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We propose a new approach to model composition, based on reducing severalmodels to the same level of complexity and subsequent combining them together.Firstly, we suggest a set of model reduction tools that can be systematicallyapplied to a given model. Secondly, we suggest a notion of a minimal complexitymodel. This model is the simplest one that can be obtained from the originalmodel using these tools and still able to approximate experimental data.Thirdly, we propose a strategy for composing the reduced models together.Connection with the detailed model is preserved, which can be advantageous insome applications. A toolbox for model reduction and composition has beenimplemented as part of the BioUML software and tested on the example ofintegrating two previously published models of the CD95 (APO-1/Fas) signalingpathways. We show that the reduced models lead to the same dynamical behaviorof observable species and the same predictions as in the precursor models. Thecomposite model is able to recapitulate several experimental datasets whichwere used by the authors of the original models to calibrate them separately,but also has new dynamical properties.

Model composition through model reduction: a combined model of CD95 and NF-{\kappa}B signaling pathways