Skip to main content
SpringerLink
Log in

Database and tools for metabolic network analysis

  • Review Paper
  • Published:
Biotechnology and Bioprocess Engineering Aims and scope Submit manuscript

Abstract

Metabolic network analysis has attracted much attention in the area of systems biology. It has a profound role in understanding the key features of organism metabolic networks and has been successfully applied in several fields of systems biology, including in silico gene knockouts, production yield improvement using engineered microbial strains, drug target identification, and phenotype prediction. A variety of metabolic network databases and tools have been developed in order to assist research in these fields. Databases that comprise biochemical data are normally integrated with the use of metabolic network analysis tools in order to give a more comprehensive result. This paper reviews and compares eight databases as well as twenty one recent tools. The aim of this review is to study the different types of tools in terms of the features and usability, as well as the databases in terms of the scope and data provided. These tools can be categorised into three main types: standalone tools; toolbox-based tools; and web-based tools. Furthermore, comparisons of the databases as well as the tools are also provided to help software developers and users gain a clearer insight and a better understanding of metabolic network analysis. Additionally, this review also helps to provide useful information that can be used as guidance in choosing tools and databases for a particular research interest.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wagner, A. (2012) Metabolic networks and their evolution. Adv. Exp. Med. Biol. DOI 10.1007/978-1-4614-3567-92.

    Google Scholar 

  2. Chen, X. W., A. P. Alonso, D. K. Allen, J. L. Reed, and Y. Shachar-Hill (2010) Synergy between 13C-metabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in E. coli. Metab. Eng. 13: 38–48.

    Article  Google Scholar 

  3. Burgard, A. P., P. Pharkya, and C. D. Maranas (2003) OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84: 647–657.

    Article  CAS  Google Scholar 

  4. Koffas, M. A., G. Y. Jung, and G. Stephanopoulos (2003) Engineering metabolism and product formation in corynebacterium glutamicum by coordinated gene overexpression. Metab. Eng. 5: 32–41.

    Article  CAS  Google Scholar 

  5. Alpher, H., Y. S. Jin, J. F. Moxley, and G. Strephanopoulos (2005) Identifying gene target for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 7: 155–164.

    Article  Google Scholar 

  6. Feng, X. Y., Y. Xu, Y. X. Chen, and Y. J. J. Tang (2012) MicrobesFlux: A web platform for drafting metabolic models from the KEGG Database. BMC Syst. Biol. 6: 94.

    Article  Google Scholar 

  7. Karp, P. D. and R. Caspi (2011) A survey of metabolic databases emphasizing the MetaCyc family. Arch. Toxicol. 85: 1015–1033.

    Article  CAS  Google Scholar 

  8. Raman, K., P. Rajagopalan, and N. Chandra (2005) Flux balance analysis of mycolic acid pathway: Targets for anti-tubercular drugs. PLoS. Comput. Biol. 1: 349–358.

    Article  CAS  Google Scholar 

  9. Beste, D. J. V., T. Hooper, G. Stewart, B. Bonde, C. Avignone-Rossa, M. E. Bushell, P. Wheeler, S. Klamt, A. M. Kierzek, and J. McFadden (2007) GSMN-TB: A web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol. 8: 89.

    Article  Google Scholar 

  10. Jamshidi, N. and B. Palsson (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst. Biol. 1: 26.

    Article  Google Scholar 

  11. Larhlimi, A. and A. Bockmayr, (2007) Constraint-based Analysis of Gene Deletion in A Metabolic Network. Workshop on Constraint based Methods for Bioinformatics. 48–55.

    Google Scholar 

  12. Covert, M. W., J. L. Reed, E. M. Knight, M. J. Herrgard, and B. O. Palsson (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429: 92–96.

    Article  CAS  Google Scholar 

  13. Price, N. D., J. L. Reed, and B. Palson (2004) Genome-scale models of microbial cells: Evaluating the consequences of constraints. Nat. Rev. Microbiol. 2: 886–897.

    Article  CAS  Google Scholar 

  14. Ogata, H., S. Goto, K. Sato, W. Fujibuchi, H. Bono, and M. Kanehisa (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27: 29–34.

    Article  CAS  Google Scholar 

  15. Moutselos, K., I. Kanaris, A. Chatziioannou, I. Maglogiannis, and F. N. Kolisis (2009) KEGG converter: A tool for the In-silico modelling of metabolic networks of the KEGG pathways data-base. BMC Bioinformat. 10: 324.

    Article  Google Scholar 

  16. Stobbe, M. D., S. M. Houten, G. A. Janse, A. H. C. V. Kampen, and P. D. Moerland (2011) Critical assessment of human metabolic pathway databases: A stepping stone for future integration. BMC Syst. Biol.5: 165.

    Article  Google Scholar 

  17. Liu, H., J. Wang, C. Zhuang, N. Han, B. Wei, and S. Rayner (2010) Development of a pathway comparison tool for analysis of bacteria genomes. Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference. December 18–18. Hongkong.

    Google Scholar 

  18. Karp, P. D., C. A. Ouzounis, C. Moore-Kochlacs, L. Goldovsky, P. Kaipa, D. Ahren, S. Tsoka, N. Darzentas, V. Kunin, and N. Lopez-Bigas (2005) Expansion of the BioCyc collection of path-way/genome databases to 160 genomes. Nucleic Acids Res. 33: 6083–6089.

    Article  CAS  Google Scholar 

  19. Krummenacker, M., S. Paley, L. Mueller, T. Yan, and P. D. Karp (2005) Querying and computing with BioCyc databases. 21: 3454–3455.

    Google Scholar 

  20. Choi, C., R. Münch, B. Bunk, J. Barthelmes, C. Ebeling, D. Schomburg, M. Schobert, and D. Jahn (2007) Combination of a data warehouse concept with web services for the establishment of the Pseudomonas systems biology database SYSTOMONAS. J. Integrative Bioinformat.. 4: 48.

    Google Scholar 

  21. Karp, P. D., M. Riley, S. M. Paley, and A. Pellegrini-Toole (2002) The MetaCyc database. Nucleic Acids Res. 30: 59–61.

    Article  CAS  Google Scholar 

  22. Krieger, C. J., P. Zhang, L. A. Mueller, A. Wang, S. Paley, M. Arnaud, J. Pick, S. Y. Rhee, and P. D. Karp (2004) MetaCyc: A multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res. 32: 439–442.

    Article  Google Scholar 

  23. Zhang, P., H. Foerster, C. P. Tissier, L. Mueller, S. Paley, P. D. Karp, and S. Y. Rhee (2005) MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol.138: 27–37.

    CAS  Google Scholar 

  24. Caspi, R., H. Foerster, C. A. Fulcher, P. Kaipa, M. Krummenacker, M. Latendresse, S. Paley, S. Y. Rhee, A. G. Shearer, C. Tissier, T. C. Walk, P. Zhang, and P. D. Karp (2008) The metacyc database of metabolic pathways and enzymes and the biocyc collection of pathway or genome databases. Nucleic Acids Res. 36: 623–631.

    Article  Google Scholar 

  25. Bairoch, A. (2000) The ENZYME database in 2000. Nucleic Acids Res. 28: 304–305.

    Article  CAS  Google Scholar 

  26. Sheu, S., D. R. L. Jr, K. H. Clodfelter, M. R. Landon, and S. Vajda (2005) PRECISE: A database of predicted and consensus interaction sites in enzymes. Nucleic Acids Res. 33: 206–211.

    Article  Google Scholar 

  27. Andreini C., I. Bertini, G. Cavallaro, G. L. Holliday, and J. M. Thornton (2008) Metal ions in biological catalysis: From enzyme databases to general principles. J. Biol. Inorg. Chem. 13: 1205–1218.

    CAS  Google Scholar 

  28. Sharma, V. K., N. Kumar, T. Prakash, and T. D. Taylor (2010) MetaBioME: A database to explore commercially useful enzymes in metagenomic datasets. Nucleic Acids Res. 38: 468–472.

    Article  Google Scholar 

  29. Schomburg, D. and I. Schomburg (2001) Springer Handbook of Enzymes. 2 nd ed. Springer, Heidelberg, Germany.

    Google Scholar 

  30. Schomburg, I., A. Chang, O. Hofmann, C. Ebeling, F. Ehrentreich, and D. Schomburg (2002) BRENDA: A resource for enzyme data and metabolic information. Trends Biochem. Sci. 27: 54–56.

    Article  CAS  Google Scholar 

  31. Schomburg, I., A. Chang, C. Ebeling, M. Gremse, C. Heldt, G. Huhn, and D. Schomburg (2004) BRENDA: The Enzyme Data-base: Updates and major new developments. Nucleic Acids Res.. 32: 431–433.

    Article  Google Scholar 

  32. Barthelmes, J., C. Ebeling, A. Chang, I. S. Chomburg, and D. Schomburg (2006) New developments at the brenda enzyme information system. 211–225.

    Google Scholar 

  33. Schellenberger, J., J. O. Park, T. M. Conrad, and B. Palsson (2010) BiGGe: A biochemical genetic and genomic knowledge-base of large scale metabolic reconstructions. BMC Bioinformat. 11: 213–222.

    Article  Google Scholar 

  34. Muja, M., R. B. Rusuy, G. Bradskiy, and D. G. Lowe (2010) REIN — A Fast, Robust, Scalable REcognition Infrastructure. 1–8.

    Google Scholar 

  35. Haw, R. A., D. Croft, C. K. Yung, N. Ndegwa, P. D. Eustachio, H. Hermjakob, and L. D. Stein (2011) The Reactome BioMart. Database: The J. Biol. Databases and Curat. 2011: bar031.

    Article  Google Scholar 

  36. Crof, D., G. O’Kelly, G. Wu, R. Haw, M. Gillespie, S. L. Matthew, M. Caudy, P. Garapati, G. Gopinath, B. Jassal, S. Jupe, I. Kalatskaya, S. Mahajan, B. May, N. Ndegwa, E. Schmidt, V. Shamovsky, C. Yung, E. Birney, H. Hermjakob, P. D’Eustachio, and L. Stein (2011) Reactome: A database of reactions, pathways and biological processes. Nucleic Acids Res. 39: 691–697.

    Article  Google Scholar 

  37. Milacic, M., R. Haw, K. Rothfels, G. Wu, D. Croft, H. Hermjakob, P. D. Eustachio, and L. Stein (2012) Annotating cancer variants and anti-cancer therapeutics in reactome. Cancers 4: 1180–1211.

    Article  CAS  Google Scholar 

  38. Pao, W. and J. Chmielecki (2010) Rational, biologically based treatment of EGFR-mutant non-small-cell lung cancer. Nat. Rev. Cancer 10: 760–774.

    Article  CAS  Google Scholar 

  39. Sakurai, N., T. Ara, Y. Ogata, R. Sano, T. Ohno, K. Sugiyama, A. Hiruta, K. Yamazaki, K. Yano, K. Aoki, A. Aharoni, K. Hamada, K. Yokoyama, S. Kawamura, H. Otsuka, T. Tokimatsu, M. Kanehisa, H. Suzuki, K. Saito, and D. Shibata (2011) KaPPA-View4: A metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite coaccumulation and omics data. Nucleic Acids Res. 39: 677–684.

    Article  Google Scholar 

  40. Tokimatsu, T., N. Sakurai, H. Suzuki, H. Ohta, K. Nishitani, T. Koyama, T. Umezawa, N. Misawa, K. Saito, and D. Shibata (2005) KaPPA-View: A web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol. 138: 1289–1300.

    Article  CAS  Google Scholar 

  41. Devappa, R. K., H. P. Makkar, and K. Becker (2010) Nutritional, biochemical, and pharmaceutical potential of proteins and peptides from jatropha: Review. J. Agric. Food Chem. 58: 6543–6555.

    Article  CAS  Google Scholar 

  42. Abdulla, R., E. S. Chan, and P. Ravindra (2011) Biodiesel production from Jatrophacurcas: A critical review. Crit. Rev. Biotechnol. 31: 53–64.

    Article  CAS  Google Scholar 

  43. Thomas, R., N. K. Sah, and P. B. Sharma, (2008) Therapeutic biology of Jatrophacurcas: A mini review. Curr. Pharm. Biotechnol. 9: 315–324.

    Article  CAS  Google Scholar 

  44. Sakurai, N., Y. Ogata, T. Ara, R. Sano, N. Akimoto, A. Hiruta, H. Suzuki, M. Kajikawa, U. Widyastuti, S. Suharsono, A. Yokota, K. Akashi, J. Kikuchi, and D. Shibata (2012) Development of KaPPA-View4 for omics studies on Jatropha and a database system KaPPA-Loader for construction of local omics databases. Plant Biotechnol. 29: 131–135.

    Article  CAS  Google Scholar 

  45. Barrett, T., D. B. Troup, S. E. Wilhite, P. Ledoux, C. Evangelista, I. F. Kim, M. Tomashevsky, K. A. Marshall, K. H. Phillippy, P. M. Sherman, R. N. Muertter, M. Holko, O. Ayanbule, A. Yefanov, and A. Soboleva (2011) NCBI GEO: Archive for functional genomics data sets- 10 years on. Nucleic Acids Res. 39: 1005–1010.

    Article  Google Scholar 

  46. McAnulty, M. J., J. Y. Yen, B. G. Freedman, and R. S. Senger (2012) Genome-scale modelling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico. BMC Syst. Biol. doi: 10.1186/1752-0509-6-42.

    Google Scholar 

  47. Chung, B. K., M. Lakshmanan, M. Klement, B. Mohanty, and D. Y. Lee (2013) Genome-scale in silico modeling and analysis for designing synthetic terpenoid-producing microbial cell factories. Chem. Eng. Sci. 103: 100–108.

    Article  CAS  Google Scholar 

  48. Park, J. M., H. Song, H. J. Lee, and D. Seung (2013) Genome-scale reconstruction and in silico analysis of Klebsiella oxytoca for 2,3-butanediol production. Microbial. Cell Fact. 12: 1–11.

    Article  Google Scholar 

  49. Hanly, T. J. and M. A. Henson (2013) Dynamic metabolic modeling of a microaerobic yeast co-culture: Predicting and optimizing ethanol production from glucose/xylose mixtures. Bioetchnol. Biofuels 6: 1–16.

    Article  Google Scholar 

  50. Karp, P. D., S. M. Paley, M. Krummenacker, M. Latendresse, J. M. Dale, T. J. Lee, P. Kaipa, F. Gilham, A. Spaulding, and L. Popescu (2010) Pathway Tools version 13.0: Integrated software for pathway/genome informatics and systems biology. Brief Bioinform. 11: 40–79.

    Article  CAS  Google Scholar 

  51. Dale, J. M., L. Popescu, and P. D. Karp (2010) Machine learning methods for metabolic pathway prediction. BMC Bioinformat. 11: 15.

    Article  Google Scholar 

  52. Sheu, S., Jr, D. R. L., K. H., Clodfelter, M. R. Landon, and S. Vajda (2005) PRECISE: A database of predicted and consensus interaction sites in enzymes. Nucleic Acids Res. 33: 206–211.

    Article  Google Scholar 

  53. Andreini, C., I. Bertini, G. Cavallaro, G. L. Holliday, and J. M. Thornton (2008) Metal ions in biological catalysis: From enzyme databases to general principles. J. Biol. Inorg. Chem. 13: 1205- 1218.

    Article  CAS  Google Scholar 

  54. Sharma, V. K., N. Kumar, T. Prakash, and T. D. Taylor (2010) MetaBioME: A database to explore commercially useful enzymes in metagenomic datasets. Nucleic Acids Res. 38: 468–472.

    Article  Google Scholar 

  55. Ranganathan, S. and C. D. Maranas (2010) Microbial 1-butanol production: Identification of non-native production routes and in silico engineering interventions. Biotechnol. J. 5: 716–725.

    Article  CAS  Google Scholar 

  56. Duarte, N. C., S. A. Becker, N. Jasmshidi, I. Thiele, M. L. Mo, T. D. Vo, R. Srivas, and B. Palsson (2006) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Nat. Acad. Sci. 104: 1777–1782.

    Article  Google Scholar 

  57. Schellenberger, J., J. P. Park, T. M. Conrad, and B. Palsson (2010) BiGG: A biochemical genetic and genomic knowledge base of large scale metabolic reconstructions. BMC Bioinform. 11: 213.

    Article  Google Scholar 

  58. Lakshmanan, M., G. Koh, B. Chung, and D. Lee (2012) Software applications for flux balance analysis. Brief. Bioinforma. 15: 108–112.

    Article  Google Scholar 

  59. Lee, S. Y., D. Y. Lee, S. H. Hong, T. Y. Kim, H. Yun, Y. G. Oh, and S. Park (2003) MetaFluxNet: A program package for metabolic pathway construction and analysis, and it used in large-scale metabolic flux analysis of Escherichia Coli. Genome Inform. 14: 23–33.

    CAS  Google Scholar 

  60. Garvey, T. D., P. Lincoln, C. J. Pederson, D. Martin, and M. Johnson (2003) BioSPICE: Access to the most current computational tools for biologists. OMICS: A J. Integrat. Biol. 7: 411–420.

    Article  CAS  Google Scholar 

  61. Luo, R. Y., S. Liao, and S. Q. Zeng (2006) FluxExplorer: A general platform for modeling and analyses of metabolic networks based on stoichiometry. Chin. Sci. Bull. 51: 689–696.

    Article  CAS  Google Scholar 

  62. Wright, J. and A. Wagner (2008) The systems biology research tool: Evolvable open-source software. BMC Syst. Biol. 2: 55.

    Article  Google Scholar 

  63. Raman, K. and N. Chandran (2008) Pathway analyser: A systems biology tool for flux analysis of metabolic pathways. Nat. Prec. 2: 38.

    Google Scholar 

  64. Rocha, I., P. Maia, P. Evangelista, P. Vilaca, S. Soares, J. P. Pinto, J. Nielsen, K. R. Patil, E. C. Ferreira, and M. Rocha (2010) Opt-Flux: An open-source software platform for in silico metabolic engineering. BMC Syst. Biol. 4: 45.

    Article  Google Scholar 

  65. Cvijovic, M., R. Olivares-Hernandez, R. Agren, N. Dahr, W. Vongsangnak, I. Nookaew, K. R. Patil, and J. Nielsen (2010) BioMet toolbox: Genome-wide analysis of metabolism. Nucleic Acid Res. 38: 144–149.

    Article  Google Scholar 

  66. Gevorgyan, A., M. E. Bushell, C. Avignone-Rossa, and A. M. Kierzek (2011) SurreyFBA: A command line tool and graphical user interface for constraint-based modeling of genome-scale metabolic reaction network. Bioinform. 27: 433–434.

    Article  CAS  Google Scholar 

  67. Hoppe, A., S. Hoffmann, A. Gerasch, C. Gille, and H. G. Holzhutter (2011) FASIMU- Flexible software for flux-balnace computation series in large metabolic networks. BMC Bioinform. 12: 28.

    Article  Google Scholar 

  68. Segre, D., J. Zucker, J. Katz, X. X. Lin, P. D’haeseleer, W. P. Rindone, P. Kharchenko, D. H. Nguyen, M. A. Wright, and G. M. Church (2003) From annotated genomes to metabolic flux models and kinetics parameter fitting. OMICS 7: 301–316.

    Article  CAS  Google Scholar 

  69. Klamt, S., J. Saez-Rodriguez, and E. D. Gilles (2007) Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst. Biol. 1: 2.

    Article  Google Scholar 

  70. Klamt, S., J. Stelling, M. Ginkel, and E. D. Gilles (2003) Flux-Analyzer: Exploring structure, pathways and flux distributions in metabolic networks on interative flux maps. Bioinformat. 19: 261–269.

    Article  CAS  Google Scholar 

  71. Urbanczik, R. (2006) SNA- A toolbox for the stoichiometric analysis of metabolic networks. BMC Bioinformat. 7: 129.

    Article  Google Scholar 

  72. Quek, L. E., C. Wittmann, L. K. Nielsen, and J. O. Kromer (2009) OpenFLUX: Efficient modelling software for 13C-based metabolic flux analysis. Microbial. Cell Fact. 8: 25.

    Article  Google Scholar 

  73. Grafahrend-Belau, E., C. Klukas, B. H. Junker, and F. Schreiber (2009) FBA-SimVis: Interactive visualization of constraint-based metabolic models. Bioinformat. Syst. Biol. 25: 2755–2757.

    CAS  Google Scholar 

  74. Becker, S. A., A. M. Feist, L. M. Monica, G. Hannum, B. Palsson, and M. J. Herrgard (2007) Quantitative prediction of cellular metabolism with constraint-based models: The COBRA toolbox. Nat. Protocols 2: 727–738.

    Article  CAS  Google Scholar 

  75. Schellenberger, J., R. Que, R. M. T. Fleming, I. Thiele, J. D. Ortho, A. M. Feist, D. C. Zielinski, A. Bordbar, N. E. Lewis, S. Rahmanian, J. Kang, D. R. Hyduke, and B. Palsson (2011) Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox v2.0. Nat. Protocols 2: 727–738.

    Google Scholar 

  76. Fevre, F. L., S. Smidtas, C. Combe, M. Durot, F. d’Alche-Buc, and V. Schachter (2009) CycSim- An online tool for exploring and experimenting with genome-scale metabolic models. Bioinformat. 25: 1987–1988.

    Article  Google Scholar 

  77. Jung, T. S., H. C. Yeo, S. G. Reddy, W. S. Cho, and D. Y. Lee (2009) WEbcoli: An interactive and asynchronous web application for in silico design and analysis of genome-scale E. coli model. Bioinformat. 25: 2850–2852.

    Article  CAS  Google Scholar 

  78. Henry, C. S., M. Dejongh, A. A. Best, P. M. Frybarger, B. Linsay, and R. L. Stevens (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28: 977–982.

    Article  CAS  Google Scholar 

  79. Sroka, J., L. Bieniasz-Krzywiec, S. Gwozdz, D. Leniowski, J. Lacki, M. Markowski, C. Avignone-Rossa, M. E. Bushell, J. McFadden, and A. M. Kierzek (2011) Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface. BMC Bioinformat. 12: 196.

    Article  Google Scholar 

  80. Boele, J., B. G. Olivier, and B. Teusink (2012) FAME, the flux analysis and modeling environment. BMC Syst. Biol. 6: 8.

    Article  Google Scholar 

  81. Borodina, I., P. Kraben, and J. Nielsen (2005) Genome-scale analysis of streptomyces coelicolorA3(2) metabolism. Genome Res. 15: 820–829.

    Article  CAS  Google Scholar 

  82. Patil, K. R., I. Rocha, J. Forster, and J. Nielsen (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformat. 6: 308.

    Article  Google Scholar 

  83. Wright, J. and W. Andreas (2008) The systems biology research tool: Evolvable open-source software. BMC Syst. Biol. 2: 55.

    Article  Google Scholar 

  84. Raman, K. and C. Nagasuma (2009) Flux balance analysis of biological systems: Applications and challenges. Brief. Bioinformat. 10: 435–449.

    Article  CAS  Google Scholar 

  85. Copeland, W. B., B. A. Bartley, D. Chandran, M. Galdzicki, K. H. Kim, S. C. Sleighta, C. D. Maranasc, and H. M. Sauroa (2012) Computational tools for metabolic engineering. Metabol. Eng. 14: 270–280.

    Article  CAS  Google Scholar 

  86. Abrusán, G. (2012) Somatic transposition in the brain has the potential to influence the biosynthesis of metabolites involved in Parkinson’s disease and schizophrenia. Biol. Direct. 7: 41.

    Article  Google Scholar 

  87. Flamm, C., C. Hemmingsen, and D. Merkle (2013) Barrier trees for metabolic adjustment landscapes. Adv. Artificial Life, ECAL. 12: 1–8.

    Google Scholar 

  88. Quirós, M., R. Martínez-Moreno, J. Albiol, P. Morales, F. Vázquez-Lima, A. Barreiro-Vázquez, P. Ferrer, and R. Gonzalez (2013) Metabolic flux analysis during the exponential growth phase of Saccharomyces cerevisiae in wine fermentations. PloS one. 8: e71909.

    Article  Google Scholar 

  89. Rios-Estepa, R. (2008) Unraveling the regulation of mint monoterpene biosynthesis: Development and experimental testing of kinetic mathematical models. Dissertation. Washington State University, USA.

    Google Scholar 

  90. Xiong, Z. and W. L. Peter (1997) COBRA: A sensitive and quantitative DNA methylation assay. Nucleic Acids Res. 25: 2532–2534.

    Article  CAS  Google Scholar 

  91. Feng, X., Y. Xu, Y. Chen, and Y. J. Tang (2012) MicrobesFlux: A web platform for drafting metabolic models from the KEGG database. BMC Syst. Biol. 6: 94.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310, Malaysia

    Lu Shi Jing, Farah Fathiah Muzaffar Shah, Mohd Saberi Mohamad, Nur Laily Hamran, Abdul Hakim Mohamed Salleh & Safaai Deris

  2. College of Pharmacy, The University of Rhode Island, 7 Greenhouse Rd, South Kingstown, RI, 0288, USA

    Hany Alashwal

Authors
  1. Lu Shi Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farah Fathiah Muzaffar Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohd Saberi Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nur Laily Hamran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdul Hakim Mohamed Salleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Safaai Deris
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hany Alashwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohd Saberi Mohamad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jing, L.S., Shah, F.F.M., Mohamad, M.S. et al. Database and tools for metabolic network analysis. Biotechnol Bioproc E 19, 568–585 (2014). https://doi.org/10.1007/s12257-014-0172-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12257-014-0172-8

Keywords

Search

Navigation