Metabolic pathway databases and model repositories

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.

Graphical abstract

Keywords

metabolic pathway / database / model repository

References

[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 & Wilkins

[2] Wren, J. D. and Bateman, A. (2008) Databases, data tombs and dust in the wind. Bioinformatics, 24, 2127–2128

[3] Likić V. A. (2006) Databases of metabolic pathways. Biochem. Mol. Biol. Educ., 34, 408–412

[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, 165

[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: Springer

[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, 535

[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 single- and double-deletion mutants. J. Bacteriol., 187, 5818–5830

[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, R69

[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, 114

[10] Zhang, C. and Hua, Q. (2015) Applications of genome-scale metabolic models in biotechnology and systems medicine. Front. Physiol., 6, 413

[11] Liu, L., Agren, R., Bordel, S. and Nielsen, J. (2010) Use of genome-scale metabolic models for understanding microbial physiology. FEBS Lett., 584, 2556–2564

[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, 129–144

[13] Wittig, U. and De Beuckelaer, A. (2001) Analysis and comparison of metabolic pathway databases. Brief. Bioinform., 2, 126–142

[14] Croft, D., Mundo, A. F., Haw, R., Milacic, M., Weiser, J., Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M. R., (2014) The Reactome pathway knowledgebase. Nucleic Acids Res., 42, D472–D477

[15] Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 44, D471–D480

[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–D462

[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 Press

[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–D522

[19] Chelliah, V., Laibe, C. and Le Novère, N. (2013) BioModels Database: a repository of mathematical models of biological processes. Methods Mol. Biol., 1021, 189–199

[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, 815–816

[21] Croft, D., O’Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res., 39, D691–D697

[22] Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., (2016) The Reactome pathway Knowledgebase. Nucleic Acids Res., 44, D481–D487

[23] Naithani, S., Preece, J., D’Eustachio, P., Gupta, P., Amarasinghe, V., Dharmawardhana, P. D., Wu, G., Fabregat, A., Elser, J. L., Weiser, J., (2017) Plant Reactome: a resource for plant pathways and comparative analysis. Nucleic Acids Res., 45, D1029–D1039

[24] Caspi, R., Altman, T., Dreher, K., Fulcher, C. A., Subhraveti, P., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 40, D742–D753

[25] Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C. A., Holland, T. A., Keseler, I. M., Kothari, A., Kubo, A., (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res., 42, D459–D471

[26] Caspi, R., Altman, T., Dale, J. M., Dreher, K., Fulcher, C. A., Gilham, F., Kaipa, P., Karthikeyan, A. S., Kothari, A., Krummenacker, M., (2010) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 38, D473–D479

[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., (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–D314

[28] Mueller, L. A., Zhang, P. and Rhee, S. Y. (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol., 132, 453–460

[29] Liang, C., Jaiswal, P., Hebbard, C., Avraham, S., Buckler, E. S., Casstevens, T., Hurwitz, B., McCouch, S., Ni, J., Pujar, A., (2008) Gramene: a growing plant comparative genomics resource. Nucleic Acids Res., 36, D947–D953

[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, R84

[31] Seo, S. and Lewin, H. A. (2009) Reconstruction of metabolic pathways for the cattle genome. BMC Syst. Biol., 3, 33

[32] Urbanczyk-Wochniak, E. and Sumner, L. W. (2007) MedicCyc: a biochemical pathway database for Medicago truncatula. Bioinformatics, 23, 1418–1423

[33] Zhang, P., Dreher, K., Karthikeyan, A., Chi, A., Pujar, A., Caspi, R., Karp, P., Kirkup, V., Latendresse, M., Lee, C., (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, 1479–1491

[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., (2009) dictyBase—a Dictyostelium bioinformatics resource update. Nucleic Acids Res., 37, D515–D519

[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, 57

[36] May, P., Christian, J. O., Kempa, S. and Walther, D. (2009) ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii. BMC Genomics, 10, 209

[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–D1155

[38] Snyder, E. E., Kampanya, N., Lu, J., Nordberg, E. K., Karur, H. R., Shukla, M., Soneja, J., Tian, Y., Xue, T., Yoo, H., (2007) PATRIC: the VBI PathoSystems Resource Integration Center. Nucleic Acids Res., 35, D401–D406

[39] Vincent, J., Dai, Z., Ravel, C., Choulet , F., Mouzeyar, S., Bouzidi, M. F., Agier, M. and Martre, P. (2013) dbWFA: a web-based database for functional annotation of Triticum aestivum transcripts. Database (Oxford). 2013, bat014

[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, 1830–1843

[41] Shiratake, K. and Suzuki, M. (2016) Omics studies of citrus, grape and rosaceae fruit trees. Breed. Sci., 66, 122–138

[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, 1519–1533

[43] Chae, L., Kim, T., Nilo-Poyanco, R. and Rhee, S. Y. (2014) Genomic signatures of specialized metabolism in plants. Science, 344, 510–513

[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, 4389–4406

[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–D205

[46] Schmeier, S., Alam, T., Essack, M. and Bajic, V. B. (2017) TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions. Nucleic Acids Res., 45, D145–D150

[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

[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, 1015–1020

[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, 19–39

[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, btw636

[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–D403

[52] Chen, I. A., Markowitz, V. M., Chu, K., Palaniappan, K., Szeto, E., Pillay, M., Ratner, A., Huang, J., Andersen, E., Huntemann, M., (2017) IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Res., 45, D507–D516

[53] Saa, P. A. and Nielsen, L. K. (2016) Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models. Bioinformatics, 32, 3807–3814

[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., (2017) The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res., 45, D271–D281

[55] Büchel, F., Rodriguez, N., Swainston, N., Wrzodek, C., Czauderna, T., Keller, R., Mittag, F., Schubert, M., Glont, M., Golebiewski, M., (2013) Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC Syst. Biol., 7, 116

[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, 1154–1159

[57] Le Novère, N. (2015) Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet., 16, 146–158

[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., bbw071

[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 genome-scale metabolic networks. Nucleic Acids Res., 44, D523–D526

[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, 35082

[61] Thompson, R. A., Dahal, S., Garcia, S., Nookaew, I. and Trinh, C. T. (2016) Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome. Biotechnol. Biofuels, 9, 194

[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, e1005085

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature