Reduction of mathematical models of signal transduction networks: simulation-based approach applied to EGF receptor signalling.
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.
Conzelmann
H
H
Institute for System Dynamics and Control Engineering, University of Stuttgart, Germany. conzelmann@isr.uni-stuttgart.de
Saez-Rodriguez
J
J
Sauter
T
T
Bullinger
E
E
Allgöwer
F
F
Gilles
E D
ED
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004815:Epidermal Growth Factor / Q000378:metabolism*; D066246:ErbB Receptors / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological; D015398:Signal Transduction / Q000502:physiology*
D004815:Epidermal Growth Factor; D066246:ErbB Receptors