Refinement-based modelling of the ErbB signalling pathway

Building large biological models is a difficult task, often attained by iteratively adding details to an initial abstraction of the modeled process. Refitting the model at every step of the development is computationally intensive. Fit-preserving data refinement offers an efficient alternative by providing adequate parameter values that preserve the fit from the previous step.We focus here on the implementation of fit-preserving data refinement of a model of the ErbB signalling pathway, which is extended to include details regarding the types of ligands and receptors involved. We obtained an extensive model ensuring a good fit by construction, with significantly less effort than any parameter estimation routine would require. Building large biological models is a difficult task, often attained by iteratively adding details to an initial abstraction of the modeled process. Refitting the model at every step of the development is computationally intensive. Fit-preserving data refinement offers an efficient alternative by providing adequate parameter values that preserve the fit from the previous step.We focus here on the implementation of fit-preserving data refinement of a model of the ErbB signalling pathway, which is extended to include details regarding the types of ligands and receptors involved. We obtained an extensive model ensuring a good fit by construction, with significantly less effort than any parameter estimation routine would require.