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Prediction of the Performance of Human Liver Cell Bioreactors by Donor Organ Data

  • Wolfgang Schmidt-Heck
  • Katrin Zeilinger
  • Gesine Pless
  • Joerg C. Gerlach
  • Michael Pfaff
  • Reinhard Guthke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)

Abstract

Human liver cell bioreactors are used in extracorporeal liver support therapy. To optimize bioreactor operation with respect to clinical application an early prediction of the long-term bioreactor culture performance is of interest. Data from 70 liver cell bioreactor runs labeled by low (n=18), medium (n=34) and high (n=18) performance were analyzed by statistical and machine learning methods. 25 variables characterizing donor organ properties, organ preservation, cell isolation and cell inoculation prior to bioreactor operation were analyzed with respect to their importance to bioreactor performance prediction. Results obtained were compared and assessed with respect to their robustness. The inoculated volume of liver cells was found to be the most relevant variable allowing the prediction of low versus medium/high bioreactor performance with an accuracy of 84 %.

Keywords

Support Vector Machine Leaf Node Donor Organ Independent Component Analysis Bioreactor Culture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Gerlach, J.C., Botsch, M., Kardassis, D., Lemmens, P., Schon, M., Janke, J., Puhl, G., Unger, J., Kraemer, M., Busse, B., Bohmer, C., Belal, R., Ingenlath, M., Kosan, M., Kosan, B., Sultmann, J., Patzold, A., Tietze, S., Rossaint, R., Mueller, C., Monch, E., Sauer, I.M., Neuhaus, P.: Experimental evaluation of a cell module for hybrid liver support. Int. J. Artif. Organs 24, 793–798 (2001)Google Scholar
  2. 2.
    Zeilinger, K., Holland, G., Sauer, I.M., Efimova, E., Kardassis, D., Obermayer, N., Liu, M., Neuhaus, P., Gerlach, J.C.: Time course of primary liver cell reorganization in threedimensional high-density bioreactors for extracorporeal liver support: an immunohistochemical and ultrastructural study. Tissue Eng. 10, 1113–1124 (2004)Google Scholar
  3. 3.
    Gerlach, J.C., Mutig, K., Sauer, I.M., Schrade, P., Efimova, E., Mieder, T., Naumann, G., Grunwald, A., Pless, G., Mas, A., Bachmann, S., Neuhaus, P., Zeilinger, K.: Use of primary human liver cells originating from discarded grafts in a bioreactor for liver support therapy and the prospects of culturing adult liver stem cells in bioreactors: a morphologic study. Transplantation 76, 781–786 (2003)CrossRefGoogle Scholar
  4. 4.
    Gerlach, J.C., Brombacher, J., Kloeppel, K., Smith, M., Schnoy, N., Neuhaus, P.: Comparison of four methods for mass hepatocyte isolation from pig and human livers. Transplantation 57, 1318–1322 (1994)CrossRefGoogle Scholar
  5. 5.
    Pfaff, M., Toepfer, S., Woetzel, D., Driesch, D., Zeilinger, K., Pless, G., Neuhaus, P., Gerlach, J.C., Schmidt-Heck, W., Guthke, R.: Fuzzy cluster and rule based analysis of the system dynamics of a bioartificial 3D human liver cell bioreactor for liver support therapy. In: Dounias, G., Magoulas, G., Linkens, D. (eds.) Intelligent Technologies in Bioinformatics and Medicine. Special Session. Proceedings of the EUNITE 2004 Symposium, p. 57. A Publication of the University of the Aegean (2004)Google Scholar
  6. 6.
    Schmidt-Heck, W., Zeilinger, K., Pfaff, M., Toepfer, S., Driesch, D., Pless, G., Neuhaus, P., Gerlach, J.C., Guthke, R.: Network analysis of the kinetics of amino acid metabolism in a liver cell bioreactor. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds.) ISBMDA 2004. LNCS, vol. 3337, pp. 427–438. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Breiman, L.: Random forests. Technical Report, Stat. Dept. UCB (2001)Google Scholar
  9. 9.
    Breiman, L.: Random forests. Mach. Learn 45, 5–32 (2001)zbMATHCrossRefGoogle Scholar
  10. 10.
  11. 11.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  12. 12.
  13. 13.
  14. 14.
    Guthke, R., Schmidt-Heck, W., Pfaff, M.: Knowledge acquisition and knowledge based control in bio-process engineering. J. Biotechnol. 65, 37–46 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Wolfgang Schmidt-Heck
    • 1
  • Katrin Zeilinger
    • 2
  • Gesine Pless
    • 2
  • Joerg C. Gerlach
    • 2
    • 3
  • Michael Pfaff
    • 4
  • Reinhard Guthke
    • 1
  1. 1.Leibniz Institute for Natural Product Research and Infection BiologyHans Knoell InstituteJenaGermany
  2. 2.Division of Experimental Surgery, Charité Campus VirchowUniversity Medicine BerlinBerlinGermany
  3. 3.Depts of Surgery and BioengineeringMcGowan Institute for Regenerative Medicine, University of PittsburghUSA
  4. 4.BioControl Jena GmbHJenaGermany

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