Path2Models: large-scale generation of computational models from biochemical pathway maps

BMC Systems Biology

Nicolas Le Novère⁴  Andreas Dräger⁵  Camille Laibe⁹  Falk Schreiber^1,10  Julio Saez-Rodriguez⁹  Claudine Chaouiya²  Andreas Zell⁵  Pedro Mendes⁸  Wolfgang Müller⁷  Douglas B Kell¹  Michael Hucka³  Henning Hermjakob⁹  Michael Wybrow⁶  Matthias Rall⁵  Sarah Keating⁹  Martijn van Iersel⁹  Martin Golebiewski⁷  Mihai Glont⁹  Michael Schubert⁹  Florian Mittag⁵  Roland Keller⁵  Tobias Czauderna^1,10  Clemens Wrzodek⁵  Neil Swainston⁸  Nicolas Rodriguez⁴  Finja Büchel⁵ 

[1] School of Chemistry, The University of Manchester, Manchester M13 9PL, UK;Instituto Gulbenkian de Ciência (IGC), Oeiras P-2780-156, Portugal;Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA;Babraham Institute, Babraham Research Campus, Cambridge, UK;Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany;Caulfield School of Information Technology, Monash University, Victoria 3800, Australia;HITS gGmbH, D-69118, Heidelberg, Germany;Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK;European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK;Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben D-06466, Germany

关键词: SBML;    SBGN;    Logical models;    Constraint based models;    Modular rate law;   

Others  :  1141963
DOI  :  10.1186/1752-0509-7-116

 received in 2013-07-19, accepted in 2013-10-23,  发布年份 2013

【 摘 要 】

Background

Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data.

Results

To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models webcite. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps.

Conclusions

To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized.

【 授权许可】

   
2013 Büchel et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Karp PD, Riley M, Saier M, Paulsen IT, Paley SM, Pellegrini-Toole A: The EcoCyc and MetaCyc databases. Nucleic Acids Res 2000, 28:56-59.
• [2]Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27-30.
• [3]Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: the pathway interaction database. Nucleic Acid Res 2009, 37:D674-D679.
• [4]Joshi-Tope G, Gillespie M, Vastrik I, D’Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L: Reactome: a knowledgebase of biological pathways. Nucleic Acid Res 2005, 33:D428-D432.
• [5]Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C: WikiPathways: pathway editing for the people. PloS Biol 2008, 6:e184.
• [6]Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012, 40:D109-D114.
• [7]Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C: BioModels database, enhanced curated and annotated resource of published quantitative kinetic models. BMC Syst Biol 2010, 4:92. BioMed Central Full Text
• [8]Lloyd CM, Lawson JR, Hunter PJ, Nielsen PF: The CellML repository. Bioinformatics 2008, 24:2122-2123.
• [9]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, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novère N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, et al.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003, 19:524-531.
• [10]Le Novère N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, Bergman FT, Gauges R, Ghazal P, Kawaji H, Li L, Matsuoka Y, Villéger A, Boyd SE, Calzone L, Courtot M, Dogrusoz U, Freeman TC, Funahashi A, Ghosh S, Jouraku A, Kim S, Kolpakov F, Luna A, Sahle S, Schmidt E, et al.: The systems biology graphical notation. Nat Biotechnol 2009, 27:735-741.
• [11]Smallbone K, Messiha HL, Carroll KM, Winder CL, Malys N, Dunn WB, Murabito E, Swainston N, Dada JO, Khan F, Pir P, Simeonidis E, Spasić I, Wishart J, Weichart D, Hayes NW, Jameson D, Broomhead DS, Oliver SG, Gaskell SJ, McCarthy JE, Paton NW, Westerhoff HV, Kell DB, Mendes P: A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Lett 2013, 587:2832-2841.
• [12]Brown M, Dunn WB, Dobson P, Patel Y, Winder CL, Francis-McIntyre S, Begley P, Carroll K, Broadhurst D, Tseng A, Swainston N, Spasic I, Goodacre R, Kell DB: Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 2009, 134:1322-1332.
• [13]Swainston N, Jameson D, Carroll K: A QconCAT informatics pipeline for the analysis, visualization and sharing of absolute quantitative proteomics data. Proteomics 2011, 11:329-333.
• [14]Kauffman SA: Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 1969, 22:437-467.
• [15]Thomas R: Boolean Formalization of genetic control circuits. J Theor Biol 1973, 42:563-585.
• [16]Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA: Logic-based models for the analysis of cell signaling networks. Biochemistry 2010, 49:3216-3224.
• [17]Laubenbacher R, Stigler B: A computational algebra approach to the reverse engineering of gene regulatory networks. J Theor Biol 2004, 229:523-537.
• [18]Glass L, Kauffman SA: The logical analysis of continuous non-linear biochemical control networks. J Theor Biol 1973, 39:103-129.
• [19]Chaouiya C: Petri net modelling of biological networks. Brief Bioinfo 2007, 8:210-219.
• [20]Whelan KE, King RD: Using a logical model to predict the growth of yeast. BMC Bioinfo 2008, 9:97. BioMed Central Full Text
• [21]Watterson S, Marshall S, Ghazal P: Logic models of pathway biology. Drug Discov Today 2008, 23:447-456.
• [22]Chaouiya C, Keating SM, Berenguier D, Naldi A, Thieffry D, Van Iersel M, Helicar T: Qualitative models, Version 1 Release 1. 2013. Available from COMBINE http://identifiers.org/combine.specifications/sbml.level-3.version-1.qual.version-1.release-1 webcite
• [23]Oberhardt MA, Puchałka J, Martins dos Santos VA, Papin JA: Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis. PLoS Comput Biol 2011, 7:e1001116.
• [24]Lee D, Smallbone K, Dunn WB, Murabito E, Winder CL, Kell DB, Mendes P, Swainston N: Improving metabolic flux predictions using absolute gene expression data. BMC Syst Biol 2012, 6:73. BioMed Central Full Text
• [25]Thiele I, Palsson BØ: A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 2010, 5:93-121.
• [26]Herrgård MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Blüthgen N, Borger S, Costenoble R, Heinemann M, Hucka M, Le Novère N, Li P, Liebermeister W, Mo ML, Oliveira AP, Petranovic D, Pettifer S, Simeonidis E, Smallbone K, Spasić I, Weichart D, Brent R, Broomhead DS, Westerhoff HV, Kirdar B, Penttilä M, Klipp E, Palsson BØ, Sauer U, et al.: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 2008, 26:1155-1160.
• [27]Dobson PD, Smallbone K, Jameson D, Simeonidis E, Lanthaler K, Pir P, Lu C, Swainston N, Dunn WB, Fisher P, Hull D, Brown M, Oshota O, Stanford NJ, Kell DB, King RD, Oliver SG, Stevens RD, Mendes P: Further developments towards a genome-scale metabolic model of yeast. BMC Syst Biol 2010, 4:145. BioMed Central Full Text
• [28]Thiele I, Hyduke DR, Steeb B, Fankam G, Allen DK, Bazzani S, Charusanti P, Chen FC, Fleming RM, Hsiung CA, De Keersmaecker SC, Liao YC, Marchal K, Mo ML, Özdemir E, Raghunathan A, Reed JL, Shin SI, Sigurbjörnsdóttir S, Steinmann J, Sudarsan S, Swainston N, Thijs IM, Zengler K, Palsson BO, Adkins JN, Bumann D: A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst Biol 2011, 5:8. BioMed Central Full Text
• [29]Thiele I, Swainston N, Fleming RM, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, Thorleifsson SG, Agren R, Bölling C, Bordel S, Chavali AK, Dobson P, Dunn WB, Endler L, Hala D, Hucka M, Hull D, Jameson D, Jamshidi N, Jonsson JJ, Juty N, Keating S, Nookaew I, Le Novère N, Malys N, Mazein A, et al.: A community-driven global reconstruction of human metabolism. Nat Biotechnol 2013, 31:419-425.
• [30]Swainston N, Mendes P, Kell DB: An analysis of a ‘community-driven’ reconstruction of the human metabolic network. Metabolomics 2013, 9:757-764.
• [31]Ananiadou S, Pyysalo S, Tsujii J, Kell DB: Event extraction for systems biology by text mining the iterature. Trends Biotechnol 2012, 28:381-390.
• [32]Nobata C, Dobson P, Iqbal SA, Mendes P, Tsujii J, Kell DB, Ananiadou S: Mining metabolites: extracting the yeast metabolome from the literature. Metabolomics 2011, 7:94-101.
• [33]Le Novère N, Finney A, Hucka M, Bhalla US, Campagne F, Collado-Vides J, Crampin EJ, Halstead M, Klipp E, Mendes P, Nielsen P, Sauro H, Shapiro B, Snoep JL, Spence HD, Wanner BL: MIRIAM, Minimum information requested in the annotation of biochemical models. Nat Biotechnol 2005, 23:1509-1515.
• [34]Krause F, Schulz M, Swainston N, Liebermeister W: Sustainable model building: the role of standards and biological semantics. Methods Enzymol 2011, 500:371-395.
• [35]Demir E, Cary MP, Paley S, Fukuda K, Lemer C, Vastrik I, Wu G, D’Eustachio P, Schaefer C, Luciano J, Schacherer F, Martinez-Flores I, Hu Z, Jimenez-Jacinto V, Joshi-Tope G, Kandasamy K, Lopez-Fuentes AC, Mi H, Pichler E, Rodchenkov I, Splendiani A, Tkachev S, Zucker J, Gopinath G, Rajasimha H, Ramakrishnan R, Shah I, Syed M, Anwar N, et al.: BioPAX – A Community Standard for Pathway Data Sharing. Nat Biotechnol 2010, 28:935-994.
• [36]Wittig U, Kania R, Golebiewski M, Rey M, Shi L, Jong L, Algaa E, Weidemann A, Sauer-Danzwith H, Mir S, Krebs O, Bittkowski M, Wetsch E, Rojas I, Müller W: SABIO-RK-database for biochemical reaction kinetics. Nucleic Acids Res 2012, 40:D790-D796.
• [37]Swainston N, Golebiewski M, Messiha HL, Malys N, Kania R, Kengne S, Krebs O, Mir S, Sauer-Danzwith H, Smallbone K, Weidemann A, Wittig U, Kell DB, Mendes P, Müller W, Paton NW, Rojas I: Enzyme kinetics informatics: from instrument to browser. FEBS J 2010, 77:3769-3779.
• [38]Swainston N, Mendes P: libAnnotationSBML: a library for exploiting SBML annotations. Bioinformatics 2009, 25:2292-2293.
• [39]Swainston N, Smallbone K, Mendes P, Kell D, Paton N: The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks. J Integr Bioinform 2011, 8:186.
• [40]Bernard T, Bridge A, Morgat A, Moretti S, Xenarios I, Pagni M: Reconciliation of metabolites and biochemical reactions for metabolic networks. Brief Bioinform 2012. Epub ahead of print doi:10.1093/bib/bbs058
• [41]Schellenberger J, Park JO, Conrad TM, Palsson BØ: BiGG: a Biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 2010, 11:213. BioMed Central Full Text
• [42]Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL: High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 2010, 28:977-982.
• [43]Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 2011, 6(9):1290-1307.
• [44]Ebrahim A, Lerman JA, Palsson BØ, Hyduke DR: COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol 2013, 7:74. BioMed Central Full Text
• [45]Letunic I, Bork P: Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res 2011, 39:W475-W478.
• [46]Path2Models whole genome metabolic models. http://itol.embl.de/external.cgi?tree=1308801712097513714825090&restore_saved=1&cT=6976 webcite
• [47]Models produced by the Path2Models project. http://www.ebi.ac.uk/biomodels-main/path2models webcite
• [48]Agren R, Liu L, Shoaie S, Vongsangnak W, Nookaew I, Nielsen J: The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol 2013, 9:e1002980.
• [49]Wrzodek C, Dräger A, Zell A: KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats. Bioinformatics 2011, 27:2314-2315.
• [50]Li P, Dada JO, Jameson D, Spasic I, Swainston N, Carroll K, Dunn W, Khan F, Malys N, Messiha HL, Simeonidis E, Weichart D, Winder C, Wishart J, Broomhead DS, Goble CA, Gaskell SJ, Kell DB, Westerhoff HV, Mendes P, Paton NW: Systematic integration of experimental data and models in systems biology. BMC Bioinfo 2010, 11:582.
• [51]Smallbone K, Simeonidis E, Swainston N, Mendes P: Towards a genome-scale kinetic model of cellular metabolism. BMC syst biol 2010, 4:6. BioMed Central Full Text
• [52]Liebermeister W, Uhlendorf J, Klipp E: Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation. Bioinformatics 2010, 26:1528-1534.
• [53]Nobeli I, Ponstingl H, Krissinel EB, Thornton JM: A structure-based anatomy of the E. coli metabolome. J Mol Biol 2003, 334:697-719.
• [54]MacNamara A, Terfve C, Henriques D, Bernabé BP, Saez-Rodriguez J: State-time spectrum of signal transduction logic models. Phys Biol 2012, 9:045003.
• [55]Gene Interaction Network simulation (GINsim). http://ginsim.org webcite
• [56]A flexible pipeline to model protein signalling networks trained to data using various logic formalisms. http://www.cellnopt.org webcite
• [57]The Cell Collective platform. http://www.thecellcollective.org webcite
• [58]Terfve CD, Cokelaer T, Henriques D, Macnamara A, Gonçalves E, Morris MK, van Iersel M, Lauffenburger DA, Saez-Rodriguez J: CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst Biol 2012, 6:133. BioMed Central Full Text
• [59]Helikar T, Kowal B, McClenathan S, Bruckner M, Rowley T, Madrahimov A, Wicks B, Shrestha M, Limbu K, Rogers JA: The Cell Collective: toward an open and collaborative approach to systems biology. BMC Syst Biol 2012, 6:96. BioMed Central Full Text
• [60]Chaouiya C, Naldi A, Thieffry D: Logical modelling of gene regulatory networks with GINsim. Methods Mol Biol 2012, 804:463-479.
• [61]Di Battista G, Eades P, Tamassia R, Tollis IG: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall; 1999.
• [62]Kaufmann M, Wagner D: Drawing Graphs: Methods and Models. Springer; 2001:2025. [Lecture Notes in Computer Science] http://books.google.co.uk/books?hl=en&lr=&id=_2qjR_uM69sC&oi=fnd&pg=PR3&dq=Drawing+Graphs:+Methods+and+Models&ots=v2kon0XRy8&sig=ip9GnbF6jbdDz_VPj2dOp5ZBgKA#v=onepage&q=Drawing%20Graphs%3A%20Methods%20and%20Models&f=false webcite
• [63]Schreiber F, Dwyer T, Marriott K, Wybrow M: A generic algorithm for layout of biological networks. BMC Bioinfo 2009, 10:375. BioMed Central Full Text
• [64]Wybrow M, Marriott K, Stuckey PJ: Orthogonal connector routing. Lecture Notes in Computer Science 2010, 5849:219-231.
• [65]Büchel F, Wrzodek C, Mittag F, Dräger A, Eichner J, Rodriguez N, Le Novère N, Zell A: Qualitative translation of relations from BioPAX to SBML qual. Bioinformatics 2012, 28:2648-2653.
• [66]Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B Jr, Assad-Garcia N, Glass JI, Covert MW: A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150:389-401.
• [67]Wrzodek C, Büchel B, Dräger A, Ruff M, Zell A: Precise generation of systems biology models from KEGG pathways. BMC Syst Biol 2013, 7:15. BioMed Central Full Text
• [68]Courtot M, Juty N, Knüpfer C, Waltemath D, Zhukova A, Dräger A, Dumontier M, Finney A, Golebiewski M, Hastings J, Hoops S, Keating S, Kell DB, Kerrien S, Lawson J, Lister A, Lu J, Machne R, Mendes P, Pocock M, Rodriguez N, Villeger A, Wilkinson DJ, Wimalaratne S, Laibe C, Hucka M, Le Novère N: Controlled vocabularies and semantics in systems biology. Mol Syst Biol 2011, 7:543.
• [69]Gauges R, Rost U, Sahle S, Wegner K: A model diagram layout extension for SBML. Bioinformatics 2006, 22:1879-1885.
• [70]Juty N, Le Novère N, Laibe C: Identifiers.org and MIRIAM Registry: community resources to provide persistent identification. Nucleic Acids Res 2012, 40:D580-D586.
• [71]BioModels.net qualifiers. http://biomodels.net/qualifiers/ webcite
• [72]Dräger A, Hassis N, Supper J, Schröder A, Zell A: SBMLsqueezer: a Cell Designer plug-in to generate kinetic rate equations for biochemical networks. BMC Syst Biol 2008, 2:39. BioMed Central Full Text
• [73]Dräger A, Schröder A, Zell A: Automating mathematical modeling of biochemical reaction networks. In Systems Biology for Signaling Networks, Systems Biology. Edited by Choi S. Springer-Verlag; 2010:159-205. Volume 1. http://books.google.co.uk/books?id=-cnVcd5X4oEC&pg=PA159&dq=Automating+mathematical+modeling+of+biochemical+reaction+networks&hl=en&sa=X&ei=AwFsUqinCuXm4QTs8ICQBg&ved=0CD4Q6AEwAA#v=onepage&q=Automating%20mathematical%20modeling%20of%20biochemical%20reaction%20networks&f=false webcite
• [74]Cornish-Bowden A: Fundamentals of Enzyme Kinetics. Portland Press; 2004:52.
• [75]Wegscheider R: Über simultane Gleichgewichte und die Beziehungen zwischen Thermodynamik und Reactionskinetik homogener Systeme. Chem Month. 1901, 32:849-906.
• [76]Cornish-Bowden A: Fundamentals of Enzyme Kinetics. Portland Press; 2004:169.
• [77]Liebermeister W, Klipp E: Bringing metabolic networks to life: convenience rate law and thermodynamic constraints. Theor Biol Med Model 2006, 3:41. BioMed Central Full Text
• [78]Schauer M, Heinrich R: Quasi-steady-state approximation in the mathematical modeling of biochemical reaction networks. Math Biosci 1983, 65:155-171.
• [79]Cornish-Bowden A: Fundamentals of Enzyme Kinetics. Portland Press; 2004:314.
• [80]Gkoutos GV, Schofield PN, Hoehndorf R: The Units Ontology: a tool for integrating units of measurement in science. Database 2012, 2012:bas033.
• [81]Dräger A, Rodriguez N, Dumousseau M, Dörr A, Wrzodek C, Le Novère N, Zell A, Hucka M: JSBML: a flexible Java library for working with SBML. Bioinformatics 2011, 27:2167-2168.
• [82]Bornstein BJ, Keating SM, Jouraku A, Hucka M: LibSBML: an API library for SBML. Bioinformatics 2008, 24:880-881.
• [83]KEGG Markup Language. http://www.genome.jp/kegg/xml/docs/ webcite
• [84]Radrich K, Tsuruoka Y, Dobson P, Gevorgyan A, Swainston N, Baart G, Schwartz JM: Integration of metabolic databases for the reconstruction of genome-scale metabolic networks. BMC Syst Biol 2010, 4:114. BioMed Central Full Text
• [85]SBML Flux Balance Constraints. http://identifiers.org/combine.specifications/sbml.level-3.version-1.fbc.version-1.release-1 webcite
• [86]Czauderna T, Klukas C, Schreiber F: Editing, validating, and translating of SBGN maps. Bioinformatics 2010, 26:2340-2341.
• [87]van Iersel MP, Villéger AC, Czauderna T, Boyd SE, Bergmann FT, Luna A, Demir E, Sorokin A, Dogrusoz U, Matsuoka Y, Funahashi A, Aladjem MI, Mi H, Moodie SL, Kitano H, Le Novère N, Schreiber F: Software support for SBGN maps: SBGN-ML and LibSBGN. Bioinformatics 2012, 28:2016-2021.
• [88]Dwyer T, Marriott K, Stuckey PJ: Fast node overlap removal. Lecture Notes in Computer Science 2006, 2006(3843):153-164.
• [89]Adaptagrams, tools for adaptive diagrams. http://www.adaptagrams.org/ webcite
• [90]van Iersel MP, Kelder T, Pico AR, Hanspers K, Coort S, Conklin BR, Evelo C: Presenting and exploring biological pathways with PathVisio. BMC Bioinfo 2008, 9:399. BioMed Central Full Text