  We propose a new approach to model composition, based on reducing several
models to the same level of complexity and subsequent combining them together.
Firstly, we suggest a set of model reduction tools that can be systematically
applied to a given model. Secondly, we suggest a notion of a minimal complexity
model. This model is the simplest one that can be obtained from the original
model 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 in
some applications. A toolbox for model reduction and composition has been
implemented as part of the BioUML software and tested on the example of
integrating two previously published models of the CD95 (APO-1/Fas) signaling
pathways. We show that the reduced models lead to the same dynamical behavior
of observable species and the same predictions as in the precursor models. The
composite model is able to recapitulate several experimental datasets which
were used by the authors of the original models to calibrate them separately,
but also has new dynamical properties.
