title: Reduced modeling of signal transduction - a modular approach.
abstract: BACKGROUND
  Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations.
  
  
  RESULTS
  We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible.
  
  
  CONCLUSIONS
  The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for macroscopic variables. It can be combined with existing reduction methods without any difficulties.
authors.affiliation: Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr, 1, 39106 Magdeburg, Germany. koschorreck@mpi-magdeburg.mpg.de
authors.forename: Markus
authors.initials: M
authors.lastname: Koschorreck
authors.forename: Holger
authors.initials: H
authors.lastname: Conzelmann
authors.forename: Sybille
authors.initials: S
authors.lastname: Ebert
authors.forename: Michael
authors.initials: M
authors.lastname: Ederer
authors.forename: Ernst Dieter
authors.initials: ED
authors.lastname: Gilles
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software
chemical_list: D020543:Proteome