Reduction of mathematical models of signal transduction networks: Simulation-based approach applied to EGF receptor signalling

H. Conzelmann, J. Saez-Rodriguez, T. Sauter, E. Bullinger, F. Allgöwer, E.D. Gilles

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52 Citations (Scopus)

Abstract

Biological systems and, in particular, cellular signal transduction pathways are characterised by their high complexity. Mathematical models describing these processes might be of great help to gain qualitative and, most importantly, quantitative knowledge about such complex systems. However, a detailed mathematical description of these systems leads to nearly unmanageably large models, especially when combining models of different signalling pathways to study cross-talk phenomena. Therefore, simplification of models becomes very important. Different methods are available for model reduction of biological models. Importantly, most of the common model reduction methods cannot be applied to cellular signal transduction pathways. Using as an example the epidermal growth factor (EGF) signalling pathway, we discuss how quantitative methods like system analysis and simulation studies can help to suitably reduce models and additionally give new insights into the signal transmission and processing of the cell.
Original languageEnglish
Pages (from-to)159-169
Number of pages10
JournalIET Systems Biology
Volume1
Issue number1
DOIs
Publication statusPublished - Jun 2004

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Keywords

  • mathematical model reduction
  • cellular signal transduction networks
  • epidermal growth factor receptor signalling
  • complex biological systems
  • cross-talk phenomena
  • system analysis
  • signal transmission
  • cell processing

Cite this

Conzelmann, H., Saez-Rodriguez, J., Sauter, T., Bullinger, E., Allgöwer, F., & Gilles, E. D. (2004). Reduction of mathematical models of signal transduction networks: Simulation-based approach applied to EGF receptor signalling. IET Systems Biology, 1(1), 159-169. https://doi.org/10.1049/sb:20045011