ABSTRACT
Abstract. Quantitative model refinement is an essential step in the model development cycle. Starting with a high level, abstract representation of a biological system, one often needs to add details to this representation to reflect changes in its constituent elements. Any such refinement step has two aspects: one structural and one quantitative. The structural aspect of the refinement defines an increase in the resolution of its representation, while the quantitative one specifies a numerical setup for the model that ensures its fit preservation at every refinement step. We discuss in this paper the implementation of quantitative model refinement in four extensively used biomodeling frameworks: ODE-based models,
From Approach
rule-based models, Petri net models, and guarded command language models, emphasizing the specificity for every model implementation. We argue that quantitative model refinement is framework-independent, being implementable in all chosen frameworks despite their different underlying modeling paradigms.
