokens such as "-ic acid" and "-ate" are treated as equivalent. PubChem and Chemspider sources were accessed through the GUI so that the curator gets as much information as possible to identify the data correctly. Phonetic matches provided clues for resolving over 159 metabolites. The iterative application of string and phonetic comparison algorithms resolved as many as 8,879 metabolites after 18 rounds of reconciliation.
Upon completion of this workflow, all genome-scale models are reformatted into a computations-ready form and Flux Balance Analysis  is performed on both the source model and the standardized model in MetRxn to ascertain the ability of the model to produce biomass before and after standardization. We performed the calculations using GAMS version 12.6. MetRxn is accessible through a web interface that indirectly generates MySQL queries. In order to facilitate analysis and use of the data, a number of tools are provided as part of MetRxn.
Data export and display
MetRxn supports a number of export capabilities. In general, any list that is displayed contains live links to the metabolite or reaction entities. These lists can consist of an entire model, data from a comparison, or query results. All items can be exported to SMBL format. In addition, the public MySQL database will be made available upon request. Because of licensing limitations, the BRENDA database cannot be exported and is not part of the public MySQL database. However, we plan to provide Java source code that allows for the integration of a local copy of the public MySQL database with the BRENDA database (provided upon request).
Source comparisons and visualization
In addition to listing the content (number of metabolites, reactions, etc.) of the selected data source(s), MetRxn contains tools for comparing two or more models and visualizing the results. These associations can be for metabolites or reactions. During these comparisons compartment information and reversibility are suppressed. Comparison tables are generated by comparing the associations between the selected data source(s) using the canonical structures.
which implies that 7,8-aminopelargonic acid (DAPA) is identical to 7,8-Diaminononanoate (dann). Examination of pelargonic acid and nonanoate reveals that they were indeed known synonyms. In many cases, we were also able to assign stereo-specific information to metabolite entries in models (e.g., stipulate the L-lysine isomer for lysine). We made use of an iterative approach that allowed us to map structures from models with explicit links to structures (e.g. to KEGG or CAS numbers) to models that only provided metabolite names. Furthermore, by using a phonetic algorithm that uses tokens for equivalent strings in metabolite names (e.g., '-ic acid' and '-ate' are equivalent) we were able to resolve more than an additional 159 metabolites. For example, phonetic searches flagged cis-4-coumarate and COUMARATE in the Acinetobacter baylyi model  as potentially identical compounds. Additional checks revealed that indeed both metabolites should map to the same structure. A more complex matching example involved 1-(5'-Phosphoribosyl)-4-(N-succinocarboxamide)-5-aminoimidazole from the Bacillus subtilis model  and 1-(5'-Phosphoribosyl)-5-amino-4-(N-succinocarboxamide)-imidazole from the Aspergillus nidulans model . We note that the phonetic algorithm only makes suggestions and orders the possible matches for the curator. Next, we detail three examples that provide an insight into the type of tasks that MetRxn can facilitate.
Utility and Discussion
1. Charge and elementally balanced metabolic models
The standardized description of metabolites and balanced reactions afforded by MetRxn enables the expedient repair of existing models for metabolite naming inconsistencies and reaction balancing errors. Here we highlight one such metabolic model repair for Acinetobacter baylyi i Abaylyiv4 . We identified that 189 out of 880 reactions are not elementally or charge balanced. Most of the reactions with charge balance errors involved a missed proton in reactions involving cofactor pairs such as NAD/NADH. For example, a proton had to be added to the reactants side in the reaction (R, R)-Butanediol-dehydrogenase in which butanediol reacts with NAD to form acetoin. In addition, the stoichiometric coefficient of water in GTP cyclohydrolase I was erroneously set at -2 which resulted in an imbalance in oxygen atoms. The re-balancing analysis changed the coefficient to -1 (as listed in BRENDA) and added a proton to the list of reactants (absent from BRENDA) in order to also balance charges.
We performed flux balance analysis (FBA) on both the published and MetRxn-based rebalanced version of the Acinetobacter baylyi model using the uptake constraints listed in  to assess the effect of re-balancing reaction entries on FBA results. We found that the maximum biomass using the glucose/ammonia uptake environment decreased by 9% primarily due to the increased energetic costs associated with maintaining the proton gradient. This result demonstrates the significant effect that lack of reaction balancing may cause in FBA calculations. Overall, we found that nearly two-thirds of the models had at least one unbalanced reaction, with over 2,400 entities across all models that were either charge or elementally imbalanced. Frequently, the same reaction was imbalanced in multiple models (each occurrence was counted separately).
2. Contrasting existing metabolic models
Representation of glucose-6-phosphate dehydrogenase in selected metabolic models
[c]: g6p + nadp < = = > 6pgl + h + nadph
[c]: g6p + nadp -- > 6pgl + h + nadph
[c]g6p + nadp < = = > 6pgl + h + nadph
[c]: f420-2 + g6p -- > 6pgl + f420-2h2
G6P + NADP < - > D6PGL + NADPH
G6P + NADP - > D6PGL + NADPH
G6P + NAD - > D6PGL + NADH
C01172 + C00006 = C01236 + C00005 + C00080
GSM mouse 
C00092 + C00006 < = > C01236 + C00005 + C00080
3. Using MetRxn to Bio-Prospect for Novel Production Routes
A "Grand Challenge" in biotechnological production is the identification of novel production routes that allow for the conversion of inexpensive resources (e.g., various sugars) into useful products (e.g., succinate, artemisinin) and bio-fuels (e.g., ethanol, butanol, biodiesel etc.). Selected production routes must exhibit high yields, avoid thermodynamic barriers, bypass toxic intermediates and circumvent existing intellectual property restrictions. Historically, the incorporation of heterologous pathways relied largely on human intuition and literature review followed by experimentation [67, 68]. Currently, rapidly expanding compilations of biotransformations such as KEGG  and BRENDA  are increasingly being prospected using search algorithms to identify biosynthetic routes to important product molecules. Several optimization and graph-based methods have been employed to computationally assemble novel biochemical routes from these sources. OptStrain  used a mixed-integer linear optimization representation to identify the minimal number of reactions to be added (i.e. knock-ins) into a genome-scale metabolic model to enable the production of the new molecule. However the combinatorial nature of the problem poses a significant challenge to the OptStrain methodology as the number of reaction database entries increase from a few to tens of thousands. At the expense of not enforcing stoichiometric balances, graph-based algorithms have inherently better-scaling properties for exhaustively identifying all min-path reaction entries that link a source with a target metabolite. Hatzimanikatis et. al.  introduced a graph-based heuristic approach (BNICE) to identify all possible biosynthetic routes from a given substrate to a target chemical by hypothesized enzymatic reaction rules. In addition, the BNICE framework was used to identify novel metabolic pathways for the synthesis of 3-hydroxypropionate in E. coli . Based on a similar approach, a new scoring algorithm  was introduced to evaluate and compare novel pathways generated using enzyme-reaction rules. In addition, several techniques such as PathMiner , PathComp , Pathway Tools [77, 78], MetaRoute , PathFinder  and UM-BBD Pathway Prediction System  have been used to search databases for bioconversion routes.
MetRxn enables the standardization, correction and utilization of rapidly growing metabolic information for over 76,000 metabolites participating in 72,000 reactions (including unresolved entries). The library of standardized and balanced reactions streamlines the process of reconstructing organism-specific metabolism and opens the way for identifying new paths for metabolic flux redirection. Moreover, the standardization of published genome-scale models enables the rapidly growing community of researchers who make use of metabolic information to understand metabolism at an organism-level and re-deploy it for various biotechnological objectives. By removing standardization and data heterogeneity bottlenecks the pace of knowledge creation and discovery from users of this resource will be accelerated. MetRxn is constructed in a way that allows for quick updating and tracking of changes that occur in the primary databases, as well as available parsing tools that allow for rapid import of new genome-scale metabolic models as they become available. By having exports in SBML, MetRxn's output can be directly interfaced with software packages such as the COBRA toolbox.
During the construction of the initial release of MetRxn, we managed to associate structures for over 8,800 metabolites and re-balanced more than 2,400 reaction instances across 44 metabolic models. This enables the genuine comparison of metabolic content between metabolic models. Preliminary results reinforce that that discrepancies between metabolic models echo not only genuine differences in metabolism but also assumptions and workflow followed by the model creator(s). Going forward, we will continue to expand MetRxn to include more genome-scale metabolic models and add additional tools to aid in their analysis. Because we anticipate that the scope and number of models will rapidly expand, we plan to invite and encourage the community to offer comments about metabolite and reaction information as well as provide feedback on MetRxn itself.
Availability and requirements
MetRxn is available at http://metrxn.che.psu.edu. Its use is freely available for all non-commercial activity.
Acknowledgements and Funding
This work was funded by DOE grant DE-FG02-05ER25684. The authors would like to gratefully acknowledge Robert Pantazes, Sridhar Ranganatha, Rajib Saha, and Alireza Zomorrodi for their help with testing and feedback on the MetRxn web interface.
- 1.Liolios K, Tavernarakis N, Hugenholtz P, Kyrpides NC: The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide. Nucleic Acids Res 2006, (34 database):D332–4.Google Scholar
- 15.Scheer M, Grote A, Chang A, Schomburg I, Munaretto C, Rother M, Sohngen C, Stelzer M, Thiele J, Schomburg D: BRENDA, the enzyme information system in 2011. Nucleic Acids Res 2011, (39 database):D670–6.Google Scholar
- 16.Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 2009, (38 database):D355–60.Google Scholar
- 17.Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, Kaipa P, Karthikeyan AS, Kothari A, Krummenacker M, Latendresse M, Mueller LA, Paley S, Popescu L, Pujar A, Shearer AG, Zhang P, Karp PD: The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2009, (38 database):D473–9.Google Scholar
- 19.Gao J, Ellis LB, Wackett LP: The University of Minnesota Biocatalysis/Biodegradation Database: improving public access. Nucleic Acids Res 2010, (38 database):D488–91.Google Scholar
- 24.Fleischmann A, Darsow M, Degtyarenko K, Fleischmann W, Boyce S, Axelsen KB, Bairoch A, Schomburg D, Tipton KF, Apweiler R: IntEnz, the integrated relational enzyme database. Nucleic Acids Res 2004, (32 database):D434–7.Google Scholar
- 27.Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, Garapati P, Hemish J, Hermjakob H, Jassal B, Kanapin A, Lewis S, Mahajan S, May B, Schmidt E, Vastrik I, Wu G, Birney E, Stein L, D'Eustachio P: Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 2009, (37 database):D619–22.Google Scholar
- 30.Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novere N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003, 19(4):524–31. 10.1093/bioinformatics/btg015CrossRefPubMedGoogle Scholar
- 32.Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BO: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 2007, 3: 121.PubMedCentralCrossRefPubMedGoogle Scholar
- 34.Pitkanen E, Akerlund A, Rantanen A, Jouhten P, Ukkonen E: ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis. J Integr Bioinform 2008., 5(2):Google Scholar
- 38.Weininger D: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences 1988, 28(1):31–36.Google Scholar
- 39.Weininger D, Weininger A, Weininger JL: SMILES. 2. Algorithm for generation of unique SMILES notation. Journal of Chemical Information and Computer Sciences 1989, 29(2):97–101.Google Scholar
- 40.Daylight Theory Manual[http://www.daylight.com/dayhtml/doc/theory/]
- 41.Weininger D: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of chemical information and computer sciences 1988, 28(1):31.Google Scholar
- 45.Durot M, Le Fevre F, de Berardinis V, Kreimeyer A, Vallenet D, Combe C, Smidtas S, Salanoubat M, Weissenbach J, Schachter V: Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data. BMC Syst Biol 2008, 2: 85. 10.1186/1752-0509-2-85PubMedCentralCrossRefPubMedGoogle Scholar
- 53.JL Reed, Encarnacion S, Collado-Vides J, Palsson BO: Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli. PLoS Comput Biol 2007, 3(10):1887–95.Google Scholar
- 55.Herrgard MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Bluthgen N, Borger S, Costenoble R, Heinemann M, Hucka M, Le Novere N, Li P, Liebermeister W, Mo ML, Oliveira AP, Petranovic D, Pettifer S, Simeonidis E, Smallbone K, Spasic I, Weichart D, Brent R, Broomhead DS, Westerhoff HV, Kirdar B, Penttila M, Klipp E, Palsson BO, Sauer U, Oliver SG, Mendes P, Nielsen J, Kell DB: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 2008, 26(10):1155–60. 10.1038/nbt1492PubMedCentralCrossRefPubMedGoogle Scholar
- 69.Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y: KEGG for linking genomes to life and the environment. Nucleic Acids Res 2008, (36 database):D480–4.Google Scholar
- 70.Chang A, Scheer M, Grote A, Schomburg I, Schomburg D: BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucleic Acids Res 2009, (37 database):D588–92.Google Scholar
- 74.Cho A, Yun H, Park JH, Lee SY, Park S: Prediction of novel synthetic pathways for the production of desired chemicals. BMC Systems Biology 2010., 4(35):Google Scholar
- 76.Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006, (34 database):D354–7.Google Scholar
- 78.Karp PD, Paley SM, Krummenacker M, Latendresse M, Dale JM, Lee TJ, Kaipa P, Gilham F, Spaulding A, Popescu L, Altman T, Paulsen I, Keseler IM, Caspi R: Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform 2009.Google Scholar
- 81.Ellis LB, Roe D, Wackett LP: The University of Minnesota Biocatalysis/Biodegradation Database: the first decade. Nucleic Acids Res 2006, (34 database):D517–21.Google Scholar
- 83.Yen JY: Finding K Shortest Loopless Paths in a Network. Management Science Series a-Theory 1971, 17(11):712–716.Google Scholar
- 88.Sillers R, Chow A, Tracy B, Papoutsakis ET: Metabolic engineering of the non-sporulating, non-solventogenic Clostridium acetobutylicum strain M5 to produce butanol without acetone demonstrate the robustness of the acid-formation pathways and the importance of the electron balance. Metab Eng 2008, 10(6):321–32. 10.1016/j.ymben.2008.07.005CrossRefPubMedGoogle Scholar
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