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Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices

  • Natalie J. Stanford
  • Martin Scharm
  • Paul D. Dobson
  • Martin Golebiewski
  • Michael Hucka
  • Varun B. Kothamachu
  • David Nickerson
  • Stuart Owen
  • Jürgen PahleEmail author
  • Ulrike Wittig
  • Dagmar Waltemath
  • Carole Goble
  • Pedro Mendes
  • Jacky Snoep
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2049)

Abstract

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR—findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.

Key words

Standards Metadata Databases Data storage Model storage FAIR Reproducible research 

References

  1. 1.
    Noble D (2008) The music of life: biology beyond genes. Oxford University Press, Oxford, MI. OUP Oxford PaperbackGoogle Scholar
  2. 2.
    Hoppensteadt FC, Peskin CS, Hoppensteadt FC (2002) Modeling and simulation in medicine and the life sciences. Texts in applied mathematics, vol 10, 2nd edn. Springer, New York, NYCrossRefGoogle Scholar
  3. 3.
    Klipp E, Liebermeister W, Wierling C, Kowald A (2016) Systems biology: a textbook. 2nd edn. Wiley-Blackwell, Hoboken, NJGoogle Scholar
  4. 4.
    Henkel R, Endler L, Peters A, Le Novere N, Waltemath D (2010) Ranked retrieval of computational biology models. BMC Bioinformatics 11:423.  https://doi.org/10.1186/1471-2105-11-423CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    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 Novere N, Laibe C (2010) BioModels Database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4:92.  https://doi.org/10.1186/1752-0509-4-92CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Yu T, Lloyd CM, Nickerson DP, Cooling MT, Miller AK, Garny A, Terkildsen JR, Lawson J, Britten RD, Hunter PJ, Nielsen PM (2011) The physiome model repository 2. Bioinformatics 27(5):743–744.  https://doi.org/10.1093/bioinformatics/btq723CrossRefPubMedGoogle Scholar
  7. 7.
    Olson GM, Zimmerman A (2008) Scientific collaboration on the Internet. MIT Press, Cambridge, MACrossRefGoogle Scholar
  8. 8.
    Hunter PJ (2006) Modeling human physiology: the IUPS/EMBS Physiome Project. Proc IEEE 94(4):678–691.  https://doi.org/10.1009/Jpoc.2006.871767CrossRefGoogle Scholar
  9. 9.
    Swainston N, Smallbone K, Hefzi H, Dobson PD, Brewer J, Hanscho M, Zielinski DC, Ang KS, Gardiner NJ, Gutierrez JM, Kyriakopoulos S, Lakshmanan M, Li S, Liu JK, Martinez VS, Orellana CA, Quek LE, Thomas A, Zanghellini J, Borth N, Lee DY, Nielsen LK, Kell DB, Lewis NE, Mendes P (2016) Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12:109.  https://doi.org/10.1007/s11306-016-1051-4CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Thiele I, Swainston N, Fleming RM, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, Thorleifsson SG, Agren R, Bolling 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 Novere N, Malys N, Mazein A, Papin JA, Price ND, Selkov E Sr, Sigurdsson MI, Simeonidis E, Sonnenschein N, Smallbone K, Sorokin A, van Beek JH, Weichart D, Goryanin I, Nielsen J, Westerhoff HV, Kell DB, Mendes P, Palsson BO (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31(5):419–425.  https://doi.org/10.1038/nbt.2488CrossRefGoogle Scholar
  11. 11.
    Holzhütter HG, Drasdo D, Preusser T, Lippert J, Henney AM (2012) The virtual liver: a multidisciplinary, multilevel challenge for systems biology. Wiley Interdiscip Rev Syst Biol Med 4(3):221–235.  https://doi.org/10.1002/wsbm.1158CrossRefPubMedGoogle Scholar
  12. 12.
    Blaustein R (2014) Reproducibility undergoes scrutiny. Bioscience 64(4):368.  https://doi.org/10.1093/biosci/biu017CrossRefGoogle Scholar
  13. 13.
    Economist TE (2014) How science goes wrong. De EconomistGoogle Scholar
  14. 14.
    Arrowsmith J (2011) Trial watch: phase II failures: 2008–2010. Nat Rev Drug Discov 10(5):328–329.  https://doi.org/10.1038/nrd3439CrossRefGoogle Scholar
  15. 15.
    Begley CG, Ellis LM (2012) Raise standards for preclinical cancer research. Nature 483(7391):531–533CrossRefGoogle Scholar
  16. 16.
    Mullard A (2011) Reliability of ‘new drug target’ claims called into question. Nat Rev Drug Discov 10(9):643–644.  https://doi.org/10.1038/nrd3545CrossRefPubMedGoogle Scholar
  17. 17.
    Prinz F, Schlange T, Asadullah K (2011) Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov 10(9):712–U781.  https://doi.org/10.1038/nrd3439-c1CrossRefPubMedGoogle Scholar
  18. 18.
    Bell AW, Deutsch EW, Au CE, Kearney RE, Beavis R, Sechi S, Nilsson T, Bergeron JJ, Group HTSW (2009) A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nat Methods 6(6):423–430.  https://doi.org/10.1038/nmeth.1333CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Ioannidis JP, Allison DB, Ball CA, Coulibaly I, Cui X, Culhane AC, Falchi M, Furlanello C, Game L, Jurman G, Mangion J, Mehta T, Nitzberg M, Page GP, Petretto E, van Noort V (2009) Repeatability of published microarray gene expression analyses. Nat Genet 41(2):149–155.  https://doi.org/10.1038/ng.295CrossRefGoogle Scholar
  20. 20.
    Garijo D, Kinnings S, Xie L, Xie L, Zhang Y, Bourne PE, Gil Y (2013) Quantifying reproducibility in computational biology: the case of the tuberculosis drugome. PLoS One 8(11):e80278.  https://doi.org/10.1371/journal.pone.0080278CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Topalidou M, Leblois A, Boraud T, Rougier NP (2015) A long journey into reproducible computational neuroscience. Front Comput Neurosci 9.  https://doi.org/10.3389/fncom.2015.00030
  22. 22.
    Errington TM, Iorns E, Gunn W, Tan FE, Lomax J, Nosek BA (2014) An open investigation of the reproducibility of cancer biology research. elife 3.  https://doi.org/10.7554/eLife.04333
  23. 23.
    Waltemath D, Wolkenhauer O (2016) How modeling standards, software, and initiatives support reproducibility in systems biology and systems medicine. IEEE Ttrans Bio-Med Eng 63(10):1999–2006.  https://doi.org/10.1109/Tbme.2016.2555481CrossRefGoogle Scholar
  24. 24.
    Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, Santos LBD, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, t’ Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) Comment: the FAIR guiding principles for scientific data management and stewardship. Sci Data 3.  https://doi.org/10.1038/sdata.2016.18
  25. 25.
    Hucka M, Nickerson DP, Bader GD, Bergmann FT, Cooper J, Demir E, Garny A, Golebiewski M, Myers CJ, Schreiber F, Waltemath D, Le Novere N (2015) Promoting coordinated development of community-based information standards for modeling in biology: the COMBINE initiative. Front Bioeng Biotechnol 3:19.  https://doi.org/10.3389/fbioe.2015.00019CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Stromback L, Hall D, Lambrix P (2007) A review of standards for data exchange within systems biology. Proteomics 7(6):857–867.  https://doi.org/10.1002/pmic.200600438CrossRefPubMedGoogle Scholar
  27. 27.
    Klipp E, Liebermeister W, Helbig A, Kowald A, Schaber J (2007) Systems biology standards—the community speaks. Nat Biotechnol 25(4):390–391.  https://doi.org/10.1038/nbt0407-390CrossRefPubMedGoogle Scholar
  28. 28.
    Taylor CF, Field D, Sansone SA, Aerts J, Apweiler R, Ashburner M, Ball CA, Binz PA, Bogue M, Booth T, Brazma A, Brinkman RR, Clark AM, Deutsch EW, Fiehn O, Fostel J, Ghazal P, Gibson F, Gray T, Grimes G, Hancock JM, Hardy NW, Hermjakob H, Julian RK, Kane M, Kettner C, Kinsinger C, Kolker E, Kuiper M, Le Novere N, Leebens-Mack J, Lewis SE, Lord P, Mallon AM, Marthandan N, Masuya H, McNally R, Mehrle A, Morrison N, Orchard S, Quackenbush J, Reecy JM, Robertson DG, Rocca-Serra P, Rodriguez H, Rosenfelder H, Santoyo-Lopez J, Scheuermann RH, Schober D, Smith B, Snape J, Stoeckert CJ, Tipton K, Sterk P, Untergasser A, Vandesompele J, Wiemann S (2008) Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotechnol 26(8):889–896.  https://doi.org/10.1038/nbt.1411CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29(4):365–371.  https://doi.org/10.1038/ng1201-365CrossRefPubMedGoogle Scholar
  30. 30.
    Field D, Garrity G, Gray T, Morrison N, Selengut J, Sterk P, Tatusova T, Thomson N, Allen MJ, Angiuoli SV, Ashburner M, Axelrod N, Baldauf S, Ballard S, Boore J, Cochrane G, Cole J, Dawyndt P, De Vos P, dePamphilis C, Edwards R, Faruque N, Feldman R, Gilbert J, Gilna P, Glockner FO, Goldstein P, Guralnick R, Haft D, Hancock D, Hermjakob H, Hertz-Fowler C, Hugenholtz P, Joint I, Kagan L, Kane M, Kennedy J, Kowalchuk G, Kottmann R, Kolker E, Kravitz S, Kyrpides N, Leebens-Mack J, Lewis SE, Li K, Lister AL, Lord P, Maltsev N, Markowitz V, Martiny J, Methe B, Mizrachi I, Moxon R, Nelson K, Parkhill J, Proctor L, White O, Sansone SA, Spiers A, Stevens R, Swift P, Taylor C, Tateno Y, Tett A, Turner S, Ussery D, Vaughan B, Ward N, Whetzel T, Gil IS, Wilson G, Wipat A (2008) The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol 26(5):541–547.  https://doi.org/10.1038/nbt1360CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Taylor CF, Paton NW, Lilley KS, Binz PA, Julian RK, Jones AR, Zhu WM, Apweiler R, Aebersold R, Deutsch EW, Dunn MJ, Heck AJR, Leitner A, Macht M, Mann M, Martens L, Neubert TA, Patterson SD, Ping PP, Seymour SL, Souda P, Tsugita A, Vandekerckhove J, Vondriska TM, Whitelegge JP, Wilkins MR, Xenarios I, Yates JR, Hermjakob H (2007) The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 25(8):887–893.  https://doi.org/10.1038/nbt1329CrossRefPubMedGoogle Scholar
  32. 32.
    Quinn TA, Granite S, Allessie MA, Antzelevitch C, Bollensdorff C, Bub G, Burton RAB, Cerbai E, Chen PS, Delmar M, DiFrancesco D, Earm YE, Efimov IR, Egger M, Entcheva E, Fink M, Fischmeister R, Franz MR, Garny A, Giles WR, Hannes T, Harding SE, Hunter PJ, Iribe G, Jalife J, Johnson CR, Kass RS, Kodama I, Koren G, Lord P, Markhasin VS, Matsuoka S, McCulloch AD, Mirams GR, Morley GE, Nattel S, Noble D, Olesen SP, Panfilov AV, Trayanova NA, Ravens U, Richard S, Rosenbaum DS, Rudy Y, Sachs F, Sachse FB, Saint DA, Schotten U, Solovyova O, Taggart P, Tung L, Varro A, Volders PG, Wang K, Weiss JN, Wettwer E, White E, Wilders R, Winslow RL, Kohl P (2011) Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): standardised reporting for model reproducibility, interoperability, and data sharing. Prog Biophys Mol Biol 107(1):4–10.  https://doi.org/10.1016/j.pbiomolbio.2011.07.001CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Stanford NJ, Wolstencroft K, Golebiewski M, Kania R, Juty N, Tomlinson C, Owen S, Butcher S, Hermjakob H, Le Novere N, Mueller W, Snoep J, Goble C (2015) The evolution of standards and data management practices in systems biology. Mol Syst Biol 11(12):851CrossRefGoogle Scholar
  34. 34.
    Hucka M, Bergmann FT, Chaoulya C, Draeger A, Hoops S, Keating SM, König M, Le Novére N, Myers CJ, Olivier B, Sahle S, Schaff JC, Sheriff R, Smith LP, Waltemath D, Wilkinson DJ (2019) The Systems Biology Markup Language (SBML): language specification for level 3 version 2 core release 2. J Integr Bioinform 16(2).  https://doi.org/10.1515/jib-2019-0021
  35. 35.
    Cuellar AA, Lloyd CM, Nielsen PF, Bullivant DP, Nickerson DP, Hunter PJ (2003) An overview of CellML 1.1, a biological model description language. Simulation 79(12):740–747CrossRefGoogle Scholar
  36. 36.
    Cannon RC, Gleeson P, Crook S, Ganapathy G, Marin B, Piasini E, Silver RA (2014) LEMS: A language for expressing complex biological models in concise and hierarchical form and its use in underpinning Neur oML 2. Frontiers in Neuroinformatics 8:79.  https://doi.org/10.3389/fninf.2014.00079
  37. 37.
    Gauges R, Rost U, Sahle S, Wengler K, Bergmann FT (2015) The Systems Biology Markup Language (SBML) Level 3 package: layout, version 1 core. J Integr Bioinform 12(2).  https://doi.org/10.2390/biecoll-jib-2015-267
  38. 38.
    Bergmann F, Olivier B (2010) SBML level 3 package proposal: flux. Nat Preced.  https://doi.org/10.1038/npre.2010.4236.1
  39. 39.
    Olivier B, Bergmann F (2015) SBML level 3 flux balance constraints package version 2 release 1Google Scholar
  40. 40.
    Smith LP, Hucka M, Hoops S, Finney A (2015) SBML level 3 package: hierarchical model composition, version 1 release 3.  https://doi.org/10.2390/biecoll-jib-2015-268CrossRefGoogle Scholar
  41. 41.
    Smith LP, Hucka M, Hoops S, Finney A (2013) SBML level 3 hierarchical model composition package version 1 release 3Google Scholar
  42. 42.
    hucka M, Smith LP (2016) SBML level 3 package: groups, version 1 release 1.  https://doi.org/10.2390/biecoll-jib-2016-290CrossRefGoogle Scholar
  43. 43.
    Maxwell Lewis Neal, Matthias König, David Nickerson, G­ksel Mısırlı, Reza Kalbasi, Andreas Drðger, Koray Atalag, Vijayalakshmi Chelliah, Michael T Cooling, Daniel L Cook, Sharon Crook, Miguel de Alba, Samuel H Friedman, Alan Garny, John H Gennari, Padraig Gleeson, Martin Golebiewski, Michael Hucka, Nick Juty, Chris Myers, Brett G Olivier, Herbert M Sauro, Martin Scharm, Jacky L Snoep, Vasundra Touré, Anil Wipat, Olaf Wolkenhauer, Dagmar Waltemath (2019) Harmonizing semantic annotations for computational models in biology. Briefings in Bioinformatics 20 (2):540–550Google Scholar
  44. 44.
    Hucka M, Bergmann F, Hoops S, Keating S, Sahle S, Wilkinson DJ (2010) The Systems Biology Markup Language (SBML): language specification for level 3 version 1 core (release 1 candidate). Nat Preced.  https://doi.org/10.1038/npre.2010.4123.1
  45. 45.
    Lassila O, Swick RR (1999) Resource Description Framework (RDF) model and syntax specification. W3C recommendation 22 Feb 1999Google Scholar
  46. 46.
    Le Novere 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 (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23(12):1509–1515.  https://doi.org/10.1038/nbt1156CrossRefPubMedGoogle Scholar
  47. 47.
    Juty N, Le Novere N, Laibe C (2012) Identifiers.org and MIRIAM registry: community resources to provide persistent identification. Nucleic Acids Res 40(Database issue):D580–D586.  https://doi.org/10.1093/nar/gkr1097CrossRefPubMedGoogle Scholar
  48. 48.
    Editors S (2017) SBML software guideGoogle Scholar
  49. 49.
    Hedley WJ, Nelson MR (2001) CellML 1.0 Specification—CellML. https://www.cellml.org/specifications/cellml_1.0Google Scholar
  50. 50.
    Lloyd CM, Halstead MD, Nielsen PF (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85(2–3):433–450.  https://doi.org/10.1016/j.pbiomolbio.2004.01.004CrossRefPubMedGoogle Scholar
  51. 51.
    Wimalaratne SM, Halstead MD, Lloyd CM, Cooling MT, Crampin EJ, Nielsen PF (2009) Facilitating modularity and reuse: guidelines for structuring CellML 1.1 models by isolating common biophysical concepts. Exp Physiol 94(5):472–485.  https://doi.org/10.1113/expphysiol.2008.045161CrossRefPubMedGoogle Scholar
  52. 52.
    Beard DA, Britten R, Cooling MT, Garny A, Halstead MD, Hunter PJ, Lawson J, Lloyd CM, Marsh J, Miller A, Nickerson DP, Nielsen PM, Nomura T, Subramanium S, Wimalaratne SM, Yu T (2009) CellML metadata standards, associated tools and repositories. Philos Trans A Math Phys Eng Sci 367(1895):1845–1867.  https://doi.org/10.1098/rsta.2008.0310CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Wimalaratne SM, Halstead MD, Lloyd CM, Crampin EJ, Nielsen PF (2009) Biophysical annotation and representation of CellML models. Bioinformatics 25(17):2263–2270.  https://doi.org/10.1093/bioinformatics/btp391CrossRefPubMedGoogle Scholar
  54. 54.
    Garny A, Nickerson DP, Cooper J, Weber dos Santos R, Miller AK, McKeever S, Nielsen PM, Hunter PJ (2008) CellML and associated tools and techniques. Philos Trans A Math Phys Eng Sci 366(1878):3017–3043.  https://doi.org/10.1098/rsta.2008.0094CrossRefPubMedGoogle Scholar
  55. 55.
    Courtot M, Juty N, Knupfer C, Waltemath D, Zhukova A, Drager 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 Novere N (2011) Controlled vocabularies and semantics in systems biology. Mol Syst Biol 7:543.  https://doi.org/10.1038/msb.2011.77CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Kitano H, Funahashi A, Matsuoka Y, Oda K (2005) Using process diagrams for the graphical representation of biological networks. Nat Biotechnol 23(8):961–966.  https://doi.org/10.1038/nbt1111CrossRefPubMedGoogle Scholar
  57. 57.
    Le Novere 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, Villeger 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, Watterson S, Wu G, Goryanin I, Kell DB, Sander C, Sauro H, Snoep JL, Kohn K, Kitano H (2009) The systems biology graphical notation. Nat Biotechnol 27(8):735–741.  https://doi.org/10.1038/nbt.1558CrossRefPubMedGoogle Scholar
  58. 58.
    Moodie S, Le Novere N, Demir E, Mi H, Villeger A (2015) Systems biology graphical notation: process description language level 1 version 1.3. J Integr Bioinform 12(2):263.  https://doi.org/10.2390/biecoll-jib-2015-263CrossRefPubMedGoogle Scholar
  59. 59.
    Sorokin A, Le Novere N, Luna A, Czauderna T, Demir E, Haw R, Mi H, Moodie S, Schreiber F, Villeger A (2015) Systems biology graphical notation: entity relationship language level 1 version 2. J Integr Bioinform 12(2):264.  https://doi.org/10.2390/biecoll-jib-2015-264CrossRefPubMedGoogle Scholar
  60. 60.
    Mi H, Schreiber F, Moodie S, Czauderna T, Demir E, Haw R, Luna A, Le Novere N, Sorokin A, Villeger A (2015) Systems biology graphical notation: activity flow language level 1 version 1.2. J Integr Bioinform 12(2):265.  https://doi.org/10.2390/biecoll-jib-2015-265CrossRefPubMedGoogle Scholar
  61. 61.
    van Iersel MP, Villeger 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 Novere N, Schreiber F (2012) Software support for SBGN maps: SBGN-ML and LibSBGN. Bioinformatics 28(15):2016–2021.  https://doi.org/10.1093/bioinformatics/bts270CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Vasundra Touré, Nicolas Le Novère, Dagmar Waltemath, Olaf Wolkenhauer, Francis Ouellette (2018) Quick tips for creating effective and impactful biological pathways using the Systems Biology Graphical Notation. PLOS Computational Biology 14(2):e1005740Google Scholar
  63. 63.
    Waltemath D, Adams R, Bergmann FT, Hucka M, Kolpakov F, Miller AK, Moraru II, Nickerson D, Sahle S, Snoep JL, Le Novere N (2011) Reproducible computational biology experiments with SED-ML—the simulation experiment description markup language. BMC Syst Biol 5:198.  https://doi.org/10.1186/1752-0509-5-198CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Waltemath D, Adams R, Beard DA, Bergmann FT, Bhalla US, Britten R, Chelliah V, Cooling MT, Cooper J, Crampin EJ, Garny A, Hoops S, Hucka M, Hunter P, Klipp E, Laibe C, Miller AK, Moraru I, Nickerson D, Nielsen P, Nikolski M, Sahle S, Sauro HM, Schmidt H, Snoep JL, Tolle D, Wolkenhauer O, Le Novere N (2011) Minimum information about a simulation experiment (MIASE). PLoS Comput Biol 7(4):e1001122.  https://doi.org/10.1371/journal.pcbi.1001122CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Bergmann FT, Cooper J, König M, Moraru I, Nickerson D, Le Novére N, Olivier BG, Sahle S, Smith L, Waltemath D (2018) Simulation Experiment Description Markup Language (SED-ML): level 1 version 3 (l1 v3). J Integr Bioinform 15(1).  https://doi.org/10.1515/jib-2017-0086
  66. 66.
    Kolpakov F (2002) BIOUML – framework for visual modeling and simualtion biological systems. Proceedings of the international conference on bioinformatics of genome regulation and structureGoogle Scholar
  67. 67.
    Kolpakov F, Tolstykh NI, Valeev TF, Kiselev IN, Kutumova E, Ryabova A, Yevshin I, Kel A (2011) BIO-UML open source plug-in based platform for bioinformatics: invitation to collaboration. Proceedings of the international Moscow conference on computational molecular biologyGoogle Scholar
  68. 68.
    Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U (2006) COPASI—a COmplex PAthway SImulator. Bioinformatics 22(24):3067–3074.  https://doi.org/10.1093/bioinformatics/btl485CrossRefPubMedGoogle Scholar
  69. 69.
    Olivier BG, Snoep JL (2004) Web-based kinetic modelling using JWS online. Bioinformatics 20(13):2143–2144.  https://doi.org/10.1093/bioinformatics/bth200CrossRefPubMedGoogle Scholar
  70. 70.
    Snoep JL, Olivier BG (2003) JWS online cellular systems modelling and microbiology. Microbiology 149(Pt 11):3045–3047.  https://doi.org/10.1099/mic.0.C0124-0CrossRefPubMedGoogle Scholar
  71. 71.
    Sauro HM, Choi K, Medley JK, Cannistra C (2016) Tellurium: a python based modeling and reproducibility platform for systems biology. bioRxiv.  https://doi.org/10.1101/054601
  72. 72.
    Zhukova A, Adams R, Laibe C, Le Novere N (2012) LibKiSAO: a Java library for Querying KiSAO. BMC Res Notes 5:520.  https://doi.org/10.1186/1756-0500-5-520CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Shafranovich Y (2005) Common format and MIME type for comma-separated values (CSV) files. The Internet Society, Reston, VACrossRefGoogle Scholar
  74. 74.
    Klink P (2016) FieldedText. http://www.fieldedtext.org/. Accessed 1 May 2017Google Scholar
  75. 75.
    Dada JO, Spasic I, Paton NW, Mendes P (2010) SBRML: a markup language for associating systems biology data with models. Bioinformatics 26(7):932–938.  https://doi.org/10.1093/bioinformatics/btq069CrossRefPubMedGoogle Scholar
  76. 76.
    Scharm M, Waltemath D (2016) A fully featured COMBINE archive of a simulation study on syncytial mitotic cycles in Drosophila embryos. F1000 Research 5:2421.  https://doi.org/10.12688/f1000research.9379.1CrossRefPubMedGoogle Scholar
  77. 77.
    Gennari JH, Neal ML, Galdzicki M, Cook DL (2011) Multiple ontologies in action: composite annotations for biosimulation models. J Biomed Inform 44(1):146–154.  https://doi.org/10.1016/j.jbi.2010.06.007CrossRefPubMedGoogle Scholar
  78. 78.
    Finney A, Hucka M, Bornstein BJ, Keating SM, Shapiro BE (2006) Software infrastructure for effective communication and reuse of computational models. In: Szallasi Z, Stelling J, Periwal V (eds) System modeling in cell biology: from concepts to nuts & bolts. MIT Press, Cambridge, MA, pp 355–378CrossRefGoogle Scholar
  79. 79.
    Misirli G, Cavaliere M, Waites W, Pocock M, Madsen C, Gilfellon O, Honorato-Zimmer R, Zuliani P, Danos V, Wipat A (2016) Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization. Bioinformatics 32(6):908–917.  https://doi.org/10.1093/bioinformatics/btv660CrossRefPubMedGoogle Scholar
  80. 80.
    Swainston N, Mendes P (2009) libAnnotationSBML: a library for exploiting SBML annotations. Bioinformatics 25(17):2292–2293.  https://doi.org/10.1093/bioinformatics/btp392CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Rodriguez N, Pettit JB, Dalle Pezze P, Li L, Henry A, van Iersel MP, Jalowicki G, Kutmon M, Natarajan KN, Tolnay D, Stefan MI, Evelo CT, Le Novere N (2016) The systems biology format converter. BMC Bioinformatics 17:154.  https://doi.org/10.1186/s12859-016-1000-2CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Dräger A, Planatscher H, Motsou Wouamba D, Schröder A, Hucka M, Endler L, Golebiewski M, Müller W, Zell A (2009) SBML2L(A)T(E)X: conversion of SBML files into human-readable reports. Bioinformatics 25(11):1455–1456.  https://doi.org/10.1093/bioinformatics/btp170CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Shen SY, Bergmann F, Sauro HM (2010) SBML2TikZ: supporting the SBML render extension in LaTeX. Bioinformatics 26(21):2794–2795.  https://doi.org/10.1093/bioinformatics/btq512CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Junker A, Rohn H, Czauderna T, Klukas C, Hartmann A, Schreiber F (2012) Creating interactive, web-based and data-enriched maps with the Systems Biology Graphical Notation. Nat Protoc 7(3):579–593.  https://doi.org/10.1038/nprot.2012.002CrossRefPubMedGoogle Scholar
  85. 85.
    Rosse C, Mejino JL Jr (2003) A reference ontology for biomedical informatics: the Foundational Model of Anatomy. J Biomed Inform 36(6):478–500.  https://doi.org/10.1016/j.jbi.2003.11.007CrossRefPubMedGoogle Scholar
  86. 86.
    Bard JB, Rhee SY (2004) Ontologies in biology: design, applications and future challenges. Nat Rev Genet 5(3):213–222.  https://doi.org/10.1038/nrg1295CrossRefGoogle Scholar
  87. 87.
    Wolstencroft K, Owen S, Horridge M, Krebs O, Mueller W, Snoep JL, du Preez F, Goble C (2011) RightField: embedding ontology annotation in spreadsheets. Bioinformatics 27(14):2021–2022.  https://doi.org/10.1093/bioinformatics/btr312CrossRefPubMedGoogle Scholar
  88. 88.
    Maguire E, Gonzalez-Beltran A, Whetzel PL, Sansone SA, Rocca-Serra P (2013) OntoMaton: a bioportal powered ontology widget for Google Spreadsheets. Bioinformatics 29(4):525–527.  https://doi.org/10.1093/bioinformatics/bts718CrossRefPubMedGoogle Scholar
  89. 89.
    Wolstencroft K, Owen S, Krebs O, Müller W, Nguyen Q, Snoep JL, Goble C (2013) Semantic data and models sharing in systems biology: the Just Enough Results Model and the SEEK platform. In: Alani H, Kagal L, Fokoue A et al (eds) The semantic web – ISWC 2013. Springer, Berlin.  https://doi.org/10.1007/978-3-642-41338-4_14CrossRefGoogle Scholar
  90. 90.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25(1):25–29.  https://doi.org/10.1038/75556CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Federhen S (2012) The NCBI taxonomy database. Nucleic Acids Res 40(Database issue):D136–D143.  https://doi.org/10.1093/nar/gkr1178CrossRefPubMedGoogle Scholar
  92. 92.
    Natale DA, Arighi CN, Blake JA, Bult CJ, Christie KR, Cowart J, D'Eustachio P, Diehl AD, Drabkin HJ, Helfer O, Huang H, Masci AM, Ren J, Roberts NV, Ross K, Ruttenberg A, Shamovsky V, Smith B, Yerramalla MS, Zhang J, AlJanahi A, Celen I, Gan C, Lv M, Schuster-Lezell E, Wu CH (2014) Protein ontology: a controlled structured network of protein entities. Nucleic Acids Res 42(Database issue):D415–D421.  https://doi.org/10.1093/nar/gkt1173CrossRefPubMedGoogle Scholar
  93. 93.
    Degtyarenko K, de Matos P, Ennis M, Hastings J, Zbinden M, McNaught A, Alcantara R, Darsow M, Guedj M, Ashburner M (2008) ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 36(Database issue):D344–D350.  https://doi.org/10.1093/nar/gkm791CrossRefPubMedGoogle Scholar
  94. 94.
    Iannella R, McKinney J (2014) vCard ointology – for describing people and organizations. W3c. https://www.w3.org/TR/vcard-rdf/. Accessed 3 Apr 2017
  95. 95.
    Scharm M, Waltemath D, Mendes P, Wolkenhauer O (2016) COMODI: an ontology to characterise differences in versions of computational models in biology. J Biomed Semantics 7(1):46.  https://doi.org/10.1186/s13326-016-0080-2CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Resat H, Petzold L, Pettigrew MF (2009) Kinetic modeling of biological systems. Methods Mol Biol 541:311–335.  https://doi.org/10.1007/978-1-59745-243-4_14CrossRefPubMedPubMedCentralGoogle Scholar
  97. 97.
    Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248CrossRefGoogle Scholar
  98. 98.
    Stanford NJ, Millard P, Swainston N (2015) RobOKoD: microbial strain design for (over)production of target compounds. Front Cell Dev Biol 3:17.  https://doi.org/10.3389/fcell.2015.00017CrossRefPubMedPubMedCentralGoogle Scholar
  99. 99.
    Tepper N, Shlomi T (2010) Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 26(4):536–543.  https://doi.org/10.1093/bioinformatics/btp704CrossRefPubMedGoogle Scholar
  100. 100.
    Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647–657.  https://doi.org/10.1002/bit.10803CrossRefPubMedGoogle Scholar
  101. 101.
    Garny A, Hunter PJ (2015) OpenCOR: a modular and interoperable approach to computational biology. Front Physiol 6:26.  https://doi.org/10.3389/fphys.2015.00026CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    Walker MA, Madduri R, Rodriguez A, Greenstein JL, Winslow RL (2016) Models and simulations as a service: exploring the use of galaxy for delivering computational models. Biophys J 110(5):1038–1043.  https://doi.org/10.1016/j.bpj.2015.12.041CrossRefPubMedPubMedCentralGoogle Scholar
  103. 103.
    Peters M, Eicher JJ, van Niekerk DD, Waltemath D, Snoep JL (2017) The JWS online simulation database. Bioinformatics 33(10):1589–1590.  https://doi.org/10.1093/bioinformatics/btw831CrossRefPubMedGoogle Scholar
  104. 104.
    Wolstencroft K, Krebs O, Snoep JL, Stanford NJ, Bacall F, Golebiewski M, Kuzyakiv R, Nguyen Q, Owen S, Soiland-Reyes S, Straszewski J, van Niekerk DD, Williams AR, Malmstrom L, Rinn B, Muller W, Goble C (2017) FAIRDOMHub: a repository and collaboration environment for sharing systems biology research. Nucleic Acids Res 45(D1):D404–D407.  https://doi.org/10.1093/nar/gkw1032CrossRefPubMedGoogle Scholar
  105. 105.
    Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, Haraldsdottir HS, Keating SM, Vlasov V, Wachowiak J, Magnusdottir S, Yu Ng C, Preciat G, Zagare A, Chan SHJ, Aurich MK, Clancy CM, Modamio J, Sauls JT, Noronha A, Bordbar A, Cousins B, El Assal DC, Ghaderi S, Ahookhosh M, Guebila MB, Apaolaza I, Kostromins A, Le Ding Ma HM, Sun Y, Valcarcel LV, Wang L, Yurkovich JT, Vuong PT, El Assal LP, Hinton S, Bryant WA, Aragon Artacho FJ, Planes FJ, Stalidzans E, Maass A, Vempala S, Hucka M, Saunders MA, Maranas CD, Lewis NE, Sauter T, Palsson BØ, Thiele I, Fleming RMT (2019) Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. Nat Protoc 14:639–702CrossRefGoogle Scholar
  106. 106.
    Boele J, Olivier BG, Teusink B (2012) FAME, the flux analysis and modeling environment. BMC Syst Biol 6:8.  https://doi.org/10.1186/1752-0509-6-8CrossRefPubMedPubMedCentralGoogle Scholar
  107. 107.
    Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30CrossRefGoogle Scholar
  108. 108.
    Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42(Database issue):D199–D205.  https://doi.org/10.1093/nar/gkt1076CrossRefPubMedGoogle Scholar
  109. 109.
    Olivier BG, Rohwer JM, Hofmeyr JH (2005) Modelling cellular systems with PySCeS. Bioinformatics 21(4):560–561.  https://doi.org/10.1093/bioinformatics/bti046CrossRefPubMedGoogle Scholar
  110. 110.
    Waltemath D, Henkel R, Halke R, Scharm M, Wolkenhauer O (2013) Improving the reuse of computational models through version control. Bioinformatics 29(6):742–748.  https://doi.org/10.1093/bioinformatics/btt018CrossRefPubMedGoogle Scholar
  111. 111.
    Wolstencroft K, Owen S, du Preez F, Krebs O, Mueller W, Goble C, Snoep JL (2011) The SEEK: a platform for sharing data and models in systems biology. Methods Enzymol 500:629–655.  https://doi.org/10.1016/B978-0-12-385118-5.00029-3CrossRefPubMedGoogle Scholar
  112. 112.
    Henkel R, Wolkenhauer O, Waltemath D (2015) Combining computational models, semantic annotations and simulation experiments in a graph database. Database (Oxford).  https://doi.org/10.1093/database/bau130
  113. 113.
    Chelliah V, Juty N, Ajmera I, Ali R, Dumousseau M, Glont M, Hucka M, Jalowicki G, Keating S, Knight-Schrijver V, Lloret-Villas A, Natarajan KN, Pettit JB, Rodriguez N, Schubert M, Wimalaratne SM, Zhao Y, Hermjakob H, Le Novere N, Laibe C (2015) BioModels: ten-year anniversary. Nucleic Acids Res 43(Database issue):D542–D548.  https://doi.org/10.1093/nar/gku1181CrossRefPubMedGoogle Scholar
  114. 114.
    van Gend C, Conradie R, du Preez FB, Snoep JL (2007) Data and model integration using JWS Online. In Silico Biol 7(2 Suppl):S27–S35PubMedGoogle Scholar
  115. 115.
    Buchel F, Rodriguez N, Swainston N, Wrzodek C, Czauderna T, Keller R, Mittag F, Schubert M, Glont M, Golebiewski M, van Iersel M, Keating S, Rall M, Wybrow M, Hermjakob H, Hucka M, Kell DB, Muller W, Mendes P, Zell A, Chaouiya C, Saez-Rodriguez J, Schreiber F, Laibe C, Drager A, Le Novere N (2013) Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC Syst Biol 7:116.  https://doi.org/10.1186/1752-0509-7-116CrossRefPubMedPubMedCentralGoogle Scholar
  116. 116.
    Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R, Jassal B, Jupe S, Korninger F, McKay S, Matthews L, May B, Milacic M, Rothfels K, Shamovsky V, Webber M, Weiser J, Williams M, Wu G, Stein L, Hermjakob H, D'Eustachio P (2016) The reactome pathway knowledgebase. Nucleic Acids Res 44(D1):D481–D487.  https://doi.org/10.1093/nar/gkv1351CrossRefPubMedGoogle Scholar
  117. 117.
    Licata L, Orchard S (2016) The MIntAct project and molecular interaction databases. Methods Mol Biol 1415:55–69.  https://doi.org/10.1007/978-1-4939-3572-7_3CrossRefPubMedGoogle Scholar
  118. 118.
    Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, Campbell NH, Chavali G, Chen C, del-Toro N, Duesbury M, Dumousseau M, Galeota E, Hinz U, Iannuccelli M, Jagannathan S, Jimenez R, Khadake J, Lagreid A, Licata L, Lovering RC, Meldal B, Melidoni AN, Milagros M, Peluso D, Perfetto L, Porras P, Raghunath A, Ricard-Blum S, Roechert B, Stutz A, Tognolli M, van Roey K, Cesareni G, Hermjakob H (2014) The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42((Database issue)):D358–D363.  https://doi.org/10.1093/nar/gkt1115CrossRefGoogle Scholar
  119. 119.
    Hastings J, de Matos P, Dekker A, Ennis M, Harsha B, Kale N, Muthukrishnan V, Owen G, Turner S, Williams M, Steinbeck C (2013) The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 41(Database issue):D456–D463.  https://doi.org/10.1093/nar/gks1146CrossRefPubMedGoogle Scholar
  120. 120.
    Abeyruwan S, Vempati UD, Kucuk-McGinty H, Visser U, Koleti A, Mir A, Sakurai K, Chung C, Bittker JA, Clemons PA, Brudz S, Siripala A, Morales AJ, Romacker M, Twomey D, Bureeva S, Lemmon V, Schurer SC (2014) Evolving BioAssay Ontology (BAO): modularization, integration and applications. J Biomed Semantics 5(Suppl 1):S5.  https://doi.org/10.1186/2041-1480-5-S1-S5CrossRefPubMedPubMedCentralGoogle Scholar
  121. 121.
    Sansone SA, Rocca-Serra P, Brandizi M, Brazma A, Field D, Fostel J, Garrow AG, Gilbert J, Goodsaid F, Hardy N, Jones P, Lister A, Miller M, Morrison N, Rayner T, Sklyar N, Taylor C, Tong W, Warner G, Wiemann S, Members of the RWG (2008) The first RSBI (ISA-TAB) workshop: “can a simple format work for complex studies?”. OMICS 12(2):143–149.  https://doi.org/10.1089/omi.2008.0019CrossRefPubMedGoogle Scholar
  122. 122.
    Sansone SA, Rocca-Serra P, Field D, Maguire E, Taylor C, Hofmann O, Fang H, Neumann S, Tong W, Amaral-Zettler L, Begley K, Booth T, Bougueleret L, Burns G, Chapman B, Clark T, Coleman LA, Copeland J, Das S, de Daruvar A, de Matos P, Dix I, Edmunds S, Evelo CT, Forster MJ, Gaudet P, Gilbert J, Goble C, Griffin JL, Jacob D, Kleinjans J, Harland L, Haug K, Hermjakob H, Ho Sui SJ, Laederach A, Liang S, Marshall S, McGrath A, Merrill E, Reilly D, Roux M, Shamu CE, Shang CA, Steinbeck C, Trefethen A, Williams-Jones B, Wolstencroft K, Xenarios I, Hide W (2012) Toward interoperable bioscience data. Nat Genet 44(2):121–126.  https://doi.org/10.1038/ng.1054CrossRefPubMedPubMedCentralGoogle Scholar
  123. 123.
    Hines ML, Morse T, Migliore M, Carnevale NT, Shepherd GM (2004) ModelDB: a database to support computational neuroscience. J Comput Neurosci 17(1):7–11.  https://doi.org/10.1023/B:JCNS.0000023869.22017.2eCrossRefPubMedPubMedCentralGoogle Scholar
  124. 124.
    McDougal RA, Morse TM, Carnevale T, Marenco L, Wang R, Migliore M, Miller PL, Shepherd GM, Hines ML (2017) Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J Comput Neurosci 42(1):1–10.  https://doi.org/10.1007/s10827-016-0623-7CrossRefPubMedGoogle Scholar
  125. 125.
    McDougal RA, Morse TM, Hines ML, Shepherd GM (2015) ModelView for ModelDB: online presentation of model structure. Neuroinformatics 13(4):459–470.  https://doi.org/10.1007/s12021-015-9269-2CrossRefPubMedPubMedCentralGoogle Scholar
  126. 126.
    Placzek S, Schomburg I, Chang A, Jeske L, Ulbrich M, Tillack J, Schomburg D (2017) BRENDA in 2017: new perspectives and new tools in BRENDA. Nucleic Acids Res 45(D1):D380–D388.  https://doi.org/10.1093/nar/gkw952CrossRefGoogle Scholar
  127. 127.
    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, Muller W (2012) SABIO-RK—database for biochemical reaction kinetics. Nucleic Acids Res 40(Database issue):D790–D796.  https://doi.org/10.1093/nar/gkr1046CrossRefPubMedGoogle Scholar
  128. 128.
    Bergmann FT, Adams R, Moodie S, Cooper J, Glont M, Golebiewski M, Hucka M, Laibe C, Miller AK, Nickerson DP, Olivier BG, Rodriguez N, Sauro HM, Scharm M, Soiland-Reyes S, Waltemath D, Yvon F, Le Novere N (2014) COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project. BMC Bioinformatics 15:369.  https://doi.org/10.1186/s12859-014-0369-zCrossRefPubMedPubMedCentralGoogle Scholar
  129. 129.
    Bechhofer S, Buchan I, De Roure D, Missier P, Ainsworth J, Bhagat J, Couch P, Cruickshank D, Delderfield M, Dunlop I, Gamble M, Michaelides D, Owen S, Newman D, Sufi S, Goble C (2013) Why linked data is not enough for scientists. Futur Gener Comput Syst 29(2):599–611.  https://doi.org/10.1016/j.future.2011.08.004CrossRefGoogle Scholar
  130. 130.
    Scharm M, Wendland F, Peters M, Wolfien M, Thiele T, Waltemath D (2014) The CombineArchiveWeb application – a web based tool to handle files associated with modelling results. CEUR workshop proceedingsGoogle Scholar
  131. 131.
    Bergmann FT, Nickerson D, Waltemath D, Scharm M (2017) SED-ML web tools: generate, modify and export standard-compliant simulation studies. Bioinformatics 33(8):1253–1254.  https://doi.org/10.1093/bioinformatics/btw812CrossRefPubMedPubMedCentralGoogle Scholar
  132. 132.
    Pattinson D (2012) Launches Reproducibility initiative EveryOne PLoS One. PLOS one community blogGoogle Scholar
  133. 133.
    Scharm M, Waltemath D (2015) Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit. Proceedings of the workshop on data management in life sciencesGoogle Scholar
  134. 134.
    Cooper J, Scharm M, Mirams GR (2016) The cardiac electrophysiology Web lab. Biophys J 110(2):292–300.  https://doi.org/10.1016/j.bpj.2015.12.012CrossRefPubMedPubMedCentralGoogle Scholar
  135. 135.
    Cooper J, Vik JO, Waltemath D (2015) A call for virtual experiments: accelerating the scientific process. Prog Biophys Mol Biol 117(1):99–106.  https://doi.org/10.1016/j.pbiomolbio.2014.10.001CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Natalie J. Stanford
    • 1
  • Martin Scharm
    • 2
  • Paul D. Dobson
    • 1
  • Martin Golebiewski
    • 3
  • Michael Hucka
    • 4
  • Varun B. Kothamachu
    • 5
  • David Nickerson
    • 6
  • Stuart Owen
    • 1
  • Jürgen Pahle
    • 7
    Email author
  • Ulrike Wittig
    • 3
  • Dagmar Waltemath
    • 10
  • Carole Goble
    • 1
  • Pedro Mendes
    • 8
  • Jacky Snoep
    • 1
    • 9
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK
  2. 2.Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
  3. 3.Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
  4. 4.Computing and Mathematical SciencesCalifornia Institute of TechnologyPasadenaUSA
  5. 5.Signalling ISPThe Babraham InstituteCambridgeUK
  6. 6.Auckland Bioengineering InstituteUniversity of AucklandAucklandNew Zealand
  7. 7.BIOMS/BioQuantHeidelberg UniversityHeidelbergGermany
  8. 8.Centre for Quantitative MedicineUniversity of ConnecticutFarmingtonUSA
  9. 9.BiochemistryStellenbosch UniversityStellenboschSouth Africa
  10. 10.Medical InformaticsUniversity Medicine GreifswaldGreifswaldGermany

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