Predictive mathematical models of cancer signalling pathways

Authors

  • J. Bachmann,

    1. From the Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg;
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    • These authors contributed equally.

  • A. Raue,

    1. Institute of Physics, University of Freiburg, BIOSS Centre for Biological Signalling Studies
    2. Freiburg Institute for Advanced Studies (FRIAS) & Zentrum für Biosystemanalyse (ZBSA), University of Freiburg, Freiburg; Germany
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    • These authors contributed equally.

  • M. Schilling,

    1. From the Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg;
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  • V. Becker,

    1. From the Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg;
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    • Present address: Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

  • J. Timmer,

    1. Institute of Physics, University of Freiburg, BIOSS Centre for Biological Signalling Studies
    2. Freiburg Institute for Advanced Studies (FRIAS) & Zentrum für Biosystemanalyse (ZBSA), University of Freiburg, Freiburg; Germany
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  • U. Klingmüller

    1. From the Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg;
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Prof. Dr. Ursula Klingmüller, Systems Biology of Signal Transduction (A150), Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
(fax: +49 6221 424488; e-mail: u.klingmueller@dkfz.de).

Abstract

Abstract.  Bachmann J, Raue A, Schilling M, Becker V, Timmer J, Klingmüller U (German Cancer Research Center, Heidelberg; BIOSS Centre for Biological Signalling Studies, Freiburg; and University of Freiburg, Freiburg; Germany). Predictive mathematical models of cancer signalling pathways (Key Symposium). J Intern Med 2012; 271:155–165.

Complex intracellular signalling networks integrate extracellular signals and convert them into cellular responses. In cancer cells, the tightly regulated and fine-tuned dynamics of information processing in signalling networks is altered, leading to uncontrolled cell proliferation, survival and migration. Systems biology combines mathematical modelling with comprehensive, quantitative, time-resolved data and is most advanced in addressing dynamic properties of intracellular signalling networks. Here, we introduce different modelling approaches and their application to medical systems biology, focusing on the identifiability of parameters in ordinary differential equation models and their importance in network modelling to predict cellular decisions. Two related examples are given, which include processing of ligand-encoded information and dual feedback regulation in erythropoietin (Epo) receptor signalling. Finally, we review the current understanding of how systems biology could foster the development of new treatment strategies in the context of lung cancer and anaemia.

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