Predictive mathematical models of cancer signalling pathways

• Published in: Journal of internal medicine 2012-02, Vol.271 (2), p.155-165
• Main Authors: Bachmann, J., Raue, A., Schilling, M., Becker, V., Timmer, J., Klingmüller, U.
• Format: Article
• Language: English
• Subjects: Anemia - chemically induced; Anemia - drug therapy; Antineoplastic Agents - adverse effects; Biological and medical sciences; cancer; cell biology; Cell Survival - physiology; cytokines; Cytokines - metabolism; Erythropoietin - adverse effects; Erythropoietin - metabolism; Forecasting; General aspects; hematology; Humans; Likelihood Functions; lung cancer; Lung Neoplasms - drug therapy; Lung Neoplasms - etiology; Lung Neoplasms - physiopathology; Mathematics; Medical sciences; Models, Biological; Pneumology; Receptors, Erythropoietin - antagonists & inhibitors; Receptors, Erythropoietin - physiology; Recombinant Proteins; Risk Factors; Signal Transduction - physiology; Systems Biology - methods; Transcription Factors - physiology; Tumors of the respiratory system and mediastinum
• ISSN: 0954-6820; 1365-2796
• DOI: 10.1111/j.1365-2796.2011.02492.x

.  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.