[en] 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
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Conzelmann, Holger
Saez-Rodriguez, Julio
Sauter, Thomas
Bullinger, Eric ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes computationnelles pour la biologie systémique
Allgöwer, Frank
Gilles, Ernst Dieter
Language :
English
Title :
Reduction of mathematical models of signal transduction networks: simulation-based approach applied to EGF receptor signalling
Publication date :
2004
Journal title :
IEE Proceedings. Systems Biology
ISSN :
1741-2471
eISSN :
1741-248X
Publisher :
Institution of Engineering & Technology, Stevenage, United Kingdom