Quantitative Biology

, Volume 6, Issue 1, pp 30–39 | Cite as

Metabolic pathway databases and model repositories

  • Abraham A. Labena
  • Yi-Zhou Gao
  • Chuan Dong
  • Hong-li Hua
  • Feng-Biao Guo
Review

Abstract

Background

The number of biological Knowledge bases/databases storing metabolic pathway information and models has been growing rapidly. These resources are diverse in the type of information/data, the analytical tools, and objectives. Here we present a review of the most popular metabolic pathway databases and model repositories, focusing on their scope, content including reactions, enzymes, compounds, and genes, and applicability. The review aims to help researchers choose a suitable database or model repository according to the information and data required, by providing an insight look of each pathway resource.

Results

Four pathways databases and three model repositories were selected on the basis of popularity and diversity. Our review showed that the pathway resources vary in many aspects, such as their scope, content, access to data and the tools. In addition, inconsistencies have been observed in nomenclature and representation of database entities. The three model repositories reviewed do not offer a brief description of the models’ characteristics such as simulation conditions.

Conclusions

The inconsistencies among the databases in representing their contents may hamper the maximal use of the knowledge accumulated in these databases in particular and the area of systems biology at large. Therefore, it is strongly recommended that the database creators and the metabolic network models developers should follow international standards for the nomenclature of reactions and metabolites. Besides, computationally generated models that could be obtained from model repositories should be utilized with manual curations as they lack some important components that are necessary for full functionality of the models.

Keywords

metabolic pathway database model repository 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 31470068), Sichuan Youth Science and Technology Foundation of China (No. 2014JQ0051) and the Fundamental Research Funds for the Central Universities of China (Nos. ZYGX2015Z006 and ZYGX2015J144). The funders had no role in study design, data collection, analysis, decision to publish, and preparation of the manuscript.

References

  1. 1.
    Berg, J. M., Tymoczko, J. L. and Stryer, L. (2002) Metabolism Is Composed of Many Coupled, Interconnecting Reactions. In Biochemistry. 5th ed. New York: Lippincott Williams & WilkinsGoogle Scholar
  2. 2.
    Wren, J. D. and Bateman, A. (2008) Databases, data tombs and dust in the wind. Bioinformatics, 24, 2127–2128CrossRefPubMedGoogle Scholar
  3. 3.
    Likić, V. A. (2006) Databases of metabolic pathways. Biochem. Mol. Biol. Educ., 34, 408–412CrossRefPubMedGoogle Scholar
  4. 4.
    Stobbe, M. D., Houten, S. M., Jansen, G. A., van Kampen, A. H. and Moerland, P. D. (2011) Critical assessment of human metabolic pathway databases: a stepping stone for future integration. BMC Syst. Biol., 5, 165CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Stobbe, M. D. (2015) Metabolic Pathway Databases: A Word of Caution. In Computational Systems Toxicology. Hoeng, J., Peitsch, M.C, eds. pp. 27–63. New York: SpringerCrossRefGoogle Scholar
  6. 6.
    Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M. and Palsson, B. Ø. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. Mol. Syst. Biol., 7, 535CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Thiele, I., Vo, T. D., Price, N. D. and Palsson, B. O. (2005) Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of singleand double-deletion mutants. J. Bacteriol., 187, 5818–5830CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Henry, C. S., Zinner, J. F., Cohoon, M. P. and Stevens, R. L. (2009) iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations. Genome Biol., 10, R69CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Radrich, K., Tsuruoka, Y., Dobson, P., Gevorgyan, A., Swainston, N., Baart, G. and Schwartz, J.-M. (2010) Integration of metabolic databases for the reconstruction of genome-scale metabolic networks. BMC Syst. Biol., 4, 114CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Zhang, C. and Hua, Q. (2015) Applications of genome—scale metabolic models in biotechnology and systems medicine. Front. Physiol., 6, 413PubMedGoogle Scholar
  11. 11.
    Liu, L., Agren, R., Bordel, S. and Nielsen, J. (2010) Use of genome-scale metabolic models for understanding microbial physiology. FEBS Lett., 584, 2556–2564CrossRefPubMedGoogle Scholar
  12. 12.
    Ooi, H. S., Schneider, G., Lim, T. T., Chan, Y. L., Eisenhaber, B. and Eisenhaber, F. (2010) Biomolecular pathway databases. Methods Mol. Biol., 609, 129–144CrossRefPubMedGoogle Scholar
  13. 13.
    Wittig, U. and De Beuckelaer, A. (2001) Analysis and comparison of metabolic pathway databases. Brief. Bioinform., 2, 126–142CrossRefPubMedGoogle Scholar
  14. 14.
    Croft, D., Mundo, A. F., Haw, R., Milacic, M., Weiser, J., Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M. R., et al. (2014) The Reactome pathway knowledgebase. Nucleic Acids Res., 42, D472–D477CrossRefPubMedGoogle Scholar
  15. 15.
    Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 44, D471–D480CrossRefPubMedGoogle Scholar
  16. 16.
    Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. and Tanabe, M. (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res., 44, D457–D462CrossRefPubMedGoogle Scholar
  17. 17.
    Dreher, K. (2014) Putting the Plant Metabolic Network Pathway Databases to Work: Going Offline to Gain New Capabilities. In Plant Metabolism: Methods and Protocols. Sriram G. Totowa, ed., pp. 151–171. NJ: Humana PressCrossRefGoogle Scholar
  18. 18.
    King, Z. A., Lu, J., Dräger, A., Miller, P., Federowicz, S., Lerman, J. A., Ebrahim, A., Palsson, B. O. and Lewis, N. E. (2016) BiGG Models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res., 44, D515–D522CrossRefPubMedGoogle Scholar
  19. 19.
    Chelliah, V., Laibe, C. and Le Novère, N. (2013) BioModels Database: a repository of mathematical models of biological processes. Methods Mol. Biol., 1021, 189–199CrossRefPubMedGoogle Scholar
  20. 20.
    Ganter, M., Bernard, T., Moretti, S., Stelling, J. and Pagni, M. (2013) MetaNetX.org: a website and repository for accessing, analysing and manipulating metabolic networks. Bioinformatics, 29, 815–816CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Croft, D., O’Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., et al. (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res., 39, D691–D697CrossRefPubMedGoogle Scholar
  22. 22.
    Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) The Reactome pathway Knowledgebase. Nucleic Acids Res., 44, D481–D487CrossRefPubMedGoogle Scholar
  23. 23.
    Naithani, S., Preece, J., D’Eustachio, P., Gupta, P., Amarasinghe, V., Dharmawardhana, P. D., Wu, G., Fabregat, A., Elser, J. L., Weiser, J., et al. (2017) Plant Reactome: a resource for plant pathways and comparative analysis. Nucleic Acids Res., 45, D1029–D1039CrossRefPubMedGoogle Scholar
  24. 24.
    Caspi, R., Altman, T., Dreher, K., Fulcher, C. A., Subhraveti, P., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/ genome databases. Nucleic Acids Res., 40, D742–D753CrossRefPubMedGoogle Scholar
  25. 25.
    Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C. A., Holland, T. A., Keseler, I. M., Kothari, A., Kubo, A., et al. (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res., 42, D459–D471CrossRefPubMedGoogle Scholar
  26. 26.
    Caspi, R., Altman, T., Dale, J. M., Dreher, K., Fulcher, C. A., Gilham, F., Kaipa, P., Karthikeyan, A. S., Kothari, A., Krummenacker, M., et al. (2010) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 38, D473–D479CrossRefPubMedGoogle Scholar
  27. 27.
    Christie, K. R., Weng, S., Balakrishnan, R., Costanzo, M. C., Dolinski, K., Dwight, S. S., Engel, S. R., Feierbach, B., Fisk, D. G., Hirschman, J. E., et al. (2004) Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res., 32, D311–D314CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Mueller, L. A., Zhang, P. and Rhee, S. Y. (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol., 132, 453–460CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Liang, C., Jaiswal, P., Hebbard, C., Avraham, S., Buckler, E. S., Casstevens, T., Hurwitz, B., McCouch, S., Ni, J., Pujar, A., et al. (2008) Gramene: a growing plant comparative genomics resource. Nucleic Acids Res., 36, D947–D953CrossRefPubMedGoogle Scholar
  30. 30.
    Evsikov, A. V., Dolan, M. E., Genrich, M. P., Patek, E. and Bult, C. J. (2009) MouseCyc: a curated biochemical pathways database for the laboratory mouse. Genome Biol., 10, R84CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Seo, S. and Lewin, H. A. (2009) Reconstruction of metabolic pathways for the cattle genome. BMC Syst. Biol., 3, 33CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Urbanczyk-Wochniak, E. and Sumner, L. W. (2007) MedicCyc: a biochemical pathway database for Medicago truncatula. Bioinformatics, 23, 1418–1423CrossRefPubMedGoogle Scholar
  33. 33.
    Zhang, P., Dreher, K., Karthikeyan, A., Chi, A., Pujar, A., Caspi, R., Karp, P., Kirkup, V., Latendresse, M., Lee, C., et al. (2010) Creation of a genome-wide metabolic pathway database for Populus trichocarpa using a new approach for reconstruction and curation of metabolic pathways for plants. Plant Physiol., 153, 1479–1491CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Fey, P., Gaudet, P., Curk, T., Zupan, B., Just, E. M., Basu, S., Merchant, S. N., Bushmanova, Y. A., Shaulsky, G., Kibbe, W. A., et al. (2009) dictyBase—a Dictyostelium bioinformatics resource update. Nucleic Acids Res., 37, D515–D519CrossRefPubMedGoogle Scholar
  35. 35.
    Doyle, M. A., MacRae, J. I., De Souza, D. P., Saunders, E. C., McConville, M. J. and Likić, V. A. (2009) LeishCyc: a biochemical pathways database for Leishmania major. BMC Syst. Biol., 3, 57CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    May, P., Christian, J. O., Kempa, S. and Walther, D. (2009) ChlamyCyc: an integrative systems biology database and webportal for Chlamydomonas reinhardtii. BMC Genomics, 10, 209CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Bombarely, A., Menda, N., Tecle, I. Y., Buels, R. M., Strickler, S., Fischer-York, T., Pujar, A., Leto, J., Gosselin, J. and Mueller, L. A. (2011) The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl. Nucleic Acids Res., 39, D1149–D1155CrossRefPubMedGoogle Scholar
  38. 38.
    Snyder, E. E., Kampanya, N., Lu, J., Nordberg, E. K., Karur, H. R., Shukla, M., Soneja, J., Tian, Y., Xue, T., Yoo, H., et al. (2007) PATRIC: the VBI PathoSystems Resource Integration Center. Nucleic Acids Res., 35, D401–D406CrossRefPubMedGoogle Scholar
  39. 39.
    Vincent, J., Dai, Z., Ravel, C., Choulet, F., Mouzeyar, S., Bouzidi, M F., Agier, M., Martre, P. (2013) dbWFA: a web-based database for functional annotation of Triticum aestivum transcripts. Database (Oxford). 2013, bat014.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Obertello, M., Shrivastava, S., Katari, M. S. and Coruzzi, G. M. (2015) Cross-species network analysis uncovers conserved nitrogen-regulated network modules in rice. Plant Physiol., 168, 1830–1843CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Shiratake, K. and Suzuki, M. (2016) Omics studies of citrus, grape and rosaceae fruit trees. Breed. Sci., 66, 122–138CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Cho, K., Cho, K. S., Sohn, H. B., Ha, I. J., Hong, S. Y., Lee, H., Kim, Y. M. and Nam, M. H. (2016) Network analysis of the metabolome and transcriptome reveals novel regulation of potato pigmentation. J. Exp. Bot., 67, 1519–1533CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Chae, L., Kim, T., Nilo-Poyanco, R. and Rhee, S. Y. (2014) Genomic signatures of specialized metabolism in plants. Science, 344, 510–513CrossRefPubMedGoogle Scholar
  44. 44.
    Tzfadia, O., Amar, D., Bradbury, L. M. T., Wurtzel, E. T. and Shamir, R. (2012) The MORPH algorithm: ranking candidate genes for membership in Arabidopsis and tomato pathways. Plant Cell, 24, 4389–4406CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M. and Tanabe, M. (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res., 42, D199–D205CrossRefPubMedGoogle Scholar
  46. 46.
    Schmeier, S., Alam, T., Essack, M. and Bajic, V. B. (2017) TcoFDB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions. Nucleic Acids Res., 45, D145–D150CrossRefPubMedGoogle Scholar
  47. 47.
    Li, P., Tompkins, R. G. and Xiao, W. (2017) KERIS: kaleidoscope of gene responses to inflammation between species. Nucleic Acids Res., 45, D908–D914.CrossRefPubMedGoogle Scholar
  48. 48.
    Wang, Y., Xu, L., Thilmony, R., You, F. M., Gu, Y. Q. and Coleman-Derr, D. (2016) PIECE 2.0: an update for the plant gene structure comparison and evolution database. Nucleic Acids Res., 45, 1015–1020CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Kotera, M., Hirakawa, M., Tokimatsu, T., Goto, S. and Kanehisa, M. (2012) The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals. Methods Mol. Biol., 802, 19–39CrossRefPubMedGoogle Scholar
  50. 50.
    Bianco, L., Riccadonna, S., Lavezzo, E., Falda, M., Formentin, E., Cavalieri, D., Toppo, S. and Fontana, P. (2016) Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms. Bioinformatics, btw636Google Scholar
  51. 51.
    Li, S., Shui, K., Zhang, Y., Lv, Y., Deng, W., Ullah, S., Zhang, L. and Xue, Y. (2016) CGDB: a database of circadian genes in eukaryotes. Nucleic Acids Res., 45, D397–D403PubMedPubMedCentralGoogle Scholar
  52. 52.
    Chen, I. A., Markowitz, V. M., Chu, K., Palaniappan, K., Szeto, E., Pillay, M., Ratner, A., Huang, J., Andersen, E., Huntemann, M., et al. (2017) IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Res., 45, D507–D516CrossRefPubMedGoogle Scholar
  53. 53.
    Saa, P. A. and Nielsen, L. K. (2016) Fast-SNP: a fast matrix preprocessing algorithm for efficient loopless flux optimization of metabolic models. Bioinformatics, 32, 3807–3814CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Rose, P. W., Prlic, A., Altunkaya, A., Bi, C., Bradley, A. R., Christie, C. H., Costanzo, L. D., Duarte, J. M., Dutta, S., Feng, Z., et al. (2017) The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res., 45, D271–D281.CrossRefPubMedGoogle Scholar
  55. 55.
    Büchel, F., Rodriguez, N., Swainston, N., Wrzodek, C., Czauderna, T., Keller, R., Mittag, F., Schubert, M., Glont, M., Golebiewski, M.,et al. (2013) Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC Syst. Biol., 7, 116CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Naldi, A., Monteiro, P. T., Müssel, C., the Consortium for Logical Models and Tools, Kestler, H. A., Thieffry, D., Xenarios, I., Saez-Rodriguez, J., Helikar, T. and Chaouiya, C. (2015) Cooperative development of logical modelling standards and tools with CoLoMoTo. Bioinformatics, 31, 1154–1159CrossRefPubMedGoogle Scholar
  57. 57.
    Le Novère, N. (2015) Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet., 16, 146–158CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Zhang, P., Tao, L., Zeng, X., Qin, C., Chen, S., Zhu, F., Li, Z., Jiang, Y., Chen, W. and Chen, Y. Z. (2016) A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks. Brief. Bioinform., bbw071Google Scholar
  59. 59.
    Moretti, S., Martin, O., Van Du Tran, T., Bridge, A., Morgat, A. and Pagni, M. (2016) MetaNetX/MNXref–reconciliation of metabolites and biochemical reactions to bring together genomescale metabolic networks. Nucleic Acids Res., 44, D523–D526CrossRefPubMedGoogle Scholar
  60. 60.
    Ye, Y. N., Ma, B. G., Dong, C., Zhang, H., Chen, L. L. and Guo, F. B. (2016) A novel proposal of a simplified bacterial gene set and the neo-construction of a general minimized metabolic network. Sci. Rep., 6, 35082CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Thompson, R. A., Dahal, S., Garcia, S., Nookaew, I. and Trinh, C. T. (2016) Exploring complex cellular phenotypes and modelguided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome. Biotechnol. Biofuels, 9, 194CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    van Heck, R. G., Ganter, M., Martins Dos Santos, V. A. and Stelling, J. (2016) Efficient reconstruction of predictive consensus metabolic network models. PLoS Comput. Biol., 12, e1005085CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  • Abraham A. Labena
    • 1
    • 2
  • Yi-Zhou Gao
    • 1
  • Chuan Dong
    • 3
  • Hong-li Hua
    • 3
  • Feng-Biao Guo
    • 3
  1. 1.School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.College of Computational and Natural SciencesDilla UniversityDillaEthiopia
  3. 3.School of Life Science and Technology, Center for Informational BiologyUniversity of Electronic Science and Technology of ChinaChengduChina

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