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

Research output: Contribution to journalArticle

51 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.
LanguageEnglish
Pages159-169
Number of pages10
JournalIET Systems Biology
Volume1
Issue number1
DOIs
Publication statusPublished - Jun 2004

Fingerprint

Signal transduction
Signal Transduction
Network Simulation
Growth Factors
Epidermal Growth Factor Receptor
Receptor
Theoretical Models
Mathematical Model
Mathematical models
Signaling Pathways
Model Reduction
Pathway
Biological Models
Systems Analysis
Epidermal Growth Factor
Crosstalk
System Simulation
Reduction Method
Biological Systems
Model

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
Conzelmann, H. ; Saez-Rodriguez, J. ; Sauter, T. ; Bullinger, E. ; Allgöwer, F. ; Gilles, E.D. / Reduction of mathematical models of signal transduction networks: Simulation-based approach applied to EGF receptor signalling. In: IET Systems Biology. 2004 ; Vol. 1, No. 1. pp. 159-169.
@article{b03c05492c6141c0af944b677e660896,
title = "Reduction of mathematical models of signal transduction networks: Simulation-based approach applied to EGF receptor signalling",
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.",
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",
author = "H. Conzelmann and J. Saez-Rodriguez and T. Sauter and E. Bullinger and F. Allg{\"o}wer and E.D. Gilles",
year = "2004",
month = "6",
doi = "10.1049/sb:20045011",
language = "English",
volume = "1",
pages = "159--169",
journal = "IET Systems Biology",
issn = "1751-8849",
publisher = "Institution of Engineering and Technology",
number = "1",

}

Conzelmann, H, Saez-Rodriguez, J, Sauter, T, Bullinger, E, Allgöwer, F & Gilles, ED 2004, 'Reduction of mathematical models of signal transduction networks: Simulation-based approach applied to EGF receptor signalling' IET Systems Biology, vol. 1, no. 1, pp. 159-169. https://doi.org/10.1049/sb:20045011

Reduction of mathematical models of signal transduction networks: Simulation-based approach applied to EGF receptor signalling. / Conzelmann, H.; Saez-Rodriguez, J.; Sauter, T.; Bullinger, E.; Allgöwer, F.; Gilles, E.D.

In: IET Systems Biology, Vol. 1, No. 1, 06.2004, p. 159-169.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Reduction of mathematical models of signal transduction networks: Simulation-based approach applied to EGF receptor signalling

AU - Conzelmann, H.

AU - Saez-Rodriguez, J.

AU - Sauter, T.

AU - Bullinger, E.

AU - Allgöwer, F.

AU - Gilles, E.D.

PY - 2004/6

Y1 - 2004/6

N2 - 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.

AB - 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.

KW - mathematical model reduction

KW - cellular signal transduction networks

KW - epidermal growth factor receptor signalling

KW - complex biological systems

KW - cross-talk phenomena

KW - system analysis

KW - signal transmission

KW - cell processing

UR - http://dx.doi.org/10.1049/sb:20045011

U2 - 10.1049/sb:20045011

DO - 10.1049/sb:20045011

M3 - Article

VL - 1

SP - 159

EP - 169

JO - IET Systems Biology

T2 - IET Systems Biology

JF - IET Systems Biology

SN - 1751-8849

IS - 1

ER -