English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Talk

Domain-oriented and modular approaches to the reduction of mathematical models of signaling networks

MPS-Authors
/persons/resource/persons86220

Saez-Rodriguez,  J.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

/persons/resource/persons86158

Conzelmann,  H.
Inst. for System Dynamics and Control Engineering, Univ. of Stuttgart;
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

/persons/resource/persons86223

Sauter,  T.
Univ. of Stuttgart, Inst. for System Dynamics and Control Engineering, Stuttgart, Germany;
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

/persons/resource/persons86172

Gilles,  E. D.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Saez-Rodriguez, J., Conzelmann, H., Sauter, T., Kholodenko, B. N., & Gilles, E. D. (2005). Domain-oriented and modular approaches to the reduction of mathematical models of signaling networks. Talk presented at 4th Workshop on Computation of Biochemical Pathways and Genetic Networks and Genetic Networks. Heidelberg, Germany. 2005-09-12 - 2005-09-13.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-9BB7-3
Abstract
A rigorous, detailed description of signaling networks gives rise to huge models, while an a priori simplified model relies on assumptions difficult to prove. Therefore, models which are manageable, yet retain the essential properties of the real network, are desirable. Herein, we first describe a method that addresses the combinatorial explosion of the number of states due to the ability of proteins to bind multiple partners via different domains. We show how a linear state transformation together with the application of the system-theoretical concept of observability allow a dramatic reduction of the number of states to be considered. Secondly, we present an approach for a further reduction of the models, based on a decomposition of the model into modules. The resulting subunits are analyzed via simulation studies, leading to the identification of less complex non-linear models showing approximately the same input/output behavior, which can replace the complex modules in the whole model.