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Predictive mathematical models of cancer signalling pathways

  • 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 couldComplex 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.show moreshow less

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Metadaten
Author:J. Bachmann, Andreas RaueORCiDGND, M. Schilling, V. Becker, J. Timmer, U. Klingmüller
URN:urn:nbn:de:bvb:384-opus4-1132652
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/113265
ISSN:0954-6820OPAC
ISSN:1365-2796OPAC
Parent Title (English):Journal of Internal Medicine
Publisher:Wiley
Place of publication:Weinheim
Type:Article
Language:English
Year of first Publication:2012
Publishing Institution:Universität Augsburg
Release Date:2024/06/03
Volume:271
Issue:2
First Page:155
Last Page:165
DOI:https://doi.org/10.1111/j.1365-2796.2011.02492.x
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Modellierung und Simulation biologischer Prozesse
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):Deutsches Urheberrecht