Weaving Knowledge into Biological Pathways in a Collaborative Manner

  • Yukiko Matsuoka
  • Kazuhiro Fujita
  • Samik Ghosh
  • Hiroaki Kitano
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

Toxicity pathway modeling is an effective approach to understanding how biological systems function under chemical perturbations. Many efforts have been made to construct pathways by data-driven or literature-based approaches to elucidate the mechanisms of action of toxicity. In this chapter, we explain how to build a literature-based pathway map in a collaborative manner using in silico platforms such as CellDesigner to draw pathways and networks, Payao as the curation platform, iPathways+ as the publishing platform, and Garuda to integrate curated pathways while adopting model-descriptive standards such as Systems Biology Markup Language as a file format and Systems Biology Graphical Notation as the graphical representation.

Key words

CellDesigner Collaboration Garuda platform Pathway curation SBGN (systems biology graphical notation) SBML (systems biology markup language) 

References

  1. 1.
    Krewski D, Acosta D Jr, Andersen M et al (2010) Toxicity testing in the 21st century: a vision and a strategy. J Toxicol Environ Health B Crit Rev 13:51–138. doi:10.1080/10937404.2010.483176 PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    Kleensang A, Maertens A, Rosenberg M et al (2014) t4 workshop report: Pathways of toxicity. ALTEX 31:53–61. doi:10.14573/altex.1309261 PubMedCentralCrossRefPubMedGoogle Scholar
  3. 3.
    Howe D, Costanzo M, Fey P et al (2008) Big data: the future of biocuration. Nature 455:47–50. doi:10.1038/455047a PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    sbv IMPROVER project team, Ansari S, Binder J et al (2013) On crowd-verification of biological networks. Bioinform Biol Insights 7:307–325. doi:10.4137/BBI.S12932 Google Scholar
  5. 5.
    Le Novère N, Hucka M, Mi H et al (2009) The systems biology graphical notation. Nat Biotechnol 27:735–741. doi:10.1038/nbt1558 CrossRefPubMedGoogle Scholar
  6. 6.
    Mi H, Lazareva-Ulitsky B, Loo R et al (2005) The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res 33:D284–D288. doi:10.1093/nar/gki078 PubMedCentralCrossRefPubMedGoogle Scholar
  7. 7.
    Mi H, Guo N, Kejariwal A et al (2007) PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic Acids Res 35:D247–D252. doi:10.1093/nar/gkl869 PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Croft D, Mundo AF, Haw R et al (2014) The Reactome pathway knowledgebase. Nucleic Acids Res 42:D472–D477. doi:10.1093/nar/gkt1102 PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Hucka M, Finney A, Sauro HM et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531. doi:10.1093/bioinformatics/btg015 CrossRefPubMedGoogle Scholar
  10. 10.
    Lloyd CM, Halstead MD, Nielsen PF (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85:433–450. doi:10.1016/j.pbiomolbio.2004.01.004 CrossRefPubMedGoogle Scholar
  11. 11.
    Demir E, Cary MP, Paley S et al (2010) The BioPAX community standard for pathway data sharing. Nat Biotechnol 28:935–942. doi:10.1038/nbt.1666 PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Stromback L, Lambrix P (2005) Representations of molecular pathways: an evaluation of SBML, PSI MI and BioPAX. Bioinformatics 21:4401–4407. doi:10.1093/bioinformatics/bti718 CrossRefPubMedGoogle Scholar
  13. 13.
    Slater T (2014) Recent advances in modeling languages for pathway maps and computable biological networks. Drug Discov Today 19:193–198. doi:10.1016/j.drudis.2013.12.011 CrossRefPubMedGoogle Scholar
  14. 14.
    Matsuoka Y, Matsumae H, Katoh M et al (2013) A comprehensive map of the influenza A virus replication cycle. BMC Syst Biol 7:97. doi:10.1186/1752-0509-7-97 PubMedCentralCrossRefPubMedGoogle Scholar
  15. 15.
    Kaizu K, Ghosh S, Matsuoka Y et al (2010) A comprehensive molecular interaction map of the budding yeast cell cycle. Mol Syst Biol 6:415. doi:10.1038/msb.2010.73 PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Caron E, Ghosh S, Matsuoka Y et al (2010) A comprehensive map of the mTOR signaling network. Mol Syst Biol 6:453. doi:10.1038/msb.2010.108 PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Oda K, Kitano H (2006) A comprehensive map of the toll-like receptor signaling network. Mol Syst Biol 2. doi:10.1038/msb4100057
  18. 18.
    Oda K, Matsuoka Y, Funahashi A et al (2005) A comprehensive pathway map of epidermal growth factor receptor signaling. Mol Syst Biol 1:2005.0010. doi:10.1038/msb4100014 PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    Kitano H, Oda K, Kimura T et al (2004) Metabolic syndrome and robustness tradeoffs. Diabetes 53(Suppl 3):S6–S15CrossRefPubMedGoogle Scholar
  20. 20.
    Calzone L, Gelay A, Zinovyev A et al (2008) A comprehensive modular map of molecular interactions in RB/E2F pathway. Mol Syst Biol 4:173. doi:10.1038/msb.2008.7 PubMedCentralCrossRefPubMedGoogle Scholar
  21. 21.
    Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30PubMedCentralCrossRefPubMedGoogle Scholar
  22. 22.
    Mi H, Thomas P (2009) PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol Biol 563:123–140. doi:10.1007/978-1-60761-175-2_7 CrossRefPubMedGoogle Scholar
  23. 23.
    Joshi-Tope G, Gillespie M, Vastrik I et al (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:D428–D432. doi:10.1093/nar/gki072 PubMedCentralCrossRefPubMedGoogle Scholar
  24. 24.
    Thiele I, Swainston N, Fleming RM et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425. doi:10.1038/nbt.2488 CrossRefPubMedGoogle Scholar
  25. 25.
    Pico AR, Kelder T, van Iersel MP et al (2008) WikiPathways: pathway editing for the people. PLoS Biol 6, e184. doi:10.1371/journal.pbio.0060184 PubMedCentralCrossRefPubMedGoogle Scholar
  26. 26.
    Bader GD, Cary MP, Sander C (2006) Pathguide: a pathway resource list. Nucleic Acids Res 34:D504–D506. doi:10.1093/nar/gkj126 PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Bockmann B, Heiden K (2013) PathGuide—model-based generation of guideline-compliant pathways for the use in different hospital information systems. Stud Health Technol Inform 192:1089PubMedGoogle Scholar
  28. 28.
    Viswanathan GA, Nudelman G, Patil S et al (2007) BioPP: a tool for web-publication of biological networks. BMC Bioinformatics 8:168. doi:10.1186/1471-2105-8-168 PubMedCentralCrossRefPubMedGoogle Scholar
  29. 29.
    Matsuoka Y, Ghosh S, Kikuchi N et al (2010) Payao: a community platform for SBML pathway model curation. Bioinformatics (Oxford, England) 26:1381–1383. doi:10.1093/bioinformatics/btq143 CrossRefGoogle Scholar
  30. 30.
    Kuperstein I, Cohen DP, Pook S et al (2013) NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps. BMC Syst Biol 7:100. doi:10.1186/1752-0509-7-100 PubMedCentralCrossRefPubMedGoogle Scholar
  31. 31.
    Bauer-Mehren A, Furlong LI, Sanz F (2009) Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol Syst Biol 5:290. doi:10.1038/msb.2009.47 PubMedCentralCrossRefPubMedGoogle Scholar
  32. 32.
    Mizuno S, Iijima R, Ogishima S et al (2012) AlzPathway: a comprehensive map of signaling pathways of Alzheimer’s disease. BMC Syst Biol 6:52. doi:10.1186/1752-0509-6-52 PubMedCentralCrossRefPubMedGoogle Scholar
  33. 33.
    Fujita KA, Ostaszewski M, Matsuoka Y et al (2014) Integrating pathways of Parkinson’s disease in a molecular interaction map. Mol Neurobiol 49:88–102. doi:10.1007/s12035-013-8489-4 PubMedCentralCrossRefPubMedGoogle Scholar
  34. 34.
    Kitano H, Ghosh S, Matsuoka Y (2011) Social engineering for virtual ‘big science’ in systems biology. Nat Chem Biol 7:323–326. doi:10.1038/nchembio.574 CrossRefPubMedGoogle Scholar
  35. 35.
    Funahashi A, Matsuoka Y, Jouraku A et al (2008) CellDesigner 3.5: a versatile modeling tool for biochemical networks. Proceedings of the IEEE 96, pp 1254–1265Google Scholar
  36. 36.
    Apweiler R, Bairoch A, Wu CH et al (2004) UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 32:D115–D119. doi:10.1093/nar/gkh131 PubMedCentralCrossRefPubMedGoogle Scholar
  37. 37.
    Maglott D, Ostell J, Pruitt KD et al (2005) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 33:D54–D58. doi:10.1093/nar/gki031 PubMedCentralCrossRefPubMedGoogle Scholar
  38. 38.
    Kitano H (2003) A graphical notation for biochemical networks. BIOSILICO 1:169–176. doi:http://dx.doi.org/10.1016/S1478-5382(03)02380-1
  39. 39.
    Kitano H, Funahashi A, Matsuoka Y et al (2005) Using process diagrams for the graphical representation of biological networks. Nat Biotechnol 23:961–966. doi:10.1038/nbt1111 CrossRefPubMedGoogle Scholar
  40. 40.
    van Iersel MP, Villeger AC, Czauderna T et al (2012) Software support for SBGN maps: SBGN-ML and LibSBGN. Bioinformatics 28:2016–2021. doi:10.1093/bioinformatics/bts270 PubMedCentralCrossRefPubMedGoogle Scholar
  41. 41.
    Machne R, Finney A, Muller S et al (2006) The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks. Bioinformatics 22:1406–1407. doi:10.1093/bioinformatics/btl086 CrossRefPubMedGoogle Scholar
  42. 42.
    Hoops S, Sahle S, Gauges R et al (2006) COPASI—a COmplex PAthway SImulator. Bioinformatics 22:3067–3074. doi:10.1093/bioinformatics/btl485 CrossRefPubMedGoogle Scholar
  43. 43.
    Keller R, Dorr A, Tabira A et al (2013) The systems biology simulation core algorithm. BMC Syst Biol 7:55. doi:10.1186/1752-0509-7-55 PubMedCentralCrossRefPubMedGoogle Scholar
  44. 44.
    Le Novere N, Bornstein B, Broicher A et al (2006) BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 34:D689–D691. doi:10.1093/nar/gkj092 PubMedCentralCrossRefPubMedGoogle Scholar
  45. 45.
    Olivier BG, Snoep JL (2004) Web-based kinetic modelling using JWS Online. Bioinformatics 20:2143–2144. doi:10.1093/bioinformatics/bth200 CrossRefPubMedGoogle Scholar
  46. 46.
    Fujibuchi W, Goto S, Migimatsu H et al (1998) DBGET/LinkDB: an integrated database retrieval system. Pac Symp Biocomput 683–694. http://www.ncbi.nlm.nih.gov/pubmed/9697222
  47. 47.
    Cherry JM, Adler C, Ball C et al (1998) SGD: Saccharomyces Genome Database. Nucleic Acids Res 26:73–79PubMedCentralCrossRefPubMedGoogle Scholar
  48. 48.
    Fernandez JM, Hoffmann R, Valencia A (2007) iHOP web services. Nucleic Acids Res 35:W21–W26. doi:10.1093/nar/gkm298 PubMedCentralCrossRefPubMedGoogle Scholar
  49. 49.
    Huss JW III, Lindenbaum P, Martone M et al (2010) The Gene Wiki: community intelligence applied to human gene annotation. Nucleic Acids Res 38:D633–D639. doi:10.1093/nar/gkp760 PubMedCentralCrossRefPubMedGoogle Scholar
  50. 50.
    Degtyarenko K, de Matos P, Ennis M et al (2008) ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 36:D344–D350. doi:10.1093/nar/gkm791 PubMedCentralCrossRefPubMedGoogle Scholar
  51. 51.
    Caspi R, Altman T, Dreher K et al (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 40:D742–D753. doi:10.1093/nar/gkr1014 PubMedCentralCrossRefPubMedGoogle Scholar
  52. 52.
    Mi H, Muruganujan A, Demir E et al (2011) BioPAX support in Cell Designer. Bioinformatics 27:3437–3438. doi:10.1093/bioinformatics/btr586 PubMedCentralCrossRefPubMedGoogle Scholar
  53. 53.
    Ghosh S, Matsuoka Y, Asai Y et al (2013) Toward an integrated software platform for systems pharmacology. Biopharm Drug Dispos 34:508–526. doi:10.1002/bdd.1875 PubMedCentralCrossRefPubMedGoogle Scholar
  54. 54.
    Matsuoka Y, Funahashi A, Ghosh S et al (2014) Modeling and simulation using Cell Designer. Methods Mol Biol 1164:121–145. doi:10.1007/978-1-4939-0805-9_11 CrossRefPubMedGoogle Scholar
  55. 55.
    Hirschman L, Burns GA, Krallinger M et al (2012) Text mining for the biocuration workflow. Database 2012:bas020. doi:10.1093/database/bas020 PubMedCentralCrossRefPubMedGoogle Scholar
  56. 56.
    Le Novère N, Finney A, Hucka M et al (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23:1509–1515. doi:10.1038/nbt1156 CrossRefPubMedGoogle Scholar
  57. 57.
    Oda K, Kim JD, Ohta T et al (2008) New challenges for text mining: mapping between text and manually curated pathways. BMC Bioinformatics 9(Suppl 3):S5. doi:10.1186/1471-2105-9-S3-S5 PubMedCentralCrossRefPubMedGoogle Scholar
  58. 58.
    Ghosh S, Matsuoka Y, Asai Y et al (2013) Software platform for metabolic network reconstruction of Mycobacterium tuberculosis. In: Beste DJV, Kierzek AM, McFadden J (eds) Systems biology of tuberculosis. Springer, New York, pp 21–35. doi:10.1007/978-1-4614-4966-9_2 CrossRefGoogle Scholar
  59. 59.
    Jasny B (2013) Realities of data sharing using the genome wars as case study—an historical perspective and commentary. EPJ Data Sci 2:1CrossRefGoogle Scholar
  60. 60.
    Meyer P, Alexopoulos LG, Bonk T et al (2011) Verification of systems biology research in the age of collaborative competition. Nat Biotechnol 29:811–815. doi:10.1038/nbt.1968 CrossRefPubMedGoogle Scholar
  61. 61.
    Friend SH, Norman TC (2013) Metcalfe’s law and the biology information commons. Nat Biotechnol 31:297–303. doi:10.1038/nbt.2555 CrossRefPubMedGoogle Scholar
  62. 62.
    Zengler K, Palsson BO (2012) A road map for the development of community systems (CoSy) biology. Nat Rev Microbiol 10:366–372. doi:10.1038/nrmicro2763 PubMedGoogle Scholar
  63. 63.
    Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science 337:337–341. doi:10.1126/science.1215842 CrossRefPubMedGoogle Scholar
  64. 64.
    Bohon W, Robinson S, Arrowsmith R et al (2013) Building an effective social media strategy for science programs. Eos 94:237–238. doi:10.1002/2013EO270001 CrossRefGoogle Scholar
  65. 65.
    Lakhani KR, Boudreau KJ, Loh PR et al (2013) Prize-based contests can provide solutions to computational biology problems. Nat Biotechnol 31:108–111. doi:10.1038/nbt.2495 PubMedCentralCrossRefPubMedGoogle Scholar
  66. 66.
    Sheridan C (2011) Industry continues dabbling with open innovation models. Nat Biotechnol 29:1063–1065. doi:10.1038/nbt1211-1063a CrossRefPubMedGoogle Scholar
  67. 67.
    Open to interpretation (2013). Nat Biotechnol 31: 661. doi:10.1038/nbt.2665

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Yukiko Matsuoka
    • 1
  • Kazuhiro Fujita
    • 1
  • Samik Ghosh
    • 1
  • Hiroaki Kitano
    • 1
  1. 1.The Systems Biology InstituteTokyoJapan

Personalised recommendations