Catalysis in Industry

, Volume 10, Issue 1, pp 83–90 | Cite as

Mathematical Simulating the Biokatalytic Transformation of Methyl Phenyl Sulfide into (R)-Sulfoxide

  • A. A. El’kin
  • T. I. Kylosova
  • M. A. Osipenko
  • Yu. I. Nyashin
  • V. V. Grishko
  • I. B. Ivshina


A mathematical model is proposed for describing the biotransformation of methyl phenyl sulfide to (R)-methyl phenyl sulfoxide by immobilized Gordonia terrae IEGM 136 cells. Kinetic patterns of the biotransformation of methyl phenyl sulfide are determined using experimental data on the initial concentration of sulfide and the amount of biocatalyst. The experimental data are compared to simulations of sulfide biotransformation scaling in a laboratory bioreactor. A mathematical model is developed for describing the biotransformation of methyl phenyl sulfide with repeated use of the biocatalyst. The resulting data can be used for optimizing the biotransformation of a wide range of organic aryl alkyl sulfides to optically active sulfoxides.


biocatalysis methyl phenyl sulfide optically active sulfoxides Gordonia terrae IEGM 136 method of least squares 


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  1. 1.
    Wojaczyńska, E. and Wojaczyński, J., Chem. Rev., 2010, vol. 110, no. 7, pp. 4303–4356.CrossRefGoogle Scholar
  2. 2.
    O’Mahony, G.E., Ford, A., and Maguire, A.R., J. Sulfur Chem., 2013, vol. 34, no. 3, pp. 301–341.CrossRefGoogle Scholar
  3. 3.
    Carreño, M.C., Ribagorda, M., Somoza, A., and Urbano, A., Angew. Chem., Int. Ed. Engl., 2002, vol. 41, no. 15, pp. 2755–2757.CrossRefGoogle Scholar
  4. 4.
    De la Pradilla, R.F., Simal, C., Bates, R.H., Viso, A., and Infantes, L., Org. Lett., 2013, vol. 15, no. 19, pp. 4936–4939.CrossRefGoogle Scholar
  5. 5.
    Raghavan, S. and Rathore, K., Tetrahedron, 2009, vol. 65, no. 48, pp. 10083–10092.CrossRefGoogle Scholar
  6. 6.
    Raghavan, S., Krishnaiah, V., and Sridhar, B., J. Org. Chem., 2010, vol. 75, no. 2, pp. 498–501.CrossRefGoogle Scholar
  7. 7.
    Chen, Y., Zhuo, J., Zheng, D., Tian, S., and Li, Z., J. Mol. Catal. B: Enzym., 2014, vol. 106, pp. 100–104.CrossRefGoogle Scholar
  8. 8.
    Matsui, T., Dekishima, Y., and Ueda, M., Appl. Microbiol. Biotechnol., 2014, vol. 98, no. 18, pp. 7699–7706.CrossRefGoogle Scholar
  9. 9.
    El'kin, A.A., Grishko, V.V., and Ivshina, I.B., Prikl. Biokhim. Mikrobiol., 2010, vol. 46, no. 6, pp. 637–643.Google Scholar
  10. 10.
    Mascotti, M.L., Orden, A.A., Bisogno, F.R., de Gonzalo, G., and Kurina-Sanz, M., J. Mol. Catal. B: Enzym., 2012, vol. 82, pp. 32–36.CrossRefGoogle Scholar
  11. 11.
    Verbelen, P.J., de Schutter, D.P., Delvaux, F., Verstrepen, K.J., and Delvaux, F.R., Biotechnol. Lett., 2006, vol. 28, no. 19, pp. 1515–1525.CrossRefGoogle Scholar
  12. 12.
    Kisukuri, C.M. and Andrade, L.H., Org. Biomol. Chem., 2015, vol. 13, no. 40, pp. 10086–10107.CrossRefGoogle Scholar
  13. 13.
    Lozinsky, V.I., Galaev, I.Yu., Plieva, F.M., Savina, I.N., Jungvid, H., and Mattiasson, B., Trends Biotechnol., 2003, vol. 21, no. 10, pp. 445–451.CrossRefGoogle Scholar
  14. 14.
    Hassan, C.M. and Peppas, N.A., Adv. Polym. Sci., 2000, vol. 153, pp. 37–65.CrossRefGoogle Scholar
  15. 15.
    Elkin, A.A., Kylosova, T.I., Grishko, V.V., and Ivshina, I.B., J. Mol. Catal. B: Enzym., 2013, vol. 89, pp. 82–85.CrossRefGoogle Scholar
  16. 16.
    Kylosova, T.I., Elkin, A.A., Grishko, V.V., and Ivshina, I.B., J. Mol. Catal. B: Enzym., 2016, vol. 123, pp. 8–13.CrossRefGoogle Scholar
  17. 17.
    Atlas, R.T., Florida: CRC Press, 1993.Google Scholar
  18. 18.
    Kuyukina, M.S., Ivshina, I.B., Gavrin, A.Yu., Podorozhko, E.A., Lozinsky, V.I., Jeffree, C.E., and Philp, J.C., J. Microbiol. Methods, 2006, vol. 65, no. 3, pp. 596–603.CrossRefGoogle Scholar
  19. 19.
    Grishko, V.V., Ivshina, I.B., and Tolstikov, A.G., Biotechnol. Russ., 2004, vol. 5, pp. 69–77.Google Scholar
  20. 20.
    Li, A.-T., Zhang, J.-D., Yu, H.-L., Pan, J., and Xu, J.-H., Process Biochem., 2011, vol. 46, no. 3, pp. 689–694.CrossRefGoogle Scholar
  21. 21.
    Linnik, Yu.V., Metod naimen’shikh kvadratov i osnovy matematiko-statisticheskoi obrabotki nablyudenii (Least Squares Technique and Mathematical Statistic Foundations of Observation Processing), Moscow: Fizmatgiz, 1962.Google Scholar
  22. 22.
    Ramadhan, S.H., Matsui, T., Nakano, K., and Minami, H., Appl. Microbiol. Biotechnol., 2013, vol. 97, no. 5, pp. 1903–1907.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • A. A. El’kin
    • 1
    • 2
  • T. I. Kylosova
    • 3
  • M. A. Osipenko
    • 3
  • Yu. I. Nyashin
    • 3
  • V. V. Grishko
    • 4
  • I. B. Ivshina
    • 1
    • 2
  1. 1.Institute of Ecology and Genetics of Microorganisms, Ural BranchRussian Academy of SciencesPermRussia
  2. 2.Perm State National Research UniversityPermRussia
  3. 3.Perm National Research Polytechnic UniversityPermRussia
  4. 4.Institute of Technical Chemistry, Ural BranchRussian Academy of SciencesPermRussia

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