Advertisement

A Text Mining Approach for the Extraction of Kinetic Information from Literature

  • Ana Alão Freitas
  • Hugo Costa
  • Miguel Rocha
  • Isabel Rocha
Conference paper
  • 610 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 375)

Abstract

Systems biology has fostered interest in the use of kinetic models to better understand the dynamic behavior of metabolic networks in a wide variety of conditions. Unfortunately, in most cases, data available in different databases are not sufficient for the development of such models, since a significant part of the relevant information is still scattered in the literature. Thus, it becomes essential to develop specific and powerful text mining tools towards this aim. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The pipeline proposed integrates the development of a novel plug-in over the text mining tool @Note2. Overall, the results validate the developed approach.

Keywords

Enzyme kinetics Metabolic models Text mining Name entity recognition Relation extraction Databases 

Notes

Acknowledgments

The work was funded by National Funds through the FCT (Portuguese Foundation for Science and Technology) within project ref. PTDC/QUI-BIQ/119657/2010 Finding the naturally evolved design principles of prevalent metabolic circuits. The authors would also like to thank the FCT Strategic Project PEst-OE/EQB/ LA0023/2013 and the Projects BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes, REF. NORTE-07-0124-FEDER-000028 and PEM Metabolic Engineering Platform, project number 23060, both co-funded by the Programa Operacional Regional do Norte (ON.2 O Novo Norte), QREN, FEDER.

References

  1. 1.
    Ananiadou, S., Kell, D.B., Tsujii, J.-I.: Text mining and its potential applications in systems biology. Trends Biotechnol. 24(12), 9–571 (2006)CrossRefGoogle Scholar
  2. 2.
    Caspi, R., Altman, T., Dreher, K., Fulcher, C.A., Subhraveti, P., Keseler, I.M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L.A., Ong, Q., Paley, S., Pujar, A., Shearer, A.G., Travers, M., Weerasinghe, D., Zhang, P., Karp, P.D.: The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 40(Database issue):D742–D753 (2012)Google Scholar
  3. 3.
    Chassagnole, C., Noisommit-Rizzi, N., Schmid, J.W., Mauch, K., Reuss, M.: Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol. Bioeng. 79(1), 53–73 (2002)CrossRefGoogle Scholar
  4. 4.
    Cohen, K.B., Hunter, L.: Getting started in text mining. PLoS Comput. Biol. 4(1), e20 (2008)CrossRefGoogle Scholar
  5. 5.
    Dis, G. F., Schomburg, I., Hofmann, O., Baensch, C.: Enzyme data and metabolic information : BRENDA, a resource for research in biology, biochemistry, and medicine, pp. 3–4 (2000)Google Scholar
  6. 6.
    Durot, M., Bourguignon, P.-Y., Schachter, V.: Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol. Rev. 33(1), 90–164 (2009)CrossRefGoogle Scholar
  7. 7.
    Edwards, J.S., Palsson, B.O.: Robustness analysis of the Escherichia coli metabolic network. Biotechnol. Prog. 16(6), 927–939 (2000)CrossRefGoogle Scholar
  8. 8.
    Gasteiger, E.: ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31(13), 3784–3788 (2003)CrossRefGoogle Scholar
  9. 9.
    Gerner, M., Nenadic, G., Bergman, C.M.: LINNAEUS : a species name identification system for biomedical literature (2010)Google Scholar
  10. 10.
    Heinen, S., Thielen, B., Schomburg, D.: KID-an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes. BMC Bioinf. 11, 375 (2010)CrossRefGoogle Scholar
  11. 11.
    Lourenço, A., Carreira, R., Carneiro, S., Maia, P., Glez-Peña, D., Fdez-Riverola, F., Ferreira, E.C., Rocha, I., Rocha, M.: @Note: a workbench for biomedical text mining. J. Biomed. Inf. 42(4), 20–710 (2009)CrossRefGoogle Scholar
  12. 12.
    Patil, K.R., Åkesson, M., Nielsen, J.: Use of genome-scale microbial models for metabolic engineering. Curr. Opin. Biotechnol. 15(1), 64–69 (2004)CrossRefGoogle Scholar
  13. 13.
    Rodriguez-Esteban, R.: Biomedical text mining and its applications. PLoS Comput. Biol. 5(12), e1000597 (2009)CrossRefGoogle Scholar
  14. 14.
    Schmeier, S., Kowald, A., Klipp, E., Leser, U.L.F.: Finding kinetic parameters using text mining. 8(2), 131–153 (2004)Google Scholar
  15. 15.
    Schomburg, I., Chang, A., Placzek, S., Söhngen, C., Rother, M., Lang, M., Munaretto, C., Ulas, S., Stelzer, M., Grote, A., Scheer, M., Schomburg, D.: BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res. 41(Database issue):D764–D772 (2013)Google Scholar
  16. 16.
    Shatkay, H., Craven, M.: Mining the biomedical literature. MIT Press (2012)Google Scholar
  17. 17.
    Wittig, U., Golebiewski, M., Kania, R., Krebs, O., Mir, S., Weidemann, A., Anstein, S., Saric, J., Rojas, I.: SABIO-RK : integration and curation of reaction kinetics data, pp. 94–103 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ana Alão Freitas
    • 1
  • Hugo Costa
    • 2
  • Miguel Rocha
    • 1
  • Isabel Rocha
    • 1
  1. 1.Centre Biological EngineeringSchool of Engineering University of MinhoBragaPortugal
  2. 2.SilicoLife LdaBragaPortugal

Personalised recommendations