Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

IAPR International Conference on Pattern Recognition in Bioinformatics

PRIB 2012: Pattern Recognition in Bioinformatics pp 118–128Cite as

  1. Home
  2. Pattern Recognition in Bioinformatics
  3. Conference paper
An Algorithm to Assemble Gene-Protein-Reaction Associations for Genome-Scale Metabolic Model Reconstruction

An Algorithm to Assemble Gene-Protein-Reaction Associations for Genome-Scale Metabolic Model Reconstruction

  • João Cardoso23,24,
  • Paulo Vilaça23,24,
  • Simão Soares23 &
  • …
  • Miguel Rocha24 
  • Conference paper
  • 1864 Accesses

  • 1 Citations

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7632)

Abstract

The considerable growth in the number of sequenced genomes and recent advances in Bioinformatics and Systems Biology fields have provided several genome-scale metabolic models (GSMs) that have been used to provide phenotype simulation methods. Given their importance in biomedical research and biotechnology applications (e.g. in Metabolic Engineering efforts), several workflows and computational platforms have been proposed for GSM reconstruction. One of the challenges of these methods is related to the assignment of gene-protein-reaction (GPR) associations that allow to add transcriptional/ translational information to GSMs, a task typically addressed through manual literature curation. This work proposes a novel algorithm to create a set of GPR rules, based on the integration of the information provided by the genome annotation with information on protein composition and function (protein complexes, sub-units, iso-enzymes, etc.) provided by the UniProt database. The methods are validated by using two state-of-the-art models for E. coli and S. cerevisiae, with competitive results.

Keywords

  • Metabolic models
  • gene-protein-reaction rules
  • genome annotation

Download conference paper PDF

References

  1. Dias, O., Rocha, M., Ferreira, E., Rocha, I.: Merlin: Metabolic models reconstruction using genome-scale information. Computer Applications in Biotechnology 11, 120–125 (2010)

    Google Scholar 

  2. Feist, A.M., Palsson, B.: The growing scope of applications of genome-scale metabolic reconstructions using escherichia coli. Nature Biotechnology 26(6), 659–667 (2008)

    CrossRef  Google Scholar 

  3. Feist, A.M., Henry, C.S., Reed, J.L., Krummenacker, M., Joyce, A.R., Karp, P.D., Broadbelt, L.J., Hatzimanikatis, V., Palsson, B.Ø.: A genome-scale metabolic reconstruction for escherichia coli k-12 mg1655 that accounts for 1260 orfs and thermodynamic information. Molecular Systems Biology 3 (2007)

    Google Scholar 

  4. Feist, A.M., Herrgård, M.J., Thiele, I., Reed, J.L., Ø Palsson, B.: Reconstruction of biochemical networks in microorganisms. Nature Reviews. Microbiology 7(2), 129–143 (2009)

    Google Scholar 

  5. Henry, C.S., DeJongh, M., Best, A.A., Frybarger, P.M., Linsay, B., Stevens, R.L.: High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature Biotechnology 28(9), 977–982 (2010)

    CrossRef  Google Scholar 

  6. Magrane, M., Uniprot Consortium: Uniprot knowledgebase: a hub of integrated protein data. Database: the Journal of Biological Databases and Curation 2011:bar009 (January 2011)

    Google Scholar 

  7. Mo, M., Palsson, B.Ø., Herrgård, M.J.: Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Systems Biology 3, 37 (2009)

    CrossRef  Google Scholar 

  8. Nielsen, J.: Metabolic engineering. Appl Microbiol Biotechnol. 55, 263–283 (2001)

    CrossRef  Google Scholar 

  9. Reed, J.L., Vo, T.D., Schilling, C.H., Palsson, B.Ø.: An expanded genome-scale model of escherichia coli k-12 (ijr904 gsm/gpr). Genome Biology 4, R54 (2006)

    Google Scholar 

  10. Rocha, M., Maia, P., Mendes, R., Pinto, J.P., Ferreira, E.C., Nielsen, J., Patil, K.R., Rocha, I.: Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinformatics 9 (2008)

    Google Scholar 

  11. Schulz, K.U., Mihov, S.: Fast string correction with levenshtein automata. International Journal on Document Analysis and Recognition 5, 67–85 (2002)

    CrossRef  MATH  Google Scholar 

  12. Thiele, I., Palsson, B.Ø.: A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protocols 5, 93–121 (2010)

    CrossRef  Google Scholar 

  13. Vilaça, P., Maia, P., Rocha, I., Rocha, M.: Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 205–216. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  14. Webb, E.C.: International Union of Biochemistry, and Molecular Biology. In: Enzyme nomenclature 1992. Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the Nomenclature and Classification of Enzymes, 6th edn., Academic Press (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SilicoLife Lda., Spinpark, Avepark, Apart. 4152, 4806-909, Guimarães, Portugal

    João Cardoso, Paulo Vilaça & Simão Soares

  2. CCTC, School of Engineering, University of Minho, Portugal

    João Cardoso, Paulo Vilaça & Miguel Rocha

Authors
  1. João Cardoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo Vilaça
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simão Soares
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miguel Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Medical Science, University of Tokyo, 4-6-1, Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Tetsuo Shibuya

  2. Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, 113-8654, Bunkyo-ku, Tokyo, Japan

    Hisashi Kashima

  3. Department of Comouter Science, Tokyo Institute of Technology, 2-12-1 Ookayamama, 152-8550, Meguro-ku, Tokyo, Japan

    Jun Sese

  4. Bioinformatics Project, National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, 567-0085, Suita, Osaka, Japan

    Shandar Ahmad

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cardoso, J., Vilaça, P., Soares, S., Rocha, M. (2012). An Algorithm to Assemble Gene-Protein-Reaction Associations for Genome-Scale Metabolic Model Reconstruction. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_11

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-34123-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34122-9

  • Online ISBN: 978-3-642-34123-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • The International Association for Pattern Recognition

    Published in cooperation with

    http://www.iapr.org/

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature