Fit-preserving refinement of the ErbB signalling pathway

The construction of large scale biological models is a laborious task, which is
often addressed by adopting iterative routines for model augmentation, adding
certain details to an initial high level abstraction of the biological phenomenon of
interest. However, refitting a model at every step of its development is time consuming
and computationally intensive. In this context, fit-preserving data refinement
brings about an effective alternative by providing adequate parameter values
that ensure fit preservation at every refinement step. We address here the implementation
of fit-preserving data refinement for a model of the ErbB signalling
pathway, which is extended to include four different types of receptor tyrosine
kinases, ErbB1-4, and two types of ligands, EGF and HRG. We build an extensive
model, which ensures a good fit by construction with notably less effort than what
a parameter estimation routine would require.